Least-Squares Fitting of Two 3-D Point Sets
IEEE Transactions on Pattern Analysis and Machine Intelligence
Congruence, similarity, and symmetries of geometric objects
SCG '87 Proceedings of the third annual symposium on Computational geometry
Farthest neighbors, maximum spanning trees and related problems in higher dimensions
Computational Geometry: Theory and Applications
Computing Largest Common Point Sets under Approximate Congruence
ESA '00 Proceedings of the 8th Annual European Symposium on Algorithms
An efficient approximation algorithm for point pattern matching under noise
LATIN'06 Proceedings of the 7th Latin American conference on Theoretical Informatics
On Protein Structure Alignment under Distance Constraint
ISAAC '09 Proceedings of the 20th International Symposium on Algorithms and Computation
Constant time approximation scheme for largest well predicted subset
COCOON'10 Proceedings of the 16th annual international conference on Computing and combinatorics
On protein structure alignment under distance constraint
Theoretical Computer Science
Computing the protein binding sites
ISBRA'11 Proceedings of the 7th international conference on Bioinformatics research and applications
Optimizing a Widely Used Protein Structure Alignment Measure in Expected Polynomial Time
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
On Complexity of Protein Structure Alignment Problem under Distance Constraint
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
P-Binder: a system for the protein-protein binding sites identification
ISBRA'12 Proceedings of the 8th international conference on Bioinformatics Research and Applications
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How to evaluate the quality of models is a basic problem for the field of protein structure prediction. Numerous evaluation criteria have been proposed, and one of the most intuitive criteria requires us to find a largest well-predicted subset-- a maximum subset of the model which matches the native structure [12]. The problem is solvable in O(n7) time, albeit too slow for practical usage. We present a (1 + 茂戮驴)ddistance approximation algorithm that runs in time O(n3logn/茂戮驴5) for general protein structures. In the case of globular proteins, this result can be enhanced to a randomized O(nlog2n) time algorithm with probability at least 1 茂戮驴 O(1/n). In addition, we propose a (1 + 茂戮驴)-approximation algorithm to compute the minimum distance to fit all the points of a model to its native structure in time O(n(loglogn+ log1/茂戮驴)/茂戮驴5). We have implemented our algorithms and results indicate our program finds much more matched pairs with less running time than TMScore, which is one of the most popular tools to assess the quality of predicted models.