Detecting network communities: an application to phylogenetic analysis.

This paper proposes a new method to identify communities in generally weighted complex networks and apply it to phylogenetic analysis. In this case, weights correspond to the similarity indexes among protein sequences, which can be used for network construction so that the network structure can be analyzed to recover phylogenetically useful information from its properties. The analyses discussed here are mainly based on the modular character of protein similarity networks, explored through the Newman-Girvan algorithm, with the help of the neighborhood matrix . The most relevant networks are found when the network topology changes abruptly revealing distinct modules related to the sets of organisms to which the proteins belong. Sound biological information can be retrieved by the computational routines used in the network approach, without using biological assumptions other than those incorporated by BLAST. Usually, all the main bacterial phyla and, in some cases, also some bacterial classes corresponded totally (100%) or to a great extent (>70%) to the modules. We checked for internal consistency in the obtained results, and we scored close to 84% of matches for community pertinence when comparisons between the results were performed. To illustrate how to use the network-based method, we employed data for enzymes involved in the chitin metabolic pathway that are present in more than 100 organisms from an original data set containing 1,695 organisms, downloaded from GenBank on May 19, 2007. A preliminary comparison between the outcomes of the network-based method and the results of methods based on Bayesian, distance, likelihood, and parsimony criteria suggests that the former is as reliable as these commonly used methods. We conclude that the network-based method can be used as a powerful tool for retrieving modularity information from weighted networks, which is useful for phylogenetic analysis.

Comparison of complex networks and tree-based methods of phylogenetic analysis and proposal of a bootstrap method.

Complex networks have been successfully applied to the characterization and modeling of complex systems in several distinct areas of Biological Sciences. Nevertheless, their utilization in phylogenetic analysis still needs to be widely tested, using different molecular data sets and taxonomic groups, and, also, by comparing complex networks approach to current methods in phylogenetic analysis. In this work, we compare all the four main methods of phylogenetic analysis (distance, maximum parsimony, maximum likelihood, and Bayesian) with a complex networks method that has been used to provide a phylogenetic classification based on a large number of protein sequences as those related to the chitin metabolic pathway and ATP-synthase subunits. In order to perform a close comparison to these methods, we selected Basidiomycota fungi as the taxonomic group and used a high-quality, manually curated and characterized database of chitin synthase sequences. This enzymatic protein plays a key role in the synthesis of one of the exclusive features of the fungal cell wall: the presence of chitin. The communities (modules) detected by the complex network method corresponded exactly to the groups retrieved by the phylogenetic inference methods. Additionally, we propose a bootstrap method for the complex network approach. The statistical results we have obtained with this method were also close to those obtained using traditional bootstrap methods.

Biochemical characterization and molecular cloning of a plasminogen activator proteinase (LV-PA) from bushmaster snake venom.

The protein (LV-PA) from bushmaster (Lachesis muta muta) venom is a serine proteinase which specifically activates the inactive proenzyme plasminogen. LV-PA is a single chain glycoprotein with an apparent molecular mass of 33 kDa that fell to 28 kDa after treatment with N-Glycosidase F (PNGase F). Approximately 93% of its protein sequence was determined by automated Edman degradation of various fragments derived from a digestion with trypsin. A cDNA library of L. m. muta was constructed to generate expressed sequence tags (ESTs) and the plasminogen activator precursor cDNA was sequenced. The complete amino acid sequence of the enzyme was deduced from the cDNA sequence. LV-PA is composed of 234 residues and contains a single asparagine-linked glycosylation site, Asn-X-Ser, bearing sugars that account for approximately 10% of the enzyme's total molecular mass of 33 kDa. The sequence of LV-PA is highly similar to the plasminogen activators (PAs) TSV-PA from Trimeresurus stejnegeri venom and Haly-PA from Agkistrodon halys. Furthermore, the mature protein sequence of LV-PA exhibits significant similarity with other viperidae venom serine proteinases which affect many steps of hemostasis, ranging from the blood coagulation cascade to platelet function. The Michaelis constant (Km) and the catalytic rate constant (kcat) of LV-PA on four chromogenic substrates were obtained from Lineweaver-Burk plots. In addition, we used an indirect enzyme-linked immunoabsorbent assay (ELISA) to explore the phylogenetic range of immunological cross-reactivity (using antibodies raised against LV-PA) with analogous serine proteinases from two viperidae venoms and mammals.

Comparative protein analysis of the chitin metabolic pathway in extant organisms: a complex network approach.

Chitin is a structural endogenous carbohydrate, which is a major component of fungal cell walls and arthropod exoskeletons. A renewable resource and the second most abundant polysaccharide in nature after cellulose, chitin is currently used for waste water clearing, cosmetics, medical, and veterinary applications. This work comprises data mining of protein sequences related to the chitin metabolic pathway of completely sequenced genomes of extant organisms pertaining to the three life domains, followed by meta-analysis using traditional sequence similarity comparison and complex network approaches. Complex networks involving proteins of the chitin metabolic pathway in extant organisms were constructed based on protein sequence similarity. Several usual network indices were estimated in order to obtain information on the topology of these networks, including those related to higher order neighborhood properties. Due to the assumed evolutionary character of the system, we also discuss issues related to modularity properties, with the concept of edge betweenness playing a particularly important role in our analysis. Complex network approach correctly identifies clusters of organisms that belong to phylogenetic groups without any a priori knowledge about the biological features of the investigated protein sequences. We envisage the prospect of using such a complex network approach as a high-throughput phylogenetic method.