ct0c00188_si_001.pdf (16.64 MB)
Protein Structure Prediction in CASP13 Using AWSEM-Suite
journal contribution
posted on 2020-05-22, 19:16 authored by Shikai Jin, Mingchen Chen, Xun Chen, Carlos Bueno, Wei Lu, Nicholas P. Schafer, Xingcheng Lin, José N. Onuchic, Peter G. WolynesRecently several techniques have
emerged that significantly enhance
the quality of predictions of protein tertiary structures. In this
study, we describe the performance of AWSEM-Suite, an algorithm that
incorporates template-based modeling and coevolutionary restraints
with a realistic coarse-grained force field, AWSEM. With its roots
in neural networks, AWSEM contains both physical and bioinformatical
energies that have been optimized using energy landscape theory. AWSEM-Suite
participated in CASP13 as a server predictor and generated reliable
predictions for most targets. AWSEM-Suite ranked eighth in both the
free-modeling category and the hard-to-model category and in one case
provided the best submitted prediction. Here we critically discuss
the prediction performance of AWSEM-Suite using several examples from
different categories in CASP13. Structure prediction tests on these
selected targets, two of them being hard-to-model targets, show that
AWSEM-Suite can achieve high-resolution structure prediction after
incorporating both template guidances and coevolutionary restraints
even when homology is weak. For targets with reliable templates (template-easy
category), introducing coevolutionary restraints sometimes damages
the overall quality of the predictions. Free energy profile analyses
demonstrate, however, that the incorporations of both of these evolutionarily
informed terms effectively increase the funneling of the landscape
toward native-like structures while still allowing sufficient flexibility
to correct for discrepancies between the correct target structure
and the provided guidance. In contrast to other predictors that are
exclusively oriented toward structure prediction, the connection of
AWSEM-Suite to a statistical mechanical basis and affiliated molecular
dynamics and importance sampling simulations makes it suitable for
functional explorations.