1
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Zhang H, Saravanan KM, Wei Y, Jiao Y, Yang Y, Pan Y, Wu X, Zhang JZH. Deep Learning-Based Bioactive Therapeutic Peptide Generation and Screening. J Chem Inf Model 2023; 63:835-845. [PMID: 36724090 DOI: 10.1021/acs.jcim.2c01485] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/02/2023]
Abstract
Many bioactive peptides demonstrated therapeutic effects over complicated diseases, such as antiviral, antibacterial, anticancer, etc. It is possible to generate a large number of potentially bioactive peptides using deep learning in a manner analogous to the generation of de novo chemical compounds using the acquired bioactive peptides as a training set. Such generative techniques would be significant for drug development since peptides are much easier and cheaper to synthesize than compounds. Despite the limited availability of deep learning-based peptide-generating models, we have built an LSTM model (called LSTM_Pep) to generate de novo peptides and fine-tuned the model to generate de novo peptides with specific prospective therapeutic benefits. Remarkably, the Antimicrobial Peptide Database has been effectively utilized to generate various kinds of potential active de novo peptides. We proposed a pipeline for screening those generated peptides for a given target and used the main protease of SARS-COV-2 as a proof-of-concept. Moreover, we have developed a deep learning-based protein-peptide prediction model (DeepPep) for rapid screening of the generated peptides for the given targets. Together with the generating model, we have demonstrated that iteratively fine-tuning training, generating, and screening peptides for higher-predicted binding affinity peptides can be achieved. Our work sheds light on developing deep learning-based methods and pipelines to effectively generate and obtain bioactive peptides with a specific therapeutic effect and showcases how artificial intelligence can help discover de novo bioactive peptides that can bind to a particular target.
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Affiliation(s)
- Haiping Zhang
- Shenzhen Institute of Synthetic Biology, Faculty of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, Guangdong, China
| | - Konda Mani Saravanan
- Department of Biotechnology, Bharath Institute of Higher Education and Research, Chennai 600073, Tamil Nadu, India
| | - Yanjie Wei
- Center for High Performance Computing, Joint Engineering Research Center for Health Big Data Intelligent Analysis Technology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, Guangdong, China
| | - Yang Jiao
- Faculty of Computer Science and Control Engineering, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
| | - Yang Yang
- Shenzhen Key Laboratory of Pathogen and Immunity, National Clinical Research Center for infectious disease, State Key Discipline of Infectious Disease, Shenzhen Third People's Hospital, Second Hospital Affiliated to Southern University of Science and Technology, Shenzhen 518112, China
| | - Yi Pan
- Center for High Performance Computing, Joint Engineering Research Center for Health Big Data Intelligent Analysis Technology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, Guangdong, China.,Faculty of Computer Science and Control Engineering, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
| | - Xuli Wu
- School of Medicine, Shenzhen University, Shenzhen 518060, Guangdong, China
| | - John Z H Zhang
- Shenzhen Institute of Synthetic Biology, Faculty of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, Guangdong, China.,East China Normal University, Shanghai 200062, China.,NYU-ECNU Center for Computational Chemistry at NYU Shanghai, Shanghai 200062, China
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2
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Murugesan A, Nguyen P, Ramesh T, Yli-Harja O, Kandhavelu M, Saravanan KM. Molecular modeling and dynamics studies of the synthetic small molecule agonists with GPR17 and P2Y1 receptor. J Biomol Struct Dyn 2022; 40:12908-12916. [PMID: 34542380 DOI: 10.1080/07391102.2021.1977707] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Abstract
