Selected Protein Sequences Research Articles

CusProSe: a customizable protein annotation software with an application to the prediction of fungal secondary metabolism genes

We report here a new application, CustomProteinSearch (CusProSe), whose purpose is to help users to search for proteins of interest based on their domain composition. The application is customizable. It consists of two independent tools, IterHMMBuild and ProSeCDA. IterHMMBuild allows the iterative construction of Hidden Markov Model (HMM) profiles for conserved domains of selected protein sequences, while ProSeCDA scans a proteome of interest against an HMM profile database, and annotates identified proteins using user-defined rules. CusProSe was successfully used to identify, in fungal genomes, genes encoding key enzyme families involved in secondary metabolism, such as polyketide synthases (PKS), non-ribosomal peptide synthetases (NRPS), hybrid PKS-NRPS and dimethylallyl tryptophan synthases (DMATS), as well as to characterize distinct terpene synthases (TS) sub-families. The highly configurable characteristics of this application makes it a generic tool, which allows the user to refine the function of predicted proteins, to extend detection to new enzymes families, and may also be applied to biological systems other than fungi and to other proteins than those involved in secondary metabolism.

Evaluation and identification of essential therapeutic proteins and vaccinomics approach towards multi-epitopes vaccine designing against Legionella pneumophila for immune response instigation

The Legionellaceae group comprises the Legionella, containing 58 species with 70 serotypes. For instance, Legionella pneumophila is the deadliest serotype to cause Legionnaires infectious and is responsible for 90% of the infections in humans. The bacterial pathogen is associated with a severe lung infection, known as legionaries' disease. It is resistant to multiple drugs, thus warranting novel vaccine candidates identification to immune the host against infections caused by the said pathogen. For this, we applied the subtractive proteomics and reverse vaccinology approaches to annotate the most essential genes suitable for vaccine designing. From the whole proteome, only five proteins (Q5ZVG4, Q5ZRZ1, Q5ZWE6, Q5ZT09, and Q5ZUZ8) as the best targets for further processing as they fulfill all the standard parameters set for in silico vaccine design. Immuno-informatics approaches were further applied to the selected protein sequences to prioritized antigenic epitopes for design a multi-epitope subunit vaccine. A multi-epitopes vaccine was designed by using suitable linkers to link the CTL (cytotoxic T lymphocytes), HTL (Helper T lymphocytes), B cell epitopes, and adjuvant to strengthen the vaccine's immunogenicity. The MEVC(multi-epitopes vaccine construct) was reported to interact with human immune receptor TLR-2 (toll-like receptor) robustly (docking score = −357.18 kcal/mol), and a higher expression was achieved in the Escherichia coli system (CAI = 0.88, and GC contents = 54.34%). Moreover, immune simulation revealed that on the 3rd day, the neutralization of the antigen started, while on the 5th day, the antigen was completely neutralized by the secreted immune factors. In conclusion, the designed vaccine candidate effectively triggered the immune response against eh pathogen; however, wet lab-based experimentations are highly recommended to prove the protective immunological proficiency of the vaccine against L. pneumophila.

Novel Virus on Filamentous Arthronema africanum Cyanobacterium.

Widely distributed in water environments and in soil, cyanobacteria are hosts of lysogenic or lytic bacterioviruses. A novel, probably lysogenic virus (phage) for which the name Arthronema africanum virus TR020 (Aa-TR020) is proposed, has been isolated from filamentous freshwater cyanobacterium Arthronema africanum. The virus formed turbid plaques on plate culture of A. africanum strain 1980/01 but not on other Arthronema strain and other bacterial species. The genome of Aa-TR020 is linear molecule of dsDNA, 44,805bp in length with 216bp long terminal repeats and with G + C content of 46%. Fifty-five genes organized on plus and minus strands were predicted there. The genome size, gene arrangement, and selected protein sequences showed relatedness to Phormidium virus Pf-WMP3 and other viruses known to infect cyanobacteria and classified in the family Podoviridae.

