Computational Biology and Chemistry最新文献

{"title":"A structure-based approach to discover a potential isomerase Pin1 inhibitor for cancer therapy using computational simulation and biological studies","authors":"Wang Wang ,&nbsp;Qizhou Jiang ,&nbsp;Jiaxin Tao ,&nbsp;Zhenxian Zhang ,&nbsp;GuoPing Liu ,&nbsp;Binxuan Qiu ,&nbsp;Qingyang Hu ,&nbsp;Yuxi Zhang ,&nbsp;Chao Xie ,&nbsp;Jiawen Song ,&nbsp;GuoZhen Jiang ,&nbsp;Hui Zhong ,&nbsp;Yanling Zou ,&nbsp;Jiaqi Li ,&nbsp;Shaoli lv","doi":"10.1016/j.compbiolchem.2024.108290","DOIUrl":"10.1016/j.compbiolchem.2024.108290","url":null,"abstract":"<div><div>Peptidyl-prolyl cis/trans isomerase Pin1 occupies a prominent role in preventing the development of certain malignant tumors. Pin1 is considered a target for the treatment of related malignant tumors, so the identification of novel Pin1 inhibitors is particularly urgent. In this study, we preliminarily predicted eight candidates from FDA-approved drug database as the potential Pin1 inhibitors through virtual screening combined with empirical screening. Therefore, we selected these eight candidates and tested their binding affinity and inhibitory activity against Pin1 using fluorescence titration and PPIase activity assays, respectively. Subsequently, we found that four FDA-approved drugs showed good binding affinities and inhibition effects. In addition, we also observed that bexarotene can reduce cell viability in a dose-dependent and time-dependent manner and induce apoptosis. Finally, we inferred that residues K63, R68 and R69 are important in the binding process between bexarotene and Pin1. All in all, repurposing of FDA-approved drugs to inhibit Pin1 may provide a promising insight into the identification and development of new treatments for certain malignant tumors.</div></div>","PeriodicalId":10616,"journal":{"name":"Computational Biology and Chemistry","volume":"114 ","pages":"Article 108290"},"PeriodicalIF":2.6,"publicationDate":"2024-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142706803","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"scSFCL:Deep clustering of scRNA-seq data with subspace feature confidence learning","authors":"Xiaokun Meng,&nbsp;Yuanyuan Zhang,&nbsp;Xiaoyu Xu,&nbsp;Kaihao Zhang,&nbsp;Baoming Feng","doi":"10.1016/j.compbiolchem.2024.108292","DOIUrl":"10.1016/j.compbiolchem.2024.108292","url":null,"abstract":"<div><div>The rapid development of single-cell RNA sequencing(scRNA-seq) technology has spawned a variety of single-cell clustering methods. These methods combine statistics and bioinformatics to reveal differences in gene expression between cells and the diversity of cell types. Deep exploration of single-cell data is more challenging due to the high dimensionality, sparsity and noise of scRNA-seq data. Discriminative attribute information is often difficult to be fully utilised, while traditional clustering methods may not accurately capture the diversity of cell types. Therefore, a deep clustering method is proposed for scRNA-seq data based on subspace feature confidence learning called scSFCL. By dividing the subspace based on kernel density, discriminative feature subsets are filtered. The feature confidence of the subset is learned by combining the graph convolutional network (GCN) with weighting. Also, scSFCL facilitates the complementary fusion of generic structural and idiosyncratic information through a mutually supervised clustering that integrates GCN and a denoising variational autoencoder based on zero-inflated negative binomials (DVAE-ZINB). By validation on multiple scRNA-seq datasets, it is shown that the clustering performance of scSFCL is significantly improved compared with traditional methods, providing an effective solution for deep clustering of scRNA-seq data.</div></div>","PeriodicalId":10616,"journal":{"name":"Computational Biology and Chemistry","volume":"114 ","pages":"Article 108292"},"PeriodicalIF":2.6,"publicationDate":"2024-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142706774","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"In-silico screening to identify phytochemical inhibitor for hP2X7: A crucial inflammatory cell death mediator in Parkinson’s disease","authors":"Sabiya Khan ,&nbsp;Dharmendra Kumar Khatri","doi":"10.1016/j.compbiolchem.2024.108285","DOIUrl":"10.1016/j.compbiolchem.2024.108285","url":null,"abstract":"<div><div>The second most prevalent neurological disease among the elderly is Parkinson’s disease, where neuroinflammation plays a significant role in its pathology. Purinergic