Article

FiTMuSiC: Leveraging structural and (co)evolutionary data for protein fitness prediction

2023-08-03

Abstract excerpt

Systematically predicting the effects of mutations on protein fitness is essential for the understanding of genetic diseases. Indeed, predictions complement experimental efforts in analyzing how variants lead to dysfunctional proteins that in turn can cause diseases. Here we present our new fitness predictor, FiTMuSiC, which leverages structural, evolutionary and coevolutionary information. We show that FiTMuSiC p...

Topics

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

Identifiers and source

Literature Corpus work: 1d506973-97b5-5610-8536-bd35ec8a3147
DOI: 10.1101/2023.08.01.551497

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

FiTMuSiC: Leveraging structural and (co)evolutionary data for protein fitness predictionDOI 10.1101/2023.08.01.551497Read

FiTMuSiC: leveraging structural and (co)evolutionary data for protein fitness prediction.Semantic proximity Read

CAGI5: Objective performance assessments of predictions based on the Evolutionary Action equation.Semantic proximity Read

Evolutionary mechanisms studied through protein fitness landscapes.Semantic proximity Read

ProteinGym: Large-Scale Benchmarks for Protein Design and Fitness PredictionSemantic proximity Read

Infer global, predict local: Quantity-relevance trade-off in protein fitness predictions from sequence data.Semantic proximity Read

TranceptEVE: Combining Family-specific and Family-agnostic Models of Protein Sequences for Improved Fitness PredictionSemantic proximity Read

Unlocking Protein Evolution Insights: Efficient and Interpretable Mutational Effect Predictions with GEMME.Semantic proximity Read

Learning protein fitness landscapes with deep mutational scanning data from multiple sources.Semantic proximity Read

A protein fitness predictive framework based on feature combination and intelligent searching.Semantic proximity Read

Mutation effects predicted from sequence co-variation.Semantic proximity Read

Improving the prediction of protein stability changes upon mutations by geometric learning and a pre-training strategy.Semantic proximity Read

Prediction of impacts of mutations on protein structure and interactions: SDM, a statistical approach, and mCSM, using machine learning.Semantic proximity Read