The SubCons webserver: A user friendly web interface for state-of-the-art subcellular localization prediction

Designing a multi-epitope chimeric protein from different potential targets: A potential vaccine candidate against Plasmodium

Malaria is an infectious disease that has been a continuous threat to mankind since the time immemorial. Owing to the complex multi-staged life cycle of the plasmodium parasite, an effective malaria vaccine which is fully protective against the parasite infection is urgently needed to deal with the challenges. In the present study, essential parasite proteins were identified and a chimeric protein with multivalent epitopes was generated. The designed chimeric protein consists of best potential B and T cell epitopes from five different essential parasite proteins. Physiochemical studies of the chimeric protein showed that the modeled vaccine construct was thermo-stable, hydrophilic and antigenic in nature. And the binding of the vaccine construct with Toll-like receptor-4 (TLR-4) as revealed by the molecular docking suggests the possible interaction and role of the vaccine construct in activating the innate immune response. The constructed vaccine being a chimeric protein containing epitopes from different potential candidates could target different stages or pathways of the parasite. Moreover, the approach used in this study is time and cost effective, and can be applied in the discoveries of new potential vaccine targets for other pathogens.

Understanding Diversity, Evolution, and Structure of Small Heat Shock Proteins in Annelida Through in Silico Analyses

Small heat shock proteins (sHsps) are oligomeric stress proteins characterized by an α-crystallin domain (ACD). These proteins are localized in different subcellular compartments and play critical roles in the stress physiology of tissues, organs, and whole multicellular eukaryotes. They are ubiquitous proteins found in all living organisms, from bacteria to mammals, but they have never been studied in annelids. Here, a data set of 23 species spanning the annelid tree of life, including mostly transcriptomes but also two genomes, was interrogated and 228 novel putative sHsps were identified and manually curated. The analysis revealed very high protein diversity and showed that a significant number of sHsps have a particular dimeric architecture consisting of two tandemly repeated ACDs. The phylogenetic analysis distinguished three main clusters, two of them containing both monomeric sHsps, and ACDs located downstream in the dimeric sHsps, and the other one comprising the upstream ACDs from those dimeric forms. Our results support an evolutionary history of these proteins based on duplication events prior to the Spiralia split. Monomeric sHsps 76) were further divided into five subclusters. Physicochemical properties, subcellular location predictions, and sequence conservation analyses provided insights into the differentiating elements of these putative functional groups. Strikingly, three of those subclusters included sHsps with features typical of metazoans, while the other two presented characteristics resembling non-metazoan proteins. This study provides a solid background for further research on the diversity, evolution, and function in the family of the sHsps. The characterized annelid sHsps are disclosed as essential for improving our understanding of this important family of proteins and their pleotropic functions. The features and the great diversity of annelid sHsps position them as potential powerful molecular biomarkers of environmental stress for acting as prognostic tool in a diverse range of environments.

Diversity and evolution of the P450 family in arthropods

The P450 family (CYP genes) of arthropods encodes diverse enzymes involved in the metabolism of foreign compounds and in essential endocrine or ecophysiological functions. The P450 sequences (CYPome) from 40 arthropod species were manually curated, including 31 complete CYPomes, and a maximum likelihood phylogeny of nearly 3000 sequences is presented. Arthropod CYPomes are assembled from members of six CYP clans of variable size, the CYP2, CYP3, CYP4 and mitochondrial clans, as well as the CYP20 and CYP16 clans that are not found in Neoptera. CYPome sizes vary from two dozen genes in some parasitic species to over 200 in species as diverse as collembolans or ticks. CYPomes are comprised of few CYP families with many genes and many CYP families with few genes, and this distribution is the result of dynamic birth and death processes. Lineage-specific expansions or blooms are found throughout the phylogeny and often result in genomic clusters that appear to form a reservoir of catalytic diversity maintained as heritable units. Among the many P450s with physiological functions, six CYP families are involved in ecdysteroid metabolism. However, five so-called Halloween genes are not universally represented and do not constitute the unique pathway of ecdysteroid biosynthesis. The diversity of arthropod CYPomes has only partially been uncovered to date and many P450s with physiological functions regulating the synthesis and degradation of endogenous signal molecules (including ecdysteroids) and semiochemicals (including pheromones and defense chemicals) remain to be discovered. Sequence diversity of arthropod P450s is extreme, and P450 sequences lacking the universally conserved Cys ligand to the heme have evolved several times. A better understanding of P450 evolution is needed to discern the relative contributions of stochastic processes and adaptive processes in shaping the size and diversity of CYPomes.

