Generalized comparative modeling (GENECOMP): A combination of sequence comparison, threading, and lattice modeling for protein structure prediction and refinement

Abstract

An improved generalized comparative modeling method, GENECOMP, for the refinement of threading models is developed and validated on the Fischer database of 68 probe-template pairs, a standard benchmark used to evaluate threading approaches. The basic idea is to perform ab initio folding using a lattice protein model, SICHO, near the template provided by the new threading algorithm PROSPECTOR. PROSPECTOR also provides predicted contacts and secondary structure for the template-aligned regions, and possibly for the unaligned regions by garnering additional information from other top-scoring threaded structures. Since the lowest-energy structure generated by the simulations is not necessarily the best structure, we employed two structure-selection protocols: distance geometry and clustering. In general, clustering is found to generate somewhat better quality structures in 38 of 68 cases. When applied to the Fischer database, the protocol does no harm and in a significant number of cases improves upon the initial threading model, sometimes dramatically. The procedure is readily automated and can be implemented on a genomic scale.

Protein fragment reconstruction using various modeling techniques

“…Cαs are treated implicitly (with their approximate coordinates computed from the positions of the three closest side chains) and provide a framework for definition of interactions that mimic the effect of the main chain hydrogen bonds. Here, in contrast to numerous previous applications of the SICHO model [13][14][15][16][17][18][19][20][21], only statistical potentials (the same for all proteins) were used. The generic force field contains the following components: sequence independent local conformational bias towards protein-like conformational stiffness, a model of main chain hydrogen bonding (dependent on the mutual positions of the Cα atoms), sequence dependent shortrange secondary preferences, orientation dependent pairwise interactions of the side chains, a one-body centrosymmetric burial potential and multibody potentials mimicking the hydrophobic effect and an implicit solvent.…”

“…Recently, we developed a series of reduced protein models [13,14] (lattice and off-lattice) that proved to be very useful tools for studying the protein folding process [15] and the prediction of protein structure [16][17][18][19][20][21][22]. These models have different geometric resolution, but similar force fields.…”

Evolution and Physics in Comparative Protein Structure Modeling

“…12,13 A third group of methods uses either distance geometry or optimization techniques to satisfy spatial restraints obtained from the sequence-template alignment. [14][15][16] There are also many methods that specialize in the modeling of loops [17][18][19] and side chains 20,21 within the restrained environment provided by the rest of the structure.…”

“…For example, statistical potentials of mean force have been used successfully to assess comparative models, 26 to improve the accuracy of loop models, 17 and to improve the overall model accuracy. 16,27,43 In contrast to the sequentially local restraints, however, the nonbonded interactions between protein atoms are probably not modeled sufficiently accurately and their description needs to be improved.…”

Protein structure prediction based on fragment assembly and parameter optimization

“…The information on the tertiary structure of a protein is quite crucial in understanding the function and biological role of the protein. Popular approaches to this problem include comparative modeling [1][2][3][4][5] and fold recognition [6][7][8][9], which are classified as knowledge-based methods [5,10,11]. These methods use statistical information on sequences and their three-dimensional structures, in structural databases such as Protein Data Bank (PDB), to predict the unknown structure of a protein.…”