Intratumoral follicular regulatory T cells curtail anti-PD-1 treatment efficacy.

Immune-checkpoint blockade (ICB) has shown remarkable clinical success in boosting antitumor immunity. However, the breadth of its cellular targets and specific mode of action remain elusive. We find that tumor-infiltrating follicular regulatory T (TFR) cells are prevalent in tumor tissues of several cancer types. They are primarily located within tertiary lymphoid structures and exhibit superior suppressive capacity and in vivo persistence as compared with regulatory T cells, with which they share a clonal and developmental relationship. In syngeneic tumor models, anti-PD-1 treatment increases the number of tumor-infiltrating TFR cells. Both TFR cell deficiency and the depletion of TFR cells with anti-CTLA-4 before anti-PD-1 treatment improve tumor control in mice. Notably, in a cohort of 271 patients with melanoma, treatment with anti-CTLA-4 followed by anti-PD-1 at progression was associated with better a survival outcome than monotherapy with anti-PD-1 or anti-CTLA-4, anti-PD-1 followed by anti-CTLA-4 at progression or concomitant combination therapy.

Multi-cell type gene coexpression network analysis reveals coordinated interferon response and cross-cell type correlations in systemic lupus erythematosus.

Systemic lupus erythematosus (SLE) is an incurable autoimmune disease disproportionately affecting women. A major obstacle in finding targeted therapies for SLE is its remarkable heterogeneity in clinical manifestations as well as in the involvement of distinct cell types. To identify cell-specific targets as well as cross-correlation relationships among expression programs of different cell types, we here analyze six major circulating immune cell types from SLE patient blood. Our results show that presence of an interferon response signature stratifies patients into two distinct groups (IFNneg vs. IFNpos). Comparing these two groups using differential gene expression and differential gene coexpression analysis, we prioritize a relatively small list of genes from classical monocytes including two known immune modulators: TNFSF13B/BAFF (target of belimumab, an approved therapeutic for SLE) and IL1RN (the basis of anakinra, a therapeutic for rheumatoid arthritis). We then develop a multi-cell type extension of the weighted gene coexpression network analysis (WGCNA) framework, termed mWGCNA. Applying mWGCNA to RNA-seq data from six sorted immune cell populations (15 SLE, 10 healthy donors), we identify a coexpression module with interferon-stimulated genes (ISGs) among all cell types and a cross-cell type correlation linking expression of specific T helper cell markers to B cell response as well as to TNFSF13B expression from myeloid cells, all of which in turn correlates with disease severity of IFNpos patients. Our results demonstrate the power of a hypothesis-free and data-driven approach to discover drug targets and to reveal novel cross-correlation across cell types in SLE with implications for other autoimmune diseases.

Single agent subcutaneous blinatumomab for advanced acute lymphoblastic leukemia.

Blinatumomab is a BiTE® (bispecific T-cell engager) molecule that redirects CD3+ T-cells to engage and lyse CD19+ target cells. Here we demonstrate that subcutaneous (SC) blinatumomab can provide high efficacy and greater convenience of administration. In the expansion phase of a multi-institutional phase 1b trial (ClinicalTrials.gov, NCT04521231), heavily pretreated adults with relapsed/refractory B-cell acute lymphoblastic leukemia (R/R B-ALL) received SC blinatumomab at two doses: (1) 250 µg once daily (QD) for week 1 and 500 µg three times weekly (TIW) thereafter (250 µg/500 µg) or (2) 500 µg QD for week 1 and 1000 µg TIW thereafter (500 µg/1000 µg). The primary endpoint was complete remission/complete remission with partial hematologic recovery (CR/CRh) within two cycles. At the data cutoff of September 15, 2023, 29 patients were treated: 14 at the 250 µg/500 µg dose and 13 at 500 µg/1000 µg dose. Data from two ineligible patients were excluded. At the end of two cycles, 12 of 14 patients (85.7%) from the 250 µg/500 µg dose achieved CR/CRh of which nine patients (75.0%) were negative for measurable residual disease (MRD; <10-4 leukemic blasts). At the 500 µg/1000 µg dose, 12 of 13 patients (92.3%) achieved CR/CRh; all 12 patients (100.0%) were MRD-negative. No treatment-related grade 4 cytokine release syndrome (CRS) or neurologic events (NEs) were reported. SC injections were well tolerated and all treatment-related grade 3 CRS and NEs responded to standard-of-care management, interruption, or discontinuation. Treatment with SC blinatumomab resulted in high efficacy, with high MRD-negativity rates and acceptable safety profile in heavily pretreated adults with R/R B-ALL.