The human Guanine Protein coupled membrane Receptor 17 (hGPR17), an orphan receptor that activates uracil nucleotides and cysteinyl leukotrienes is considered as a crucial target for the neurodegenerative diseases. Yet, the detailed molecular interaction of potential synthetic ligands of GPR17 needs to be characterized. Here, we have studied a comparative analysis on the interaction specificity of GPR17-ligands with hGPR17 and human purinergic G protein-coupled receptor (hP2Y1) receptors. Previously, we have simulated the interaction stability of synthetic ligands such as T0510.3657, AC1MLNKK, and MDL29951 with hGPR17 and hP2Y1 receptor in the lipid environment. In the present work, we have comparatively studied the protein-ligand interaction of hGPR17-T0510.3657 and P2Y1-MRS2500. Sequence analysis and structural superimposition of hGPR17 and hP2Y1 receptor revealed the similarities in the structural arrangement with the local backbone root mean square deviation (RMSD) value of 1.16 Å and global backbone RMSD value of 5.30 Å. The comparative receptor-ligand interaction analysis between hGPR17 and hP2Y1 receptor exposed the distinct binding sites in terms of geometrical properties. Further, the molecular docking of T0510.3657 with the hP2Y1 receptor have shown non-specific interaction. The experimental validation also revealed that Gi-coupled activation of GPR17 by specific ligands leads to the adenylyl cyclase inhibition, while there is no inhibition upon hP2Y1 activation. Overall, the above findings suggest that T0510.3657-GPR17 binding specificity could be further explored for the treatment of numerous neuronal diseases. Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Akshaya Murugesan
- Molecular Signaling Lab, Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland.,Department of Biotechnology, Lady Doak College, Thallakulam, Madurai, India
| | - Phung Nguyen
- Molecular Signaling Lab, Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
| | - Thiyagarajan Ramesh
- Department of Basic Medical Sciences, College of Medicine, Prince Sattam Bin Abdulaziz University, Al Kharj, Kingdom of Saudi Arabia
| | - Olli Yli-Harja
- Computational Systems Biology Group, Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland.,Institute for Systems Biology, Seattle, WA, USA
| | | | - Konda Mani Saravanan
- Scigen Research and Innovation Pvt Ltd, Periyar Technology Business Incubator, Thanjavur, Tamil Nadu, India
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3
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Saravanan KM, Zhang H, Zhang H, Xi W, Wei Y. On the Conformational Dynamics of β-Amyloid Forming Peptides: A Computational Perspective. Front Bioeng Biotechnol 2020; 8:532. [PMID: 32656188 PMCID: PMC7325929 DOI: 10.3389/fbioe.2020.00532] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2019] [Accepted: 05/04/2020] [Indexed: 12/12/2022] Open
Abstract
Understanding the conformational dynamics of proteins and peptides involved in important functions is still a difficult task in computational structural biology. Because such conformational transitions in β-amyloid (Aβ) forming peptides play a crucial role in many neurological disorders, researchers from different scientific fields have been trying to address issues related to the folding of Aβ forming peptides together. Many theoretical models have been proposed in the recent years for studying Aβ peptides using mathematical, physicochemical, and molecular dynamics simulation, and machine learning approaches. In this article, we have comprehensively reviewed the developmental advances in the theoretical models for Aβ peptide folding and interactions, particularly in the context of neurological disorders. Furthermore, we have extensively reviewed the advances in molecular dynamics simulation as a tool used for studying the conversions between polymorphic amyloid forms and applications of using machine learning approaches in predicting Aβ peptides and aggregation-prone regions in proteins. We have also provided details on the theoretical advances in the study of Aβ peptides, which would enhance our understanding of these peptides at the molecular level and eventually lead to the development of targeted therapies for certain acute neurological disorders such as Alzheimer's disease in the future.
