Molecular Property Diagnostic Suite for COVID-19 (MPDSCOVID-19): an open-source disease-specific drug discovery portal.

Molecular Property Diagnostic Suite (MPDS) was conceived and developed as an open-source disease-specific web portal based on Galaxy. MPDSCOVID-19 was developed for COVID-19 as a one-stop solution for drug discovery research. Galaxy platforms enable the creation of customized workflows connecting various modules in the web server. The architecture of MPDSCOVID-19 effectively employs Galaxy v22.04 features, which are ported on CentOS 7.8 and Python 3.7. MPDSCOVID-19 provides significant updates and the addition of several new tools updated after six years. Tools developed by our group in Perl/Python and open-source tools are collated and integrated into MPDSCOVID-19 using XML scripts. Our MPDS suite aims to facilitate transparent and open innovation. This approach significantly helps bring inclusiveness in the community while promoting free access and participation in software development. Availability & Implementation: The MPDSCOVID-19 portal can be accessed at https://mpds.neist.res.in:8085/.

Prediction of IL4 inducing peptides.

The secretion of Interleukin-4 (IL4) is the characteristic of T-helper 2 responses. IL4 is a cytokine produced by CD4+ T cells in response to helminthes and other extracellular parasites. It has a critical role in guiding antibody class switching, hematopoiesis and inflammation, and the development of appropriate effector T-cell responses. In this study, it is the first time an attempt has been made to understand whether it is possible to predict IL4 inducing peptides. The data set used in this study comprises 904 experimentally validated IL4 inducing and 742 noninducing MHC class II binders. Our analysis revealed that certain types of residues are preferred at certain positions in IL4 inducing peptides. It was also observed that IL4 inducing and noninducing epitopes differ in compositional and motif pattern. Based on our analysis we developed classification models where the hybrid method of amino acid pairs and motif information performed the best with maximum accuracy of 75.76% and MCC of 0.51. These results indicate that it is possible to predict IL4 inducing peptides with reasonable precession. These models would be useful in designing the peptides that may induce desired Th2 response.

CCDB: a curated database of genes involved in cervix cancer.

The Cervical Cancer gene DataBase (CCDB, http://crdd.osdd.net/raghava/ccdb) is a manually curated catalog of experimentally validated genes that are thought, or are known to be involved in the different stages of cervical carcinogenesis. In spite of the large women population that is presently affected from this malignancy still at present, no database exists that catalogs information on genes associated with cervical cancer. Therefore, we have compiled 537 genes in CCDB that are linked with cervical cancer causation processes such as methylation, gene amplification, mutation, polymorphism and change in expression level, as evident from published literature. Each record contains details related to gene like architecture (exon-intron structure), location, function, sequences (mRNA/CDS/protein), ontology, interacting partners, homology to other eukaryotic genomes, structure and links to other public databases, thus augmenting CCDB with external data. Also, manually curated literature references have been provided to support the inclusion of the gene in the database and establish its association with cervix cancer. In addition, CCDB provides information on microRNA altered in cervical cancer as well as search facility for querying, several browse options and an online tool for sequence similarity search, thereby providing researchers with easy access to the latest information on genes involved in cervix cancer.

Predicting sub-cellular localization of tRNA synthetases from their primary structures.

Since endo-symbiotic events occur, all genes of mitochondrial aminoacyl tRNA synthetase (AARS) were lost or transferred from ancestral mitochondrial genome into the nucleus. The canonical pattern is that both cytosolic and mitochondrial AARSs coexist in the nuclear genome. In the present scenario all mitochondrial AARSs are nucleus-encoded, synthesized on cytosolic ribosomes and post-translationally imported from the cytosol into the mitochondria in eukaryotic cell. The site-based discrimination between similar types of enzymes is very challenging because they have almost same physico-chemical properties. It is very important to predict the sub-cellular location of AARSs, to understand the mitochondrial protein synthesis. We have analyzed and optimized the distinguishable patterns between cytosolic and mitochondrial AARSs. Firstly, support vector machines (SVM)-based modules have been developed using amino acid and dipeptide compositions and achieved Mathews correlation coefficient (MCC) of 0.82 and 0.73, respectively. Secondly, we have developed SVM modules using position-specific scoring matrix and achieved the maximum MCC of 0.78. Thirdly, we developed SVM modules using N-terminal, intermediate residues, C-terminal and split amino acid composition (SAAC) and achieved MCC of 0.82, 0.70, 0.39 and 0.86, respectively. Finally, a SVM module was developed using selected attributes of split amino acid composition (SA-SAAC) approach and achieved MCC of 0.92 with an accuracy of 96.00%. All modules were trained and tested on a non-redundant data set and evaluated using fivefold cross-validation technique. On the independent data sets, SA-SAAC based prediction model achieved MCC of 0.95 with an accuracy of 97.77%. The web-server 'MARSpred' based on above study is available at http://www.imtech.res.in/raghava/marspred/.

