Table of Contents
Overview
Definition and Importance
Protein engineering is defined as the process of creating new proteins or modifying existing ones to achieve specific functions, utilizing computational approaches and insights into protein structure.[2.1] This field has seen exponential growth, particularly in the in vitro manipulation of microbial enzymes through rational, directed, and combinatorial approaches.[1.1] The importance of protein engineering lies in its ability to enhance the biosynthesis of natural products by improving enzymatic activity, stabilizing proteins, and engineering sensor-regulators for better screening or dynamic regulation.[3.1] Two primary strategies in protein engineering are rational design and directed evolution. Rational design leverages detailed knowledge of enzyme structures and functions, often combining site-directed mutagenesis with computational models.[3.1] In contrast, directed evolution mimics natural selection by generating a library of diverse proteins through random or focused mutagenesis, followed by rigorous screening to identify variants with desirable properties.[18.1] This iterative process allows for the rapid development of proteins with enhanced functions, addressing limitations in catalytic properties that traditional methods may encounter.[19.1] The advancements in computational tools have significantly contributed to the field, enabling more effective protein engineering by predicting protein structures and interactions.[5.1] These tools facilitate the design of proteins with tailored functions, thereby expanding the potential applications of engineered proteins in various biotechnological fields.[20.1] Understanding the fundamental concepts of protein structure and function is essential for both rational design and directed evolution, as the specific arrangement of amino acids determines a protein's functionality.[38.1] Thus, protein engineering not only enhances our understanding of biological processes but also opens avenues for innovative applications in medicine, industry, and environmental science.Applications in Industry and Medicine
Recent advancements in protein engineering have catalyzed significant innovations in both industrial and medical fields. In the pharmaceutical industry, engineered proteins are increasingly recognized as viable alternatives to traditional small molecule drugs, with projections indicating that they will constitute half of the top ten selling drugs in 2023. This trend is driven by novel design strategies that improve the stability, affinity, and specificity of protein-based therapeutics, thereby enhancing their clinical efficacy.[6.1] In industrial biotechnology, the development of engineered proteins and enzymes has revolutionized the production of high-value compounds through microbial systems like Escherichia coli and Saccharomyces cerevisiae. These advancements have broadened the scope of applications across various sectors, including food processing, where synthetic enzymes are becoming increasingly prevalent.[7.1] The integration of enzyme evolution and metabolic engineering has further optimized the productivity of valuable substances, underscoring the adaptability of protein engineering in industrial settings.[7.1] The application of directed evolution and rational design has effectively addressed challenges related to enzyme catalytic efficiency and stability, particularly in biocatalyst production.[8.1] Advances in laboratory automation have facilitated high-throughput data acquisition, crucial for developing machine learning models that enhance protein engineering processes.[9.1] Machine learning techniques are now instrumental in identifying promising enzymes and suggesting beneficial mutations, expediting the creation of new biocatalysts.[10.1] In medical applications, tools like AlphaFold have transformed protein structure prediction, achieving remarkable accuracy in modeling protein structures and complexes. This capability is vital for understanding protein functions and advancing biological research.[14.1] Molecular dynamics simulations are employed to design stable protein variants, offering insights into protein folding and stability, which are essential for the rational design of therapeutic proteins.[12.1] CRISPR technology has also significantly impacted protein engineering, particularly in developing new therapeutic agents. For example, CRISPR-Cas9 has been used to engineer T-cells for CAR-T cell therapy, enhancing their specificity and efficacy against cancer cells.[17.1] The approval of the first CRISPR-based human therapy in late 2023 marks a pivotal moment in the application of gene editing technologies in medicine.[16.1] The convergence of protein engineering with advanced technologies such as CRISPR and machine learning is fostering innovations that promise to advance both industrial and medical applications.In this section:
History
Early Developments in Protein Engineering
The early developments in protein engineering were characterized by significant milestones that established the foundation for modern techniques. Initial efforts in de novo protein design aimed to construct sequences capable of folding in aqueous environments and membranes, leading to stable conformations. A notable example from 1979 is Bernd Gutte's pioneering work in manual protein design, where he used physical models to create a 35-residue RNA-binding peptide, exemplifying early manual approaches to protein engineering.[53.1] During the 1980s and 1990s, the focus of de novo protein design shifted towards exploring the relationship between protein sequence and its resultant structure and function. This era saw the rise of rational design strategies, which enhanced the understanding of specific protein structures, such as α-helical coiled coils characterized by amphipathic α helices encoded by heptad sequence repeats of hydrophobic and polar residues.[73.1] These rational approaches evolved from empirical methods to sophisticated computational designs, enabling the creation of complex protein structures and functionalities.[73.1] Despite these advancements, challenges remained in understanding protein folding and stability. Insight into the structural, thermodynamic, and kinetic properties of protein folding intermediates was crucial, as issues related to protein stability and folding were central to understanding diseases linked to protein misfolding.[51.1] Early techniques, such as manual model building and physical modeling, were limited in accurately predicting protein behavior, prompting the development of advanced computational methods.[72.1] The advent of computational design strategies allowed for the screening of a broader array of sequences, significantly enhancing the understanding of protein folding and stability.[72.1] Additionally, rapid mixing techniques became pivotal for studying protein folding, enabling researchers to investigate early folding stages with improved time resolution.[75.1] These advancements in experimental methods and computational models have revolutionized the field, providing detailed insights into protein folding mechanisms and facilitating the prediction of previously intractable structures.[52.1]Milestones in Protein Engineering Techniques
Milestones in protein engineering techniques have evolved significantly over the decades, marking important advancements in the field. An initial set of milestones focused on constructing sequences that could fold in aqueous environments and membranes to adopt stable conformations. Early protein designs were based on first-principles using simple physical models, which laid the groundwork for future developments as computational power increased, allowing for more sophisticated approaches such as the rotamer approximation to discover amino acid interactions.[42.1] Throughout the years, protein engineering techniques have been employed to understand protein functions in various physiological and pathological contexts, as well as to tailor proteins for specific applications. A notable recent milestone occurred when Verve Therapeutics dosed its first patient in a clinical trial, showcasing the practical applications of engineered proteins in medicine.[43.1] The 20th century saw significant advancements in biocatalyst engineering, beginning with Rosenthaler's use of crude enzyme preparations to convert benzaldehyde to mandelonitrile. The introduction of directed evolution in the 1980s enabled the generation of customized proteins, exemplified by an engineered aldolase that demonstrated high selectivity and substrate tolerance.[44.1] Recent advancements in computational methods have further transformed protein engineering. Techniques such as structure-based design, machine learning integration, and protein language models have enhanced the ability to predict protein properties and guide engineering efforts. These computational approaches have become invaluable for optimizing therapeutic proteins, significantly advancing structural biology applications.[56.1] Moreover, the integration of computational design has facilitated the creation of new protein structures and sequences, including those incorporating non-biological components. This has allowed for the design of water-soluble variants of integral membrane proteins, which are crucial for studying their structures and functions.[57.1] The reliance on approximations, such as fixed backbone and rotamer approximations, has enabled researchers to efficiently navigate the vast sequence space available to proteins, framing protein design as a discrete combinatorial optimization problem.[59.1] In the realm of enzyme engineering, directed evolution has led to remarkable breakthroughs. For instance, the evolution of an alkene anti-Markovnikov oxygenase (aMOx) demonstrated how enzyme active sites could be engineered to favor specific reaction pathways, resulting in a highly active and enantioselective catalyst.[60.1] Additionally, machine learning has been applied to guide library generation for evolving novel enzymes, overcoming challenges related to the scarcity of labeled data by utilizing unlabeled sequence data to derive functional protein rules.[61.1] The recognition of directed evolution's impact was underscored when Frances Hamilton Arnold received half of the Nobel Prize in Chemistry in 2018 for her pioneering work in engineering proteins and enzymes through Darwinian principles.[62.1] As the field continues to advance, the integration of bioinformatics tools and artificial intelligence is expected to further enhance the experimental validation process in protein engineering, although challenges remain in aligning computational predictions with laboratory results.[63.1]In this section:
