1,4-Dihydropyridines (DHPs) have been developed to treat hypertension, angina, and nerve system disease. They are thought to mainly target the L-type calcium channels, but low selectivity prompts them to block Cav1.2 and Cav3.1 channels simultaneously. Recently, some novel DHPs with different hydrophobic groups have been synthesized and among them M12 has a higher selectivity for Cav3.1. However, the structural information about Cav3.1-DHPs complexes is not available in the experiment. Thus, we combined homology modeling, molecular docking, molecular dynamics simulations, and binding free energy calculations to quantitatively elucidate the inhibition mechanism of DHPs. The calculated results indicate that our model is in excellent agreement with experimental results. On the basis of conformational analysis, we identify the main interactions between DHPs and calcium channels and further elaborate on the different selectivity of ligands from the micro perspective. In conjunction with energy distribution, we propose that the binding sites of Cav3.1-DHPs is characterized by several interspersed hydrophobic amino acid residues on the IIIS6 and IVS6 segments. We also speculate the favorable function groups on prospective DHPs. Besides, our model provides important information for further mutagenesis experiments.
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http://dx.doi.org/10.1002/pro.2763 | DOI Listing |
Prostate
January 2025
Research Department, School of Medicine, Autonomous University of Sinaloa, Culiacan, México.
Introduction: Prostate cancer (PCa) is the second most common cancer in men worldwide, with significant incidence and mortality, particularly in Mexico, where diagnosis at advanced stages is common. Early detection through screening methods such as digital rectal examination and prostate-specific antigen testing is essential to improve outcomes. Despite current efforts, compliance with prostate screening (PS) remains low due to several barriers.
View Article and Find Full Text PDFBMC Res Notes
January 2025
Department of Computer Engineering, Chungbuk National University, Chungdae-ro 1, Cheongju, 28644, Republic of Korea.
Background: Drug response prediction can infer the relationship between an individual's genetic profile and a drug, which can be used to determine the choice of treatment for an individual patient. Prediction of drug response is recently being performed using machine learning technology. However, high-throughput sequencing data produces thousands of features per patient.
View Article and Find Full Text PDFBMC Nutr
January 2025
Centre for Lifecourse Nutrition, Department of Nutrition and Public Health, Faculty of Health and Sport Sciences, University of Agder, Postbox 422, Kristiansand, 4604, Norway.
Background: Early Childhood Education and Care (ECEC) centers play an important role in fostering healthy dietary habits. The Nutrition Now project focusing on improving dietary habits during the first 1000 days of life. Central to the project is the implementation of an e-learning resource aimed at promoting feeding practices among staff and healthy dietary behaviours for children aged 0-3 years in ECEC.
View Article and Find Full Text PDFJ Transl Med
January 2025
Department of Stem Cell and Regenerative Medicine, Southwest Cancer Center, Southwest Hospital, Third Military Medical University (Army Medical University), Chongqing, 400038, China.
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Microbiome
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Key Laboratory of Environment Remediation and Ecological Health, Ministry of Education, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou, 310058, China.
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