Predicting Protein Function and Binding Profile via Matching of Local Evolutionary and Geometric Surface Patterns Export

@article{citeulike:4209212, abstract = {Inferring protein functions from structures is a challenging task, as a large number of orphan protein structures from structural genomics project are now solved without their biochemical functions characterized. For proteins binding to similar substrates or ligands and carrying out similar functions, their binding surfaces are under similar physicochemical constraints, and hence the sets of allowed and forbidden residue substitutions are similar. However, it is difficult to isolate such selection pressure due to protein function from selection pressure due to protein folding, and evolutionary relationship reflected by global sequence and structure similarities between proteins is often unreliable for inferring protein function. We have developed a method, called pevoSOAR (pocket-based evolutionary search of amino acid residues), for predicting protein functions by solving the problem of uncovering amino acids residue substitution pattern due to protein function and separating it from amino acids substitution pattern due to protein folding. We incorporate evolutionary information specific to an individual binding region and match local surfaces on a large scale with millions of precomputed protein surfaces to identify those with similar functions. Our pevoSOAR method also generates a probablistic model called the computed binding a profile that characterizes protein-binding activities that may involve multiple substrates or ligands. We show that our method can be used to predict enzyme functions with accuracy. Our method can also assess enzyme binding specificity and promiscuity. In an objective large-scale test of 100 enzyme families with thousands of structures, our predictions are found to be sensitive and specific: At the stringent specificity level of 99.98\%, we can correctly predict enzyme functions for 80.55\% of the proteins. The overall area under the receiver operating characteristic curve measuring the performance of our prediction is 0.955, close to the perfect value of 1.00. The best Matthews coefficient is 86.6\%. Our method also works well in predicting the biochemical functions of orphan proteins from structural genomics projects.}, author = {Tseng, Yan Y. and Dundas, Joseph and Liang, Jie}, citeulike-article-id = {4209212}, citeulike-linkout-0 = {http://dx.doi.org/10.1016/j.jmb.2008.12.072}, citeulike-linkout-1 = {http://linkinghub.elsevier.com/retrieve/pii/S0022283608016094}, day = {27}, doi = {10.1016/j.jmb.2008.12.072}, issn = {00222836}, journal = {Journal of Molecular Biology}, keywords = {evolution, function, prediction, protein, structure}, month = {March}, number = {2}, pages = {451--464}, posted-at = {2009-04-01 15:47:21}, priority = {0}, title = {Predicting Protein Function and Binding Profile via Matching of Local Evolutionary and Geometric Surface Patterns}, url = {http://dx.doi.org/10.1016/j.jmb.2008.12.072}, volume = {387}, year = {2009} }

TY - JOUR ID - citeulike:4209212 L3 - citeulike-article-id:4209212 N2 - Inferring protein functions from structures is a challenging task, as a large number of orphan protein structures from structural genomics project are now solved without their biochemical functions characterized. For proteins binding to similar substrates or ligands and carrying out similar functions, their binding surfaces are under similar physicochemical constraints, and hence the sets of allowed and forbidden residue substitutions are similar. However, it is difficult to isolate such selection pressure due to protein function from selection pressure due to protein folding, and evolutionary relationship reflected by global sequence and structure similarities between proteins is often unreliable for inferring protein function. We have developed a method, called pevoSOAR (pocket-based evolutionary search of amino acid residues), for predicting protein functions by solving the problem of uncovering amino acids residue substitution pattern due to protein function and separating it from amino acids substitution pattern due to protein folding. We incorporate evolutionary information specific to an individual binding region and match local surfaces on a large scale with millions of precomputed protein surfaces to identify those with similar functions. Our pevoSOAR method also generates a probablistic model called the computed binding a profile that characterizes protein-binding activities that may involve multiple substrates or ligands. We show that our method can be used to predict enzyme functions with accuracy. Our method can also assess enzyme binding specificity and promiscuity. In an objective large-scale test of 100 enzyme families with thousands of structures, our predictions are found to be sensitive and specific: At the stringent specificity level of 99.98%, we can correctly predict enzyme functions for 80.55% of the proteins. The overall area under the receiver operating characteristic curve measuring the performance of our prediction is 0.955, close to the perfect value of 1.00. The best Matthews coefficient is 86.6%. Our method also works well in predicting the biochemical functions of orphan proteins from structural genomics projects. IS - 2 JF - Journal of Molecular Biology SN - 00222836 EP - 464 TI - Predicting Protein Function and Binding Profile via Matching of Local Evolutionary and Geometric Surface Patterns VL - 387 SP - 451 KW - evolution KW - function KW - prediction KW - protein KW - structure AU - Tseng, Yan AU - Dundas, Joseph AU - Liang, Jie PY - 2009/03/27/ UR - http://dx.doi.org/10.1016/j.jmb.2008.12.072 ER -