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Protein-protein interactions are important in carrying out many biological processes and functions. These interactions may be either permanent or of temporary nature. Several studies have employed tools like solvent accessibility and graph theory to identify these interactions, but still more studies need to be performed to quantify and validate them. Although we now have many databases available with predicted and experimental results on protein-protein interactions, we still do not have many databases which focus on providing structural details of the interacting complexes, their oligomerisation state and homologues. In this work, protein-protein interactions have been thoroughly investigated within the structural regime and quantified for their strength using calculated pseudoenergies. The PPCheck server, an in-house webserver, has been used for calculating the pseudoenergies like van der Waals, hydrogen bonds and electrostatic energy based on distances between atoms of amino acids from two interacting proteins. PPCheck can be visited at . Based on statistical data, as obtained by studying established protein-protein interacting complexes from earlier studies, we came to a conclusion that an average protein-protein interface consisted of about 51 to 150 amino acid residues and the generalized energy per residue ranged from -2 kJ mol(-1) to -6 kJ mol(-1). We found that some of the proteins have an exceptionally higher number of amino acids at the interface and it was purely because of their elaborate interface or extended topology i.e. some of their secondary structure regions or loops were either inter-mixing or running parallel to one another or they were taking part in domain swapping. Residue networks were prepared for all the amino acids of the interacting proteins involved in different types of interactions (like van der Waals, hydrogen-bonding, electrostatic or intramolecular interactions) and were analysed between the query domain-interacting partner pair and its remote homologue-interacting partner pair. We found that, in exceptional cases, homologous proteins belonging to the same superfamily, but with remote sequence similarity, can share similar interfaces.
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http://dx.doi.org/10.1039/c3mb25484d | DOI Listing |
IEEE Trans Comput Biol Bioinform
September 2025
Accurately identifying associations between human genes (proteins) and clinical phenotypes is critical for advancing drug development and precision medicine. While the human phenotype ontology (HPO) standardizes clinical phenotypes, current computational approaches for predicting human protein-phenotype associations suffer from two limitations: (1) underutilization of multimodal protein-related information and (2) lack of state-of-the-art deep learning representations tailored to diverse data modalities, such as text and sequence. To overcome these limitations, we introduce MultiFusion2HPO, a novel multimodal model that integrates diverse features and advanced learning methods from multiple data sources to enhance the prediction of human protein-HPO associations.
View Article and Find Full Text PDFBiomed Environ Sci
August 2025
Gastrointestinal Disease Centre, Hebei Key Laboratory of Colorectal Cancer Precision Diagnosis and Treatment, The First Hospital of Hebei Medical University, Shijiazhuang 050031, Hebei, China.
Objective: To explore the correlation between chromosome 8 open reading frame 76 (C8orf76) and cyclin-dependent kinase 4 (CDK4) and the potential predictive effect of C8orf76 and CDK4 on the prognosis of colorectal cancer (CRC).
Methods: We constructed a protein-protein interaction network of C8orf76-related genes and analyzed the prognostic signatures of C8orf76 and CDK4. Clinicopathological features of C8orf76 and CDK4 were visualized using a nomogram.
Front Immunol
September 2025
Department of Thoracic Surgery, Shenzhen People's Hospital (The First Affiliated Hospital, Southern University of Science and Technology; The Second Clinical Medical College, Jinan University), Shenzhen, Guangdong, China.
Background: Lung cancer remains the leading cause of cancer-related mortality globally, primarily due to late-stage diagnosis, molecular heterogeneity, and therapy resistance. Key biomarkers such as EGFR, ALK, KRAS, and PD-1 have revolutionized precision oncology; however, comprehensive structural and clinical validation of these targets is crucial to enhance therapeutic efficacy.
Methods: Protein sequences for EGFR, ALK, KRAS, and PD-1 were retrieved from UniProt and modeled using SWISS-MODEL to generate high-confidence 3D structures.
Biochem Biophys Rep
December 2025
Division of Breast Surgery, Department of Surgery, Taipei Veterans General Hospital, Taipei, 112, Taiwan.
Purpose: This study aimed to conduct functional proteomics across breast cancer subtypes with bioinformatics analyses.
Methods: Candidate proteins were identified using nanoscale liquid chromatography with tandem mass spectrometry (NanoLC-MS/MS) from core needle biopsy samples of early stage (0-III) breast cancers, followed by external validation with public domain gene-expression datasets (TCGA TARGET GTEx and TCGA BRCA).
Results: Seventeen proteins demonstrated significantly differential expression and protein-protein interaction (PPI) found the strong networks including COL2A1, COL11A1, COL6A1, COL6A2, THBS1 and LUM.
Biochem Biophys Rep
June 2025
Department of Public Health, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, China.
Background: Synaptic dysfunction and synapse loss occur in Alzheimer's disease (AD). The current study aimed to identify synaptic-related genes with diagnostic potential for AD.
Methods: Differentially expressed genes (DEGs) were overlapped with phenotype-associated module selected through weighted gene co-expression network analysis (WGCNA), and synaptic-related genes.