Related problems.
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Biological Science Disciplines↗
Biomedical subjects
A C May
Publications and source records attributed to A C May.
Amino acid substitution matrices from an artificial neural network model.
An amino acid substitution matrix specifies probabilities of substitutions for each pair of the 20 amino acids. Log-odds scores transformed from the values in substitution matrices are widely used to construct protein sequence alignments. Any given substitution matrix is suited to matching sequences diverged by a specific evolutionary distance. However, for a given set of sequences, it is not always clear what matrix should be used. We used an artificial neural network model to predict probabilities of amino acid substitutions with alignment samples of different evolutionary distances. From this internal description, substitution matrices suitable for detecting relationships at any chosen evolutionary distance can be instantly generated. By using the additional information of evolutionary distances, the average cross entropy error of our neural network model is lower than that of a series of BLOSUM and PET matrices over all testing sets. Our model is more accurate on the prediction of amino acid substitution probabilities.
Amino Acid Substitution↗
Optimal classification of protein sequences and selection of representative sets from multiple alignments: application to homologous families and lessons for structural genomics.
Hierarchical classification is probably the most popular approach to group related proteins. However, there are a number of problems associated with its use for this purpose. One is that the resulting tree showing a nested sequence of groups may not be the most suitable representation of the data. Another is that visual inspection is the most common method to decide the most appropriate number of subsets from a tree. In fact, classification of proteins in general is bedevilled with the need for subjective thresholds to define group membership (e.g., 'significant' sequence identity for homologous families). Such arbitrariness is not only intellectually unsatisfying but also has important practical consequences. For instance, it hinders meaningful identification of protein targets for structural genomics. I describe an alternative approach to cluster related proteins without the need for an a priori threshold: one, through its use of dynamic programming, which is guaranteed to produce globally optimal solutions at all levels of partition granularity. Grouping proteins according to weights assigned to their aligned sequences makes it possible to delineate dynamically a 'core-periphery' structure within families. The 'core' of a protein family comprises the most typical sequences while the 'periphery' consists of the atypical ones. Further, a new sequence weighting scheme that combines the information in all the multiply aligned positions of an alignment in a novel way is put forward. Instead of averaging over all positions, this procedure takes into account directly the distribution of sequence variability along an alignment. The relationships between sequence weights and sequence identity are investigated for 168 families taken from HOMSTRAD, a database of protein structure alignments for homologous families. An exact solution is presented for the problem of how to select the most representative pair of sequences for a protein family. Extension of this approach by a greedy algorithm allows automatic identification of a minimal set of aligned sequences. The results of this analysis are available on the Web at http://mathbio.nimr.mrc.ac.uk/~amay.
Algorithms↗
Retinal morphology and ERG response in the DBA/2NNia mouse model of angle-closure glaucoma.
PURPOSE: To document the time course of retinal dysfunction by scotopic electroretinography (ERG) and by quantitative morphology in eyes of the DBA/2NNia substrain of mouse (DBA) with inherited angle-closure glaucoma. METHODS: DBA and control C57BL/6J (C57) mice were studied by ERG recordings from 5 to 15 months of age, and by morphology from 1 to 14 months of age. Scotopic ERGs were simultaneously recorded from both eyes of dark-adapted anesthetized mice. Changes in the central neuronal retina were evaluated by quantitative morphometry performed on serial semithin sections of Epon-embedded eyes. RESULTS: When compared with normal C57 mice, DBA mice showed significant reductions of the a-wave and b-wave amplitudes by 7 to 8 months, and the decline continued as the animals aged. The b-wave implicit time in DBA mice showed a gradual prolongation beginning at 8 months of age, when compared with C57 mice. Logistic regression analyses revealed significant correlations in a- and b-wave amplitude reductions between ipsilateral and contralateral eyes of DBA mice at ages when ERG parameters were greatly altered. Morphologically, thinning of the whole retina was already evident in DBA mice at 4 months of age, but loss of ganglion cells and thinning of the outer plexiform layer were first seen in 7- to 8-month-old animals. These changes progressed to the end of the 13-month period studied. CONCLUSIONS: Progressive thinning of the outer retinal layers in DBA mice was found to correlate with decreases in ERG a- and b-wave amplitudes, both occurring from the age of 7 to 8 months onward. Similarities with the findings in human late-stage glaucomatous retinopathy indicate the relevance of this animal model in further glaucoma research.
Animals↗
Toward more meaningful hierarchical classification of protein three-dimensional structures.
Recently, several hierarchical classifications of protein three-dimensional (3D) structures have been published. However, none of them provides any assessment of the validity of a hierarchical representation or test individual clusters contained within. In fact, testing here of published trees reveals that they vary in meaning. Protein structure similarity measures are then assessed in terms of the robustness of the resulting trees for 24 protein families. A meaningful tree is defined as one in which all the clusters are found to be reliable according to a jackknife test. With the use of this criterion, a previously published similarity measure described as a "better RMS" is shown in fact to be usually less suited to protein fold classification than normal RMS after superposition. Here the "best" protein structure similarity measure for hierarchical classification-in terms of that which after clustering produces the highest number of meaningful trees, 20, for the 24 families-is found to be a new one. This measure includes information on the relationship of a distance at a given aligned position in a pair to the rest of the unique distances at that position in a protein family. There are only 2 families of the 24 tested, the globins (3 trees) and Kazal-type serine proteinase inhibitors (21 trees), in which the topology (branching order) of the meaningful 3D structure-based trees is constant. Thus, a new view of protein family sequence-structure relationships is afforded by comparing meaningful trees for each family. More generally, there is a need for care in interpretation of the results of those molecular biology algorithms that force a tree structure on data without assessing its applicability. Proteins 1999;37:20-29.
