Computational Approaches to Predicting Protein Structure and Molecular Interactions

Abstract

The three-dimensional structure of a protein determines its function, yet experimental structure determination remains slow and costly relative to the rapid growth of sequence data. This article reviews computational approaches for predicting protein structure and molecular interactions, beginning with the folding problem and the role of experimental data and databases. Template-based modeling, physics-based and fragment assembly methods, coevolution analysis and modern deep learning systems are compared in terms of principles, strengths and limitations. Extensions of structure prediction to protein-protein complexes, molecular docking of small ligands and molecular dynamics simulation are discussed, together with approaches to validation and confidence estimation. Despite remarkable progress, challenges remain in modeling conformational ensembles, disordered regions, mutation effects, transient interactions and binding affinity. Integration of data-driven prediction with physics-based simulation and experiment is highlighted as a promising direction for understanding protein function and accelerating biomedical discovery.