AntigenDB: an immunoinformatics database of pathogen antigens.

The continuing threat of infectious disease and future pandemics, coupled to the continuous increase of drug-resistant pathogens, makes the discovery of new and better vaccines imperative. For effective vaccine development, antigen discovery and validation is a prerequisite. The compilation of information concerning pathogens, virulence factors and antigenic epitopes has resulted in many useful databases. However, most such immunological databases focus almost exclusively on antigens where epitopes are known and ignore those for which epitope information was unavailable. We have compiled more than 500 antigens into the AntigenDB database, making use of the literature and other immunological resources. These antigens come from 44 important pathogenic species. In AntigenDB, a database entry contains information regarding the sequence, structure, origin, etc. of an antigen with additional information such as B and T-cell epitopes, MHC binding, function, gene-expression and post translational modifications, where available. AntigenDB also provides links to major internal and external databases. We shall update AntigenDB on a rolling basis, regularly adding antigens from other organisms and extra data analysis tools. AntigenDB is available freely at http://www.imtech.res.in/raghava/antigendb and its mirror site http://www.bic.uams.edu/raghava/antigendb.

Prediction of mitochondrial proteins of malaria parasite using split amino acid composition and PSSM profile.

The rate of human death due to malaria is increasing day-by-day. Thus the malaria causing parasite Plasmodium falciparum (PF) remains the cause of concern. With the wealth of data now available, it is imperative to understand protein localization in order to gain deeper insight into their functional roles. In this manuscript, an attempt has been made to develop prediction method for the localization of mitochondrial proteins. In this study, we describe a method for predicting mitochondrial proteins of malaria parasite using machine-learning technique. All models were trained and tested on 175 proteins (40 mitochondrial and 135 non-mitochondrial proteins) and evaluated using five-fold cross validation. We developed a Support Vector Machine (SVM) model for predicting mitochondrial proteins of P. falciparum, using amino acids and dipeptides composition and achieved maximum MCC 0.38 and 0.51, respectively. In this study, split amino acid composition (SAAC) is used where composition of N-termini, C-termini, and rest of protein is computed separately. The performance of SVM model improved significantly from MCC 0.38 to 0.73 when SAAC instead of simple amino acid composition was used as input. In addition, SVM model has been developed using composition of PSSM profile with MCC 0.75 and accuracy 91.38%. We achieved maximum MCC 0.81 with accuracy 92% using a hybrid model, which combines PSSM profile and SAAC. When evaluated on an independent dataset our method performs better than existing methods. A web server PFMpred has been developed for predicting mitochondrial proteins of malaria parasites ( http://www.imtech.res.in/raghava/pfmpred/).

Computer-aided biotechnology: from immuno-informatics to reverse vaccinology.

Genome sequences from many organisms, including humans, have been completed, and high-throughput analyses have produced burgeoning volumes of 'omics' data. Bioinformatics is crucial for the management and analysis of such data and is increasingly used to accelerate progress in a wide variety of large-scale and object-specific functional analyses. Refined algorithms enable biotechnologists to follow 'computer-aided strategies' based on experiments driven by high-confidence predictions. In order to address compound problems, current efforts in immuno-informatics and reverse vaccinology are aimed at developing and tuning integrative approaches and user-friendly, automated bioinformatics environments. This will herald a move to 'computer-aided biotechnology': smart projects in which time-consuming and expensive large-scale experimental approaches are progressively replaced by prediction-driven investigations.