signaling mediated by P2X7 plays a significant role in neuroinflammation and pyroptotic cell death pathways through mediators like NLRP3, Caspase-1, and Caspase-3, instigating pyroptotic cell death. No synthetic agent advanced in late-stage clinical trials due to their inefficacy and toxicity. Hence, in this study, we aimed to identify a phytoconstituent inhibitor against the hP2X7 receptor to ameliorate the inflammatory processes involved. To achieve this aim, we performed homology modeling of the receptor and screened phytoconstituents from a library of over 3500 commercially available phytoconstituents. Molecular docking through the Maestro program of the Schrödinger suite was performed considering evaluation parameters like docking score, docking pose and spatial arrangement, and MMGBSA binding free energy. Predictive pharmacokinetic and toxicity profiling was done using tools like QikProp, ADMETLab 2.0, SwissADME, and Protox-II. Molecular dynamic simulation was performed using Schrödinger’s Desmond tool for the top 10 phytoconstituents. The complex stability was evaluated based on the ligand- and protein-RMSD, protein-ligand contact stability over a simulation period of 100 ns, protein RMSF, and ligand properties like RMSF, radius of gyration, intramolecular hydrogen bonding, and SASA. Based on the studies' results, silychristin, silybin, rosmarinic acid, nordihydroguaiaretic acid, and aurantiamide were shortlisted as the top 5 phytoconstituents against hP2X7. Further in-vitro and in-vivo studies would offer better clarity on the mechanism of action of these agents specifically related to pyroptotic cell death in various disease models.</div></div>","PeriodicalId":10616,"journal":{"name":"Computational Biology and Chemistry","volume":"115 ","pages":"Article 108285"},"PeriodicalIF":2.6,"publicationDate":"2024-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142757598","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Pharmacoinformatics-based prediction of Checkpoint kinase-1 inhibitors from Momordica charantia Linn. for cancer","authors":"Subramanian Haripriya ,&nbsp;Muniyandi Vijayalakshmi ,&nbsp;Chandu Ala ,&nbsp;Sankaranarayanan Murugesan ,&nbsp;Parasuraman Pavadai ,&nbsp;Selvaraj Kunjiappan ,&nbsp;Sureshbabu Ram Kumar Pandian","doi":"10.1016/j.compbiolchem.2024.108286","DOIUrl":"10.1016/j.compbiolchem.2024.108286","url":null,"abstract":"<div><div>Checkpoint kinase 1 (Chk-1), a serine/threonine kinase family protein, is an emerging target in cancer research owing to its crucial role in cell cycle arrest. Therefore, we aimed to predict potential Chk-1 inhibitors from <em>Momordica charantia</em> Linn., using high-throughput molecular docking. We used a graph theoretical network approach to determine the target protein, Chk-1. Among 86 compounds identified from <em>M. charantia</em> L., five molecules such as α-spinasterol (-9.7 kcal × mol⁻¹), stigmasterol (-9.6 kcal × mol⁻¹), stigmasta-7,22,25-trienol (-9.5 kcal × mol⁻¹), campesterol (-9.5 kcal × mol⁻¹), and stigmasta-7,25-dien-3beta-ol (-9.5 kcal × mol⁻¹) and standard drug CCT245737 (-8.3 kcal × mol^-1) displayed highest binding affinity with Chk-1. Besides, pharmacokinetic studies have demonstrated the non-toxic and drug-like properties of these compounds. Furthermore, molecular dynamics (MD) simulation studies confirmed the strong intermolecular interactions and stability of the compounds with Chk-1. The estimation of binding free-energy derived from molecular docking was fully recognized by the Molecular Mechanics-Generalized Born Surface Area (MM-GBSA) produced from the MD simulation paths. Altogether, these five compounds may serve as effective inhibitors of Chk-1, thereby could be used to develop new medications for cancer treatment.</div></div>","PeriodicalId":10616,"journal":{"name":"Computational Biology and Chemistry","volume":"115 ","pages":"Article 108286"},"PeriodicalIF":2.6,"publicationDate":"2024-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142756057","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A multi-perspective deep learning framework for enhancer characterization and identification","authors":"Liwei Liu ,&nbsp;Zhebin Tan ,&nbsp;Yuxiao Wei ,&nbsp;Qianhui Sun","doi":"10.1016/j.compbiolchem.2024.108284","DOIUrl":"10.1016/j.compbiolchem.2024.108284","url":null,"abstract":"<div><div>Enhancers are vital elements in the genome that boost the transcriptional activity of neighboring genes and are essential in regulating cell-specific gene expression. Therefore, accurately identifying and