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Affiliation(s)
| | | | | | - Wenhui Xi
- Center for High Performance Computing, Joint Engineering Research Center for Health Big Data Intelligent Analysis Technology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Yanjie Wei
- Center for High Performance Computing, Joint Engineering Research Center for Health Big Data Intelligent Analysis Technology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
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4
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Zhang H, Saravanan KM, Yang Y, Hossain MT, Li J, Ren X, Pan Y, Wei Y. Deep Learning Based Drug Screening for Novel Coronavirus 2019-nCov. Interdiscip Sci 2020; 12:368-376. [PMID: 32488835 PMCID: PMC7266118 DOI: 10.1007/s12539-020-00376-6] [Citation(s) in RCA: 89] [Impact Index Per Article: 22.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2020] [Revised: 04/20/2020] [Accepted: 05/25/2020] [Indexed: 01/09/2023]
Abstract
A novel coronavirus, called 2019-nCoV, was recently found in Wuhan, Hubei Province of China, and now is spreading across China and other parts of the world. Although there are some drugs to treat 2019-nCoV, there is no proper scientific evidence about its activity on the virus. It is of high significance to develop a drug that can combat the virus effectively to save valuable human lives. It usually takes a much longer time to develop a drug using traditional methods. For 2019-nCoV, it is now better to rely on some alternative methods such as deep learning to develop drugs that can combat such a disease effectively since 2019-nCoV is highly homologous to SARS-CoV. In the present work, we first collected virus RNA sequences of 18 patients reported to have 2019-nCoV from the public domain database, translated the RNA into protein sequences, and performed multiple sequence alignment. After a careful literature survey and sequence analysis, 3C-like protease is considered to be a major therapeutic target and we built a protein 3D model of 3C-like protease using homology modeling. Relying on the structural model, we used a pipeline to perform large scale virtual screening by using a deep learning based method to accurately rank/identify protein-ligand interacting pairs developed recently in our group. Our model identified potential drugs for 2019-nCoV 3C-like protease by performing drug screening against four chemical compound databases (Chimdiv, Targetmol-Approved_Drug_Library, Targetmol-Natural_Compound_Library, and Targetmol-Bioactive_Compound_Library) and a database of tripeptides. Through this paper, we provided the list of possible chemical ligands (Meglumine, Vidarabine, Adenosine, D-Sorbitol, D-Mannitol, Sodium_gluconate, Ganciclovir and Chlorobutanol) and peptide drugs (combination of isoleucine, lysine and proline) from the databases to guide the experimental scientists and validate the molecules which can combat the virus in a shorter time.
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Affiliation(s)
- Haiping Zhang
- Center for High Performance Computing, Joint Engineering Research Center for Health Big Data Intelligent Analysis Technology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, 518055, People's Republic of China
| | - Konda Mani Saravanan
- Center for High Performance Computing, Joint Engineering Research Center for Health Big Data Intelligent Analysis Technology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, 518055, People's Republic of China
| | - Yang Yang
- Shenzhen Key Laboratory of Pathogen and Immunity, Guangdong Key Laboratory for Diagnosis and Treatment of Emerging Infectious Diseases, State Key Discipline of Infectious Disease, Second Hospital Affiliated to Southern University of Science and Technology, Shenzhen Third People's Hospital, Shenzhen, 518112, People's Republic of China
| | - Md Tofazzal Hossain
- Center for High Performance Computing, Joint Engineering Research Center for Health Big Data Intelligent Analysis Technology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, 518055, People's Republic of China
- University of Chinese Academy of Sciences, No. 19(A) Yuquan Road, Shijingshan District, Beijing, 100049, People's Republic of China
| | - Junxin Li
- Shenzhen Laboratory of Human Antibody Engineering, Institute of Biomedicine and Biotechnology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, 1068 Xueyuan Boulevard, University City of Shenzhen, XiliNanshan, Shenzhen, 518055, People's Republic of China
| | - Xiaohu Ren
- Institute of Toxicology, Shenzhen Center for Disease Control and Prevention, No 8 Longyuan Road, Nanshan District, Shenzhen, 518055, China
| | - Yi Pan
- Department of Computer Science, Georgia State University, Atlanta, 30302-5060, USA
| | - Yanjie Wei
- Center for High Performance Computing, Joint Engineering Research Center for Health Big Data Intelligent Analysis Technology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, 518055, People's Republic of China.
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5
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Saravanan KM, Ponnuraj K. Sequence and structural analysis of fibronectin-binding protein reveals importance of multiple intrinsic disordered tandem repeats. J Mol Recognit 2018; 32:e2768. [PMID: 30397967 DOI: 10.1002/jmr.2768] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2018] [Revised: 09/03/2018] [Accepted: 10/06/2018] [Indexed: 12/24/2022]
Abstract
The location of certain amino acid sequences like repeats along the polypeptide chain is very important in the context of forming the overall shape of the protein molecule which in fact determines its function. In gram-positive bacteria, fibronectin-binding protein (FnBP) is one such repeat containing protein, and it is a cell wall-attached protein responsible for various acute infections in human. Several studies on sequence, structure, and function of fibronectin-binding regions of FnBPs were reported; however, no detailed study was carried out on the full-length protein sequence. In the present study, we have made a thorough sequence and structure analysis on FnBP_A of Staphylococcus aureus and explored the presence of dual ligand-binding ability of fibrinogen (fg)-binding region and its molecular recognition processes. Multiple sequence alignment and protein-protein docking analysis reveal the regions which are likely involved in dual ligand binding. Further analysis of docking of FnBP_A fg-binding region and fn N-terminal modules suggests that if the latter binds to the fg-binding region of FnBP_A, it would inhibit the subsequent binding of fg because of steric hindrance. The sequence analysis further suggests that the abundance of disorder promoting residue glutamic acid and dual personality (both order/disorder promoting) residue threonine in tandem repeats of FnBP_A and B proteins possibly would help the molecule to undergo a conformational change while binding with fn by β-zipper mechanism. The segment-based power spectral analysis was carried out which helps to understand the distribution of hydrophobic residues along the sequence particularly in intrinsic disordered tandem repeats. The results presented here will help to understand the role of internal repeats and intrinsic disorder in the molecular recognition process of a pathogenic cell surface protein.
