Using physical potentials and learned models to distinguish native binding interfaces from de novo designed interfaces that do not bind

Author:

Demerdash Omar N. A.12,Mitchell Julie C.34

Affiliation:

1. Medical Scientist Training Program; University of Wisconsin-Madison; Madison Wisconsin

2. Biophysics Program; University of Wisconsin-Madison; Madison Wisconsin

3. Department of Biochemistry; University of Wisconsin-Madison; Madison Wisconsin

4. Department of Mathematics; University of Wisconsin-Madison; Madison Wisconsin

Funder

US Department of Energy Genomics: GTL and SciDAC Programs

Publisher

Wiley

Subject

Molecular Biology,Biochemistry,Structural Biology

Cited by 7 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. MODELOS DE INTERACCIONES ATÓMICAS EN MECÁNICA MOLECULAR;Revista de la Facultad de Ciencias;2024-01-01

2. Using diverse potentials and scoring functions for the development of improved machine-learned models for protein–ligand affinity and docking pose prediction;Journal of Computer-Aided Molecular Design;2021-10-28

3. Prediction of peptide binding to MHC using machine learning with sequence and structure-based feature sets;Biochimica et Biophysica Acta (BBA) - General Subjects;2020-04

4. Predicting nsSNPs that Disrupt Protein-Protein Interactions Using Docking;IEEE/ACM Transactions on Computational Biology and Bioinformatics;2017-09-01

5. Understanding Proteins as Digital Widgets;A Mathematical Approach to Protein Biophysics;2017

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