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Su Z, Griffin B, Emmons S, Wu Y. Prediction of interactions between cell surface proteins by machine learning. Proteins 2024; 92:567-580. [PMID: 38050713 DOI: 10.1002/prot.26648] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2023] [Revised: 11/15/2023] [Accepted: 11/20/2023] [Indexed: 12/06/2023]
Abstract
Cells detect changes in their external environments or communicate with each other through proteins on their surfaces. These cell surface proteins form a complicated network of interactions in order to fulfill their functions. The interactions between cell surface proteins are highly dynamic and, thus, challenging to detect using traditional experimental techniques. Here, we tackle this challenge using a computational framework. The primary focus of the framework is to develop new tools to identify interactions between domains in the immunoglobulin (Ig) fold, which is the most abundant domain family in cell surface proteins. These interactions could be formed between ligands and receptors from different cells or between proteins on the same cell surface. In practice, we collected all structural data on Ig domain interactions and transformed them into an interface fragment pair library. A high-dimensional profile can then be constructed from the library for a given pair of query protein sequences. Multiple machine learning models were used to read this profile so that the probability of interaction between the query proteins could be predicted. We tested our models on an experimentally derived dataset that contains 564 cell surface proteins in humans. The cross-validation results show that we can achieve higher than 70% accuracy in identifying the PPIs within this dataset. We then applied this method to a group of 46 cell surface proteins in Caenorhabditis elegans. We screened every possible interaction between these proteins. Many interactions recognized by our machine learning classifiers have been experimentally confirmed in the literature. In conclusion, our computational platform serves as a useful tool to help identify potential new interactions between cell surface proteins in addition to current state-of-the-art experimental techniques. The tool is freely accessible for use by the scientific community. Moreover, the general framework of the machine learning classification can also be extended to study the interactions of proteins in other domain superfamilies.
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Affiliation(s)
- Zhaoqian Su
- Department of Systems and Computational Biology, Albert Einstein College of Medicine, Bronx, New York, USA
| | - Brian Griffin
- Department of Genetics, Albert Einstein College of Medicine, Bronx, New York, USA
| | - Scott Emmons
- Department of Genetics, Albert Einstein College of Medicine, Bronx, New York, USA
| | - Yinghao Wu
- Department of Systems and Computational Biology, Albert Einstein College of Medicine, Bronx, New York, USA
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2
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Gromiha MM, Orengo CA, Sowdhamini R, Thornton AJM. Srinivasan (1962-2021) in Bioinformatics and beyond. Bioinformatics 2022; 38:2377-2379. [PMID: 35134112 PMCID: PMC9004639 DOI: 10.1093/bioinformatics/btac054] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2022] [Accepted: 01/27/2022] [Indexed: 02/05/2023] Open
Affiliation(s)
- M Michael Gromiha
- Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai, Tamil Nadu 600036, India
| | - Christine A Orengo
- Department of Structural and Molecular Biology, University College London, London WC1E 6BT, UK
| | - Ramanathan Sowdhamini
- National Centre for Biological Sciences (NCBS-TIFR), GKVK Campus, Bangalore, Karnataka 560065, India,Molecular Biophysics Unit, Indian Institute of Science, Bangalore 560012, India,Institute of Bioinformatics and Applied Biotechnology, Bangalore 560100, India
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3
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Kumar G, Srinivasan N, Sandhya S. Profiles of Natural and Designed Protein-Like Sequences Effectively Bridge Protein Sequence Gaps: Implications in Distant Homology Detection. Methods Mol Biol 2022; 2449:149-167. [PMID: 35507261 DOI: 10.1007/978-1-0716-2095-3_5] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Sequence-based approaches are fundamental to guide experimental investigations in obtaining structural and/or functional insights into uncharacterized protein families. Powerful profile-based sequence search methods rely on a sequence space continuum to identify non-trivial relationships through homology detection. The computational design of protein-like sequences that serve as "artificial linkers" is useful in identifying relationships between distant members of a structural fold. Such sequences act as intermediates and guide homology searches between distantly related proteins. Here, we describe an approach that represents natural intermediate sequences and designed protein-like sequences as HMM (Hidden Markov Models) profiles, to improve the sensitivity of existing search methods. Searches made within the "Profile database" were shown to recognize the parent structural fold for 90% of the search queries at query coverage better than 60%. For 1040 protein families with no available structure, fold associations were made through searches in the database of natural and designed sequence profiles. Most of the associations were made with the Alpha-alpha superhelix, Transmembrane beta-barrels, TIM barrel, and Immunoglobulin-like beta-sandwich folds. For 11 domain families of unknown functions, we provide confident fold associations using the profiles of designed sequences and a consensus from other fold recognition methods. For two DUFs (Domain families of Unknown Functions), we performed detailed functional annotation through comparisons with characterized templates of families of known function.