Prediction of RNA binding sites in a protein using SVM and PSSM profile.

RNA-binding proteins (RBPs) play key roles in post-transcriptional control of gene expression, which, along with transcriptional regulation, is a major way to regulate patterns of gene expression during development. Thus, the identification and prediction of RNA binding sites is an important step in comprehensive understanding of how RBPs control organism development. Combining evolutionary information and support vector machine (SVM), we have developed an improved method for predicting RNA binding sites or RNA interacting residues in a protein sequence. The prediction models developed in this study have been trained and tested on 86 RNA binding protein chains and evaluated using fivefold cross validation technique. First, a SVM model was developed that achieved a maximum Matthew's correlation coefficient (MCC) of 0.31. The performance of this SVM model further improved the MCC from 0.31 to 0.45, when multiple sequence alignment in the form of PSSM profiles was used as input to the SVM, which is far better than the maximum MCC achieved by previous methods (0.41) on the same dataset. In addition, SVM models were also developed on an alternative dataset that contained 107 RBP chains. Utilizing PSSM as input information to the SVM, the training/testing on this alternate dataset achieved a maximum MCC of 0.32. Conclusively, the prediction performance of SVM models developed in this study is better than the existing methods on the same datasets. A web server 'Pprint' was also developed for predicting RNA binding residues in a protein sequence which is freely available at http://www.imtech.res.in/raghava/pprint/.

CytoPred: a server for prediction and classification of cytokines.

Cytokines are messengers of immune system. They are small secreted proteins that mediate and regulate the immune system, inflammation and hematopoiesis. Recent studies have revealed important roles played by the cytokines in adjuvants as therapeutic targets and in cancer therapy. In this paper, an attempt has been made to predict this important class of proteins and classify further them into families and subfamilies. A PSI-BLAST+Support Vector Machine-based hybrid approach is adopted to develop the prediction methods. CytoPred is capable of predicting cytokines with an accuracy of 98.29%. The overall accuracy of classification of cytokines into four families and further classification into seven subfamilies is 99.77 and 97.24%, respectively. It has been shown by comparison that CytoPred performs better than the already existing CTKPred. A user-friendly server CytoPred has been developed and available at http://www.imtech.res.in/raghava/cytopred.

DPROT: prediction of disordered proteins using evolutionary information.

The association of structurally disordered proteins with a number of diseases has engendered enormous interest and therefore demands a prediction method that would facilitate their expeditious study at molecular level. The present study describes the development of a computational method for predicting disordered proteins using sequence and profile compositions as input features for the training of SVM models. First, we developed the amino acid and dipeptide compositions based SVM modules which yielded sensitivities of 75.6 and 73.2% along with Matthew's Correlation Coefficient (MCC) values of 0.75 and 0.60, respectively. In addition, the use of predicted secondary structure content (coil, sheet and helices) in the form of composition values attained a sensitivity of 76.8% and MCC value of 0.77. Finally, the training of SVM models using evolutionary information hidden in the multiple sequence alignment profile improved the prediction performance by achieving a sensitivity value of 78% and MCC of 0.78. Furthermore, when evaluated on an independent dataset of partially disordered proteins, the same SVM module provided a correct prediction rate of 86.6%. Based on the above study, a web server ("DPROT") was developed for the prediction of disordered proteins, which is available at http://www.imtech.res.in/raghava/dprot/.

AlgPred: prediction of allergenic proteins and mapping of IgE epitopes.

