Table of Contents
Overview
Definition of Biophysics
Biophysics is an interdisciplinary science that applies the theories and methods traditionally used in physics to study biological phenomena across all scales of biological organization, from molecular to organismic and populations.[1.1] This field is concerned with understanding how biological systems work by employing quantitative sciences such as physics, math, and chemistry.[2.1] Biophysics encompasses a wide range of topics, including the mechanics of molecular structures, cellular functions, and complex systems within the body, such as the brain and immune system.[2.1] It also involves the study of biological functions that depend on physical agents like electricity and mechanical force, as well as interactions between living organisms and their environment.[4.1] The term "biophysics" was first introduced by Karl Pearson in 1892, and while some academic institutions have dedicated biophysics departments, many integrate biophysics into related fields such as biochemistry, molecular biology, and physics.[1.1]Interdisciplinary Nature
Biophysics is inherently interdisciplinary, integrating principles and methodologies from various scientific domains to address complex biological questions. One significant area of intersection is between biophysics and artificial intelligence (AI), where AI algorithms are employed to analyze medical images and patient data, enhancing the accuracy and efficiency of disease diagnosis and treatment. This integration has the potential to transform medical diagnostics by improving patient outcomes and streamlining healthcare systems.[27.1] AI techniques, including machine learning and deep learning, are extensively used in healthcare for tasks such as disease diagnosis, drug discovery, and patient risk identification. These techniques encompass a wide range of models, such as support vector machines (SVM), k-nearest neighbors (KNN), and convolutional neural networks (CNN), which are applied to detect various diseases.[28.1] The adoption of AI in healthcare is further accelerated by advancements in mobile technology, the Internet of Things (IoT), and computing power, although it also presents challenges related to data integrity and ethical considerations.[29.1] Machine learning, a subset of AI, plays a crucial role in healthcare by improving the speed and accuracy of medical professionals' work and offering individualized treatment options, thereby enhancing the overall efficiency of healthcare systems.[30.1] In computational biophysics, machine learning is used to predict enzyme properties and model complex biological systems without relying on strong assumptions, providing a benchmark for biomedical models.[32.1] This approach has been particularly successful in protein structure prediction, molecular dynamics simulations, and protein engineering, where machine learning aids in designing proteins that fold into specified structures.[33.1] A notable example of AI's application in biophysics is the work by researchers at Auburn University, who integrated AI with molecular dynamics simulations to predict binding sites on the PD-L1 protein, a critical target in cancer treatment. This advancement highlights the potential of combining computational tools with AI to accelerate the development of personalized cancer therapies.[34.1] The interdisciplinary nature of biophysics, therefore, not only bridges the gap between biology and physics but also incorporates cutting-edge technologies like AI to push the boundaries of scientific discovery and application.History
Early Foundations
The early foundations of biophysics were significantly influenced by the scientific advancements of the 19th century. During this period, the study of heat evolved into the science of thermodynamics, which was grounded in mathematical analysis and had profound implications for understanding various engine cycles and refrigeration processes.[44.1] This era also witnessed the introduction of structured and professional approaches to scientific inquiry, which laid the groundwork for modern scientific disciplines, including biophysics.[46.1] The study of metabolism was further illuminated by essential chemical facts and concepts discovered by prominent scientists such as Friedrich Wöhler, Justus von Liebig, and Louis Pasteur. These discoveries contributed to the understanding of biological processes and paved the way for future research in biophysics.[45.1] Additionally, Claude Bernard's concept of the "milieu intérieur" led to the discovery of homeostasis mechanisms, which became a cornerstone in the study of physiological processes.[45.1] The transition from traditional physiology to a more interdisciplinary approach incorporating physical principles was facilitated by key figures such as Marcello Malpighi, Giovanni Alfonso Borelli, and René Descartes. These individuals were instrumental in establishing the iatrophysical approach to medicine, which emphasized mechanics over chemistry in understanding the human body's functioning.[55.1] The foundations for biophysics as a distinct field were further solidified by a group of physiologists in Berlin during the 19th century, setting the stage for its emergence as a subfield in the early to mid-20th century.[56.1]Development as a Subfield
Biophysics is a relatively young branch of science that emerged as a distinct subfield in the early to mid-20th century, although its foundational concepts were already being explored in the 19th century by a group of pioneering physiologists in Berlin, including Hermann von Helmholtz, Emil DuBois-Reymond, and Ernst von Brücke.[38.1] The term "biophysics" was originally introduced by Karl Pearson in 1892, marking a significant milestone in its development as an academic discipline.[40.1] This interdisciplinary field applies approaches and methods traditionally used in physics to study biological phenomena, covering all scales of biological organization, from molecular to organismic and populations.[42.1] Biophysics shares significant overlap with various disciplines, including biochemistry, molecular biology, and bioengineering, and is increasingly recognized in academic settings, although many institutions incorporate it within related departments rather than having dedicated biophysics departments.[42.1] The early contributions to biophysics included the study of osmotic phenomena by René Dutrochet in 1828, who identified endosmotic and exosmotic processes as a new class of physical phenomena integral to vital biological functions.[39.1] This was followed by the quantitative measurements of these phenomena by botanist W.F.P. Pfeffer and the formulation of the fundamental laws of diffusion by Adolf Fick in 1856, who published what is considered one of the first biophysics texts, "Die medizinische Physik" ("Medical Physics").[39.1] These early studies laid the groundwork for biophysics to evolve into a formal subfield, with research efforts expanding to include the study of physical quantities such as electric current, temperature, stress, and entropy in biological systems.[42.1] Despite its relatively young status as a distinct scientific discipline, biophysics has established itself as a crucial area of research, contributing significantly to advancements in understanding biological systems and processes.[41.1]In this section:
Key Concepts In Biophysics
Molecular Biophysics
Molecular biophysics is an interdisciplinary field that applies methods and concepts from physics, chemistry, engineering, mathematics, and biology to understand biomolecular systems. It aims to explain biological functions in terms of molecular structure, structural organization, and dynamic behavior at various levels of complexity, from single molecules to supramolecular structures, viruses, and small living systems.[76.1] This field is crucial for investigating the physical properties and behavior of biological molecules, providing unique insights into the fundamental workings of life.[75.1] One of the key techniques in molecular biophysics is Nuclear Magnetic Resonance (NMR) spectroscopy, which is used to investigate the structure, dynamics, and molecular interactions of biomolecules such as peptides, proteins, and nucleic acids.[84.1] NMR spectroscopy provides detailed information about the molecular structure, dynamics, reaction state, and chemical environment of molecules, making it a versatile tool for studying the complexities of biological macromolecules and their interactions within cellular environments.[85.1] It captures both static structures and conformational ensembles, revealing flexibility and multiple states within proteins.[85.1] Another significant technique is X-ray crystallography, which has long been a key method in solving the three-dimensional structure of proteins. This technique enables the determination of the three-dimensional structures of large biologically interesting molecules, such as proteins and nucleic acids, which are vital for elucidating protein function and intermolecular interactions.[87.1] These structures improve our understanding of basic biological and biochemical mechanisms and diseases.[87.1] Computational biophysics also plays a pivotal role in molecular biophysics by applying computational techniques to understand the physical principles underlying biological processes and structures. This multidisciplinary field uses numerical methods and theoretical models to simulate and analyze the behavior of biological systems at an atomic or molecular level.[89.1] Classical mechanics-based models, including molecular dynamics (MD) and Monte Carlo (MC) simulations, allow scientists to visualize molecular interactions in three-dimensional space, providing invaluable insights into the structure-function relationships of biological molecules.[89.1] These computational tools are particularly notable in drug discovery, aiding in the design and optimization of new therapeutic molecules by predicting how they interact with their biological targets.[89.1]In this section:
Recent Advancements
Innovations in Biotechnology
