Rosetta Custom Score Functions Accurately Predict ΔΔG of Mutations at Protein-Protein Interfaces Using Machine Learning

doi 10.1101/2020.03.17.996066
Full Text
Abstract

Available in full text

Date
Authors
Publisher

Cold Spring Harbor Laboratory

Related search

Prediction of Hot Spot Residues at Protein-Protein Interfaces by Combining Machine Learning and Energy-Based Methods

BMC Bioinformatics
BiochemistryApplied MathematicsComputer Science ApplicationsStructural BiologyMolecular Biology
2009English

Protein Conformational Flexibility Prediction Using Machine Learning

Journal of Magnetic Resonance
BiochemistryNuclearHigh Energy PhysicsCondensed Matter PhysicsBiophysics
2008English

Protein–lipid Interfaces Can Drive the Functions of Membrane-Embedded Protein–protein Complexes

ACS Chemical Biology
BiochemistryMedicineMolecular Medicine
2018English

How Accurately Can Z-Score Predict Bank Failure?

Financial Markets, Institutions and Instruments
EconomicsEconometricsFinance
2016English

Predicting Protein–protein Association Rates Using Coarse-Grained Simulation and Machine Learning

Scientific Reports
Multidisciplinary
2017English

Structural and Computational Characterization of Disease-Related Mutations Involved in Protein-Protein Interfaces

International Journal of Molecular Sciences
Organic ChemistryMolecular BiologyTheoretical ChemistryInorganic ChemistryComputer Science ApplicationsSpectroscopyMedicineCatalysisPhysical
2019English

Improving Propensity Score Weighting Using Machine Learning

Statistics in Medicine
EpidemiologyStatisticsProbability
2009English

Protein Structure Prediction With Machine Learning

2020English

Creation of Artificial Protein–protein Interactions Using Α-Helices as Interfaces

Biophysical Reviews
Molecular BiologyStructural BiologyBiophysics
2017English

Amanote Research

Note-taking for researchers

Follow Amanote

© 2024 Amaplex Software S.P.R.L. All rights reserved.

Privacy PolicyRefund Policy