@article {109312, title = {PASS2.7: a database containing structure-based sequence alignments and associated features of protein domain superfamilies from SCOPe.}, journal = {Database (Oxford)}, volume = {2022}, year = {2022}, month = {2022 Apr 12}, abstract = {

Sequence alignments are models that capture the structural, functional and evolutionary relationships between proteins. Structure-guided sequence alignments are helpful in the case of distantly related proteins with poor sequence identity, thus rendering routine sequence alignment methods ineffective. Protein Alignment organized as Structural Superfamilies or PASS2 database provides such sequence alignments of protein domains within a superfamily as per the Structural Classification of Proteins extended (SCOPe) database. The current update of PASS2 (i.e. PASS2.7) is following the latest release of SCOPe (2.07) and we provide data for 14 323 protein domains that are \<40\% identical and are organized into 2024 superfamilies. Several useful features derived from the alignments, such as conserved secondary structural motifs, HMMs and residues conserved across the superfamily, are also reported. Protein domains that are deviant from the rest of the members of a superfamily may compromise the quality of the alignment, and we found this to be the case in \~{}7\% of the total superfamilies we considered. To improve the alignment by objectively identifying such {\textquoteright}outliers{\textquoteright}, in this update, we have used a k-means-based unsupervised machine learning method for clustering superfamily members, where features provided were length of domains aligned, Cα-RMSD derived from the rigid-body superposition of all members and gaps contributed to the alignment by each domain. In a few cases, we have split the superfamily as per the clusters predicted and provided complete data for each cluster. A new feature included in this update is absolutely conserved interactions (ACIs) between residue backbones and side chains, which are obtained by aligning protein structure networks using structure-guided sequence alignments of superfamilies. ACIs provide valuable information about functionally important residues and the structure-function relationships of proteins. The ACIs and the corresponding conserved networks for backbone and sidechain have been marked on the superimposed structure separately.

DATABASE URL: The updated version of the PASS2 database is available at http://caps.ncbs.res.in/pass2/.

}, keywords = {Databases, Protein, Protein Domains, Protein Structure, Tertiary, Proteins, Sequence Alignment}, issn = {1758-0463}, doi = {10.1093/database/baac025}, author = {Bhattacharyya, Teerna and Nayak, Soumya and Goswami, Smit and Gadiyaram, Vasundhara and Mathew, Oommen K and Sowdhamini, Ramanathan} } @article {78096, title = {Dataset for the combined transcriptome assembly of and functional annotation.}, journal = {Data Brief}, volume = {30}, year = {2020}, month = {2020 Jun}, pages = {105416}, abstract = {

In this paper, we present the data acquired during transcriptome analysis of the Moringa oleifera [1] from five different tissues (root, stem, leaf, flower and seed) by RNA sequencing. A total of 271 million reads were assembled with an N50 of 2094 bp. The combined transcriptome was assessed for transcript abundance across five tissues. The protein coding genes identified from the transcripts were annotated and used for orthology analysis. Further, enzymes involved in the biosynthesis of select medicinally important secondary metabolites, vitamins and ion transporters were identified and their expression levels across tissues were examined. The data generated by RNA sequencing has been deposited to NCBI public repository under the accession number PRJNA394193 (https://www.ncbi.nlm.nih.gov/bioproject/PRJNA394193).

}, issn = {2352-3409}, doi = {10.1016/j.dib.2020.105416}, author = {Shafi, K Mohamed and Joshi, Adwait G and Meenakshi, Iyer and Pasha, Shaik Naseer and Harini, K and Mahita, Jarjapu and Sajeevan, Radha Sivarajan and Karpe, Snehal D and Ghosh, Pritha and Nitish, Sathyanarayanan and Gandhimathi, A and Mathew, Oommen K and Prasanna, Subramanian Hari and Malini, Manoharan and Mutt, Eshita and Naika, Mahantesha and Ravooru, Nithin and Rao, Rajas M and Shingate, Prashant N and Sukhwal, Anshul and Sunitha, Margaret S and Upadhyay, Atul K and Vinekar, Rithvik S and Sowdhamini, Ramanathan} } @article {83641, title = {A knowledge-driven protocol for prediction of proteins of interest with an emphasis on biosynthetic pathways.}, journal = {MethodsX}, volume = {7}, year = {2020}, month = {2020}, pages = {101053}, abstract = {

