Hierarchical algorithm for computer modeling of protein tertiary structure: folding of myoglobin to 6.2 .ANG. resolution

Gunn, John R.; Monge, Alessandro; Friesner, Richard A.; Marshall, Colin

doi:10.1021/j100053a053

Cited by 40 publications

(27 citation statements)

References 4 publications

(4 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Minimization was carried out using two stages of Monte Carlo-simulated annealing on a high-resolution lattice. Results similar to those reported by Smith-Brown et al (1993) were obtained (4.0-5.6 A Ê RMS) using fewer distance constraints (10-35 total).Using a hierarchical Monte Carlo-based algorithm we have obtained low-resolution (5-6 A Ê RMS) structures for proteins such as myoglobin (Gunn et al, 1994), BPTI (Standley et al, 1998), and CTF (Monge et al, 1995) given a knowledge of secondary structure alone. Our results con®rm that applying the statistical energy function we used to typical protein folds leads to a small number of low energy basins of attraction, one of which is generally in the native region; however, the native region is not always lowest in energy.…”

supporting

confidence: 71%

See 1 more Smart Citation

A branch and bound algorithm for protein structure refinement from sparse NMR data sets

Standley

Eyrich

Felts

et al. 1999

Journal of Molecular Biology

Self Cite

View full text Add to dashboard Cite

We describe new methods for predicting protein tertiary structures to low resolution given the speci®cation of secondary structure and a limited set of long-range NMR distance constraints. The NMR data sets are derived from a realistic protocol involving completely deuterated 15 N and 13 C-labeled samples. A global optimization method, based upon a modi®cation of the aBB (branch and bound) algorithm of Floudas and co-workers, is employed to minimize an objective function combining the NMR distance restraints with a residue-based protein folding potential containing hydrophobicity, excluded volume, and van der Waals interactions. To assess the ef®cacy of the new methodology, results are compared with benchmark calculations performed via the X-PLOR program of Bru È nger and co-workers using standard distance geometry/molecular dynamics (DGMD) calculations. Seven mixed a/b proteins are examined, up to a size of 183 residues, which our methods are able to treat with a relatively modest computational effort, considering the size of the conformational space. In all cases, our new approach provides substantial improvement in root-mean-square deviation from the native structure over the DGMD results; in many cases, the DGMD results are qualitatively in error, whereas the new method uniformly produces high quality low-resolution structures. The DGMD structures, for example, are systematically non-compact, which probably results from the lack of a hydrophobic term in the X-PLOR energy function. These results are highly encouraging as to the possibility of developing computational/ NMR protocols for accelerating structure determination in larger proteins, where data sets are often underconstrained. # 1999 Academic PressKeywords: protein folding; global optimization; annealing; distance restraints *Corresponding author IntroductionEnergy minimization in the absence of longrange constraints has not yet contributed to the determination of new protein structures. Two requirements for a solution to this problem are (1) a potential energy function which is accurate enough to distinguish the native from all other conformations; and (2) a means of globally minimizing such a function. In the absence of an accurate energy function, some effort has been spent on augmenting existing functions with experimentally derived constraints. Provided the quality and quantity of the constraints are suf®cient to guarantee a global minimum near the native conformation, the augmented energy functions (or target functions) may be useful for testing and developing minimization algorithms. The resulting constrained energy minimization (CEM) techniques avoid one serious problem associated with unconstrained minimization, which is that the global minimum is not, in general, known.In addition to providing an objective target for optimization, CEM is immediately applicable to the problem of NMR structure re®nement. Typi-E-mail address of the corresponding author: rich@chem.columbia.edu Abbreviations used: DGMD, distance geometry/ molecular dynamics; CEM,...

show abstract

supporting

confidence: 71%

“…The model and a previous version of the potential have been described elsewhere (Gunn et al, 1994). Signi®cant modi®cations to the energy function, as well as the new optimization algorithm, are presented for the ®rst time.…”

Section: Protein Structure Refinement Protocols Overviewmentioning

confidence: 99%

A branch and bound algorithm for protein structure refinement from sparse NMR data sets

