.png){kind=icon}
返回
Toward Developing Intuitive Rules for Protein Variant Effect Prediction Using Deep Mutational Scanning Data
.png){kind=icon}
OA
AI
DOI：10.1021/acsomega.0c02402.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Protein structure and function can be severely altered by even a single amino acid mutation. Predictions of mutational effects using extensive artificial intelligence (AI)-based models, although accurate, remain as enigmatic as the experimental observations in terms of improving intuitions about the contributions of various factors. Inspired by Lipinski's rules for drug-likeness, we devise simple thresholding criteria on five different descriptors such as conservation, which have so far been limited to qualitative interpretations such as high conservation implies high mutational effect. We analyze systematic deep mutational scanning data of all possible single amino acid substitutions on seven proteins (25153 mutations) to first define these thresholds and then to evaluate the scope and limits of the predictions. At this stage, the approach allows us to comment easily and with a low error rate on the subset of mutations classified as neutral or deleterious by all of the descriptors. We hope that complementary to the accurate AI predictions, these thresholding rules or their subsequent modifications will serve the purpose of codifying the knowledge about the effects of mutations.
Keyword:
SOLUBILITY
MECHANISM
FITNESS
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
4.3
论文数:
3.4W
被引数:
9.8W
机构
引用论文
Comprehensive mutational scanning of a kinase in vivo reveals substrate-dependent fitness landscapes.png)
NUCLEIC ACIDS RESEARCHOAAI
NUCLEIC ACIDS RESEARCHIF13.1
OAAI分享收藏Systematic Mutant Analyses Elucidate General and Client-Specific Aspects of Hsp90 FunctionCELL REPORTSOAAI
CELL REPORTSIF6.9
OAAI分享收藏Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings在药物发现和开发环境中估算溶解度和渗透性的实验和计算方法
PREAI
分享收藏Deep generative models of genetic variation capture the effects of mutations遗传变异的深度生成模型捕捉了突变的影响NATURE METHODSOAAI
NATURE METHODSIF32.1
OAAI分享收藏Activity-enhancing mutations in an E3 ubiquitin ligase identified by high-throughput mutagenesis通过高通量诱变鉴定的E3泛素连接酶中的活性增强突变OAAI
OAAI分享收藏
查看更多内容