[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123673-en":3,"doc-seo-123673-105":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},123673,962075006959,"Anda","https://ap-avatar.wpscdn.com/avatar/e0002397efbe92a78e?_k=1776741047341049297",8,"Research & Report","Mathematical and Machine Learning Approaches for Classification of Protein Secondary Structure Elements from Cα Coordinates - read online free","The secondary structure elements (SSEs) of proteins are essential intermediate targets for determining tertiary structures, yet they are often difficult to infer when spatial atomic details are missing. Common identification tools, such as DSSP and STRIDE, rely on atomic information and hydrogen-bond detection, creating a bottleneck under incomplete data. To mitigate this limitation, three Ca-atom–based classification approaches are proposed: a mathematical method, a deep learning method, and an ensemble of five machine learning models. The approaches are evaluated against one another and compared with the state-of-the-art method PCASSO.","Tennessee State University  \nDigital Scholarship @ Tennessee State University  \n\n| Computer Science Faculty Research | Department of Computer Science |\n| --- | --- |\n| 5-31-2023\u003Cbr>Mathematical and Machine Learning Approaches for Classification of Protein Secondary Structure Elements from Cα Coordinates\u003Cbr>Ali Sekmen\u003Cbr>Tennessee State University\u003Cbr>Kamal Al Nasr\u003Cbr>Tennessee State University\u003Cbr>Bahadir Bilgin\u003Cbr>Middle East Technical University\u003Cbr>Ahmet Bugra Koku\u003Cbr>Middle East Technical University Christopher Jones\u003Cbr>Tennessee State University\u003Cbr>Follow this and additional works at: [https://digitalscholarship.tnstate.edu/computerscience](https://digitalscholarship.tnstate.edu/computerscience)\u003Cbr> Part of the Computer Engineering Commons |  |\n\nRecommended Citation  \nSekmen, A.; Al Nasr, K.; Bilgin, B.; Koku, A. B.; Jones, C. Mathematical and Machine Learning Approaches for Classification of Protein Secondary Structure Elements from Cα Coordinates. Biomolecules 2023, 13, 923. [https://doi.org/10.3390/biom13060923](https://doi.org/10.3390/biom13060923)  \nThis Article is brought to you for free and open access by the Department of Computer Science at Digital Scholarship @ Tennessee State University. It has been accepted for inclusion in Computer Science Faculty Research by an authorized administrator of Digital Scholarship @ Tennessee State University. For more information, please [contact XGE@Tnstate.edu](contact XGE@Tnstate.edu).  \n biomolecules  \nArticle  \nMathematical and Machine Learning Approaches for Classification of Protein Secondary Structure Elements from Ca Coordinates  \nAli Sekmen 1, Kamal Al Nasr 1,*, Bahadir Bilgin 1,2, Ahmet Bugra Koku 2,3 and Christopher Jones 1  \nCitation: Sekmen, A.; Al Nasr, K.; Bilgin, B.; Koku, A.B.; Jones, C. Mathematical and Machine Learning Approaches for Classification of Protein Secondary Structure Elements from Ca Coordinates. Biomolecules 2023, 13, 923. [https://](https://)[ ](https://)[doi.org/10.3390/biom13060923](doi.org/10.3390/biom13060923)  \nAcademic Editors: Jose M. Guisan, Yung-Chuan Liu and Antonio Zuorro  \nReceived: 3 May 2023  \nRevised: 16 May 2023  \nAccepted: 16 May 2023  \nPublished: 31 May 2023  \nCopyright: © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://](https://)[ ](https://)[creativecommons.org/licenses/by/](creativecommons.org/licenses/by/)[ ](creativecommons.org/licenses/by/)[4.0/](4.0/)) .  \n1 Department of Computer Science, Tennessee State University, Nashville, TN 37209, USA;  \n[asekmen@tnstate.edu](asekmen@tnstate.edu) (A.S.); [bbilgin@tnstate.edu](bbilgin@tnstate.edu) (B.B.); [cjone141@tnstate.edu](cjone141@tnstate.edu) (C.J.)  \n2 Department of Mechanical Engineering, Middle East Technical University, Ankara 06800, Türkiye; [kbugra@metu.edu.tr](kbugra@metu.edu.tr)  \n3 Center for Robotics and AI, Middle East Technical University, Ankara 06800, Türkiye  \n* Correspondence: [kalnasr@tnstate.edu](kalnasr@tnstate.edu)  \nAbstract: Determining Secondary Structure Elements (SSEs) for any protein is crucial as an intermediate step for experimental tertiary structure determination. SSEs are identified using popular tools such as DSSP and STRIDE. These tools use atomic information to locate hydrogen bonds to identify SSEs. When some spatial atomic details are missing, locating SSEs becomes a hinder. To address the problem, when some atomic information is missing, three approaches for classifying SSE types using Ca atoms in protein chains were developed: (1) a mathematical approach,(2) a deep learning approach, and (3) an ensemble of five machine learning models. The proposed methods were compared against each other and with a state-of-the-art approach, PCASSO.  \nKeywords: protein structure modeling; protein secondary structure; secondary structure identification; machine learning; protein trace; mathematical modeling  \n1.","cbCainehPHeSV5H4","https://ap.wps.com/l/cbCainehPHeSV5H4","pdf",1362087,1,19,"English","en",105,"# Introduction\n## Protein structures and importance of SSEs\n## Experimental methods for 3D structure determination\n# Proposed approaches\n## Mathematical approach\n## Deep learning approach\n## Ensemble of five machine learning models\n# Evaluation and comparison\n## Comparison among proposed methods\n## Benchmark against PCASSO","[{\"question\":\"Why is classifying protein secondary structure elements (SSEs) important?\",\"answer\":\"SSEs are key intermediate information for determining protein tertiary structure, which in turn relates to protein function.\"},{\"question\":\"What problem do the proposed methods address?\",\"answer\":\"They address difficulty in locating SSEs when some spatial atomic details are missing, which limits hydrogen-bond–based tools like DSSP and STRIDE.\"},{\"question\":\"What are the three Ca-based SSE classification approaches?\",\"answer\":\"They include a mathematical approach, a deep learning approach, and an ensemble that combines five machine learning models.\"}]","Mathematical and Machine Learning Approaches for Classification of Protein Secondary Structure Elements from Cα Coordinates - 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