Identification of proteins associated with Mycobacterium tuberculosis virulence pathway by their polar profile.

With almost one third of the world population infected, tuberculosis is one of the most devastating diseases worldwide and it is a major threat to any healthcare system. With the mathematical-computational method named "Polarity Index Method", already published by this group, we identified, with high accuracy (70%), proteins related to Mycobacterium tuberculosis bacteria virulence pathway from the Tuberculist Database. The test considered the totality of proteins cataloged in the main domains: fungi, bacteria, and viruses from three databases: Antimicrobial Peptide Database (APD2), Tuberculist Database, Uniprot Database, and four antigens of Mycobacterium tuberculosis: PstS-1, 38-kDa, 19-kDa, and H37Rv ORF. The method described was calibrated with each database to achieve the same performance, showing a high percentage of coincidence in the identification of proteins associated with Mycobacterium tuberculosis bacteria virulence pathway located in the Tuberculist Database, and identifying a polar pattern regardless of the group studied. This method has already been used in the identification of diverse groups of proteins and peptides, showing that it is an effective discriminant. Its metric considers only one physico-chemical property, i.e. polarity.

Identification of proteins associated with amyloidosis by polarity index method.

There is a natural protein form, insoluble and resistant to proteolysis, adopted by many proteins independently of their amino acid sequences via specific misfolding-aggregation process. This dynamic process occurs in parallel with or as an alternative to physiologic folding, generating toxic protein aggregates that are deposited and accumulated in various organs and tissues. These proteinaceous deposits typically represent bundles of ß-sheet-enriched fibrillar species known as the amyloid fibrils that are responsible for serious pathological conditions, including but not limited to neurodegenerative diseases, grouped under the term amyloidoses. The proteins that might adopt this fibrillar conformation are some globular proteins and natively unfolded (or intrinsically disordered) proteins. Our work shows that intrinsically disordered and intrinsically ordered proteins can be reliably identified, discriminated, and differentiated by analyzing their polarity profiles generated using a computational tool known as the polarity index method (Polanco & Samaniego, 2009; Polanco et al., 2012; 2013; 2013a; 2014; 2014a; 2014b; 2014c; 2014d). We also show that proteins expressed in neurons can be differentiated from proteins in these two groups based on their polarity profiles, and also that this computational tool can be used to identify proteins associated with amyloidoses. The efficiency of the proposed method is high (i.e. 70%) as evidenced by the analysis of peptides and proteins in the APD2 database (2012), AVPpred database (2013), and CPPsite database (2013), the set of selective antibacterial peptides from del Rio et al. (2001), the sets of natively unfolded and natively folded proteins from Oldfield et al. (2005), the set of human revised proteins expressed in neurons, and non-human revised proteins expressed in neurons, from the Uniprot database (2014), and also the set of amyloidogenic proteins from the AmyPDB database (2014).

Discrete dynamic system oriented on the formation of prebiotic dipeptides from Rode's experiment.

This work attempts to rationalize the possible prebiotic profile of the first dipeptides of about 4 billion years ago based on a computational discrete dynamic system that uses the final yields of the dipeptides obtained in Rode's experiments of salt-induced peptide formation (Rode et al., 1999, Peptides 20: 773-786). The system built a prebiotic scenario that allowed us to observe that (i) the primordial peptide generation was strongly affected by the abundances of the amino acid monomers, (ii) small variations in the concentration of the monomers have almost no effect on the final distribution pattern of the dipeptides and (iii) the most plausible chemical reaction of prebiotic peptide bond formation can be linked to Rode's hypothesis of a salt-induced scenario. The results of our computational simulations were related to former simulations of the Miller, and Fox & Harada experiments on amino acid monomer and oligomer generation, respectively, offering additional information to our approach.

Possible computational filter to detect proteins associated to influenza A subtype H1N1.

The design of drugs with bioinformatics methods to identify proteins and peptides with a specific toxic action is increasingly recurrent. Here, we identify toxic proteins towards the influenza A virus subtype H1N1 located at the UniProt database. Our quantitative structure-activity relationship (QSAR) approach is based on the analysis of the linear peptide sequence with the so-called Polarity Index Method that shows an efficiency of 90% for proteins from the Uniprot Database. This method was exhaustively verified with the APD2, CPPsite, Uniprot, and AmyPDB databases as well as with the set of antibacterial peptides studied by del Rio et al. and Oldfield et al.

Polar profile of antiviral peptides from AVPpred Database.

Diseases of viral origin in humans are among the most serious threats to health and the global economy. As recent history has shown the virus has a high pandemic potential, among other reasons, due to its ability to spread by air, hence the identification, investigation, containment, and treatment of viral diseases should be considered of paramount importance. In this sense, the bioinformatics research has focused on finding fast and efficient algorithms that can identify highly toxic antiviral peptides and to serve as a first filter, so that trials in the laboratory are substantially reduced. The work presented here contributes to this effort through the use of an algorithm already published by this team, called polarity index method, which identifies with high efficiency antiviral peptides from the exhaustive analysis of the polar profile, using the linear sequence of the peptide. The test carried out included all peptides in APD2 Database and 60 antiviral peptides identified by Kumar and co-workers (Nucleic Acids Res 40:W199-204, 2012), to build its AVPpred algorithm. The validity of the method was focused on its discriminating capacity so we included the 15 sub-classifications of both Databases.

