TopScore: Using Deep Neural Networks and Large Diverse Data Sets for Accurate Protein Model Quality Assessment-Reference-Cited by-同舟云学术

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TopScore: Using Deep Neural Networks and Large Diverse Data Sets for Accurate Protein Model Quality Assessment

Published:2018-09-25 Issue:11 Volume:14 Page:6117-6126
ISSN:1549-9618
Container-title:Journal of Chemical Theory and Computation
language:en
Short-container-title:J. Chem. Theory Comput.

Author:

Mulnaes Daniel¹,Gohlke Holger¹²^ORCID

Affiliation:

1. Department of Mathematics and Natural Sciences, Institute for Pharmaceutical and Medicinal Chemistry, Heinrich Heine University Düsseldorf, Universitätsstrasse 1, 40225 Düsseldorf, Germany

2. John von Neumann Institute for Computing (NIC), Jülich Supercomputing Centre (JSC) & Institute for Complex Systems - Structural Biochemistry (ICS 6), Forschungszentrum Jülich GmbH, Jülich, Germany

Funder

Deutsche Forschungsgemeinschaft

Publisher

American Chemical Society (ACS)

Subject

Physical and Theoretical Chemistry,Computer Science Applications

Link

https://pubs.acs.org/doi/pdf/10.1021/acs.jctc.8b00690

Reference52 articles.

1. ProQ3: Improved model quality assessments using Rosetta energy terms

2. Assessment of the assessment: Evaluation of the model quality estimates in CASP10

3. Statistical potential for assessment and prediction of protein structures

4. GOAP: A Generalized Orientation-Dependent, All-Atom Statistical Potential for Protein Structure Prediction

5. Novel knowledge-based mean force potential at atomic level

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