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Affiliation(s)
- Gayatri Kumar
- Molecular Biophysics Unit, Indian Institute of Science, Bangalore, Karnataka, India
| | | | - Sankaran Sandhya
- Molecular Biophysics Unit, Indian Institute of Science, Bangalore, Karnataka, India.
- Department of Biotechnology, Faculty of Life and Allied Health Sciences, M.S. Ramaiah University of Applied Sciences, Bangalore, Karnataka, India.
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4
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Mehta D, Ramesh A. Diversity and prevalence of ANTAR RNAs across actinobacteria. BMC Microbiol 2021; 21:159. [PMID: 34051745 PMCID: PMC8164766 DOI: 10.1186/s12866-021-02234-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2020] [Accepted: 05/18/2021] [Indexed: 11/13/2022] Open
Abstract
Background Computational approaches are often used to predict regulatory RNAs in bacteria, but their success is limited to RNAs that are highly conserved across phyla, in sequence and structure. The ANTAR regulatory system consists of a family of RNAs (the ANTAR-target RNAs) that selectively recruit ANTAR proteins. This protein-RNA complex together regulates genes at the level of translation or transcriptional elongation. Despite the widespread distribution of ANTAR proteins in bacteria, their target RNAs haven’t been identified in certain bacterial phyla such as actinobacteria. Results Here, by using a computational search model that is tuned to actinobacterial genomes, we comprehensively identify ANTAR-target RNAs in actinobacteria. These RNA motifs lie in select transcripts, often overlapping with the ribosome binding site or start codon, to regulate translation. Transcripts harboring ANTAR-target RNAs majorly encode proteins involved in the transport and metabolism of cellular metabolites like sugars, amino acids and ions; or encode transcription factors that in turn regulate diverse genes. Conclusion In this report, we substantially diversify and expand the family of ANTAR RNAs across bacteria. These findings now provide a starting point to investigate the actinobacterial processes that are regulated by ANTAR. Supplementary Information The online version contains supplementary material available at 10.1186/s12866-021-02234-x.
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Affiliation(s)
- Dolly Mehta
- National Centre for Biological Sciences, Tata Institute of Fundamental Research, GKVK Campus, Bellary Road, Bangalore, 560065, India.,SASTRA University, Tirumalaisamudram, Thanjavur, 613401, India
| | - Arati Ramesh
- National Centre for Biological Sciences, Tata Institute of Fundamental Research, GKVK Campus, Bellary Road, Bangalore, 560065, India.
