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In fact, only a small subset of the native contact map is necessary to successfully drive the model conformational search, so one important goal is to obtain the set of constraints with the highest true-positive rate, lowest redundancy and greatest amount of information. In this work, we introduce a constraint evaluation and selection method based on the point-biserial correlation coefficient, which utilizes structural information from an ensemble of models to indirectly measure the power of each constraint in biasing the conformational search toward consensus structures.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Residue contact maps obtained by direct coupling analysis are systematically improved by means of discriminant analysis, reaching in some cases accuracies often seen only in modern deep-learning-based approaches. When combined with an iterative modeling workflow, the proposed constraint classification optimizes the selection of the constraint set and maximizes the probability of obtaining successful models. The use of discriminant analysis for the valorization of the information of constraint datasets is a general concept with possible applications to other constraint types and modeling problems.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Availability and implementation<\/jats:title>\n                    <jats:p>MSA for the targets in this work is available on https:\/\/github.com\/m3g\/2021_Bottino_Biserial. Modeling data supporting the findings of this study was generated at the Center for Computing in Engineering and Sciences, and is available from the corresponding author LM on request.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Supplementary information<\/jats:title>\n                    <jats:p>Supplementary data are available at Bioinformatics online.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btab425","type":"journal-article","created":{"date-parts":[[2021,6,3]],"date-time":"2021-06-03T16:38:08Z","timestamp":1622738288000},"page":"3766-3773","source":"Crossref","is-referenced-by-count":0,"title":["Structural discrimination analysis for constraint selection in protein modeling"],"prefix":"10.1093","volume":"37","author":[{"given":"Guilherme F","family":"Bottino","sequence":"first","affiliation":[{"name":"Institute of Chemistry, University of Campinas , Campinas, SP,","place":["Brazil"]},{"name":"Center for Computational Engineering & Science, University of Campinas , Campinas, SP,","place":["Brazil"]}]},{"given":"Allan J R","family":"Ferrari","sequence":"additional","affiliation":[{"name":"Institute of Chemistry, University of Campinas , Campinas, SP,","place":["Brazil"]},{"name":"Center for Computational Engineering & Science, University of Campinas , Campinas, SP,","place":["Brazil"]}]},{"given":"Fabio C","family":"Gozzo","sequence":"additional","affiliation":[{"name":"Institute of Chemistry, University of Campinas , Campinas, SP,","place":["Brazil"]}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6857-1884","authenticated-orcid":false,"given":"Leandro","family":"Mart\u00ednez","sequence":"additional","affiliation":[{"name":"Institute of Chemistry, University of Campinas , Campinas, SP,","place":["Brazil"]},{"name":"Center for Computational Engineering & Science, University of Campinas , Campinas, SP,","place":["Brazil"]}]}],"member":"286","published-online":{"date-parts":[[2021,6,4]]},"reference":[{"key":"2026041420022718100_btab425-B1","doi-asserted-by":"crossref","first-page":"1100","DOI":"10.1002\/prot.25787","article-title":"A further leap of improvement in tertiary structure prediction in CASP13 prompts new routes for future assessments","volume":"87","author":"Abriata","year":"2019","journal-title":"Proteins"},{"key":"2026041420022718100_btab425-B2","doi-asserted-by":"crossref","first-page":"470","DOI":"10.1093\/bioinformatics\/btz593","article-title":"DEEPCON: protein contact prediction using dilated convolutional neural networks with dropout","volume":"36","author":"Adhikari","year":"2020","journal-title":"Bioinformatics"},{"key":"2026041420022718100_btab425-B3","doi-asserted-by":"crossref","first-page":"1466","DOI":"10.1093\/bioinformatics\/btx781","article-title":"DNCON2: improved protein contact prediction using two-level deep convolutional neural networks","volume":"34","author":"Adhikari","year":"2018","journal-title":"Bioinformatics"},{"key":"2026041420022718100_btab425-B4","doi-asserted-by":"crossref","first-page":"e92721","DOI":"10.1371\/journal.pone.0092721","article-title":"Fast and accurate multivariate Gaussian modeling of protein families: predicting residue contacts and protein-interaction partners","volume":"9","author":"Baldassi","year":"2014","journal-title":"PLoS One"},{"key":"2026041420022718100_btab425-B5","doi-asserted-by":"crossref","first-page":"13115","DOI":"10.1021\/bi000402j","article-title":"Crystal structure of oxidized Bacillus pasteurii cytochrome c553 at 0.97-A resolution","volume":"39","author":"Benini","year":"2000","journal-title":"Biochemistry"},{"key":"2026041420022718100_btab425-B6","article-title":"ProSPr: democratized implementation of alphafold protein distance prediction network","volume":"830273, 1\u201312","author":"Billings","year":"2019","journal-title":"Cold Spring Harbor Lab"},{"key":"2026041420022718100_btab425-B7","doi-asserted-by":"crossref","first-page":"905","DOI":"10.1107\/S0907444998003254","article-title":"Crystallography & NMR system: a new software suite for macromolecular structure determination","volume":"54","author":"Br\u00fcnger","year":"1998","journal-title":"Acta Crystallogr. 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