Ligand Binding Prediction on Pharmaceutical and Nucleic Acid Targets by the CoDock Group in CASP16.

Abstract

Ligand binding prediction is a critical component of structure-based drug design, gaining prominence in Critical Assessment of protein Structure Prediction (CASP) since its introduction in CASP15. In CASP16, the challenges expanded to include protein-ligand and nucleic acid-ligand binding predictions, alongside binding affinity ranking, posing greater computational and methodological demands. This study presents a sophisticated prediction strategy combining template-based docking, multiple receptor conformations, and AI-driven scoring to address these challenges. For protein-ligand systems (L1000-L4000), we leveraged structural templates from PDB, ligand similarity analysis, and tools like CoDock-Ligand and AutoDock Vina to predict binding poses. Key successes included accurate predictions for targets like SARS-CoV-2 Mpro (L4000) and Autotaxin (L3000), though challenges persisted with binding site flexibility and pose ranking. The prediction of ligand pose achieved satisfactory results, with more than 66% of the distribution having RMSD less than 3 Å. Nucleic acid-ligand predictions (e.g., ZTP riboswitch) yielded mixed results, highlighting limitations in RNA/DNA structural accuracy. Affinity prediction employed diverse methods, with machine learning-based SVR_Conjoint outperforming physics-based approaches (Kendall's Tau = 0.43). Our strategy demonstrated robustness in CASP16, yet underscored the need for advancements in handling conformational dynamics and scoring accuracy. This work provides a framework for future ligand binding prediction efforts in computational drug discovery.