In 2023, significant advancements in biotechnology have been marked by breakthroughs in various fields, including genetic medicine and protein engineering. A notable milestone was the FDA's approval of LEQEMBI (lecanemab-irmb) for Alzheimer's disease treatment, highlighting progress in neurodegenerative disease therapies.[110.1] Additionally, the UK approved Casgevy, the world's first CRISPR-based therapy, representing a historic achievement in genetic medicine.[110.1] The integration of artificial intelligence (AI) has propelled advancements in protein engineering, enabling the prediction of protein structures and functions with unprecedented precision, thus facilitating the design of novel proteins.[110.1] Stem cell research has also seen remarkable progress, particularly in the development of therapies for neurodegenerative diseases. Stem cell therapy has emerged as a promising approach, offering the potential to replace damaged neurons and secrete neuromodulators and neuroprotectors.[116.1] Post-transplantation improvements have been observed in synaptic plasticity, apoptosis inhibition, and reduction in tau-phosphorylation and amyloid beta production in Alzheimer's patients.[116.1] Furthermore, stem cell therapy has demonstrated the ability to repair damaged neural tissues and establish synaptic connections between neurons, providing a unique modality for treating neurodegenerative disorders.[116.1] In the realm of tissue engineering, three-dimensional (3D) bioprinting has shown immense promise for advancing stem cell research and developing novel therapeutic strategies. Recent breakthroughs in 3D bioprinting have focused on its application in neural tissue engineering and disease modeling, enhancing the potential for creating functional tissues or organs for transplantation.[132.1] These innovations underscore the transformative impact of biotechnology advancements in recent years, offering new avenues for therapeutic interventions and disease modeling.Applications of AI in Biophysics
Artificial intelligence (AI) has significantly advanced the field of biophysics, particularly in the prediction and analysis of protein interactions. AI-driven methodologies have enhanced key aspects such as ligand binding site prediction, protein-ligand binding pose estimation, and scoring function development. Traditional docking methods often lack accuracy, but AI models, including graph neural networks and diffusion models, have improved predictive performance, leading to more robust virtual screening strategies.[137.1] Recent breakthroughs in structure prediction and the availability of extensive genomic sequence data have enabled computational methods to predict protein-protein interactions (PPIs) with accuracy approaching that of experimental approaches. These methods utilize evolutionary information from homologous sequences that coevolve in interacting partners, allowing for accurate AI-based modeling of protein structures and PPIs at a proteome-wide scale.[138.1] A notable application of AI in biophysics is the development of FragFold, a program that leverages AlphaFold to predict protein fragments capable of binding to and inhibiting full-length proteins in E. coli. This novel application of AlphaFold has demonstrated high accuracy in predicting binding and inhibition, even without prior structural data, thus offering a generalizable approach for identifying binding modes likely to inhibit protein function.[139.1] Furthermore, researchers have integrated AI with molecular dynamics simulations and network analysis to enhance the prediction of binding sites on the PD-L1 protein, a critical target in cancer treatment. This approach, which combines AlphaFold2-based AI tools with experimental validation techniques, promises to accelerate the development of personalized cancer therapies by identifying critical interaction points in cancer-related proteins.[140.1]In this section:
Applications Of Biophysics
Medical Imaging Techniques
Medical imaging techniques have significantly benefited from advancements in biophysical methods, enhancing the ability to observe and analyze biological structures and processes at a molecular level. One such technique is fluorescence microscopy, which has become a versatile and powerful tool in live-cell investigations. This method allows for the study of chemical and biological systems in real time, providing insights into the structure, interconnectivity, and motion of target proteins. The use of fluorescent proteins to label and monitor the localization of proteins within cells has been instrumental in revealing their relationships with other molecules, contributing to a deeper understanding of cellular processes.[163.1] The development of super-resolved fluorescence microscopy, pioneered by Nobel Laureates Eric Betzig, Stefan W. Hell, and William E. Moerner, has further advanced the field by allowing for the visualization of structures at a resolution beyond the diffraction limit of light. This innovation has opened new avenues for exploring protein chemistry and biophysics, particularly in the study of protein conformational changes.[163.1] Another significant advancement in medical imaging is the application of X-ray crystallography and Nuclear Magnetic Resonance (NMR) spectroscopy. These techniques have revolutionized drug discovery by enabling detailed examination of protein crystal structures and their interactions with ligands. The introduction of Cryo-Electron Microscopy (cryo-EM) has also been transformative, providing 3D structures of specific proteins and offering predictions for multiprotein complexes, protein dynamics, and ligand interactions. These imaging techniques, combined with artificial intelligence and protein prediction models, hold the potential to accelerate the development of new methods for visualizing intact cells and characterizing subcellular organization.[164.1]Protein Engineering and Drug Development
Protein misfolding is a significant factor in various genetic disorders, including Alzheimer's disease, Parkinson's disease, Huntington's disease, sickle cell anemia, cystic fibrosis, cancer, and other degenerative and neurodegenerative conditions.[150.1] Understanding the mechanisms of protein folding and misfolding is crucial, as misfolded proteins contribute to the pathogenesis of these diseases.[151.1] Recent advancements in structural biophysics have provided novel insights into the "protein folding problem" and the effects of forces as denaturants, which are essential for comprehending how proteins transition from a string of amino acids to their functional forms in the cellular environment.[153.1] Additionally, these advancements have facilitated the structural characterization and detection of amyloid fibrils, which are associated with protein aggregation and misfolding.[152.1] By employing improved imaging techniques and computational modeling, scientists can better investigate the factors responsible for protein misfolding and develop strategies to combat these aggregation processes.[152.1] In the realm of drug development, biophysics contributes significantly to the design of advanced drug delivery systems. These systems are formulated using cutting-edge technology to ensure targeted delivery of therapeutics, thereby maximizing efficacy and minimizing off-target effects.[155.1] The integration of biophysical principles in drug delivery systems is expected to evolve, with a focus on creating biocompatible and biodegradable materials that enhance drug delivery efficiency. This evolution is driven by the need to address challenges such as drug bio-acceptability and the development of single-dose therapies that prevent excessive drug accumulation in the body.[155.1]Prominent Biophysicists
Contributions of Historical Figures
Maurice Hugh Frederick Wilkins CBE FRS (15 December 1916 – 5 October 2004) was a New Zealand-born British biophysicist and Nobel laureate whose research significantly advanced the fields of physics and biophysics, particularly in areas such as phosphorescence, isotope separation, optical microscopy, and X-ray diffraction.[195.1] His work on DNA can be divided into two distinct phases, with the second phase occurring between 1951 and 1952, during which he produced clear "B form" X-shaped images from squid sperm. These images were shared with James Watson and Francis Crick, prompting Watson to commend the quality of Wilkins' X-ray diffraction photographs.[195.1] Notably, Wilkins played a crucial role in sharing findings from his colleague Rosalind Franklin, specifically the pivotal Photo 51, with Watson and Crick without her consent, which directly influenced their development of the double-helix model of DNA.[181.1] The collaborative efforts of Wilkins, Watson, and Crick, alongside Franklin's contributions, marked a monumental shift in the understanding of genetics and biotechnology, leading to accelerated progress in these fields.[183.1] Max Ludwig Henning Delbrück, another prominent figure in biophysics, applied his knowledge of theoretical physics to biological systems, significantly impacting the field. Delbrück's work on bacteriophages and gene replication demonstrated that bacteria undergo random genetic mutations to resist phage infections, linking bacterial genetics to the genetics of higher organisms.[190.1] His approach to experimental design emphasized the role of chance in scientific discovery, a concept that has influenced methodologies in biophysics today. Delbrück's quantum mechanical model of the gene, which he referred to as an "Atomverband," connected genetics with physics and chemistry, paving the way for a more concrete analysis of genes using exact sciences.[191.1] His recognition of the role of serendipity in scientific discovery further underscored the importance of chance in advancing scientific knowledge.[193.1]Modern Influencers in Biophysics
