Article

Techniques for Developing Reliable Machine Learning Classifiers Applied to Understanding and Predicting Protein:Protein Interaction Hot Spots

2022-12-27

Abstract excerpt

With machine learning now transforming the sciences, successful prediction of biological structure or activity is mainly limited by the extent and quality of data available for training, the astute choice of features for prediction, and thorough assessment of the robustness of prediction on a variety of new cases. Here we address these issues while developing and sharing protocols to build a robust dataset and rig...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work: f408aa4d-457a-5bed-a822-a52e520bdbb1
DOI: 10.1101/2022.12.26.521948

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

Techniques for Developing Reliable Machine Learning Classifiers Applied to Understanding and Predicting Protein:Protein Interaction Hot SpotsDOI 10.1101/2022.12.26.521948Read

Characterizing changes in the rate of protein-protein dissociation upon interface mutation using hotspot energy and organization.Semantic proximity Read

Context-based identification of protein-protein interfaces and "hot-spot" residues.Semantic proximity Read

Categorization of hotspots into three types - weak, moderate and strong to distinguish protein-protein versus protein-peptide interactions.Semantic proximity Read

RosettaDDGPrediction for high-throughput mutational scans: from stability to bindingSemantic proximity Read

PIIMS Server: A Web Server for Mutation Hotspot Scanning at the Protein-Protein Interface.Semantic proximity Read

Rosetta custom score functions accurately predict ΔΔG of mutations at protein-protein interfaces using machine learning.Semantic proximity Read

Hot spots in protein-protein interfaces: towards drug discovery.Semantic proximity Read

Expanding the frontiers of protein-protein modeling: from docking and scoring to binding affinity predictions and other challenges.Semantic proximity Read

RosettaDDGPrediction for high-throughput mutational scans: From stability to binding.Semantic proximity Read

Benchmarking the accuracy of structure-based binding affinity predictors on Spike-ACE2 deep mutational interaction set.Semantic proximity Read

Co-Occurring Atomic Contacts for the Characterization of Protein Binding Hot Spots.Semantic proximity Read

HotSpot Wizard 2.0: automated design of site-specific mutations and smart libraries in protein engineering.Semantic proximity Read