In this study a systematic attempt has been made to integrate various approaches in order to predict allergenic proteins with high accuracy. The dataset used for testing and training consists of 578 allergens and 700 non-allergens obtained from A. K. Bjorklund, D. Soeria-Atmadja, A. Zorzet, U. Hammerling and M. G. Gustafsson (2005) Bioinformatics, 21, 39-50. First, we developed methods based on support vector machine using amino acid and dipeptide composition and achieved an accuracy of 85.02 and 84.00%, respectively. Second, a motif-based method has been developed using MEME/MAST software that achieved sensitivity of 93.94 with 33.34% specificity. Third, a database of known IgE epitopes was searched and this predicted allergenic proteins with 17.47% sensitivity at specificity of 98.14%. Fourth, we predicted allergenic proteins by performing BLAST search against allergen representative peptides. Finally hybrid approaches have been developed, which combine two or more than two approaches. The performance of all these algorithms has been evaluated on an independent dataset of 323 allergens and on 101 725 non-allergens obtained from Swiss-Prot. A web server AlgPred has been developed for the predicting allergenic proteins and for mapping IgE epitopes on allergenic proteins (http://www.imtech.res.in/raghava/algpred/). AlgPred is available at www.imtech.res.in/raghava/algpred/.

Analysis and prediction of antibacterial peptides.

BACKGROUND: Antibacterial peptides are important components of the innate immune system, used by the host to protect itself from different types of pathogenic bacteria. Over the last few decades, the search for new drugs and drug targets has prompted an interest in these antibacterial peptides. We analyzed 486 antibacterial peptides, obtained from antimicrobial peptide database APD, in order to understand the preference of amino acid residues at specific positions in these peptides. RESULTS: It was observed that certain types of residues are preferred over others in antibacterial peptides, particularly at the N and C terminus. These observations encouraged us to develop a method for predicting antibacterial peptides in proteins from their amino acid sequence. First, the N-terminal residues were used for predicting antibacterial peptides using Artificial Neural Network (ANN), Quantitative Matrices (QM) and Support Vector Machine (SVM), which resulted in an accuracy of 83.63%, 84.78% and 87.85%, respectively. Then, the C-terminal residues were used for developing prediction methods, which resulted in an accuracy of 77.34%, 82.03% and 85.16% using ANN, QM and SVM, respectively. Finally, ANN, QM and SVM models were developed using N and C terminal residues, which achieved an accuracy of 88.17%, 90.37% and 92.11%, respectively. All the models developed in this study were evaluated using five-fold cross validation technique. These models were also tested on an independent or blind dataset. CONCLUSION: Among antibacterial peptides, there is preference for certain residues at N and C termini, which helps to demarcate them from non-antibacterial peptides. Both the termini play a crucial role in imparting the antibacterial property to these peptides. Among the methods developed, SVM shows the best performance in predicting antibacterial peptides followed by QM and ANN, in that order. AntiBP (Antibacterial peptides) will help in discovering efficacious antibacterial peptides, which we hope will prove to be a boon to combat the dreadful antibiotic resistant bacteria. A user friendly web server has also been developed to help the biological community, which is accessible at http://www.imtech.res.in/raghava/antibp/.

TAPPred prediction of TAP-binding peptides in antigens.

The transporter associated with antigen processing (TAP) plays a crucial role in the transport of the peptide fragments of the proteolysed antigenic or self-altered proteins to the endoplasmic reticulum where the association between these peptides and the major histocompatibility complex (MHC) class I molecules takes place. Therefore, prediction of TAP-binding peptides is highly helpful in identifying the MHC class I-restricted T-cell epitopes and hence in the subunit vaccine designing. In this chapter, we describe a support vector machine (SVM)-based method TAPPred that allows users to predict TAP-binding affinity of peptides over web. The server allows user to predict TAP binders using a simple SVM model or cascade SVM model. The server also allows user to customize the display/output. It is freely available for academicians and noncommercial organization at the address http://www.imtech.res.in/raghava/tappred.

A hybrid approach for predicting promiscuous MHC class I restricted T cell epitopes.

