RNA-binding proteins (RBPs) play crucial roles in gene regulation. Their dysregulation has been increasingly linked to neurodegenerative diseases, liver cancer, and lung cancer. Although experimental methods like CLIP-seq accurately identify RNA-protein binding sites, they are time-consuming and costly. To address this, we propose RMDNet-a deep learning framework that integrates CNN, CNN-Transformer, and ResNet branches to capture features at multiple sequence scales. These features are fused with structural representations derived from RNA secondary structure graphs. The graphs are processed using a graph neural network with DiffPool. To optimize feature integration, we incorporate an improved dung beetle optimization algorithm, which adaptively assigns fusion weights during inference. Evaluations on the RBP-24 benchmark show that RMDNet outperforms state-of-the-art models including GraphProt, DeepRKE, and DeepDW across multiple metrics. On the RBP-31 dataset, it demonstrates strong generalization ability, while ablation studies on RBPsuite2.0 validate the contributions of individual modules. We assess biological interpretability by extracting candidate binding motifs from the first-layer CNN kernels. Several motifs closely match experimentally validated RBP motifs, confirming the model's capacity to learn biologically meaningful patterns. A downstream case study on YTHDF1 focuses on analyzing interpretable spatial binding patterns, using a large-scale prediction dataset and CLIP-seq peak alignment. The results confirm that the model captures localized binding signals and spatial consistency with experimental annotations. Overall, RMDNet is a robust and interpretable tool for predicting RNA-protein binding sites. It has broad potential in disease mechanism research and therapeutic target discovery. The source code is available https://github.com/cskyan/RMDNet .

Nucleosomes represent hubs in chromatin organization and gene regulation and interact with a plethora of chromatin factors through different modes. In addition, alterations in histone proteins such as cancer mutations and post-translational modifications have profound effects on histone/nucleosome interactions. To elucidate the principles of histone interactions and the effects of those alterations, we developed histone interactomes for comprehensive mapping of histone-histone interactions (HHIs), histone-DNA interactions (HDIs), histone-partner interactions (HPIs) and DNA-partner interactions (DPIs) of 37 organisms, which contains a total of 3808 HPIs from 2544 binding proteins and 339 HHIs, 100 HDIs and 142 DPIs across 110 histone variants. With the developed networks, we explored histone interactions at different levels of granularities (protein-, domain- and residue-level) and performed systematic analysis on histone interactions at a large scale. Our analyses have characterized the preferred binding hotspots on both nucleosomal/linker DNA and histone octamer and unraveled diverse binding modes between nucleosome and different classes of binding partners. Last, to understand the impact of histone cancer-associated mutations on histone/nucleosome interactions, we complied one comprehensive cancer mutation dataset including 7940 cancer-associated histone mutations and further mapped those mutations onto 419,125 histone interactions at the residue level. Our quantitative analyses point to histone cancer-associated mutations' strongly disruptive effects on HHIs, HDIs and HPIs. We have further predicted 57 recurrent histone cancer mutations that have large effects on histone/nucleosome interactions and may have driver status in oncogenesis.

MOTIVATION: Mutations in protein-protein interactions can affect the corresponding complexes, impacting function and potentially leading to disease. Given the abundance of membrane proteins, it is crucial to assess the impact of mutations on the binding affinity of these proteins. Although several methods exist to predict the binding free energy change due to mutations in protein-protein complexes, most require structural information of the protein complex and are primarily trained on the SKEMPI database, which is composed mainly of soluble proteins. RESULTS: A novel sequence-based method (SAAMBE-MEM) for predicting binding free energy changes (ΔΔG) in membrane protein-protein complexes due to mutations has been developed. This method utilized the MPAD database, which contains binding affinities for wild-type and mutant membrane protein complexes. A machine learning model was developed to predict ΔΔG by leveraging features such as amino acid indices and position-specific scoring matrices (PSSM). Through extensive dataset curation and feature extraction, SAAMBE-MEM was trained and validated using the XGBoost regression algorithm. The optimal feature set, including PSSM-related features, achieved a Pearson correlation coefficient of 0.64, outperforming existing methods trained on the SKEMPI database. Furthermore, it was demonstrated that SAAMBE-MEM performs much better when utilizing evolution-based features in contrast to physicochemical features. AVAILABILITY AND IMPLEMENTATION: The method is accessible via a web server and standalone code at http://compbio.clemson.edu/SAAMBE-MEM/. The cleaned MPAD database is available at the website.

