Modeling of protein loops by simulated annealing

Collura, Vincent; Higo, Junichi; Garnier, Jean

doi:10.1002/pro.5560020915

Cited by 84 publications

(59 citation statements)

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“…For five out of six systems used in this study, our results could be compared with those of Bruccoleri and Karplus (1987) and Collura et al (1993), as shown in Table 5. For FXN and PCY loops, we compare our results with the lowest energy loops listed in Table 7 of Bruccoleri and Karplus (1987).…”

Section: Resultsmentioning

confidence: 99%

“…For FXN and PCY loops, we compare our results with the lowest energy loops listed in Table 7 of Bruccoleri and Karplus (1987). For BPTI, MCP-L2, and MCP-H3, we compare our results with the lowest energy loops listed in Table 2 of Collura et al (1993). It should be noted that potential energy is used as the filtering criterion in both the above-cited works.…”

Section: Resultsmentioning

confidence: 99%

“…For FXN and PCY, we compare our results with the RMSDs corresponding to the lowest energy in Table 7 of Karplus and Bruccoleri (1987). For MCP-L2 and MCP-H3, we compare our results with the RMSDs corresponding to the lowest energy in Table 2 of Collura et al (1993).…”

Section: Resultsmentioning

confidence: 99%

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Modeling protein loops using a ϕ_i+1, Ψ_i dimer database

et al. 1995

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We present an automated method for modeling backbones of protein loops. The method samples a database of 4,+, and $; angles constructed from a nonredundant version of the Protein Data Bank (PDB). The dihedral angles c$j+l and $, completely define the backbone conformation of a dimer when standard bond lengths, bond angles, and a trans planar peptide configuration are used. For the 400 possible dimers resulting from 20 natural amino acids, a list of allowed 4,+, , $; pairs for each dimer is created by pooling all such pairs from the loop segments of each protein in the nonredundant version of the PDB. Starting from the N-terminus of the loop sequence, conformations are generated by assigning randomly selected pairs of $, for each dimer from the respective pool using standard bond lengths, bond angles, and a frans peptide configuration. We use this database to simulate protein loops of lengths varying from 5 to 11 amino acids in five proteins of known three-dimensional structures. Typically, 10,000-50,000 models are simulated for each protein loop and are evaluated for stereochemical consistency. Depending on the length and sequence of a given loop, 50-80Vo of the models generated have no stereochemical strain in the backbone atoms. We demonstrate that, when simulated loops are extended to include flanking residues from homologous segments, only very few loops from an ensemble of sterically allowed conformations orient the flanking segments consistent with the protein topology. The presence of near-native backbone conformations for loops from five different proteins suggests the completeness of the dimeric database for use in modeling loops of homologous proteins. Here, we take advantage of this observation to design a method that filters near-native loop conformations from an ensemble of sterically allowed conformations. We demonstrate that our method eliminates the need for a loop-closure algorithm and hence allows for the use of topological constraints of the homologous proteins or disulfide constraints to filter near-native loop conformations.Keywords: de novo folding; dihedral angles; homology modeling; knowledge-based method; loop modelingIn the absence of models derived from X-ray or N M R techniques, homology modeling methods remain as the single most effective way to obtain three-dimensional models of structurally uncharacterized proteins. Homology modeling relies on the observation that proteins in a given family have a similar tertiary topology. The polypeptide segments that are structurally conserved within a family of proteins are generally known as structurally conserved regions (SCRs) and those polypeptide segments that are variable are known as structurally variable regions (SVRs, herein termed "loops"). Although several methods exist for modeling SCRs, modeling SVRs remains as one of the most significant challenges in homology modeling.In its most basic form, homology modeling transfers structural information from one member of a family, designated as the template protein, to one or more m...

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

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Modeling protein loops using a ϕ_i+1, Ψ_i dimer database

et al. 1995

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“…Several taxonomy schemes have been developed for the classification of &turns (Wilmot & Thornton, 1990) and more general loop conformations (Ring et al, 1992). Efforts to predict the conformation of protein loops have included comparison to similar known loop structures (Chothia et al, 1989) conformational searches utilizing Monte Carlo, simulated annealing, and exhaustive grid search techniques (Fine et al, 1986;Bruccoleri et al, 1988;Collura et al, 1993). Further work toward the understanding of the dominant interactions that determine loop conformations is important to the future of protein engineering and rational drug design.…”

Section: Introductionmentioning

confidence: 99%

Stabilization of a strained protein loop conformation through protein engineering

