Neoantigen Identification and Dendritic Cell-Based Vaccines for Lung Cancer Immunotherapy.

Immunotherapies can treat many cancers, including difficult-to-treat cases such as lung cancer. Due to its tolerability, long-lasting therapeutic responses, and efficacy in a wide spectrum of patients, immunotherapy can also help to treat lung cancer, which has few treatment choices. Tumor-specific antigens (TSAs) for cancer vaccinations and T-cell therapies are difficult to discover. Neoantigens (NeoAgs) from genetic mutations, irregular RNA splicing, protein changes, or viral genetic sequences in tumor cells provide a solution. NeoAgs, unlike TSAs, are non-self and can cause an immunological response. Next-generation sequencing (NGS) and bioinformatics can swiftly detect and forecast tumor-specific NeoAgs. Highly immunogenic NeoAgs provide personalized or generalized cancer immunotherapies. Dendritic cells (DCs), which originate and regulate T-cell responses, are widely studied potential immunotherapeutic therapies for lung cancer and other cancers. DC vaccines are stable, reliable, and safe in clinical trials. The purpose of this article is to evaluate the current status, limitations, and prospective clinical applications of DC vaccines, as well as the identification and selection of major histocompatibility complex (MHC) class I and II genes for NeoAgs. Our goal is to explain DC biology and activate DC manipulation to help researchers create extremely potent cancer vaccines for patients.

Partial genomic characterization of Chromobacterium piscinae from India reveals multi drug resistance.

Species of genus Chromobacterium have been isolated from diverse geographical settings, which exhibits significant metabolic flexibility as well as biotechnological and pathogenic properties. This study describes the isolation, characterization, draft assembly, and detailed sequence analysis of Chromobacterium piscinae strain W1B-CG-NIBSM isolated from water samples from multi use community pond. The organism was characterized by biochemical tests, Matrix Assisted Laser Desorption/Ionization Time of Flight Mass Spectrometry (MALDI TOF-MS) and partial genome sequencing. The partial genomic data of Chromobacterium pisciane isolate W1B NIBSM strain was submitted to GenBank with Bio project number PRJNA803347 and accession no CP092474. An integrated genome analysis of Chromobacterium piscinae has been accomplished with PATRIC which indicates good quality genome. DNA sequencing using the illumina HiSeq 4000 system generated total length of 4,155,481 bp with 63 contig with G + C content is 62.69%. This partial genome contains 4,126 protein-coding sequences (CDS), 27 repeats region and 78 transfer RNA (tRNA) genes as well as 3 ribosomal RNA (rRNA) genes. The genomic annotation of Chromobacterium W1B depicts 2,925 proteins with functional assignments and 1201 hypothetical proteins. A repertoire of specialty genes implicated in antibiotic resistance (45 genes), drug target (6 genes), Transporter (3 genes) and virulence factor (10 genes). The genomic analysis reveals the adaptability, displays metabolic varied pathways and shows specific structural complex and various virulence factors which makes this strain multi drug resistant. The isolate was found to be highly resistant to ß-lactam antibiotics whereas it showed sensitivity towards aminoglycosides and fluoroquinolone antibiotics. Hence, the recovery of Chromobacterium piscinae from community pond evidenced for uncertain hidden source of public health hazard. To the best of authors knowledge this is first report of isolation and genomic description of C. piscinae from India.

Coevolution based immunoinformatics approach considering variability of epitopes to combat different strains: A case study using spike protein of SARS-CoV-2.

In the recent past several vaccines were developed to combat the COVID-19 disease. Unfortunately, the protective efficacy of the current vaccines has been reduced due to the high mutation rate in SARS-CoV-2. Here, we successfully implemented a coevolution based immunoinformatics approach to design an epitope-based peptide vaccine considering variability in spike protein of SARS-CoV-2. The spike glycoprotein was investigated for B- and T-cell epitope prediction. Identified T-cell epitopes were mapped on previously reported coevolving amino acids in the spike protein to introduce mutation. The non-mutated and mutated vaccine components were constructed by selecting epitopes showing overlapping with the predicted B-cell epitopes and highest antigenicity. Selected epitopes were linked with the help of a linker to construct a single vaccine component. Non-mutated and mutated vaccine component sequences were modelled and validated. The in-silico expression level of the vaccine constructs (non-mutated and mutated) in E. coli K12 shows promising results. The molecular docking analysis of vaccine components with toll-like receptor 5 (TLR5) demonstrated strong binding affinity. The time series calculations including root mean square deviation (RMSD), radius of gyration (RGYR), and energy of the system over 100 ns trajectory obtained from all atom molecular dynamics simulation showed stability of the system. The combined coevolutionary and immunoinformatics approach used in this study will certainly help to design an effective peptide vaccine that may work against different strains of SARS-CoV-2. Moreover, the strategy used in this study can be implemented on other pathogens.

