Biology subjects

Skrhak, V.

Publications and source records attributed to Skrhak, V..

3 recordsLinked to original sources

bioRxiv2026

Seq2Pocket: Augmenting protein language models for spatially consistent binding site prediction

Protein-ligand binding site prediction (LBS) is important for many areas of structural biology and molecular modeling, where, as in other tasks, protein language models (pLMs) have shown a great promise. In their application to LBS, the pLM classifies each amino acid as binding or not, but translating these predictions into three-dimensional binding pockets remains challenging; in particular, residue-centric predictions tend to produce spatially fragmented pockets. We present Seq2Pocket, a methodology for pocket-level LBS prediction that combines pLM finetuning, data enhancement, and structure-aware post-processing. First, we introduce sc-PDBenhanced, an extended training dataset that augments sc-PDB with additional small-molecule and ion-binding sites, improving coverage of non-obvious and small pockets. Second, we employ an embedding-supported smoothing classifier to refine residue-level predictions. Third, we define the Pocket Fragmentation Index and use it to select a clustering approach that preserves a consistent mapping between predictions and ground-truth pockets. We evaluate Seq2Pocket on two tasks: general binding site prediction using the LIGYSIS benchmark and cryptic binding site prediction using CryptoBench benchmark. Across both benchmarks, the proposed methodology achieves state-of-the-art performance. In particular, for general binding site prediction on the LIGYSIS benchmark, it improves distance-center-to-center recall by up to 12%, outperforming existing predictors. We believe that these findings contribute to more reliable evaluation practices in ligand binding site prediction, highlight the importance of training data curation, and provide pocket-level prediction tool that are better suited for downstream applications such as drug discovery.

bioinformatics↗

bioRxiv2026

Protein Language Models and Structure-Based Machine Learning for Prediction of Allosteric Binding Sites in Protein Kinases: An Explainable AI Framework Grounded in Energy Landscape-Encoded Frustration

Reliable identification of allosteric binding sites remains a major bottleneck in structure-based drug discovery, particularly in protein kinase families where such sites are often structurally cryptic, evolutionarily non-conserved, and sparsely populated. In this work, we present a systematic analysis of binding site prediction across a rigorously curated dataset of human kinase-ligand complexes, encompassing 453 kinases and spanning five inhibitor classes: Type I, Type I.5, and Type II (orthosteric ATP-competitive) and Type III/IV (non-ATP allosteric) modulators. We employed the pretrained protein language model (PLM) ESM2-650M model that was fine-tuned for prediction of protein-ligand binding sites by replacing the original masked language modeling head with a token-level classification head that acts as a projection layer that maps the high-dimensional latent representation of each residue to a scalar probability score for a given protein residue to be part of the binding site. We employed this fine-tuned sequence-based PLM and structure-based detection approach P2Rank for identification of orthosteric and allosteric binding sites in protein kinases. Our analysis reveals a stark performance divergence: while both methods achieve high precision-recall (AUPR = 0.64-0.76) on orthosteric sites, PLM performance collapses on allosteric sites (AUPR = 0.06), despite retaining moderate ranking ability (AUROC = 0.70). This deficit persists even after strict control for sequence similarity, structural redundancy, and extreme class imbalance (allosteric residues constitute <3% of the kinase domain). To mechanistically interpret this discrepancy, we integrate large-scale local frustration analysis, a physics-based framework derived from energy landscape theory that quantifies the energetic stability of residue-residue interactions under mutational and conformational perturbations. We find that, although the global frustration landscape is conserved across kinase states dominated by neutral frustration (55-75% of residues), local binding sites exhibit fundamentally distinct mutational constraints. Orthosteric pockets are enriched in minimally frustrated residues, whereas allosteric sites are characterized by neutral mutational frustration, indicating evolutionary permissiveness and sequence degeneracy. This study reframes the performance of AI approaches in predicting protein binding sites as a reflection of functional design that can be rationalized through lens of the landscape-encoded protein frustration as an explainable AI framework.

biophysics↗

bioRxiv2024

CryptoBench: Cryptic protein-ligand binding sites dataset and benchmark

Structure-based methods for detecting protein-ligand binding sites play a crucial role in various domains, from fundamental research to biomedical applications. However, current prediction methodologies often rely on holo (ligand-bound) protein conformations for training and evaluation, overlooking the significance of the apo (ligand-free) states. This oversight is particularly problematic in the case of cryptic binding sites (CBSs) where holo-based assessment yields unrealistic performance expectations. To advance the development in this domain, we introduce CryptoBench, a benchmark dataset tailored for training and evaluating novel CBS prediction methodologies. CryptoBench is constructed upon a large collection of apo-holo protein pairs, grouped by UniProtID, clustered by sequence identity, and filtered to contain only structures with substantial structural change in the binding site. CryptoBench comprises 1,107 structures with predefined cross-validation splits, making it the most extensive CBS dataset to date. To establish a performance baseline, we measured the predictive power of sequence- and structure-based CBS residue prediction methods using the benchmark. We selected PocketMiner as the state-of-the-art representative of the structure-based methods for CBS detection, and P2Rank, a widely-used structure-based method for general binding site prediction that is not specifically tailored for cryptic sites. For sequence-based approaches, we trained a neural network to classify binding residues using protein language model embeddings. Our sequence-based approach outperformed PocketMiner and P2Rank across key metrics, including AUC, AUPRC, MCC, and F1 scores. These results provide baseline benchmark results for future CBS and potentially also non-CBS prediction endeavors, leveraging CryptoBench as the foundational platform for further advancements in the field.

bioinformatics↗