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5
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Janaki C, Gowri VS, Srinivasan N. Master Blaster: an approach to sensitive identification of remotely related proteins. Sci Rep 2021; 11:8746. [PMID: 33888741 PMCID: PMC8062480 DOI: 10.1038/s41598-021-87833-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2019] [Accepted: 04/06/2021] [Indexed: 11/11/2022] Open
Abstract
Genome sequencing projects unearth sequences of all the protein sequences encoded in a genome. As the first step, homology detection is employed to obtain clues to structure and function of these proteins. However, high evolutionary divergence between homologous proteins challenges our ability to detect distant relationships. In the past, an approach involving multiple Position Specific Scoring Matrices (PSSMs) was found to be more effective than traditional single PSSMs. Cascaded search is another successful approach where hits of a search are queried to detect more homologues. We propose a protocol, ‘Master Blaster’, which combines the principles adopted in these two approaches to enhance our ability to detect remote homologues even further. Assessment of the approach was performed using known relationships available in the SCOP70 database, and the results were compared against that of PSI-BLAST and HHblits, a hidden Markov model-based method. Compared to PSI-BLAST, Master Blaster resulted in 10% improvement with respect to detection of cross superfamily connections, nearly 35% improvement in cross family and more than 80% improvement in intra family connections. From the results it was observed that HHblits is more sensitive in detecting remote homologues compared to Master Blaster. However, there are true hits from 46-folds for which Master Blaster reported homologs that are not reported by HHblits even using the optimal parameters indicating that for detecting remote homologues, use of multiple methods employing a combination of different approaches can be more effective in detecting remote homologs. Master Blaster stand-alone code is available for download in the supplementary archive.
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Affiliation(s)
- Chintalapati Janaki
- Molecular Biophysics Unit, Indian Institute of Science, Bangalore, 560012, India.,Centre for Development of Advanced Computing, Knowledge Park, Byappanahalli, Bangalore, 560038, India
| | - Venkatraman S Gowri
- Molecular Biophysics Unit, Indian Institute of Science, Bangalore, 560012, India.,Department of Chemistry, Auxilium College, Gandhinagar, Vellore, 632006, India
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Kumar G, Srinivasan N, Sandhya S. Artificial protein sequences enable recognition of vicinal and distant protein functional relationships. Proteins 2020; 88:1688-1700. [PMID: 32725917 DOI: 10.1002/prot.25986] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2020] [Revised: 06/29/2020] [Accepted: 07/26/2020] [Indexed: 11/07/2022]
Abstract
High divergence in protein sequences makes the detection of distant protein relationships through homology-based approaches challenging. Grouping protein sequences into families, through similarities in either sequence or 3-D structure, facilitates in the improved recognition of protein relationships. In addition, strategically designed protein-like sequences have been shown to bridge distant structural domain families by serving as artificial linkers. In this study, we have augmented a search database of known protein domain families with such designed sequences, with the intention of providing functional clues to domain families of unknown structure. When assessed using representative query sequences from each family, we obtain a success rate of 94% in protein domain families of known structure. Further, we demonstrate that the augmented search space enabled fold recognition for 582 families with no structural information available a priori. Additionally, we were able to provide reliable functional relationships for 610 orphan families. We discuss the application of our method in predicting functional roles through select examples for DUF4922, DUF5131, and DUF5085. Our approach also detects new associations between families that were previously not known to be related, as demonstrated through new sub-groups of the RNA polymerase domain among three distinct RNA viruses. Taken together, designed sequences-augmented search databases direct the detection of meaningful relationships between distant protein families. In turn, they enable fold recognition and offer reliable pointers to potential functional sites that may be probed further through direct mutagenesis studies.