Cloning and functional characterization of arsenite oxidase (aoxB) gene associated with arsenic transformation in Pseudomonas sp. strain AK9.

Arsenic transforming bacterial strains belong to genus Pseudomonas sp.AK9 (KY569424), were isolated from the middle Gangetic plains of Bihar, India. The Pseudomonas sp. AK9 strains were able to transform toxic arsenite to a less toxic arsenate. In the present work, the presence of different arsenic resistance genes (aoxB, arsB, acr3 and aoxAB) were observed in isolated strain. Furthermore, the aoxB gene was amplified from genomic DNA of AK9, cloned in E.coli/DH5αcells, and sequenced. The BLASTn results and phylogenetic study of the aoxB gene showed 95.32 % and 90.07 % identity with the large subunit of aoxB gene of previous reported Thiomonas arsenivorans strain DSM16361 and Thiomonas arsenivorans strain b6, respectively. Further overhang primers were designed for amplifications of full length aoxB gene (∼1200 bp), and cloned in to the expression vector and host E.coli/BL21 cells. The GST-aoxB gene was expressed in BL21 cells, and a profound expression product of âˆ¼ 72 kDa was observed in SDS PAGE. The detection of a large subunit (aoxB) of arsenate oxidase protein in western blotting assay affirmed the expression of aoxB gene in recombinant E.coli/BL21 clone. Further, the recombinant E.coli/BL21cells showed increased growth than the normal E.coli/BL21 cells against As (III). Thus, this study showed the presence of aoxB gene in Pseudomonas sp. AK9 genome which regulates the resistant ability to arsenic toxicity.

AutomAted RepeaT Identifier (AARTI): A tool to identify common, polymorphic, and unique microsatellites.

Here we are presenting an automated computational pipeline used to mine 5976 mitochondrial genomes to identify common, polymorphic, and unique microsatellites also known as simple sequence repeats (SSRs). Microsatellites are repetitive motifs of 1-6 bases in a DNA sequence. Due to their abundance and highly polymorphic nature, microsatellites have become one of the widely used molecular/genetic markers valuable for many studies including gene tagging, genetic diversity, and species identification. Several computational tools dedicated to mine and categorize microsatellites in nucleotide sequences were developed; however, there is no tool which can identify unique, common and polymorphic microsatellites between each pair of nucleotide sequences. To explore such microsatellites, we have developed a fully automated computational pipeline named AutomAted RepeaT Identifier (AARTI). The AARTI is the only tool till date, which identifies common, polymorphic, and unique microsatellites between each pair of nucleotide sequences. The computational pipeline was constructed using the PERL programming language and the web server for the pipeline was developed with the help of PHP, HTML, CSS, and JavaScript. It was successfully tested to reproduce the results of previous study on 7 mitochondrial genomes of genus Orthotrichum. Moreover, the pipeline was also applied on 5846 (Metazoa) and 130 (Viridiplantae) mitochondrial genomes. The AARTI is freely available at https://lms.snu.edu.in/aarti/ and will certainly accelerate the studies of length variation in microsatellites between species. Additionally, it will be useful in comparative genomic studies, for the computational characterization of microsatellites, and has the potential to be a routine genome analysis pipeline for mitochondrial genomes.

pSATdb: a database of mitochondrial common, polymorphic, and unique microsatellites.

Microsatellites, also termed as simple sequence repeats, are repetitive tracts in a DNA sequence, typically consisting of one to six nucleotides. These repeats are found in all genomes and play key roles in phylogeny and species identification. Microsatellites are highly polymorphic, and their length may differ from species to species. There are several online resources dedicated to mitochondria; however, comprehensive information is not available about the length variation of mitochondrial microsatellites. Therefore, to explore it between species among a genus, we have developed a database named pSATdb (polymorphic microSATellites database; https://lms.snu.edu.in/pSATdb/). pSATdb contains 28,710 perfect microsatellites identified across 5,976 mitochondrial genome (mt-genome) sequences from 1,576 genera which includes 1,535 (5,846 mt-genome) and 41 (130 mt-genome) genera of Metazoa and Viridiplantae, respectively. pSATdb is the only database which provides genus-wise information about the length variation of mitochondrial microsatellites. Because of the emerging role of microsatellites in genomics studies, the identified common, polymorphic, and unique microsatellites stored in pSATdb will be effectively useful in various studies including genetic diversity, mapping, marker-assisted selection, and comparative population studies.