Joachim Frank, a German-American biophysicist, has significantly influenced modern biophysics through his pioneering work in cryo-electron microscopy (cryo-EM). His development of image-processing techniques was crucial for the advancement of cryo-EM, allowing for the high-resolution structure determination of biomolecules in solution, a breakthrough that earned him the Nobel Prize in Chemistry in 2017.[187.1] Frank's innovations have transformed cryo-EM into a mainstream tool for structural biology, offering advantages over traditional methods like X-ray crystallography and NMR. These advantages include the ability to analyze proteins and complexes of large molecular weight without the need for crystals, reduced radiation damage, and the capability to capture multiple conformational states in a single experiment.[189.1] His work has particularly advanced the understanding of ribosome structure and function, as well as the elucidation of membrane channel structures.[186.1] Carlos Bustamante, a Peruvian-born American biophysicist, is renowned for his contributions to the field of single-molecule biophysics, particularly in the study of molecular motors and biological polymer physics.[178.1] A significant focus of his research is the bacteriophage phi 29 connector, which, along with its associated ATPase (gp16), is responsible for packaging viral DNA within the capsid during bacteriophage assembly. This molecular motor has been shown to generate forces as high as 57 pN, highlighting its powerful capabilities.[199.1] Through his innovative single-molecule studies, Bustamante has advanced our understanding of the mechanics of these molecular motors, illustrating the impact of his work on the field.[199.1]In this section:
Future Directions
Emerging Trends in Research
Biophysics is an interdisciplinary science that applies the principles and methods of physics to understand biological systems at every level, from atoms and molecules to cells, organisms, and ecosystems.[214.1] This field serves as a bridge between the principles of physics and the complexities of biological systems, offering insights into the mechanisms underlying health and disease.[215.1] Modern biophysics encompasses a broad range of research areas, including bioelectronics and quantum biology, and involves both experimental and theoretical tools.[213.1] One of the emerging trends in biophysics research is the integration of artificial intelligence (AI) and machine learning (ML) technologies. These technologies are transforming the pharmaceutical industry by revolutionizing drug development and discovery processes. AI and ML facilitate the quick analysis of complex biological data, enabling the identification of new therapeutic targets and the prediction of molecular interactions.[218.1] The integration of AI with advanced imaging technologies, such as high-throughput microscopy and single-cell sequencing, provides new avenues for discovering and validating drug targets, thereby enhancing the understanding of complex disease mechanisms.[220.1] Another significant trend is the development of predictive biophysical computer models for personalized medicine. These models are designed to predict individual responses to different medications, allowing for more effective and personalized treatment strategies. For instance, the QSPainRelief Consortium focuses on developing computational models to optimize the personalized treatment of chronic pain.[221.1] Additionally, multi-scale modeling approaches are being developed to couple biomolecular networks with biophysical models, enabling detailed comparisons of various treatments through virtual clinical trials. These models help translate therapies from in vitro studies to in vivo applications, facilitating the transition from animal models to human clinical trials.[222.1]Potential Impact on Healthcare and Environment
Biophysics is poised to significantly impact healthcare by advancing drug development and personalized medicine. In drug development, biophysics provides a deeper understanding of biological processes and molecular interactions, which is crucial for identifying potential drug targets and designing effective molecules.[236.1] Biophysical methods, such as surface plasmon resonance (SPR), isothermal titration calorimetry (ITC), nuclear magnetic resonance (NMR), and X-ray crystallography, are instrumental in drug discovery. These techniques offer insights into the binding, stability, and dynamics of drug-target interactions, aiding in the identification of promising drug candidates and the optimization of lead compounds.[241.1] Furthermore, biophysical methods facilitate the qualitative detection of small molecule binding to targets and the quantitative determination of binding parameters, supporting compound progression and mechanistic understanding of drug-receptor interactions.[243.1] In the realm of personalized medicine, biophysics contributes to tailoring treatments based on individual genetic profiles. Genomics, a key area in personalized medicine, analyzes genetic information to predict disease susceptibility and optimize treatment strategies.[246.1] Pharmacogenomics, a subset of genomics, explores the relationship between genetic variations and drug responses, aiming to enhance treatment efficacy and minimize adverse effects.[246.1] Personalized medicine also considers environmental exposures, developmental phenomena, and epigenetic changes, employing patient-derived cell and organoid models to determine optimal therapies.[247.1] This approach enables the development of personalized digital therapeutics and individualized diagnostic protocols, improving patient outcomes across diverse medical fields.[247.1]In this section:
References
[1] Biophysics - Wikipedia — Biophysics is an interdisciplinary science that applies approaches and methods traditionally used in physics to study biological phenomena. Biophysics covers all scales of biological organization, from molecular to organismic and populations. Biophysical research shares significant overlap with biochemistry, molecular biology, physical chemistry, physiology, nanotechnology, bioengineering, computational biology, biomechanics, developmental biology and systems biology. The term biophysics was originally introduced by Karl Pearson in 1892. The term biophysics is also regularly used in academia to indicate the study of the physical quantities (e.g. electric current, temperature, stress, entropy) in biological systems. While some colleges and universities have dedicated departments of biophysics, usually at the graduate level, many do not have university-level biophysics departments, instead having groups in related departments such as biochemistry, cell biology, chemistry, computer science, engineering, mathematics, medicine, molecular biology, neuroscience, pharmacology, physics, and physiology.
[2] What Is Biophysics? | The Biophysical Society — Biophysics is the field that applies the theories and methods of physics to understand how biological systems work. Biophysics has been critical to understanding the mechanics of how the molecules of life are made, how different parts of a cell move and function, and how complex systems in our bodies—the brain, circulation, immune system, and others— work. Biophysicists are uniquely trained in the quantitative sciences of physics, math, and chemistry and they are able tackle a wide array of topics, ranging from how nerve cells communicate, to how plant cells capture light and transform it into energy, to how changes in the DNA of healthy cells can trigger their transformation into cancer cells, to so many other biological problems. Biophysicists work to develop methods to overcome disease, eradicate global hunger, produce renewable energy sources, design cutting-edge technologies, and solve countless scientific mysteries.
[4] biophysics summary | Britannica — biophysics summary Our editors will review what you’ve submitted and determine whether to revise the article. biophysics, Discipline concerned with applications of the principles and methods of the physical sciences to biological problems. Biophysics deals with biological functions that depend on physical agents such as electricity or mechanical force, with the interaction of living organisms with physical agents such as light, sound, or ionizing radiation, and with interactions between living things and their environment as in locomotion, navigation, and communication. Its subjects include bone, nerve impulses, muscle, and vision as well as organic molecules, using such tools as paper chromatography and X-ray crystallography.
[27] Artificial Intelligence for Medical Diagnostics—Existing and Future AI ... — AI algorithms can analyze medical images (e.g., X-rays, MRIs, ultrasounds, CT scans, and DXAs) and assist healthcare providers in identifying and diagnosing diseases more accurately and quickly. AI can analyze large amounts of patient data, including medical 2D/3D imaging, bio-signals (e.g., ECG, EEG, EMG, and EHR), vital signs (e.g., body temperature, pulse rate, respiration rate, and blood pressure), demographic information, medical history, and laboratory test results. By using AI algorithms to analyze vast amounts of medical data and identify patterns and relationships, general AI for medical diagnostics can transform the field of medicine, leading to improved patient outcomes and a more efficient and effective healthcare system.
[28] Artificial intelligence in disease diagnosis: a systematic literature ... — Artificial intelligence techniques ranging from machine learning to deep learning are prevalent in healthcare for disease diagnosis, drug discovery, and patient risk identification. Researchers have used various AI-based techniques such as machine and deep learning models to detect the diseases such as skin, liver, heart, alzhemier, etc. To fully understand how AI assists in the diagnosis and prediction of a disease, it is essential to understand the use and applicability of diverse techniques such as SVM, KNN, Naïve Bayes, Decision Tree, Ada Boost, Random Forest, K-Mean clustering, RNN, Convolutional neural networks (CNN), Deep-CNN, Generative Adversarial Networks (GAN), and Long short-term memory (LSTM) and many others for various disease detection system (Owasis et al.
