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Prediction of the conformation and geometry of loops in globular proteins: Testing ArchDB, a structural classification of loops
Author(s) -
FernandezFuentes Narcis,
Querol Enrique,
Aviles Francesc X.,
Sternberg Michael J. E.,
Oliva Baldomero
Publication year - 2005
Publication title -
proteins: structure, function, and bioinformatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.699
H-Index - 191
eISSN - 1097-0134
pISSN - 0887-3585
DOI - 10.1002/prot.20516
Subject(s) - loop modeling , protein secondary structure , structural alignment , globular protein , protein structure prediction , threading (protein sequence) , homology modeling , loop (graph theory) , computer science , sequence (biology) , protein structure , ab initio , structural motif , structural bioinformatics , algorithm , sequence alignment , artificial intelligence , mathematics , peptide sequence , biology , crystallography , combinatorics , physics , chemistry , genetics , biochemistry , enzyme , quantum mechanics , gene
In protein structure prediction, a central problem is defining the structure of a loop connecting 2 secondary structures. This problem frequently occurs in homology modeling, fold recognition, and in several strategies in ab initio structure prediction. In our previous work, we developed a classification database of structural motifs, ArchDB. The database contains 12,665 clustered loops in 451 structural classes with information about ϕ–ψ angles in the loops and 1492 structural subclasses with the relative locations of the bracing secondary structures. Here we evaluate the extent to which sequence information in the loop database can be used to predict loop structure. Two sequence profiles were used, a HMM profile and a PSSM derived from PSI‐BLAST. A jack‐knife test was made removing homologous loops using SCOP superfamily definition and predicting afterwards against recalculated profiles that only take into account the sequence information. Two scenarios were considered: (1) prediction of structural class with application in comparative modeling and (2) prediction of structural subclass with application in fold recognition and ab initio. For the first scenario, structural class prediction was made directly over loops with X‐ray secondary structure assignment, and if we consider the top 20 classes out of 451 possible classes, the best accuracy of prediction is 78.5%. In the second scenario, structural subclass prediction was made over loops using PSI‐PRED (Jones, J Mol Biol 1999;292:195–202) secondary structure prediction to define loop boundaries, and if we take into account the top 20 subclasses out of 1492, the best accuracy is 46.7%. Accuracy of loop prediction was also evaluated by means of RMSD calculations. Proteins 2005. © 2005 Wiley‐Liss, Inc.