Coevolutionary forces shaping the fitness of SARS-CoV-2 spike glycoprotein against human receptor ACE2.

The current global health problem caused by SARS-CoV-2 has challenged the scientific community in various ways. Therefore, worldwide several scientific groups are exploring SARS-CoV-2 from different aspects including its origin, spread, severe infectivity, and also to find a cure. It is now well known that spike glycoprotein helps SARS-CoV-2 to enter inside the human host through a cellular receptor ACE2. However, the role of coevolutionary forces that makes SARS-CoV-2 spike glycoprotein more fit towards its human host remains unexplored. Therefore, in present bioinformatics study we identify coevolving amino acids in spike glycoprotein. Additionally, the effects of coevolution on the stability of the spike glycoprotein as well as its binding with receptor ACE2 were predicted. The results clearly indicate that coevolutionary forces play a pivotal role in increasing the fitness of spike glycoprotein against ACE2.

Genome wide annotation and characterization of young, intact long terminal repeat retrotransposons (In-LTR-RTs) of seven legume species.

Availability of genome sequence of different legume species has provided an opportunity to characterize the abundance, distribution, and divergence of canonical intact long terminal retrotransposons (In-LTR-RT) superfamilies. Among seven legume species, Arachis ipaensis (Aip) showed the highest number of full-length canonical In-LTR-RTs (3325), followed by Glycine max (Gma, 2328), Vigna angularis (Van, 1625), Arachis durensis (Adu, 1348), Lotus japonicus (Lja, 1294), Medicago truncatula (Mtr, 788), and Circer arietinum (Car, 124). Divergence time analysis demonstrated that the amplification timeframe of LTR-RTs dramatically varied in different families. The average insertion time of Copia element varied from 0.51 (Van) to 1.37 million years ago (Mya) (Adu, and Aip), whereas that of Gypsy was between 0.22 (Mtr) and 1.82 Mya (Adu). Bayesian phylogenetic tree analysis suggested that the 1397 and 1917 reverse transcriptase (RT) domains of Copia and Gypsy families of the seven legume species were clustered into 7 and 14 major groups, respectively. The highest proportion (approximately 94.79-100%) of transposable element (TE)-associated genes assigned to pathways was mapped to metabolism-related pathways in all species. The results enabled the structural understanding of full-length In-LTR-RTs and will be valuable resource for the further study of the impact of TEs on gene structure and expression in legume species.

Structural basis for drug resistance mechanisms against anaplastic lymphoma kinase.

Drug resistance to anaplastic lymphoma kinase (ALK) inhibitors (crizotinib and ceritinib) is caused by mutation in the region encoding kinase domain of ALK. Compounds with potential ability to inhibit all strains of ALK are a solution to tackle the problem of drug resistance. In this study, we delineated positions of residues possessing the ability to make ALK drug resistant upon mutation by assessing them using five parameters (conservation index, binding-site root-mean-square deviation, protein structure stability, change in ATP, and drug-binding affinity). Four residual positions (Leu 1122, Thr 1151, Phe 1245, and Gly 1269) were ascertained. This study will be beneficial for designing drugs with better proficiency against ALK and the issues of drug resistance. This study can be taken as a pipeline for investigating drug-resistant mutations in other diseases as well.

Groups of coevolving positions provide drug resistance to Mycobacterium tuberculosis: A study using targets of first-line antituberculosis drugs.