characterizing enhancers is essential for comprehending gene regulatory networks and the development of related diseases. This study introduces MPDL-Enhancer, a novel multi-perspective deep learning framework aimed at enhancer characterization and identification. In this study, enhancer sequences are encoded using the dna2vec model along with features derived from the structural properties of DNA sequences. Subsequently, these representations are processed through a novel dual-scale deep neural network designed to discern subtle correlations and extended interactions embedded within the semantic content of DNA. The predictive phase of our methodology employs a Support Vector Machine classifier to render the final classification. To rigorously assess the efficacy of our approach, a comprehensive evaluation was executed utilizing an independent test dataset, thereby substantiating the robustness and accuracy of our model. Our methodology demonstrated superior performance over existing computational techniques, with an accuracy (ACC) of 81.00 %, a sensitivity (SN) of 79.00 %, and specificity (SP) of 83.00 %. The innovative dual-scale deep neural network and the unique feature representation strategy contributed to this performance improvement. MPDL-Enhancer has effectively characterized enhancer sequences and achieved excellent predictive performance. Building upon this foundation, we conducted an interpretability analysis of the model, which can assist researchers in identifying key features and patterns that affect the functionality of enhancers, thereby promoting a deeper understanding of gene regulatory networks.</div></div>","PeriodicalId":10616,"journal":{"name":"Computational Biology and Chemistry","volume":"114 ","pages":"Article 108284"},"PeriodicalIF":2.6,"publicationDate":"2024-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142694045","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Vector embeddings by sequence similarity and context for improved compression, similarity search, clustering, organization, and manipulation of cDNA libraries","authors":"Daniel H. Um ,&nbsp;David A. Knowles ,&nbsp;Gail E. Kaiser","doi":"10.1016/j.compbiolchem.2024.108251","DOIUrl":"10.1016/j.compbiolchem.2024.108251","url":null,"abstract":"<div><div>This paper demonstrates the utility of organized numerical representations of genes in research involving flat string gene formats (i.e., FASTA/FASTQ⁵). By assigning a unique vector embedding to each short sequence, it is possible to more efficiently cluster and improve upon compression performance for the string representations of cDNA libraries. Furthermore, by studying alternative coordinate vector embeddings trained on the context of codon triplets, we can demonstrate clustering based on amino acid properties. Employing this sequence embedding method to encode barcodes and cDNA sequences, we can improve the time complexity of similarity searches. By pairing vector embeddings with an algorithm that determines the vector proximity in Euclidean space, this approach enables quicker and more flexible sequence searches.</div></div>","PeriodicalId":10616,"journal":{"name":"Computational Biology and Chemistry","volume":"114 ","pages":"Article 108251"},"PeriodicalIF":2.6,"publicationDate":"2024-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142722570","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The interaction of methotrexate with the human C5a and its potential therapeutic implications","authors":"Manaswini Ghosh,&nbsp;Pulkit Kr. Gupta,&nbsp;Shobhan Jena,&nbsp;Soumendra Rana","doi":"10.1016/j.compbiolchem.2024.108283","DOIUrl":"10.1016/j.compbiolchem.2024.108283","url":null,"abstract":"<div><div>Methotrexate (MTX) is an antimetabolite drug that mimics folate and inhibits dihydrofolic acid reductase, resulting in the impairment of malignant growth in actively proliferating tissues. MTX is approved by the FDA for primarily treating non-Hodgkin lymphoma, lymphoblastic leukemia, and osteosarcoma. In addition, MTX is also prescribed as a preferred anti-rheumatic medication for the management of rheumatoid arthritis, including psoriasis, indicating that MTX has a multipronged mechanism of action. MTX is also known to exert anti-inflammatory effects, and interestingly, the role of C5a, a pro-inflammatory glycoprotein of the complement system, is well established in several chronic inflammatory diseases, including rheumatoid arthritis and psoriasis, through the recruitment of C5a receptors (C5aR1/C5aR2) expressed in both immune and non-immune