FMOPhore for hotspot identification and efficient fragment-to-lead growth strategies

TY - UNPB

T1 - FMOPhore for hotspot identification and efficient fragment-to-lead growth strategies

AU - Ezzat Girgis Fahmy Ibrahim, Peter

AU - Zachariae, Ulrich

AU - Gilbert, Ian

AU - Bodkin, Mike

PY - 2024/10/1

Y1 - 2024/10/1

N2 - Fragment based drug design (FBDD) is like a chess game in that a good or a bad move can dramatically influence the outcome. At the start of the design process, it is important to identify the key binding site residues (hotspots) that can have a substantial impact on ligand efficiency (LE) and binding. Here, we introduce a novel, fully automated algorithm named FMOPhore, which performs Quantum Mechanics Fragment Molecular Orbital (QM-FMO) calculations on 3D-protein-ligand pharmacophore models. This is implemented in a novel scoring function named FP-score to classify binding site residues in two classes: 1) Hotspot residues (further delineated into three categories; Anchor, Transient, and Accessible) and 2) Non-hotspot residues. We apply our algorithm in two different scenarios: holo-complex and apo-structure scenarios, testing its robustness on sixteen different protein targets including the application for fragment growing and target selectivity. We demonstrate that handling protein binding site flexibility using Dy-FMOPhore improves hotspots detection. The FMOPhore algorithm can be a powerful tool in identifying and quantifying binding site hotspots to enable an efficient design strategy for fragment-to-lead optimization.

AB - Fragment based drug design (FBDD) is like a chess game in that a good or a bad move can dramatically influence the outcome. At the start of the design process, it is important to identify the key binding site residues (hotspots) that can have a substantial impact on ligand efficiency (LE) and binding. Here, we introduce a novel, fully automated algorithm named FMOPhore, which performs Quantum Mechanics Fragment Molecular Orbital (QM-FMO) calculations on 3D-protein-ligand pharmacophore models. This is implemented in a novel scoring function named FP-score to classify binding site residues in two classes: 1) Hotspot residues (further delineated into three categories; Anchor, Transient, and Accessible) and 2) Non-hotspot residues. We apply our algorithm in two different scenarios: holo-complex and apo-structure scenarios, testing its robustness on sixteen different protein targets including the application for fragment growing and target selectivity. We demonstrate that handling protein binding site flexibility using Dy-FMOPhore improves hotspots detection. The FMOPhore algorithm can be a powerful tool in identifying and quantifying binding site hotspots to enable an efficient design strategy for fragment-to-lead optimization.

KW - Fragment based drug design

KW - Hotspots

KW - pharmacophore model

KW - Quantum Mechanics Fragment Molecular Orbital (QM-FMO)

KW - FP-score

KW - fragment-to-lead growing strategies

U2 - 10.26434/chemrxiv-2024-r3rjr D O I: 10.26434/chemrxiv-2024-r3rjr [opens in a new tab]

DO - 10.26434/chemrxiv-2024-r3rjr D O I: 10.26434/chemrxiv-2024-r3rjr [opens in a new tab]

M3 - Working paper

BT - FMOPhore for hotspot identification and efficient fragment-to-lead growth strategies

PB - ChemRxiv

ER -