Using increment of diversity to predict mitochondrial proteins of malaria parasite: integrating pseudo-amino acid composition and structural alphabet

Due to the complexity of Plasmodium falciparum (PF) genome, predicting mitochondrial proteins of PF is more difficult than other species. In this study, using the n-peptide composition of reduced amino acid alphabet (RAAA) obtained from structural alphabet named Protein Blocks as feature parameter, the increment of diversity (ID) is firstly developed to predict mitochondrial proteins. By choosing the 1-peptide compositions on the N-terminal regions with 20 residues as the only input vector, the prediction performance achieves 86.86% accuracy with 0.69 Mathew’s correlation coefficient (MCC) by the jackknife test. Moreover, by combining with the hydropathy distribution along protein sequence and several reduced amino acid alphabets, we achieved maximum MCC 0.82 with accuracy 92% in the jackknife test by using the developed ID model. When evaluating on an independent dataset our method performs better than existing methods. The results indicate that the ID is a simple and efficient prediction method for mitochondrial proteins of malaria parasite.     

Efficient Prediction of Co-Complexed Proteins Based on Coevolution

The prediction of the network of protein-protein interactions (PPI) of an organism is crucial for the understanding of biological processes and for the development of new drugs. Machine learning methods have been successfully applied to the prediction of PPI in yeast by the integration of multiple direct and indirect biological data sources. However, experimental data are not available for most organisms. We propose here an ensemble machine learning approach for the prediction of PPI that depends solely on features independent from experimental data. We developed new estimators of the coevolution between proteins and combined them in an ensemble learning procedure.We applied this method to a dataset of known co-complexed proteins in Escherichia coli and compared it to previously published methods. We show that our method allows prediction of PPI with an unprecedented precision of 95.5% for the first 200 sorted pairs of proteins compared to 28.5% on the same dataset with the previous best method.A close inspection of the best predicted pairs allowed us to detect new or recently discovered interactions between chemotactic components, the flagellar apparatus and RNA polymerase complexes in E. coli.     

A step-by-step classification algorithm of protein secondary structures based on double-layer SVM model

In this paper, a step-by-step classification algorithm based on double-layer SVM model is constructed to predict the secondary structure of proteins. The most important feature of this algorithm is to improve the prediction accuracy of α+β and α/β classes through transforming the prediction of two classes of proteins, α+β and α/β classes, with low accuracy in the past, into the prediction of all-α and all-β classes with high accuracy. A widely-used dataset, 25PDB dataset with sequence similarity lower than 40%, is used to evaluate this method. The results show that this method has good performance, and on the basis of ensuring the accuracy of other three structural classes of proteins, the accuracy of α+β class proteins is improved significantly.     

Prediction of Mitochondrial Proteins Using Discrete Wavelet Transform

A new method was proposed for prediction of mitochondrial proteins by the discrete wavelet transform, based on the sequence–scale similarity measurement. This sequence–scale similarity, revealing more information than other conventional methods, does not rely on subcellular location information and can directly predict protein sequences with different length. In our experiments, 499 mitochondrial protein sequences, constituting a mitochondria database, were used as training dataset, and 681 non-mitochondrial protein sequences were tested. The system can predict these sequences with sensitivity, specificity, accuracy and MCC of 50.30%, 95.74%, 76.53% and 0.54, respectively. Source code of the new program is available on request from the authors.     

Characterization of mitochondrial carrier proteins of malaria parasite Plasmodium falciparum based on in vitro translation and reconstitution

Members of the mitochondrial carrier (MC) family of membrane transporters play important roles in cellular metabolism. We previously established an in vitro reconstitution system for membrane transporters based on wheat germ cell-free translation system. We have now applied this reconstitution system to the comparative analysis of MC proteins from the malaria parasite Plasmodium falciparum and Saccharomyces cerevisiae. We synthesized twelve putative P. falciparum MCs and determined the transport activities of four of these proteins including PF3D7_1037300 protein (ADP/ATP translocator), PF3D7_1004800 protein (ADP/ATP translocator), PF3D7_1202200 protein (phosphate carrier), and PF3D7_1241600 protein (S-adenosylmethionine transporter). In addition, we tested the effect of cardiolipin on the activity of MC proteins. The transport activities of the yeast MCs, ScAac2p, ScGgc1p, ScDic1p, ScPic1p, and ScSam5p, which localize to the mitochondrial inner membrane, were increased by cardiolipin supplementation, whereas that of ScAnt1p, which localizes to the peroxisome membrane, was not significantly affected. Together, this indicates that the functional properties of the reconstituted MCs reflect the lipid content of their native membranes. Except for PF3D7_1241600 protein, these P. falciparum proteins manifested cardiolipin-dependent transport activities. Immunofluorescence analysis showed that PF3D7_1241600 protein is not mainly localized to the mitochondria of P. falciparum cells. We thus revealed the functions of four MC proteins of the malaria parasite and the effects of cardiolipin on their activities.     

