Accurate Protein Structure Prediction by Embeddings and Deep Learning Representations

Proteins are the major building blocks of life, and actuators of almost all chemical and biophysical events in living organisms. Their native structures in turn enable their biological functions which have a fundamental role in drug design. This motivates predicting the structure of a protein from i...

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Bibliographic Details
Published in:arXiv.org 2019-11
Main Authors: Drori, Iddo, Thaker, Darshan, Srivatsa, Arjun, Jeong, Daniel, Wang, Yueqi, Linyong Nan, Wu, Fan, Leggas, Dimitri, Jinhao Lei, Lu, Weiyi, Fu, Weilong, Gao, Yuan, Karri, Sashank, Kannan, Anand, Moretti, Antonio, AlQuraishi, Mohammed, Chen Keasar, Pe'er, Itsik
Format: Article
Language:English
Subjects:
Actuators
Amino acids
Backbone
Datasets
Deep learning
Machine learning
Optimization
Organic chemistry
Predictions
Proteins
Torsion
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Summary:Proteins are the major building blocks of life, and actuators of almost all chemical and biophysical events in living organisms. Their native structures in turn enable their biological functions which have a fundamental role in drug design. This motivates predicting the structure of a protein from its sequence of amino acids, a fundamental problem in computational biology. In this work, we demonstrate state-of-the-art protein structure prediction (PSP) results using embeddings and deep learning models for prediction of backbone atom distance matrices and torsion angles. We recover 3D coordinates of backbone atoms and reconstruct full atom protein by optimization. We create a new gold standard dataset of proteins which is comprehensive and easy to use. Our dataset consists of amino acid sequences, Q8 secondary structures, position specific scoring matrices, multiple sequence alignment co-evolutionary features, backbone atom distance matrices, torsion angles, and 3D coordinates. We evaluate the quality of our structure prediction by RMSD on the latest Critical Assessment of Techniques for Protein Structure Prediction (CASP) test data and demonstrate competitive results with the winning teams and AlphaFold in CASP13 and supersede the results of the winning teams in CASP12. We make our data, models, and code publicly available.
ISSN:2331-8422