Consensus Phylogenetic trees of Fifteen Prokaryotic Aminoacyl-tRNA Synthetase Polypeptides based on Euclidean Geometry of All-Pairs Distances and Concatenation

An interesting one from my friend. 

Read full here:  http://biorxiv.org/content/early/2016/05/06/051623

For comments and quarries please write to  rhishi.sr[at]gmail.com 

Most molecular phylogenetic trees depict the relative closeness or the extent of similarity among a set of taxa based on comparison of sequences of homologous genes or proteins. Since the tree topology for individual monogenic traits varies among the same set of organisms and does not overlap taxonomic hierarchy, hence there is a need to generate multidimensional phylogenetic trees. Phylogenetic trees were constructed for 119 prokaryotes representing 2 phyla under Archaea and 11 phyla under Bacteria after comparing multiple sequence alignments for 15 different aminoacyl-tRNA synthetase polypeptides. 

The topology of Neighbor Joining (NJ) trees for individual tRNA synthetase polypeptides varied substantially. We use Euclidean geometry to estimate all-pairs distances in order to construct phylogenetic trees. Further, we used a novel 'Taxonomic fidelity' algorithm to estimate clade by clade similarity between the phylogenetic tree and the taxonomic tree. We find that, as compared to trees for individual tRNA synthetase polypeptides and rDNA sequences, the topology of our Euclidean tree and that for aligned and concatenated sequences of 15 proteins are closer to the taxonomic trees and offer the best consensus. We have also aligned sequences after concatenation, and find that by changing the order of sequence joining prior to alignment, the tree topologies vary. In contrast, changing the types of polypeptides in the grouping for Euclidean trees does not affect the tree topologies. 

We show that a consensus phylogenetic tree of 15 polypeptides from 14 aminoacyl-tRNA synthetases for 119 prokaryotes using Euclidean geometry exhibits better taxonomic fidelity than trees for individual tRNA synthetase polypeptides as well as 16S rDNA. We have also examined Euclidean N-dimensional trees for 15 tRNA synthetase polypeptides which give the same topology as that constructed after amalgamating 3-dimensional Euclidean trees for groups of 3 polypeptides. Euclidean N-dimensional trees offer a reliable future to multi-genic molecular phylogenetics.

Functional Annotation of Hypothetical proteins

Experimental work is though time taking but is direct approach for functional annotation of hypothetical proteins; however, at times it is difficult to decide upon the experimental design for a relatively new class of a protein. With increasing size and quality of various protein databases, it is becoming relatively easier to look for the experimental design for the probable function of a protein. Following are the steps that can be used in choosing the type of experimental analysis that needs to be performed and the substrate to be used during laboratory tests.

1.     If the protein is predicted to be an enzyme, BLAST results normally indicates its closely related proteins that can be looked upon for the experimental procedures to be performed as indicated by the matching hits (look for the papers on those proteins that might indicate the type of related function the protein might perform).

2.   With the increasing domain databases, it is possible to analyze the protein domain wise indicating the ability to perform certain kind of biochemical reactions if any. The NCBI’s Conserved Doamin Database (CDD), Pfam and InterProScan databases have a large number of conserved domains that defines a functional class. Presence of certain domain is also indicative of the possible activity of the protein and therefore the type of substrate to be used for defining its chemical activity in laboratory could be helpful.

3.     Composition based analysis of protein: there are various bioinformatics tools available online to studying the amino acid composition based analysis of protein informing various properties which help in indicating the properties of protein which later help with the functional annotation of the proteins i.e. Protparam, SPAAN, MP3 and a lot more etc.

4.  Homology based modeling: this is an important step in determining the functional annotation of protein based on the structure of the protein, though it may be difficult for the proteins with low identity (<30%) with the already known crystal structures of the protein. However, a good homology model can be an important step towards determining functional annotation for a protein. So also the secondary and tertiary structure prediction of the protein will tell the similar functional categories thereby help in designing relative experimental assays. Some of the commonly used homology based modeling tools are listed here http://bioinformatictools.blogspot.in/2012/01/homology-modeling-of-proteins.html.

5.  Phylogenetic analysis: Phylogenetic analysis not only shows evolutionary divergence of the protein but also act as an important step towards functional conservation of the protein. This helps in determining the degree of functional similarity with other related homologous proteins. Thus, determining the appropriate experimental assays towards functional annotation of the protein. With the help of molecular dynamic simulation, this also helps in-silico assessment of the ability of substrate to bind to the protein. In fact it can cut down from large number of substrate molecules to the top most hits, helping to prioritize the experimental analysis, saving time and resources.

6.     It is sometimes a bit difficult while working with novel proteins for which relevant data is almost negligible worldwide, so you can wait till you get more information.

Let me know if you have more suggestions to add on.