Molecular Evidence of Increased Resistance to Anti-Folate Drugs in Plasmodium falciparum in North-East India: A Signal for Potential Failure of Artemisinin Plus Sulphadoxine-Pyrimethamine Combination Therapy

North-east India, being a corridor to South-east Asia, is believed to play an important role in transmitting drug resistant Plasmodium falciparum malaria to India and South Asia. North-east India was the first place in India to record the emergence of drug resistance to chloroquine as well as sulphadoxine/pyrimethamine. Presently chloroquine resistance is widespread all over the North-east India and resistance to other anti-malarials is increasing. In this study both in vivo therapeutic efficacy and molecular assays were used to screen the spectrum of drug resistance to chloroquine and sulphadoxine/pyrimethamine in the circulating P. falciparum strains. A total of 220 P. falciparum positives subjects were enrolled in the study for therapeutic assessment of chloroquine and sulphadoxine/pyrimethamine and assessment of point mutations conferring resistances to these drugs were carried out by genotyping the isolates following standard methods. Overall clinical failures in sulphadoxine/pyrimethamine and chloroquine were found 12.6 and 69.5% respectively, while overall treatment failures recorded were 13.7 and 81.5% in the two arms. Nearly all (99.0%) the isolates had mutant pfcrt genotype (76T), while 68% had mutant pfmdr-1 genotype (86Y). Mutation in dhps 437 codon was the most prevalent one while dhfr codon 108 showed 100% mutation. A total of 23 unique haplotypes at the dhps locus and 7 at dhfr locus were found while dhps-dhfr combined loci revealed 49 unique haplotypes. Prevalence of double, triple and quadruple mutations were common while 1 haplotype was found with all five mutated codons (F/AGEGS/T) at dhps locus. Detection of quadruple mutants (51I/59R/108N/164L) in the present study, earlier recorded from Car Nicobar Island, India only, indicates the presence of high levels of resistance to sulphadoxine/pyrimethamine in north-east India. Associations between resistant haplotypes and the clinical outcomes and emerging resistance in sulphadoxine/pyrimethamine in relation to the efficacy of the currently used artemisinin combination therapy are discussed.     

Deciphering apicoplast targeting signals – feature extraction from nuclear-encoded precursors of Plasmodium falciparum apicoplast proteins

The malaria causing protozoan Plasmodium falciparum contains a vestigal, non-photosynthetic plastid, the apicoplast. Numerous proteins encoded by nuclear genes are targeted to the apicoplast courtesy of N-terminal extensions. With the impending sequence completion of an entire genome of the malaria parasite, it is important to have software tools in place for prediction of subcellular locations for all proteins. Apicoplast targeting signals are bipartite; containing a signal peptide and a transit peptide. Nuclear-encoded apicoplast protein precursors were analyzed for characteristic features by statistical methods, principal component analysis, self-organizing maps, and supervised neural networks. The transit peptide contains a net positive charge and is rich in asparagine, lysine, and isoleucine residues. A novel prediction system (PATS, predict apicoplast-targeted sequences) was developed based on various sequence features, yielding a Matthews correlation coefficient of 0.91 (97% correct predictions) in a 40-fold cross-validation study. This system predicted 22% apicoplast proteins of the 205 potential proteins on P. falciparum chromosome 2, and 21% of 243 chromosome 3 proteins. A combination of the PATS results with a signal peptide prediction yields 15% potentially nuclear-encoded apicoplast proteins on chromosomes 2 and 3. The prediction tool will advance P. falciparum genome analysis, and it might help to identify apicoplast proteins as drug targets for the development of novel anti-malaria agents.     

