TopSuite Web Server: A Meta-Suite for Deep-Learning-Based Protein Structure and Quality Prediction.

Journal: Journal of chemical information and modeling
Published Date:

Abstract

Proteins carry out the most fundamental processes of life such as cellular metabolism, regulation, and communication. Understanding these processes at a molecular level requires knowledge of their three-dimensional structures. Experimental techniques such as X-ray crystallography, NMR spectroscopy, and cryogenic electron microscopy can resolve protein structures but are costly and time-consuming and do not work for all proteins. Computational protein structure prediction tries to overcome these problems by predicting the structure of a new protein using existing protein structures as a resource. Here we present TopSuite, a web server for protein model quality assessment (TopScore) and template-based protein structure prediction (TopModel). TopScore provides meta-predictions for global and residue-wise model quality estimation using deep neural networks. TopModel predicts protein structures using a top-down consensus approach to aid the template selection and subsequently uses TopScore to refine and assess the predicted structures. The TopSuite Web server is freely available at https://cpclab.uni-duesseldorf.de/topsuite/.

Authors

  • Daniel Mulnaes
    Department of Mathematics and Natural Sciences , Institute for Pharmaceutical and Medicinal Chemistry, Heinrich Heine University Düsseldorf , Universitätsstrasse 1 , 40225 Düsseldorf , Germany.
  • Filip Koenig
    Institut für Pharmazeutische und Medizinische Chemie, Heinrich-Heine-Universität Düsseldorf, 40225 Düsseldorf, Germany.
  • Holger Gohlke
    Institute for Pharmaceutical and Medicinal Chemistry, Heinrich Heine University Düsseldorf 40225 Düsseldorf, Germany; Institute of Bio- and Geosciences (IBG4: Bioinformatics), Forschungszentrum Jülich GmbH, 52425 Jülich, Germany. Electronic address: gohlke@hhu.de.