Recent Advancements
Integration of Machine Learning and AI
The integration of machine learning (ML) and artificial intelligence (AI) into protein engineering has significantly transformed the field, enhancing the efficiency and effectiveness of protein design processes. The incorporation of these technologies has enabled the development of predictive models based on data-driven strategies, which can guide protein design by elucidating how these models function. This approach is particularly beneficial for methodologies such as machine learning-assisted directed evolution, which aims to optimize protein properties through iterative cycles of mutation and selection.[91.1] Machine learning has notably increased the throughput of computational protein design, allowing for a greater diversity of sequence solutions for specific protein structures and functions. By leveraging in silico modeling, ML enhances both the quality and variety of potential protein designs, addressing the inherent challenges of designing proteins with desired characteristics.[90.1] Furthermore, the integration of ML with traditional computational methods, such as structure-based design, has proven invaluable in engineering therapeutic proteins with improved properties, thereby advancing the field of structural biology.[89.1] The application of computational methods, including machine learning integration and protein language models, has dramatically improved the ability to predict protein properties and guide engineering efforts. These advancements contribute to the optimization of protein design, making it a more systematic and reliable process.[87.1] Despite the successes achieved through these technologies, challenges remain in ensuring that the models are interpretable and explainable, which is crucial for their practical application in protein engineering.[91.1]Protein Engineering Methods
Rational Design
Rational design in protein engineering is fundamentally grounded in the principles of enzyme kinetics, which elucidate the mechanisms of enzyme catalysis and its regulation as a function of substrate concentration. This approach begins with a thorough understanding of enzyme kinetics, including key concepts such as the enzyme-substrate complex, the Michaelis-Menten equation, and the steady-state assumption, which are critical for predicting how engineered proteins will behave under various conditions.[111.1] By applying these kinetic principles, researchers can systematically design proteins with desired catalytic properties. For instance, the lock and key and induced-fit models serve as frameworks for understanding how modifications to an enzyme's structure can enhance its interaction with specific substrates, thereby improving its efficiency and specificity.[111.1] This methodical approach allows for the creation of engineered enzymes that not only meet specific functional requirements but also exhibit optimized performance in biochemical reactions, demonstrating the practical application of enzyme kinetics in protein engineering.[110.1]Directed Evolution
Directed evolution is a pivotal technique in protein engineering that has significantly advanced the development of customized proteins. This method mimics the process of natural selection to evolve proteins or nucleic acids toward a user-defined goal. The origins of directed evolution can be traced back to the 1980s, when advances in molecular biology facilitated the generation of tailored proteins. Notably, this period saw the engineering of an aldolase enzyme that exhibited high selectivity and substrate tolerance, showcasing the potential of directed evolution in creating proteins with desirable traits.[108.1] The success of directed evolution is largely attributed to its ability to generate enzyme variants with enhanced catalytic activity, altered substrate specificity, and modified stereoselectivity.[107.1] This approach has been instrumental in the field of enzyme catalysis, which is one of the most frequently engineered protein functions due to its wide-ranging applications in industrial and biomedical processes.[112.1] For instance, computational protein design methodologies have been employed to engineer enzymes capable of catalyzing complex reactions, demonstrating the interplay between computational techniques and experimental evolution.[113.1] Moreover, directed evolution has been complemented by bioinformatics tools that aid in predicting the stability and functionality of engineered proteins. These tools can identify key residues that, when mutated, can enhance protein stability and activity.[120.1] The integration of structural bioinformatics with directed evolution strategies has led to the development of proteins with improved properties, further underscoring the significance of this method in modern protein engineering.[117.1]In this section:
Enzyme CatalysisFunctional RequirementsNucleic AcidsMolecular BiologyStructural Bioinformatics
Applications In Biotechnology
Therapeutic Proteins and Vaccines
Therapeutic proteins and vaccines represent a significant advancement in biotechnology, particularly through the application of engineered proteins and gene editing technologies. Engineered proteins, including exosomal proteins, have shown promise in regenerative medicine by activating signaling pathways that promote cell proliferation and differentiation, which are crucial for processes such as wound healing and tissue repair. For instance, studies have demonstrated that exosomal proteins can enhance wound healing by stimulating angiogenesis and collagen deposition, highlighting their therapeutic potential in regenerative applications.[158.1] The future of regenerative medicine is expected to leverage a multi-faceted approach involving various components such as proteins, peptides, and exosomes. Each of these elements plays a vital role in orchestrating the body's response to regenerative therapies, necessitating a comprehensive understanding of the underlying biological mechanisms.[159.1] Recent advancements in engineered extracellular vesicles (EVs) have further facilitated tissue repair by delivering proteins, lipids, and nucleic acids to target cells, thereby acting as signaling molecules.[160.1] The customizable nature of engineered protein materials allows for the development of next-generation tissue repair systems that can integrate various therapeutic approaches.[161.1] In the realm of gene editing, CRISPR technologies have significantly influenced the design and modification of proteins for therapeutic applications. Advanced CRISPR techniques, such as base editing and prime editing, enable precise modifications in the genome, which can enhance the functionality of therapeutic proteins.[162.1] For example, CRISPR-Cas9 technology has been instrumental in engineering T-cells to express chimeric antigen receptors (CARs), allowing for targeted elimination of cancer cells.[164.1] This approach has been further refined to improve T-cell functionality and specificity, demonstrating the potential of CRISPR in developing effective cancer therapies.[165.1] Moreover, the application of CRISPR in drug development has led to the creation of the world's first CRISPR-based medicine for sickle cell disease, marking a significant milestone in the therapeutic landscape.[165.1] The integration of computational methods in protein design also plays a crucial role in overcoming challenges associated with the accuracy and efficiency of engineered proteins. These methods involve a systematic approach to evaluating protein structures and functions, which is essential for successful therapeutic applications.[172.1]Industrial Enzymes and Biocatalysis
Industrial enzymes and biocatalysis represent a significant application of protein engineering in biotechnology, particularly in the context of sustainable industrial processes. Recent advancements in protein and enzyme engineering have facilitated the production of a diverse array of high-value compounds using microbial systems, which are increasingly recognized for their potential industrial applications.[146.1] These engineered enzymes are not only pivotal in medical applications but also play a crucial role in the food industry, where they are utilized for processing various products.[146.1] The development of industrial enzymes often involves techniques such as directed evolution, which generates a library of mutated proteins through focused or random mutagenesis. This process mimics natural selection, allowing for the identification of proteins with desirable properties.[147.1] Additionally, the integration of machine learning methods has enhanced the efficiency of enzyme engineering by leveraging existing experimental and simulation data to discover promising enzymes and suggest beneficial mutations.[154.1] This computational approach allows for a more systematic exploration of enzyme functionality, thereby accelerating the development of effective biocatalysts.[153.1] Factors influencing enzyme activity and efficiency are critical in the optimization of engineered enzymes for specific industrial applications. Key parameters include temperature, pH, substrate concentration, and regulatory mechanisms, all of which uniquely affect how enzymes function under varying conditions.[166.1] For instance, pH can influence the ionization of an enzyme's active site, which is essential for substrate binding and catalysis.[166.1] By carefully considering these factors, researchers can enhance both the efficiency and sustainability of engineered enzymes in industrial settings.In this section:
