Information-Driven Modeling of Biomolecular Complexes

TL;DR

This paper reviews how experimental and bioinformatics data enhance molecular docking methods for modeling complex biomolecular assemblies, emphasizing recent software advances and applications in antibody-antigen and membrane protein complexes.

Contribution

It provides a comprehensive overview of integrating experimental and computational data in protein-protein docking, highlighting recent methodological developments and specific applications.

Findings

Recent software improvements enable more accurate docking models.

Integration of evolutionary and shape data improves complex predictions.

Applications include antibody-antigen and membrane protein complex modeling.

Abstract

Proteins play crucial roles in every cellular process by interacting with each other, with nucleic acids, metabolites, and other molecules. The resulting assemblies can be very large and intricate and pose challenges to experimental methods. In the current era of integrative modeling, it is often only by a combination of various experimental techniques and computations that 3D models of those molecular machines can be obtained. Among the various computational approaches available, molecular docking is often the method of choice when it comes to predicting 3D structures of complexes. Docking can generate particularly accurate models when taking into account the available information on the complex of interest. We review here the use of experimental and bioinformatics data in protein-protein docking, describing recent software developments and highlighting applications for the modeling of antibody-antigen complexes and membrane protein complexes, and the use of evolutionary and shape information.