This protocol describes a stepwise process to identify proteins of interest from a query proteome derived from NGS data. We implemented this protocol on transcriptome to identify proteins involved in secondary metabolite and vitamin biosynthesis and ion transport. This knowledge-driven protocol identifies proteins using an integrated approach involving sensitive sequence search and evolutionary relationships. We make use of functionally important residues (FIR) specific for the query protein family identified through its homologous sequences and literature. We screen protein hits based on the clustering with true homologues through phylogenetic tree reconstruction complemented with the FIR mapping. The protocol was validated for the protein hits through qRT-PCR and transcriptome quantification. Our protocol demonstrated a higher specificity as compared to other methods, particularly in distinguishing cross-family hits. This protocol was effective in transcriptome data analysis of as described in Pasha et al.{\textbullet}Knowledge-driven protocol to identify secondary metabolite synthesizing protein in a highly specific manner.{\textbullet}Use of functionally important residues for screening of true hits.{\textbullet}Beneficial for metabolite pathway reconstruction in any (species, metagenomics) NGS data.

}, issn = {2215-0161}, doi = {10.1016/j.mex.2020.101053}, author = {Joshi, Adwait G and Harini, K and Meenakshi, Iyer and Shafi, K Mohamed and Pasha, Shaik Naseer and Mahita, Jarjapu and Sajeevan, Radha Sivarajan and Karpe, Snehal D and Ghosh, Pritha and Nitish, Sathyanarayanan and Gandhimathi, A and Mathew, Oommen K and Prasanna, Subramanian Hari and Malini, Manoharan and Mutt, Eshita and Naika, Mahantesha and Ravooru, Nithin and Rao, Rajas M and Shingate, Prashant N and Sukhwal, Anshul and Sunitha, Margaret S and Upadhyay, Atul K and Vinekar, Rithvik S and Sowdhamini, Ramanathan} } @article {57789, title = {The transcriptome enables the identification of candidate genes behind medicinal value of Drumstick tree (Moringa oleifera).}, journal = {Genomics}, volume = {112}, year = {2020}, month = {01/2020}, pages = {621-628}, abstract = {

Moringa oleifera is a plant well-known for its nutrition value, drought resistance and medicinal properties. cDNA libraries from five different tissues (leaf, root, stem, seed and flower) of M. oleifera cultivar Bhagya were generated and sequenced. We developed a bioinformatics pipeline to assemble transcriptome, along with the previously published M. oleifera genome, to predict 17,148 gene models. Few candidate genes related to biosynthesis of secondary metabolites, vitamins and ion transporters were identified. Expressions were further confirmed by real-time quantitative PCR experiments for few promising leads. Quantitative estimation of metabolites, as well as elemental analysis, was also carried out to support our observations. Enzymes in the biosynthesis of vitamins and metabolites like quercetin and kaempferol are highly expressed in leaves, flowers and seeds. The expression of iron transporters and calcium storage proteins were observed in root and leaves. In general, leaves retain the highest amount of small molecules of interest.

}, issn = {1089-8646}, doi = {10.1016/j.ygeno.2019.04.014}, author = {Pasha, Shaik Naseer and Shafi, K Mohamed and Joshi, Adwait G and Meenakshi, Iyer and Harini, K and Mahita, Jarjapu and Sajeevan, Radha Sivarajan and Karpe, Snehal D and Ghosh, Pritha and Nitish, Sathyanarayanan and Gandhimathi, A and Mathew, Oommen K and Prasanna, Subramanian Hari and Malini, Manoharan and Mutt, Eshita and Naika, Mahantesha and Ravooru, Nithin and Rao, Rajas M and Shingate, Prashant N and Sukhwal, Anshul and Sunitha, Margaret S and Upadhyay, Atul K and Vinekar, Rithvik S and Sowdhamini, Ramanathan} } @article {53182, title = {PASS2 version 6: a database of structure-based sequence alignments of protein domain superfamilies in accordance with SCOPe.}, journal = {Database (Oxford)}, volume = {2019}, year = {2019}, month = {2019 Jan 01}, abstract = {