Human Eosinophils Express a Distinct Gene Expression Program in Response to IL-3 Compared with Common ß-Chain Cytokines IL-5 and GM-CSF.

Despite recent advances in asthma management with anti-IL-5 therapies, many patients have eosinophilic asthma that remains poorly controlled. IL-3 shares a common ß subunit receptor with both IL-5 and GM-CSF but, through α-subunit-specific properties, uniquely influences eosinophil biology and may serve as a potential therapeutic target. We aimed to globally characterize the transcriptomic profiles of GM-CSF, IL-3, and IL-5 stimulation on human circulating eosinophils and identify differences in gene expression using advanced statistical modeling. Human eosinophils were isolated from the peripheral blood of healthy volunteers and stimulated with either GM-CSF, IL-3, or IL-5 for 48 h. RNA was then extracted and bulk sequencing performed. DESeq analysis identified differentially expressed genes and weighted gene coexpression network analysis independently defined modules of genes that are highly coexpressed. GM-CSF, IL-3, and IL-5 commonly upregulated 252 genes and downregulated 553 genes, producing a proinflammatory and survival phenotype that was predominantly mediated through TWEAK signaling. IL-3 stimulation yielded the most numbers of differentially expressed genes that were also highly coexpressed (n = 119). These genes were enriched in pathways involving JAK/STAT signaling. GM-CSF and IL-5 stimulation demonstrated redundancy in eosinophil gene expression. In conclusion, IL-3 produces a distinct eosinophil gene expression program among the ß-chain receptor cytokines. IL-3-upregulated genes may provide a foundation for research into therapeutics for patients with eosinophilic asthma who do not respond to anti-IL-5 therapies.

Comparison of spinocerebellar ataxia type 3 mouse models identifies early gain-of-function, cell-autonomous transcriptional changes in oligodendrocytes.

Spinocerebellar ataxia type 3 (SCA3) is a neurodegenerative disorder caused by a polyglutamine-encoding CAG repeat expansion in the ATXN3 gene. This expansion leads to misfolding and aggregation of mutant ataxin-3 (ATXN3) and degeneration of select brain regions. A key unanswered question in SCA3 and other polyglutamine diseases is the extent to which neurodegeneration is mediated through gain-of-function versus loss-of-function. To address this question in SCA3, we performed transcriptional profiling on the brainstem, a highly vulnerable brain region in SCA3, in a series of mouse models with varying degrees of ATXN3 expression and aggregation. We include two SCA3 knock-in mouse models: our previously published model that erroneously harbors a tandem duplicate of the CAG repeat-containing exon, and a corrected model, introduced here. Both models exhibit dose-dependent neuronal accumulation and aggregation of mutant ATXN3, but do not exhibit a behavioral phenotype. We identified a molecular signature that correlates with ATXN3 neuronal aggregation yet is primarily linked to oligodendrocytes, highlighting early white matter dysfunction in SCA3. Two robustly elevated oligodendrocyte transcripts, Acy3 and Tnfrsf13c, were confirmed as elevated at the protein level in SCA3 human disease brainstem. To determine if mutant ATXN3 acts on oligodendrocytes cell-autonomously, we manipulated the repeat expansion in the variant SCA3 knock-in mouse by cell-type specific Cre/LoxP recombination. Changes in oligodendrocyte transcripts are driven cell-autonomously and occur independent of neuronal ATXN3 aggregation. Our findings support a primary toxic gain of function mechanism and highlight a previously unrecognized role for oligodendrocyte dysfunction in SCA3 disease pathogenesis.

Algorithms for modeling global and context-specific functional relationship networks.