iHyd-LysSite (EPSV): Identifying Hydroxylysine Sites in Protein Using Statistical Formulation by Extracting Enhanced Position and Sequence Variant Feature Technique

Introduction

Hydroxylation is one of the most important post-translational modifications (PTM) in cellular functions and is linked to various diseases. The addition of one of the hydroxyl groups (OH) to the lysine sites produces hydroxylysine when undergoes chemical modification.

Methods

The method which is used in this study for identifying hydroxylysine sites based on powerful mathematical and statistical methodology incorporating the sequence-order effect and composition of each object within protein sequences. This predictor is called “iHyd-LysSite (EPSV)” (identifying hydroxylysine sites by extracting enhanced position and sequence variant technique). The prediction of hydroxylysine sites by experimental methods is difficult, laborious and highly expensive. In silico technique is an alternative approach to identify hydroxylysine sites in proteins.

Results

The experimental results require that the predictive model should have high sensitivity and specificity values and must be more accurate. The self-consistency, independent, 10-fold cross-validation and jackknife tests are performed for validation purposes. These tests are resulted by using three renowned classifiers, Neural Networks (NN), Random Forest (RF) and Support Vector Machine (SVM) with the demanding prediction rate. The overall predictive outcomes are extraordinarily superior to the results obtained by previous predictors. The proposed model contributed an excellent prediction rate in the system for NN, RF, and SVM classifiers. The sensitivity and specificity results using all these classifiers for jackknife test are 96.08%, 94.99%, 98.16% and 97.52%, 98.52%, 80.95%.

Conclusion

The results obtained by the proposed tool show that this method may meet the future demand of hydroxylysine sites with a better prediction rate over the existing methods.

Ultrastructural, Cytochemical, and Comparative Genomic Evidence of Peroxisomes in Three Genera of Pathogenic Free-Living Amoebae, Including the First Morphological Data for the Presence of This Organelle in Heteroloboseans

Peroxisomes perform various metabolic processes that are primarily related to the elimination of reactive oxygen species and oxidative lipid metabolism. These organelles are present in all major eukaryotic lineages, nevertheless, information regarding the presence of peroxisomes in opportunistic parasitic protozoa is scarce and in many cases it is still unknown whether these organisms have peroxisomes at all. Here, we performed ultrastructural, cytochemical, and bioinformatic studies to investigate the presence of peroxisomes in three genera of free-living amoebae from two different taxonomic groups that are known to cause fatal infections in humans. By transmission electron microscopy, round structures with a granular content limited by a single membrane were observed in Acanthamoeba castellanii, Acanthamoeba griffini, Acanthamoeba polyphaga, Acanthamoeba royreba, Balamuthia mandrillaris (Amoebozoa), and Naegleria fowleri (Heterolobosea). Further confirmation for the presence of peroxisomes was obtained by treating trophozoites in situ with diaminobenzidine and hydrogen peroxide, which showed positive reaction products for the presence of catalase. We then performed comparative genomic analyses to identify predicted peroxin homologues in these organisms. Our results demonstrate that a complete set of peroxins-which are essential for peroxisome biogenesis, proliferation, and protein import-are present in all of these amoebae. Likewise, our in silico analyses allowed us to identify a complete set of peroxins in Naegleria lovaniensis and three novel peroxin homologues in Naegleria gruberi. Thus, our results indicate that peroxisomes are present in these three genera of free-living amoebae and that they have a similar peroxin complement despite belonging to different evolutionary lineages.

