Journal of Chemical Information and Modeling 最新文献

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.

Drug discovery and medicinal chemistry efforts are increasingly influenced by machine learning (ML), with compound property prediction as a central application. ML models have demonstrated strong performance in predicting various compound properties from chemical structure. However, these models can exhibit varying levels of prediction error, making uncertainty quantification (UQ) essential for informed decisions. Standard UQ metrics include the distance to the molecules in the training set and prediction variance, obtained through methods such as model ensembles or Bayesian modeling. Although several UQ methodologies have been developed in recent years, no single approach consistently outperformed others. Herein, we present a comprehensive benchmark of UQ strategies for ML-based prediction of absorption, distribution, metabolism, and excretion (ADME) properties, using both in-house and public data sets. We employed the recently introduced UNIQUE (UNcertaInty QUantification bEnchmarking) framework and evaluated UQ method performance under data shifts. Our findings indicate data-based UQ metrics (e.g., chemical distance), and model-based UQ metrics (e.g., predicted value and variance) may capture complementary aspects of uncertainty. Their combination through error models, designed to predict the original ML model’s error, yielded higher-quality uncertainty estimates. These error models emerged as a promising strategy for enhancing UQ, showing robustness in under various degrees and types of data shift. Taken together, our work highlights the potential of combining diverse UQ metrics and error modeling to improve reliability in molecular property prediction. By establishing standardized evaluation setups and assessing UQ under data shifts, we provide a foundation for future UQ method development and benchmarking in the field.

Pancreatic cancer remains a formidable health challenge due to its late-stage diagnosis and limited therapeutic options, underscoring the need for novel targets and modalities. Our previous work revealed that the natural product, BE-43547A₂, could effectively inhibit the progression of pancreatic cancer by the covalent binding to eukaryotic translation elongation factor 1 α 1 (eEF1A1) at Cys234 (C234). Considering the critical role in protein synthesis and the association with pancreatic cancer progression, eEF1A1 is a novel promising target for pancreatic cancer. However, the rational drug design methods for eEF1A1 are extremely lacking. Herein, using microsecond-scale molecular dynamics (MD) simulations, we identify a suitable eEF1A1 conformation for structure-based virtual screening (SBVS) by targeting the residue of C234. Through a tailored SBVS pipeline, we identified AKOS-04 as a novel small-molecule covalent inhibitor with nanomolar-level potency (IC₅₀ = 28.5 ± 2.86 nM in the PATU8988T cell line). Notably, cellular thermal shift assays (CETSA), with the treatments of dithiothreitol (DTT) and iodoacetamide (IAM), confirmed the covalent Cys-involved interaction of AKOS-04 and eEF1A1. Further structural modification validated the critical contribution of a double bond in the acrylamide group of AKOS-04 for its covalent binding with eEF1A1, manifested by the abolished inhibitory activity of compound 9 with the changed single bond in the acrylamide group. MST experiments confirmed direct binding of the compounds to eEF1A1 protein. AKOS-04 exhibited the strongest binding among the tested compounds, consistent with effective covalent target. Finally, MD simulations and pair-interaction energy analyses highlighted Lys84, Arg218, and Glu230 of eEF1A1 as key residues for driving its binding interactions to AKOS-04. These results reveal that AKOS-04, screened by SBVS against C234 of eEF1A1, represents a promising lead for eEF1A1-targeted pancreatic cancer therapy, highlighting the power of computational approaches in covalent drug discovery.

Peptides featuring synthetic modifications, such as noncanonical amino acids, backbone modifications, cyclic structures, and polymer units have become central to modern drug design due to their enhanced stability and functional diversity. However, current machine learning (ML) approaches are restricted by challenges associated with transforming peptide sequences into atom-level representations, leading ML efforts to focus largely on datasets containing linear peptides comprised of standard residues. Here, we present PepFoundry, a Python package that handles peptide sequences beyond canonical amino acids and linear topologies by using SMILES strings in the CHUCKLES format. PepFoundry generates atom-mapped RDKit molecule objects, enabling the extraction of atom-level features, such as Morgan fingerprints and graph representations. We demonstrate its utility by processing a dataset of peptide sequences containing noncanonical amino acids and generating atomic level features for downstream property prediction. We show that atomic-level representations of peptides containing noncanonical amino acids consistently outperform sequence-level representations, regardless of model type. We additionally explore the representation of noncanonical peptides through latent space visualization and show that models with atomic-level information can effectively learn relationships between analogous sequences of l-peptides, d-peptides, and peptoids. This framework allows for the flexible incorporation of new amino acid chemistries, enabling existing ML methods to be straightforwardly applied to datasets of peptides containing nonstandard features. It also facilitates the rapid construction of customized peptide libraries and provides a scalable platform to accelerate ML-driven peptide discovery and optimization.

DNA barcoding is a powerful tool for exploring biodiversity, and DNA language models have significantly facilitated its construction and identification. However, since DNA barcodes come from a specific region of mitochondrial DNA and there are structural differences between DNA barcodes and reference genomes used to train existing DNA language models, it is difficult to directly apply the existing DNA language models to the DNA barcoding task. To address this, this paper introduces DNACSE (DNA Contrastive Learning for Sequence Embeddings), an unsupervised noise-contrastive learning framework designed to fine-tune the DNA language foundation model while enhancing the distribution of the embedding space. The results demonstrate that DNACSE outperforms the direct usage of DNA language models in DNA barcoding-related tasks. Specifically, in fine-tuning and linear probe tasks, it achieves accuracy rates of 99.17 and 98.31%, respectively, surpassing the current state-of-the-art BarcodeBERT by 6.44 and 6.44%. In zero-shot clustering tasks, it raises the adjusted mutual information (AMI) score to 92.25%, an improvement of 8.36%. In addition, zero-shot benchmarking and genomic benchmarking tests are evaluated, indicating that DNACSE enhances the performance of DNA language models in generalized genomic tasks. In summary, DNACSE has demonstrated excellent performance in DNA barcode species classification by making full use of multispecies information and DNA barcode information, providing a feasible way to further explore and protect biodiversity. The code repository is available at https://github.com/Kavicy/DNACSE.

While contiguous subsequences of hydrophobic residues are essential to protein structure and function, as in the hydrophobic core and transmembrane regions, there are no current bioinformatics tools for module identification focused on hydrophobicity. To fill this gap, we created the blobulator toolkit for detecting, visualizing, and characterizing hydrophobic modules in protein sequences. This toolkit uses our previously developed algorithm, blobulation, which was critical in both interpreting intraprotein contacts in a series of intrinsically disordered protein simulations (Lohia et al., 2019) and defining the “local context” around disease-associated mutations across the human proteome (Lohia et al., 2022). The blobulator toolkit provides accessible, interactive, and scalable implementations of blobulation. These are available via a webtool, a visual molecular dynamics (VMD) plugin, and a command line interface. We highlight use cases for visualization, interaction analysis, and modular annotation through three example applications: a globular protein, two orthologous membrane proteins, and an intrinsically disordered protein. The blobulator webtool can be found at www.blobulator.branniganlab.org, and the source code with pip installable command line tool, as well as the VMD plugin with installation instructions, can be found on GitHub at www.GitHub.com/BranniganLab/blobulator.