title: Automated ligand- and structure-based protocol for in silico prediction of human serum albumin binding.
abstract: 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.
authors.affiliation: Novartis Institutes for Biomedical Research, 100 Technology Square, Cambridge, Massachusetts 02143, USA.
authors.forename: Michelle Lynn
authors.initials: ML
authors.lastname: Hall
authors.forename: William L
authors.initials: WL
authors.lastname: Jorgensen
authors.forename: Lewis
authors.initials: L
authors.lastname: Whitehead
mesh_terms: D001499:Bayes Theorem; D001665:Binding Sites; D055808:Drug Discovery; D006801:Humans; D020407:Internet; D008024:Ligands; D062105:Molecular Docking Simulation; D015999:Multivariate Analysis; D055553:Prescription Drugs / Q000737:chemistry*; D011485:Protein Binding; D012709:Serum Albumin / Q000737:chemistry*; D054852:Small Molecule Libraries / Q000737:chemistry*; D012984:Software
chemical_list: D008024:Ligands; D055553:Prescription Drugs; D012709:Serum Albumin; D054852:Small Molecule Libraries