Topological representations for protein mutation problem.

NIAID Data Ecosystem2026-03-10 收录

蛋白质突变预测

拓扑表示方法

数据链接：

https://figshare.com/articles/dataset/Topological_representations_for_protein_mutation_problem_/5253868 数据链接链接失效反馈

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资源简介：

Topological representations for protein mutation problem.

创建时间：

2017-08-08

Structure-informed direct coupling analysis improves protein mutational landscape predictions

蛋白质突变预测

结构信息整合计算模型

Data repository for StructureDCA Data repository for the publication:Matsvei Tsishyn, Hugo Talibart, Marianne Rooman, Fabrizio Pucci. Structure-informed direct coupling analysis improves protein muta

Zenodo2026-03-16 更新70

iGEMME Missense Mutational Effect Predictions for Entire Human Proteome

生物信息学

蛋白质突变预测

This dataset contains iGEMME single point mutation predictions of about ~19000 human proteins. In iGEMME predictions, only evolutionary data coming from multiple sequence alignment files is used. Desc

Mendeley Data2024-06-27 更新70

Data_Sheet_1_Prediction of Protein Mutational Free Energy: Benchmark and Sampling Improvements Increase Classification Accuracy.ZIP

蛋白质突变预测

计算生物学基准

Software to predict the change in protein stability upon point mutation is a valuable tool for a number of biotechnological and scientific problems. To facilitate the development of such software and

NIAID Data Ecosystem20

SeprotHub/Dataset-RASH_HUMAN

蛋白质突变预测

深度突变扫描

该数据集包含了人类RASH蛋白质单点突变及其对应的突变效应分数，这些分数来自于深度突变扫描实验。蛋白质的格式为氨基酸序列。数据集分为训练集、验证集和测试集，分别包含2479、338和317个样本。标签表示基于深度突变扫描实验的每个蛋白质的突变效应分数，范围从负无穷大到正无穷大，其中0代表野生型的值，数值越大表示适应性越高。

Hugging Face2025-05-13 更新70

Table_2_Predicting the Disease Risk of Protein Mutation Sequences With Pre-training Model.XLSX

蛋白质突变预测

疾病风险评估

Accurately identifying the missense mutations is of great help to alleviate the loss of protein function and structural changes, which might greatly reduce the risk of disease for tumor suppressor gen

NIAID Data Ecosystem30

Topological representations for protein mutation problem.

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