In the present study, a systematic attempt has been made to develop an accurate method for predicting MHC class I restricted T cell epitopes for a large number of MHC class I alleles. Initially, a quantitative matrix (QM)-based method was developed for 47 MHC class I alleles having at least 15 binders. A secondary artificial neural network (ANN)-based method was developed for 30 out of 47 MHC alleles having a minimum of 40 binders. Combination of these ANN-and QM-based prediction methods for 30 alleles improved the accuracy of prediction by 6% compared to each individual method. Average accuracy of hybrid method for 30 MHC alleles is 92.8%. This method also allows prediction of binders for 20 additional alleles using QM that has been reported in the literature, thus allowing prediction for 67 MHC class I alleles. The performance of the method was evaluated using jack-knife validation test. The performance of the methods was also evaluated on blind or independent data. Comparison of our method with existing MHC binder prediction methods for alleles studied by both methods shows that our method is superior to other existing methods. This method also identifies proteasomal cleavage sites in antigen sequences by implementing the matrices described earlier. Thus, the method that we discover allows the identification of MHC class I binders (peptides binding with many MHC alleles) having proteasomal cleavage site at C-terminus. The user-friendly result display format (HTML-II) can assist in locating the promiscuous MHC binding regions from antigen sequence. The method is available on the web at www.imtech.res.in/raghava/nhlapred and its mirror site is available at http://bioinformatics.uams.edu/mirror/nhlapred/.

Support vector machine based prediction of glutathione S-transferase proteins.

Glutathione S-transferase (GST) proteins play vital role in living organism that includes detoxification of exogenous and endogenous chemicals, survivability during stress condition. This paper describes a method developed for predicting GST proteins. We have used a dataset of 107 GST and 107 non-GST proteins for training and the performance of the method was evaluated with five-fold cross-validation technique. First a SVM based method has been developed using amino acid and dipeptide composition and achieved the maximum accuracy of 91.59% and 95.79% respectively. In addition we developed a SVM based method using tripeptide composition and achieved maximum accuracy 97.66% which is better than accuracy achieved by HMM based searching (96.26%). Based on above study a web-server GSTPred has been developed (http://www.imtech.res.in/raghava/gstpred/).

PEPstr: a de novo method for tertiary structure prediction of small bioactive peptides.

Among secondary structure elements, beta-turns are ubiquitous and major feature of bioactive peptides. We analyzed 77 biologically active peptides with length varying from 9 to 20 residues. Out of 77 peptides, 58 peptides were found to contain at least one beta-turn. Further, at the residue level, 34.9% of total peptide residues were found to be in beta-turns, higher than the number of helical (32.3%) and beta-sheet residues (6.9%). So, we utilized the predicted beta-turns information to develop an improved method for predicting the three-dimensional (3D) structure of small peptides. In principle, we built four different structural models for each peptide. The first 'model I' was built by assigning all the peptide residues an extended conformation (phi = Psi = 180 degrees ). Second 'model II' was built using the information of regular secondary structures (helices, beta-strands and coil) predicted from PSIPRED. In third 'model III', secondary structure information including beta-turn types predicted from BetaTurns method was used. The fourth 'model IV' had main-chain phi, Psi angles of model III and side chain angles assigned using standard Dunbrack backbone dependent rotamer library. These models were further refined using AMBER package and the resultant C(alpha) rmsd values were calculated. It was found that adding the beta-turns to the regular secondary structures greatly reduces the rmsd values both before and after the energy minimization. Hence, the results indicate that regular and irregular secondary structures, particularly beta-turns information can provide valuable and vital information in the tertiary structure prediction of small bioactive peptides. Based on the above study, a web server PEPstr (http://www.imtech.res.in/raghava/pepstr/) was developed for predicting the tertiary structure of small bioactive peptides.

Oxypred: prediction and classification of oxygen-binding proteins.

This study describes a method for predicting and classifying oxygen-binding proteins. Firstly, support vector machine (SVM) modules were developed using amino acid composition and dipeptide composition for predicting oxygen-binding proteins, and achieved maximum accuracy of 85.5% and 87.8%, respectively. Secondly, an SVM module was developed based on amino acid composition, classifying the predicted oxygen-binding proteins into six classes with accuracy of 95.8%, 97.5%, 97.5%, 96.9%, 99.4%, and 96.0% for erythrocruorin, hemerythrin, hemocyanin, hemoglobin, leghemoglobin, and myoglobin proteins, respectively. Finally, an SVM module was developed using dipeptide composition for classifying the oxygen-binding proteins, and achieved maximum accuracy of 96.1%, 98.7%, 98.7%, 85.6%, 99.6%, and 93.3% for the above six classes, respectively. All modules were trained and tested by five-fold cross validation. Based on the above approach, a web server Oxypred was developed for predicting and classifying oxygen-binding proteins (available from http://www.imtech.res.in/raghava/oxypred/).