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Affiliation(s)
- Konda Mani Saravanan
- Centre of Advanced Study in Crystallography & Biophysics, University of Madras, Chennai, India
| | - Karthe Ponnuraj
- Centre of Advanced Study in Crystallography & Biophysics, University of Madras, Chennai, India
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6
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Saravanan KM, Selvaraj S. Dihedral angle preferences of amino acid residues forming various non-local interactions in proteins. J Biol Phys 2017; 43:265-278. [PMID: 28577238 PMCID: PMC5471173 DOI: 10.1007/s10867-017-9451-x] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2016] [Accepted: 04/13/2017] [Indexed: 12/22/2022] Open
Abstract
In theory, a polypeptide chain can adopt a vast number of conformations, each corresponding to a set of backbone rotation angles. Many of these conformations are excluded due to steric overlaps. Ramachandran and coworkers were the first to look into this problem by plotting backbone dihedral angles in a two-dimensional plot. The conformational space in the Ramachandran map is further refined by considering the energetic contributions of various non-bonded interactions. Alternatively, the conformation adopted by a polypeptide chain may also be examined by investigating interactions between the residues. Since the Ramachandran map essentially focuses on local interactions (residues closer in sequence), out of interest, we have analyzed the dihedral angle preferences of residues that make non-local interactions (residues far away in sequence and closer in space) in the folded structures of proteins. The non-local interactions have been grouped into different types such as hydrogen bond, van der Waals interactions between hydrophobic groups, ion pairs (salt bridges), and ππ-stacking interactions. The results show the propensity of amino acid residues in proteins forming local and non-local interactions. Our results point to the vital role of different types of non-local interactions and their effect on dihedral angles in forming secondary and tertiary structural elements to adopt their native fold.
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Affiliation(s)
- Konda Mani Saravanan
- Centre of Advanced Study in Crystallography & Biophysics, University of Madras, Guindy Campus, Chennai, Tamil Nadu, 600 025, India
| | - Samuel Selvaraj
- Centre of Advanced Study in Crystallography & Biophysics, University of Madras, Guindy Campus, Chennai, Tamil Nadu, 600 025, India.
- Department of Bioinformatics, School of Life Sciences, Bharathidasan University, Tiruchirappalli, Tamil Nadu, 620024, India.
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7
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Bahramali G, Goliaei B, Minuchehr Z, Marashi SA. A network biology approach to understanding the importance of chameleon proteins in human physiology and pathology. Amino Acids 2016; 49:303-315. [DOI: 10.1007/s00726-016-2361-6] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2016] [Accepted: 11/05/2016] [Indexed: 12/20/2022]
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8
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Saravanan KM, Suvaithenamudhan S, Parthasarathy S, Selvaraj S. Pairwise contact energy statistical potentials can help to find probability of point mutations. Proteins 2016; 85:54-64. [PMID: 27761949 DOI: 10.1002/prot.25191] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2016] [Revised: 06/16/2016] [Accepted: 10/13/2016] [Indexed: 11/10/2022]
Abstract
To adopt a particular fold, a protein requires several interactions between its amino acid residues. The energetic contribution of these residue-residue interactions can be approximated by extracting statistical potentials from known high resolution structures. Several methods based on statistical potentials extracted from unrelated proteins are found to make a better prediction of probability of point mutations. We postulate that the statistical potentials extracted from known structures of similar folds with varying sequence identity can be a powerful tool to examine probability of point mutation. By keeping this in mind, we have derived pairwise residue and atomic contact energy potentials for the different functional families that adopt the (α/β)8 TIM-Barrel fold. We carried out computational point mutations at various conserved residue positions in yeast Triose phosphate isomerase enzyme for which experimental results are already reported. We have also performed molecular dynamics simulations on a subset of point mutants to make a comparative study. The difference in pairwise residue and atomic contact energy of wildtype and various point mutations reveals probability of mutations at a particular position. Interestingly, we found that our computational prediction agrees with the experimental studies of Silverman et al. (Proc Natl Acad Sci 2001;98:3092-3097) and perform better prediction than iMutant and Cologne University Protein Stability Analysis Tool. The present work thus suggests deriving pairwise contact energy potentials and molecular dynamics simulations of functionally important folds could help us to predict probability of point mutations which may ultimately reduce the time and cost of mutation experiments. Proteins 2016; 85:54-64. © 2016 Wiley Periodicals, Inc.
