预测蛋白质结构和人工智能

IF 2.6 3区 生物学 Q2 GENETICS & HEREDITY
Shiho Ohno, Noriyoshi Manabe, Yoshiki Yamaguchi
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引用次数: 0

摘要

AlphaFold 是一种基于人工智能(AI)的蛋白质三维结构预测工具,因其在人类蛋白质折叠方面的高准确性和多功能性而得到广泛认可。AlphaFold 有助于从蛋白质三维结构模型中理解结构-功能关系,并可作为实验结构分析(包括 X 射线晶体学、核磁共振和冷冻电镜分析)的模板或参考。不仅在结构生物学领域,在其他研究领域,研究人员对它的使用也在不断扩大。研究人员目前正在探索 AlphaFold 生成的蛋白质模型的全部潜力。预测错义突变导致的疾病严重程度就是其中一项应用。本文概述了基于深度学习技术的 AlphaFold 三维结构建模,并重点介绍了预测错义突变致病性所面临的挑战。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Prediction of protein structure and AI

Prediction of protein structure and AI

Prediction of protein structure and AI
AlphaFold, an artificial intelligence (AI)-based tool for predicting the 3D structure of proteins, is now widely recognized for its high accuracy and versatility in the folding of human proteins. AlphaFold is useful for understanding structure-function relationships from protein 3D structure models and can serve as a template or a reference for experimental structural analysis including X-ray crystallography, NMR and cryo-EM analysis. Its use is expanding among researchers, not only in structural biology but also in other research fields. Researchers are currently exploring the full potential of AlphaFold-generated protein models. Predicting disease severity caused by missense mutations is one such application. This article provides an overview of the 3D structural modeling of AlphaFold based on deep learning techniques and highlights the challenges in predicting the pathogenicity of missense mutations.
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来源期刊
Journal of Human Genetics
Journal of Human Genetics 生物-遗传学
CiteScore
7.20
自引率
0.00%
发文量
101
审稿时长
4-8 weeks
期刊介绍: The Journal of Human Genetics is an international journal publishing articles on human genetics, including medical genetics and human genome analysis. It covers all aspects of human genetics, including molecular genetics, clinical genetics, behavioral genetics, immunogenetics, pharmacogenomics, population genetics, functional genomics, epigenetics, genetic counseling and gene therapy. Articles on the following areas are especially welcome: genetic factors of monogenic and complex disorders, genome-wide association studies, genetic epidemiology, cancer genetics, personal genomics, genotype-phenotype relationships and genome diversity.
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