In-silico structural modeling and epitope prediction of highly conserved Plasmodium falciparum protein AMR1

Malaria caused by Plasmodium falciparum is the most deadly and a major health issue worldwide. In spite of several control programs, there hasn't been much improvement in keeping the disease under control. The appearance of drug resistant strains of Plasmodium in addition to insecticide resistance of the Anopheles vector has been a hurdle. Therefore, it is highly desirable to identify new potential candidates that can be targeted for therapeutic intervention. The present study identifies AMR1, a highly conserved essential protein of Plasmodium falciparum, as a potential candidate for vaccine development. AMR1 is an exposed surface protein with high antigenic property and conservancy among other species of the parasite. Reverse vaccinology approach (RV) is adopted to determine the best epitopes of AMR1 protein. The protein was further evaluated for several important physiochemical parameters. The study revealed the 3D structure of AMR1, as well as the best B cell and helper T-cell epitopes of the protein. These resulted epitopes might be of great importance in the development of an effective vaccine to combat the deadly disease.

Advances in the Prediction of Protein Subcellular Locations with Machine Learning

Background:

Revealing the subcellular location of a newly discovered protein can bring insight into their function and guide research at the cellular level. The experimental methods currently used to identify the protein subcellular locations are both time-consuming and expensive. Thus, it is highly desired to develop computational methods for efficiently and effectively identifying the protein subcellular locations. Especially, the rapidly increasing number of protein sequences entering the genome databases has called for the development of automated analysis methods.

Methods:

In this review, we will describe the recent advances in predicting the protein subcellular locations with machine learning from the following aspects: i) Protein subcellular location benchmark dataset construction, ii) Protein feature representation and feature descriptors, iii) Common machine learning algorithms, iv) Cross-validation test methods and assessment metrics, v) Web servers.

Result & Conclusion:

Concomitant with a large number of protein sequences generated by highthroughput technologies, four future directions for predicting protein subcellular locations with machine learning should be paid attention. One direction is the selection of novel and effective features (e.g., statistics, physical-chemical, evolutional) from the sequences and structures of proteins. Another is the feature fusion strategy. The third is the design of a powerful predictor and the fourth one is the protein multiple location sites prediction.

Prediction of Apoptosis Protein Subcellular Localization with Multilayer Sparse Coding and Oversampling Approach

The prediction of apoptosis protein subcellular localization plays an important role in understanding the progress in cell proliferation and death. Recently computational approaches to this issue have become very popular, since the traditional biological experiments are so costly and time-consuming that they cannot catch up with the growth rate of sequence data anymore. In order to improve the prediction accuracy of apoptosis protein subcellular localization, we proposed a sparse coding method combined with traditional feature extraction algorithm to complete the sparse representation of apoptosis protein sequences, using multilayer pooling based on different sizes of dictionaries to integrate the processed features, as well as oversampling approach to decrease the influences caused by unbalanced data sets. Then the extracted features were input to a support vector machine to predict the subcellular localization of the apoptosis protein. The experiment results obtained by Jackknife test on two benchmark data sets indicate that our method can significantly improve the accuracy of the apoptosis protein subcellular localization prediction.

BUSCA: an integrative web server to predict subcellular localization of proteins

Here, we present BUSCA (http://busca.biocomp.unibo.it), a novel web server that integrates different computational tools for predicting protein subcellular localization. BUSCA combines methods for identifying signal and transit peptides (DeepSig and TPpred3), GPI-anchors (PredGPI) and transmembrane domains (ENSEMBLE3.0 and BetAware) with tools for discriminating subcellular localization of both globular and membrane proteins (BaCelLo, MemLoci and SChloro). Outcomes from the different tools are processed and integrated for annotating subcellular localization of both eukaryotic and bacterial protein sequences. We benchmark BUSCA against protein targets derived from recent CAFA experiments and other specific data sets, reporting performance at the state-of-the-art. BUSCA scores better than all other evaluated methods on 2732 targets from CAFA2, with a F1 value equal to 0.49 and among the best methods when predicting targets from CAFA3. We propose BUSCA as an integrated and accurate resource for the annotation of protein subcellular localization.