Automated Ligand- and Structure-Based Protocol for in Silico Prediction of Human Serum Albumin Binding

详细信息    查看全文

• 作者：Michelle Lynn Hall ; William L. Jorgensen ; Lewis Whitehead
• 刊名：Journal of Chemical Information and Modeling
• 出版年：2013
• 出版时间：April 22, 2013
• 年：2013
• 卷：53
• 期：4
• 页码：907-922
• 全文大小：738K
• 年卷期：v.53,no.4(April 22, 2013)
• ISSN：1549-960X

文摘

Plasma protein binding has a profound impact on the pharmacokinetic and pharmacodynamic properties of many drug candidates and is thus an integral component of drug discovery. Nevertheless, extant methods to examine small-molecule interactions with plasma protein have various limitations, thus creating a need for alternative methods. Herein we present a comprehensive and cross-validated in silico workflow for the prediction of small-molecule binding to Human Serum Albumin (HSA), the most ubiquitous plasma protein. This protocol reliably predicts small-molecule interactions with HSA, including a binding affinity calculation using multiple linear regression methods, binding site prediction using a naive-Bayes classifier, and a three-dimensional binding pose using induced fit docking. Furthermore, this workflow is implemented in a portable and automated format that can be downloaded and used by other end users, either as is or with customization.