Estimation and Use of Protein Backbone Angle Probabilities

Kang, Hong Seok; Kurochkina, Natalya; Lee, B.

doi:10.1006/jmbi.1993.1045

Cited by 61 publications

(43 citation statements)

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“…Secondary structure predictions have also been incorporated into ab initio structure prediction methods (14)(15)(16)(17). Again the results are encouraging, with several structures predicted in the low-resolution range (14,15,18,19).…”

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confidence: 94%

Toward predicting protein topology: An approach to identifying β hairpins

Cruz

Hutchinson

Shepherd

et al. 2002

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

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Although secondary structure prediction methods have recently improved, progress from secondary to tertiary structure prediction has been limited. A promising but largely unexplored route to this goal is to predict structure motifs from secondary structure knowledge. Here we present a novel method for the recognition of ␤ hairpins that combines secondary structure predictions and threading methods by using a database search and a neural network approach. The method successfully predicts 48 and 77%, respectively, of all of hairpin and nonhairpin ␤-coil-␤ motifs in a protein database. We find that the main contributors to motif recognition are predicted accessibility and turn propensities.

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mentioning

confidence: 94%

Toward predicting protein topology: An approach to identifying β hairpins

Cruz

Hutchinson

Shepherd

et al. 2002

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

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“…These frequencies generate a statistical potential that accurately reproduces the known helical, ␤-sheet, and PPII propensities (25). The statistical potential can be chosen to include correlations between adjacent residues to account for both residue type and conformation, as emphasized in our previous studies (25,35) and other earlier studies of nearest neighbor (NN) effects (20,23,(36)(37)(38)(39)(40). After assigning backbone dihedral angles according to the statistical potential, unfolded chain conformations are slightly ''nudged'' to satisfy excluded volume constraints.…”

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confidence: 99%

Statistical coil model of the unfolded state: Resolving the reconciliation problem

Jha

Colubri

Freed

et al. 2005

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

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415

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An unfolded state ensemble is generated by using a self-avoiding statistical coil model that is based on backbone conformational frequencies in a coil library, a subset of the Protein Data Bank. The model reproduces two apparently contradicting behaviors observed in the chemically denatured state for a variety of proteins, random coil scaling of the radius of gyration and the presence of significant amounts of local backbone structure (NMR residual dipolar couplings). The most stretched members of our unfolded ensemble dominate the residual dipolar coupling signal, whereas the uniformity of the sign of the couplings follows from the preponderance of polyproline II and ␤ conformers in the coil library. Agreement with the NMR data substantially improves when the backbone conformational preferences include correlations arising from the chemical and conformational identity of neighboring residues. Although the unfolded ensembles match the experimental observables, they do not display evidence of nativelike topology. By providing an accurate representation of the unfolded state, our statistical coil model can be used to improve thermodynamic and kinetic modeling of protein folding. denatured state ͉ protein folding ͉ residual dipolar coupling ͉ nearest neighbor ͉ radius of gyration D enatured proteins are the initial state for mechanistic and thermodynamic studies of folding. The recent resurgence of interest in the unfolded state is partly motivated by the development of NMR methods that are capable of providing site-resolved structural information (1-7). These measurements indicate that unfolded proteins have far richer structural diversity than earlier believed, possibly even encoding the native topology (1,(8)(9)(10)(11).These works seem at odds with the classic studies by Tanford et al. (12,13), who demonstrated by using hydrodynamic methods that the global dimensions of denatured proteins exhibit the size dependence expected for self-avoiding ''random coil'' polymers. More recent measurements of the radius of gyration, R g , using small-angle scattering methods exhibit the same random coil scaling behavior with length R g ϰ N 0.585 (8,14). These observations are consistent with denatured proteins being random coils in good solvent conditions (15). This finding leads to the so-called ''reconciliation problem'' between the random coil scaling behavior and the presence of significant amounts of local structure in unfolded state (14, 16).However, Rose and Fitzkee (17) demonstrate that even a ''deliberately extreme'' model of chains composed of native-like segments connected by flexible residues also can reproduce random coil scaling behavior. Hence, the recapitulation of the scaling behavior provides only a weak test for any unfolded state model. Nevertheless, spectroscopic measurements, such as circular dichroism, indicate that most unfolded states, particularly chemicaldenatured proteins (8,13,18), have little secondary structure. Accordingly, the unrealistic native-like segment model is ruled out. More exacting...

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“…from frequencies of amino acids in search procedures aiming at finding the global free energy mini-contact [6,15,16], separated by a certain distance [17,18], mum and in the effective potentials used to evaluate the free solvent accessible [18,19] or adopting certain values of backenergies of the conformations. Recently, a number of solutions bone dihedral angles [20,21]. These potentials are particularly to the former problem have been proposed which suggests the well suited for structure prediction, where certain degrees of possibility of getting a satisfactory solution in the near future.…”

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confidence: 99%

Different derivations of knowledge‐based potentials and analysis of their robustness and context‐dependent predictive power

Rooman

Gilis

1998

European Journal of Biochemistry

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The possibility of defining effective potentials from known protein structures, which are sufficiently accurate to be used for protein-structure-prediction purposes, is investigated. Three types of distance potentials and three types of backbone torsion potentials are defined, based on propensities of amino acid pairs to be separated by a given spatial distance or to be associated to a backbone torsion angle domain. Their differences reside in the way the physical correlations between the amino acids and the conformational states are extracted from the bulk interactions due to the presence of many residues in a protein.For the distance potentials, a physical meaning can be associated to the different definitions, given that some of the potentials favor hydrophobic interactions and others favor interactions between oppositely charged residues. The performance of the different torsion and distance potentials in structure prediction procedures, in particular native-fold recognition and evaluation of protein stability changes upon point mutations, is analyzed. It appears to differ according to the specific proteins and protein environments. In particular, one of the distance potentials performs better than the others for membrane proteins and in protein regions involving charged residues, but less well in other protein regions. Furthermore, the dependence of the potentials on the characteristics of the proteins from which they are derived is analyzed. It is shown that the dependence of the potentials on the length, amino acid composition and secondarystructure content of the proteins from the dataset is either very limited or rather strong, according to the type of potential. The results obtained suggest that the main problem limiting the performance of database-derived potentials is their lack of universality : each potential describes with satisfactory accuracy only the interactions present in certain protein environments.Keywords : folding free energy; fold recognition; stability changes upon mutation; protein length.The success of protein-structure prediction from the amino rived from joint frequencies of sequence elements and structural acid sequence is limited by deficiencies in the conformational states in the dataset, e.g. from frequencies of amino acids in search procedures aiming at finding the global free energy mini-contact [6,15,16], separated by a certain distance [17,18], mum and in the effective potentials used to evaluate the free solvent accessible [18,19] or adopting certain values of backenergies of the conformations. Recently, a number of solutions bone dihedral angles [20,21]. These potentials are particularly to the former problem have been proposed which suggests the well suited for structure prediction, where certain degrees of possibility of getting a satisfactory solution in the near future. freedom may be neglected or must be neglected to keep comThe most promising procedure is constraint-based exhaustive puter time within reasonable limits. search [1,2] or branch-and-bound algorithm [3]....

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Estimation and Use of Protein Backbone Angle Probabilities

Cited by 61 publications

References 0 publications

Toward predicting protein topology: An approach to identifying β hairpins

Toward predicting protein topology: An approach to identifying β hairpins

Statistical coil model of the unfolded state: Resolving the reconciliation problem

Different derivations of knowledge‐based potentials and analysis of their robustness and context‐dependent predictive power

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