Journal of Chemical Information and Modeling 最新文献

Understanding protein-peptide interactions is essential for uncovering cellular signaling mechanisms and advancing therapeutic development, as these interactions play central roles in numerous biological processes. Gaining structural insight into such complexes is crucial, yet traditional methods like nuclear magnetic resonance (NMR) and X-ray crystallography are often time-consuming and experimentally demanding. Computational approaches─including physics-based docking and deep-learning (DL) structure predictors such as AlphaFold3, Boltz-2, and Chai-1─offer powerful alternatives. Accurately modeling flexible peptides that bind to shallow, surface-exposed regions remains difficult for physics-based methods, and although multiple sequence alignment-driven DL models can achieve excellent performance in well-behaved systems, they too can struggle when the peptide adopts noncanonical conformations or when sequence identity is low. In such cases, distance restraints are often required to guide the docking toward accurate and biologically meaningful solutions, yet acquiring multiple high-quality restraints is often difficult. To address the limitation of physics and DL approaches, we developed a restraint scoring function that integrates evolutionary conservation, spatial proximity, and geometric distribution to assess the informativeness of restraint sets. This enables a more accurate evaluation of docking inputs and overcomes the shortcomings of relying solely on restraint count. Building on this framework, we introduce a minimal-restraint docking strategy, capable of identifying optimized subsets of restraints that lead to high-quality structural models. We evaluate a comprehensive set of protein–peptide systems, including 43 SH3 domain complexes, 8 WW domain complexes, and 19 medium-difficulty cases from the PepPCBench benchmark. Our approach shows that model quality improves as the restraint score increases, supporting restraint score as a simple, interpretable indicator of docking success. We further identify clear, domain-specific restraint-score thresholds for the SH3 and WW systems that enable accurate model selection. Together, these results offer a scalable and efficient strategy for structure prediction in data-limited contexts and lay the groundwork for restraint-informed modeling with quantifiable confidence, as well as a powerful foundation for data-efficient machine learning-based peptide–protein docking.

Single-cell multiomics technologies provide unprecedented opportunities to dissect cellular heterogeneity by capturing multidimensional information on complex cellular states and regulatory networks. However, challenges such as high dimensionality, extreme data sparsity, and modality-specific discrepancies hinder the accuracy, interpretability, and scalability of the existing integration methods. Existing integration paradigms, including horizontal, vertical, and diagonal strategies, are further limited by their inability to fully capture nonlinear biological relationships, their reliance on high-quality data, and their substantial computational demands. Here, we present scII (Dual-Threshold Adaptive Integration of Single-Cell Multiomics Data Driven by Imputation), an adaptive framework designed to integrate gene expression (scRNA-seq) and chromatin accessibility (scATAC-seq) data. Our approach is built on several key conceptual innovations: (i) scRNA-seq–guided signal imputation to enhance information integrity in scATAC-seq; (ii) a multilayer perceptron with the Maxout activation function to improve the modeling of complex nonlinear relationships and mitigate the vanishing gradient problem; (iii) a dynamic dual-threshold adaptive selection mechanism that jointly evaluates cross-modality feature similarity and classification reliability to select high-quality cells; and (iv) Bayesian Information Criterion (BIC)-based optimization to dynamically determine the number of Gaussian Mixture Model components according to data distribution, thereby eliminating reliance on manually preset parameters. Extensive experiments on multiple real-world and simulated data sets demonstrate that scII not only enables efficient integration of unpaired scRNA-seq and scATAC-seq data but also achieves accurate transfer of cell-type annotations, allowing high-precision cell-type prediction for scATAC-seq.

Type III CRISPR systems provide adaptive immunity against invasion of foreign nucleic acids by generating cyclic oligoadenylate (cA_n) second messengers, which activate effector proteins containing CRISPR-associated Rossmann fold (CARF) domains. The apo form of CARF adopts a closed state, distinct from its cA₄-bound open state conformation. To investigate the conformational transition, we performed multiple type molecular dynamics (MD) simulations, revealing a unidirectional conformational shift toward the closed state. This transition was hindered by reduced flexibility in cA₄-binding residues. Notably, the conformational change primarily occurs between the two monomers, with minimal structural rearrangement within individual monomers. Comparative analysis showed that while the number of hydrogen bonds and contacts between CARF and cA₄ decreases in the closed state, intermonomer interactions are strengthened. Binding free-energy calculations between the two chains of CARF further confirmed higher affinity in the closed state. Our findings support an energy-driven conformational change model, providing insights for optimizing CRISPR-based genetic manipulation tools.

G Protein-Coupled Receptors (GPCRs) are important targets for drug discovery owing to their ability to respond to a broad range of stimuli and their involvement in numerous pathologies. Although traditional ligand-based and structure-based approaches have facilitated the development of effective therapeutics for many GPCRs, these approaches often fall short when applied to receptors with limited ligand or structural data. This limitation highlights the critical need for advanced strategies capable of accurately predicting ligand bioactivity across the entire GPCR family, especially for understudied receptor subtypes. In this study, we introduce BOLD-GPCRs (BERT-Optimized Ligand Discovery for GPCRs), a deep learning framework designed to enhance the prediction of ligand bioactivity across class A GPCRs. Accessible via a user-friendly web interface, BOLD-GPCRs employs transfer learning and leverages curated data sets of known class A GPCR ligands, receptor sequences, and signaling-relevant mutations. By integrating dense neural network classifiers with transformer-based protein language models, BOLD-GPCRs captures complex relationships between receptor sequence/function and ligand activity. Our results demonstrate that BOLD-GPCRs achieves robust predictive performance for both ligand bioactivity and mutational effects across a broad range of class A GPCRs, underscoring its potential as a valuable tool for ligand discovery, especially for poorly characterized receptors.

Atom surface site Interaction Points (AIP) which were previously used to predict association constants for synthetic host–guest systems has been extended to protein–ligand complexes. AIP descriptions of protein binding sites were obtained by combining a library of precomputed AIP descriptors for all protein functional groups with a graph-based substructure matching algorithm. The corresponding AIP description of ligands was obtained directly by footprinting the molecular electrostatic potential surface calculated using density functional theory. These AIP descriptions were projected onto X-ray crystal structures of protein–ligand complexes to identify pairs of AIPs that were sufficiently close in space to constitute an intermolecular interaction. The overall free energy of binding was calculated by summing the contributions of each AIP contact and associated desolvation. Application to the 94 complexes involving uncharged ligands in CASF benchmark data set showed that the method achieves a Pearson correlation coefficient of 0.76 and an RMSD of 11 kJ mol^–1 for absolute free energies of binding.