Challenges And Future Directions
Limitations of Current Techniques
Current techniques in protein engineering face several limitations that hinder their effectiveness and efficiency. One significant challenge is associated with directed evolution, a method that emulates natural selection to evolve proteins with desired traits. Despite its successes in identifying beneficial mutations, directed evolution is constrained by difficulties in efficiently mutagenizing the target sequence without introducing unwanted changes, which can complicate the desired outcomes.[215.1] Additionally, rational design, while powerful, encounters obstacles related to its high therapeutic potential yet limited screening capacity. This limitation necessitates innovative approaches to enhance the efficacy of biotherapeutics through rational engineering methods.[211.1] The integration of engineered proteins into synthetic biological systems is also challenged by the need for advanced genetic tools and a deeper understanding of protein biochemistry, cell physiology, and metabolism.[191.1] Moreover, the translation of engineered proteins into clinical applications is fraught with difficulties, including high costs, challenges in recruiting suitable patient populations for clinical trials, and navigating complex regulatory pathways.[216.1] These barriers underscore the necessity for more strategic business models and careful analysis of the technologies involved to improve the successful translation of engineered proteins.[216.1] Finally, the development of effective delivery systems for engineered proteins presents additional challenges. Techniques for cytosolic protein delivery can be broadly categorized into physical methods, direct protein engineering, and nanocarrier-mediated delivery, each facing unique hurdles that require further research and innovation.[217.1] The unpredictable behavior of nanoparticles in human systems complicates the translation of nanotechnology and biomaterials into clinical applications, highlighting the urgent need for advancements in this area.[218.1]Emerging Trends and Technologies
Emerging trends and technologies in protein engineering are increasingly focused on integrating advanced computational methods and machine learning techniques to enhance the design and functionality of proteins. The success of AlphaFold in protein structure prediction exemplifies the potential of data-driven approaches in this field, although challenges remain in developing machine learning models that can effectively design proteins with desired functions.[185.1] Recent advancements have highlighted the role of deep learning in addressing various challenges in industrial production, healthcare, and environmental sustainability, framing these issues from the perspective of protein understanding and engineering.[187.1] Machine learning methods are becoming integral to the engineering of biocatalysts, utilizing existing experimental and simulation data to discover promising enzymes and suggest beneficial mutations for known targets.[188.1] This integration of machine learning with traditional directed evolution techniques represents a new paradigm, where computational models are trained on protein sequence fitness data to improve the efficiency of the directed evolution cycle.[189.1] Furthermore, the application of computational methodologies, including structure-based design and protein language models, has significantly enhanced the ability to predict protein properties and guide engineering efforts, thereby optimizing therapeutic protein engineering applications.[192.1] Innovative approaches such as de novo protein design are also emerging, which aim to overcome the limitations of traditional methods by addressing the challenges of designing both structure and function.[193.1] Additionally, advancements in conjugation technologies are enabling the construction of more stable and site-selective antibody-drug conjugates (ADCs), which are crucial for improving therapeutic efficacy.[194.1] The exploration of biomolecular engineering tools allows for the custom design of protein interactions, enhancing specificity and functionality.[195.1] As protein engineering continues to evolve, it is essential to address the industrial challenges associated with the production of recombinant proteins, particularly in the context of personalized medicine. These challenges include achieving optimal cell density, protein expression, and purification processes, which are critical for meeting the growing demand for biotherapeutic proteins tailored to individual patient needs.[197.1] Overall, the integration of these emerging technologies and methodologies is poised to reshape the future of protein engineering, driving advancements in various sectors, including biotechnology and medicine.[184.1]References
[1] Protein Engineering - an overview | ScienceDirect Topics — Protein engineering is an exponentially growing field of molecular biology, where several developments have taken place in in vitro manipulations of microbial enzymes by rational, directed and combinatorial approaches.
[2] Protein Engineering - an overview | ScienceDirect Topics — Protein engineering is the process of creating new proteins or modifying existing ones to achieve specific functions, using computational approaches and information on protein structure. AI generated definition based on: Encyclopedia of Bioinformatics and Computational Biology, 2019
[3] Protein Engineering - an overview | ScienceDirect Topics — Protein engineering has improved the biosynthesis of natural products through enhancement of enzymatic activity, colocalization of enzyme complexes, improvement of protein stability, and engineering of sensor-regulators for better screening or dynamic regulation. Recently, protein engineering has become a favored method to improve enzymatic activity, increase enzyme stability, and expand product spectra in natural product biosynthesis. The two main strategies in protein engineering are rational design, which combines site-directed mutagenesis with the detailed knowledge of enzyme structures and functions or computational models, and directed evolution, which does not require scientific knowledge since it is based on the random synthesis of a pool of mutated enzymes and the subsequent selection by an iterative process .
[5] Protein engineering in the computational age: An open source framework ... — The authors highlight how the 2022 iGEM Team, ‘Sporadicate’, set out to develop InFinity 1.0, a computational framework for increased accessibility to effective protein engineering, hoping to increase awareness and accessibility to novel in silico tools. Computational tools addressing both these issues have been developed extensively in the last decade, with in silico strategies for predicting protein structure (AlphaFold, RoseTTAFold), their interactions with other ligands (AutoDock Vina, UCSF Dock, P2Rank), as well as dynamic system simulations (molecular dynamics software, such as GROMACS, NAMD, and AMBER) finding regular applications in the cutting edge. With the advent of computational tools that can accurately predict protein structures and significant progress in the field of molecular docking, we pose the question if said developments could be incorporated into a framework for streamlined protein engineering .
[6] Engineering protein-based therapeutics through structural and ... - Nature — Projected to be half of the top ten selling drugs in 2023, proteins have emerged as rivaling and, in some cases, superior alternatives to historically used small molecule-based medicines. This review chronicles both well-established and emerging design strategies that have enabled this paradigm shift by transforming protein-based structures that are often prone to denaturation, degradation, and aggregation in vitro and in vivo into highly effective therapeutics. In particular, we discuss strategies for creating structures with increased affinity and targetability, enhanced in vivo stability and pharmacokinetics, improved cell permeability, and reduced amounts of undesired immunogenicity. Advances in rational design and ability to deliberately introduce chemical and structural modifications have driven a paradigm shift in how these properties can be tuned13,14,15.
[7] New advances in protein engineering for industrial applications: Key ... — Recent advancements in protein/enzyme engineering have enabled the production of a diverse array of high-value compounds in microbial systems with the potential for industrial applications. Keywords: protein and enzyme engineering, industrial biotechnology, thermostability, Escherichia coli, Saccharomyces cerevisiae, yeast, bacteria, fungi, algae While medical applications currently represent the most lucrative market for engineered protein products, synthetic enzymes are also utilized in the food industry for processing. In recent years, protein/enzyme engineering has seen numerous advancements, resulting in remarkable outcomes with potential for industrial application. coli, has been a foundation of recent advancements in protein engineering, enabling the efficient production of valuable substances. Integrating Enzyme Evolution and Metabolic Engineering to Improve the Productivity of Γ-Aminobutyric Acid by Whole-Cell Biosynthesis in Escherichia coli.
