Jul 2026· Journal of Chemical Information and Modeling· 0 citations· 40 references
Computer ScienceMedicine
TL;DR
A novel "induce-and-identify" workflow that integrates mixed solvent molecular dynamics simulations with SiteMap is developed and validated, enabling the exploration of a broader range of druggable targets by effectively inducing and identifying cryptic binding sites.
Abstract
Identifying cryptic binding sites in proteins remains a challenge in structure-based drug discovery because these sites are often not apparent in apo structures. Here, we developed and validated a novel "induce-and-identify" workflow that integrates mixed solvent molecular dynamics (MxMD) simulations with SiteMap. This approach leverages MxMD to sample protein conformations to expose hidden pockets, which are then effectively identified and ranked by SiteMap. Using a challenging data set of 65 cryptic binding sites, the developed workflow identified the cryptic binding site within the top 5 predictions in 78.5% of cases. These results suggest that the proposed MxMD + SiteMap workflow provides a robust and valuable tool for early phase drug discovery, enabling the exploration of a broader range of druggable targets by effectively inducing and identifying cryptic binding sites.
Lacuna, an open-source Python tool for discovering cryptic binding pockets, generates a conformational ensemble from any input structure, detects pockets independently in every conformer, clusters the detections into persistent sites across the ensemble, and ranks those sites with a model fitted on within-structure pairs.
Cryptic pockets are transient ligand-binding sites that remain hidden in apo protein structures and become accessible only through conformational change, making them difficult to identify and exploit in structure-based ligand discovery. Although recent AI/ML approaches can identify residues associated with cryptic pocket formation, they do not directly yield the corresponding holo-like conformations. Enhanced-sampling and mixed-solvent strategies can promote pocket opening, but often under non-native conditions and without demonstrating stable ligand-bound holo-like states. Here, we present a computational framework in which residues with high cryptic-pocket propensity are used as mutation handles to perturb the free-energy landscape and expose ligand-compatible open states even in the absence of ligand binding. We establish this strategy on TEM-1 β-lactamase, a canonical cryptic-pocket system, and show that mutation-induced perturbations can shift the conformational ensemble toward an open pocket state prior to ligand binding. The resulting open state supports ligand binding in both docked structures and unbiased simulations, and the ligand remains stably bound after reversion to the wild-type sequence, consistent with recovery of a holo-like wild-type conformation. Importantly, the residues targeted for mutation are not the principal determinants of the ligand interactions observed in the bound state, indicating that their role is to reshape the conformational landscape and promote access to an open, ligand-compatible pocket rather than to form the binding interface itself. We further generalize this framework to LfrR, FtsZ, and Bombyx mori pheromone-binding protein, where mutation of predicted cryptic residues likewise generates open conformations capable of supporting ligand binding. Together, these results show that cryptic residue predictions can be used not only to identify hidden binding sites but also to recover holo-like conformations from apo structures, providing a practical framework for studying and targeting cryptic pockets.
Reshob Routh, Mithun Radhakrishna· Journal of Chemical Informat...· 0 citations
Certain proteins known to be involved in life-threatening diseases have remained challenging therapeutic targets for decades, simply because a suitable binding pocket for a potent molecular inhibitor could not be identified in their ground-state apo structures. In the past decade, the discovery of cryptic pockets in challenging targets like KRAS and Werner helicase has proven to be a major turning point for therapeutic development. However, the alternate protein conformations required for these cryptic pockets to exist were only revealed by experiments conducted in the presence of ligands that can bind to them. Time-consuming and expensive experiments currently used to uncover these biologically rare events could be usefully complemented by a computational method capable of reliably identifying cryptic pockets. We have previously shown that aqueous and mixed-solvent Weighted Ensemble molecular dynamics (WEMD) simulations, driven by normal modes representing the direction of the most collective motion of a protein, can predict known cryptic pockets in the KRAS oncoprotein. Here, we evaluate this cryptic pocket detection technique on a data set of diverse proteins and show that it successfully samples cryptic pockets within 2 Å of the known holo conformation 57% of the time, starting with just the apo structure. The predicted pockets in the most holo-like conformations show at least 20%, 50%, and 80% volume overlap with the bound ligand in the holo structure, with success rates of 92%, 84%, and 46%, respectively. We also show that we can successfully rank candidate pockets from WEMD using our pocket ligandability prediction model, Target X.
N. Vithani, She Zhang, Judith Günther et al.· Journal of the American Chem...· 0 citations
Cryptic binding sites (CBSs) are crucial functional sites that become accessible following conformational changes by ligand binding. They play a significant role in expanding the scope of druggable targets and revealing the dynamic regulatory mechanisms of proteins. Most existing computational methods rely on holo (ligand-bound) structures and struggle to effectively identify CBSs in the apo (ligand-free) state. Furthermore, these methods fail to account for protein three-dimensional conformational changes and spatial geometric information, often making it difficult to explain the dynamic characteristics of CBS formation.To address these challenges, we present CrypKANet, an innovative multimodal predictive framework for cryptic binding site identification that integrates EGNN, gated attention mechanisms, and Kolmogorov-Arnold Network (KAN). Our framework employs a two-branch design to independently encode geometric and biochemical features: the EGNN branch is dedicated to explicitly capturing three-dimensional spatial restraints and conformational dynamics, whereas the GINE branch strengthens the representation of residue-level chemical interactions and topological connectivity. Finally, the model predicts cryptic binding sites through the KAN classifier. Experimental results on the CBS benchmark dataset demonstrate that CrypKANet outperforms the existing state-of-the-art methods, and exhibits excellent generalization performance on protein-protein interaction sites and ligand binding site tasks.
Yongxian Fan, Xianchen Zheng, Yangfeng Zhu et al.· IEEE journal of biomedical a...· 0 citations
HydroDock is extended to challenging targets with open binding pockets, populated by highly mobile water molecules that are the most troublesome for drug design, and improved docking accuracy with increases of 28% (10%) in median structural (and ranking) performance.
Bayar Bayarsaikhan, C. Hetényi, B. Z. Zsidó· ACS Physical Chemistry Au· 0 citations
Co-folding models hold immense potential for allosteric drug discovery, but have been severely hampered by their systematic bias toward orthosteric ligand binding. While fragment screening has been proposed for allosteric binding site discovery, we show that co-folding models still suffer from memorization in which chemically simpler fragments also default to canonical orthosteric binding sites. To overcome these limitations, we introduce CAFE (Co-folding Approach for Fragment Exploration), a co-folding protocol that uses competitive orthosteric blockers to divert fragments into non-canonical sites as illustrated here with the Boltz-2 co-folding model. Using ADP as an orthosteric blocker for the kinase family, we find CAFE substantially increases the allosteric binding site exploration for fragments, with notably strong absolute binding free energies that match or exceed those of known crystallographic poses, without post-hoc refinement of the Boltz-2 prediction. We also show that CAFE identifies cryptic binding pockets undetected by conventional pocket prediction tools, some of which are more thermodynamically favorable than the allosteric or orthosteric pockets. To demonstrate generality, we apply CAFE using Type I orthosteric blockers for kinase proteins, known orthosteric ligands as blockers for non-kinase proteins in the RAS-MAPK signaling pathway, and for virtual screening campaigns using fragment libraries for new fragments that selectively engage allosteric and cryptic binding sites. CAFE establishes orthosteric blocking and fragment screening as a training-free, inference-time protocol that helps overcome some of the limitations of current co-folding models while elevating their great promise for allosteric and cryptic binding drug discovery.
Justin Purnomo, Kunyang Sun, T. Head-Gordon· bioRxiv· 0 citations
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