Standley

Eyrich

Felts

et al. 1999

Journal of Molecular Biology

Self Cite

View full text Add to dashboard Cite

show abstract

“…The feasibility of biasing search algorithms with secondary structure knowledge was ®rst explored using``exact'' secondary structure, as observed in the experimental native conformation. In this regard, using an off-lattice model and exact knowledge of the native secondary structure, Friesner et al (1996) have obtained very encouraging results, successfully folding two four-helix bundles (cytochrome b562 (B256) and myohemerythrin (2MHR)), a large a-helical protein (myoglobin (1MBO)), and a relatively complicated a/b fold, the C-terminal domain of the L7/L12 50 S ribosomal protein (1CTF) (Friesner & Gunn, 1996;Gunn et al, 1994). Similarly, Dandekar & Argos (1994, using a genetic algorithm to search conformational space, obtained encouraging results for a test set of 19 small proteins, including all a and some a/b proteins.…”

Section: Introductionmentioning

confidence: 99%

Fold assembly of small proteins using Monte Carlo simulations driven by restraints derived from multiple sequence alignments

Ortíz

Koliński

Skolnick

1998

Journal of Molecular Biology

View full text Add to dashboard Cite

The feasibility of predicting the global fold of small proteins by incorporating predicted secondary and tertiary restraints into ab initio folding simulations has been demonstrated on a test set comprised of 20 nonhomologous proteins, of which one was a blind prediction of target 42 in the recent CASP2 contest. These proteins contain from 37 to 100 residues and represent all secondary structural classes and a representative variety of global topologies. Secondary structure restraints are provided by the PHD secondary structure prediction algorithm that incorporates multiple sequence information. Predicted tertiary restraints are derived from multiple sequence alignments via a two-step process. First, seed side-chain contacts are identi®ed from correlated mutation analysis, and then a threading-based algorithm is used to expand the number of these seed contacts. A lattice-based reduced protein model and a folding algorithm designed to incorporate these predicted restraints is described. Depending upon fold complexity, it is possible to assemble native-like topologies whose coordinate root-mean-square deviation from native is between 3.0 A Ê and 6.5 A Ê . The requisite level of accuracy in side-chain contact map prediction can be roughly 25% on average, provided that about 60% of the contact predictions are correct within AE1 residue and 95% of the predictions are correct within AE4 residues. Precision in tertiary contact prediction is more critical than absolute accuracy. Furthermore, only a subset of the tertiary contacts, on the order of 25% of the total, is suf®cient for successful topology assembly. Overall, this study suggests that the use of restraints derived from multiple sequence alignments combined with a fold assembly algorithm holds considerable promise for the prediction of the global topology of small proteins.

show abstract

“…It has been shown that, on the basis of the hydrophobicity or conservation of amino acids, as well as the secondary structure, the approximate structure of small proteins can be determined (Gunn et al, 1994;Hanggi & Braun, 1994;Asz6di et al, 1995). Hydrophobicity-based potentials have also helped approximate sbucture determination of protein fragments (Srinivasan & Rose, 1995).…”

Section: Discussionmentioning

confidence: 99%

Relationship between protein structure and geometrical constraints

et al. 1996

View full text Add to dashboard Cite

We evaluate to what extent the structure of proteins can be deduced from incomplete knowledge of disulfide bridges, surface assignments, secondary structure assignments, and additional distance constraints. A cost function taking such constraints into account was used to obtain protein structures using a simple minimization algorithm. For small proteins, the approximate structure could be obtained using one additional distance constraint for each amino acid in the protein.We also studied the effect of using predicted secondary structure and surface assignments. The constraints used in this approach typically may be obtained from low-resolution experimental data. When using a cost function based on distances, half of the resulting structures will be mirrored, because the resulting structure and its mirror image will have the same cost. The secondary structure assignments were therefore divided into chirality constraints and distance constraints. Here we report that the problem of mirrored structures, in some cases, can be solved by using a chirality term in the cost function.

show abstract

Hierarchical algorithm for computer modeling of protein tertiary structure: folding of myoglobin to 6.2 .ANG. resolution

Cited by 40 publications

References 4 publications

A branch and bound algorithm for protein structure refinement from sparse NMR data sets

A branch and bound algorithm for protein structure refinement from sparse NMR data sets

Fold assembly of small proteins using Monte Carlo simulations driven by restraints derived from multiple sequence alignments

Relationship between protein structure and geometrical constraints

Contact Info

Product

Resources

About