cells. Notably, through drug repurposing studies, we have earlier shown that non-steroidal anti-inflammatory drugs (NSAIDS) can potentially neutralize the function of C5a. Though MTX binds to serum albumin and can affect the immune system, whether its interaction with C5a could be therapeutically beneficial due to the downregulation of both extracellular and intracellular signaling of C5a is not yet established in the literature. In the current study, we have hypothesized and provided preliminary evidence through computational studies that MTX can strongly bind to the hotspot regions on C5a involved in the interactions with its receptors, which is likely to alter the downstream signaling of C5a and contribute to the overall therapeutic efficacy of MTX.</div></div>","PeriodicalId":10616,"journal":{"name":"Computational Biology and Chemistry","volume":"114 ","pages":"Article 108283"},"PeriodicalIF":2.6,"publicationDate":"2024-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142696163","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"MuSE: A deep learning model based on multi-feature fusion for super-enhancer prediction","authors":"Wenying He ,&nbsp;Haolu Zhou ,&nbsp;Yun Zuo ,&nbsp;Yude Bai ,&nbsp;Fei Guo","doi":"10.1016/j.compbiolchem.2024.108282","DOIUrl":"10.1016/j.compbiolchem.2024.108282","url":null,"abstract":"<div><div>Although bioinformatics-based methods accurately identify SEs (Super-enhancers), the results depend on feature design. It is foundational to representing biological sequences and automatically extracting their key features for improving SE identification. We propose a deep learning model MuSE (<strong><u>Mu</u></strong>lti-Feature Fusion for <strong><u>S</u></strong>uper-<strong><u>E</u></strong>nhancer), based on multi-feature fusion. This model utilizes two encoding methods, one-hot and DNA2Vec, to signify DNA sequences. Specifically, one-hot encoding reflects single nucleotide information, while k-mer representations based on DNA2Vec capture both local sequence fragment information and global sequence characteristics. These types of feature vectors are conducted and combined by neural networks, which aim at SE prediction. To validate the effectiveness of MuSE, we design extensive experiments on human and mouse species datasets. Compared to baselines such as SENet, MuSE improves the prediction of F1 score to a maximum improvement exceeding 0.05 on mouse species. The k-mer representations based on DNA2Vec among the given features have the most important impact on predictions. This feature effectively captures context semantic knowledge and positional information of DNA sequences. However, its representation of the individuality of each species negatively affects MuSE's generalization ability. Nevertheless, the cross-species prediction results of MuSE improve again to reach an AUC of nearly 0.8, after removing this type of feature. Source codes are available at <span><span>https://github.com/15831959673/MuSE</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":10616,"journal":{"name":"Computational Biology and Chemistry","volume":"113 ","pages":"Article 108282"},"PeriodicalIF":2.6,"publicationDate":"2024-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142678000","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"In-silico study and in-vitro validations for an affinity of mangiferin with aldose reductase: Investigating potential in tackling diabetic retinopathy","authors":"Arvind B. Chavhan ,&nbsp;Hemamalini Kola ,&nbsp;Babitha Bobba ,&nbsp;Yogendra Kumar Verma ,&nbsp;Mahendra Kumar Verma","doi":"10.1016/j.compbiolchem.2024.108281","DOIUrl":"10.1016/j.compbiolchem.2024.108281","url":null,"abstract":"<div><div>Type II Diabetes mellitus (T2DM) and associated complications primarily diabetic retinopathy cases are rising with an alarming rate. Prolong hyperglycemia along with the aldose reductase (AR) activity play a pivotal role in the development of oxidative stress in the aqueous humor and diabetic retinopathy. AR catalyzes conversion of glucose into sorbitol and or fructose get diffuse into lens leading to impaired electrolyte balance and cataract formation. Here in the study, affinity of mangiferin was evaluated first using in silico approaches (Docking studies) and then validated via isothermal titration calorimetry. Here in the present study aim was to check the does mangiferin do have affinity with AR, does mangiferin inhibit the AR and polyol pathway as key pathway involve in the diabetic retinopathy. Both <em>in silico</em> and laboratory