The Amino Acid Composition of Quadruplex Binding Proteins Reveals a Shared Motif and Predicts New Potential Quadruplex Interactors

The importance of local DNA structures in the regulation of basic cellular processes is an emerging field of research. Amongst local non-B DNA structures, G-quadruplexes are perhaps the most well-characterized to date, and their presence has been demonstrated in many genomes, including that of humans. G-quadruplexes are selectively bound by many regulatory proteins. In this paper, we have analyzed the amino acid composition of all seventy-seven described G-quadruplex binding proteins of Homo sapiens. Our comparison with amino acid frequencies in all human proteins and specific protein subsets (e.g., all nucleic acid binding) revealed unique features of quadruplex binding proteins, with prominent enrichment for glycine (G) and arginine (R). Cluster analysis with bootstrap resampling shows similarities and differences in amino acid composition of particular quadruplex binding proteins. Interestingly, we found that all characterized G-quadruplex binding proteins share a 20 amino acid long motif/domain (RGRGR GRGGG SGGSG GRGRG) which is similar to the previously described RG-rich domain (RRGDG RRRGG GGRGQ GGRGR GGGFKG) of the FRM1 G-quadruplex binding protein. Based on this protein fingerprint, we have predicted a new set of potential G-quadruplex binding proteins sharing this interesting domain rich in glycine and arginine residues.

Clustering of multi-domain protein sequences

The overall function of a multi-domain protein is determined by the functional and structural interplay of its constituent domains. Traditional sequence alignment-based methods commonly utilize domain-level information and provide classification only at the level of domains. Such methods are not capable of taking into account the contributions of other domains in the proteins, and domain-linker regions and classify multi-domain proteins. An alignment-free protein sequence comparison tool, CLAP (CLAssification of Proteins) was previously developed in our laboratory to especially handle multi-domain protein sequences without a requirement of defining domain boundaries and sequential order of domains. Through this method we aim to achieve a biologically meaningful classification scheme for multi-domain protein sequences. In this article, CLAP-based classification has been explored on 5 datasets of multi-domain proteins and we present detailed analysis for proteins containing (1) Tyrosine phosphatase and (2) SH3 domain. At the domain-level CLAP-based classification scheme resulted in a clustering similar to that obtained from an alignment-based method. CLAP-based clusters obtained for full-length datasets were shown to comprise of proteins with similar functions and domain architectures. Our study demonstrates that multi-domain proteins could be classified effectively by considering full-length sequences without a requirement of identification of domains in the sequence.

Classification of Protein Kinases Influenced by Conservation of Substrate Binding Residues

With the advent of genome sequencing projects in the recent past, several kinases have come to light as regulating different signaling pathways. These kinases are generally classified into different subfamilies based on their sequence similarity with members of known subfamilies of kinases. A functional association is then defined to the kinase based on the subfamily to which it has been characterized. However, one of the key factors that give identity to a kinase in a subfamily is its ability to phosphorylate a given set of substrates. Substrate specificity of a kinase is largely determined by the residues at the substrate binding site. Though in general the sequence similarity based measure for classification more or less gives the preliminary idea on subfamily, understanding the molecular basis of kinase substrate recognition could further refine the classification scheme for kinases and render a better understanding of their functional role. In this analysis we emphasize on the possibility of using putative substrate binding information in the classification of a given kinase into a particular subfamily.

CLAP: a web-server for automatic classification of proteins with special reference to multi-domain proteins

BACKGROUND

The function of a protein can be deciphered with higher accuracy from its structure than from its amino acid sequence. Due to the huge gap in the available protein sequence and structural space, tools that can generate functionally homogeneous clusters using only the sequence information, hold great importance. For this, traditional alignment-based tools work well in most cases and clustering is performed on the basis of sequence similarity. But, in the case of multi-domain proteins, the alignment quality might be poor due to varied lengths of the proteins, domain shuffling or circular permutations. Multi-domain proteins are ubiquitous in nature, hence alignment-free tools, which overcome the shortcomings of alignment-based protein comparison methods, are required. Further, existing tools classify proteins using only domain-level information and hence miss out on the information encoded in the tethered regions or accessory domains. Our method, on the other hand, takes into account the full-length sequence of a protein, consolidating the complete sequence information to understand a given protein better.

RESULTS

Our web-server, CLAP (Classification of Proteins), is one such alignment-free software for automatic classification of protein sequences. It utilizes a pattern-matching algorithm that assigns local matching scores (LMS) to residues that are a part of the matched patterns between two sequences being compared. CLAP works on full-length sequences and does not require prior domain definitions.Pilot studies undertaken previously on protein kinases and immunoglobulins have shown that CLAP yields clusters, which have high functional and domain architectural similarity. Moreover, parsing at a statistically determined cut-off resulted in clusters that corroborated with the sub-family level classification of that particular domain family.

CONCLUSIONS

CLAP is a useful protein-clustering tool, independent of domain assignment, domain order, sequence length and domain diversity. Our method can be used for any set of protein sequences, yielding functionally relevant clusters with high domain architectural homogeneity. The CLAP web server is freely available for academic use at http://nslab.mbu.iisc.ernet.in/clap/.

Hybrid and rogue kinases encoded in the genomes of model eukaryotes

The highly modular nature of protein kinases generates diverse functional roles mediated by evolutionary events such as domain recombination, insertion and deletion of domains. Usually domain architecture of a kinase is related to the subfamily to which the kinase catalytic domain belongs. However outlier kinases with unusual domain architectures serve in the expansion of the functional space of the protein kinase family. For example, Src kinases are made-up of SH2 and SH3 domains in addition to the kinase catalytic domain. A kinase which lacks these two domains but retains sequence characteristics within the kinase catalytic domain is an outlier that is likely to have modes of regulation different from classical src kinases. This study defines two types of outlier kinases: hybrids and rogues depending on the nature of domain recombination. Hybrid kinases are those where the catalytic kinase domain belongs to a kinase subfamily but the domain architecture is typical of another kinase subfamily. Rogue kinases are those with kinase catalytic domain characteristic of a kinase subfamily but the domain architecture is typical of neither that subfamily nor any other kinase subfamily. This report provides a consolidated set of such hybrid and rogue kinases gleaned from six eukaryotic genomes-S.cerevisiae, D. melanogaster, C.elegans, M.musculus, T.rubripes and H.sapiens-and discusses their functions. The presence of such kinases necessitates a revisiting of the classification scheme of the protein kinase family using full length sequences apart from classical classification using solely the sequences of kinase catalytic domains. The study of these kinases provides a good insight in engineering signalling pathways for a desired output. Lastly, identification of hybrids and rogues in pathogenic protozoa such as P.falciparum sheds light on possible strategies in host-pathogen interactions.