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Distance-Guided Forward and Backward Chain-Growth Monte Carlo Method for Conformational Sampling and Structural Prediction of Antibody CDR-H3 Loops

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Title: Distance-Guided Forward and Backward Chain-Growth Monte Carlo Method for Conformational Sampling and Structural Prediction of Antibody CDR-H3 Loops
Author(s): Tang, K.; Zhang, J.F.; Liang, J.
Subject(s): Forward and Backward Chain-Growth
Abstract: Antibodies recognize antigens through the complementary determining regions (CDR) formed by six-loop hypervariable regions crucial for the diversity of antigen specificities. Among the six CDR loops, the H3 loop is the most challenging to predict because of its much higher variation in sequence length and identity, resulting in much larger and complex structural space, compared to the other five loops. We developed a novel method based on a chain-growth sequential Monte Carlo method, called Distanceguided Sequential chain-Growth Monte Carlo for H3 loops (DiSGro-H3). The new method samples protein chains in both forward and backward directions. It can ef- ficiently generate low energy, near-native H3 loop structures using the conformation types predicted from the sequences of H3 loops. DiSGro-H3 performs significantly better than another ab initio method, RosettaAntibody, in both sampling and prediction, while taking less computational time. It performs comparably to template-based methods. As an ab initio method, DiSGro-H3 offers satisfactory accuracy while being able to predict any H3 loops without templates.
Issue Date: 2016-11-30
Publisher: American Chemical Society
Citation Info: Tang, Ke, Jinfeng Zhang, and Jie Liang. "Distance-Guided Forward and Backward Chain-Growth Monte Carlo Method for Conformational Sampling and Structural Prediction of Antibody CDR-H3 Loops." Journal of Chemical Theory and Computation (2016). DOI: 10.1021/acs.jctc.6b00845.
Type: Article
Description: Copyright © 2016 American Chemical Society
URI: http://hdl.handle.net/10027/21678
ISSN: 1549-9618
Date Available in INDIGO: 2017-11-27
 

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