[8] Recent Advances in Protein Engineering and Synthetic Applications of ... — More crucially, we delve into the recent advancements in protein engineering of ATs through directed evolution and rational/semi-rational design strategies, which have been instrumental in addressing limitations such as low catalytic efficiency and stability. Furthermore, we survey the recent synthetic applications of ATs in the production of
[9] In vitro continuous protein evolution empowered by machine ... - PubMed — Furthermore, recent advancements in laboratory automation have enabled the rapid execution of long, complex experiments for high-throughput data acquisition in both industrial and academic settings, thus providing the means to collect a large quantity of data required to develop ML models for protein engineering.
[10] Machine Learning-Guided Protein Engineering | ACS Catalysis — Recent progress in engineering highly promising biocatalysts has increasingly involved machine learning methods. These methods leverage existing experimental and simulation data to aid in the discovery and annotation of promising enzymes, as well as in suggesting beneficial mutations for improving known targets. The field of machine learning for protein engineering is gathering steam, driven
[12] Insights from molecular dynamics simulations for computational protein ... — Molecular dynamics simulations can be used to (A) design stable protein variants, (B) engineer functional regions, or (C) provide insights from protein unfolding / folding pathways. (D) [Indirect Use] MD simulations may also be used to rationalize the mechanism of protein stabilization and in so doing, provide insights for optimizing designs. The designs make use of alternating D- and L-amino acids to help lock in the α-sheet structure, making use of conformational propensity125,213–215 and rotamer libraries216–218 for D- and L-amino acids from our Dynameomics project,219–220 which contains simulations of representatives of essentially all known protein folds and multiple host-guest peptide systems. Assessing protein conformational sampling and structural stability via de novo design and molecular dynamics simulations.
[14] AlphaFold Protein Structure Database — In CASP14, AlphaFold was the top-ranked protein structure prediction method by a large margin, producing predictions with high accuracy. While the system still has some limitations, the CASP results suggest AlphaFold has immediate potential to help us understand the structure of proteins and advance biological research.
[16] Past, present, and future of CRISPR genome editing technologies — It has far-reaching applications, from unraveling fundamental biological processes to driving advancements in medicine, agriculture, and biotechnology. With the approval of the first CRISPR-based human therapy in late 2023, 1 CRISPR genome editing is entering a new era.
[17] Advances in CRISPR-Cas technology and its applications: revolutionising ... — | Blood | Hemophilia B | F9 | Corrected F9 gene in iPSCs using CRISPR-Cas9; restored F9 expression in hepatocyte-like cells | Morishige et al. One prominent application of CRISPR-Cas9 technology is its application in engineering T-cells express CARs. CAR-T cell therapy is a genetically modified T-cell that expresses CARs, targeting tumour-associated antigens (TAAs) or tumour-specific antigens (TSAs) with high specificity, thereby targeting and eliminating cancer cells (Jogalekar et al., 2022). CRISPR-Cas9 technology has enhanced CAR-T therapy by enabling precise genetic edits that improve T cell functionality, persistence, and specificity (Dimitri et al., 2022). CRISPR-Cas gene editing is utilised to introduce oncolytic viruses with therapeutic genes, enhancing their cancer tissue selectivity and suppressing antiviral protective mechanisms employed by malignant cells (Wang et al., 2022b).
[18] Introduction to Directed Evolution and Rational Design as Protein ... — Introduction to Directed Evolution and Rational Design as Protein Engineering Techniques - Enzyme Engineering - Wiley Online Library Directed evolution and rational design are the two main approaches in protein engineering, which were developed in the quest to solve the limitations of enzymes regarding insufficient catalytic properties. Directed enzyme evolution utilizes random introduction of mutations or focused mutations of the type combinatorial active-site saturation test (CAST)/iterative saturation mutagenesis (ISM), while rational design exploits structural and mechanistic information with computational aids. 10.1016/0734-9750(92)91451-J J. 8 ( 1 ): 115 – 120 . Directed evolution of an enantioselective enzyme through combinatorial multiple-cassette mutagenesis . Directed evolution of enantioselective enzymes as catalysts for organic synthesis . (b) Reetz , M.T. and Carballeira , J.D.
[19] Evolutionary approaches in protein engineering towards biomaterial ... — Rational design and directed evolution are the two main strategies to reengineer proteins or create chimeric structures. Rational engineering is often limited by insufficient knowledge about proteins' structure-function relationships; directed evolution overcomes this restriction but poses challenges in the screening of candidates.
[20] Beyond directed evolution—semi-rational protein engineering and design ... — The advances in understanding protein structure and function, in no insignificant part a result of directed evolution studies, are increasingly empowering scientists and engineers to device more effective methods for manipulating and tailoring biocatalysts. Often referred to as semi-rational, smart or knowledge-based library design, these approaches utilize information on protein sequence, structure and function, as well as computational predictive algorithms to preselect promising target sites and limited amino acid diversity for protein engineering. To highlight the rapidly growing number of successful enzyme engineering studies by semi-rational and computer-guided protein design, this review concentrates (with few exceptions) on recent studies that required libraries of less than 1000 members (Table 1).
[38] The Basics of Protein - Its Structure and Functions - BYJU'S — The body uses proteins for a variety of purposes, and their structure determines how they work. Several notable functions include: Digestion - The digestive enzymes, which are primarily proteinaceous in origin, carry out digestion.; Movement - Muscles include a protein called myosin, which helps muscles contract, allowing for movement.; Structure and Support - The structural protein
[42] Computational Advances in Protein Engineering and Enzyme Design — An initial set of milestones in de novo protein design focused on the construction of sequences that folded in water and membranes to adopt folded conformations. The first proteins were designed from first-principles using very simple phys. models. As computers became more powerful, the use of the rotamer approxn. allowed one to discover amino
[43] Protein engineering technologies for development of next-generation ... — Protein engineering techniques have been utilized for decades to understand protein function in physiological or pathological conditions as well as to tailor different functions to accomplish specific goals. ... Another significant milestone was recently achieved in a clinical trial (NCT05398029) when Verve Therapeutics dosed its first patient
[44] Protein Engineering: Past, Present, and Future | SpringerLink — Advances in protein engineering for tailoring biocatalysts. (a) A century ago, Rosenthaler used a crude enzyme preparation from almonds to convert benzaldehyde to mandelonitrile.(b) In the 1980s, advances in molecular biology and the introduction of directed evolution enabled generation of customized proteins as exemplified by an aldolase engineered for high selectivity and substrate tolerance
[51] Early Events in Protein Folding Explored by Rapid Mixing Methods — Insight into the structural, thermodynamic and kinetic properties of protein folding intermediates is critical for understanding a wide range of diseases that can be linked to aggregation of partially denatured or misfolded forms of proteins. 15-20 Issues related to protein stability and folding also play a central role in understanding the
[52] Unveiling the Complexity of Protein Folding: Significance, Challenges ... — HAV/HBV/HCV/HDV/HEV Antigen Influenza A/B Antigen Coronaviruses Dengue HIV SIV HSV Zika Virus Antigens Ebola HPV Other Viral Antigens Another critical advancement is the application of new experimental methods and computational models to improve protein folding predictions. This breakthrough has resolved many long-standing challenges in structural biology, providing detailed insights into protein folding mechanisms and enabling researchers to predict structures that were previously intractable . This process, known as structure-based drug design, has been revolutionized by advancements in protein folding research. These models will not only predict static structures but also simulate dynamic folding processes, providing real-time insights into how proteins fold and misfold . AlphaFold, developed by DeepMind, has revolutionized protein folding research by using artificial intelligence to predict protein structures from amino acid sequences with remarkable accuracy.
