{"doi":"10.1109/sieds.2019.8735646","title":"Machine Learning for Classification of Protein Helix Capping Motifs","abstract":"The biological function of a protein stems from its 3-dimensional structure, which is thermodynamically determined by the energetics of interatomic forces between its amino acid building blocks (the order of amino acids, known as the sequence, defines a protein). Given the costs (time, money, human resources) of determining protein structures via experimental means like X-ray crystallography, can we better describe and compare protein 3D structures in a robust and efficient manner, so as to gain meaningful biological insights? We begin by considering a relatively simple problem, limiting ourselves to just protein secondary structural elements. Historically, many computational methods have been devised to classify amino acid residues in a protein chain into one of several discrete \"secondary structures\", of which the most well-characterized are the geometrically regular a-helix and β-sheet; irregular structural patterns, such as `turns' and `loops', are less understood. Here, we present a study of Deep Learning techniques to classify the loop-like end cap structures which delimit a-helices. Previous work used highly empirical and heuristic methods to manually classify helix capping motifs. Instead, we use structural data directly-including (i) backbone torsion angles computed from 3D structures, (ii) macromolecular feature sets (e.g., physicochemical properties), and (iii) helix cap classification data (from CAPS-DB)-as the ground truth to train a bidirectional long short-term memory (BiLSTM) model to classify helix cap residues. We tried different network architectures and scanned hyperparameters in order to train and assess several models; we also trained a Support Vector Classifier (SVC) to use as a baseline. Ultimately, we achieved 85% class-balanced accuracy with a deep BiLSTM model.","journal":"2019 Systems and Information Engineering Design Symposium (SIEDS)","year":2019,"id":2649,"datarank":0.3357946214963396,"base_score":1.0986122886681096,"endowment":1.0986122886681096,"self_citation_contribution":0.16479184330021646,"citation_network_contribution":0.17100277819612314,"self_endowment_contribution":0.16479184330021646,"citer_contribution":0.17100277819612314,"corpus_percentile":null,"corpus_rank":null,"citation_count":2,"citer_count":1,"citers_with_citation_signal":1,"citers_with_endowment":1,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.0469,"is_data_producer":false,"deposit_databanks":null,"is_oa":false,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2019-04-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":30943,"name":"Ruoyan Chen","orcid":"0000-0003-4421-5815","position":1,"is_corresponding":false},{"id":30944,"name":"Sri Vaishnavi Vemulapalli","orcid":null,"position":2,"is_corresponding":false},{"id":6103,"name":"Eli J. Draizen","orcid":"0000-0002-5645-5050","position":3,"is_corresponding":false},{"id":30945,"name":"Ke Wang","orcid":"0000-0001-5783-1655","position":4,"is_corresponding":false},{"id":1690,"name":"Cameron Mura","orcid":"0000-0001-7985-2561","position":5,"is_corresponding":false},{"id":125,"name":"Philip  E. Bourne","orcid":"0000-0002-7618-7292","position":6,"is_corresponding":false},{"id":30942,"name":"Sean Mullane","orcid":null,"position":0,"is_corresponding":true}],"reference_count":24,"raw_metadata":{"citation_network_status":"fetched"},"created_at":"2026-03-01T18:20:47.508186Z","pmid":null,"pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}