Drug resistance has been associated with point mutations in coding regions leading to an altered protein sequence and structure. Such changes have been seen as isolated events occurring at various positions in a sequence. However, we hypothesise that it is not a single mutation at a specific position but a group of positions that coevolve in a correlated fashion to increase the fitness of a target protein against a drug. To prove the hypothesis, selected protein sequences of Mycobacterium tuberculosis drug resistance genes were successfully screened using a bioinformatics approach to detect groups of coevolving amino acids at important structural and functional positions in the targets of first-line antituberculosis drugs (isoniazid, rifampicin, ethambutol and pyrazinamide). The algorithmically characterised genetic mutations and the lineage-specific single nucleotide polymorphisms (SNPs) detected previously in drug resistance genes of M. tuberculosis complex genomes were also found in the identified coevolving groups. Mapping of coevolving positions to the secondary structure of proteins clearly indicates the preference of amino acid residues in the helix to coevolve. Moreover, active-site residues of some candidate proteins were also found in coevolving groups. The coevolving groups detected in this study will be useful to gain new insights into the molecular and evolutionary basis of drug resistance. This work provides an important first step towards finding solutions to the multidrug resistance problem through coevolution analysis of proteins, in turn helping to develop new drug regimens against pathogens, including M. tuberculosis.

Correction: Quantum curcumin mediated inhibition of gingipains and mixed-biofilm of Porphyromonas gingivalis causing chronic periodontitis.

[This corrects the article DOI: 10.1039/C8RA08435A.].

Quantum curcumin mediated inhibition of gingipains and mixed-biofilm of Porphyromonas gingivalis causing chronic periodontitis.

Periodontitis is a biofilm-associated irreversible inflammation of the periodontal tissues. Reports suggest the role of Porphyromonas gingivalis specific Arg- and Lys-specific proteinases in the orchestration of the initiation and progression of periodontal diseases. These proteinases are precisely termed as gingipains R and K. Curcumin is an active polyphenol that is extracted from the rhizomes of Curcuma longa. However, the molecule curcumin owing to its high hydropathy index and poor stability has not been able to justify its role as frontline drug modality in the treatment of infectious and non-infectious diseases as claimed by several investigators. In the present study, at first, we synthesized and characterized quantum curcumin, and investigated its biocompatibility. This was subsequently followed by the evaluation of the role of quantum curcumin as an antimicrobial, anti-gingipains and antibiofilm agent against Porphyromonas gingivalis and select reference strains. We have successfully synthesized the quantum curcumin utilizing a top-down approach with the average size of 3.5 nm. Apart from its potent antimicrobial as well as antibiofilm properties, it also significantly inhibited the gingipains in a dose-dependent manner. At the minimal concentration of 17.826 µM, inhibition up to 98.7% and 89.4% was noted for gingipain R and K respectively. The data was also supported by the in silico docking experiments which revealed high exothermic enthalpies (-7.01 and -7.02 cal mol-1). Besides, the inhibition constant was found to be 7.24 µM and 7.1 µM against gingipains R and K respectively. The results suggest that quantum curcumin is a potential drug candidate which needs further clinical validation.

A tree of life based on ninety-eight expressed genes conserved across diverse eukaryotic species.

Rapid advances in DNA sequencing technologies have resulted in the accumulation of large data sets in the public domain, facilitating comparative studies to provide novel insights into the evolution of life. Phylogenetic studies across the eukaryotic taxa have been reported but on the basis of a limited number of genes. Here we present a genome-wide analysis across different plant, fungal, protist, and animal species, with reference to the 36,002 expressed genes of the rice genome. Our analysis revealed 9831 genes unique to rice and 98 genes conserved across all 49 eukaryotic species analysed. The 98 genes conserved across diverse eukaryotes mostly exhibited binding and catalytic activities and shared common sequence motifs; and hence appeared to have a common origin. The 98 conserved genes belonged to 22 functional gene families including 26S protease, actin, ADP-ribosylation factor, ATP synthase, casein kinase, DEAD-box protein, DnaK, elongation factor 2, glyceraldehyde 3-phosphate, phosphatase 2A, ras-related protein, Ser/Thr protein phosphatase family protein, tubulin, ubiquitin and others. The consensus Bayesian eukaryotic tree of life developed in this study demonstrated widely separated clades of plants, fungi, and animals. Musa acuminata provided an evolutionary link between monocotyledons and dicotyledons, and Salpingoeca rosetta provided an evolutionary link between fungi and animals, which indicating that protozoan species are close relatives of fungi and animals. The divergence times for 1176 species pairs were estimated accurately by integrating fossil information with synonymous substitution rates in the comprehensive set of 98 genes. The present study provides valuable insight into the evolution of eukaryotes.

Structural Basis for Drug Resistance Mechanisms Against EGFR.

