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Loopholes and missing links in protein modeling
Author(s) -
Rossi Karen A.,
Weigelt Carolyn A.,
Nayeem Akbar,
Krystek Stanley R.
Publication year - 2007
Publication title -
protein science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.353
H-Index - 175
eISSN - 1469-896X
pISSN - 0961-8368
DOI - 10.1110/ps.072887807
Subject(s) - loop (graph theory) , loop modeling , computer science , ranking (information retrieval) , loop tiling , prime (order theory) , rank (graph theory) , data mining , algorithm , protein structure , protein structure prediction , mathematics , biology , artificial intelligence , combinatorics , programming language , biochemistry , compiler
This paper provides an unbiased comparison of four commercially available programs for loop sampling, Prime, Modeler, ICM, and Sybyl, each of which uses a different modeling protocol. The study assesses the quality of results and examines the relative strengths and weaknesses of each method. The set of loops to be modeled varied in length from 4–12 amino acids. The approaches used for loop modeling can be classified into two methodologies: ab initio loop generation (Modeler and Prime) and database searches (Sybyl and ICM). Comparison of the modeled loops to the native structures was used to determine the accuracy of each method. All of the protocols returned similar results for short loop lengths (four to six residues), but as loop length increased, the quality of the results varied among the programs. Prime generated loops with RMSDs <2.5 Å for loops up to 10 residues, while the other three methods met the 2.5 Å criteria at seven‐residue loops. Additionally, the ability of the software to utilize disulfide bonds and X‐ray crystal packing influenced the quality of the results. In the final analysis, the top‐ranking loop from each program was rarely the loop with the lowest RMSD with respect to the native template, revealing a weakness in all programs to correctly rank the modeled loops.