Time-Frequency Approach Applied to Finding Interaction Regions in Pathogenic Proteins.

Protein-protein interactions govern all molecular processes for living organisms, even those involved in pathogen infection. Pathogens such as virus, bacteria, and parasites contain proteins that help the pathogen to attach, penetrate, and settle inside the target cell. Thus, it is necessary to know the regions in pathogenic proteins that interact with host cell receptors. Currently, powerful pathogen databases are available and many pathogenic proteins have been recognized, but many pathogenic proteins have not been characterized. This work developed a program in MATLAB environment based on the time-frequency analysis to recognize important sites in proteins. Our program highlights the highest energy patches in proteins from their time-frequency distribution and matches the corresponding frequency. We sought to know if this approach is able to recognize stretches residues related to interaction. Our approach was applied to five study cases from pathogenic co-crystallized structures that have been well characterized. We searched the frequencies that characterize interaction regions in pathogenic proteins and with this information tried to identify new interaction patches in either paralogs or orthologs. We found that our program generates a well-interpretable graphic under several descriptors that can show important regions in proteins even those related to interaction. We propose that this MATLAB program could be used as a tool to explore outstanding regions in uncharacterized proteins.

R Script Approach to Infer Toxoplasma Infection Mechanisms From Microarrays and Domain-Domain Protein Interactions.

Pathogen-host protein-protein interaction systems examine the interactions between the protein repertoires of 2 distinct organisms. Some of these pathogen proteins interact with the host protein system and may manipulate it for their own advantages. In this work, we designed an R script by concatenating 2 functions called rowDM and rowCVmed to infer pathogen-host interaction using previously reported microarray data, including host gene enrichment analysis and the crossing of interspecific domain-domain interactions. We applied this script to the Toxoplasma-host system to describe pathogen survival mechanisms from human, mouse, and Toxoplasma Gene Expression Omnibus series. Our outcomes exhibited similar results with previously reported microarray analyses, but we found other important proteins that could contribute to toxoplasma pathogenesis. We observed that Toxoplasma ROP38 is the most differentially expressed protein among toxoplasma strains. Enrichment analysis and KEGG mapping indicated that the human retinal genes most affected by Toxoplasma infections are those related to antiapoptotic mechanisms. We suggest that proteins PIK3R1, PRKCA, PRKCG, PRKCB, HRAS, and c-JUN could be the possible substrates for differentially expressed Toxoplasma kinase ROP38. Likewise, we propose that Toxoplasma causes overexpression of apoptotic suppression human genes.

MSCA: a spectral comparison algorithm between time series to identify protein-protein interactions.

BACKGROUND: The interactions between pathogen proteins and their hosts allow pathogens to manipulate host cellular mechanisms to their advantage. The identification of host proteins that are targeted by virulent pathogen proteins is crucial to increase our understanding of infection mechanisms and to propose new therapeutics that target pathogens. Understanding the virulence mechanisms of pathogens requires a detailed molecular description of the proteins involved, but acquiring this knowledge is time consuming and prohibitively expensive. Therefore, we develop a statistical method based on hypothesis testing to compare the time series obtained from conversion of the physicochemical characteristics of the amino acids that form the primary structure of proteins and thus to propose potential functional relation between proteins. We called this algorithm the multiple spectral comparison algorithm (MSCA); the MSCA was inspired by the BLASTP tool and was implemented in R code. The algorithm compares and relates multiple time series according to their spectral similarities, and the biological relation between them could be interpreted as either a similar function or protein-protein interaction (PPI). RESULTS: A simulation study showed that the MSCA works satisfactorily well when we compare unequal time series generated from ARMA processes because its power was close to 1. The MSCA presented a 70% average accuracy of detecting protein interactions using a threshold of 0.7 for our spectral measure, indicating that this algorithm could predict novel PPIs and pathogen-host interactions (PHIs) with acceptable confidence. The MSCA also was validated by its identification of well-known interactions of the human proteins MAGI1, SCRIB and JAK1, as well as interactions of the virulence proteins ROP16, ROP18, ROP17 and ROP5. We verified the spectral similarities for human intraspecific PPIs and PHIs that were previously demonstrated experimentally by other authors. We suggest that human GBP (GTPase group induced by interferon) and the CREB transcription factor family could be human substrates for the complex of ROP18, ROP17 and ROP5. CONCLUSIONS: Using multiple-hypothesis testing between the spectral densities of a set of unequal time series, we developed an algorithm that is able to identify the similarities or interactions between a set of proteins.

Cluster analysis identifies aminoacid compositional features that indicate Toxoplasma gondii adhesin proteins.

Toxoplasma gondii invade host cells using a multi-step process that depends on the regulated secretion of adhesions. To identify key primary sequence features of adhesins in this parasite, we analyze the relative frequency of individual amino acids, their dipeptide frequencies, and the polarity, polarizability and Van der Waals volume of the individual amino acids by using cluster analysis. This method identified cysteine as a key amino acid in the Toxoplasma adhesin group. The best vector algorithm of non-concatenated features was for 2 attributes: the single amino acid relative frequency and the dipeptide frequency. Polarity, polarizability and Van der Waals volume were not good classificatory attributes. Single amino acid attributes clustered unambiguously 67 apicomplexan hypothetical adhesins. This algorithm was also useful for clustering hypothetical Toxoplasma target host receptors. All of the cluster performances had over 70% sensitivity and 80% specificity. Compositional aminoacid data can be useful for improving machine learning-based prediction software when homology and structural data are not sufficient.