Classification↗
A cautionary note on interpretation of hierarchical classifications of protein folds.
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Glutamate Decarboxylase↗
Finding local structural similarities among families of unrelated protein structures: a generic non-linear alignment algorithm.
We have developed a generic tool for the automatic identification of regions of local structural similarity in unrelated proteins having different folds, as well as for defining more global similarities that result from homologous protein structures. The computer program GENFIT has evolved from the genetic algorithm-based three-dimensional protein structure comparison program GA_FIT. GENFIT, however, can locate and superimpose regions of local structural homology regardless of their position in a pair of structures, the fold topology, or the chain direction. Furthermore, it is possible to restrict the search to a volume centered about a region of interest (e.g., catalytic site, ligand-binding site) in two protein structures. We present a number of examples to illustrate the function of the program, which is a parallel processing implementation designed for distribution to multiple machines over a local network or to run on a single multiprocessor computer.
Algorithms↗
Towards more meaningful hierarchical classification of amino acid scoring matrices.
Hierarchical classifications of the 20 amino acids according to residue relationships within scoring matrices have not hitherto been tested for reliability. In fact, testing here of the residue groupings obtained thus from 18 published matrices shows that they vary considerably in reliability. This behaviour gives a new insight then into the matrices with respect to the relationships between the amino acid scores contained therein. For example, other than the trivial grouping of the 20 amino acids, no reliable residue groupings are present in all 18 matrix amino acid hierarchical classifications. Hierarchical classification of the 18 scoring matrices themselves is investigated in terms of matrix representation and choice of similarity and dissimilarity measures for matrix comparison. There is no absolute standard against which to compare a matrix clustering, of course, but it is possible to assess the usefulness of a measure for the purpose in terms of the reliability of the calculated tree. Matrix representation is shown to be important. Finally, a novel two-step approach for hierarchical classification of the 18 amino acid scoring matrices is described.
Amino Acids↗
Discrimination of common protein folds: application of protein structure to sequence/structure comparisons.
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Amino Acid Sequence↗
Pairwise iterative superposition of distantly related proteins and assessment of the significance of 3-D structural similarity.
A challenge lies in identifying distant protein 3-D structural similarity by rigid-body superposition. The most common measure of structural similarity is r.m.s. distance (r.m.s.d.) between topologically equivalent residues, and most automated methods of protein modelling rely on the assembly of rigid fragments from known 3-D structures. A fast method of improving the definition of a common protein fold by superposition, especially for distant relationships, is described. The definition of topological equivalence by the standard dynamic programming sequence alignment algorithm is extended by refining the entire structure alignment (not just those equivalenced residues within a given cut-off distance) and determining whether the alignment can be continued at the termini. The most appropriate distance-based definition of topological equivalence for a given comparison is identified. Despite the fact that hitherto the distant similarity between the globin fold and colicin A has not been recognized directly by rigid-body superposition, this new approach defines more equivalent residues with a lower r.m.s.d. between them than that obtained by the superposition of equivalences identified by a more elaborate method. A previous distance metric of 3-D structural similarity derived from rigid-body superposition has been extended to the assessment of superpositions where topological equivalences have been determined by methods other than rigid-body ones.
Algorithms↗
Improved genetic algorithm-based protein structure comparisons: pairwise and multiple superpositions.
Three major improvements to a previously described method for automatic protein structure comparison are described. First, a limit to translations for the rigid-body superposition is now assigned according to the dimensions of the structures being compared. Second, examination of the effect of the gap penalty on the derivation of a sequence alignment corresponding to a given structure superposition has led to a method to evaluate alternative structure-based sequence alignments. Third, the pairwise procedure has been generalized to multiple structure alignment. This implementation of rigid-body superposition can recognize well documented distant relationships which hitherto have required consideration of additional features and properties as well as those relationships between proteins of different sizes. A much larger common scaffold or framework between six globins can be extracted than that obtained using a standard algorithm for multiple structure superposition.
Algorithms↗
The recognition of protein structure and function from sequence: adding value to genome data.
The explosion of DNA sequence data from genome projects presents many challenges. For instance, we must extend our current knowledge of protein structure and function so that it can be applied to these new sequences. The derivation of rules for the relationships between sequence and structure allow us to recognize a common fold by the use of tertiary templates. New techniques enable us to begin to meet the challenge of rule-based modelling of distantly related proteins. This paper describes an integrated and knowledge-based approach to the prediction of protein structure and function which can maximize the value of sequence information.
Amino Acid Sequence↗
Automated comparative modelling of protein structures.
Most automated methods for protein modelling rely on the assembly of rigid fragments from known three-dimensional structures. Although the modelling of side chains has received attention recently, loop regions continue to be neglected, and these conformations contribute most errors to models. Comparative modelling procedures using distance restraints are particularly useful for modelling distantly related proteins with a common fold. Progress has also recently been made in the automatic evaluation of model structures.
Computer Simulation↗
Protein structure comparisons using a combination of a genetic algorithm, dynamic programming and least-squares minimization.
We introduce a completely automatic and objective procedure for the comparison of protein structures. A genetic algorithm is used to search for a near optimal solution of the rigid-body superposition of two whole protein structures. The specification of an initial set of equivalences is not required. Topological equivalences in the final structural alignment are defined by a conventional dynamic programming routine, which is commonly used to compare protein sequences. A least-squares fitting algorithm is then used to optimize the fit between the final set of equivalences. We have applied our method to the comparison of ribonucleic acid structures, as well as protein structures. The structural alignments are generally consistent with those previously published. In fact, on most occasions our method defines at least the same number of topological equivalences as other procedures, but always with a lower r.m.s. distance between them.
Algorithms↗