Predicting the conformation of proteins man versus machine

Benner, Steven A.; Gerloff, Dietlind L.

doi:10.1016/0014-5793(93)81408-r

Cited by 27 publications

(12 citation statements)

References 38 publications

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“…Recently, several protein structures have been successfully predicted from the primary structure by using a combination of computational methods and human expertise (reviewed by Rost et al, 1993;Benner & Gerloff, 1993). These predictions appeared to be accurate when compared with subsequently determined crystal structures.…”

Section: Introductionmentioning

confidence: 97%

Molecular Evolution of the C-terminal Cytoplasmic Domain of a Superfamily of Bacterial Receptors Involved in Taxis

Moual

Koshland

1996

Journal of Molecular Biology

157

230

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Section: Introductionmentioning

confidence: 97%

Molecular Evolution of the C-terminal Cytoplasmic Domain of a Superfamily of Bacterial Receptors Involved in Taxis

Moual

Koshland

1996

Journal of Molecular Biology

157

230

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“…The picture that arises is that large-scale applicability (automatability) and physicochemical interpretability are somehow incompatible-a view emphasized by some proponents of the more manual methods (Benner & Gerloff, 1993;Benner et al, 1997). We certainly agree with the scientific necessity for using inspectable models to further understanding, and with the criticism of the current state-of-the-art automated schemes in this regard.…”

mentioning

confidence: 59%

Predicting protein secondary structure with probabilistic schemata of evolutionarily derived information

Thompson¹,

Goldstein²

1997

Protein Science

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We demonstrate the applicability of our previously developed Bayesian probabilistic approach for predicting residue solvent accessibility to the problem of predicting secondary structure. Using only single-sequence data, this method achieves a three-state accuracy of 67% over a database of 473 non-homologous proteins. This approach is more amenable to inspection and less likely to overlearn specifics of a dataset than "black box" methods such as neural networks. It is also conceptually simpler and less computationally costly.We also introduce a novel method for representing and incorporating multiple-sequence alignment information within the prediction algorithm, achieving 72% accuracy over a dataset of 304 non-homologous proteins. This is accomplished by creating a statistical model of the evolutionarily derived correlations between patterns of amino acid substitution and local protein structure. This model consists of parameter vectors, termed "substitution schemata," which probabilistically encode the structure-based heterogeneity in the distributions of amino acid substitutions found in alignments of homologous proteins. The model is optimized for structure prediction by maximizing the mutual information between the set of schemata and the database of secondary structures.Unlike "expert heuristic" methods, this approach has been demonstrated to work well over large datasets. Unlike the opaque neural network algorithms, this approach is physicochemically intelligible. Moreover, the model optimization procedure, the formalism for predicting one-dimensional structural features, and our previously developed method for tertiary structure recognition all share a common Bayesian probabilistic basis. This consistency starkly contrasts with the hybrid and ad hoc nature of methods that have dominated this field in recent years.Keywords: Bayesian statistics; evolutionary information; mutual information; probabilistic schemata; secondary structure prediction The prediction of protein secondary structure by a number of methods has benefitted from the use of aligned sets of homologous proteins. The patterns of conservation and variation in amino acid residue substitutions at a particular site in a protein convey implicit information about the long-range interactions involved in determining the local structure at that site. Various techniques have been devised to use this information to raise the three-state accuracies to around 70-72%. While this summary statistic is universally reported, a broader evaluation of prediction performance would consider both the practical and scientific value of the prediction scheme in terms of its statistical performance, physicochemical interpretability, and general applicability (reproducibility and robustness). Until now, the success of multiple-sequence alignment-based secondary structure prediction methods has been restricted to one or two of these attributes. The most common technique for using multiple-sequence alignments is the consensus method. This "signal averaging" appro...

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“…Recently, several successful predictions of folded protein structures have been made and announced before the experimental structures were known (see [2], [9]). While most of these have been made with a blend of a human expert's abilities and computer assistance, fully automated methods have shown promise for producing previously unattainable accuracy.…”

Section: P Hillips Rosen Walkementioning

confidence: 99%

Molecular structure determination by convex global underestimation of local energy minima

Phillips¹,

Rosen²,

Walke³

1995

Global Minimization of Nonconvex Energy Functions: Molecular Conformation and Protein F

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The determination of a stable molecular structure can often be formulated in terms of calculating the global (or approximate global) minimum of a potential energy function. Computing the global minimum of this function is very difficult because it typically has a very large number of local minima which may grow exponentially with molecule size. The optimization method presented involves collecting a large number of conformers, each attained by finding a local minimum of the potential energy function from a random starting point. The information from these conformers is then used to form a convex quadratic global underestimating function for the potential energy of all known conformers. This underestimator is an L 1 approximation to all known local minima, and is obtained by a linear programming formulation and solution. The minimum of this underestimator is used to predict the global minimum for the function, allowing a localized conformer search to be performed based on the predicted minimum. The new set of conformers generated by the localized search serves as the basis for another quadratic underestimation step in an iterative algorithm. This algorithm has been used to determine the structures of homopolymers of length n ≤ 30 with no sidechains. While it is estimated that there are O(3 n) local minima for a chain of length n, this method requires O(n 4) computing time on average. It is also shown that the global minimum potential energy values lie on a concave quadratic curve for n ≤ 30. This important property permits estimation of the minimum energy for larger molecules, and also can be used to accelerate the global minimization algorithm.

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Predicting the conformation of proteins man versus machine

Cited by 27 publications

References 38 publications

Molecular Evolution of the C-terminal Cytoplasmic Domain of a Superfamily of Bacterial Receptors Involved in Taxis

Molecular Evolution of the C-terminal Cytoplasmic Domain of a Superfamily of Bacterial Receptors Involved in Taxis

Predicting protein secondary structure with probabilistic schemata of evolutionarily derived information

Molecular structure determination by convex global underestimation of local energy minima

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