[53] PDF — An initial set of milestones in de novo protein design focused on the construction of sequences that folded in water and membranes to adopt folded conformations. The first proteins were ... Manual protein design As early as 1979, Bernd Gutte used manual model building and physical models to design a 35-residue RNA-binding peptide (Gutte et al
[56] Integrating Computational Design and Experimental Approaches for Next ... — Computational methods like structure-based design, machine learning integration, and protein language models have dramatically enhanced our ability to predict protein properties and guide engineering efforts. Structure-based computational design has become an invaluable tool for engineering therapeutic proteins with improved properties . The integration of these algorithms and the advancement of structure-based computational techniques contribute to the optimization and advancement of structural biology for therapeutic protein engineering applications. doi: 10.1155/2014/149185. doi: 10.1080/19420862.2023.2245111. 124.Patel S.G., Sayers E.J., He L., Narayan R., Williams T.L., Mills E.M., Allemann R.K., Luk L.Y.P., Jones A.T., Tsai Y.H. Cell-penetrating peptide sequence and modification dependent uptake and subcellular distribution of green florescent protein in different cell lines. doi: 10.1038/s41598-019-42456-8. doi: 10.1007/s12033-023-00679-1. doi: 10.1158/0008-5472.CAN-12-2796. doi: 10.1038/s41573-020-0090-8. doi: 10.1007/s13346-011-0052-0. doi: 10.1038/s41467-021-27804-5. doi: 10.1017/S0033583519000131.
[57] Computational protein design: Advances in the design and redesign of ... — Computational protein design facilitates the continued development of methods for the design of biomolecular structure, sequence and function. In addition to protein redesign, new protein structures (and sequences) have been computationally designed, which may incorporate non-biological components. Specificity of interactions as well as structure has been studied with the aid of computational protein design. The fraction of fluorescent, functional proteins was largest for the library designed using a structure-based computational method. Experimental characterization of such a computationally designed protein is consistent with high specificity of binding to the desired cofactors and a well-structured protein [22•]. Using computational design, water-soluble variants of integral membrane proteins have been designed, potentially facilitating studies of their structures and functions. Full-sequence computational design and solution structure of a thermostable protein variant.
[59] Protein Design Using Continuous Rotamers - PLOS — The protein design system can be represented as a rotamer vector, , which is an assignment of a rotamer at each design position . Then we define the total energy of the system : (1) The dead-end elimination criterion states that for a rotamer , if there is a rotamer such that: (2) then is provably not part of the GMEC, and can therefore be pruned.
[60] Directed Evolution: Bringing New Chemistry to Life - PMC — In a powerful example of how an enzyme active site can be engineered to promote one reaction pathway over another, postdoctoral fellow Stephan Hammer directed the evolution of an alkene anti‐Markovnikov oxygenase (aMOx), which catalyzes the conversion of alkenes into the anti‐Markovnikov carbonyl compounds.12 Intrigued by a report that the cytochrome P450 from Labrenzia aggregata made some phenyacetaldehyde as a side product when it oxidized styrene to the epoxide, Hammer looked more deeply and discovered that this promiscuous reactivity did not involve epoxidation followed by isomerization to the aldehyde, as had been proposed.13 He correctly surmised that it instead went through a competing, stepwise mechanism involving radical/cation intermediates and a 1,2‐hydride migration (Figure 4 A).14 He then exploited this side activity to direct the evolution of by far the most active, and the first enantioselective, direct aMOx catalyst.12 Using earth‐abundant iron, dioxgen, and a recyclable cofactor (NADPH), the laboratory‐evolved P450 enzyme catalyzes thousands of turnovers for anti‐Markovnikov oxidation of different substituted styrenes, including hindered substrates such as internal and 1,1‐disubstituted alkenes.
[61] A primer to directed evolution: current methodologies and future ... — Machine learning (ML) has also been applied to guide library generation by modelling the fitness landscape incorporating multiple data sources of tested variants, achieving success at even evolving novel enantiospecific enzymes.65,66 One of the main difficulties found when applying ML to directed evolution is the scarcity of labeled data, i.e., biological sequences with an associated measurement of the target property.67 One currently active research line aims to employ unlabeled sequence data to capture a set of underlying rules assumed to be followed by any functional protein, which can then be employed to generate a sort of compressed numerical representation of protein sequences (known as “embeddings”). More recently, mutagenesis methods for genome-scale directed evolution have been devised, including several techniques based on Multiplex Automated Genome Engineering (MAGE) which employ combinations of multiple oligonucleotides to target up to thousands of genomic locations simultaneously.120,121 The potential of such approaches to develop new variant organisms serving as optimised whole-cell catalysts was demonstrated by Wang et al., who managed to obtain an E.
[62] Synthetic Biology, Directed Evolution, and the Rational Design of New ... — Recent advances, however, in the field of directed evolution coupled with deep learning may change all this. In 2018, Frances Hamilton Arnold was awarded one half of the Nobel Prize in Chemistry for her work on engineering proteins and enzymes using the principles of Darwinian evolution to select candidate proteins with desirable properties.
[63] Validation of an LLM-based Multi-Agent Framework for Protein ... — The engineering process proceeded in two distinct phases. During the initial screening phase, TourSynbio-Agent generated 200 single-site mutation candidates within two weeks, followed by a three-week experimental validation period to collect comprehensive activity and selectivity data.
[72] Protein WISDOM: A Workbench for In silico De novo Design of ... — In contrast, computational design strategies allow for the screening of a much larger set of sequences covering a wide variety of properties and functionality. We have developed a range of computational de novo protein design methods capable of tackling several important areas of protein design.
[73] A Brief History of De Novo Protein Design: Minimal, Rational, and ... — The rational approach to protein design and engineering has led to a high level of understanding for one class of protein structure in particular; namely, the α-helical coiled coils.137, 138 In these structures, amphipathic α helices are usually encoded by ‘heptad’ sequence repeats of hydrophobic (h) and polar (p) residues, (hpphppp)3-5. The field has moved on from largely empirical and minimalist approaches that test our basic understanding of protein folding, through rational approaches that develop and apply sequence-to-structure relationships or rules for protein design, and onto computational protein design, which is delivering complex protein structures and functions. Towards functional de novo designed proteins The de novo design of protein structures Computational protein design has been improved significantly in recent years and has successfully produced de novo stable backbone structures with optimized sequences and functions.
[75] Early Events in Protein Folding Explored by Rapid Mixing Methods — Solution mixing techniques have experienced a renaissance due to advances in mixer design and detection methods, which made it possible to extend the time resolution well into the microsecond time range.70–73 Efficient turbulent mixers coupled with a variety of detection methods have yielded a wealth of information on early stages of protein folding.61,71,74–91 Although rapid mixing techniques cannot compete with the perturbation methods mentioned above in terms of time resolution, they remain the method of choice for studies of protein folding reactions far from the equilibrium transition region where intermediate states are most likely to accumulate.
