Spatially resolved transcriptomics (SRT) allows for the comprehensive profiling of gene expression while preserving spatial context, advancing the study of tissue architecture. However, existing computational approaches still face key limitations, particularly the insufficient exploitation of histology information and the lack of cross-modal meaningful contrastive strategies for biological analyses. To overcome these challenges, we propose GCAST, a graph contrastive autoencoder framework for spatial transcriptomics that seamlessly integrates multimodal SRT data. GCAST adopts a self-supervised strategy to derive biologically meaningful representations directly from histology images when available. GCAST constructs dual graph views based on data augmentation and introduces a novel contrastive learning designed to leverage histology-weighted and gene-weighted features and improve biological interpretability. In addition, GCAST employs a block-diagonal graph construction to automatically align multiple datasets, achieving batch-effect correction without manual intervention. The framework not only captures spatial gene expression patterns to identify tissue domains but also adapts to datasets with or without histological images and supports the integration of multiple datasets for joint analyses. Overall, GCAST provides a unified and biologically informed framework that has the potential to facilitate deeper analyses of spatial transcriptomics.

Accurately predicting the pathogenicity of missense variants is crucial for improving disease diagnosis and advancing clinical research. However, existing computational methods primarily focus on general pathogenicity predictions, overlooking assessments of disease-specific conditions. In this study, we propose DS-MVP, a method capable of predicting disease-specific pathogenicity of missense variants in human genomes. DS-MVP first leverages a deep learning model pre-trained on a large general pathogenicity dataset to learn rich representation of missense variants. It then fine-tunes these representations with an XGBoost model on smaller datasets for specific diseases. We evaluated the learned representation by testing it on multiple binary pathogenicity datasets and gene-level statistics, demonstrating that DS-MVP outperforms existing state-of-the-art methods, such as MetaRNN and AlphaMissense. Additionally, DS-MVP excels in multi-label and multi-class classification, effectively classifying disease-specific pathogenic missense variants based on disease conditions. It further enhances predictions by fine-tuning the pre-trained model on disease-specific datasets. Finally, we analyzed the contributions of the pre-trained model and various feature types, with gene description corpus features from large language model and genetic feature fusion contributing the most. These results underscore that DS-MVP represents a broader perspective on pathogenicity prediction and holds potential as an effective tool for disease diagnosis.

MOTIVATION: Binding of peptides to major histocompatibility complex (MHC) molecules plays a crucial role in triggering T cell recognition mechanisms essential for immune response. Accurate prediction of MHC-peptide binding is vital for the development of cancer therapeutic vaccines. While recent deep learning-based methods have achieved significant performance in predicting MHC-peptide binding affinity, most of them separately encode MHC molecules and peptides as inputs, potentially overlooking critical interaction information between the two. RESULTS: In this work, we propose RPEMHC, a new deep learning approach based on residue-residue pair encoding to predict the binding affinity between peptides and MHC, which encode an MHC molecule and a peptide as a residue-residue pair map. We evaluate the performance of RPEMHC on various MHC-II-related datasets for MHC-peptide binding prediction, demonstrating that RPEMHC achieves better or comparable performance against other state-of-the-art baselines. Moreover, we further construct experiments on MHC-I-related datasets, and experimental results demonstrate that our method can work on both two MHC classes. These extensive validations have manifested that RPEMHC is an effective tool for studying MHC-peptide interactions and can potentially facilitate the vaccine development. AVAILABILITY: The source code of the method along with trained models is freely available at https://github.com/lennylv/RPEMHC.

Effectively and accurately predicting the effects of interactions between proteins after amino acid mutations is a key issue for understanding the mechanism of protein function and drug design. In this study, we present a deep graph convolution (DGC) network-based framework, DGCddG, to predict the changes of protein-protein binding affinity after mutation. DGCddG incorporates multi-layer graph convolution to extract a deep, contextualized representation for each residue of the protein complex structure. The mined channels of the mutation sites by DGC is then fitted to the binding affinity with a multi-layer perceptron. Experiments with results on multiple datasets show that our model can achieve relatively good performance for both single and multi-point mutations. For blind tests on datasets related to angiotensin-converting enzyme 2 binding with the SARS-CoV-2 virus, our method shows better results in predicting ACE2 changes, may help in finding favorable antibodies. Code and data availability: https://github.com/lennylv/DGCddG.

Proteinprotein interactions are the basis of many cellular biological processes, such as cellular organization, signal transduction, and immune response. Identifying proteinprotein interaction sites is essential for understanding the mechanisms of various biological processes, disease development, and drug design. However, it remains a challenging task to make accurate predictions, as the small amount of training data and severe imbalanced classification reduce the performance of computational methods. We design a deep learning method named ctP2ISP to improve the prediction of proteinprotein interaction sites. ctP2ISP employs Convolution and Transformer to extract information and enhance information perception so that semantic features can be mined to identify proteinprotein interaction sites. A weighting loss function with different sample weights is designed to suppress the preference of the model toward multi-category prediction. To efficiently reuse the information in the training set, a preprocessing of data augmentation with an improved sample-oriented sampling strategy is applied. The trained ctP2ISP was evaluated against current state-of-the-art methods on six public datasets. The results show that ctP2ISP outperforms all other competing methods on the balance metrics: F1, MCC, and AUPRC. In particular, our prediction on open tests related to viruses may also be consistent with biological insights.

MOTIVATION: Histone modifications are epigenetic markers that impact gene expression by altering the chromatin structure or recruiting histone modifiers. Their accurate identification is key to unraveling the mechanisms by which they regulate gene expression. However, the solutions for this task can be improved by exploiting multiple relationships from dataset and exploring designs of learning models, for example jointly learning technology. RESULTS: This article proposes a deep learning-based multi-objective computational approach, iHMnBS, to identify which of the seven typical histone modifications a DNA sequence may choose to bind, and which parts of the DNA sequence bind to them. iHMnBS employs a customized dataset that allows the marking of modifications contained in histones that may bind to any position in the DNA sequence. iHMnBS tries to mine the information implicit in this richer data by means of deep neural networks. In comprehensive comparisons, iHMnBS outperforms a baseline method, and the probability of binding to modified histones assigned to a representative nucleotide of a DNA sequence can serve as a reference for biological experiments. Since the interaction between transcription factors and histone modifications has an important role in gene expression, we extracted a number of sequence patterns that may bind to transcription factors, and explored their possible impact on disease. AVAILABILITY AND IMPLEMENTATION: The source code is available at https://github.com/lennylv/iHMnBS. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.