PT  - JOURNAL ARTICLE
AU  - Mikita Misiura
AU  - Raghav Shroff
AU  - Ross Thyer
AU  - Anatoly B. Kolomeisky
TI  - DLPacker: Deep Learning for Prediction of Amino Acid Side Chain Conformations in Proteins
AID  - 10.1101/2021.05.23.445347
DP  - 2021 Jan 01
TA  - bioRxiv
PG  - 2021.05.23.445347
4099  - http://biorxiv.org/content/early/2021/07/22/2021.05.23.445347.short
4100  - http://biorxiv.org/content/early/2021/07/22/2021.05.23.445347.full
AB  - Prediction of side chain conformations of amino acids in proteins (also termed ‘packing’) is an important and challenging part of protein structure prediction with many interesting applications in protein design. A variety of methods for packing have been developed but more accurate ones are still needed. Machine learning (ML) methods have recently become a powerful tool for solving various problems in diverse areas of science, including structural biology. In this work we evaluate the potential of Deep Neural Networks (DNNs) for prediction of amino acid side chain conformations. We formulate the problem as image-to-image transformation and train a U-net style DNN to solve the problem. We show that our method outperforms other physics-based methods by a significant margin: reconstruction RMSDs for most amino acids are about 20% smaller compared to SCWRL4 and Rosetta Packer with RMSDs for bulky hydrophobic amino acids Phe, Tyr and Trp being up to 50% smaller.Competing Interest StatementThe authors have declared no competing interest.