Vaccinomics strategy to concoct a promising subunit vaccine for visceral leishmaniasis targeting sandfly and leishmania antigens

Visceral leishmaniasis (VL) has been declared as one of the most severely neglected tropical diseases by the World Health Organization report 2017. Cumulative incidences of treatment failure and drug resistance, demanding a potential treatment and preventive strategy for VL. In this study, we have devised a multi-epitope vaccine by targeting sandfly saliva and parasite-derived membrane and secretory antigens. We have predicted the immunogenic B-cell, HTL, and CTL epitopes from all the selected protein sequences. The epitopes were then linked to the spacer sequences for providing stability and flexibility, and the construct was linked with a synthetic TLR-4 agonist namely RS09 as an adjuvant. The 3D structure of vaccine was modelled, refined and validated by generating a Ramachandran plot. Later, molecular docking was performed between the TLR-4 receptor and vaccine. The obtained docked complex was then checked for their stability by performing MD simulation. The immune dynamics simulation was done to check the probable immune response generated when the host will be exposed to the vaccine candidate. This novel vaccine strategy will provide functional and mechanistic evidence on parasite and vector-derived epitopes that could activate B- and T-cells and potentially elicit a long-lasting memory cell response.

Boosting phosphorylation site prediction with sequence feature-based machine learning.

Protein phosphorylation is one of the essential posttranslation modifications playing a vital role in the regulation of many fundamental cellular processes. We propose a LightGBM-based computational approach that uses evolutionary, geometric, sequence environment, and amino acid-specific features to decipher phosphate binding sites from a protein sequence. Our method, while compared with other existing methods on 2429 protein sequences taken from standard Phospho.ELM (P.ELM) benchmark data set featuring 11 organisms reports a higher F1 score = 0.504 (harmonic mean of the precision and recall) and ROC AUC = 0.836 (area under the curve of the receiver operating characteristics). The computation time of our proposed approach is much less than that of the recently developed deep learning-based framework. Structural analysis on selected protein sequences informs that our prediction is the superset of the phosphorylation sites, as mentioned in P.ELM data set. The foundation of our scheme is manual feature engineering and a decision tree-based classification. Hence, it is intuitive, and one can interpret the final tree as a set of rules resulting in a deeper understanding of the relationships between biophysical features and phosphorylation sites. Our innovative problem transformation method permits more control over precision and recall as is demonstrated by the fact that if we incorporate output probability of the existing deep learning framework as an additional feature, then our prediction improves (F1 score = 0.546; ROC AUC = 0.849). The implementation of our method can be accessed at http://cse.iitkgp.ac.in/~pralay/resources/PPSBoost/ and is mirrored at https://cosmos.iitkgp.ac.in/PPSBoost.

Predicting regions prone to protein aggregation based on SVM algorithm

The phenomenon of protein aggregation has been associated with several neurodegenerative diseases, such as Parkinson's and Alzheimer's. Computational tools have been used to predict regions prone to aggregate in proteins with relative success. We have developed a tool called MAGRE for such predictions, based on the machine learning and sliding window techniques. We have applied the Support Vector Machine algorithm to generate classification models. In order to accomplish classification training, we adopted information of primary structure - protein sequence - from the Amyloid Data Bank. We have implemented two predictor categories according to protein structural information: General and Folding Class. We have selected the best performances of the sliding windows method and considered the folding class in order to develop the predictor. We conducted testing with randomly selected protein sequences from the PDB data bank - MAGRE's performance was compared with two predictors from literature: Aggrescan and Zyggregator, being considered satisfactory.

Strong Uranium(VI) Binding onto Bovine Milk Proteins, Selected Protein Sequences, and Model Peptides.

Hexavalent uranium is ubiquitous in the environment. In view of the chemical and radiochemical toxicity of uranium(VI), a good knowledge of its possible interactions in the environment is crucial. The aim of this work was to identify typical binding and sorption characteristics of uranium(VI) with both the pure bovine milk protein β-casein and diverse related protein mixtures (caseins, whey proteins). For comparison, selected model peptides representing the amino acid sequence 13-16 of β-casein and dephosphorylated β-casein were also studied. Complexation studies using potentiometric titration and time-resolved laser-induced fluorescence spectroscopy revealed that the phosphoryl-containing proteins form uranium(VI) complexes of higher stability than the structure-analog phosphoryl-free proteins. That is in agreement with the sorption experiments showing a significantly higher affinity of caseins toward uranium(VI) in comparison to whey proteins. On the other hand, the total sorption capacity of caseins is lower than that of whey proteins. The discussed binding behavior of milk proteins to uranium(VI) might open up interesting perspectives for sustainable techniques of uranium(VI) removal from aqueous solutions. This was further demonstrated by batch experiments on the removal of uranium(VI) from mineral water samples.

