{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,31]],"date-time":"2025-10-31T14:21:57Z","timestamp":1761920517175,"version":"3.32.0"},"reference-count":20,"publisher":"Wiley","issue":"4","license":[{"start":{"date-parts":[[2018,12,1]],"date-time":"2018-12-01T00:00:00Z","timestamp":1543622400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2018,12,1]],"date-time":"2018-12-01T00:00:00Z","timestamp":1543622400000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2017YFE0111900"],"award-info":[{"award-number":["2017YFE0111900"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Quant. Biol."],"published-print":{"date-parts":[[2018,12]]},"abstract":"<jats:sec><jats:title>Background<\/jats:title><jats:p><jats:italic>Ab initio<\/jats:italic> protein structure prediction is to predict the tertiary structure of a protein from its amino acid sequence alone. As an important topic in bioinformatics, considerable efforts have been made on designing the <jats:italic>ab initio<\/jats:italic> methods. Unfortunately, lacking of a perfect energy function, it is a difficult task to select a good near\u2010native structure from the predicted decoy structures in the last step.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>Here we propose an ensemble clustering method based on <jats:italic>k<\/jats:italic>\u2010medoids to deal with this problem. The <jats:italic>k<\/jats:italic>\u2010medoids method is run many times to generate clustering ensembles, and then a voting method is used to combine the clustering results. A confidence score is defined to select the final near\u2010native model, considering both the cluster size and the cluster similarity.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>We have applied the method to 54 single\u2010domain targets in CASP\u201011. For about 70.4% of these targets, the proposed method can select better near\u2010native structures compared to the SPICKER method used by the I\u2010TASSER server.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>The experiments show that, the proposed method is effective in selecting the near\u2010native structure from decoy sets for different targets in terms of the similarity between the selected structure and the native structure.<\/jats:p><\/jats:sec>","DOI":"10.1007\/s40484-018-0158-1","type":"journal-article","created":{"date-parts":[[2018,11,24]],"date-time":"2018-11-24T03:18:36Z","timestamp":1543029516000},"page":"307-312","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Selecting near\u2010native protein structures from <i>ab initio<\/i> models using ensemble clustering"],"prefix":"10.1002","volume":"6","author":[{"given":"Li","family":"Li","sequence":"first","affiliation":[{"name":"School of Information Science and Engineering Lanzhou University Lanzhou 730000 China"}]},{"given":"Huanqian","family":"Yan","sequence":"additional","affiliation":[{"name":"School of Information Science and Engineering Lanzhou University Lanzhou 730000 China"}]},{"given":"Yonggang","family":"Lu","sequence":"additional","affiliation":[{"name":"School of Information Science and Engineering Lanzhou University Lanzhou 730000 China"}]}],"member":"311","published-online":{"date-parts":[[2018,12]]},"reference":[{"key":"e_1_2_8_2_2","unstructured":"UniProtKB\/TrEMBL Protein Database Release 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