Mutations in the kinase domain encoding region of EGFR gene causes drug resistance to EGFR kinase inhibitors such as erlotinib and gefitinib. This problem can be addressed by a new lead compound effective against all mutants of EGFR. To predict positions of residues possessing the potential to render EGFR drug resistant upon mutation, residual positions known to interact with Erlotinib and Gefitinib were assessed using five parameters (conservation index, binding site RMSD, protein structure stability and change in ATP and drug binding affinity). Structural screening protocol was followed to identify novel lead compound. Four positions, Lys 745, Cys 797, Asp 800 and Thr 854, were most likely observed to acquire drug resistance by altering drug binding affinity without destabilizing the protein and ATP binding ability. A compound DHO was observed to possess better binding affinity for all EGFR models in comparison to Erlotinib and Gefitinib, using docking protocol. This information would pave the way for designing drugs effective against wild-type (WT) EGFR as well as against variant EGFRs models. Thus, authors report a lead compound as a long-term potential with the ability to inhibit predicted models of mutant, wild and known SNPs EGFR.

Predicting neurological Adverse Drug Reactions based on biological, chemical and phenotypic properties of drugs using machine learning models.

Adverse drug reactions (ADRs) have become one of the primary reasons for the failure of drugs and a leading cause of deaths. Owing to the severe effects of ADRs, there is an urgent need for the generation of effective models which can accurately predict ADRs during early stages of drug development based on integration of various features of drugs. In the current study, we have focused on neurological ADRs and have used various properties of drugs that include biological properties (targets, transporters and enzymes), chemical properties (substructure fingerprints), phenotypic properties (side effects (SE) and therapeutic indications) and a combinations of the two and three levels of features. We employed relief-based feature selection technique to identify relevant properties and used machine learning approach to generated learned model systems which would predict neurological ADRs prior to preclinical testing. Additionally, in order to explain the efficiency and applicability of the models, we tested them to predict the ADRs for already existing anti-Alzheimer drugs and uncharacterized drugs, respectively in side effect resource (SIDER) database. The generated models were highly accurate and our results showed that the models based on chemical (accuracy 93.20%), phenotypic (accuracy 92.41%) and combination of three properties (accuracy 94.18%) were highly accurate while the models based on biological properties (accuracy 82.11%) were highly informative.

Machine Learning and Molecular Dynamics Based Insights into Mode of Actions of Insulin Degrading Enzyme Modulators.

BACKGROUND: Alzheimer's disease (AD) is one of the most common lethal neurodegenerative disorders having impact on the lives of millions of people worldwide. The disease lacks effective treatment options and the unavailability of the drugs to cure the disease necessitates the development of effectual anti-Alzheimer drugs. Several mechanisms have been reported underlying the association of the two disorders, diabetes and dementia, one among which is the insulin-degrading enzyme (IDE) which is known to degrade insulin as well beta-amyloid peptides. METHODS: The present study is aimed to generate accurate classification models using machine learning techniques, which could identify IDE modulators from a bioassay dataset consisting of IDE inhibitors as well as non-inhibitors. The identified compounds were subjected to docking and Molecular dynamics (MD) studies for an in-depth analysis of the binding modes along with the complex stability. This study proposes that the identified potential active compounds, STK026154 (PubChem ID: CID2927418) with Glide score of -7.70 kcal/mol and BAS05901102 (PubChem ID: CID3152845) with Glide score of -7.06 kcal/mol, could serve as promising leads for the development of novel drugs against AD. CONCLUSION: The present study shows that such in silico approaches can be effectively used to discover and select active compounds from unseen data for accelerated drug development process. The machine learning models generated in the present study were used to screen Traditional Chinese Medicine (TCM) database to identify the phytocompounds already been reported to have therapeutic effects against AD.

Computational Screening and Exploration of Disease-Associated Genes in Alzheimer's Disease.

Alzheimer's is a neurodegenerative disease affecting large populations worldwide characterized mainly by progressive loss of memory along with various other symptoms. The foremost cause of the disease is still unclear, however various mechanisms have been proposed to cause the disease that include amyloid hypothesis, tau hypothesis, and cholinergic hypothesis in addition to genetic factors. Various genes have been known to be involved which are APOE, PSEN1, PSEN2, and APP among others. In the present study, we have used computational methods to examine the pathogenic effects of non-synonymous single nucleotide polymorphisms (SNPs) associated with ABCA7, CR1, MS4A6A, CD2AP, PSEN1, PSEN2, and APP genes. The SNPs were obtained from dbSNP database followed by identification of deleterious SNPs and prediction of their functional impact. Prediction of disease-associated mutations was performed and the impact of the mutations on the stability of the protein was carried out. To study the structural significance of the computationally prioritized mutations on the proteins, molecular dynamics simulation studies were carried out. On analysis, the SNPs with IDs rs76282929 ABCA7; CR1 rs55962594; MS4A6A rs601172; CD2AP rs61747098; PSEN1 rs63750231, rs63750265, rs63750526, rs63750577, rs63750687, rs63750815, rs63750900, rs63751037, rs63751163, rs63751399; PSEN2 rs63749851; and APP rs63749964, rs63750066, rs63750734, and rs63751039 were predicted to be deleterious and disease-associated having significant structural impact on the proteins. The current study proposes a precise computational methodology for the identification of disease-associated SNPs. J. Cell. Biochem. 118: 1471-1479, 2017. © 2016 Wiley Periodicals, Inc.

