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

Identification of Toxoplasma gondii adhesins through a machine learning approach

Toxoplasma gondii, as other apicomplexa, employs adhesins transmembrane proteins for binding and invasion to host cells. Search and characterization of adhesins is pivotal in understanding Apicomplexa invasion mechanisms and targeting new druggable candidates. This work developed a machine learning software called ApiPredictor UniQE V2.0, based on two approaches: support vector machines and multilayer perceptron, to predict adhesins proteins from amino acid sequences. By using ApiPredictor UniQE V2.0, five SAG-Related Sequences (SRSs) were identified within the Toxoplasma gondii proteome. One of those candidates, TgSRS12B, was cloned in plasmid pEXP5-CT/TOPO and expressed in E. coli BL21 DE3. The resulting recombinant protein was purified via affinity chromatography. Co-precipitation assays in CaCo and Muller cells showed interactions between TgSRS12B-His-tagged and the membrane fractions from both human cell lines. In conclusion, we demonstrated that ApiPredictor UniQE V2.0, a bioinformatic free software, was able to identify TgSRS12B as a new adhesin protein.

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.

Protein targets of thiazolidinone derivatives in Toxoplasma gondii and insights into their binding to ROP18

Background

Thiazolidinone derivatives show inhibitory activity (IC₅₀) against the Toxoplasma gondii parasite, as well as high selectivity with high therapeutic index. To disclose the target proteins of the thiazolidinone core in this parasite, we explored in silico the active sites of different T. gondii proteins and estimated the binding-free energy of reported thiazolidinone molecules with inhibitory effect on invasion and replication of the parasite inside host cells. This enabled us to describe some of the most suitable structural characteristics to design a compound derived from the thiazolidinone core.

Results

The best binding affinity was observed in the active site of kinase proteins, we selected the active site of the T. gondii ROP18 kinase, because it is an important factor for the virulence and survival of the parasite. We present the possible effect of a derivative of thiazolidinone core in the active site of T. gondii ROP18 and described some characteristics of substituent groups that could improve the affinity and specificity of compounds derived from the thiazolidinone core against T. gondii.

Conclusions

The results of our study suggest that compounds derived from the thiazolidinone core have a preference for protein kinases of T. gondii, being promising compounds for the development of new drugs with potential anti-toxoplasmosis activity. Our findings highlight the importance of use computational studies for the understanding of the action mechanism of compounds with biological activity.

Electronic supplementary material

The online version of this article (10.1186/s12864-018-5223-7) contains supplementary material, which is available to authorized users.

Automated shape-based clustering of 3D immunoglobulin protein structures in chronic lymphocytic leukemia

Background

Although the etiology of chronic lymphocytic leukemia (CLL), the most common type of adult leukemia, is still unclear, strong evidence implicates antigen involvement in disease ontogeny and evolution. Primary and 3D structure analysis has been utilised in order to discover indications of antigenic pressure. The latter has been mostly based on the 3D models of the clonotypic B cell receptor immunoglobulin (BcR IG) amino acid sequences. Therefore, their accuracy is directly dependent on the quality of the model construction algorithms and the specific methods used to compare the ensuing models. Thus far, reliable and robust methods that can group the IG 3D models based on their structural characteristics are missing.

Results

Here we propose a novel method for clustering a set of proteins based on their 3D structure focusing on 3D structures of BcR IG from a large series of patients with CLL. The method combines techniques from the areas of bioinformatics, 3D object recognition and machine learning. The clustering procedure is based on the extraction of 3D descriptors, encoding various properties of the local and global geometrical structure of the proteins. The descriptors are extracted from aligned pairs of proteins. A combination of individual 3D descriptors is also used as an additional method. The comparison of the automatically generated clusters to manual annotation by experts shows an increased accuracy when using the 3D descriptors compared to plain bioinformatics-based comparison. The accuracy is increased even more when using the combination of 3D descriptors.

Conclusions

The experimental results verify that the use of 3D descriptors commonly used for 3D object recognition can be effectively applied to distinguishing structural differences of proteins. The proposed approach can be applied to provide hints for the existence of structural groups in a large set of unannotated BcR IG protein files in both CLL and, by logical extension, other contexts where it is relevant to characterize BcR IG structural similarity. The method does not present any limitations in application and can be extended to other types of proteins.