CLIPS-1D: analysis of multiple sequence alignments to deduce for residue-positions a role in catalysis, ligand-binding, or protein structure

Abstract

BackgroundOne aim of the in silico characterization of proteins is to identify all residue-positions, which are crucial for function or structure. Several sequence-based algorithms exist, which predict functionally important sites. However, with respect to sequence information, many functionally and structurally important sites are hard to distinguish and consequently a large number of incorrectly predicted functional sites have to be expected. This is why we were interested to design a new classifier that differentiates between functionally and structurally important sites and to assess its performance on representative datasets.ResultsWe have implemented CLIPS-1D, which predicts a role in catalysis, ligand-binding, or protein structure for residue-positions in a mutually exclusive manner. By analyzing a multiple sequence alignment, the algorithm scores conservation as well as abundance of residues at individual sites and their local neighborhood and categorizes by means of a multiclass support vector machine. A cross-validation confirmed that residue-positions involved in catalysis were identified with state-of-the-art quality; the mean MCC-value was 0.34. For structurally important sites, prediction quality was considerably higher (mean MCC = 0.67). For ligand-binding sites, prediction quality was lower (mean MCC = 0.12), because binding sites and structurally important residue-positions share conservation and abundance values, which makes their separation difficult. We show that classification success varies for residues in a class-specific manner. This is why our algorithm computes residue-specific p-values, which allow for the statistical assessment of each individual prediction. CLIPS-1D is available as a Web service at http://www-bioinf.uni-regensburg.de/.ConclusionsCLIPS-1D is a classifier, whose prediction quality has been determined separately for catalytic sites, ligand-binding sites, and structurally important sites. It generates hypotheses about residue-positions important for a set of homologous proteins and focuses on conservation and abundance signals. Thus, the algorithm can be applied in cases where function cannot be transferred from well-characterized proteins by means of sequence comparison.

Accounting for undetected compounds in statistical analyses of mass spectrometry ‘omic studies

“…We assume a log-normal distribution because log transformed metabolomics data are usually approximately normally distributed (Karpievitch et al 2012). Further, using a log-normal distribution is consistent with typical analytical approaches to ‘omics data that log transform intensity values and then use a t -test, ANOVA, or linear regression which assume normally distributed data.…”

“…In mass spectrometry ‘omics studies, however, missing values can originate from detection limit censoring and hence are MNAR. Because most imputation techniques produce unbiased results only if the missing data are MCAR or missing at random, but not MNAR (Karpievitch et al 2012, Lee 2004), using the imputation methods developed for microarrary studies in mass spectrometry ‘omics studies could lead to biased results. Further, the choice of imputation method can substantially affect the results and interpretation of analyses of metabolomics data (Hrydziuszko and Viant 2012).…”

Inference of Functionally-Relevant N-acetyltransferase Residues Based on Statistical Correlations

“…Given these results, we investigated whether functional residue prediction programs specifically designed to identify catalytic, ligand-binding and subtype-specific residues yield similar results. S2.1A–S2.1D Fig compares our analysis of the Gna1 subgroup to that of two such programs: FRpred [43] and CLIPS-1D [45]. This reveals that the structural bipartitioning of residues is unique to our hiMSA analysis, which therefore, at least in this case, is finding protein structural features that these other methods fail to identify.…”

A Novel Sequence-Based Feature for the Identification of DNA-Binding Sites in Proteins Using Jensen–Shannon Divergence

“…Since the 3D structures are often unavailable, Capra and Singh [34] developed a window score for such predictions. The concrete shape of our scores takes pattern form Janda et al [45], who in turn refer to Fischer et al [33]. Our scores are convex combinations of the Jensen-Shannon terms associated with the residues belonging to the surrounding window w(k).…”