Integrating network, sequence and functional features using machine learning approaches towards identification of novel Alzheimer genes.

BACKGROUND: Alzheimer's disease (AD) is a complex progressive neurodegenerative disorder commonly characterized by short term memory loss. Presently no effective therapeutic treatments exist that can completely cure this disease. The cause of Alzheimer's is still unclear, however one of the other major factors involved in AD pathogenesis are the genetic factors and around 70 % risk of the disease is assumed to be due to the large number of genes involved. Although genetic association studies have revealed a number of potential AD susceptibility genes, there still exists a need for identification of unidentified AD-associated genes and therapeutic targets to have better understanding of the disease-causing mechanisms of Alzheimer's towards development of effective AD therapeutics. RESULTS: In the present study, we have used machine learning approach to identify candidate AD associated genes by integrating topological properties of the genes from the protein-protein interaction networks, sequence features and functional annotations. We also used molecular docking approach and screened already known anti-Alzheimer drugs against the novel predicted probable targets of AD and observed that an investigational drug, AL-108, had high affinity for majority of the possible therapeutic targets. Furthermore, we performed molecular dynamics simulations and MM/GBSA calculations on the docked complexes to validate our preliminary findings. CONCLUSIONS: To the best of our knowledge, this is the first comprehensive study of its kind for identification of putative Alzheimer-associated genes using machine learning approaches and we propose that such computational studies can improve our understanding on the core etiology of AD which could lead to the development of effective anti-Alzheimer drugs.

Identification of common, unique and polymorphic microsatellites among 73 cyanobacterial genomes.

Microsatellites also known as Simple Sequence Repeats are short tandem repeats of 1-6 nucleotides. These repeats are found in coding as well as non-coding regions of both prokaryotic and eukaryotic genomes and play a significant role in the study of gene regulation, genetic mapping, DNA fingerprinting and evolutionary studies. The availability of 73 complete genome sequences of cyanobacteria enabled us to mine and statistically analyze microsatellites in these genomes. The cyanobacterial microsatellites identified through bioinformatics analysis were stored in a user-friendly database named CyanoSat, which is an efficient data representation and query system designed using ASP.net. The information in CyanoSat comprises of perfect, imperfect and compound microsatellites found in coding, non-coding and coding-non-coding regions. Moreover, it contains PCR primers with 200 nucleotides long flanking region. The mined cyanobacterial microsatellites can be freely accessed at www.compubio.in/CyanoSat/home.aspx. In addition to this 82 polymorphic, 13,866 unique and 2390 common microsatellites were also detected. These microsatellites will be useful in strain identification and genetic diversity studies of cyanobacteria.

ProTSAV: A protein tertiary structure analysis and validation server.

Quality assessment of predicted model structures of proteins is as important as the protein tertiary structure prediction. A highly efficient quality assessment of predicted model structures directs further research on function. Here we present a new server ProTSAV, capable of evaluating predicted model structures based on some popular online servers and standalone tools. ProTSAV furnishes the user with a single quality score in case of individual protein structure along with a graphical representation and ranking in case of multiple protein structure assessment. The server is validated on ~64,446 protein structures including experimental structures from RCSB and predicted model structures for CASP targets and from public decoy sets. ProTSAV succeeds in predicting quality of protein structures with a specificity of 100% and a sensitivity of 98% on experimentally solved structures and achieves a specificity of 88%and a sensitivity of 91% on predicted protein structures of CASP11 targets under 2Å.The server overcomes the limitations of any single server/method and is seen to be robust in helping in quality assessment. ProTSAV is freely available at http://www.scfbio-iitd.res.in/software/proteomics/protsav.jsp.