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Affiliation(s)
- K M Saravanan
- Centre of Advanced Study in Crystallography and Biophysics, University of Madras, Guindy Campus, Chennai, Tamilnadu, 600 025, India
| | - S Suvaithenamudhan
- Department of Bioinformatics, School of Life Sciences, Bharathidasan University, Tirchirappalli, Tamilnadu, 620 024, India
| | - S Parthasarathy
- Department of Bioinformatics, School of Life Sciences, Bharathidasan University, Tirchirappalli, Tamilnadu, 620 024, India
| | - S Selvaraj
- Department of Bioinformatics, School of Life Sciences, Bharathidasan University, Tirchirappalli, Tamilnadu, 620 024, India
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9
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Saravanan KM, Senthil R. PreFRP: Prediction and visualization of fluctuation residues in proteins. J Nat Sci Biol Med 2016; 7:124-6. [PMID: 27433060 PMCID: PMC4934099 DOI: 10.4103/0976-9668.184696] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022] Open
Abstract
Aim: The PreFRP web server extracts sequence and basic information of a protein structure and groups amino acid residues in a protein into three important types such as high, moderate, and weak fluctuating residues. Materials and Methods: The server takes a protein data bank file or an amino acid sequence as input and prints the probability of amino acid residues to fluctuate. The server also provides a link to Jmol, a molecular visualization program to visualize the high, moderate, and weak fluctuating residues in three different colors. Results: Prediction and visualization of fluctuating amino acid residues in proteins may help to understand the complex three-dimensional structure of proteins and may further help in docking and mutation experiments. Availability: The web server is freely accessible through the web page of the author's institution http://www.mpi.edu.in/prefrp/link.html.
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Affiliation(s)
- Konda Mani Saravanan
- Centre of Excellence in Bioinformatics, School of Biotechnology, Madurai Kamaraj University, Madurai, India
| | - Renganathan Senthil
- Department of Bioinformatics, School of Biosciences, The Marudupandiyar Institutions (Affiliated to Bharathidasan University), Thanjavur, Tamil Nadu, India
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10
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Comparative Analysis of the Molecular Adjuvants and Their Binding Efficiency with CR1. Interdiscip Sci 2015; 8:35-40. [PMID: 26264056 DOI: 10.1007/s12539-015-0279-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2014] [Revised: 01/16/2015] [Accepted: 01/16/2015] [Indexed: 12/20/2022]
Abstract
There are so many obstacles in developing a vaccine or vaccine technology for diseases like cancer and human immunodeficiency virus infection. While developing vaccines that target specific infection, molecular adjuvants are indispensable. These molecular adjuvants act as a vaccine delivery vehicle to the immune system to increase the effectiveness of the specific antigens. In the present work, a computational study has been done on molecular adjuvants like IgGFc, GMCSF and C3d to find out how efficiently they are binding to CR1. Sequence, structure and mutational analysis are performed on the molecular adjuvants to understand the features important for their binding with the receptor. Results obtained from our study indicate that the adjuvant IgGFc complexed with the receptor CR1 has the best binding efficiency, which can be used further to develop better vaccine technologies.
