Partially-supervised protein subclass discovery with simultaneous annotation of functional residues

Recognition of sites of functional specialisation in all known eukaryotic protein kinase families

The conserved function of protein phosphorylation, catalysed by members of protein kinase superfamily, is regulated in different ways in different kinase families. Further, differences in activating triggers, cellular localisation, domain architecture and substrate specificity between kinase families are also well known. While the transfer of γ-phosphate from ATP to the hydroxyl group of Ser/Thr/Tyr is mediated by a conserved Asp, the characteristic functional and regulatory sites are specialized at the level of families or sub-families. Such family-specific sites of functional specialization are unknown for most families of kinases. In this work, we systematically identify the family-specific residue features by comparing the extent of conservation of physicochemical properties, Shannon entropy and statistical probability of residue distributions between families of kinases. An integrated discriminatory score, which combines these three features, is developed to demarcate the functionally specialized sites in a kinase family from other sites. We achieved an area under ROC curve of 0.992 for the discrimination of kinase families. Our approach was extensively tested on well-studied families CDK and MAPK, wherein specific protein interaction sites and substrate recognition sites were successfully detected (p-value < 0.05). We also find that the known family-specific oncogenic driver mutation sites were scored high by our method. The method was applied to all known kinases encompassing 107 families from diverse eukaryotic organisms leading to a comprehensive list of family-specific functional sites. Apart from other uses, our method facilitates identification of specific protein interaction sites and drug target sites in a kinase family.

Functional evolution of PLP-dependent enzymes based on active-site structural similarities

Families of distantly related proteins typically have very low sequence identity, which hinders evolutionary analysis and functional annotation. Slowly evolving features of proteins, such as an active site, are therefore valuable for annotating putative and distantly related proteins. To date, a complete evolutionary analysis of the functional relationship of an entire enzyme family based on active-site structural similarities has not yet been undertaken. Pyridoxal-5'-phosphate (PLP) dependent enzymes are primordial enzymes that diversified in the last universal ancestor. Using the comparison of protein active site structures (CPASS) software and database, we show that the active site structures of PLP-dependent enzymes can be used to infer evolutionary relationships based on functional similarity. The enzymes successfully clustered together based on substrate specificity, function, and three-dimensional-fold. This study demonstrates the value of using active site structures for functional evolutionary analysis and the effectiveness of CPASS.

Bayesian classification of residues associated with protein functional divergence: Arf and Arf-like GTPases

Background

Certain residues within proteins are highly conserved across very distantly related organisms, yet their (presumably critical) structural or mechanistic roles are completely unknown. To obtain clues regarding such residues within Arf and Arf-like (Arf/Arl) GTPases--which function as on/off switches regulating vesicle trafficking, phospholipid metabolism and cytoskeletal remodeling--I apply a new sampling procedure for comparative sequence analysis, termed multiple category Bayesian Partitioning with Pattern Selection (mcBPPS).

Results

The mcBPPS sampler classified sequences within the entire P-loop GTPase class into multiple categories by identifying those evolutionarily-divergent residues most likely to be responsible for functional specialization. Here I focus on categories of residues that most distinguish various Arf/Arl GTPases from other GTPases. This identified residues whose specific roles have been previously proposed (and in some cases corroborated experimentally and that thus serve as positive controls), as well as several categories of co-conserved residues whose possible roles are first hinted at here. For example, Arf/Arl/Sar GTPases are most distinguished from other GTPases by a conserved aspartate residue within the phosphate binding loop (P-loop) and by co-conserved residues nearby that, together, can form a network of salt-bridge and hydrogen bond interactions centered on the GTPase active site. Residues corresponding to an N-[VI] motif that is conserved within Arf/Arl GTPases may play a role in the interswitch toggle characteristic of the Arf family, whereas other, co-conserved residues may modulate the flexibility of the guanine binding loop. Arl8 GTPases conserve residues that strikingly diverge from those typically found in other Arf/Arl GTPases and that form structural interactions suggestive of a novel interswitch toggle mechanism.

