Mavchen-1: A Conformational Ensemble Platform for Protein–Ligand Pose Prediction That Substantially Outperforms Static Structure Prediction in a Category-Stratified Benchmark
A category-stratified, statistically powered benchmark comparing pose prediction from receptor conformational ensembles against AlphaFold2, used as a matched static-structure baseline, across 29 protein–ligand systems spanning cryptic-pocket, induced-fit, water-mediated, and autoimmune-indication target classes is presented.
Protein structure predictors achieve high single-state accuracy, but it remains unclear whether they can recover functionally relevant conformational ensembles or account for the presence of ligands and/or binding partners. Here, we benchmark AlphaFold3, Boltz-2, Chai-1, and BioEmu on four canonical multi-state proteins (Pf-MATE, LAO, SecA, and β2AR), quantifying state bias and sampling breadth against experimental reference structures. Models frequently default to a dominant state represented in the PDB; small-molecule ligands have weak or inconsistent effects, while large protein partners drive clear conformational switching between states. Multiple sequence alignment (MSA)-based approaches (AF-Cluster and random subsampling) recapitulate similar biases, indicating that this behavior is not unique to newer architectures. These results underscore current limitations for multi-state protein structure prediction and structure-guided ligand discovery. TOC Graphic
Muhui Ye, Yu-Hong Wang, M. Brogi et al.· bioRxiv· 0 citations
MultiGeo is a DTA prediction framework that explicitly leverages multiple protein conformations rather than a single snapshot, and introduces a disagreement-aware gating mechanism that adaptively fuses this ensemble representation with the dominant structure only when the additional conformers provide complementary information.
Ruida Zeng, Cheng Guo, Yajie Meng et al.· 0 citations
Vilya-2 is the structure-prediction oracle that de novo peptide design pipelines require--establishing the all-atom approach as a general foundation for the design and evaluation of de novo peptide therapeutics.
Vilya Research Pascal Sturmfels, Naozumi Hiranuma, M. Salem et al.· 0 citations
Accurate prediction of peptide structures and peptide-receptor complexes is essential for rational peptide drug development. However, the inherent conformational flexibility of short and disordered peptides presents a fundamental challenge. The AlphaFold model series, which has progressed from AlphaFold2 through AlphaFold-Multimer to AlphaFold3, has substantially advanced computational peptide structure prediction through innovations in geometric reasoning (invariant point attention) and interface-focused confidence metrics (ipTM score), achieving high accuracy for both monomeric peptide structures and multi-chain complexes. However, these models output static conformations, whereas many bioactive peptides adopt their functional conformations only upon binding-often corresponding to low-probability states that static predictions may overlook, leading to failures in virtual screening. This review synthesizes recent advances in the AlphaFold series for peptide studies and applications, discusses their current strengths in structure prediction and receptor-binding analysis, and examines the limitations in capturing conformational dynamics, transient interactions, and chemical modifications. Recent studies have suggested that integrated computational strategies that combine AlphaFold predictions with molecular dynamics simulations, free energy calculations, and ensemble sampling to enhance predictive accuracy and better represent the dynamic nature of peptide-drug interactions. These complementary approaches position AlphaFold as a central computational platform in structure-guided peptide drug design, enabling more efficient lead identification and optimization while bridging the gap between static computational predictions and the complex biophysical reality of peptide therapeutics.
It is shown that prospective structure selection, rather than structure generation, represents the primary bottleneck in ensemble-based VS, highlighting an urgent need for novel structural descriptors to identify high-performing conformations.
Jaeoh Shin, K. Joo, Jejoong Yoo· Journal of Chemical Informat...· 0 citations
CMD-PLA is proposed as a dynamics-aware framework for protein-ligand affinity prediction, and the conformational evolution of the ligand is explicitly modeled as a pocket-dependent dynamical process rather than an isolated static update.
Hao Li, Dongjiang Niu, Xiaofeng Wang et al.· Computational biology and ch...· 0 citations