[29] Revolutionizing clinical laboratories: The impact of artificial ... — References (58) However, the benefits of AI come with challenges, including concerns about data integrity, ethical implications, and potential biases in algorithms, requiring careful management as AI becomes more integrated into clinical practice. Additionally, advancements in mobile technology, the Internet of Things (IoT), computing power, and data security further accelerate the adoption of AI in healthcare delivery models . REFERENCES (58) Big Data in Laboratory Medicine—FAIR Quality for AI? View more references View full text © 2025 Elsevier Inc. All rights are reserved, including those for text and data mining, AI training, and similar technologies. All rights are reserved, including those for text and data mining, AI training, and similar technologies.
[30] Significance of machine learning in healthcare: Features, pillars and ... — Significance of machine learning in healthcare: Features, pillars and applications - ScienceDirect Machine Learning (ML) is a subtype of Artificial Intelligence (AI) technology that aims to improve the speed and accuracy of physicians' work. Paper identifies and discusses the significant applications of ML for Healthcare. Paper explores how ML-based tools are used to provide various treatment alternatives and individualised treatments and improve the overall efficiency of healthcare systems. Machine Learning (ML) applications are making a considerable impact on healthcare. Finally, it identified and discussed the significant applications of ML for healthcare. ML-based tools are used to provide various treatment alternatives and individualised treatments and improve the overall efficiency of hospitals and healthcare systems while lowering the cost of care. For all open access content, the relevant licensing terms apply.
[32] The Roles of Machine Learning in Biomedical Science — Thanks to engineering applications, machine learning is making it possible to model data extremely well, without using strong assumptions about the modeled system. Machine learning can usually better describe data than biomedical models and thus provides both engineering solutions and an essential benchmark. The vast field of machine learning is a radically different way of approaching modeling that relies on minimal human insight (Bishop 2006). For example, in psychiatric medicine, studies have used smartphone recordings of everyday behaviors (e.g., when patients wake up or how much they exercise) to predict mood using machine learning (Wang et al. Because machine learning is a useful tool for making predictions, it may provide close to an upper bound for human-produced models.
[33] Physics Meets Machine Learning: Machine learning in biological physics ... — Machine learning has been proposed as an alternative to theoretical modeling when dealing with complex problems in biological physics.We summarize recent efforts to establish these connections and provide examples on how each of these formulations integrating physical modeling and machine learning have been successful in tackling recent problems in biomolecular structure, dynamics, function, evolution, and design.Instances include protein structure prediction; improvement in computational complexity and accuracy of molecular dynamics simulations; better inference of the effects of mutations in proteins leading to improved evolutionary modeling and finally how machine learning is revolutionizing protein engineering and design.In this perspective, we aim to provide a glimpse at the state-of-the-art algorithms in machine learning and how they are utilized for several applications in biological physics.We also look into the problem of protein design and how a combination of physical models and machine learning can be used to engineer possible proteins that fold to specified structures. The problem of protein folding or that of inferring a three-dimensional molecular structure of a protein using a sequence of amino acids has been relevant in the past five decades.Modern molecular dynamics approaches are looking into advances in machine learning to help bridge the gap between all-atomic models and coarse-grained ones.
[34] AI meets biophysics: New approach identifies critical interaction ... — Researchers at Auburn University, in collaboration with scientists from the University of Basel and ETH Zurich, have made an advance in the fight against cancer.The team, led by Dr. Rafael Bernardi, Associate Professor of Biophysics in the Department of Physics, has developed a novel approach integrating artificial intelligence (AI) with molecular dynamics simulations and network analysis to enhance the prediction of binding sites on the PD-L1 protein.This breakthrough promises to accelerate the development of personalized cancer treatments by identifying critical interaction points in cancer-related proteins.Dr. Bernardi and his team have developed a sophisticated method that combines AlphaFold2-based AI tools with molecular dynamics simulations and dynamic network analysis.Their approach allowed them to predict and confirm key binding regions in the PD-L1 protein that are critical for drug interaction.The implications of this study go far beyond PD-L1."This research stresses the potential of computational tools like NAMD and VMD, combined with cutting-edge hardware such as NVIDIA DGX systems, to advance cancer therapeutics.
[38] Understanding Biophysics: Key Concepts and Historical Insights | Course ... — BRIEF HISTORY Biophysics is a relatively young branch of science. • It became a definite subfield in the early to mid-20th Century. • Foundations for the study of biophysics were already in existence. • In the 19th Century, a group of physiologists in Berlin pioneered this. • The Berlin school of physiologists included Hermann von Helmholtz, Emil DuBois-Reymond, Ernst von Brücke, and
[39] Biophysics | Molecular Biology, Physics & Chemistry | Britannica — Ask the Chatbot Games & Quizzes History & Society Science & Tech Biographies Animals & Nature Geography & Travel Arts & Culture ProCon Money Videos The semipermeable membranes required to produce the fluid flow that characterizes osmotic phenomena initially came from biological sources; French scientist René Dutrochet wrote in 1828, “it appears from these new studies that the endosmotic and exosmotic phenomena, which I discovered, belong to a new class of physical phenomena, whose powerful intervention in the vital phenomenon is no longer doubtful.” Following the first quantitative measurements by the botanist W.F.P. Pfeffer, the fundamental laws governing diffusion were enunciated by Adolf Fick, who in 1856 published what is probably the first biophysics text, Die medizinische Physik (“Medical Physics”).
[40] Biophysics - Wikiwand — Biophysics Biophysics is an interdisciplinary science that applies approaches and methods traditionally used in physics to study biological phenomena.Biophysics covers all scales of biological organization, from molecular to organismic and populations. The term biophysics was originally introduced by Karl Pearson in 1892. The term biophysics is also regularly used in academia to indicate the study of the physical quantities (e.g. electric current, temperature, stress, entropy) in biological systems. While some colleges and universities have dedicated departments of biophysics, usually at the graduate level, many do not have university-level biophysics departments, instead having groups in related departments such as biochemistry, cell biology, chemistry, computer science, engineering, mathematics, medicine, molecular biology, neuroscience, pharmacology, physics, and physiology. List of publications in biology – Biophysics Biophysics
[41] Biophysics: Definition, History, Major and Careers - Biology Dictionary — Research in biophysics has helped prevent and treat disease, advance drug development, and create technology to allow humans to live more sustainably and protect the changing environment. History of Biophysics. Biophysics is a relatively young branch of science; it arose as a definite subfield in the early to mid-20th Century.
[42] Biophysics - Wikipedia — Biophysics is an interdisciplinary science that applies approaches and methods traditionally used in physics to study biological phenomena. Biophysics covers all scales of biological organization, from molecular to organismic and populations. Biophysical research shares significant overlap with biochemistry, molecular biology, physical chemistry, physiology, nanotechnology, bioengineering, computational biology, biomechanics, developmental biology and systems biology. The term biophysics was originally introduced by Karl Pearson in 1892. The term biophysics is also regularly used in academia to indicate the study of the physical quantities (e.g. electric current, temperature, stress, entropy) in biological systems. While some colleges and universities have dedicated departments of biophysics, usually at the graduate level, many do not have university-level biophysics departments, instead having groups in related departments such as biochemistry, cell biology, chemistry, computer science, engineering, mathematics, medicine, molecular biology, neuroscience, pharmacology, physics, and physiology.
[44] Exploring Key Discoveries in Physics and Their Impact — Some key impacts of physical discoveries include: ... The development of thermodynamics in the 19th century facilitated the understanding of various engine cycles and refrigeration processes, which had profound implications for industry and society. ... Biophysics: Focuses on the physical principles underlying biological structures and
[45] History of Biology: The 19th Century - The Linda Hall Library — The study of metabolism was also illuminated by knowledge of essential chemical facts and concepts that came to light through the work of Wohler (1800-1882), Liebig (1803-1873), Pasteur (1822-1895), and many others. Bernard's (1813-1878) concept of milieu interior led to the discovery of the mechanisms of homeostasis in the following century.