Computational model of abiogenic amino acid condensation to obtain a polar amino acid profile.

In accordance with the second law of thermodynamics, the Universe as a whole tends to higher entropy. However, the sequence of far-from-equilibrium events that led to the emergence of life on Earth could have imposed order and complexity during the course of chemical reactions in the so-called primordial soup of life. Hence, we may expect to find characteristic profiles or biases in the prebiotic product mixtures, as for instance among the first amino acids. Seeking to shed light on this hypothesis, we have designed a high performance computer program that simulates the spontaneous formation of the amino acid monomers in closed environments. The program was designed in reference to a prebiotic scenario proposed by Sydney W. Fox. The amino acid abundances and their polarities as the two principal biases were also taken into consideration. We regarded the computational model as exhaustive since 200,000 amino acid dimers were formed by simulation, subsequently expressed in a vector and compared with the corresponding amino acid dimers that were experimentally obtained by Fox. We found a very high similarity between the experimental results and our simulations.

Detection of severe respiratory disease epidemic outbreaks by CUSUM-based overcrowd-severe-respiratory-disease-index model.

A severe respiratory disease epidemic outbreak correlates with a high demand of specific supplies and specialized personnel to hold it back in a wide region or set of regions; these supplies would be beds, storage areas, hemodynamic monitors, and mechanical ventilators, as well as physicians, respiratory technicians, and specialized nurses. We describe an online cumulative sum based model named Overcrowd-Severe-Respiratory-Disease-Index based on the Modified Overcrowd Index that simultaneously monitors and informs the demand of those supplies and personnel in a healthcare network generating early warnings of severe respiratory disease epidemic outbreaks through the interpretation of such variables. A post hoc historical archive is generated, helping physicians in charge to improve the transit and future allocation of supplies in the entire hospital network during the outbreak. The model was thoroughly verified in a virtual scenario, generating multiple epidemic outbreaks in a 6-year span for a 13-hospital network. When it was superimposed over the H1N1 influenza outbreak census (2008-2010) taken by the National Institute of Medical Sciences and Nutrition Salvador Zubiran in Mexico City, it showed that it is an effective algorithm to notify early warnings of severe respiratory disease epidemic outbreaks with a minimal rate of false alerts.

A toy model of prebiotic peptide evolution: the possible role of relative amino acid abundances.

This paper presents a mathematical-computational toy model based on the assumed dynamic principles of prebiotic peptide evolution. Starting from a pool of amino acid monomers, the model describes in a generalized manner the generation of peptides and their sequential information. The model integrates the intrinsic and dynamic key elements of the initiation of biopolymerization, such as the relative amino acid abundances and polarities, as well as the oligomer reversibility, i.e. fragmentation and recombination, and peptide self-replication. Our modeling results suggest that the relative amino acid abundances, as indicated by Miller-Urey type electric discharge experiments, played a principal role in the early sequential information of peptide profiles. Moreover, the computed profiles display an astonishing similarity to peptide profiles observed in so-called biological common ancestors found in the following three microorganisms; E. coli, M. jannaschii, and S. cereviasiae. The prebiotic peptide fingerprint was obtained by the so-called polarity index method that was earlier reported as a tool for the identification of cationic amphipathic antibacterial short peptides.

Detection of selective antibacterial peptides by the Polarity Profile method.

Antimicrobial peptides occupy a prominent place in the production of pharmaceuticals, because of their effective contribution to the protection of the immune system against almost all types of pathogens. These peptides are thoroughly studied by computational methods designed to shed light on their main functions. In this paper, we propose a computational approach, named the Polarity Profile method that represents an improvement to the former Polarity Index method. The Polarity Profile method is very effective in detecting the subgroup of antibacterial peptides called selective cationic amphipathic antibacterial peptides (SCAAP) that show high toxicity towards bacterial membranes and exhibit almost zero toxicity towards mammalian cells. Our study was restricted to the peptides listed in the antimicrobial peptides database (APD2) of December 19, 2012. Performance of the Polarity Profile method is demonstrated through a comparison to the former Polarity Index method by using the same sets of peptides. The efficiency of the Polarity Profile method exceeds 85% taking into account the false positive and/or false negative peptides.

Characterization of a possible uptake mechanism of selective antibacterial peptides.

Selective antibacterial peptides containing less than 30 amino acid residues, cationic, with amphipathic properties, have been the subject of several studies due to their active participation and beneficial effects in strengthening the immune system of all living organisms. This manuscript reports the results of a comparison between the group of selective antibacterial peptides and another group called "cell penetrating peptides". An important number of the selective antibacterial peptides are cell penetrating peptides, suggesting that their toxicity is related to their uptake mechanism. The verification of this observation also includes the adaptation of a method previously published, called Polarity index, which reproduces and confirms the action of this new set of peptides. The efficiency of this method was verified based on four different databases, yielding a high score. The verification was based exclusively on the peptides already reported in the databases which have been experimentally verified.