Contact potential that recognizes the correct folding of globular proteins

Maiorov, Vladimir N.; Grippen, Gordon M.

doi:10.1016/0022-2836(92)90228-c

Cited by 336 publications

(259 citation statements)

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“…[17][18][19][20][21][22][23][24][25] This requirement is also a necessary condition for the simulated annealing to lead to the native state from an open conformation. Thus…”

Section: Perceptron Methods For Learning the Interaction Potentialsmentioning

confidence: 99%

Assembly of protein tertiary structures from secondary structures using optimized potentials

et al. 2003

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We present a simulated annealingbased method for the prediction of the tertiary structures of proteins given knowledge of the secondary structure associated with each amino acid in the sequence. The backbone is represented in a detailed fashion whereas the sidechains and pairwise interactions are modeled in a simplified way, following the LINUS model of Srinivasan and Rose. A perceptron-based technique is used to optimize the interaction potentials for a training set of three proteins. For these proteins, the procedure is able to reproduce the tertiary structures to below 3 Å in root mean square deviation (rmsd) from the PDB targets. We present the results of tests on twelve other proteins. For half of these, the lowest energy decoy has a rmsd from the native state below 6 Å and, in 9 out of 12 cases, we obtain decoys whose rmsd from the native states are also well below 5 Å. Proteins 2003;52:155-165.

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“…[17][18][19][20][21][22][23][24][25] This requirement is also a necessary condition for the simulated annealing to lead to the native state from an open conformation. Thus…”

Section: Perceptron Methods For Learning the Interaction Potentialsmentioning

confidence: 99%

Assembly of protein tertiary structures from secondary structures using optimized potentials

et al. 2003

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“…In this approach, the amino acid sequence of a protein of unknown structure is compared with a library of known structures, and a likely structure is identified from the library. Recently, various groups developed fold recognition methods (e.g., Sippl, 1990;Bowie et al, 1991;Godzik et al, 1992;Jones et al, 1992;Maiorov & Crippen, 1992;Bryant & Lawrence, 1993;Ouzounis et al, 1993;Wilmanns & Eisenberg, 1993;Kocher et al, 1994). We also developed a method that evaluates the sequencestructure compatibility in terms of side-chain packing, solvation, hydrogen bonding, and local conformation (Nishikawa & Matsuo, 1993;Matsuo & Nishikawa, 1994a, 1994b, 1995Matsuo et ai., 1995).…”

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confidence: 99%

A distant evolutionary relationship between bacterial sphingomyelinase and mammalian DNase I

et al. 1996

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The three-dimensional structure of bacterial sphingomyelinase (SMase) was predicted using a protein fold recognition method; the search of a library of known structures showed that the SMase sequence is highly compatible with the mammalian DNase I structure, which suggested that SMase adopts a structure similar to that of DNase I. The amino acid sequence alignment based on the prediction revealed that, despite the lack of overall sequence similarity (less than 10% identity), those residues of DNase I that are involved in the hydrolysis of the phosphodiester bond, including two histidine residues (His 134 and His 252) of the active center, are conserved in SMase. In addition, a conserved pentapeptide sequence motif was found, which includes two catalytically critical residues, Asp 251 and His 252. A sequence database search showed that the motif is highly specific to mammalian DNase I and bacterial SMase. The functional roles of SMase residues identified by the sequence comparison were consistent with the results from mutant studies. Two Bacillus cereus SMase mutants (H134A and H252A) were constructed by site-directed mutagenesis. They completely abolished their catalytic activity. A model for the SMase-sphingomyelin complex structure was built to investigate how the SMase specifically recognizes its substrate. The model suggested that a set of residues conserved among bacterial SMases, including Trp 28 and Phe 55, might be important in the substrate recognition. The predicted structural similarity and the conservation of the functionally important residues strongly suggest a distant evolutionary relationship between bacterial SMase and mammalian DNase I. These two phosphodiesterases must have acquired the specificity for different substrates in the course of evolution.

