[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124928-en":3,"doc-seo-124928-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},124928,4398048950312,"Violet","https://ap-avatar.wpscdn.com/avatar/400002538284de19e3c?_k=1778320343897328908",8,"Research & Report","Structural bioinformatics - Utilizing biological experimental data and molecular dynamics for the classification of mutational hotspots through machine learning","Combining molecular dynamics with machine learning, this work evaluates how BPDE-Guanine DNA adducts deform DNA helices across multiple gene contexts. It applies a random-forest classification pipeline with feature selection to identify precise topological descriptors that separate BPDE hotspot and nonhotspot sites. Models trained on TP53 duplex helical data are tested on TP53, cII, and lacZ genes, achieving accuracy, precision, and F1 scores above 91%. Regional base-pair rotation variance, conserved across genes and linked to GC content, emerges as a potential biomarker of mutational hotspots.","Structural bioinformatics  \nUtilizing biological experimental data and molecular dynamics for the classification of mutational hotspots through machine learning  \nJames G. Davies 1 and Georgina E. Menzies  1,􀀃  \n1 Molecular Bioscience Division, School of Biosciences, Cardiff University, Cardiff, CF10 3AX, United Kingdom  \n􀀃 Corresponding author. Molecular Bioscience Division, School of Biosciences, Cardiff University, Cardiff CF10 3AX, United Kingdom.  \n[E-mail: menziesg@cardiff.ac.uk](E-mail: menziesg@cardiff.ac.uk)  \nAssociate Editor: Franca Fraternali  \nAbstract  \nMotivation: Benzo[a]pyrene, a notorious DNA-damaging carcinogen, belongs to the family of polycyclic aromatic hydrocarbons commonly found in tobacco smoke. Surprisingly, nucleotide excision repair (NER) machinery exhibits inefficiency in recognizing specific bulky DNA adducts including Benzo[a]pyrene Diol-Epoxide (BPDE), a Benzo[a]pyrene metabolite. While sequence context is emerging as the leading factor linking the inadequate NER response to BPDE adducts, the precise structural attributes governing these disparities remain inadequately understood. We therefore combined the domains of molecular dynamics and machine learning to conduct a comprehensive assessment of helical distortion caused by BPDE-Guanine adducts in multiple gene contexts. Specifically, we implemented a dual approach involving a random forest classification-based analysis and subsequent feature selection to identify precise topological features that may distinguish adduct sites of variable repair capacity. Our models were trained using helical data extracted from duplexes representing both BPDE hotspot and nonhotspot sites within the TP53 gene, then applied to sites within TP53, cII, and lacZ genes.  \nResults: We show our optimized model consistently achieved exceptional performance, with accuracy, precision, and f1 scores exceeding 91% . Our feature selection approach uncovered that discernible variance in regional base pair rotation played a pivotal role in informing the decisions of our model. Notably, these disparities were highly conserved among TP53 and lacZ duplexes and appeared to be influenced by the regional GC content. As such, our findings suggest that there are indeed conserved topological features distinguishing hotspots and nonhotpot sites, highlighting regional GC content as a potential biomarker for mutation.  \nAvailability and implementation: Code for comparing machine learning classifiers and evaluating their performance is available at [https://](https://)[ ](https://)[github.com/jdavies24/ML-Classifier-Comparison](github.com/jdavies24/ML-Classifier-Comparison), and code for analysing DNA structure with Curvesþ and Canal using Random Forest is available at [https://github.com/jdavies24/ML-classification-of-DNA-trajectories](https://github.com/jdavies24/ML-classification-of-DNA-trajectories).  \n1 Introduction  \nMutations serve as the fundamental source of genetic variation, fostering adaptive evolution, whilst contributing to the development of diseases such as cancer and age-related illnesses (Alexandrov et al. 2013, Bae et al. 2018) . A nucleotides mutation rate exhibits considerable heterogeneity throughout the genome, with the rate of site-to-site mutation shown to vary by > 100-fold, prompting sequence dependant contribution to both evolution and genetic pathologies (Ellegren et al. 2003). Analysing the precise features that promote this dependency may unlock valuable insights into the  \nmutagenic mechanisms underlying various human diseases.  \nIt is widely accepted that site-specific mutational frequencies are contingent upon three generalized factors: (i) nucleotide stability and vulnerability to mutagenesis; (ii) the fidelity of DNA replication processes; and (iii) the efficiency of DNA repair machinery (Baer et al. 2007) . Consequently, a natural gradient manifests, revealing the degree to which mutational occurrences are influenced by the underlying sequence compositi","cbCaigFXTfBZeRmx","https://ap.wps.com/l/cbCaigFXTfBZeRmx","pdf",1727479,1,13,"English","en",105,"# Abstract\n## Motivation\n## Results\n## Availability and implementation\n# Introduction\n## Mutations and genetic disease relevance\n## Factors shaping mutational frequencies\n## Role of TP53 in cancer","[{\"question\":\"What problem does the study address regarding BPDE and DNA repair?\",\"answer\":\"It investigates why nucleotide excision repair shows inefficient recognition of specific bulky DNA adducts such as BPDE-Guanine, focusing on the structural features underlying hotspot versus nonhotspot behavior.\"},{\"question\":\"How does the method classify mutational hotspots in the DNA structure?\",\"answer\":\"The approach extracts helical data from duplexes and uses a random-forest classifier followed by feature selection to identify topological features distinguishing hotspot and nonhotspot sites.\"},{\"question\":\"Which structural feature most strongly informs the model’s hotspot predictions?\",\"answer\":\"Feature selection highlights regional base-pair rotation variance, which is conserved across TP53 and lacZ duplexes and appears influenced by regional GC content.\"}]","Structural bioinformatics - Utilizing biological experimental data and molecular dynamics for the classification of mutational hotspots through machine learning | PDF",1785895424,33,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"structural-bioinformatics-utilizing-biological-experimental-data-and-molecular-dynamics-for-the-classification-of-mutational-hotspots-through-machine-learning","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/structural-bioinformatics-utilizing-biological-experimental-data-and-molecular-dynamics-for-the-classification-of-mutational-hotspots-through-machine-learning/124928/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What problem does the study address regarding BPDE and DNA repair?","Question",{"text":75,"@type":76},"It investigates why nucleotide excision repair shows inefficient recognition of specific bulky DNA adducts such as BPDE-Guanine, focusing on the structural features underlying hotspot versus nonhotspot behavior.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the method classify mutational hotspots in the DNA structure?",{"text":80,"@type":76},"The approach extracts helical data from duplexes and uses a random-forest classifier followed by feature selection to identify topological features distinguishing hotspot and nonhotspot sites.",{"name":82,"@type":73,"acceptedAnswer":83},"Which structural feature most strongly informs the model’s hotspot predictions?",{"text":84,"@type":76},"Feature selection highlights regional base-pair rotation variance, which is conserved across TP53 and lacZ duplexes and appears influenced by regional GC content.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]