The number of protein structures is increasing due to the individual initiatives and rapid development of structure determination techniques. Structure-based sequence alignments of distantly related proteins enable the investigation of structural, evolutionary and functional relationships between proteins and their domains leading to their common evolutionary origin. Protein Alignments organized as Structural Superfamilies (PASS2) is a database that provides such alignments of members of protein domain superfamilies of known structure and with less than 40\% sequence identity. PASS2 has been continuously updated in accordance to Structural Classification of Proteins (SCOP), and now Structural Classification of Proteins - extended (SCOPe). The current update directly corresponds to SCOPe 2.06, dealing with 2006 domain superfamilies of known structure and about 14\ 000 domains. Alignments have been augmented by features such as hidden Markov models, highly conserved residues, structural motifs and gene ontology terms, which are available for download. In this update, we introduce the concepts of {\textquoteright}extreme structural outliers{\textquoteright} and {\textquoteright}split superfamilies{\textquoteright} as well.

}, issn = {1758-0463}, doi = {10.1093/database/baz028}, author = {Ghosh, Pritha and Bhattacharyya, Teerna and Mathew, Oommen K and Sowdhamini, Ramanathan} } @article {3545, title = {An Approach to Function Annotation for Proteins of Unknown Function (PUFs) in the Transcriptome of Indian Mulberry.}, journal = {PLoS One}, volume = {11}, year = {2016}, month = {2016}, pages = {e0151323}, abstract = {

The modern sequencing technologies are generating large volumes of information at the transcriptome and genome level. Translation of this information into a biological meaning is far behind the race due to which a significant portion of proteins discovered remain as proteins of unknown function (PUFs). Attempts to uncover the functional significance of PUFs are limited due to lack of easy and high throughput functional annotation tools. Here, we report an approach to assign putative functions to PUFs, identified in the transcriptome of mulberry, a perennial tree commonly cultivated as host of silkworm. We utilized the mulberry PUFs generated from leaf tissues exposed to drought stress at whole plant level. A sequence and structure based computational analysis predicted the probable function of the PUFs. For rapid and easy annotation of PUFs, we developed an automated pipeline by integrating diverse bioinformatics tools, designated as PUFs Annotation Server (PUFAS), which also provides a web service API (Application Programming Interface) for a large-scale analysis up to a genome. The expression analysis of three selected PUFs annotated by the pipeline revealed abiotic stress responsiveness of the genes, and hence their potential role in stress acclimation pathways. The automated pipeline developed here could be extended to assign functions to PUFs from any organism in general. PUFAS web server is available at http://caps.ncbs.res.in/pufas/ and the web service is accessible at http://capservices.ncbs.res.in/help/pufas.

}, issn = {1932-6203}, doi = {10.1371/journal.pone.0151323}, author = {Dhanyalakshmi, K H and Naika, Mahantesha B N and Sajeevan, R S and Mathew, Oommen K and Shafi, K Mohamed and Sowdhamini, Ramanathan and N Nataraja, Karaba} } @article {3558, title = {PASS2 database for the structure-based sequence alignment of distantly related SCOP domain superfamilies: update to version 5 and added features.}, journal = {Nucleic Acids Res}, volume = {44}, year = {2016}, month = {2016 Jan 4}, pages = {D410-4}, abstract = {

Structure-based sequence alignment is an essential step in assessing and analysing the relationship of distantly related proteins. PASS2 is a database that records such alignments for protein domain superfamilies and has been constantly updated periodically. This update of the PASS2 version, named as PASS2.5, directly corresponds to the SCOPe 2.04 release. All SCOPe structural domains that share less than 40\% sequence identity, as defined by the ASTRAL compendium of protein structures, are included. The current version includes 1977 superfamilies and has been assembled utilizing the structure-based sequence alignment protocol. Such an alignment is obtained initially through MATT, followed by a refinement through the COMPARER program. The JOY program has been used for structural annotations of such alignments. In this update, we have automated the protocol and focused on inclusion of new features such as mapping of GO terms, absolutely conserved residues among the domains in a superfamily and inclusion of PDBs, that are absent in SCOPe 2.04, using the HMM profiles from the alignments of the superfamily members and are provided as a separate list. We have also implemented a more user-friendly manner of data presentation and options for downloading more features. PASS2.5 version is available at http://caps.ncbs.res.in/pass2/.

