Modeling Protein–Protein and Protein–Ligand Interactions by the <scp>ClusPro</scp> Team in <scp>CASP16</scp>
Ryota Ashizawa, Sergei Kotelnikov, Omeir Khan, Stan Xiaogang Li, Ernest Glukhov, Xin Cao, Maria Lazou, Ayse A. Bekar-Cesaretli, Derara Hailegeorgis, Veranika Averkava, Y. Zhu, George Jones, Yu Hao, Dmytro Kalitin, Darya Stepanenko, Kushal Koirala, T. Patsahan, Dmitri Beglov, Mark Lukin, Diane Joseph‐McCarthy, Carlos Simmerling, Alexander Tropsha, Evangelos A. Coutsias, Ken A. Dill, Dzmitry Padhorny, Sándor Vajda, Dima Kozakov
Abstract
In the CASP16 experiment, our team employed hybrid computational strategies to predict both protein-protein and protein-ligand complex structures. For protein-protein docking, we combined physics-based sampling-using ClusPro FFT docking and molecular dynamics-with AlphaFold (AF)-based sampling, followed by AF-based refinement. Our method produced numerous high-accuracy complex models, including cases where AF alone failed, underscoring the critical role of physics-based sampling alongside deep learning-based refinement. For protein-ligand docking, we integrated the ClusPro LigTBM template-based approach with a machine learning-based confidence model for rescoring. The method preserves conserved interaction fragments derived from homologous complexes, followed by local resampling using physics-based sampling and a diffusion model. Our template-based strategy achieved a mean lDDT-PLI of 0.69 across 233 targets, which was highly competitive. These results demonstrate that combining physics-based modeling with AI-driven refinement can significantly enhance the accuracy of both protein-protein and protein-ligand structure predictions.