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Affiliation(s)
- Gayatri Kumar
- Molecular Biophysics Unit, Indian Institute of Science, Bangalore, India
| | | | - Sankaran Sandhya
- Molecular Biophysics Unit, Indian Institute of Science, Bangalore, India
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7
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Kumar G, Mudgal R, Srinivasan N, Sandhya S. Use of designed sequences in protein structure recognition. Biol Direct 2018; 13:8. [PMID: 29776380 PMCID: PMC5960202 DOI: 10.1186/s13062-018-0209-6] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2017] [Accepted: 04/18/2018] [Indexed: 12/13/2022] Open
Abstract
Background Knowledge of the protein structure is a pre-requisite for improved understanding of molecular function. The gap in the sequence-structure space has increased in the post-genomic era. Grouping related protein sequences into families can aid in narrowing the gap. In the Pfam database, structure description is provided for part or full-length proteins of 7726 families. For the remaining 52% of the families, information on 3-D structure is not yet available. We use the computationally designed sequences that are intermediately related to two protein domain families, which are already known to share the same fold. These strategically designed sequences enable detection of distant relationships and here, we have employed them for the purpose of structure recognition of protein families of yet unknown structure. Results We first measured the success rate of our approach using a dataset of protein families of known fold and achieved a success rate of 88%. Next, for 1392 families of yet unknown structure, we made structural assignments for part/full length of the proteins. Fold association for 423 domains of unknown function (DUFs) are provided as a step towards functional annotation. Conclusion The results indicate that knowledge-based filling of gaps in protein sequence space is a lucrative approach for structure recognition. Such sequences assist in traversal through protein sequence space and effectively function as ‘linkers’, where natural linkers between distant proteins are unavailable. Reviewers This article was reviewed by Oliviero Carugo, Christine Orengo and Srikrishna Subramanian. Electronic supplementary material The online version of this article (10.1186/s13062-018-0209-6) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Gayatri Kumar
- Lab 103, Molecular Biophysics Unit, Indian Institute of Science, Bangalore, Karnataka, 560012, India
| | - Richa Mudgal
- Lab 103, Molecular Biophysics Unit, Indian Institute of Science, Bangalore, Karnataka, 560012, India.,Present address: Institute for Research in Biomedicine (IRB), Parc Cientific de Barcelona, C/ Baldiri Reixac 10, 08028, Barcelona, Spain
| | - Narayanaswamy Srinivasan
- Lab 103, Molecular Biophysics Unit, Indian Institute of Science, Bangalore, Karnataka, 560012, India.
| | - Sankaran Sandhya
- Lab 103, Molecular Biophysics Unit, Indian Institute of Science, Bangalore, Karnataka, 560012, India.
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8
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Topham CM, Barbe S, André I. An Atomistic Statistically Effective Energy Function for Computational Protein Design. J Chem Theory Comput 2016; 12:4146-68. [PMID: 27341125 DOI: 10.1021/acs.jctc.6b00090] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Shortcomings in the definition of effective free-energy surfaces of proteins are recognized to be a major contributory factor responsible for the low success rates of existing automated methods for computational protein design (CPD). The formulation of an atomistic statistically effective energy function (SEEF) suitable for a wide range of CPD applications and its derivation from structural data extracted from protein domains and protein-ligand complexes are described here. The proposed energy function comprises nonlocal atom-based and local residue-based SEEFs, which are coupled using a novel atom connectivity number factor to scale short-range, pairwise, nonbonded atomic interaction energies and a surface-area-dependent cavity energy term. This energy function was used to derive additional SEEFs describing the unfolded-state ensemble of any given residue sequence based on computed average energies for partially or fully solvent-exposed fragments in regions of irregular structure in native proteins. Relative thermal stabilities of 97 T4 bacteriophage lysozyme mutants were predicted from calculated energy differences for folded and unfolded states with an average unsigned error (AUE) of 0.84 kcal mol(-1) when compared to experiment. To demonstrate the utility of the energy function for CPD, further validation was carried out in tests of its capacity to recover cognate protein sequences and to discriminate native and near-native protein folds, loop conformers, and small-molecule ligand binding poses from non-native benchmark decoys. Experimental ligand binding free energies for a diverse set of 80 protein complexes could be predicted with an AUE of 2.4 kcal mol(-1) using an additional energy term to account for the loss in ligand configurational entropy upon binding. The atomistic SEEF is expected to improve the accuracy of residue-based coarse-grained SEEFs currently used in CPD and to extend the range of applications of extant atom-based protein statistical potentials.