[46] Discover 19TH CENTURY SCIENCE & Its MODERN IMPACT! — The transformative 19th century science era established a structured and professional approach to scientific inquiry, which has significantly influenced the world we live in today. From the introduction of the word "scientist" to groundbreaking discoveries, the 19th century was a period of rapid advancement and enduring legacies that continue to shape modern science.
[55] The Original Biomechanists - American Society of Biomechanics — (Imagine, theoretical medicine, in 1658!) Malpighi was to become the greatest of the early microscopists, and the father of embryology. He, Borelli, and Descartes were key figures in establishing the iatrophysical approach to medicine, which held that mechanics rather than chemistry was the key to understanding the functioning of the human body.
[56] Biophysics - Definition, Meaning, Research Topics and FAQs - Vedantu — Biophysics is a relatively new branch of science, the need and importance of biophysics arose as a definite subfield between the early 20th century to mid 20th century. The foundations for the study of biophysics were laid down much earlier, within the 19th Century, by a gaggle of physiologists in Berlin.
[75] Biophysics Concept Map: From Interdisciplinary Approach to Real-World ... — Biophysics Concept Map: From Interdisciplinary Approach to Real-World Applications Biophysics is a fascinating interdisciplinary field that bridges the gap between physics and biology. This concept map provides a comprehensive overview of the key aspects of biophysics, from its fundamental approach to its real-world applications. At the heart of our concept map lies biophysics, a field that applies physical principles and methods to biological systems. Molecular Biophysics: Investigating the physical properties and behavior of biological molecules. The knowledge and techniques developed in biophysics have numerous practical applications: Biophysics represents a powerful fusion of physics and biology, offering unique insights into the fundamental workings of life. Biophysics Concept Map: From Interdisciplinary Approach to Real-World Applications Biophysics
[76] Outline of biophysics - Wikipedia — 1.1 Biophysics is Biophysics – interdisciplinary science that uses the methods of physics to study biological systems. Biophysics is Medical biophysics – interdisciplinary field that applies methods and concepts from physics to medicine or healthcare, ranging from radiology to microscopy and nanomedicine. Membrane biophysics – study of biological membranes using physical, computational, mathematical, and biophysical methods. Molecular biophysics – interdisciplinary field that applies methods and concepts from physics, chemistry, engineering, mathematics and biology to understand biomolecular systems and explain biological function in terms of molecular structure, structural organization, and dynamic behaviour at various levels of complexity, from single molecules to supramolecular structures, viruses and small living systems. Biophysical techniques – methods used for gaining information about biological systems on an atomic or molecular level. Biophysics
[84] NMR Spectroscopy: Instrumentation, Applications, Limitation - Science Info — Applications Of NMR Spectroscopy. Organic, organometallic, and biological compounds are identified and structurally elucidated by using NMR spectroscopy. In biophysics and molecular biology, NMR spectroscopy is used to investigate the structure, dynamics, and molecular interactions of biomolecules such as peptides, proteins, nucleic acids
[85] Applications of NMR in Biochemistry and Biophysics — All Posts, NMR in Biochemistry and Biophysics / May 28, 2023 By applying a magnetic field and radiofrequency pulses, NMR provides detailed information about the molecular structure, dynamics, reaction state, and chemical environment of molecules. In biochemistry and biophysics, NMR serves as a versatile tool for investigating the complexities of biological macromolecules and their interactions within cellular environments. Structural Dynamics: NMR captures both static structures and conformational ensembles, revealing flexibility and multiple states within proteins. NMR enables the detailed study of these structures: NMR is invaluable for studying how small molecules (ligands) interact with proteins: Ligand Observed Methods: Techniques like WaterLOGSY and STD-NMR assess ligand binding without detailed structural information. X-ray Crystallography: Offers high-resolution structures, while NMR provides dynamic information. NMR in Structural Biology
[87] Developments in X-ray Crystallographic Structure ... - Science — Macromolecular crystallography enables the three-dimensional (3D) structures of large biologically interesting molecules to be determined. Structures of proteins and nucleic acids determined by macromolecular crystallography are vital for elucidating protein function and intermolecular interactions and for improving our understanding of basic biological and biochemical mechanisms and disease
[89] Computational Biophysics | Modeling, Simulation & Analysis — Computational biophysics represents a pivotal intersection between biology, physics, chemistry, and computer science, focusing on the application of computational techniques to understand the physical principles underlying biological processes and structures.This multidisciplinary field leverages numerical methods and theoretical models to simulate and analyze the behavior of biological systems at an atomic or molecular level.At the heart of computational biophysics is the modeling and simulation of biological molecules.These simulations employ various levels of theoretical abstraction and computational approaches, from quantum mechanics to classical mechanics models. Classical mechanics-based models, including molecular dynamics (MD) and Monte Carlo (MC) simulations, offer a balance between computational demand and the ability to simulate larger systems over longer time scales.These tools allow scientists to visualize molecular interactions in three-dimensional space, providing invaluable insights into the structure-function relationships of biological molecules.One of the most notable is in drug discovery, where it aids in the design and optimization of new therapeutic molecules by predicting how they interact with their biological targets.
[110] The top biotech breakthroughs that defined 2023 — 2023 marked a significant milestone in Alzheimer’s disease (AD) treatment with the FDA’s approval of LEQEMBI (lecanemab-irmb).The development of Alzheimer’s vaccines has been another biotech highlight of 2023.2023 marked a historic year in the field of genetic medicine with the UK’s approval of Casgevy, the world’s first CRISPR-based therapy, another biotech breakthrough.2023 has been a pivotal year in biotech, marked by significant breakthroughs in protein engineering, largely driven by the integration of AI.AI’s computational prowess is leveraged to predict protein structures and functions, enabling the design of novel proteins with a level of precision previously unattainable.2023 marked a year of significant progress in the field of oncology, notably in prostate cancer research. Immunotherapy, which leverages the body’s immune system to fight cancer, had a few ups and downs in 2023.
[116] Recent advances in stem cell therapy: efficacy, ethics, safety concerns ... — Stem cell therapy has emerged as a hope for neurodegenerative disorders since it is not only the damaged neurons that might be replaced, but other neuromodulators and neuroprotectors are secreted.Post-transplantation improved synaptic plasticity, apoptosis inhibition, and reduction in tau-phosphorylation and amyloid beta (Aβ) production has been observed in Alzheimer’s patients.A large number of experimental, preclinical, and clinical studies have been conducted, and encouraging results have been obtained.Stem cell or regenerative therapy enables the repairing of damaged and degenerated neural tissues.In the presence of exogenous stem cells, wide varieties of bioactive products are secreted that improve neural growth, reduce apoptosis, subside inflammation, and establish synaptic connections between damaged neurons.Stem cell therapy is the only modality that can cure neurodegenerative disorders.In experiments conducted in animal models and in some preclinical and clinical studies, structural and functional improvements have been observed in neurodegenerative disorders.
[132] 3D bioprinting approaches for enhancing stem cell-based neural tissue ... — Three-dimensional (3D) bioprinting holds immense promise for advancing stem cell research and developing novel therapeutic strategies in the field of neural tissue engineering and disease modeling. This paper critically analyzes recent breakthroughs in 3D bioprinting, specifically focusing on its application in these areas.
[137] Recent advances in AI-driven protein-ligand interaction ... - PubMed — AI-driven methodologies are significantly improving key aspects of the field, including ligand binding site prediction, protein-ligand binding pose estimation, scoring function development, and virtual screening.Traditional docking methods based on empirical scoring functions often lack accuracy, whereas AI models, including graph neural networks, mixture density networks, transformers, and diffusion models, have enhanced predictive performance.Ligand binding site prediction has been refined using geometric deep learning and sequence-based embeddings, aiding in the identification of potential druggable target sites.Binding pose prediction has evolved with sampling-based and regression-based models, as well as protein-ligand co-generation frameworks.AI-powered scoring functions now integrate physical constraints and deep learning techniques to improve binding affinity estimation, leading to more robust virtual screening strategies.As AI technologies continue to evolve, they are expected to revolutionize molecular docking and affinity prediction, increasing both the accuracy and efficiency of structure-based drug discovery.In this review, we summarize the recent AI-driven advances in various protein-ligand interaction prediction tasks.