Signal Peptide Sequence Analysis of Selected Protein Sequences from Cryptosporidium parvum

Background and Objective: Studies on signal peptides remain little and lacking to Cryptosporidium. This study represents the first attempt to investigate selected putative protein sequences of Cryptosporidium parvum in its established genome database for signal peptide. Methods: Prediction analysis of protein sequences and identification of signal peptides on this parasite were analysed by SignalP 4.1 server. A total of 100 protein sequences were randomly selected and analysed, of which 6 (6%) corresponded to be predicted as signal peptide sequences. Results: Based on the comparison of sequences with database protein sequences in GenBank NCBI, almost all protein sequences were highly conserved with other protein sequences from different species of Cryptosporidium. Considering the secretory proteins in Cryptosporidium, several virulence protein sequences of this parasite may also show presence of N-terminal signal peptide or not. Conclusions: Hence, further studies need to be carried out for identification of N-terminal signal peptide function in Cryptosporidium for facilitating its virulence and pathogenesis pathway.

Accelerating Information Retrieval from Profile Hidden Markov Model Databases.

Profile Hidden Markov Model (Profile-HMM) is an efficient statistical approach to represent protein families. Currently, several databases maintain valuable protein sequence information as profile-HMMs. There is an increasing interest to improve the efficiency of searching Profile-HMM databases to detect sequence-profile or profile-profile homology. However, most efforts to enhance searching efficiency have been focusing on improving the alignment algorithms. Although the performance of these algorithms is fairly acceptable, the growing size of these databases, as well as the increasing demand for using batch query searching approach, are strong motivations that call for further enhancement of information retrieval from profile-HMM databases. This work presents a heuristic method to accelerate the current profile-HMM homology searching approaches. The method works by cluster-based remodeling of the database to reduce the search space, rather than focusing on the alignment algorithms. Using different clustering techniques, 4284 TIGRFAMs profiles were clustered based on their similarities. A representative for each cluster was assigned. To enhance sensitivity, we proposed an extended step that allows overlapping among clusters. A validation benchmark of 6000 randomly selected protein sequences was used to query the clustered profiles. To evaluate the efficiency of our approach, speed and recall values were measured and compared with the sequential search approach. Using hierarchical, k-means, and connected component clustering techniques followed by the extended overlapping step, we obtained an average reduction in time of 41%, and an average recall of 96%. Our results demonstrate that representation of profile-HMMs using a clustering-based approach can significantly accelerate data retrieval from profile-HMM databases.

Genetic and structural analyses of cytochrome P450 hydroxylases in sex hormone biosynthesis: Sequential origin and subsequent coevolution

Biosynthesis of steroid hormones in vertebrates involves three cytochrome P450 hydroxylases, CYP11A1, CYP17A1 and CYP19A1, which catalyze sequential steps in steroidogenesis. These enzymes are conserved in the vertebrates, but their origin and existence in other chordate subphyla (Tunicata and Cephalochordata) have not been clearly established. In this study, selected protein sequences of CYP11A1, CYP17A1 and CYP19A1 were compiled and analyzed using multiple sequence alignment and phylogenetic analysis. Our analyses show that cephalochordates have sequences orthologous to vertebrate CYP11A1, CYP17A1 or CYP19A1, and that echinoderms and hemichordates possess CYP11-like but not CYP19 genes. While the cephalochordate sequences have low identity with the vertebrate sequences, reflecting evolutionary distance, the data show apparent origin of CYP11 prior to the evolution of CYP19 and possibly CYP17, thus indicating a sequential origin of these functionally related steroidogenic CYPs. Co-occurrence of the three CYPs in early chordates suggests that the three genes may have coevolved thereafter, and that functional conservation should be reflected in functionally important residues in the proteins. CYP19A1 has the largest number of conserved residues while CYP11A1 sequences are less conserved. Structural analyses of human CYP11A1, CYP17A1 and CYP19A1 show that critical substrate binding site residues are highly conserved in each enzyme family. The results emphasize that the steroidogenic pathways producing glucocorticoids and reproductive steroids are several hundred million years old and that the catalytic structural elements of the enzymes have been conserved over the same period of time. Analysis of these elements may help to identify when precursor functions linked to these enzymes first arose.

