Machine learning and template based modeling for improving and expanding the functionality of rigid body docking

Proteins govern practically every process in living organisms through inhibiting, activating, or acting on other proteins in different ways. With the large and growing number of known interactions through high throughput screening technologies, experimental determination of atomic-level details of these interactions is nigh impossible. Computational methods such as docking can speed up efforts of understanding these interactions. However, several issues ought to be addressed before docking can replace experimental methods. This thesis describes work on assessment of the state of the art in docking methods, implementation of a machine learning algorithm to improve model ranking and integration of docking with template-based modeling to expand its usage with a special focus on antibody-antigen interactions. Firstly, the performance of docking methods was rigorously assessed by using a diverse set of protein complexes with a special focus on ClusPro, one of the leading rigid-body docking servers. Different strengths and potential areas of improvement for ClusPro and rigid-body docking methods in general were highlighted. Secondly, one of the major short-comings of docking noted in the first project, poor ranking of good models, was addressed. A regression-based machine learning algorithm was introduced to improve the ranking. Finally, a server was developed to tackle the challenge of epitope mapping by integrating template-based modeling with docking. An intuitive ensemble approach to scoring residue likelihood using docking poses and different homologues is shown to yield great success. In addition to shifting docking’s purpose of conformational search to interface identification, this server also allows users to start with protein sequence inputs.2022-09-28T00:00:00