[138] Recent advances in predicting and modeling protein-protein interactions ... — With recent breakthroughs in structure prediction and a deluge of genomic sequence data, computational methods to predict PPIs and model spatial structures of protein complexes are now approaching the accuracy of experimental approaches for permanent interactions and show promise for elucidating transient interactions.As we describe here, the key to this success is rich evolutionary information deciphered from thousands of homologous sequences that coevolve in interacting partners.This covariation signal, revealed by sophisticated statistical and machine learning (ML) algorithms, predicts physiological interactions.Accurate artificial intelligence (AI)-based modeling of protein structures promises to provide accurate 3D models of PPIs at a proteome-wide scale.Revolutionizing protein-protein interaction prediction with deep learning.Application of Machine Learning Approaches for Protein-protein Interactions Prediction.Systematic evaluation of machine learning methods for identifying human-pathogen protein-protein interactions.
[139] AI system predicts protein fragments that can bind to or inhibit a ... — Recently published in Proceedings of the National Academy of Sciences, a new method developed in the Department of Biology builds on existing artificial intelligence models to computationally predict protein fragments that can bind to and inhibit full-length proteins in E. coli.The program, called FragFold, leverages AlphaFold, an AI model that has led to phenomenal advancements in biology in recent years due to its ability to predict protein folding and protein interactions.The goal of the project was to predict fragment inhibitors, which is a novel application of AlphaFold.The researchers on this project confirmed experimentally that more than half of FragFold’s predictions for binding or inhibition were accurate, even when researchers had no previous structural data on the mechanisms of those interactions.“Our results suggest that this is a generalizable approach to find binding modes that are likely to inhibit protein function, including for novel protein targets, and you can use these predictions as a starting point for further experiments,” says co-first and corresponding author Andrew Savinov, a postdoc in the Li Lab.“This is one example of how AlphaFold is fundamentally changing how we can study molecular and cell biology,” Keating says.“The big surprise was that we can predict binding with such high accuracy and, in fact, often predict binding that corresponds to inhibition,” Savinov says.
[140] AI meets biophysics: New approach identifies critical interaction ... — Researchers at Auburn University, in collaboration with scientists from the University of Basel and ETH Zurich, have made an advance in the fight against cancer.The team, led by Dr. Rafael Bernardi, Associate Professor of Biophysics in the Department of Physics, has developed a novel approach integrating artificial intelligence (AI) with molecular dynamics simulations and network analysis to enhance the prediction of binding sites on the PD-L1 protein.This breakthrough promises to accelerate the development of personalized cancer treatments by identifying critical interaction points in cancer-related proteins.Their work, published in the Journal of the American Chemical Society, focuses on understanding how therapeutic proteins interact with PD-L1, a protein known to help cancer cells evade detection by the immune system.Dr. Bernardi and his team have developed a sophisticated method that combines AlphaFold2-based AI tools with molecular dynamics simulations and dynamic network analysis.Their approach allowed them to predict and confirm key binding regions in the PD-L1 protein that are critical for drug interaction.The computational approach was validated with cutting-edge experimental techniques, including cross-linking mass spectrometry and next-generation sequencing.
[150] Frequent contiguous pattern mining over biological sequences of protein ... — Protein misfolding is believed to be one of the primary causes of genetic disorder diseases such as Alzheimer's disease, Parkinson's disease, Huntington's disease, Sickle cell anemia, Cystic fibrosis, Cancer and many other degenerative and neurodegenerative disorders . Protein misfolding may occur due to an unwanted mutation in their
[151] A Symphony of Bonds: Sonification Unlocks Protein Folding Pathways — Misfolded proteins contribute to Alzheimer's disease, Parkinson's disease, cystic fibrosis and other disorders. To better understand how this process goes awry, scientists must first determine how a string of amino acids shape-shifts into its final form in the watery environment of the cell.
[152] Biophysical Insight into Protein Folding, Aggregate Formation and its ... — This review aims to summarise the underlying mechanisms of protein folding, misfolding and aggregation. It also highlights the recent technologies for the structural characterisation and detection of amyloid fibrils in addition to the various factors responsible for the aggregate formation and the strategies to combat the aggregation process.
[153] Force Spectroscopy Tools for Probing the Mechanochemistry of Protein ... — The current chapter discusses our previous understanding of the "protein folding problem," effect of force as a denaturant, and principles and working of the single-molecule force spectroscopy tools and novel insights they provided on the process of protein folding and unfolding.
[155] Advances in drug delivery systems, challenges and future directions — Advances in molecular pharmacology and an improved understanding of the mechanism of most diseases have created the need to specifically target the cells involved in the initiation and progression of diseases.Recent drug delivery systems (DDS) are formulated using advanced technology to accelerate systemic drug delivery to the specific target site, maximizing therapeutic efficacy and minimizing off-target accumulation in the body.They are made of nanomaterials or miniaturized devices with multifunctional components that are biocompatible, biodegradable, and have high viscoelasticity with an extended circulating half-life.It updates the most recent drug delivery systems, their therapeutic applications, challenges associated with their use, and future directions for improved performance and use.Recently, several drug delivery systems (NDDS) have been developed using advanced systems for more convenient, controlled, and targeted delivery. Drug delivery and nanomedicine have become a very fascinating area of research in modern science, in the past years, it has gotten a lot of attention in both research, experimentation and in a number of clinical trials. Vargason et al. believe that the use of cell therapies can go a long way to solve the bio-acceptability issues that drug delivery systems face, they also think it will create a single dose that is effective that avoids high accumulation of drugs in the system.
[163] Applications of fluorescence spectroscopy in protein conformational ... — Emission fluorescence is a versatile and powerful biophysical technique.Fluorescence microscopy provides valuable live-cell investigations.Using fluorescence tools, chemical, and biological systems are being studied at a molecular level and in real time.The fluorescence images are successfully used in structure elucidation, but also for elucidation of interconnectivity and motion of target proteins.In the study of protein conformational changes, the fluorescence technique can be very informative and provide valuable answers on protein chemistry and biophysics through emission maximum shift phenomenon that can be combined with quenching/self-quenching.The Nobel Laureates in Chemistry 2014, Eric Betzig, Stefan W. Hell, and William E. Moerner were the pioneers in the development of super-resolved fluorescence microscopy.Fluorescent microscopy makes use of fluorescent proteins (FPs) to label target proteins and monitor their localisation in cells, for example, and reveal information about the target protein's relationships with other molecules within cells.
[164] Advancements in Biophysics and Emerging Drug Modalities — Biophysics has always provided the most powerful tools for molecular observation and description – indeed, some of the most important drug discoveries in recent memory are a direct result of innovations in these tools.The appearance of X-ray crystallography in the 1990s revolutionized the field of drug discovery, allowing for detailed examination of the crystal structures of proteins and the ligands that bind to them.Similar innovations were brought about in structure-based drug discovery with the entry of Nuclear Magnetic Resonance (NMR) spectroscopy into the mainstream.Beyond the two main established methods for structural biology, one new technique stands out in particular: Cryo-Electron Microscopy (cryo-EM). These platforms focus on providing the 3D structures of specific proteins, but variations on AlphaFold also provide predictions for multiprotein complexes, protein dynamics, ligand interactions and even RNA structure.Artificial Intelligence and protein prediction also hold unique potential to accelerate the development of cryo-EM-derived methods.These include cryo-electron tomography and focused-ion beam scanning electron microscopy, which can visualize intact cells and characterize subcellular organization and localization.
