Journal Article

FZJ-2020-00491

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png TopModel: Template-based protein structure prediction at low sequence identity using top-down consensus and deep neural networks

Mulnaes, D. ; Porta, N. ; Clemens, R. ; Apanasenko, I.FZJ* ; Reiners, J. ; Gremer, L.FZJ* ; Neudecker, P.FZJ* ; Smits, S. H. J. ; Gohlke, H. (Corresponding author)FZJ*

2020
Washington, DC

This record in other databases:      

Please use a persistent id in citations: http://hdl.handle.net/2128/24531  doi:10.1021/acs.jctc.9b00825

Abstract: Knowledge of protein structures is essential to understand the proteins’ functions, evolution, dynamics, stabilities, interactions, and for data-driven protein- or drug-design. Yet, experimental structure determination rates are far exceeded by that of next-generation sequencing. Computational structure prediction seeks to alleviate this problem, and the Critical Assessment of protein Structure Prediction (CASP) has shown the value of consensus- and meta-methods that utilize complementary algorithms. However, traditionally, such methods employ majority voting during template selection and model averaging during refinement, which can drive the model away from the native fold if it is underrepresented in the ensemble. Here, we present TopModel, a fully automated meta-method for protein structure prediction. In contrast to traditional consensus- and meta-methods, TopModel uses top-down consensus and deep neural networks to select templates and identify and correct wrongly modeled regions. TopModel combines a broad range of state-of-the-art methods for threading, alignment and model quality estimation and provides a versatile work-flow and toolbox for template-based structure prediction. TopModel shows a superior template selection, alignment accuracy, and model quality for template-based structure prediction on the CASP10-12 datasets. TopModel was validated by prospective predictions of the nisin resistance protein NSR protein from S. agalactiae and LipoP from C. difficile, showing far better agreement with experimental data than any of its constituent primary predictors. These results, in general, demonstrate the utility of TopModel for protein structure prediction and, in particular, show how combining computational structure prediction with sparse or low-resolution experimental data can improve the final model.

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Contributing Institute(s):

1. Jülich Supercomputing Center (JSC)
2. John von Neumann - Institut für Computing (NIC)
3. Strukturbiochemie (ICS-6)

Research Program(s):

1. 511 - Computational Science and Mathematical Methods (POF3-511) (POF3-511)
2. 551 - Functional Macromolecules and Complexes (POF3-551) (POF3-551)
3. Forschergruppe Gohlke (hkf7_20170501) (hkf7_20170501)

Appears in the scientific report 2020

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Database coverage:
; ; Clarivate Analytics Master Journal List ; Current Contents - Physical, Chemical and Earth Sciences ; IF >= 5 ; JCR ; NCBI Molecular Biology Database ; SCOPUS ; Science Citation Index ; Science Citation Index Expanded ; Web of Science Core Collection

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