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Biologically inspired de novo protein structure prediction

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Protein structures can elucidate functional understanding, explain disease mechanisms and inform drug design. However, experimental structure determination is costly, and technically difficult. However, while the three-dimensional structure of proteins is difficult to obtain amino acid sequences are easily available and far outnumber solved structures. There are two main methods for protein structure prediction template based and de novo. Current de novo protein structure prediction methods are heuristics limited by the enormous search space, with successful prediction largely restricted to small, single domain proteins. The three key components of most de novo methods for protein structure prediction are the fragment library, the “energy” function and the search method. In this talk I will give an overview of my groups work on improving each of these stages. Firstly, describing the development of a novel fragment library Flib that uses predicted secondary structure to determine library generation strategy [1]. Secondly, giving a comparison of the different co-evolution contact predictors in terms of their ability to improve protein structure prediction [2]. Finally demonstrating how sequential prediction approaches using SAINT2 can improve both search heuristics and final model quality [3].

[1] Saulo H P de Oliveira, Jiye Shi, Charlotte M Deane, Building a better fragment library for de novo protein structure prediction, Plos One, 2015, 10(4), e0123998 [2] Saulo H P de Oliveira, Jiye Shi, Charlotte M Deane, Comparing co-evolution methods and their application to template-free protein structure prediction, Bioinformatics, 2017; 33 (3): 373-381. [3] Saulo H P de Oliveira, Eleanor C. Law, Jiye Shi, Charlotte M. Deane, Sequential search leads to faster, more efficient fragment-based de novo protein structure prediction, Bioinformatics, 2017, btx722

This talk is part of the Biophysical Seminars series.

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