Diversidad molecular de candidatos vacunales en Leptospira spp.

El objetivo de este estudio fue determinar la diversidad molecular de las proteínas OmpL1, LipL32, LipL41, LigA y LigB y de los genes que las codifican mediante análisis bioinformáticos en diferentes cepas patógenas de Leptospira spp., a partir de la información disponible en las bases de datos. Se utilizaron las secuencias de aminoácidos de las proteínas OmpL1, LipL32, LipL41, LigA y LigB, así como las de los genes que las codifican en las cepas de Leptospira spp. registradas en The National Center for Biotechnology Information (NCBI). Los análisis de las proteínas y los genes se realizaron mediante los recursos Protein, Nucleotide y Gene del NCBI. La alineación de las secuencias consenso se realizó con las herramientas PSI-BLAST y BLASTn. El porcentaje de cobertura de las secuencias seleccionadas de los genes ompL1, lipL32, lipL41, ligA y ligB en cepas patógenas de Leptospira spp. es de 100 % para ompL1, lipL32 y lipL41, 75 % para ligA y 99 % para ligB con porcentajes de identidad de 85, 98, 88, 90 y 80 % respectivamente; el porcentaje de cobertura de las secuencias seleccionadas de las proteínas es de 100, 77, 99, 100 y 100 % con porcentajes de identidad de 90, 99, 92, 63 y 60 % respectivamente, lo cual indica que los genes y las proteínas, excepto las proteínas LigA y LigB, son bastante conservadas en los diferentes serovares patógenos de Leptospira spp. Según dichos resultados, se recomienda realizar análisis complementarios de estas proteínas con el fin de determinar si es viable su uso como candidatos vacunales.

In vitro selection of proteins with desired characteristics using mRNA-display

mRNA-display is an amplification-based, iterative rounds of in vitro protein selection technique that circumvents a number of difficulties associated with yeast two-hybrid and phage display. Because of the covalent linkage between the genotype and the phenotype, mRNA-display provides a powerful means for reading and amplifying a peptide or protein sequence after it has been selected from a library with very high diversity. The purpose of this article is to provide a summary of the field and practical framework of mRNA-display-based selections. We summarize the advantages and limitations of selections using mRNA-display as well as the recent applications, namely, the identification of novel affinity reagents, target-binding partners, and enzyme substrates from synthetic peptide or natural proteome libraries. Practically, we provide a detailed procedure for performing mRNA-display-based selections with the aim of identifying protease substrates and binding partners of a target protein. Furthermore, we describe how to confirm the function of the selected protein sequences by biochemical assays and bioinformatic tools.

Quality control for a large‐scale study using protein arrays and protein beads to measure immune response in serum and plasma

We report on quality control performed in the context of a large-scale, multi-institutional study of the immune response in blood samples from prostate cancer patients. The measurements were performed by two commercially available techniques/services: protein arrays and an automated bead-based ELISA-like technique on 871 patient samples. The project started with a wide screen using standard arrays with 8302 protein sequences for 113 patients, followed by three studies using custom arrays with 215 selected protein sequences. These studies were followed up by three studies using the bead-based approach on 57 protein sequences chosen from the 215 selected before. We find similar responses in plasma and serum samples. Samples from the two European projects from which the samples originated also appeared comparable. In the data from the high-density standard arrays, we see for ~12% of the protein sequences high cross-correlation (R(2) > 0.8) with signals from unrelated protein sequences that are physically nearby on the array, suggesting production issues. The custom array and bead-based techniques both have good reproducibility, but the techniques do not agree with each other for ~50% of the protein sequences measured. We discuss the consequences of the observed data quality for the design and interpretation of the study.