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Affiliation(s)
- Christopher M Topham
- Université de Toulouse; INSA, UPS, INP; LISBP , 135 Avenue de Rangueil, F-31077 Toulouse, France.,CNRS, UMR5504 , F-31400 Toulouse, France.,INRA, UMR792 Ingénierie des Systèmes Biologiques et des Procédés , F-31400 Toulouse, France
| | - Sophie Barbe
- Université de Toulouse; INSA, UPS, INP; LISBP , 135 Avenue de Rangueil, F-31077 Toulouse, France.,CNRS, UMR5504 , F-31400 Toulouse, France.,INRA, UMR792 Ingénierie des Systèmes Biologiques et des Procédés , F-31400 Toulouse, France
| | - Isabelle André
- Université de Toulouse; INSA, UPS, INP; LISBP , 135 Avenue de Rangueil, F-31077 Toulouse, France.,CNRS, UMR5504 , F-31400 Toulouse, France.,INRA, UMR792 Ingénierie des Systèmes Biologiques et des Procédés , F-31400 Toulouse, France
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9
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Sandhya S, Mudgal R, Kumar G, Sowdhamini R, Srinivasan N. Protein sequence design and its applications. Curr Opin Struct Biol 2016; 37:71-80. [PMID: 26773478 DOI: 10.1016/j.sbi.2015.12.004] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2015] [Revised: 12/07/2015] [Accepted: 12/15/2015] [Indexed: 01/14/2023]
Abstract
Design of proteins has far-reaching potentials in diverse areas that span repurposing of the protein scaffold for reactions and substrates that they were not naturally meant for, to catching a glimpse of the ephemeral proteins that nature might have sampled during evolution. These non-natural proteins, either in synthesized or virtual form have opened the scope for the design of entities that not only rival their natural counterparts but also offer a chance to visualize the protein space continuum that might help to relate proteins and understand their associations. Here, we review the recent advances in protein engineering and design, in multiple areas, with a view to drawing attention to their future potential.
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Affiliation(s)
- Sankaran Sandhya
- Molecular Biophysics Unit, Indian Institute of Science, Bangalore 560 012, India
| | - Richa Mudgal
- Molecular Biophysics Unit, Indian Institute of Science, Bangalore 560 012, India; IISc Mathematics Initiative, Indian Institute of Science, Bangalore 560 012, India
| | - Gayatri Kumar
- Molecular Biophysics Unit, Indian Institute of Science, Bangalore 560 012, India
| | - Ramanathan Sowdhamini
- National Centre for Biological Sciences-TIFR, UAS-GKVK Campus, Bangalore 560065, India
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10
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Oh Brother, Where Art Thou? Finding Orthologs in the Twilight and Midnight Zones of Sequence Similarity. Evol Biol 2016. [DOI: 10.1007/978-3-319-41324-2_22] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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Kaushik S, Nair AG, Mutt E, Subramanian HP, Sowdhamini R. Rapid and enhanced remote homology detection by cascading hidden Markov model searches in sequence space. Bioinformatics 2015; 32:338-44. [DOI: 10.1093/bioinformatics/btv538] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2015] [Accepted: 09/06/2015] [Indexed: 11/14/2022] Open
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Mudgal R, Sandhya S, Chandra N, Srinivasan N. De-DUFing the DUFs: Deciphering distant evolutionary relationships of Domains of Unknown Function using sensitive homology detection methods. Biol Direct 2015; 10:38. [PMID: 26228684 PMCID: PMC4520260 DOI: 10.1186/s13062-015-0069-2] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2015] [Accepted: 07/20/2015] [Indexed: 12/23/2022] Open
Abstract
Background In the post-genomic era where sequences are being determined at a rapid rate, we are highly reliant on computational methods for their tentative biochemical characterization. The Pfam database currently contains 3,786 families corresponding to “Domains of Unknown Function” (DUF) or “Uncharacterized Protein Family” (UPF), of which 3,087 families have no reported three-dimensional structure, constituting almost one-fourth of the known protein families in search for both structure and function. Results We applied a ‘computational structural genomics’ approach using five state-of-the-art remote similarity detection methods to detect the relationship between uncharacterized DUFs and domain families of known structures. The association with a structural domain family could serve as a start point in elucidating the function of a DUF. Amongst these five methods, searches in SCOP-NrichD database have been applied for the first time. Predictions were classified into high, medium and low- confidence based on the consensus of results from various approaches and also annotated with enzyme and Gene ontology terms. 