{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,11]],"date-time":"2026-06-11T04:15:48Z","timestamp":1781151348127,"version":"3.54.1"},"reference-count":37,"publisher":"Oxford University Press (OUP)","issue":"3","license":[{"start":{"date-parts":[[2016,10,2]],"date-time":"2016-10-02T00:00:00Z","timestamp":1475366400000},"content-version":"vor","delay-in-days":2494,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/2.0\/uk\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2010,2,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Motivation: A major limitation in modeling protein interactions is the difficulty of assessing the over-fitting of the training set. Recently, an experimentally based approach that integrates crystallographic information of C2H2 zinc finger\u2013DNA complexes with binding data from 11 mutants, 7 from EGR finger I, was used to define an improved interaction code (no optimization). Here, we present a novel mixed integer programming (MIP)-based method that transforms this type of data into an optimized code, demonstrating both the advantages of the mathematical formulation to minimize over- and under-fitting and the robustness of the underlying physical parameters mapped by the code.<\/jats:p>\n               <jats:p>Results: Based on the structural models of feasible interaction networks for 35 mutants of EGR\u2013DNA complexes, the MIP method minimizes the cumulative binding energy over all complexes for a general set of fundamental protein\u2013DNA interactions. To guard against over-fitting, we use the scalability of the method to probe against the elimination of related interactions. From an initial set of 12 parameters (six hydrogen bonds, five desolvation penalties and a water factor), we proceed to eliminate five of them with only a marginal reduction of the correlation coefficient to 0.9983. Further reduction of parameters negatively impacts the performance of the code (under-fitting). Besides accurately predicting the change in binding affinity of validation sets, the code identifies possible context-dependent effects in the definition of the interaction networks. Yet, the approach of constraining predictions to within a pre-selected set of interactions limits the impact of these potential errors to related low-affinity complexes.<\/jats:p>\n               <jats:p>Contact: \u00a0ccamacho@pitt.edu; droleg@pitt.edu<\/jats:p>\n               <jats:p>Supplementary information: \u00a0Supplementary data are available at Bioinformatics online.<\/jats:p>","DOI":"10.1093\/bioinformatics\/btp664","type":"journal-article","created":{"date-parts":[[2009,12,5]],"date-time":"2009-12-05T02:17:54Z","timestamp":1259979474000},"page":"319-325","source":"Crossref","is-referenced-by-count":14,"title":["Optimization of minimum set of protein\u2013DNA interactions: a quasi exact solution with minimum over-fitting"],"prefix":"10.1093","volume":"26","author":[{"given":"N. A.","family":"Temiz","sequence":"first","affiliation":[{"name":"1 Department of Computational Biology, School of Medicine and 2 Department of Industrial Engineering, School of Engineering, University of Pittsburgh, Pittsburgh, PA 15260, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"A.","family":"Trapp","sequence":"additional","affiliation":[{"name":"1 Department of Computational Biology, School of Medicine and 2 Department of Industrial Engineering, School of Engineering, University of Pittsburgh, Pittsburgh, PA 15260, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"O. A.","family":"Prokopyev","sequence":"additional","affiliation":[{"name":"1 Department of Computational Biology, School of Medicine and 2 Department of Industrial Engineering, School of Engineering, University of Pittsburgh, Pittsburgh, PA 15260, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"C. 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