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Recognition of binding patterns common to a set of protein structure
 Lecture Notes in Computer Science, 3500:440
, 2005
"... Abstract. We present a novel computational method, MultiBind, for recognition of binding patterns common to a set of protein structures. It is the first method which performs a multiple alignment between protein binding sites in the absence of overall sequence, fold or binding partner similarity. Mu ..."
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Cited by 12 (3 self)
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Abstract. We present a novel computational method, MultiBind, for recognition of binding patterns common to a set of protein structures. It is the first method which performs a multiple alignment between protein binding sites in the absence of overall sequence, fold or binding partner similarity. MultiBind recognizes common spatial arrangements of physicochemical properties in the binding sites. These should be important for recognition of function, prediction of binding and drug design. We discuss the theoretical aspects of the computational problem of multiple structure alignment. This problem involves solving a 3D kpartite matching problem, which we show to be NPHard. The MultiBind method, applies an efficient Geometric Hashing technique to detect a potential set of multiple alignments of the given binding sites. To overcome the exponential number of possible multiple combinations it applies a very efficient filtering procedure which is heavily based on the selected scoring function. Our method guarantees detection of an approximate solution in terms of pattern proximity as well as cardinality of multiple alignment. We show applications of MultiBind to several biological targets. The method recognizes patterns which are responsible for binding small molecules such as estradiol, ATP/ANP and transition state analogues. The presented computational results agree with the available biological ones.
Maximizing the Area of Overlap of two
, 2004
"... Let A and B be two sets of n resp. m (m n) disjoint unit disks in the plane. We consider the problem of nding a rigid motion of A that maximizes the total area of its overlap with B. The function describing the area of overlap is quite complex, even for combinatorially equivalent translations, ..."
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Let A and B be two sets of n resp. m (m n) disjoint unit disks in the plane. We consider the problem of nding a rigid motion of A that maximizes the total area of its overlap with B. The function describing the area of overlap is quite complex, even for combinatorially equivalent translations, and hence, we turn our attention to approximation algorithms. First, we give a deterministic (1 )approximation algorithm for the maximum area of overlap under rigid motion that runs in O((n ) log m)) time. If is the diameter of set A, we get an (1 )approximation in O( 3 ) time. Under the condition that the maximum is at least a constant fraction of the area of A, we give a probabilistic (1 ) approximation algorithm that runs in O((m m) time and succeeds with high probability. Our algorithms generalize to the case where A and B consist of possibly intersecting disks of dierent radii provided that (i) the ratio of the radii of any two disks in A[B is bounded, and (ii) within each set, the maximum number of disks with a nonempty intersection is bounded.
New Approaches to Robust, PointBased Image Registration
"... We consider various algorithmic solutions to image registration based on the alignment of a set of feature points. We present a number of enhancements to a branchandbound algorithm introduced by Mount, Netanyahu, and Le Moigne (Pattern Recognition, Vol. 32, 1999, pp. 17–38), which presented a regi ..."
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We consider various algorithmic solutions to image registration based on the alignment of a set of feature points. We present a number of enhancements to a branchandbound algorithm introduced by Mount, Netanyahu, and Le Moigne (Pattern Recognition, Vol. 32, 1999, pp. 17–38), which presented a registration algorithm based on the partial Hausdorff distance. Our enhancements include a new distance measure, the discrete Gaussian mismatch, and a number of improvements and extensions to the above search algorithm. Both distance measures are robust to the presence of outliers, that is, data points from either set that do not match any point of the other set. We present experimental studies, which show that the new distance measure considered can provide significant improvements over the partial Hausdorff distance in instances where the number of outliers is not known in advance. These experiments also show that our other algorithmic improvements can offer tangible improvements. We demonstrate the algorithm’s efficacy by considering images involving different sensors and different spectral bands, both in a traditional framework and in a multiresolution framework.