Computational Biochemistry Overview

Principles

Definition

Computational biochemistry is an interdisciplinary research field and methodological approach that applies theoretical principles, computational algorithms, and simulation techniques to investigate the structure, dynamics, interactions, and function of biological molecules and complex biochemical systems. This approach provides crucial insights that complement experimental studies and advance understanding in areas like molecular mechanisms, drug design, and cellular processes.

Ontological type

Core Methods

Key Applications

Modeling Scales

Key developments

Key Figures

Energy-Based Modeling era

Martin Karplus [1], with affiliations at Harvard University [3] and the Massachusetts Institute of Technology [4] during this era, was a central figure in Energy-Based Modeling (1972-1992). His key contributions include the CHARMM program for macromolecular energy, minimization, and dynamics calculations [6], the prediction of folding of short polypeptide segments by uniform conformational sampling [7], and the evaluation of configurational entropy for proteins in molecular dynamics simulations [8], which collectively defined the energy-centered framework championed by Karplus [1]. Bernard R. Brooks [2], with affiliations at Harvard University [3] and the University of California, Berkeley [5], contributed to the same era by advancing practical energy-based modeling through his work. Brooks [2] is best known for the CHARMM program [6], which provided a practical, extensible platform for macromolecular energy calculations, minimization, and dynamics that helped standardize energy-based force-field approaches in biomolecular simulations.

Multiscale QM/MM Integration era

Herman J. C. Berendsen [1] held appointments at University of Groningen [3] and Imperial College London [4] during the Multiscale QM/MM Integration era. Berendsen [1] introduced LINCS: A linear constraint solver for molecular simulations [7], providing a robust framework that stabilizes constrained dynamics and enables efficient large-scale biomolecular simulations. Richard A. Friesner [2] had appointments with Massachusetts Institute of Technology [5] and University of California, San Francisco [6] during this era. Friesner [2] contributed to the Evaluation and Reparametrization of the OPLS-AA Force Field for Proteins via Comparison with Accurate Quantum Chemical Calculations on Peptides [8], the New Model for Calculation of Solvation Free Energies: Correction of Self-Consistent Reaction Field Continuum Dielectric Theory for Short-Range Hydrogen-Bonding Effects [9], and the A mixed quantum mechanics/molecular mechanics (QM/MM) method for large-scale modeling of chemistry in protein environments [10], collectively advancing force-field accuracy, solvation modeling, and QM/MM integration essential for realistic multiscale simulations in proteins.

AI-Augmented Predictive Modeling era

Alexander D. MacKerell [1] is a leading figure in computational biochemistry during the AI-Augmented Predictive Modeling era, with affiliations spanning the University of California, San Francisco [3] and the University of Maryland, Baltimore [4]. His key contribution in this era centers on the paper Optimization of the Additive CHARMM All-Atom Protein Force Field Targeting Improved Sampling of the Backbone ϕ, ψ and Side-Chain χ1 and χ2 Dihedral Angles [7], which advanced sampling and accuracy in protein simulations. Robert B. Best [2] has been associated with the American College of Medical Genetics [5] and Baylor College of Medicine [6], reflecting his role in integrating genetics-informed evaluation with computational modeling during this era. His contributions are exemplified by the same 2012 CHARMM force field optimization paper [7], highlighting the era's emphasis on physics-based refinements to improve predictive capabilities within ML-augmented workflows.