[87] Integrating Computational Design and Experimental Approaches for Next ... — Computational methods like structure-based design, machine learning integration, and protein language models have dramatically enhanced our ability to predict protein properties and guide engineering efforts. Structure-based computational design has become an invaluable tool for engineering therapeutic proteins with improved properties . The integration of these algorithms and the advancement of structure-based computational techniques contribute to the optimization and advancement of structural biology for therapeutic protein engineering applications. doi: 10.1155/2014/149185. doi: 10.1080/19420862.2023.2245111. 124.Patel S.G., Sayers E.J., He L., Narayan R., Williams T.L., Mills E.M., Allemann R.K., Luk L.Y.P., Jones A.T., Tsai Y.H. Cell-penetrating peptide sequence and modification dependent uptake and subcellular distribution of green florescent protein in different cell lines. doi: 10.1038/s41598-019-42456-8. doi: 10.1007/s12033-023-00679-1. doi: 10.1158/0008-5472.CAN-12-2796. doi: 10.1038/s41573-020-0090-8. doi: 10.1007/s13346-011-0052-0. doi: 10.1038/s41467-021-27804-5. doi: 10.1017/S0033583519000131.
[89] Integrating Computational Design and Experimental Approaches for Next ... — Computational methods like structure-based design, machine learning integration, and protein language models have dramatically enhanced our ability to predict protein properties and guide engineering efforts. Structure-based computational design has become an invaluable tool for engineering therapeutic proteins with improved properties . The integration of these algorithms and the advancement of structure-based computational techniques contribute to the optimization and advancement of structural biology for therapeutic protein engineering applications. doi: 10.1155/2014/149185. doi: 10.1080/19420862.2023.2245111. 124.Patel S.G., Sayers E.J., He L., Narayan R., Williams T.L., Mills E.M., Allemann R.K., Luk L.Y.P., Jones A.T., Tsai Y.H. Cell-penetrating peptide sequence and modification dependent uptake and subcellular distribution of green florescent protein in different cell lines. doi: 10.1038/s41598-019-42456-8. doi: 10.1007/s12033-023-00679-1. doi: 10.1158/0008-5472.CAN-12-2796. doi: 10.1038/s41573-020-0090-8. doi: 10.1007/s13346-011-0052-0. doi: 10.1038/s41467-021-27804-5. doi: 10.1017/S0033583519000131.
[90] Deep Dive into Machine Learning Models for Protein Engineering — Computational protein design has a wide variety of applications. Despite its remarkable success, designing a protein for a given structure and function is still a challenging task. ... By greatly increasing throughput with in silico modeling, machine learning enhances the quality and diversity of sequence solns. for a protein engineering
[91] Interpretable and explainable predictive machine learning models for ... — Interpretable and explainable predictive machine learning models for data-driven protein engineering - ScienceDirect Interpretable and explainable predictive machine learning models for data-driven protein engineering The integration of artificial intelligence methods has further accelerated protein engineering process by enabling the development of predictive models based on data-driven strategies. The incorporation of explainable strategies in protein engineering holds significant potential, as it can guide protein design by revealing how predictive models function, benefiting approaches such as machine learning-assisted directed evolution. Finally, the remaining challenges of explainable artificial intelligence in protein engineering and future directions for its development as a support tool for traditional protein engineering methodologies are discussed. For all open access content, the Creative Commons licensing terms apply.
[107] Protein Engineering: Past, Present, and Future - PubMed — Protein Engineering: Past, Present, and Future - PubMed Search: Search Your saved search Name of saved search: Add to Search doi: 10.1007/978-1-4939-7366-8_1. DOI: 10.1007/978-1-4939-7366-8_1 Add to Search doi: 10.1007/978-1-4939-7366-8_1. DOI: 10.1007/978-1-4939-7366-8_1 A review of the literature quickly reveals the tremendous success of this approach; protein engineering has generated enzyme variants with improved catalytic activity, broadened or altered substrate specificity, as well as raised or reversed stereoselectivity. Herein, we use history to guide a brief overview of the major strategies for protein engineering-past, present, and future. doi: 10.1007/s00253-013-5370-3. doi: 10.1186/s12859-021-04323-0. Add to Search Add to Search Add to Search Add to Search Add to Search Add to Search Add to Search Add to Search Add to Search Add to Search
[108] Protein Engineering: Past, Present, and Future | SpringerLink — Advances in protein engineering for tailoring biocatalysts. (a) A century ago, Rosenthaler used a crude enzyme preparation from almonds to convert benzaldehyde to mandelonitrile.(b) In the 1980s, advances in molecular biology and the introduction of directed evolution enabled generation of customized proteins as exemplified by an aldolase engineered for high selectivity and substrate tolerance
[110] PDF — 4. Enzyme kinetics Enzyme kinetics, deals with enzyme reactions which are time-dependent and explains the mechanisms of enzyme catalysis and its regulation. Let's understand enzyme kinetics as a function for the concentration of the substrate available for the enzyme. Start the experiment with a series of tubes which contains substrate, [S].
[111] Enzyme Kinetics - SpringerLink — In this chapter, you will learn the fundamental concepts of chemical and enzyme catalysis as well as kinetics such as the lock and key and the induced-fit models, the idea of an enzyme-substrate complex, the Michaelis-Menten equation, or the steady-state assumption.Key basic concepts such as catalysts and their classification, reaction rate, enzyme inhibition, and the order of a reaction are
[112] Protein Engineering in the 21st Century - PMC — The first set of articles included in this special issue deal with enzyme catalysis, one of the most frequently engineered protein functions due to the many applications of designed enzymes in industrial and biomedical processes. Three articles that use computational protein design methodologies to engineer proteins are included in this issue. A third article by Borgo and Havranek describes the engineering of an enzyme capable of catalyzing substrate-assisted Edman degradation.10 Using a computational approach that included quantum calculations, docking, and computational protein design, they were able to develop an Edmanase displaying high catalytic efficiencies towards several substrates but only a modest rate acceleration. The three articles described above illustrate how computational protein design methods can be applied to various protein engineering problems but also highlight the limitations of current approaches.
[113] Building Enzymes through Design and Evolution | ACS Catalysis — Designing efficient enzymes is a formidable challenge at the forefront of modern biocatalysis. Here, we review recent developments in the field and illustrate how the interplay between computational design and advanced protein engineering has given rise to enzymes with diverse activities. Natural proteins have been re-engineered computationally to embed designed catalytic sites, affording
[117] Protein Stability Prediction Workflow - DNASTAR — Our protein stability prediction tools also make it easy to search for amino acid positions that are important for protein stability by performing computational alanine scanning or serine scanning to detect hot-spots, residues whose variants destabilize the structure. ... By combining structural bioinformatics with sequencing technologies, this
[120] Robust enzyme design: Bioinformatic tools for improved protein stability — Evolution has created a diversity of protein properties that are encoded in genomic sequences and structural data. Bioinformatics has the power to uncover this evolutionary code and provide a reproducible selection of hotspots - key residues to be mutated in order to produce more stable and functionally diverse proteins and enzymes.
[146] New advances in protein engineering for industrial applications: Key ... — Recent advancements in protein/enzyme engineering have enabled the production of a diverse array of high-value compounds in microbial systems with the potential for industrial applications. Keywords: protein and enzyme engineering, industrial biotechnology, thermostability, Escherichia coli, Saccharomyces cerevisiae, yeast, bacteria, fungi, algae While medical applications currently represent the most lucrative market for engineered protein products, synthetic enzymes are also utilized in the food industry for processing. In recent years, protein/enzyme engineering has seen numerous advancements, resulting in remarkable outcomes with potential for industrial application. coli, has been a foundation of recent advancements in protein engineering, enabling the efficient production of valuable substances. Integrating Enzyme Evolution and Metabolic Engineering to Improve the Productivity of Γ-Aminobutyric Acid by Whole-Cell Biosynthesis in Escherichia coli.