Functional genomics has enormous potential to facilitate our understanding of normal and disease-specific physiology. In the past decade, intensive research efforts have been focused on modeling functional relationship networks, which summarize the probability of gene co-functionality relationships. Such modeling can be based on either expression data only or heterogeneous data integration. Numerous methods have been deployed to infer the functional relationship networks, while most of them target the global (non-context-specific) functional relationship networks. However, it is expected that functional relationships consistently reprogram under different tissues or biological processes. Thus, advanced methods have been developed targeting tissue-specific or developmental stage-specific networks. This article brings together the state-of-the-art functional relationship network modeling methods, emphasizes the need for heterogeneous genomic data integration and context-specific network modeling and outlines future directions for functional relationship networks.

miRmine: a database of human miRNA expression profiles.

MOTIVATION: MicroRNAs (miRNAs) are small non-coding RNAs that are involved in post-transcriptional regulation of gene expression. In this high-throughput sequencing era, a tremendous amount of RNA-seq data is accumulating, and full utilization of publicly available miRNA data is an important challenge. These data are useful to determine expression values for each miRNA, but quantification pipelines are in a primitive stage and still evolving; there are many factors that affect expression values significantly. RESULTS: We used 304 high-quality microRNA sequencing (miRNA-seq) datasets from NCBI-SRA and calculated expression profiles for different tissues and cell-lines. In each miRNA-seq dataset, we found an average of more than 500 miRNAs with higher than 5x coverage, and we explored the top five highly expressed miRNAs in each tissue and cell-line. This user-friendly miRmine database has options to retrieve expression profiles of single or multiple miRNAs for a specific tissue or cell-line, either normal or with disease information. Results can be displayed in multiple interactive, graphical and downloadable formats. AVAILABILITY AND IMPLEMENTATION: http://guanlab.ccmb.med.umich.edu/mirmine. CONTACT: bharatpa@umich.edu. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Genome-Wide Functional Annotation of Human Protein-Coding Splice Variants Using Multiple Instance Learning.

The vast majority of human multiexon genes undergo alternative splicing and produce a variety of splice variant transcripts and proteins, which can perform different functions. These protein-coding splice variants (PCSVs) greatly increase the functional diversity of proteins. Most functional annotation algorithms have been developed at the gene level; the lack of isoform-level gold standards is an important intellectual limitation for currently available machine learning algorithms. The accumulation of a large amount of RNA-seq data in the public domain greatly increases our ability to examine the functional annotation of genes at isoform level. In the present study, we used a multiple instance learning (MIL)-based approach for predicting the function of PCSVs. We used transcript-level expression values and gene-level functional associations from the Gene Ontology database. A support vector machine (SVM)-based 5-fold cross-validation technique was applied. Comparatively, genes with multiple PCSVs performed better than single PCSV genes, and performance also improved when more examples were available to train the models. We demonstrated our predictions using literature evidence of ADAM15, LMNA/C, and DMXL2 genes. All predictions have been implemented in a web resource called "IsoFunc", which is freely available for the global scientific community through http://guanlab.ccmb.med.umich.edu/isofunc .

Identification of protein-interacting nucleotides in a RNA sequence using composition profile of tri-nucleotides.

The RNA-protein interactions play a diverse role in the cells, thus identification of RNA-protein interface is essential for the biologist to understand their function. In the past, several methods have been developed for predicting RNA interacting residues in proteins, but limited efforts have been made for the identification of protein-interacting nucleotides in RNAs. In order to discriminate protein-interacting and non-interacting nucleotides, we used various classifiers (NaiveBayes, NaiveBayesMultinomial, BayesNet, ComplementNaiveBayes, MultilayerPerceptron, J48, SMO, RandomForest, SMO and SVM(light)) for prediction model development using various features and achieved highest 83.92% sensitivity, 84.82 specificity, 84.62% accuracy and 0.62 Matthew's correlation coefficient by SVM(light) based models. We observed that certain tri-nucleotides like ACA, ACC, AGA, CAC, CCA, GAG, UGA, and UUU preferred in protein-interaction. All the models have been developed using a non-redundant dataset and are evaluated using five-fold cross validation technique. A web-server called RNApin has been developed for the scientific community (http://crdd.osdd.net/raghava/rnapin/).

Computational Inferences of the Functions of Alternative/Noncanonical Splice Isoforms Specific to HER2+/ER-/PR- Breast Cancers, a Chromosome 17 C-HPP Study.