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“…In protein folding studies, knowledge-based potentials derived from a statistical analysis of known protein structures (Ueda et al, 1978;Maiorov & Crippen, 1992;Kolinski & Skolnick, 1994;Sippl, 1995; Mimy & Domany, 1996;Miyazawa & Jernigan, 1996;Liwo et al, 1997;Park et al, 1997) are frequently used in simplified models of proteins. The quality of such potentials is often assessed by socalled Z-scores, which test how well the potentials differentiate the native fold of a protein from an ensemble of misfolded structures.…”

Section: Abstract: Knowledge-based Potentials; Protein Folding; Z-scoresmentioning

confidence: 99%

What should the Z‐score of native protein structures be?

Zhang

Skolnick

1998

Protein Science

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The Z-score of a protein is defined as the energy separation between the native fold and the average of an ensemble of misfolds in the units of the standard deviation of the ensemble. The Z-score is often used as a way of testing the knowledge-based potentials for their ability to recognize the native fold from other alternatives. However, it is not known what range of values the Z-scores should have if one had a correct potential. Here, we offer an estimate of Z-scores extracted from calorimetric measurements of proteins. The energies obtained from these experimental data are compared with those from computer simulations of a lattice model protein. It is suggested that the Z-scores calculated from different knowledge-based potentials are generally too small in comparison with the experimental values. Keywords: knowledge-based potentials; protein folding; Z-scoresIn protein folding studies, knowledge-based potentials derived from a statistical analysis of known protein structures (Ueda et al., 1978;Maiorov & Crippen, 1992;Kolinski & Skolnick, 1994;Sippl, 1995; Mimy & Domany, 1996;Miyazawa & Jernigan, 1996;Liwo et al., 1997;Park et al., 1997) are frequently used in simplified models of proteins. The quality of such potentials is often assessed by socalled Z-scores, which test how well the potentials differentiate the native fold of a protein from an ensemble of misfolded structures. These Z-scores are calculated by (Bowie et al., 1991;Sippl, 1993): where Enotive is the energy of the native structure of a protein, is the average energy of an ensemble of misfolded structures, and arn,,f;,/ds is the standard deviation of the energy in this ensemble.However, it is not known what range of values of Z-scores should be expected for real proteins with exact potentials. Table 1 lists the Z-score ranges calculated from several typical knowledgebased potentials reported in the literature. The Z-scores vary tremendously from protein to protein. The widest spread of Z-scores was found for hydrophobic fitness potentials (Huang et al., 1996), which give a range between 1 and 24 for similar sized proteins, although on average the Z-score is quite high, around 11. In fact, because many approximations are introduced in the derivation of the knowledge-based potentials, there is a great deal of uncertainty as to the validity of these potentials (Jones & Thornton, 1996; Reprint requests to: Jeffrey Skolnick, Department of Molecular Biology. TPC-5, The Scripps Research Institute, 10550 North Torrey Pines Road, La Jolla, California 92037; e-mail: skolnick@scripps.edu.Pereira & Pochapsky, 1996;Thomas & Dill, 1996;Skolnick et al., 1997;Wang & Ben-Naim, 1997). This conflicting situation calls for examination of the physical meaning of the Z-scores.In this paper, we suggest a means of extracting Z-scores from the experimental data of proteins. Such Z-scores are defined differently than Zmr,,f,,,d.y, but it is possible to relate the two quantities. Thus, it is hoped that the results presented here can provide guidance into reaso...

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Contact potential that recognizes the correct folding of globular proteins

Cited by 336 publications

References 13 publications

Assembly of protein tertiary structures from secondary structures using optimized potentials

Assembly of protein tertiary structures from secondary structures using optimized potentials

A distant evolutionary relationship between bacterial sphingomyelinase and mammalian DNase I

What should the Z‐score of native protein structures be?

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