MOTIVATION: Mutations that alter protein-DNA interactions may be pathogenic and cause diseases. Therefore, it is extremely important to quantify the effect of mutations on protein-DNA binding free energy to reveal the molecular origin of diseases and to assist the development of treatments. Although several methods that predict the change of protein-DNA binding affinity upon mutations in the binding protein were developed, the effect of DNA mutations was not considered yet. RESULTS: Here, we report a new version of SAMPDI, the SAMPDI-3D, which is a gradient boosting decision tree machine learning method to predict the change of the protein-DNA binding free energy caused by mutations in both the binding protein and the bases of the corresponding DNA. The method is shown to achieve Pearson correlation coefficient of 0.76 and 0.80 in a benchmarking test against experimentally determined change of the binding free energy caused by mutations in the binding protein or DNA, respectively. Furthermore, three datasets collected from literature were used to do blind benchmark for SAMPDI-3D and it is shown that it outperforms all existing state-of-the-art methods. The method is very fast allowing for genome-scale investigations. AVAILABILITYAND IMPLEMENTATION: It is available as a web server and a stand-code at http://compbio.clemson.edu/SAMPDI-3D/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Motivation: Protein-DNA interactions are essential for regulating many cellular processes, such as transcription, replication, recombination and translation. Amino acid mutations occurring in DNA-binding proteins have profound effects on protein-DNA binding and are linked with many diseases. Hence, accurate and fast predictions of the effects of mutations on protein-DNA binding affinity are essential for understanding disease-causing mechanisms and guiding plausible treatments. Results: Here we report a new method Single Amino acid Mutation binding free energy change of Protein-DNA Interaction (SAMPDI). The method utilizes modified Molecular Mechanics Poisson-Boltzmann Surface Area (MM/PBSA) approach along with an additional set of knowledge-based terms delivered from investigations of the physicochemical properties of protein-DNA complexes. The method is benchmarked against experimentally determined binding free energy changes caused by 105 mutations in 13 proteins (compiled ProNIT database and data from recent references), and results in correlation coefficient of 0.72. Availability and implementation: http://compbio.clemson.edu/SAMPDI. Contact: [email protected]. Supplementary information: Supplementary data are available at Bioinformatics online.

SUMMARY: Electrostatic force is an essential component of the total force acting between atoms and macromolecules. Therefore, accurate calculations of electrostatic forces are crucial for revealing the mechanisms of many biological processes. We developed a DelPhiForce web server to calculate and visualize the electrostatic forces at molecular level. DelPhiForce web server enables modeling of electrostatic forces on individual atoms, residues, domains and molecules, and generates an output that can be visualized by VMD software. Here we demonstrate the usage of the server for various biological problems including protein-cofactor, domain-domain, protein-protein, protein-DNA and protein-RNA interactions. AVAILABILITY AND IMPLEMENTATION: The DelPhiForce web server is available at: http://compbio.clemson.edu/delphi-force. CONTACT: [email protected]. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

AUTOSAR, derived from OSEK/VDX, is the most popular industrial standard in the automotive electric development. It is challenging to manually verify or validate the correctness and safety of AUTOSAR Operating System (OS) as well as mission-critical or real-time applications built on it. In this paper, we adopt timed CSP to describe and reason about the Schedule Table, a new task scheduling mechanism in AUTOSAR. We also employ timed CSP to model AUTOSAR OS and a realtime application, i.e., the Engine Management System (EMS), based on the Schedule Table mechanism, and verify some safety properties. In addition, we simulate and verify our models in Process Analysis Toolkit (PAT). The result indicates that both AUTOSAR OS and EMS application conform to the specifications and requirements.

The remote fault diagnosis for complex equipment based on virtual instrument (VI) technology is studied. It can promote the maintenance and fault diagnosis methods under the network environment. Firstly, the technology necessity of remote fault diagnosis for complex equipment is analyzed. Secondly, the system model of remote fault diagnosis system including base station and center station is discussed. Then the versatile computer supported cooperate work (CSCW) environments for remote fault diagnosis are developed, which are based on LabWindows/CVI and Datasocket technology of National Instrument respectively. LabWindows/CVI is adopted to set up the VI environment, which support DataSocket transmission protocol for communication between base station and center station. Finally, a test system is setup to validate the proposed system model. An engine is taken as an example to establish remote fault diagnosis system. Several key technology designs are discussed. It is proved to be efficient, and the design scheme of remote fault diagnosis is feasible