This study was conducted as a part of the Chromosome-Centric Human Proteome Project (C-HPP) of the Human Proteome Organization. The main objective is to identify and evaluate functionality of a set of specific noncanonical isoforms expressed in HER2-neu positive, estrogen receptor negative (ER-), and progesterone receptor negative (PR-) breast cancers (HER2+/ER-/PR- BC), an aggressive subtype of breast cancers that cause significant morbidity and mortality. We identified 11 alternative splice isoforms that were differentially expressed in HER2+/ER-/PR- BC compared to normal mammary, triple negative breast cancer and triple positive breast cancer tissues (HER2+/ER+/PR+). We used a stringent criterion that differentially expressed noncanonical isoforms (adjusted p value < 0.05) and have to be expressed in all replicates of HER2+/ER-/PR- BC samples, and the trend in differential expression (up or down) is the same in all comparisons. Of the 11 protein isoforms, six were overexpressed in HER2+/ER-/PR- BC. We explored possible functional roles of these six proteins using several complementary computational tools. Biological processes including cell cycle events and glycolysis were linked to four of these proteins. For example, glycolysis was the top ranking functional process for DMXL2 isoform 3, with a fold change of 27 compared to just two for the canonical protein. No previous reports link DMXL2 with any metabolic processes; the canonical protein is known to participate in signaling pathways. Our results clearly indicate distinct functions for the six overexpressed alternative splice isoforms, and these functions could be specific to HER2+/ER-/PR- tumor progression. Further detailed analysis is warranted as these proteins could be explored as potential biomarkers and therapeutic targets for HER2+/ER-/PR- BC patients.

MI-PVT: A Tool for Visualizing the Chromosome-Centric Human Proteome.

We have developed the web-based Michigan Proteome Visualization Tool (MI-PVT) to visualize and compare protein expression and isoform-level function across human chromosomes and tissues (http://guanlab.ccmb.med.umich.edu/mipvt). As proof of principle, we have populated the tool with Human Proteome Map (HPM) data. We were able to observe many biologically interesting features. From the vantage point of our chromosome 17 team, for example, we found more than 300 proteins from chromosome 17 expressed in each of the 30 tissues and cell types studied, with the highest number of expressed proteins being 685 in testis. Comparisons of expression levels across tissues showed low numbers of proteins expressed in esophagus, but esophagus had 12 cytoskeletal proteins coded on chromosome 17 with very high expression (>1000 spectral counts). This customized MI-PVT should be helpful for biologists to browse and study specific proteins and protein data sets across tissues and chromosomes. Users can upload any data of interest in MI-PVT for visualization. Our aim is to integrate extensive mass-spectrometric proteomic data into the tool to facilitate finding chromosome-centric protein expression and correlation across tissues.

Functional Networks of Highest-Connected Splice Isoforms: From The Chromosome 17 Human Proteome Project.

Alternative splicing allows a single gene to produce multiple transcript-level splice isoforms from which the translated proteins may show differences in their expression and function. Identifying the major functional or canonical isoform is important for understanding gene and protein functions. Identification and characterization of splice isoforms is a stated goal of the HUPO Human Proteome Project and of neXtProt. Multiple efforts have catalogued splice isoforms as "dominant", "principal", or "major" isoforms based on expression or evolutionary traits. In contrast, we recently proposed highest connected isoforms (HCIs) as a new class of canonical isoforms that have the strongest interactions in a functional network and revealed their significantly higher (differential) transcript-level expression compared to nonhighest connected isoforms (NCIs) regardless of tissues/cell lines in the mouse. HCIs and their expression behavior in the human remain unexplored. Here we identified HCIs for 6157 multi-isoform genes using a human isoform network that we constructed by integrating a large compendium of heterogeneous genomic data. We present examples for pairs of transcript isoforms of ABCC3, RBM34, ERBB2, and ANXA7. We found that functional networks of isoforms of the same gene can show large differences. Interestingly, differential expression between HCIs and NCIs was also observed in the human on an independent set of 940 RNA-seq samples across multiple tissues, including heart, kidney, and liver. Using proteomic data from normal human retina and placenta, we showed that HCIs are a promising indicator of expressed protein isoforms exemplified by NUDFB6 and M6PR. Furthermore, we found that a significant percentage (20%, p = 0.0003) of human and mouse HCIs are homologues, suggesting their conservation between species. Our identified HCIs expand the repertoire of canonical isoforms and are expected to facilitate studying main protein products, understanding gene regulation, and possibly evolution. The network is available through our web server as a rich resource for investigating isoform functional relationships (http://guanlab.ccmb.med.umich.edu/hisonet). All MS/MS data were available at ProteomeXchange Web site (http://www.proteomexchange.org) through their identifiers (retina: PXD001242, placenta: PXD000754).

