[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120096-en":3,"doc-seo-120096-105":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":20,"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},120096,8796095462418,"Noah","https://ap-avatar.wpscdn.com/avatar/80000253c1241d02b47?x-image-process=image/resize,m_fixed,w_180,h_180&k=1778826106357471780",8,"Research & Report","Raman spectra of amino acids and peptides from machine learning polarizabilities","Raman spectroscopy enables non-destructive analysis of vibrational properties and molecular composition for amino acids, peptides, and proteins. Raman spectra can be simulated from the vibrational dependence of electronic polarizability, which can be learned efficiently from first-principles data using machine learning models. Transferability from small molecules to larger structures is uncertain, and direct training on large systems is costly. This work trains two ML models for all 20 amino acids, benchmarks them against DFT, and combines them with classical force-field MD to obtain MD-based Raman spectra. Extending the approach to small peptides shows that including peptide-bond-containing structures in training substantially improves predictions, even for unseen peptide structures.","arXiv :2401 . 14808v1 [physics .comp-ph] 26 Jan 2024  \nRaman spectra of amino acids and peptides from machine learning polarizabilities  \nEthan Berger,† Juha Niemelä,‡ Outi Lampela,¶ André H. Juffer,¶ and  \nHannu-Pekka Komsa∗ ,†  \n† Microelectronics Research Unit, Faculty of Information Technology and Electrical Engineering,  \nUniversity of Oulu, P.O. Box 4500, Oulu, FIN-90014, Finland ‡ Faculty of Biochemistry and Molecular Medicine, University of Oulu, Oulu, Finland ¶ Biocenter Oulu and Faculty of Biochemistry and Molecular Medicine, University of Oulu, Oulu,  \nFinland  \n[E-mail: hannu-pekka.komsa@oulu.fi](E-mail: hannu-pekka.komsa@oulu.fi)  \nAbstract  \nRaman spectroscopy is an important tool in the study of vibrational properties and composition of molecules, peptides and even proteins. Raman spectra can be simulated based on the change of the electronic polarizability with vibrations, which can nowadays be efficiently obtained via machine learning models trained on first-principles data. However, the transferability of the models trained on small molecules to larger structures is unclear and direct training on large structures in prohibitively expensive. In this work, we first train two machine learning models to predict polarizabilities of all 20 amino acids. Both models are carefully benchmarked and compared to DFT calculations, with neural network method found to offer better transferability. By combining machine learning models with classical force field molecular dynamics, Raman spectra of all amino acids are also obtained and investigated, showing good agreement with experiments. The models are further extended to small peptides. We find that adding structures containing peptide bonds to the training set greatly improves predictions even for peptides not included in training sets.  \nIntroduction  \nRaman spectroscopy is an important tool in modern research, as it represents a non-destructive method to study the vibrational properties of both solids and molecules. In biochemistry, it can be used to study the complex structures of large molecules such as proteins or peptides. 1,2 For instance, Raman spectra contain important information on the folding of proteins. 3,4 In this case, peaks pertaining to the peptide bonds (typically amide I and II) can be used to understand the impact of folding on the Raman spectra. 5,6 Another highly promising development is the use of surface-enhanced Raman spectroscopy to study the primary structures of protein, i.e. their sequence of amino acids. As the protein moves across the plasmonic hotspot, this technique could allow one to record sequentially the spectra of individual amino acids. 7,8 In all these cases, however, it is still necessary to have good reference spectra in order to perform correct assignment. While spectra of biomolecules 9 and amino acids 10,11 have been investigated many times, their interpretation remains challenging. In this context, simulation of Raman spectra could produce such reference data and further help understanding the experimental observations.  \nSimulations of Raman spectra usually rely on calculating harmonic vibrational modes and Ra-  \nman tensors. 12–14 For large molecules such as peptides, calculations have to be repeated many times for each possible conformations, making it highly tedious and computationally expensive. By using molecular dynamics (MD), Raman spectra is naturally obtained for every visited conformations, making the analysis much easier. Raman spectra from MD is obtained using the Fourier transform of the polarizability autocorrelation function. 15–18 High quality Raman spectra therefore rely on producing accurate MD trajectories as well as correctly predicting polarizabilities along these trajectories. First-principle calculations could be used to obtain both trajectories and polarizabilities, but the computational cost increases quickly with system size. Such methods are limited to small systems and not applicable to peptides or","cbCaimR9XEFHEFRu","https://ap.wps.com/l/cbCaimR9XEFHEFRu","pdf",1383633,1,24,"English","en",105,"# Abstract\n# Introduction\n# Simulation methods for Raman spectra\n# Polarizability modeling approaches\n# Machine learning for tensorial properties","[{\"question\":\"How are Raman spectra simulated in this study?\",\"answer\":\"Raman spectra are simulated from vibrational changes in electronic polarizability. The polarizability along molecular dynamics trajectories is predicted using machine learning models.\"},{\"question\":\"What models are trained and how are they evaluated?\",\"answer\":\"Two machine learning models are trained to predict polarizabilities of all 20 amino acids. Their predictions are benchmarked and compared to DFT calculations, with neural networks showing better transferability.\"},{\"question\":\"What improves predictions for peptides in the extended models?\",\"answer\":\"Adding structures containing peptide bonds to the training set greatly improves predictions, including for peptides not included in the training sets.\"}]","Raman spectra of amino acids and peptides from machine learning polarizabilities | PDF",1785728155,60,{"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},"raman-spectra-of-amino-acids-and-peptides-from-machine-learning-polarizabilities","",{"@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/raman-spectra-of-amino-acids-and-peptides-from-machine-learning-polarizabilities/120096/",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","2026-08-03",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"How are Raman spectra simulated in this study?","Question",{"text":75,"@type":76},"Raman spectra are simulated from vibrational changes in electronic polarizability. The polarizability along molecular dynamics trajectories is predicted using machine learning models.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What models are trained and how are they evaluated?",{"text":80,"@type":76},"Two machine learning models are trained to predict polarizabilities of all 20 amino acids. Their predictions are benchmarked and compared to DFT calculations, with neural networks showing better transferability.",{"name":82,"@type":73,"acceptedAnswer":83},"What improves predictions for peptides in the extended models?",{"text":84,"@type":76},"Adding structures containing peptide bonds to the training set greatly improves predictions, including for peptides not included in the training sets.","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},[93,97,101,105,109,114,119,122,127,130,134],{"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":29,"slug":108},5,"Comic","comic",{"id":110,"doc_module":4,"doc_module_name":46,"category_name":111,"show_sort_weight":112,"slug":113},6,"Technology",50,"technology",{"id":115,"doc_module":4,"doc_module_name":46,"category_name":116,"show_sort_weight":117,"slug":118},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":120,"slug":121},30,"research-report",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]