614 uncharacterized DUFs could be associated with a known structural domain, of which high confidence predictions, involving at least four methods, were made for 54 families. These structure-function relationships for the 614 DUF families can be accessed on-line at http://proline.biochem.iisc.ernet.in/RHD_DUFS/. For potential enzymes in this set, we assessed their compatibility with the associated fold and performed detailed structural and functional annotation by examining alignments and extent of conservation of functional residues. Detailed discussion is provided for interesting assignments for DUF3050, DUF1636, DUF1572, DUF2092 and DUF659. Conclusions This study provides insights into the structure and potential function for nearly 20 % of the DUFs. Use of different computational approaches enables us to reliably recognize distant relationships, especially when they converge to a common assignment because the methods are often complementary. We observe that while pointers to the structural domain can offer the right clues to the function of a protein, recognition of its precise functional role is still ‘non-trivial’ with many DUF domains conserving only some of the critical residues. It is not clear whether these are functional vestiges or instances involving alternate substrates and interacting partners. Reviewers This article was reviewed by Drs Eugene Koonin, Frank Eisenhaber and Srikrishna Subramanian. Electronic supplementary material The online version of this article (doi:10.1186/s13062-015-0069-2) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Richa Mudgal
- IISc Mathematics Initiative, Indian Institute of Science, Bangalore, 560 012, India.
| | - Sankaran Sandhya
- Molecular Biophysics Unit, Indian Institute of Science, Bangalore, 560 012, India.
| | - Nagasuma Chandra
- Department of Biochemistry, Indian Institute of Science, Bangalore, 560 012, India.
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Mudgal R, Sandhya S, Kumar G, Sowdhamini R, Chandra NR, Srinivasan N. NrichD database: sequence databases enriched with computationally designed protein-like sequences aid in remote homology detection. Nucleic Acids Res 2014; 43:D300-5. [PMID: 25262355 PMCID: PMC4384005 DOI: 10.1093/nar/gku888] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
NrichD (http://proline.biochem.iisc.ernet.in/NRICHD/) is a database of computationally designed protein-like sequences, augmented into natural sequence databases that can perform hops in protein sequence space to assist in the detection of remote relationships. Establishing protein relationships in the absence of structural evidence or natural ‘intermediately related sequences’ is a challenging task. Recently, we have demonstrated that the computational design of artificial intermediary sequences/linkers is an effective approach to fill naturally occurring voids in protein sequence space. Through a large-scale assessment we have demonstrated that such sequences can be plugged into commonly employed search databases to improve the performance of routinely used sequence search methods in detecting remote relationships. Since it is anticipated that such data sets will be employed to establish protein relationships, two databases that have already captured these relationships at the structural and functional domain level, namely, the SCOP database and the Pfam database, have been ‘enriched’ with these artificial intermediary sequences. NrichD database currently contains 3 611 010 artificial sequences that have been generated between 27 882 pairs of families from 374 SCOP folds. The data sets are freely available for download. Additional features include the design of artificial sequences between any two protein families of interest to the user.
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Affiliation(s)
- Richa Mudgal
- IISc Mathematics Initiative, Indian Institute of Science, Bangalore 560 012, Karnataka, India
| | - Sankaran Sandhya
- Department of Biochemistry, Indian Institute of Science, Bangalore 560 012, Karnataka, India
| | - Gayatri Kumar
- Molecular Biophysics Unit, Indian Institute of Science, Bangalore 560 012, Karnataka, India
| | - Ramanathan Sowdhamini
- National Centre for Biological Sciences, Gandhi Krishi Vignan Kendra Campus, Bellary road, Bangalore 560 065, Karnataka, India
| | - Nagasuma R Chandra
- Department of Biochemistry, Indian Institute of Science, Bangalore 560 012, Karnataka, India
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