[178] List of biophysicists - Wikipedia — Christian B. Anfinsen (American, 1916–1995) — author of the postulate about spontaneous protein folding, for which he received a Nobel PrizeDavid Baker — Protein structure prediction; protein design; Rosetta softwareAdriaan (Ad) Bax (Dutch-born American, 1956–) — development of methodology for NMR (Nuclear magnetic resonance) spectroscopyCarlos Bustamante (Peruvian-born American, 1951–) — known for single-molecule biophysics of molecular motors and biological polymer physicsG. Marius Clore FRS (British and American) — pioneer of multidimensional macromolecular NMR spectroscopy laying foundations of 3D structure determination of proteins in solution, and discovery of rare, invisible conformational states of macromoleculesKen A. Dill (American, 1947–) — research on folding pathways of proteins.Hans Frauenfelder (1922–2022) — pioneering work on experiment and theory to understand dynamic behavior in protein structure
[181] What was the significance of Rosalind Franklin and Maurice Wilkins ... — Collaboration and Information Sharing: Although Franklin's work was not fully acknowledged during her lifetime, Maurice Wilkins played a key role in sharing her findings with James Watson and Francis Crick without her consent. Wilkins showed Watson Photo 51, which directly influenced their development of the double-helix model .
[183] DNA Double-Helix Structure Discovery: Key Scientists, Impact, and ... — DNA Double-Helix Structure Discovery: Key Scientists, Impact, and Ethical Considerations - Science Oxford Live DNA Double-Helix Structure Discovery: Key Scientists, Impact, and Ethical Considerations The discovery of the DNA double-helix structure in 1953 can be attributed to the collaborative efforts of several key scientists. Together, these elements formed the foundation of the discovery process of the DNA double-helix structure that revolutionized genetics. The discovery of DNA’s double-helix structure revolutionized science, impacting multiple fields such as genetics and biotechnology. The DNA double-helix structure’s discovery led to accelerated progress in genetics. The discovery of the DNA double-helix structure marked a monumental shift in our understanding of genetics and biotechnology. The Historic DNA Double-Helix Structure Discovery:… Exploring DNA Double-Helix Structure Discovery:…
[186] New Book: Single-Particle Cryo-Electron Microscopy | Frank Lab — The book reproduces 55 of more than 300 articles written by the author, representing milestones in methods development of single-particle cryo-EM as well as important results obtained by this technique in the study of biological macromolecules and their interactions.Although the biological applications are mainly in the area of ribosome structure and function, the elucidation of membrane channel structures and their activation and gating mechanisms are represented, as well.The book is introduced by a commentary that explains the original development of concepts, describes the contributions of the author’s colleagues and students, and shows how challenges were overcome as the technique matured.Along the way, the ribosome served as an example for a macromolecule with intricate structure and conformational dynamics that pose challenges for three-dimensional visualization.Toward the end of the book — bringing us to the present time — molecular structures with near-atomic resolution are presented, and a novel type of computational analysis, manifold embedding, is introduced.Secondly, the development of the technique over the years is reflected by ever-expanding discoveries in the field of ribosome structure and function.
[187] The 2017 Nobel Prize in Chemistry: cryo-EM comes of age — The 2017 Nobel Prize in Chemistry was awarded to Jacques Dubochet, Joachim Frank, and Richard Henderson for "developing cryo-electron microscopy (cryo-EM) for the high-resolution structure determination of biomolecules in solution."This feature article summarizes some of the major achievements leading to the development of cryo-EM and recent technological breakthroughs that have transformed the method into a mainstream tool for structure determination.Profile of Joachim Frank, Richard Henderson, and Jacques Dubochet, 2017 Nobel Laureates in Chemistry. Proc Natl Acad Sci U S A. 2018.PMID: 29196527 Free PMC article.No abstract available.
[189] Developments, applications, and prospects of cryo‐electron microscopy — This has made cryo‐EM faster and more efficient, so that it can compete or even replace X‐ray crystallography in many aspects.4 Compared with traditional structural biology methods such as X‐ray crystallography and NMR, cryo‐EM has the following advantages: (a) it does not need crystals; (b) it is suitable for proteins and their complexes of large molecular weight; (c) it reduces radiation damage and maintains the native activity and functional state of samples, including posttranslational modifications; (d) multiple different conformational states can be captured in one experiment; (e) it is suitable for the structural analysis of membrane proteins such as GPCR and their complexes; (f) when encountering some structures that cannot be resolved by conventional X‐ray crystallography, cryo‐EM is still the mainstream.
[190] Max Ludwig Henning Delbruck (1906-1981) - The Embryo Project Encyclopedia — Max Ludwig Henning Delbrick applied his knowledge of theoretical physics to biological systems such as bacterial viruses called bacteriophages, or phages, and gene replication during the twentieth century in Germany and the US. Delbrück demonstrated that bacteria undergo random genetic mutations to resist phage infections. Those findings linked bacterial genetics to the genetics of higher
[191] Max Delbrück - PMC — After presenting quantitative results of the effects of ionizing radiation on mutation frequency, in a separate chapter Max worked out a quantum mechanical model of the gene, calling it an “Atomverband”—a collection of atoms—thereby connecting genetics with physics and chemistry and opening the abstract gene for a concrete analysis using the exact sciences. To use an expression Max liked: We are still waiting for a “Niels Bohr” in biology as Max used to say, recognizing that in James Watson, we already have had an “Einstein of biology.” As I have discussed, Bohr introduced what became Max's favorite idea into science: the idea of complementarity, which at this point should be stated in a general way.
[193] Serendipity: The Role of Chance in Scientific Discoveries — It may seem odd to refer to chance when discussing science. Scientific research supposedly operates in a very methodical, precise, and controlled way, with no room for chance in any area of the investigation. In fact, chance plays an important role in science and technology and has been responsible for some significant discoveries in the past.
[195] Maurice Wilkins - Wikipedia — Maurice Hugh Frederick Wilkins CBE FRS (15 December 1916 – 5 October 2004) was a New Zealand-born British biophysicist and Nobel laureate whose research spanned multiple areas of physics and biophysics, contributing to the scientific understanding of phosphorescence, isotope separation, optical microscopy, and X-ray diffraction.Wilkins' work on DNA falls into two distinct phases.During the second phase, 1951–52, Wilkins produced clear "B form" X-shaped images from squid sperm, images he sent to James Watson and Francis Crick, causing Watson to write "Wilkins... has obtained extremely excellent X-ray diffraction photographs" [of DNA].At King's College, Wilkins pursued, among other things, X-ray diffraction work on ram sperm and DNA that had been obtained from calf thymus by the Swiss scientist Rudolf Signer.Using a carefully bundled group of these DNA threads and keeping them hydrated, Wilkins and a graduate student Raymond Gosling obtained X-ray photographs of DNA that showed that the long, thin DNA molecule in the sample from Signer had a regular, crystal-like structure in these threads.Early in 1952, Wilkins began a series of experiments on sepia sperm which were very encouraging.Following the initial 1953 series of publications on the double helix structure of DNA, Wilkins continued research as leader of a team that performed a range of meticulous experiments to establish the helical model as valid among different biological species, as well as in living systems, to establish the universality of the double helix structure.
[199] Carlos Bustamante | Physics — A molecular motor of special interest is the bacteriophage phi 29 connector, which is responsible, together with its associated ATPase (gp16) for the packaging of the viral DNA inside the capsid during bacteriophage assembly. Our single molecule studies have revealed that this is a powerful motor, capable of generating forces as high as 57 pN.