Prediction of mitochondrial proteins based on genetic algorithm – partial least squares and support vector machine

Mitochondria are essential cell organelles of eukaryotes. Hence, it is vitally important to develop an automated and reliable method for timely identification of novel mitochondrial proteins. In this study, mitochondrial proteins were encoded by dipeptide composition technology; then, the genetic algorithm-partial least square (GA-PLS) method was used to evaluate the dipeptide composition elements which are more important in recognizing mitochondrial proteins; further, these selected dipeptide composition elements were applied to support vector machine (SVM)-based classifiers to predict the mitochondrial proteins. All the models were trained and validated by the jackknife cross-validation test. The prediction accuracy is 85%, suggesting that it performs reasonably well in predicting the mitochondrial proteins. Our results strongly imply that not all the dipeptide compositions are informative and indispensable for predicting proteins. The source code of MATLAB and the dataset are available on request under liml@scu.edu.cn.     

A high-accuracy protein structural class prediction algorithm using predicted secondary structural information

One major problem with the existing algorithm for the prediction of protein structural classes is low accuracies for proteins from α/β and α+β classes. In this study, three novel features were rationally designed to model the differences between proteins from these two classes. In combination with other rational designed features, an 11-dimensional vector prediction method was proposed. By means of this method, the overall prediction accuracy based on 25PDB dataset was 1.5% higher than the previous best-performing method, MODAS. Furthermore, the prediction accuracy for proteins from α+β class based on 25PDB dataset was 5% higher than the previous best-performing method, SCPRED. The prediction accuracies obtained with the D675 and FC699 datasets were also improved.     

Analysis of 953 Human Proteins from a Mitochondrial HEK293 Fraction by Complexome Profiling

Complexome profiling is a novel technique which uses shotgun proteomics to establish protein migration profiles from fractionated blue native electrophoresis gels. Here we present a dataset of blue native electrophoresis migration profiles for 953 proteins by complexome profiling. By analysis of mitochondrial ribosomal complexes we demonstrate its potential to verify putative protein-protein interactions identified by affinity purification – mass spectrometry studies. Protein complexes were extracted in their native state from a HEK293 mitochondrial fraction and separated by blue native gel electrophoresis. Gel lanes were cut into gel slices of even size and analyzed by shotgun proteomics. Subsequently, the acquired protein migration profiles were analyzed for co-migration via hierarchical cluster analysis. This dataset holds great promise as a comprehensive resource for de novo identification of protein-protein interactions or to underpin and prioritize candidate protein interactions from other studies. To demonstrate the potential use of our dataset we focussed on the mitochondrial translation machinery. Our results show that mitoribosomal complexes can be analyzed by blue native gel electrophoresis, as at least four distinct complexes. Analysis of these complexes confirmed that 24 proteins that had previously been reported to co-purify with mitoribosomes indeed co-migrated with subunits of the mitochondrial ribosome. Co-migration of several proteins involved in biogenesis of inner mitochondrial membrane complexes together with mitoribosomal complexes suggested the possibility of co-translational assembly in human cells. Our data also highlighted a putative ribonucleotide complex that potentially contains MRPL10, MRPL12 and MRPL53 together with LRPPRC and SLIRP.     

Co-endemicity of Plasmodium falciparum and Intestinal Helminths Infection in School Age Children in Rural Communities of Kwara State Nigeria

Background

Malaria and intestinal helminths co-infection are major public health problems particularly among school age children in Nigeria. However the magnitude and possible interactions of these infections remain poorly understood. This study determined the prevalence, impact and possible interaction of Plasmodium falciparum and intestinal helminths co-infection among school children in rural communities of Kwara State, Nigeria.

Methods

Blood, urine and stool samples were collected from 1017 primary school pupils of ages 4–15 years. Stool samples were processed using both Kato-Katz and formol-ether concentration techniques and microscopically examined for intestinal helminths infection. Urine samples were analyzed using sedimentation method for Schistosoma haematobium. Plasmodium falciparum was confirmed by microscopy using thick and thin blood films methods and packed cell volume (PCV) was determined using hematocrit reader. Univariate analysis and chi-square statistical tests were used to analyze the data.