}, issn = {1362-4962}, doi = {10.1093/nar/gkv1205}, author = {Gandhimathi, Arumugam and Ghosh, Pritha and Hariharaputran, Sridhar and Mathew, Oommen K and Sowdhamini, R} } @article {7043, title = {PIMADb: A Database of Protein-Protein Interactions in Huge Macromolecular Assemblies.}, journal = {Bioinform Biol Insights}, volume = {10}, year = {2016}, month = {2016}, pages = {105-9}, abstract = {

Protein-protein interactions play a very important role in the process of cellular functionality. Intricate details about the interactions between the proteins in a macromolecular assembly are important to understand the function and significance of protein complexes. We are reporting about a database of protein-protein interactions in huge macromolecular assemblies (PIMADb) that records the intrinsic details of 189,532 interchain interactions in 40,049 complexes from the Protein Data Bank. These details include the results of the quantification and analysis of all the interactions in the complex. The availability of interprotomer interaction networks can enable the design of point mutation experiments. PIMADb can be accessed from the URL: http://caps.ncbs.res.in/pimadb.

}, issn = {1177-9322}, doi = {10.4137/BBI.S38416}, author = {Mathew, Oommen K and Sowdhamini, Ramanathan} } @article {8756, title = {RStrucFam: a web server to associate structure and cognate RNA for RNA-binding proteins from sequence information.}, journal = {BMC Bioinformatics}, volume = {17}, year = {2016}, month = {2016 Oct 7}, pages = {411}, abstract = {

BACKGROUND: RNA-binding proteins (RBPs) interact with their cognate RNA(s) to form large biomolecular assemblies. They are versatile in their functionality and are involved in a myriad of processes inside the cell. RBPs with similar structural features and common biological functions are grouped together into families and superfamilies. It will be useful to obtain an early understanding and association of RNA-binding property of sequences of gene products. Here, we report a web server, RStrucFam, to predict the structure, type of cognate RNA(s) and function(s) of proteins, where possible, from mere sequence information.

RESULTS: The web server employs Hidden Markov Model scan (hmmscan) to enable association to a back-end database of structural and sequence families. The database (HMMRBP) comprises of 437 HMMs of RBP families of known structure that have been generated using structure-based sequence alignments and 746 sequence-centric RBP family HMMs. The input protein sequence is associated with structural or sequence domain families, if structure or sequence signatures exist. In case of association of the protein with a family of known structures, output features like, multiple structure-based sequence alignment (MSSA) of the query with all others members of that family is provided. Further, cognate RNA partner(s) for that protein, Gene Ontology (GO) annotations, if any and a homology model of the protein can be obtained. The users can also browse through the database for details pertaining to each family, protein or RNA and their related information based on keyword search or RNA motif search.

CONCLUSIONS: RStrucFam is a web server that exploits structurally conserved features of RBPs, derived from known family members and imprinted in mathematical profiles, to predict putative RBPs from sequence information. Proteins that fail to associate with such structure-centric families are further queried against the sequence-centric RBP family HMMs in the HMMRBP database. Further, all other essential information pertaining to an RBP, like overall function annotations, are provided. The web server can be accessed at the following link: http://caps.ncbs.res.in/rstrucfam .

}, issn = {1471-2105}, doi = {10.1186/s12859-016-1289-x}, author = {Ghosh, Pritha and Mathew, Oommen K and Sowdhamini, Ramanathan} } @article {869, title = {DOCKSCORE: a webserver for ranking protein-protein docked poses.}, journal = {BMC Bioinformatics}, volume = {16}, year = {2015}, month = {2015}, pages = {127}, abstract = {

BACKGROUND: Proteins interact with a variety of other molecules such as nucleic acids, small molecules and other proteins inside the cell. Structure-determination of protein-protein complexes is challenging due to several reasons such as the large molecular weights of these macromolecular complexes, their dynamic nature, difficulty in purification and sample preparation. Computational docking permits an early understanding of the feasibility and mode of protein-protein interactions. However, docking algorithms propose a number of solutions and it is a challenging task to select the native or near native pose(s) from this pool. DockScore is an objective scoring scheme that can be used to rank protein-protein docked poses. It considers several interface parameters, namely, surface area, evolutionary conservation, hydrophobicity, short contacts and spatial clustering at the interface for scoring.