1995

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Staphylococcal nuclease is found in two folded conformations that differ in the isomerization of the Lys 116-Pro 117 peptide bond, resulting in two different conformations of the residue 112-1 17 loop. The cis form is favored over the trans with an occupancy of 90%. Previous mutagenesis studies have shown that when Lys 116 is replaced by glycine, a trans conformation is stabilized relative to the cis conformation by the release of steric strain in the trans form. However, when Lys 116 is replaced with alanine, the resulting variant protein is identical to the wild-type protein in its structure and in the dominance of the cis configuration. The results of these studies suggested that any nuclease variant with a non-glycine residue at position 116 should also favor the cis form because of steric requirements of the 0-carbon at this position. In this report, we present a structural analysis of four nuclease variants with substitutions at position 116. Two variants, K116E and K116M, follow the "0-carbon" hypothesis by favoring the cis form. Furthermore, the crystal structure of KI 16E is nearly identical to that of the wild-type protein. Two additional variants, K116D and K116N, provide exceptions to this simple "0-carbon" rule in that the trans conformation is stabilized relative to the cis configuration by these substitutions. Crystallographic data indicate that this stabilization is effected through the addition of tertiary interactions between the side chain of position 116 with the surrounding protein and water structure. The detailed trans conformation of the K116D variant appears to be similar to the trans conformation observed in the K116G variant, suggesting that these two mutations stabilize the same conformation but through different mechanisms.Keywords: NMR; proline isomerism; protein engineering; staphylococcal nuclease In work toward rational design and redesign of protein molecules, determining the relationship between the amino acid sequence and the three-dimensional structure of protein loops and &turns is essential. These elements of protein structure comprise most of the surface of proteins (Richardson, 1981) and are often critical in protein function such as catalysis and molecular recognition (e.g., antigenhnmunoglobulin recognition; Foote & Winter, 1992). Because of their aperiodic structure, loops are the most difficult element of protein secondary structure to model. Several taxonomy schemes have been developed for the classification of &turns (Wilmot & Thornton, 1990) and more general loop conformations (Ring et al., 1992). Efforts to predict the conformation of protein loops have included comparison to similar known loop structures (Chothia et al., 1989) conformational searches utilizing Monte Carlo, simulated annealing, and exhaustive grid search techniques (Fine et al., 1986;Bruccoleri et al., 1988;Collura et al., 1993). Further work toward the understanding of the dominant interactions that determine loop conformations is important to the future of protein engineering and ration...

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“…Loop closure is obtained by using methods such as random tweak (17), energy penalties (18), or analytical closure (19). Loop prediction accuracy depends on the effectiveness of the conformational search procedure and on the quality of the force field used to evaluate the conformational energy.…”

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confidence: 99%

Evaluating conformational free energies: The colony energy and its application to the problem of loop prediction

Xiang

Soto²,

Honig³

2002

Proc. Natl. Acad. Sci. U.S.A.

353

398

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In this paper, we introduce a method to account for the shape of the potential energy curve in the evaluation of conformational free energies. The method is based on a procedure that generates a set of conformations, each with its own force-field energy, but adds a term to this energy that favors conformations that are close in structure (have a low rmsd) to other conformations. The sum of the force-field energy and rmsd-dependent term is defined here as the ''colony energy'' of a given conformation, because each conformation that is generated is viewed as representing a colony of points. The use of the colony energy tends to select conformations that are located in broad energy basins. The approach is applied to the ab initio prediction of the conformations of all of the loops in a dataset of 135 nonredundant proteins. By using an rmsd from a native criterion based on the superposition of loop stems, the average rmsd of 5-, 6-, 7-, and 8-residue long loops is 0.85, 0.92, 1.23, and 1.45 Å, respectively. force field ͉ energy minimization ͉ protein structure prediction P rotein loops are usually defined as segments of the polypeptide chain that do not contain regular units of secondary structure. Although some loops seem to serve as no more than connectors between secondary structure elements, others have been implicated as determinants of protein stability and folding pathways, while others may play important functional roles. The loop prediction problem involves finding the correct conformation for a given loop under the constraint that both ends are fixed through their connection to the rest of the protein. The problem has taken on considerable importance with the increased application of homology modeling methods in protein structure prediction. Although secondary structure elements can, in many cases, be predicted with considerable accuracy because they are often well conserved, sequence and structural variability are integral properties of many loops where, as in the case of antibodies, specificity differences among family members often reside. This variability makes the problem of loop prediction particularly complicated because, by its very nature, homology methods will often not be applicable. Indeed, loop prediction can to some extent be viewed as a mini ab initio folding problem, because the necessary information will not necessarily be found in databases. As such, the problem also serves as an important test of our understanding of the physical chemical principles that determine protein structure.Two general approaches have been applied to the prediction of loop conformation: database search and ab initio techniques. In the database search method (1-6), a library of segments derived from known protein structures is searched for conformations that fit the topological constraint of the loop stems. The stems correspond to the main-chain atoms that precede and follow the loop, but are not part of the loop itself. Loop candidates found in this way then can be evaluated by different criteria such as sequence...

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Modeling of protein loops by simulated annealing

Cited by 84 publications

References 38 publications

Modeling protein loops using a ϕ_i+1, Ψ_i dimer database

Modeling protein loops using a ϕ_i+1, Ψ_i dimer database

Stabilization of a strained protein loop conformation through protein engineering

Evaluating conformational free energies: The colony energy and its application to the problem of loop prediction

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Modeling of protein loops by simulated annealing

Cited by 84 publications

References 38 publications

Modeling protein loops using a ϕi+1, Ψi dimer database

Modeling protein loops using a ϕi+1, Ψi dimer database

Stabilization of a strained protein loop conformation through protein engineering

Evaluating conformational free energies: The colony energy and its application to the problem of loop prediction

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About

Modeling protein loops using a ϕ_i+1, Ψ_i dimer database

Modeling protein loops using a ϕ_i+1, Ψ_i dimer database