Results

Overall, 61.2% of all school children had at least an infection of either P. falciparum, S. haematobium, or intestinal helminth. S. haematobium accounted for the largest proportion (44.4%) of a single infection followed by P. falciparum (20.6%). The prevalence of malaria and helminth co-infection in the study was 14.4%. Four species of intestinal helminths were recovered from the stool samples and these were hookworm (22.5%), Hymenolepis species (9.8%), Schistosoma mansoni (2.9%) and Enterobius vermicularis (0.6%). The mean densities of P. falciparum in children co-infected with S. haematobium and hookworm were higher compared to those infected with P. falciparum only though not statistically significant (p = 0.062). The age distribution of both S. haematobium (p = 0.049) and hookworm (p = 0.034) infected children were statistically significant with the older age group (10–15 years) recording the highest prevalence of 47.2% and 25% respectively. Children who were infected with S. haematobium (RR = 1.3) and hookworm (RR = 1.4) have equal chances of being infected with P. falciparum as children with no worm infection. On the other hand children infected with Hymenolepis spp. (p<0.0001) are more likely to be infected with P. falciparum than Hymenolepis spp. uninfected children (RR = 2.0)

Conclusions

These findings suggest that multiple parasitic infections are common in school age children in rural communities of Kwara State Nigeria. The Hymenolepis spp. induced increase susceptibility to P. falciparum could have important consequences on how concurrent infections affect the expression or pathogenesis of these infections.     

Multi-class protein fold recognition using support vector machines and neural networks

MOTIVATION: Protein fold recognition is an important approach to structure discovery without relying on sequence similarity. We study this approach with new multi-class classification methods and examined many issues important for a practical recognition system. RESULTS: Most current discriminative methods for protein fold prediction use the one-against-others method, which has the well-known 'False Positives' problem. We investigated two new methods: the unique one-against-others and the all-against-all methods. Both improve prediction accuracy by 14-110% on a dataset containing 27 SCOP folds. We used the Support Vector Machine (SVM) and the Neural Network (NN) learning methods as base classifiers. SVMs converges fast and leads to high accuracy. When scores of multiple parameter datasets are combined, majority voting reduces noise and increases recognition accuracy. We examined many issues involved with large number of classes, including dependencies of prediction accuracy on the number of folds and on the number of representatives in a fold. Overall, recognition systems achieve 56% fold prediction accuracy on a protein test dataset, where most of the proteins have below 25% sequence identity with the proteins used in training.     

FIEFDom: a transparent domain boundary recognition system using a fuzzy mean operator

Protein domain prediction is often the preliminary step in both experimental and computational protein research. Here we present a new method to predict the domain boundaries of a multidomain protein from its amino acid sequence using a fuzzy mean operator. Using the nr-sequence database together with a reference protein set (RPS) containing known domain boundaries, the operator is used to assign a likelihood value for each residue of the query sequence as belonging to a domain boundary. This procedure robustly identifies contiguous boundary regions. For a dataset with a maximum sequence identity of 30%, the average domain prediction accuracy of our method is 97% for one domain proteins and 58% for multidomain proteins. The presented model is capable of using new sequence/structure information without re-parameterization after each RPS update. When tested on a current database using a four year old RPS and on a database that contains different domain definitions than those used to train the models, our method consistently yielded the same accuracy while two other published methods did not. A comparison with other domain prediction methods used in the CASP7 competition indicates that our method performs better than existing sequence-based methods.     

Evaluation of the Accuracy of the EasyTest? Malaria Pf/Pan Ag,a Rapid Diagnostic Test,in Uganda

In recent years, rapid diagnostic tests (RDTs) have been widely used for malaria detection, primarily because of their simple operation, fast results, and straightforward interpretation. The Asan EasyTest™ Malaria Pf/Pan Ag is one of the most commonly used malaria RDTs in several countries, including Korea and India. In this study, we tested the diagnostic performance of this RDT in Uganda to evaluate its usefulness for field diagnosis of malaria in this country. Microscopic and PCR analyses, and the Asan EasyTest™ Malaria Pf/Pan Ag rapid diagnostic test, were performed on blood samples from 185 individuals with suspected malaria in several villages in Uganda. Compared to the microscopic analysis, the sensitivity of the RDT to detect malaria infection was 95.8% and 83.3% for Plasmodium falciparum and non-P. falciparum, respectively. Although the diagnostic sensitivity of the RDT decreased when parasitemia was ≤500 parasites/µl, it showed 96.8% sensitivity (98.4% for P. falciparum and 93.8% for non-P. falciparum) in blood samples with parasitemia ≥100 parasites/µl. The specificity of the RDT was 97.3% for P. falciparum and 97.3% for non-P. falciparum. These results collectively suggest that the accuracy of the Asan EasyTest™ Malaria Pf/Pan Ag makes it an effective point-of-care diagnostic tool for malaria in Uganda.