RESULTS: We have implemented DockScore in form of a webserver for its use by the scientific community. DockScore webserver can be employed, subsequent to docking, to perform scoring of the docked solutions, starting from multiple poses as inputs. The results, on scores and ranks for all the poses, can be downloaded as a csv file and graphical view of the interface of best ranking poses is possible.

CONCLUSIONS: The webserver for DockScore is made freely available for the scientific community at: http://caps.ncbs.res.in/dockscore/ .

}, keywords = {Algorithms, Humans, Internet, Molecular Docking Simulation, Protein Binding, Protein Interaction Domains and Motifs, Proteins, ras GTPase-Activating Proteins, Software}, issn = {1471-2105}, doi = {10.1186/s12859-015-0572-6}, author = {Malhotra, Sony and Mathew, Oommen K and Sowdhamini, Ramanathan} } @article {161, title = {Genome sequencing of herb Tulsi (Ocimum tenuiflorum) unravels key genes behind its strong medicinal properties.}, journal = {BMC Plant Biol}, volume = {15}, year = {2015}, month = {2015}, pages = {212}, abstract = {

BACKGROUND: Krishna Tulsi, a member of Lamiaceae family, is a herb well known for its spiritual, religious and medicinal importance in India. The common name of this plant is {\textquoteright}Tulsi{\textquoteright} (or {\textquoteright}Tulasi{\textquoteright} or {\textquoteright}Thulasi{\textquoteright}) and is considered sacred by Hindus. We present the draft genome of Ocimum tenuiflurum L (subtype Krishna Tulsi) in this report. The paired-end and mate-pair sequence libraries were generated for the whole genome sequenced with the Illumina Hiseq 1000, resulting in an assembled genome of 374~Mb, with a genome coverage of 61~\% (612~Mb estimated genome size). We have also studied transcriptomes (RNA-Seq) of two subtypes of O. tenuiflorum, Krishna and Rama Tulsi and report the relative expression of genes in both the varieties.

RESULTS: The pathways leading to the production of medicinally-important specialized metabolites have been studied in detail, in relation to similar pathways in Arabidopsis thaliana and other plants. Expression levels of anthocyanin biosynthesis-related genes in leaf samples of Krishna Tulsi were observed to be relatively high, explaining the purple colouration of Krishna Tulsi leaves. The expression of six important genes identified from genome data were validated by performing q-RT-PCR in different tissues of five different species, which shows the high extent of urosolic acid-producing genes in young leaves of the Rama subtype. In addition, the presence of eugenol and ursolic acid, implied as potential drugs in the cure of many diseases including cancer was confirmed using mass spectrometry.

CONCLUSIONS: The availability of the whole genome of O.tenuiflorum and our sequence analysis suggests that small amino acid changes at the functional sites of genes involved in metabolite synthesis pathways confer special medicinal properties to this herb.

}, issn = {1471-2229}, doi = {10.1186/s12870-015-0562-x}, author = {Upadhyay, Atul K and Chacko, Anita R and Gandhimathi, A and Ghosh, Pritha and Harini, K and Joseph, Agnel P and Joshi, Adwait G and Karpe, Snehal D and Kaushik, Swati and Kuravadi, Nagesh and Lingu, Chandana S and Mahita, J and Malarini, Ramya and Malhotra, Sony and Malini, Manoharan and Mathew, Oommen K and Mutt, Eshita and Naika, Mahantesha and Nitish, Sathyanarayanan and Pasha, Shaik Naseer and Raghavender, Upadhyayula S and Rajamani, Anantharamanan and Shilpa, S and Shingate, Prashant N and Singh, Heikham Russiachand and Sukhwal, Anshul and Sunitha, Margaret S and Sumathi, Manojkumar and Ramaswamy, S and Gowda, Malali and Sowdhamini, Ramanathan} }