[147] Tools and Applications of Protein Engineering: an Overview — Directed evolution is based on generating many mutated copies of genes, henceforth their corresponding proteins, using focused or random mutagenesis or computational techniques, consequently generating a library of diverse proteins followed by rigorous screening and selection of favorable ones having desired properties, just mimicking the process of evolution, which has led to existence of a number of diverse proteins families in many years through the process of natural selection. Besides, engineered certain novel techniques for externally controlling protein activity and delivery have been successfully developed , as well as developed new approaches for the directed evolution of protein and enzyme function.
[153] Machine Learning for Protein Engineering - PMC - PubMed Central (PMC) — However, a new paradigm is emerging, fusing the library generation and screening approaches of traditional directed evolution with computation through the training of machine learning models on protein sequence fitness data. This chapter highlights successful applications of machine learning to protein engineering and directed evolution, organized by the improvements that have been made with respect to each step of the directed evolution cycle. In this chapter, we review the core concepts that have enabled successful integration of machine learning in protein engineering by interpreting the process through the directed evolution cycle. The next steps are additional for protein engineering methods guided by machine learning, including (4) fitting the models and (5) selecting the next library based on these models. C. Structure based thermostability prediction models for protein single point mutations with machine learning tools.
[154] Machine Learning-Guided Protein Engineering | ACS Catalysis — Recent progress in engineering highly promising biocatalysts has increasingly involved machine learning methods. These methods leverage existing experimental and simulation data to aid in the discovery and annotation of promising enzymes, as well as in suggesting beneficial mutations for improving known targets. The field of machine learning for protein engineering is gathering steam, driven
[158] Exosomes Treatment and Future Prospects in Regenerative Medicine — These proteins activate signaling pathways in target cells, promoting processes like cell proliferation and differentiation. A study demonstrated that exosomal proteins could enhance wound healing by stimulating angiogenesis and collagen deposition, highlighting their therapeutic potential in regenerative medicine. Transfer Of RNA
[159] The Future of Regenerative Medicine: A Comprehensive Guide for ... — They often include components like proteins, peptides, and exosomes. ... we can appreciate the multi-faceted approach required in the future of regenerative medicine. Each component plays a vital role in orchestrating the body's response to regenerative therapies, underscoring the need for a deep understanding of both biological mechanisms
[160] Engineered extracellular vesicles for tissue repair and regeneration ... — Recent studies have shown that EVs from specific sources regulate tissue repair and regeneration by delivering proteins, lipids, and nucleic acids to target cells as signaling molecules. Nanotechnology breakthroughs have facilitated the development and exploration of engineered EVs for tissue repair.
[161] Protein-engineered biomaterials for cartilage therapeutics and repair ... — The highly customizable nature of protein engineered materials can benefit from combining therapeutic approaches from those and other related applications to develop next generation customized tissue repair systems.
[162] CRISPR Advancements for Human Health - PMC - PubMed Central (PMC) — Advanced CRISPR approaches such as base editing and prime editing use modified Cas enzymes which can induce precise single nucleotide changes in the genome without creating double-strand DNA breaks.2 CRISPR can also be used to activate genes (CRISPRa) or inactivate genes (CRISPRi) by targeting modified sgRNA/Cas complexes to the gene’s promoter region, recruiting transcription factors for increased gene expression or repressors for decreasing gene expression.3 In addition to engineering patient’s own T-cells (autologous T-cells), there is increasing interest in using T-cells from healthy donors (allogeneic T-cells) as an off-the-shelf cell therapy product.42 Gene-edited allogeneic T-cells, with mechanisms to reduce graft-vs-host rejection, have shown promise as a strategy to broaden access to engineered T-cell therapies.43 Recent studies have demonstrated the feasibility of disrupting genes such as PD-1 and TCR using CRISPR-Cas9 in allogeneic T-cells before adoptive transfer into patients.44,45 Allogeneic CRISPR-edited T-cell therapies are now being evaluated in early-phase clinical trials, with the goals of maintaining anti-tumor potency while minimizing the risk of graft-vs-host disease.46
[164] Advances in CRISPR-Cas technology and its applications: revolutionising ... — | Blood | Hemophilia B | F9 | Corrected F9 gene in iPSCs using CRISPR-Cas9; restored F9 expression in hepatocyte-like cells | Morishige et al. One prominent application of CRISPR-Cas9 technology is its application in engineering T-cells express CARs. CAR-T cell therapy is a genetically modified T-cell that expresses CARs, targeting tumour-associated antigens (TAAs) or tumour-specific antigens (TSAs) with high specificity, thereby targeting and eliminating cancer cells (Jogalekar et al., 2022). CRISPR-Cas9 technology has enhanced CAR-T therapy by enabling precise genetic edits that improve T cell functionality, persistence, and specificity (Dimitri et al., 2022). CRISPR-Cas gene editing is utilised to introduce oncolytic viruses with therapeutic genes, enhancing their cancer tissue selectivity and suppressing antiviral protective mechanisms employed by malignant cells (Wang et al., 2022b).
[165] Creating the World's First CRISPR Medicine, for Sickle Cell Disease — Creating the World’s First CRISPR Medicine, for Sickle Cell Disease | Harvard Medical School When Vijay Sankaran was an MD-PhD student at Harvard Medical School in the mid-2000s, one of his first clinical encounters was with a 24-year-old patient whose sickle cell disease left them with almost weekly pain episodes. In 2008, Orkin, Sankaran, and colleagues achieved their vision by identifying a new therapeutic target for sickle cell disease. The decision has ushered in a new era for sickle cell disease treatment — and marked the world’s first approval of a medicine based on CRISPR/Cas9 gene-editing technology. Plus, researchers including Orkin, Sankaran, and those at Vertex continue to conduct research to make sickle cell treatment more effective, more efficient, and appropriate for even more patients.
[166] Factors Affecting Enzyme Activity and Efficiency — Factors Affecting Enzyme Activity and Efficiency - BiologyInsights Explore the key factors influencing enzyme activity and efficiency, including temperature, pH, substrate concentration, and regulatory mechanisms. Factors such as temperature, pH levels, substrate concentration, inhibitors, allosteric regulation, and enzyme modifications each contribute uniquely to how enzymes function under different conditions. This is because pH can affect the ionization of the enzyme’s active site, which is essential for substrate binding and catalysis. This is because there are ample active sites available on the enzyme for binding, making the reaction rate dependent on how frequently enzyme and substrate molecules encounter each other. One common type of inhibitor is the competitive inhibitor, which competes with the substrate for binding to the enzyme’s active site.
[172] Challenges in the computational design of proteins - PubMed — Challenges in the computational design of proteins - PubMed In this paper, we will describe the use of energy functions in computational protein design, the use of atomic models to evaluate the free energy in the unfolded and folded states, the exploration and optimization of amino acid sequences, the problem of negative design and the design of biomolecular function. The solvated rotamers can afterwards be introduced in computational protein design (Jiang et al. Example of positive and negative design states: schematic of competing states included in the design of ligand binding-induced allosteric changes ((a) open conformation, no binding; (b) open conformation with binding; (c) aggregated state; (d) closed conformation, no binding ligand; (e) closed conformation with binding; (f) unfolded state and ligand).