Conclusions

This analysis suggests specific mutagenesis experiments to explore mechanisms underlying GTP hydrolysis, nucleotide exchange and interswitch toggling within Arf/Arl GTPases. More generally, it illustrates how the mcBPPS sampler can complement traditional evolutionary analyses by providing an objective, quantitative and statistically rigorous way to explore protein functional-divergence in molecular detail. Because the sampler classifies the input sequences at the same time, it can be used to generate subgroup profiles, in which functionally-divergent categories of residues are annotated automatically.

Reviewers

This article was reviewed by Frank Eisenhaber, L Aravind and Daniel Gaston (nominated by Eric Bapteste). For the full reviews, go to the Reviewers' comments section.

Clustering of protein families into functional subtypes using Relative Complexity Measure with reduced amino acid alphabets

Background

Phylogenetic analysis can be used to divide a protein family into subfamilies in the absence of experimental information. Most phylogenetic analysis methods utilize multiple alignment of sequences and are based on an evolutionary model. However, multiple alignment is not an automated procedure and requires human intervention to maintain alignment integrity and to produce phylogenies consistent with the functional splits in underlying sequences. To address this problem, we propose to use the alignment-free Relative Complexity Measure (RCM) combined with reduced amino acid alphabets to cluster protein families into functional subtypes purely on sequence criteria. Comparison with an alignment-based approach was also carried out to test the quality of the clustering.

Results

We demonstrate the robustness of RCM with reduced alphabets in clustering of protein sequences into families in a simulated dataset and seven well-characterized protein datasets. On protein datasets, crotonases, mandelate racemases, nucleotidyl cyclases and glycoside hydrolase family 2 were clustered into subfamilies with 100% accuracy whereas acyl transferase domains, haloacid dehalogenases, and vicinal oxygen chelates could be assigned to subfamilies with 97.2%, 96.9% and 92.2% accuracies, respectively.

Conclusions

The overall combination of methods in this paper is useful for clustering protein families into subtypes based on solely protein sequence information. The method is also flexible and computationally fast because it does not require multiple alignment of sequences.

Multi-Harmony: detecting functional specificity from sequence alignment

Many protein families contain sub-families with functional specialization, such as binding different ligands or being involved in different protein–protein interactions. A small number of amino acids generally determine functional specificity. The identification of these residues can aid the understanding of protein function and help finding targets for experimental analysis. Here, we present multi-Harmony, an interactive web sever for detecting sub-type-specific sites in proteins starting from a multiple sequence alignment. Combining our Sequence Harmony (SH) and multi-Relief (mR) methods in one web server allows simultaneous analysis and comparison of specificity residues; furthermore, both methods have been significantly improved and extended. SH has been extended to cope with more than two sub-groups. mR has been changed from a sampling implementation to a deterministic one, making it more consistent and user friendly. For both methods Z-scores are reported. The multi-Harmony web server produces a dynamic output page, which includes interactive connections to the Jalview and Jmol applets, thereby allowing interactive analysis of the results. Multi-Harmony is available at http://www.ibi.vu.nl/ programs/shmrwww.

PyMix--the python mixture package--a tool for clustering of heterogeneous biological data

BACKGROUND

Cluster analysis is an important technique for the exploratory analysis of biological data. Such data is often high-dimensional, inherently noisy and contains outliers. This makes clustering challenging. Mixtures are versatile and powerful statistical models which perform robustly for clustering in the presence of noise and have been successfully applied in a wide range of applications.

RESULTS

PyMix - the Python mixture package implements algorithms and data structures for clustering with basic and advanced mixture models. The advanced models include context-specific independence mixtures, mixtures of dependence trees and semi-supervised learning. PyMix is licenced under the GNU General Public licence (GPL). PyMix has been successfully used for the analysis of biological sequence, complex disease and gene expression data.

CONCLUSIONS

PyMix is a useful tool for cluster analysis of biological data. Due to the general nature of the framework, PyMix can be applied to a wide range of applications and data sets.