Prediction of uridine modifications in tRNA sequences.

BACKGROUND: In past number of methods have been developed for predicting post-translational modifications in proteins. In contrast, limited attempt has been made to understand post-transcriptional modifications. Recently it has been shown that tRNA modifications play direct role in the genome structure and codon usage. This study is an attempt to understand kingdom-wise tRNA modifications particularly uridine modifications (UMs), as majority of modifications are uridine-derived. RESULTS: A three-steps strategy has been applied to develop an efficient method for the prediction of UMs. In the first step, we developed a common prediction model for all the kingdoms using a dataset from MODOMICS-2008. Support Vector Machine (SVM) based prediction models were developed and evaluated by five-fold cross-validation technique. Different approaches were applied and found that a hybrid approach of binary and structural information achieved highest Area under the curve (AUC) of 0.936. In the second step, we used newly added tRNA sequences (as independent dataset) of MODOMICS-2012 for the kingdom-wise prediction performance evaluation of previously developed (in the first step) common model and achieved performances between the AUC of 0.910 to 0.949. In the third and last step, we used different datasets from MODOMICS-2012 for the kingdom-wise individual prediction models development and achieved performances between the AUC of 0.915 to 0.987. CONCLUSIONS: The hybrid approach is efficient not only to predict kingdom-wise modifications but also to classify them into two most prominent UMs: Pseudouridine (Y) and Dihydrouridine (D). A webserver called tRNAmod (http://crdd.osdd.net/raghava/trnamod/) has been developed, which predicts UMs from both tRNA sequences and whole genome.

Prediction and classification of ncRNAs using structural information.

BACKGROUND: Evidence is accumulating that non-coding transcripts, previously thought to be functionally inert, play important roles in various cellular activities. High throughput techniques like next generation sequencing have resulted in the generation of vast amounts of sequence data. It is therefore desirable, not only to discriminate coding and non-coding transcripts, but also to assign the noncoding RNA (ncRNA) transcripts into respective classes (families). Although there are several algorithms available for this task, their classification performance remains a major concern. Acknowledging the crucial role that non-coding transcripts play in cellular processes, it is required to develop algorithms that are able to precisely classify ncRNA transcripts. RESULTS: In this study, we initially develop prediction tools to discriminate coding or non-coding transcripts and thereafter classify ncRNAs into respective classes. In comparison to the existing methods that employed multiple features, our SVM-based method by using a single feature (tri-nucleotide composition), achieved MCC of 0.98. Knowing that the structure of a ncRNA transcript could provide insights into its biological function, we use graph properties of predicted ncRNA structures to classify the transcripts into 18 different non-coding RNA classes. We developed classification models using a variety of algorithms (BayeNet, NaiveBayes, MultilayerPerceptron, IBk, libSVM, SMO and RandomForest) and observed that model based on RandomForest performed better than other models. As compared to the GraPPLE study, the sensitivity (of 13 classes) and specificity (of 14 classes) was higher. Moreover, the overall sensitivity of 0.43 outperforms the sensitivity of GraPPLE (0.33) whereas the overall MCC measure of 0.40 (in contrast to MCC of 0.29 of GraPPLE) was significantly higher for our method. This clearly demonstrates that our models are more accurate than existing models. CONCLUSIONS: This work conclusively demonstrates that a simple feature, tri-nucleotide composition, is sufficient to discriminate between coding and non-coding RNA sequences. Similarly, graph properties based feature set along with RandomForest algorithm are most suitable to classify different ncRNA classes. We have also developed an online and standalone tool-- RNAcon ( http://crdd.osdd.net/raghava/rnacon).