XANNpred: Neural nets that predict the propensity of a protein to yield diffraction‐quality crystals

Production of diffracting crystals is a critical step in determining the three-dimensional structure of a protein by X-ray crystallography. Computational techniques to rank proteins by their propensity to yield diffraction-quality crystals can improve efficiency in obtaining structural data by guiding both protein selection and construct design. XANNpred comprises a pair of artificial neural networks that each predict the propensity of a selected protein sequence to produce diffraction-quality crystals by current structural biology techniques. Blind tests show XANNpred has accuracy and Matthews correlation values ranging from 75% to 81% and 0.50 to 0.63 respectively; values of area under the receiver operator characteristic (ROC) curve range from 0.81 to 0.88. On blind test data XANNpred outperforms the other available algorithms XtalPred, PXS, OB-Score, and ParCrys. XANNpred also guides construct design by presenting graphs of predicted propensity for diffraction-quality crystals against residue sequence position. The XANNpred-SG algorithm is likely to be most useful to target selection in structural genomics consortia, while the XANNpred-PDB algorithm is more suited to the general structural biology community. XANNpred predictions that include sliding window graphs are freely available from http://www.compbio.dundee.ac.uk/xannpred Proteins 2011. © 2010 Wiley-Liss, Inc.

The Membrane Proximal Extracellular Domain of Human hGC-B Folds Independently

Human Guanylyl Cyclase B (hGC-B) is a single-transmembrane receptor protein which upon binding C-type natriuretic peptide (CNP) to its extracellular domain catalyzes the intracellular conversion of GTP to the second messenger cGMP. cGMP in turn affects various physiological processes such as smooth muscle contraction, cell proliferation, phototransduction, and salt as well as fluid homeostasis. The 3-dimensional binding site of the peptide hormone is unknown, and the binding mechanism is not yet understood. Therefore, a model of the C-terminal moiety of the extracellular domain of human GC-B containing the potential binding site was derived from the crystal structure of (GC-A). The selected protein sequence was provided with an N-terminal TEV-cleavage site and fused with a 109 aa thioredoxin-tag and a hexahistidine-tag. The identity of the purified 25 kDa protein was confirmed by protein mass fingerprint and its secondary structure was determined by CD- and NMR-spectroscopy. The protein proved to be properly folded with the observed secondary structure matching the predicted secondary structure and the homologous structure in the extracellular domain of GC-A. Size exclusion chromatography confirmed the monomeric state of P-hGC-B.

Crystal structure of an alkaline serine protease from Nesterenkonia sp. defines a novel family of secreted bacterial proteases

Crystal structure of an alkaline serine protease from Nesterenkonia sp. defines a novel family of secreted bacterial proteases Na Yang, Jie Nan, Erik Brostromer, Rajni Hatti-Kaul, and Xiao-Dong Su* 1 Shenzhen Graduate School of Peking University, Shenzhen 518055, China 2 National Laboratory of Protein Engineering and Plant Genetic Engineering, Peking University, Beijing 100871, China 3 Department of Biotechnology, Center for Chemistry and Chemical Engineering, Lund University, S-221 00 Lund, Sweden

Analysis of Secreted Protein Profile and Enzymatic Activities from Corynebacterium diphtheriae and Bordetella pertussis on Production Batch Media using Peptide Quenched Fluorescent Substrates

Proteases were identified and characterized from the culture supernatant of the C. diphtheriae and B. pertussis bacteria. The proteases were secreted in the media and detected at the end of the exponential growth phase. Activity was detected in some fluorescent substrates, based on selected protein sequences such as insuline β‐chain, bradykinin, and synaptobrevin. The proteases were purified by means of gel filtration chromatography. Sodium dodecyl sulfate‐polyacrylamide gel electrophoresis (SDS‐PAGE) analysis of the purified proteins indicated, for the main secreted proteins, an estimated molecular mass of 30 kDa in C. diphtheriae and 69 kDa in B. pertussis culture media. The proteases were stable and presented enzymatic activity at 37°C. These proteases were not related to the main toxic compounds described in these two bacteria, but could represent good markers for the fermentation process when the enzyme activity was measured with the fluorescent substrates.

