Molecular interaction fingerprint approaches for GPCR drug discovery

TY - JOUR

T1 - Molecular interaction fingerprint approaches for GPCR drug discovery

AU - Vass, Márton

AU - Kooistra, Albert J.

AU - Ritschel, Tina

AU - Leurs, Rob

AU - de Esch, Iwan JP

AU - de Graaf, Chris

PY - 2016/10/1

Y1 - 2016/10/1

N2 - Protein–ligand interaction fingerprints (IFPs) are binary 1D representations of the 3D structure of protein–ligand complexes encoding the presence or absence of specific interactions between the binding pocket amino acids and the ligand. Various implementations of IFPs have been developed and successfully applied for post-processing molecular docking results for G Protein-Coupled Receptor (GPCR) ligand binding mode prediction and virtual ligand screening. Novel interaction fingerprint methods enable structural chemogenomics and polypharmacology predictions by complementing the increasing amount of GPCR structural data. Machine learning methods are increasingly used to derive relationships between bioactivity data and fingerprint descriptors of chemical and structural information of binding sites, ligands, and protein–ligand interactions. Factors that influence the application of IFPs include structure preparation, binding site definition, fingerprint similarity assessment, and data processing and these factors pose challenges as well possibilities to optimize interaction fingerprint methods for GPCR drug discovery.

AB - Protein–ligand interaction fingerprints (IFPs) are binary 1D representations of the 3D structure of protein–ligand complexes encoding the presence or absence of specific interactions between the binding pocket amino acids and the ligand. Various implementations of IFPs have been developed and successfully applied for post-processing molecular docking results for G Protein-Coupled Receptor (GPCR) ligand binding mode prediction and virtual ligand screening. Novel interaction fingerprint methods enable structural chemogenomics and polypharmacology predictions by complementing the increasing amount of GPCR structural data. Machine learning methods are increasingly used to derive relationships between bioactivity data and fingerprint descriptors of chemical and structural information of binding sites, ligands, and protein–ligand interactions. Factors that influence the application of IFPs include structure preparation, binding site definition, fingerprint similarity assessment, and data processing and these factors pose challenges as well possibilities to optimize interaction fingerprint methods for GPCR drug discovery.

UR - http://www.scopus.com/inward/record.url?scp=84979870754&partnerID=8YFLogxK

U2 - 10.1016/j.coph.2016.07.007

DO - 10.1016/j.coph.2016.07.007

M3 - Review

C2 - 27479316

AN - SCOPUS:84979870754

SN - 1471-4892

VL - 30

SP - 59

EP - 68

JO - Current Opinion in Pharmacology

JF - Current Opinion in Pharmacology

ER -