Safety and tolerability of AMG 330 in adults with relapsed/refractory AML: a phase 1a dose-escalation study.

AMG 330, a bispecific T-cell engager (BiTE®) that binds CD33 and CD3 on T cells facilitates T-cell-mediated cytotoxicity against CD33+ cells. This first-in-human, open-label, dose-escalation study evaluated the safety, pharmacokinetics, pharmacodynamics, and preliminary efficacy of AMG 330 in adults with relapsed/refractory acute myeloid leukemia (R/R AML). Amongst 77 patients treated with AMG 330 (0.5 µg/day-1.6 mg/day) on 14-day or 28-day cycles, maximum tolerated dose was not reached; median duration of treatment was 29 days. The most frequent treatment-related adverse events were cytokine release syndrome (CRS; 78%) and rash (30%); 10% of patients experienced grade 3/4 CRS. CRS was mitigated with stepwise dosing of AMG 330, prophylactic dexamethasone, and early treatment with tocilizumab. Among 60 evaluable patients, eight achieved complete remission or morphologic leukemia-free state; of the 52 non-responders, 37% had ≥50% reduction in AML bone marrow blasts. AMG 330 is a promising CD33-targeted therapeutic strategy for R/R AML.

Prediction of vitamin interacting residues in a vitamin binding protein using evolutionary information.

BACKGROUND: The vitamins are important cofactors in various enzymatic-reactions. In past, many inhibitors have been designed against vitamin binding pockets in order to inhibit vitamin-protein interactions. Thus, it is important to identify vitamin interacting residues in a protein. It is possible to detect vitamin-binding pockets on a protein, if its tertiary structure is known. Unfortunately tertiary structures of limited proteins are available. Therefore, it is important to develop in-silico models for predicting vitamin interacting residues in protein from its primary structure. RESULTS: In this study, first we compared protein-interacting residues of vitamins with other ligands using Two Sample Logo (TSL). It was observed that ATP, GTP, NAD, FAD and mannose preferred {G,R,K,S,H}, {G,K,T,S,D,N}, {T,G,Y}, {G,Y,W} and {Y,D,W,N,E} residues respectively, whereas vitamins preferred {Y,F,S,W,T,G,H} residues for the interaction with proteins. Furthermore, compositional information of preferred and non-preferred residues along with patterns-specificity was also observed within different vitamin-classes. Vitamins A, B and B6 preferred {F,I,W,Y,L,V}, {S,Y,G,T,H,W,N,E} and {S,T,G,H,Y,N} interacting residues respectively. It suggested that protein-binding patterns of vitamins are different from other ligands, and motivated us to develop separate predictor for vitamins and their sub-classes. The four different prediction modules, (i) vitamin interacting residues (VIRs), (ii) vitamin-A interacting residues (VAIRs), (iii) vitamin-B interacting residues (VBIRs) and (iv) pyridoxal-5-phosphate (vitamin B6) interacting residues (PLPIRs) have been developed. We applied various classifiers of SVM, BayesNet, NaiveBayes, ComplementNaiveBayes, NaiveBayesMultinomial, RandomForest and IBk etc., as machine learning techniques, using binary and Position-Specific Scoring Matrix (PSSM) features of protein sequences. Finally, we selected best performing SVM modules and obtained highest MCC of 0.53, 0.48, 0.61, 0.81 for VIRs, VAIRs, VBIRs, PLPIRs respectively, using PSSM-based evolutionary information. All the modules developed in this study have been trained and tested on non-redundant datasets and evaluated using five-fold cross-validation technique. The performances were also evaluated on the balanced and different independent datasets. CONCLUSIONS: This study demonstrates that it is possible to predict VIRs, VAIRs, VBIRs and PLPIRs from evolutionary information of protein sequence. In order to provide service to the scientific community, we have developed web-server and standalone software VitaPred (http://crdd.osdd.net/raghava/vitapred/).

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/.

Prediction and classification of aminoacyl tRNA synthetases using PROSITE domains.