Pcleavage: an SVM based method for prediction of constitutive proteasome and immunoproteasome cleavage sites in antigenic sequences.

This manuscript describes a support vector machine based method for the prediction of constitutive as well as immunoproteasome cleavage sites in antigenic sequences. This method achieved Matthew's correlation coefficents of 0.54 and 0.43 on in vitro and major histocompatibility complex ligand data, respectively. This shows that the performance of our method is comparable to that of the NetChop method, which is currently considered to be the best method for proteasome cleavage site prediction. Based on the method, a web server, Pcleavage, has also been developed. This server accepts protein sequences in any standard format and present results in a user-friendly format. The server is available for free use by all academic users at the URL http://www.imtech.res.in/raghava/pcleavage/ or http://bioinformatics.uams.edu/mirror/pcleavage/.

GPCRsclass: a web tool for the classification of amine type of G-protein-coupled receptors.

The receptors of amine subfamily are specifically major drug targets for therapy of nervous disorders and psychiatric diseases. The recognition of novel amine type of receptors and their cognate ligands is of paramount interest for pharmaceutical companies. In the past, Chou and co-workers have shown that different types of amine receptors are correlated with their amino acid composition and are predictable on its basis with considerable accuracy [Elrod and Chou (2002) Protein Eng., 15, 713-715]. This motivated us to develop a better method for the recognition of novel amine receptors and for their further classification. The method was developed on the basis of amino acid composition and dipeptide composition of proteins using support vector machine. The method was trained and tested on 167 proteins of amine subfamily of G-protein-coupled receptors (GPCRs). The method discriminated amine subfamily of GPCRs from globular proteins with Matthew's correlation coefficient of 0.98 and 0.99 using amino acid composition and dipeptide composition, respectively. In classifying different types of amine receptors using amino acid composition and dipeptide composition, the method achieved an accuracy of 89.8 and 96.4%, respectively. The performance of the method was evaluated using 5-fold cross-validation. The dipeptide composition based method predicted 67.6% of protein sequences with an accuracy of 100% with a reliability index > or =5. A web server GPCRsclass has been developed for predicting amine-binding receptors from its amino acid sequence [http://www.imtech.res.in/raghava/gpcrsclass/ and http://bioinformatics.uams.edu/raghava/gpersclass/ (mirror site)].

BhairPred: prediction of beta-hairpins in a protein from multiple alignment information using ANN and SVM techniques.

This paper describes a method for predicting a supersecondary structural motif, beta-hairpins, in a protein sequence. The method was trained and tested on a set of 5102 hairpins and 5131 non-hairpins, obtained from a non-redundant dataset of 2880 proteins using the DSSP and PROMOTIF programs. Two machine-learning techniques, an artificial neural network (ANN) and a support vector machine (SVM), were used to predict beta-hairpins. An accuracy of 65.5% was achieved using ANN when an amino acid sequence was used as the input. The accuracy improved from 65.5 to 69.1% when evolutionary information (PSI-BLAST profile), observed secondary structure and surface accessibility were used as the inputs. The accuracy of the method further improved from 69.1 to 79.2% when the SVM was used for classification instead of the ANN. The performances of the methods developed were assessed in a test case, where predicted secondary structure and surface accessibility were used instead of the observed structure. The highest accuracy achieved by the SVM based method in the test case was 77.9%. A maximum accuracy of 71.1% with Matthew's correlation coefficient of 0.41 in the test case was obtained on a dataset previously used by X. Cruz, E. G. Hutchinson, A. Shephard and J. M. Thornton (2002) Proc. Natl Acad. Sci. USA, 99, 11157-11162. The performance of the method was also evaluated on proteins used in the '6th community-wide experiment on the critical assessment of techniques for protein structure prediction (CASP6)'. Based on the algorithm described, a web server, BhairPred (http://www.imtech.res.in/raghava/bhairpred/), has been developed, which can be used to predict beta-hairpins in a protein using the SVM approach.