Gaining knowledge from previously unexplained spectra‐application of the PTM‐Explorer software to detect PTM in HUPO BPP MS/MS data

A novel software tool named PTM-Explorer has been applied to LC-MS/MS datasets acquired within the Human Proteome Organisation (HUPO) Brain Proteome Project (BPP). PTM-Explorer enables automatic identification of peptide MS/MS spectra that were not explained in typical sequence database searches. The main focus was detection of PTMs, but PTM-Explorer detects also unspecific peptide cleavage, mass measurement errors, experimental modifications, amino acid substitutions, transpeptidation products and unknown mass shifts. To avoid a combinatorial problem the search is restricted to a set of selected protein sequences, which stem from previous protein identifications using a common sequence database search. Prior to application to the HUPO BPP data, PTM-Explorer was evaluated on excellently manually characterized and evaluated LC-MS/MS data sets from Alpha-A-Crystallin gel spots obtained from mouse eye lens. Besides various PTMs including phosphorylation, a wealth of experimental modifications and unspecific cleavage products were successfully detected, completing the primary structure information of the measured proteins. Our results indicate that a large amount of MS/MS spectra that currently remain unidentified in standard database searches contain valuable information that can only be elucidated using suitable software tools.

Phylogenomics reveal a robust fungal tree of life

Our understanding of the tree of life (TOL) is still fragmentary. Until recently, molecular phylogeneticists have built trees based on ribosomal RNA sequences and selected protein sequences, which, however, usually suffered from lack of support for the deeper branches and inconsistencies probably due to limited subsampling of the entire genome. Now, phylogenetic hypotheses can be based on the analysis of full genomes. We used available complete genome data as well as the eukaryote orthologous group (KOG) proteins to reconstruct with confidence basal branches of the fungal TOL. Phylogenetic analysis of a core of 531 KOGs shared among 21 fungal genomes, three animal genomes and one plant genome showed a single tree with high support resulting from four different methods of phylogenetic reconstruction. The single tree that we inferred from our dataset showed excellent nodal support for each branch, suggesting that it reflects the true phylogenetic relationships of the species involved.

Prediction of catalytic residues using Support Vector Machine with selected protein sequence and structural properties.

BackgroundThe number of protein sequences deriving from genome sequencing projects is outpacing our knowledge about the function of these proteins. With the gap between experimentally characterized and uncharacterized proteins continuing to widen, it is necessary to develop new computational methods and tools for functional prediction. Knowledge of catalytic sites provides a valuable insight into protein function. Although many computational methods have been developed to predict catalytic residues and active sites, their accuracy remains low, with a significant number of false positives. In this paper, we present a novel method for the prediction of catalytic sites, using a carefully selected, supervised machine learning algorithm coupled with an optimal discriminative set of protein sequence conservation and structural properties.ResultsTo determine the best machine learning algorithm, 26 classifiers in the WEKA software package were compared using a benchmarking dataset of 79 enzymes with 254 catalytic residues in a 10-fold cross-validation analysis. Each residue of the dataset was represented by a set of 24 residue properties previously shown to be of functional relevance, as well as a label {+1/-1} to indicate catalytic/non-catalytic residue. The best-performing algorithm was the Sequential Minimal Optimization (SMO) algorithm, which is a Support Vector Machine (SVM). The Wrapper Subset Selection algorithm further selected seven of the 24 attributes as an optimal subset of residue properties, with sequence conservation, catalytic propensities of amino acids, and relative position on protein surface being the most important features.ConclusionThe SMO algorithm with 7 selected attributes correctly predicted 228 of the 254 catalytic residues, with an overall predictive accuracy of more than 86%. Missing only 10.2% of the catalytic residues, the method captures the fundamental features of catalytic residues and can be used as a "catalytic residue filter" to facilitate experimental identification of catalytic residues for proteins with known structure but unknown function.