[184] Advancements and Future Directions in Machine Learning-Assisted Protein ... — Protein engineering, a rapidly evolving field in biotechnology, has the potential to revolutionize various sectors, including antibody design, drug discovery, food security, and ecology. Traditional methods such as directed evolution and rational design have been instrumental. However, the vast mutational space makes these approaches expensive, time-consuming, and limited scope. Leveraging
[185] Critical Assessment of Protein Engineering (CAPE): A Student Challenge ... — The success of AlphaFold in protein structure prediction highlights the power of data-driven approaches in scientific research. However, developing machine learning models to design and engineer proteins with desirable functions is hampered by
[187] Protein engineering in the deep learning era - Wiley Online Library — Advances in deep learning have significantly aided protein engineering in addressing challenges in industrial production, healthcare, and environmental sustainability. This review frames frequently researched problems in protein understanding and engineering from the perspective of deep learning.
[188] Machine Learning-Guided Protein Engineering | ACS Catalysis — Recent progress in engineering highly promising biocatalysts has increasingly involved machine learning methods. These methods leverage existing experimental and simulation data to aid in the discovery and annotation of promising enzymes, as well as in suggesting beneficial mutations for improving known targets. The field of machine learning for protein engineering is gathering steam, driven
[189] Machine Learning for Protein Engineering - PMC — However, a new paradigm is emerging, fusing the library generation and screening approaches of traditional directed evolution with computation through the training of machine learning models on protein sequence fitness data. This chapter highlights successful applications of machine learning to protein engineering and directed evolution, organized by the improvements that have been made with respect to each step of the directed evolution cycle. In this chapter, we review the core concepts that have enabled successful integration of machine learning in protein engineering by interpreting the process through the directed evolution cycle. The next steps are additional for protein engineering methods guided by machine learning, including (4) fitting the models and (5) selecting the next library based on these models. C. Structure based thermostability prediction models for protein single point mutations with machine learning tools.
[191] Protein Engineering - an overview | ScienceDirect Topics — As genetic tools and understanding of protein biochemistry, cell physiology, and metabolism continue to grow, so does the integration of engineered proteins into synthetic biological systems. While not exhaustive, this chapter first summarizes the key methods and tools in protein engineering.
[192] Integrating Computational Design and Experimental Approaches for Next ... — This review examines recent advances in computational and experimental approaches for engineering improved protein therapeutics. Computational methods like structure-based design, machine learning integration, and protein language models have dramatically enhanced our ability to predict protein properties and guide engineering efforts. The integration of these algorithms and the advancement of structure-based computational techniques contribute to the optimization and advancement of structural biology for therapeutic protein engineering applications. Experimental protein engineering has achieved significant advancements through directed evolution as well as rational design and structure-guided engineering. An overview and applications of therapeutic protein engineering using advanced biological molecules. An overview and applications of therapeutic protein engineering using advanced biological molecules. Engineering protein-based therapeutics through structural and chemical design.
[193] De novo protein design—From new structures to programmable functions — Advances in artificial intelligence are revolutionizing protein engineering and design. This Perspective discusses the concepts and approaches of de novo protein design, emerging challenges in designing structure and function, and the frontiers that lie ahead in deconstructing cellular processes with de novo proteins.
[194] Emerging conjugation strategies and protein engineering technologies ... — Here we discuss advances in protein engineering strategies and emerging technologies that are being developed to improve the functional properties of ADcs. this includes the maturation of conjugation technolo-gies that enable: (1) the construction of more stable, site-selective, and homogenous ADcs, (2) the development of bispecific ADcs to
[195] Emerging technologies in protein interface engineering for biomedical ... — Whereas the natural repertoire of protein interfaces is finite, biomolecular engineering tools provide access to an unlimited scope of potential interactions that can be custom-designed for affinity, specificity, mechanism, or other properties of interest. We cover three innovative and translationally promising approaches: (1) reprogramming receptor oligomerization to manipulate signaling pathways; (2) computational protein interface design strategies; and (3) engineering bioorthogonal protein interaction networks. We specifically address three approaches to interface engineering: (1) illuminating the receptor oligomerization space; (2) computational design of molecular interfaces; and (3) engineering bioorthogonal protein interactions. Emerging strategies such as engineering receptor oligomerization to tune signaling outcomes, computational design of protein interfaces with customized functionalities, and development of bioorthogonal protein networks offer unprecedented biological insights that are
[197] Industrial Challenges of Recombinant Proteins - PubMed — Industrial Challenges of Recombinant Proteins - PubMed Search: Search Your saved search Name of saved search: Add to Search Industrial Challenges of Recombinant Proteins Industrial Challenges of Recombinant Proteins Add to Search The use of recombinant DNA methods to produce large quantities of protein for therapeutic uses has revolutionized medicine. Industrial challenges for manufacture of biotherapeutic proteins are related to the characteristics of these proteins and the increasing quantities required to address needs of patients, worldwide. Challenges addressed include achieving cell density, protein expression, separations of cells and protein, protein purification, and segmentation of protein production into smaller quantities with the evolution of personalized medicine and products designed for increasingly small patient populations. Add to Search Add to Search Recombinant Proteins / therapeutic use* Add to Search Add to Search
[211] Rational design and engineering of therapeutic proteins — Engineering a system with such high therapeutic potential yet limited screening capacity will be an exciting challenge for rational protein design. State of the art rational engineering The numerous examples discussed in this review illustrate both the demand for and power of rational engineering methods to improve the efficacy of biotherapeutics.
[215] Directed Evolution - an overview | ScienceDirect Topics — Directed evolution is a tool for protein engineering that mimics the process of natural evolution to evolve protein to user defined manner. This laboratory process functions on a molecular level and focus specific molecular properties. ... Despite certain limitations, directed evolution has demonstrated success in identifying remote mutations
[216] Clinical translation of controlled protein delivery systems for tissue ... — Challenges to translation include high cost, difficulties in recruiting appropriate and large enough patient populations for clinical trials, and complex regulatory pathways. More careful analysis of the technologies and more strategic business models may improve the successful translation of CR systems for tissue engineering [ 104 ].
[217] Intracellular Protein Delivery: Approaches, Challenges, and Clinical ... — Then, we summarize strategies that have been reported to achieve protein internalization. These techniques can be broadly classified into 3 categories: physical methods, direct protein engineering, and nanocarrier-mediated delivery. Finally, we highlight existing challenges for cytosolic protein delivery and offer an outlook for future advances.
[218] Translating nanomedicines from the lab to the clinic — Author links open overlay panelInge Herrmann, Zhong Alan Li, Raman Bahal, João Conde The development of organ- and cell-specific selectively targeted delivery technology to minimize off-target effects and improve the safety of RNA-based therapeutics is an area that requires urgent and further research and development. The translation of nanotechnology and biomaterials research into clinical applications, particularly in nanomedicine, remains challenging because of the unpredictable behavior of nanoparticles in complex human systems. Digital technologies, such as artificial intelligence and machine learning, have demonstrated significant potential in expediting the translation of nanomedicine to clinical applications (https://doi.org/10.1038/s41565-024-01673-7). Through the implementation of personalized approaches, advanced preclinical models, artificial intelligence integration, and regulatory reforms, nanomedicine has the potential to transform therapeutic strategies and provide solutions that are more efficacious and tailored to individual patient requirements.