Open AccessGuiding Protein Conformation Sampling with Conformation Space Maps
Author(s) -
Ahmed Bin Zaman,
Kenneth De Jong,
Amarda Shehu
Publication year - 2022
Publication title -
epic series in computing
Language(s) - English
Resource type - Conference proceedings
SCImago Journal Rank - 0.21
H-Index - 7
ISSN - 2398-7340
DOI - 10.29007/gfdc
Subject(s) - computer science , space (punctuation) , sequence (biology) , range (aeronautics) , artificial intelligence , sampling (signal processing) , process (computing) , algorithm , computer vision , biology , engineering , genetics , filter (signal processing) , aerospace engineering , operating system
Deep learning research, from ResNet to AlphaFold2, convincingly shows that deep learning can predict the native conformation of a given protein sequence with high accu- racy. Accounting for the plasticity of protein molecules remains challenging, and powerful algorithms are needed to sample the conformation space of a given amino-acid sequence. In the complex and high-dimensional energy surface that accompanies this space, it is critical to explore a broad range of areas. In this paper, we present a novel evolutionary algorithm that guides its optimization process with a memory of the explored conformation space, so that it can avoid searching already explored regions and search in the unexplored regions. The algorithm periodically consults an evolving map that stores already sampled non- redundant conformations to enhance exploration during selection. Evaluation on diverse datasets shows superior performance of the algorithm over the state-of-the-art algorithms.