investigations were carried out to explore the affinity of mangiferin with the aldose reductase. Swiss target prediction study showed that the AR is prime target of mangiferin in the human proteome. The molecular docking study and affinity searches were performed to seek the bonding pattern and forces involved. Docking (affinity 34.37 kcal/mol) for AR pose 1 was reported superior over the AR pose 2 (affinity −35.46 kcal/mol) against mangiferin. Mangiferin showed significant AR inhibition where IC₅₀ reported 67.711 µg/ml and highest inhibition was reported at 300 µg/ml i.e. 86.44 %. On the contrary, Quercetin showed much higher inhibition of aldose reductase at similar concentration i.e. 94.47 % at 300 µg/ml with IC₅₀ 59.6014 µg/ml. Here, AR pose 1 showed higher affinity with the mangiferin and confirmed via Isothermal Titration Calorimetry clearly showed higher binding affinity parameters. Binding affinity of AR pose 1 with the mangiferin was higher as showed with affinity parameter determined via ITC i.e. floating association constant (Ka) reported 6.47×10⁶, binding enthalpy (ΔH) −46.11 kJ/mol and higher binding sites (n) i.e. 1.84. Findings demonstrates that the mangiferin is promising AR inhibitor with the ADME prediction (CL_R 1.119 ml/min and t_1/2 1.162 h).</div></div>","PeriodicalId":10616,"journal":{"name":"Computational Biology and Chemistry","volume":"114 ","pages":"Article 108281"},"PeriodicalIF":2.6,"publicationDate":"2024-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142706772","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Screening and computational characterization of novel antimicrobial cathelicidins from amphibian transcriptomic data","authors":"H. Varela-Rodríguez,&nbsp;A. Guzman-Pando,&nbsp;J. Camarillo-Cisneros","doi":"10.1016/j.compbiolchem.2024.108276","DOIUrl":"10.1016/j.compbiolchem.2024.108276","url":null,"abstract":"<div><div>As cold-blooded organisms living in damp and dark environments, amphibians have evolved robust defense mechanisms to protect themselves from predators and infections. Among the wide repertoire of bioactive compounds they produce are antimicrobial peptides (AMPs), which are required as part of innate immunity. One important class of AMPs is cathelicidins, known for their broad-spectrum activity against pathogens and their immunoregulatory roles. However, despite their promising biomedical potential and the increasing availability of omics data, few cathelicidins have been studied in amphibians, mostly through conventional experimental techniques. Here, we present 210 novel cathelicidin sequences from amphibian transcriptomes, identified through a comprehensive computational pipeline, which employed HMMER and BLAST tools to screen cathelicidin domains. These sequences reveal a typical tripartite domain architecture that was confirmed by SignalP and InterProScan analysis. Phylogenetic inference with IQ-TREE classified the sequences into six categories based on evolutionary relationships. Compared to cathelicidins from other vertebrates, amphibian mature peptides exhibit longer average lengths (around 50 amino acids), fewer aromatic and hydrophobic residues, and reduced thermal stability. Furthermore, these amphibian cathelicidins were characterized for their physicochemical and biological properties, revealing significant antimicrobial potential with lower hemolytic capability, especially in anurans, which suggests a balance between their antimicrobial and hemolytic activities predicted through AMPlify, ampir, AmpGram, and HemoPI. Secondary structure estimations, including three-dimensional modeling using AlphaFold2, indicate that amphibian cathelicidins predominantly feature <span><math><mi>α</mi></math></span>-helices and coils. Some representative models also display a high <span><math><mi>α</mi></math></span>-helix composition with amphipathic topology, facilitating interactions with simulated bacterial membranes as assessed by the PPM approach. Thus, these findings highlight the functional role of cathelicidins in amphibian immunity and their promising biomedical applicability, emphasizing the importance of applying computational methods to expand the scope and reveal the diverse landscape of cathelicidins across vertebrates.</div></div>","PeriodicalId":10616,"journal":{"name":"Computational Biology and Chemistry","volume":"113 ","pages":"Article 108276"},"PeriodicalIF":2.6,"publicationDate":"2024-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142640495","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}