Mapping Protein Conformational Landscapes from Crystallographic Drug Fragment Screens.

Publication date: Nov 12, 2024

Proteins are dynamic macromolecules. Knowledge of a protein’s thermally accessible conformations is critical to determining important transitions and designing therapeutics. Accessible conformations are highly constrained by a protein’s structure such that concerted structural changes due to external perturbations likely track intrinsic conformational transitions. These transitions can be thought of as paths through a conformational landscape. Crystallographic drug fragment screens are high-throughput perturbation experiments, in which thousands of crystals of a drug target are soaked with small-molecule drug precursors (fragments) and examined for fragment binding, mapping potential drug binding sites on the target protein. Here, we describe an open-source Python package, COnformational LAndscape Visualization (COLAV), to infer conformational landscapes from such large-scale crystallographic perturbation studies. We apply COLAV to drug fragment screens of two medically important systems: protein tyrosine phosphatase 1B (PTP1B), which regulates insulin signaling, and the SARS CoV-2 Main Protease (MPro). With enough fragment-bound structures, we find that such drug screens enable detailed mapping of proteins’ conformational landscapes.

Concepts Keywords
Concerted Accessible
Crystallographic Conformational
Drug Conformations
Ptp1b Crystallographic
Python Drug
Fragment
Important
Landscape
Landscapes
Mapping
Perturbation
Protein
Screens
Target
Transitions

Semantics

Type Source Name
disease IDO protein

Original Article

(Visited 1 times, 1 visits today)