Evaluation of Structural and Evolutionary Contributions to Deleterious Mutation Prediction

Saunders, Christopher T.; Baker, David

doi:10.1016/s0022-2836(02)00813-6

Cited by 197 publications

(178 citation statements)

References 23 publications

Supporting

Mentioning

169

Contrasting

Order By: Relevance

“…In an effort to enable researchers to access our method, we have developed a Web site and coordinate Across all experiments, the best features are based on sequence conservation, structural conservation, or structural uniqueness. This finding is similar to those that have been seen in other methods that predict catalytic residues (Gutteridge et al 2003) and predictions of other functional sites including deleterious mutation prediction (Saunders and Baker 2002). Although these are the best features, many noncatalytic residues are sequence and structurally conserved (Fig.…”

Section: Discussionsupporting

confidence: 86%

Evaluation of features for catalytic residue prediction in novel folds

et al. 2007

View full text Add to dashboard Cite

Structural genomics projects are determining the three-dimensional structure of proteins without full characterization of their function. A critical part of the annotation process involves appropriate knowledge representation and prediction of functionally important residue environments. We have developed a method to extract features from sequence, sequence alignments, three-dimensional structure, and structural environment conservation, and used support vector machines to annotate homologous and nonhomologous residue positions based on a specific training set of residue functions. In order to evaluate this pipeline for automated protein annotation, we applied it to the challenging problem of prediction of catalytic residues in enzymes. We also ranked the features based on their ability to discriminate catalytic from noncatalytic residues. When applying our method to a wellannotated set of protein structures, we found that top-ranked features were a measure of sequence conservation, a measure of structural conservation, a degree of uniqueness of a residue's structural environment, solvent accessibility, and residue hydrophobicity. We also found that features based on structural conservation were complementary to those based on sequence conservation and that they were capable of increasing predictor performance. Using a family nonredundant version of the ASTRAL 40 v1.65 data set, we estimated that the true catalytic residues were correctly predicted in 57.0% of the cases, with a precision of 18.5%. When testing on proteins containing novel folds not used in training, the best features were highly correlated with the training on families, thus validating the approach to nonhomologous catalytic residue prediction in general. We then applied the method to 2781 coordinate files from the structural genomics target pipeline and identified both highly ranked and highly clustered groups of predicted catalytic residues.

show abstract

Section: Discussionsupporting

confidence: 86%

Evaluation of features for catalytic residue prediction in novel folds

et al. 2007

View full text Add to dashboard Cite

show abstract

“…Assuming the temperature factor is a reliable measure of flexibility, perhaps epitopes are not as flexible as suggested. Yet, it has been claimed that the temperature factor may not provide a reliable indication to flexibility (Saunders and Baker, 2002). Alternatively, crystallized protein structures might be biased towards non-flexible proteins that are easy to crystallize.…”

Section: Discussionmentioning

confidence: 99%

“…It is claimed that amino acid replacements in surface loops usually do not perturb the three-dimensional structure of the protein since surface loops are relatively flexible (Saunders and Baker, 2002). Thus, the conservation variability of epitopes might be biased by the abundance of loops in epitopes.…”

Section: Evolutionary Conservation Of Epitopesmentioning

confidence: 99%

Computational characterization of B-cell epitopes

Rubinstein

Mayrose

Halperin

et al. 2008

Molecular Immunology

220

233

View full text Add to dashboard Cite

Characterizing B-cell epitopes is a fundamental step for understanding the immunological basis of bio-recognition. To date, epitope analyses have either been based on limited structural data, or sequence data alone. In this study, our null hypothesis was that the surface of the antigen is homogeneously antigenic. To test this hypothesis, a large dataset of antibody-antigen complex structures, together with crystal structures of the native antigens, has been compiled. Computational methods were developed and applied to detect and extract physico-chemical, structural, and geometrical properties that may distinguish an epitope from the remaining antigen surface. Rigorous statistical inference was able to clearly reject the null hypothesis showing that epitopes are distinguished from the remaining antigen surface in properties such as amino acid preference, secondary structure composition, geometrical shape, and evolutionary conservation. Specifically, epitopes were found to be significantly enriched with tyrosine and tryptophan, and to show a general preference for charged and polar amino acids. Additionally, epitopes were found to show clear preference for residing on planar parts of the antigen that protrude from the surface, yet with a rugged surface shape at the atom level. The effects of complex formation on the structural properties of the antigen were also computationally characterized and it is shown that epitopes undergo compression upon antibody binding. This correlates with the finding that epitopes are enriched with unorganized secondary structure elements that render them flexible. Thus, this study extends the understanding of the underlying processes required for antibody binding, and reveals new aspects of the antibody-antigen interaction.

show abstract

“…Exhaustive amino acid substitution assays on HIV-1 protease (Loeb et al 1989), T4 lysozyme (Rennell et al 1991), and Lac repressors (Markiewicz et al 1994) showed that 59, 12, and 34% of the examined amino acid substitutions were deleterious (summarized in Saunders and Baker's 2002 Table 1). If we choose 35%, the average of these three values, as the fraction of amino acid substitutions that are deleterious, we have a phenotypic mutation rate of 0.08 deleterious mutations per synthesized protein.…”

Section: Discussionmentioning

confidence: 99%

Why Are Phenotypic Mutation Rates Much Higher Than Genotypic Mutation Rates?

2006

View full text Add to dashboard Cite

The evolution of genotypic mutation rates has been investigated in numerous theoretical and experimental studies. Mutations, however, occur not only when copying DNA, but also when building the phenotype, especially when translating and transcribing DNA to RNA and protein. Here we study the effect of such phenotypic mutations. We find a maximum phenotypic mutation rate, u max , that is compatible with maintaining a certain function of the organism. This may be called a phenotypic error threshold. In particular, we find a minimum phenotypic mutation rate, u min , with the property that there is (nearly) no selection pressure to reduce the rate of phenotypic mutations below this value. If there is a cost for lowering the phenotypic mutation rate, then u min is close to the optimum phenotypic mutation rate that maximizes the fitness of the organism. In our model, there is selective pressure to decrease the rate of genotypic mutations to zero, but to decrease the rate of phenotypic mutations only to a positive value. Despite its simplicity, our model can explain part of the huge difference between genotypic and phenotypic mutation rates that is observed in nature. The relevant data are summarized.

show abstract

Evaluation of Structural and Evolutionary Contributions to Deleterious Mutation Prediction

Cited by 197 publications

References 23 publications

Evaluation of features for catalytic residue prediction in novel folds

Evaluation of features for catalytic residue prediction in novel folds

Computational characterization of B-cell epitopes

Why Are Phenotypic Mutation Rates Much Higher Than Genotypic Mutation Rates?

Contact Info

Product

Resources

About