[213] Biophysics - latest research news and features — Biophysics - latest research news and features News on biophysics Biophysics is an interdisciplinary science that uses the methods of physical science to study biological systems. Studies included under the branches of biophysics span all levels of biological organization, from the molecular scale to whole organisms and ecosystems. Biophysical research shares significant overlap with biochemistry, nanotechnology, bioengineering, agrophysics and systems biology. In addition to traditional (i.e. molecular and cellular) biophysical topics like structural biology or enzyme kinetics, modern biophysics encompasses an extraordinarily broad range of research, from bioelectronics to quantum biology involving both experimental and theoretical tools. Science X Daily and the Weekly Email Newsletter are free features that allow you to receive your favorite sci-tech news updates in your email inbox
[214] Biophysics News - SciTechDaily — Biophysics is an interdisciplinary science that applies the principles and methods of physics to understand how biological systems function at every level, from atoms and molecules to cells, organisms, and ecosystems. It integrates concepts from fields like quantum chemistry, thermodynamics, and mec ... New research revealed that cell nuclei
[215] Biophysics in Health and Disease: And Biological Systems - Life Conceptual — Biophysics in Health and Disease : And Biological Systems Home Blog Biophysics in Health and Disease: And Biological Systems Biophysics in Health and Disease: And Biological Systems Biophysics serves as a bridge between the principles of physics and the complexities of biological systems, offering insights into the mechanisms underlying health and disease. In this article, we delve into the significance of biophysics in understanding biological systems, its role in promoting health, its applications in disease research, and the future directions of this interdisciplinary field. Biophysics is the interdisciplinary field that applies the principles of physics to understand biological systems. By studying the biophysical properties of biological molecules and systems, biophysicists can unravel the mechanisms underlying disease pathology.
[218] The Role of AI in Drug Discovery: Challenges, Opportunities, and ... — Artificial intelligence (AI) has the potential to revolutionize the drug discovery process, offering improved efficiency, accuracy, and speed. AI-based approaches, on the other hand, have the ability to improve the efficiency and accuracy of drug discovery processes and can lead to the development of more effective medications. By combining the predictive power of AI with the expertise and experience of human researchers , it is possible to optimize the drug discovery process and accelerate the development of new medications . Recent developments in AI, including the use of data augmentation, explainable AI, and the integration of AI with traditional experimental methods, offer promising strategies for overcoming the challenges and limitations of AI in the context of drug discovery.
[220] Unleashing the future: The revolutionary role of machine learning and ... — These advanced technologies have revamped traditional drug discovery methods by allowing quick analysis of complex biological data and finding new therapeutic targets (Paul et al., 2021; Patel and Shah, 2022). Additionally, integrating AI and ML with advanced imaging technologies like high-throughput microscopy and single-cell sequencing provides new avenues for discovering and validating drug targets (Singh et al., 2023). By merging imaging data with AI and deep learning algorithms, researchers can uncover fresh drug targets and gain deeper insights into disease mechanisms, developing more potent therapies (Pun et al., 2023). By examining molecular and clinical data from various origins, including electronic health records and preclinical investigations, AI algorithms can unveil previously unknown correlations between drugs and biological targets while also predicting potential side effects and drug-drug interactions (Qureshi et al., 2023; Yang and Kar, 2023).
[221] Home - In Silico Biosciences — ISB is a founding member of the QSPainRelief Consortium, a five-year, $6,000,000 pharmacological pain relief project.Academic and industry leaders in chronic pain, pharmacology, pharmacogenomics, personalized medicine, systems biology, and predictive biophysical computer modeling combined their expertise in a consortium partnership.to develop validated computational models to optimize the personalized treatment of chronic pain. The consortium focuses on developing predictive biophysical computer models for personalized treatment of chronic pain.These models will be used to predict individual responses to different pain medications, allowing for more effective and personalized treatment strategies.The ultimate goal is to improve the lives of patients suffering from chronic pain through the development and application of advanced computational models.
[222] Multiscale Systems Biology | Interagency Modeling and Analysis Group — Modelling the coupling of biomolecular networks with biophysical models on lesser and greater spatiotemporal scales.Development of multi-scale models of the key molecular biology, cellular biology, heterogeneous tissue architecture, and physiology, could lead to detailed comparison of many different treatments, including different drugs, routes of administration, doses and schedules by virtual clinical trials that incorporate models of many patients.Due to the multi-scale nature of the models, clinicians could identify emergent therapeutic or toxic effects of treatments, as well as conditions under which therapies fail.These models can translate knowledge from in vitro cell culture to in vivo preclinical and clinical studies, which is important because it is known that observed mechanisms in vitro do not always hold in vivo.These models can help researchers and clinicians translate therapies from animals to humans, or from microphysiological systems (‘body-on-a-chip’) to patients.Approaches that enable multiscale modeling of pharmacology has applications for therapeutics at different scales: gene therapy, small molecules, proteins, cell therapy, organ transplant, and more, across multiple diseasesAnother connective topic is drug delivery and response to therapeutics; many models incorporate a therapeutic component.
[236] Biophysics - Biology Simple — Biophysics In Drug Development. Biophysics plays a key role in the development of new drugs and therapies by providing a deeper understanding of biological processes and molecular interactions. By studying the physical properties of proteins, enzymes, and other biomolecules, biophysicists can identify potential drug targets and design molecules
[241] Biophysical Methods in Drug Discovery - Pharma.Tips — Biophysical methods are powerful tools in drug discovery, providing insights into the binding, stability, and dynamics of drug-target interactions.These techniques can help identify promising drug candidates, optimize lead compounds, and understand the molecular mechanisms of action.Surface plasmon resonance (SPR) is a widely used technique to study the binding kinetics of small molecules or biologics to a target protein.ITC is highly effective for studying complex interactions and determining the energetics of drug-target binding, which is crucial for optimizing lead compounds.NMR is particularly useful for studying protein-ligand interactions in solution and for understanding how small molecules influence the structure of target proteins.X-ray crystallography is essential for studying the binding modes of small molecules, identifying key interactions, and guiding the design of more potent drug candidates.Fluorescence polarization (FP) and fluorescence resonance energy transfer (FRET) are biophysical techniques used to study molecular interactions in solution.
[243] Applications of biophysical techniques in drug discovery and ... — Generally speaking, biophysical methods are in two ways involved in drug design: the qualitative detection of small molecule binding to a target (hit identification), and the quantitative determination of physical parameters associated to binding (hit-to-lead progression) .High-resolution mechanistic, kinetic, thermodynamic and structural information on drug-target interactions are now available.In particular, biophysical measurements are useful in supporting compound progression, mechanistic understanding of the drug-receptor binding, validating potency data from biochemical and cellular assays in the discovery phases and quality control of the investigational drug, including the evaluations of drug release and stability, in the development phases.The use of various biophysical techniques is discussed, including thorough assay development, primary screening, hit confirmation, and mechanistic characterization.The use of NMR spectroscopy in the various drug discovery and development processes is discussed in the manuscript written by M. Zloh .Applications of hydrogen nuclear magnetic spectroscopy (1H NMR spectroscopy), not often reported as a tool for evaluating some ADMET properties, are explored.The work by I. Quiroga and T. Scior describes the role of induced fit mechanism in binding to cytochrome P450 3A4.
[246] Personalized Medicine and Advancements in Pharmacology: Shaping the ... — One of the key areas in personalized medicine is genomics, which focuses on analyzing an individual’s genetic information to predict disease susceptibility, determine optimal treatment strategies, and identify potential adverse reactions.1 Pharmacogenomics, a subset of genomics, explores the relationship between an individual’s genetic variations and their response to drugs. By considering individual patient characteristics, including genetic variations, biomarkers, and clinical data, personalized medicine aims to optimize treatment efficacy, minimize adverse effects, and improve patient outcomes across diverse medical fields. Personalized medicine enables tailored treatment approaches based on individual patient characteristics, such as genetic makeup, biomarkers, and clinical data.
[247] Personalized Medicine: Motivation, Challenges and Progress — These extreme genetic variation explains, in part, why individuals vary so much with respect to phenotypes, in particular their susceptibilities to disease and their responses to interventions.(13) It should be emphasized that although personalized medicine has its roots in the results of genetic studies, it is widely accepted that other factors, e.g., environmental exposures, developmental phenomena and epigenetic changes, and behaviors, all need to be taken into account when determining the optimal way to treat an individual patient (see Figure 1).(14-16) These activities include the use of patient-derived cell and organoid ‘avatars’ for determining the best therapies for that patient, the use of intense individualized diagnostic and monitoring protocols to detect signs of disease, the development of personalized digital therapeutics, and the use of personalized medicine approaches in treating patients with fertility issues.