Predicting Allosteric Effects from Orthosteric Binding in Hsp90-Ligand Interactions: Implications for Fragment-Based Drug Design

Arun Chandramohan, Srinath Krishnamurthy, Andreas Larsson, Paer Nordlund, Anna Jansson, Ganesh S. Anand

Research output: Contribution to journalArticlepeer-review

18 Scopus citations

Abstract

A key question in mapping dynamics of protein-ligand interactions is to distinguish changes at binding sites from those associated with long range conformational changes upon binding at distal sites. This assumes a greater challenge when considering the interactions of low affinity ligands (dissociation constants, KD, in the μM range or lower). Amide hydrogen deuterium Exchange mass spectrometry (HDXMS) is a robust method that can provide both structural insights and dynamics information on both high affinity and transient protein-ligand interactions. In this study, an application of HDXMS for probing the dynamics of low affinity ligands to proteins is described using the N-terminal ATPase domain of Hsp90. Comparison of Hsp90 dynamics between high affinity natural inhibitors (KD ~ nM) and fragment compounds reveal that HDXMS is highly sensitive in mapping the interactions of both high and low affinity ligands. HDXMS reports on changes that reflect both orthosteric effects and allosteric changes accompanying binding. Orthosteric sites can be identified by overlaying HDXMS onto structural information of protein-ligand complexes. Regions distal to orthosteric sites indicate long range conformational changes with implications for allostery. HDXMS, thus finds powerful utility as a high throughput method for compound library screening to identify binding sites and describe allostery with important implications for fragment-based ligand discovery (FBLD).

Original languageEnglish (US)
Article numbere1004840
JournalPLoS computational biology
Volume12
Issue number6
DOIs
StatePublished - Jun 2016

All Science Journal Classification (ASJC) codes

  • Ecology, Evolution, Behavior and Systematics
  • Modeling and Simulation
  • Ecology
  • Molecular Biology
  • Genetics
  • Cellular and Molecular Neuroscience
  • Computational Theory and Mathematics

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