Machine learning prediction of interchain contacts and structural model quality for protein complexes
No Thumbnail Available
Authors
Meeting name
Sponsors
Date
Journal Title
Format
Thesis
Subject
Abstract
[EMBARGOED UNTIL 12/01/2025] Protein is the building block of life. It takes part in various biochemical reactions to sustain life. The role that a protein will play in a reaction predominantly depends on its structure. As a result, determining the structure of the proteins could unravel the mystery of life. Hence it has become a field of principal interest to researchers worldwide. Traditional approaches used to determine the structure of protein are time-consuming and expensive. Therefore a computational based method must be employed to speed up the process. In the last decade deep learning has established the impact of the computational based method. Now deep learning is extensively used in predicting the interaction and the structure of protein, quality assessment and also in determining the cryo electron density map(EDM) from 2D micrographs. This dissertation presents 3 contributions. First DRCON, introduces a deep dilated deep residual network to predict the interchain contact map of homodimers by features utilizing its sequence information only . Secondly, Multicom_qa, it uses a hybrid approach utilizing pairwise structural similarity and interface contact predicted by deep learning tools to estimate protein complex model accuracy . In the CASP15,it was ranked top in estimating the global structure accuracy of assembly models. Thirdly, Benchmarking Techniques for Electron Density Map Reconstruction from Imaging Data: A Comparative Study. Currently, cryo-em has become very popular for the study of large protein molecules but still it has not received much attention. Hence we provided a comprehensive overview of current methodologies and their effectiveness in 3D cryo-EM EDM reconstruction from images, offering insights for future research and development.
Table of Contents
DOI
PubMed ID
Degree
Ph. D.