RECOMB '01 Proceedings of the fifth annual international conference on Computational biology
Structural alignment of large—size proteins via lagrangian relaxation
Proceedings of the sixth annual international conference on Computational biology
Mining Residue Contacts in Proteins Using Local Structure Predictions
BIBE '00 Proceedings of the 1st IEEE International Symposium on Bioinformatics and Biomedical Engineering
Prediction of Contact Maps Using Support Vector Machines
BIBE '03 Proceedings of the 3rd IEEE Symposium on BioInformatics and BioEngineering
Performance of Various Computers Using Standard Linear Equations Software
Performance of Various Computers Using Standard Linear Equations Software
Algorithmic aspects of protein folding and protein structure similarity
Algorithmic aspects of protein folding and protein structure similarity
Optimal Protein Structure Alignment Using Maximum Cliques
Operations Research
Joining softassign and dynamic programming for the contact map overlap problem
BIRD'07 Proceedings of the 1st international conference on Bioinformatics research and development
A similarity matrix-based hybrid algorithm for the contact map overlaps problem
Computers in Biology and Medicine
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A fundamental problem in molecular biology is the comparison of 3-dimensional protein folds in order to develop similarity measures and exploit them for protein clustering, database searches, and drug design. Contact map overlap (CMO) is one of the most reliable and robust measures of protein structure similarity. Fold comparison can be done by aligning the amino acid residues of two proteins in a way that maximizes the number of common residue contacts. CMO maximization is gaining increasing attention because it results in protein clusterings in good agreement with classification by experts. However, CMO maximization is an ${\mathcal{NP}}$-hard problem and few exact algorithms exist for solving this problem to global optimality. In this paper, we propose a branch-and-reduce exact algorithm for the CMO problem. Contrary to previous approaches, we do not transform CMO to other combinatorial optimization problems for solution. Instead, we address the problem directly in its natural form. By exploiting the problem's mathematical structure, we develop bounding and reduction procedures that lead to a very efficient algorithm. We present extensive computational results for over 36000 test problems from the literature. These results demonstrate that our algorithm is significantly faster and solves many more challenging test sets than the best previous algorithms for CMO. Furthermore, the algorithm results in protein clusters that are in excellent agreement with the SCOP database.