开发一种蛋白质配体扩展连接性（PLEC）指纹及其在结合亲和力预测中的应用。
Development of a protein-ligand extended connectivity (PLEC) fingerprint and its application for binding affinity predictions.
机构信息
Institute of Biochemistry and Biophysics PAS, Pawinskiego 5a, Warsaw, Poland.
Faculty of Mathematics, Informatics, and Mechanics, University of Warsaw, Banacha 2, Warsaw, Poland.
出版信息
Bioinformatics. 2019 Apr 15;35(8):1334-1341. doi: 10.1093/bioinformatics/bty757.
MOTIVATION
Fingerprints (FPs) are the most common small molecule representation in cheminformatics. There are a wide variety of FPs, and the Extended Connectivity Fingerprint (ECFP) is one of the best-suited for general applications. Despite the overall FP abundance, only a few FPs represent the 3D structure of the molecule, and hardly any encode protein-ligand interactions.
RESULTS
Here, we present a Protein-Ligand Extended Connectivity (PLEC) FP that implicitly encodes protein-ligand interactions by pairing the ECFP environments from the ligand and the protein. PLEC FPs were used to construct different machine learning models tailored for predicting protein-ligand affinities (pKi∕d). Even the simplest linear model built on the PLEC FP achieved Rp = 0.817 on the Protein Databank (PDB) bind v2016 'core set', demonstrating its descriptive power.
AVAILABILITY AND IMPLEMENTATION
The PLEC FP has been implemented in the Open Drug Discovery Toolkit (https://github.com/oddt/oddt).
SUPPLEMENTARY INFORMATION
Supplementary data are available at Bioinformatics online.
动机
指纹（FPs）是化学生物信息学中最常见的小分子表示形式。有各种各样的 FPs，而扩展连接指纹（ECFP）是最适合一般应用的指纹之一。尽管 FP 总体上很丰富，但只有少数几个 FPs 代表分子的 3D 结构，几乎没有任何 FP 编码蛋白质-配体相互作用。
结果
在这里，我们提出了一种蛋白质-配体扩展连接（PLEC）指纹，它通过将配体和蛋白质的 ECFP 环境配对，隐式地编码蛋白质-配体相互作用。PLEC FPs 用于构建不同的机器学习模型，专门用于预测蛋白质-配体亲和力（pKi∕d）。即使是基于 PLEC FP 构建的最简单的线性模型，在 Protein Databank（PDB）bind v2016“核心集”上的 Rp 值也达到了 0.817，证明了其描述能力。
可用性和实现
PLEC FP 已在 Open Drug Discovery Toolkit（https://github.com/oddt/oddt）中实现。
补充信息
补充数据可在生物信息学在线获得。
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