Suppr超能文献

蛋白质中配体结合位点的自动预测。

Automated prediction of ligand-binding sites in proteins.

作者信息

Harris Rodney, Olson Arthur J, Goodsell David S

机构信息

Department of Molecular Biology, The Scripps Research Institute, La Jolla, California 92037, USA.

出版信息

Proteins. 2008 Mar;70(4):1506-17. doi: 10.1002/prot.21645.

Abstract

We present a method, termed AutoLigand, for the prediction of ligand-binding sites in proteins of known structure. The method searches the space surrounding the protein and finds the contiguous envelope with the specified volume of atoms, which has the largest possible interaction energy with the protein. It uses a full atomic representation, with atom types for carbon, hydrogen, oxygen, nitrogen and sulfur (and others, if desired), and is designed to minimize the need for artificial geometry. Testing on a set of 187 diverse protein-ligand complexes has shown that the method is successful in predicting the location and approximate volume of the binding site in 73% of cases. Additional testing was performed on a set of 96 protein-ligand complexes with crystallographic structures of apo and holo forms, and AutoLigand was able to predict the binding site in 80% of the apo structures.

摘要

我们提出了一种名为AutoLigand的方法,用于预测已知结构蛋白质中的配体结合位点。该方法搜索蛋白质周围的空间,找到具有指定原子体积的连续包络,其与蛋白质具有尽可能大的相互作用能。它使用全原子表示法,包含碳、氢、氧、氮和硫的原子类型(如有需要,还包括其他类型),并且旨在尽量减少对人工几何形状的需求。对一组187种不同的蛋白质 - 配体复合物进行测试表明,该方法在73%的情况下成功预测了结合位点的位置和大致体积。对一组96种具有脱辅基和全辅基形式晶体结构的蛋白质 - 配体复合物进行了额外测试,AutoLigand能够在80%的脱辅基结构中预测结合位点。

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验