Comparison of conformational characteristics in structurally similar protein pairs

Flores, Tomas P.; Orengo, Christine A.; Moss, David S.; Thornton, Janet M.

doi:10.1002/pro.5560021104

Cited by 172 publications

(146 citation statements)

References 44 publications

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“…To obtain more precise estimates, data on the analysis of the correlation of the identity fraction q and the RMS deviation ⌬, made by three independent research groups Table 2, data on all topologically equivalent residue pairs); Flores et al (1993, Fig. 1a, Table 1)] are used.…”

Section: Parameter Estimation and Discussionmentioning

confidence: 99%

“…Therefore, the variance of the RMS deviation between any two proteins with the same d (for example, for identical proteins, where d ‫ס‬ 0) should be small. Analysis of protein structures shows that it is not true, and the RMS deviations vary substantially with protein pairs Hubbard and Blundell 1987;Flores et al 1993). Identical proteins (Table 1 in Flores et al (1993), for proteins refined to a resolution 2 Å or better only) of average length of 60 residues have ⌬ 0 2 ≈ 0.2 ± 0.13.…”

Section: Limited Independent Diffusionmentioning

confidence: 99%

“…Analysis of protein structures shows that it is not true, and the RMS deviations vary substantially with protein pairs Hubbard and Blundell 1987;Flores et al 1993). Identical proteins (Table 1 in Flores et al (1993), for proteins refined to a resolution 2 Å or better only) of average length of 60 residues have ⌬ 0 2 ≈ 0.2 ± 0.13. The model of independent diffusion predicts a value of standard deviation to be √(2 · 0.2 2 )/(60) = 0.04.…”

Section: Limited Independent Diffusionmentioning

confidence: 99%

“…The correlation between RMS (root mean square) deviation in C ␣ atom coordinates ⌬ ij and fraction of identical residues q ij was analyzed Lesk and Chotia 1986;Hubbard and Blundell 1987;Flores et al 1993;Gutin and Badretdinov 1994). This correlation was rationalized by an empirical exponential formula and in a more sophisticated way, based on similarities of substitution process with the diffusion in a multidimensional space (Gutin and Badretdinov 1994).…”

Section: Introductionmentioning

confidence: 99%

“…In this article, the latter approach is developed: corrections are made for the upper limit of RMS deviation between two proteins with similar fold and for correlation between residues linked in a polypeptide chain. Using the derived relation between evolutionary distance d ij and RMS deviation ⌬ ij , and experimental data on correlation of identity fraction q ij and RMS deviation ⌬ ij Hubbard and Blundell 1987;Flores et al 1993), d ij is estimated as a function of q ij . This estimate uses comparisons of three-dimensional structures and evolutionary models in terms of structural changes and thus differs methodologically from the others (Dayhoff et al 1978-like sequence-based models).…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Estimation of evolutionary distances from protein spatial structures

Grishin

1997

J Mol Evol

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Abstract. New equations are derived to estimate the number of amino acid substitutions per site between two homologous proteins from the root mean square (RMS) deviation between two spatial structures and from the fraction of identical residues between two sequences. The equations are based on evolutionary models, analyzing predominantly structural changes and not sequence changes. Evolution of spatial structure is treated as a diffusion in an elastic force field. Diffusion accounts for structural changes caused by amino acid substitutions, and elastic force reflects selection, which preserves protein fold. Obtained equations are supported by analysis of protein spatial structures.

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Section: Parameter Estimation and Discussionmentioning

confidence: 99%

Section: Limited Independent Diffusionmentioning

confidence: 99%

Section: Limited Independent Diffusionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Estimation of evolutionary distances from protein spatial structures

Grishin

1997

J Mol Evol

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How many fold types of protein are there in nature?

Wang

1996

Proteins

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Many protein structures have now been determined and reveal that protein molecules can adopt the same fold despite having very different sequences. It has been suggested that, owing to different stereochemical constraints, the number of ways that a sequence can fold may be limited. Therefore, it is reasonable to ask how many fold types exist in nature. Several groups have tackled this problem with very different results. In the present study, a novel statistical sampling approach is used to reestimate this number. The results suggest that the number of protein folds in nature is probably several hundreds.

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Assessment of comparative modeling in CASP2

Martin¹,

MacArthur²,

Thornton³

1997

Proteins

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An assessment is presented for all submissions to the comparative modeling challenge in the 1996 Critical Assessment of Structure Prediction (CASP2). Of the original 12 target structures, 9 were solved prior to the meeting: 8 by X‐ray crystallography and 1 by NMR spectroscopy. These targets varied over a large range of difficulty, as assessed by the percentage sequence identity with the principal parent structure, which ranged from 20% up to 85%. The overall quality of the models reflected the similarity of the principal parent. As expected, when the sequence alignment was correct, the core was accurately modeled, with the largest deviations occurring in the loops. Models were built which gave Cα root‐mean‐square deviations (RMSDs) compared with the observed structure of <1 Å for targets with high parental similarity; even at 26% sequence identity, the best model structures had Cα deviations of only 2.2 Å. Overall, these deviations are comparable with those observed between the parent structure and the target, but locally there are several examples where the model approaches closer to the target than does the parent. There were three targets below 25% sequence identity, and the models generated for these targets were, in general, significantly less accurate. This principally reflects errors in the alignment which, if systematically shifted, can generate Cα RMSDs <18 Å. Compared with CASP1, the geometry of the models was significantly improved with no D‐amino acids. By far the major contribution to RMSD error was the alignment accuracy, which varied from 100% down to 7% over the range of targets. In the structurally variable regions, global shifts, caused by hinge bending, were the major source of error, giving significantly lower local RMSDs than global RMSDs. In over 50% of these noncore regions, the difference between global and local RMSDs was more than 3 Å, and was as high as 10 Å for one structurally variable region. For the side chains, the χ1 RMSDs are strongly correlated with the Cα RMSDs. For models with Cα deviations less than 1 Å, on average 78.5% of side chains are placed in the correct rotamer, although the χ1 RMSDs, though clearly better than random, were disappointing at around 46°. As the backbone deviations increased, the side chain placement became less accurate, with an average χ1 RMSD of 75° on a 1.5–2.5 Å Cα backbone (average 51.4% correct rotamer). Refinement by energy minimization or molecular dynamics made only minor adjustments to improve local geometry and generally made small, but not significant, improvements to the RMSD. In total, 19 groups submitted 62 models (89 coordinate sets) that could be assessed. Most modelers used manual adjustments to sequence alignments and, in general, good alignments were obtained down to 25% sequence identity. The modeling methods ranged from “classical” modeling, involving core building followed by loop and side chain addition, to more sophisticated approaches based on probability distributions, Monte Carlo sampling or distance geometry. For ea...

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Comparison of conformational characteristics in structurally similar protein pairs

Cited by 172 publications

References 44 publications

Estimation of evolutionary distances from protein spatial structures

Estimation of evolutionary distances from protein spatial structures

How many fold types of protein are there in nature?

Assessment of comparative modeling in CASP2

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