BACKGROUND: Aminoacyl tRNA synthetases (aaRSs) catalyse the first step of protein synthesis in all organisms. They are responsible for the precise attachment of amino acids to their cognate transfer RNAs. There are twenty different types of aaRSs, unique for each amino acid. These aaRSs have been divided into two classes, each comprising ten enzymes. It is important to predict and classify aaRSs in order to understand protein synthesis. RESULTS: In this study, all models were developed on a non-redundant dataset containing 117 aaRSs and an equal number of non-aaRSs, in which no two sequences have more than 30% similarity. First, we applied the similarity search technique, BLAST, and achieved a maximum accuracy of 67.52%. We observed that 62% of tRNA synthetases contain one or more domains from amongst the following four PROSITE domains: PS50862, PS00178, PS50860 and PS50861. An SVM-based model was developed to discriminate between aaRSs, and non-aaRSs, and achieved a maximum MCC of 0.68 with accuracy of 83.73%, using selective dipeptide composition. We developed a hybrid approach and achieved a maximum MCC of 0.72 with accuracy of 85.49%, where SVM model developed using selected dipeptide composition and information of four PROSITE domains. We further developed an SVM-based model for classifying the aaRSs into class-1 and class-2, using selective dipeptide composition and achieved an MCC of 0.79. We also observed that two domains (PS00178, PS50889) in class-1 and three domains (PS50862, PS50860, PS50861) in class-2 were preferred. A hybrid method was developed using these domains as descriptor, along with selected dipeptide composition, and achieved an MCC of 0.87 with a sensitivity of 94.55% and an accuracy of 93.19%. All models were evaluated using a five-fold cross-validation technique. CONCLUSIONS: We have analyzed protein sequences of aaRSs (class-1 and class-2) and non-aaRSs and identified interesting patterns. The high accuracy achieved by our SVM models using selected dipeptide composition demonstrates that certain types of dipeptide are preferred in aaRSs. We were able to identify PROSITE domains that are preferred in aaRSs and their classes, providing interesting insights into tRNA synthetases. The method developed in this study will be useful for researchers studying aaRS enzymes and tRNA biology. The web-server based on the above study, is available at http://www.imtech.res.in/raghava/icaars/.

BIAdb: a curated database of benzylisoquinoline alkaloids.

BACKGROUND: Benzylisoquinoline is the structural backbone of many alkaloids with a wide variety of structures including papaverine, noscapine, codeine, morphine, apomorphine, berberine, protopine and tubocurarine. Many benzylisoquinoline alkaloids have been reported to show therapeutic properties and to act as novel medicines. Thus it is important to collect and compile benzylisoquinoline alkaloids in order to explore their usage in medicine. DESCRIPTION: We extract information about benzylisoquinoline alkaloids from various sources like PubChem, KEGG, KNApSAcK and manual curation from literature. This information was processed and compiled in order to create a comprehensive database of benzylisoquinoline alkaloids, called BIAdb. The current version of BIAdb contains information about 846 unique benzylisoquinoline alkaloids, with multiple entries in term of source, function leads to total number of 2504 records. One of the major features of this database is that it provides data about 627 different plant species as a source of benzylisoquinoline and 114 different types of function performed by these compounds. A large number of online tools have been integrated, which facilitate user in exploring full potential of BIAdb. In order to provide additional information, we give external links to other resources/databases. One of the important features of this database is that it is tightly integrated with Drugpedia, which allows managing data in fixed/flexible format. CONCLUSIONS: A database of benzylisoquinoline compounds has been created, which provides comprehensive information about benzylisoquinoline alkaloids. This database will be very useful for those who are working in the field of drug discovery based on natural products. This database will also serve researchers working in the field of synthetic biology, as developing medicinally important alkaloids using synthetic process are one of important challenges. This database is available from http://crdd.osdd.net/raghava/biadb/.

Bioinformatics drives discovery in Biomedicine.

Bioinformatics has evolved from providing basic solutions, such as sequence alignment, structure predictions, and phylogenetic analysis to an independent data-driven field. The unprecedented growth of genomic technologies and the enormous data have opened an avenue for bioinformaticians (Bioinformatics professionals) never been seen before in the history of mankind. The novel opportunity also requires creative solutions that need skills to deal with noisy, unstructured information to offer valuable biological insights. Currently, we are seeing only the tip of an iceberg and the future will revolve around big data sets in all forms of biological research. The emerging challenge is to unfold the hidden iceberg of data.