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Attention-aware differential learning for predicting peptide-MHC class I binding and T cell receptor recognition.
Brief Bioinform
November 2024
School of Computer Science, Northwestern Polytechnical University, Xi'an, 710129 Shaanxi, China.
The identification of neoantigens is crucial for advancing vaccines, diagnostics, and immunotherapies. Despite this importance, a fundamental question remains: how to model the presentation of neoantigens by major histocompatibility complex class I molecules and the recognition of the peptide-MHC-I (pMHC-I) complex by T cell receptors (TCRs). Accurate prediction of pMHC-I binding and TCR recognition remains a significant computational challenge in immunology due to intricate binding motifs and the long-tail distribution of known binding pairs in public databases.
View Article and Find Full Text PDFA comprehensive benchmarking for evaluating TCR embeddings in modeling TCR-epitope interactions.
Brief Bioinform
November 2024
Department of Computer Science, City University of Hong Kong, 83 Tat Chee Avenue, Kowloon Tong, Hong Kong, 999077, China.
The complexity of T cell receptor (TCR) sequences, particularly within the complementarity-determining region 3 (CDR3), requires efficient embedding methods for applying machine learning to immunology. While various TCR CDR3 embedding strategies have been proposed, the absence of their systematic evaluations created perplexity in the community. Here, we extracted CDR3 embedding models from 19 existing methods and benchmarked these models with four curated datasets by accessing their impact on the performance of TCR downstream tasks, including TCR-epitope binding affinity prediction, epitope-specific TCR identification, TCR clustering, and visualization analysis.
View Article and Find Full Text PDFCardiovascular Outcomes in Patients with Complex Type 2 Diabetes Mellitus Treated with the Dual SGLT Inhibitor Sotagliflozin: A Meta-analysis.
Diabetes Ther
January 2025
The State Key Laboratory Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, People's Republic of China.
Introduction: Scientific publications have shown sodium-glucose co-transporter-2 (SGLT2) inhibitors to have several beneficial effects in patients with complex type 2 diabetes mellitus (T2DM). However, sodium-glucose co-transporter-1 (SGLT-1) inhibitor is still under investigation in clinical trials. Recently, a dual inhibitor of sodium-glucose co-transporter (SGLT1/2), sotagliflozin, has been approved for use in patients with T2DM.
View Article and Find Full Text PDFQuantum chemical studies of carbon-based graphene-like nanostructures: from benzene to coronene.
J Mol Model
January 2025
Department of Chemistry, Federal Institute of Education, Science and Technology of Espírito Santo, Av. Min. Salgado Filho, Vila Velha, 29106-010, Espírito Santo, Brazil.
Context: This study presents quantum chemical analysis of 14 distinct carbon-based nanostructures (CBN), ranging from simple molecules, like benzene, to more complex structures, such as coronene, which serves as an exemplary graphene-like model. The investigation focuses on elucidating the relationships between molecular orbital (MO) energies, the energy band gaps, electron occupation numbers (eON), electronic conduction, and the compound topologies, seeking to find the one that approaches most of a graphene-like structure for in silico studies. Through detailed examination of molecular properties including chemical hardness and chemical potential, we demonstrate that the electronic exchange between orbitals is directly influenced by the structural topology of the carbon-based nanostructures, as the electron occupation numbers and the molecular orbital energies.
View Article and Find Full Text PDFProtein kinase C iota (PKCι) and pVHL are both needed for lysosomal degradation of α5 integrin in renal carcinoma cells.
Mol Biol Rep
January 2025
Department of Biology, Adelphi University, One South Avenue, P.O. Box 701, Garden City, NY, 11530-0701, USA.
Background: von Hippel-Lindau (VHL) hereditary cancer syndrome is caused by mutations in the VHL tumor suppressor gene and is characterized by a predisposition to form various types of tumors, including renal cell carcinomas, hemangioblastomas, and pheochromocytomas. The protein products of the VHL gene, pVHL, are part of an ubiquitin ligase complex that tags hypoxia inducible factor alpha (HIF-α) for proteosomal degradation. pVHL has also been reported to bind to atypical protein kinase C (aPKC).
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