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11
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Li W, Kinch LN, Karplus PA, Grishin NV. ChSeq: A database of chameleon sequences. Protein Sci 2015; 24:1075-86. [PMID: 25970262 DOI: 10.1002/pro.2689] [Citation(s) in RCA: 37] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2015] [Revised: 04/15/2015] [Accepted: 04/24/2015] [Indexed: 11/11/2022]
Abstract
Chameleon sequences (ChSeqs) refer to sequence strings of identical amino acids that can adopt different conformations in protein structures. Researchers have detected and studied ChSeqs to understand the interplay between local and global interactions in protein structure formation. The different secondary structures adopted by one ChSeq challenge sequence-based secondary structure predictors. With increasing numbers of available Protein Data Bank structures, we here identify a large set of ChSeqs ranging from 6 to 10 residues in length. The homologous ChSeqs discovered highlight the structural plasticity involved in biological function. When compared with previous studies, the set of unrelated ChSeqs found represents an about 20-fold increase in the number of detected sequences, as well as an increase in the longest ChSeq length from 8 to 10 residues. We applied secondary structure predictors on our ChSeqs and found that methods based on a sequence profile outperformed methods based on a single sequence. For the unrelated ChSeqs, the evolutionary information provided by the sequence profile typically allows successful prediction of the prevailing secondary structure adopted in each protein family. Our dataset will facilitate future studies of ChSeqs, as well as interpretations of the interplay between local and nonlocal interactions. A user-friendly web interface for this ChSeq database is available at prodata.swmed.edu/chseq.
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Affiliation(s)
- Wenlin Li
- Department of Biophysics, University of Texas Southwestern Medical Center, Dallas, Texas, 75390-9050.,Department of Biochemistry, University of Texas Southwestern Medical Center, Dallas, Texas, 75390-9050
| | - Lisa N Kinch
- Howard Hughes Medical Institute, University of Texas Southwestern Medical Center, Dallas, Texas, 75390-9050
| | - P Andrew Karplus
- Department of Biochemistry and Biophysics, Oregon State University, Corvallis, Oregon, 97331
| | - Nick V Grishin
- Department of Biophysics, University of Texas Southwestern Medical Center, Dallas, Texas, 75390-9050.,Department of Biochemistry, University of Texas Southwestern Medical Center, Dallas, Texas, 75390-9050.,Howard Hughes Medical Institute, University of Texas Southwestern Medical Center, Dallas, Texas, 75390-9050
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12
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Saranya B, Saxena S, Saravanan KM, Shakila H. Comparative analysis of the molecular adjuvants and their binding efficiency with CR1. Interdiscip Sci 2015. [PMID: 25682380 DOI: 10.1007/s12539-014-0247-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2014] [Revised: 01/16/2015] [Accepted: 01/16/2015] [Indexed: 06/04/2023]
Abstract
There are so many obstacles in developing a vaccine or vaccine technology for diseases like Cancer and Human Immunodeficiency Virus (HIV) infection. While developing vaccines that targets specific infection, molecular adjuvants are indispensable. These molecular adjuvants act as a vaccine delivery vehicle to the immune system to increase the effectiveness of the specific antigens. In the present work, a computational study has been done on molecular adjuvants like IgGFc, GMCSF and C3d to find out how efficiently they are binding to CR1. Sequence, structure and mutational analysis are performed on the molecular adjuvants to understand the features important for their binding with the receptor. Results obtained from our study indicate that the adjuvant IgGFc complexed with the receptor CR1 has the best binding efficiency, which can be used further to develop better vaccine technologies.
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Affiliation(s)
- B Saranya
- Centre of Excellence in Bioinformatics School of Biotechnology Madurai Kamaraj University, Madurai, 625 021, Tamilnadu, India
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13
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Saravanan KM, Selvaraj S. Performance of secondary structure prediction methods on proteins containing structurally ambivalent sequence fragments. Biopolymers 2013; 100:148-53. [DOI: 10.1002/bip.22178] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2012] [Revised: 09/04/2012] [Accepted: 09/23/2012] [Indexed: 11/12/2022]
Affiliation(s)
- K. Mani Saravanan
- Department of Bioinformatics; School of Life Sciences; Bharathidasan University; Tiruchirappalli; 620024; Tamil Nadu; India
| | - Samuel Selvaraj
- Department of Bioinformatics; School of Life Sciences; Bharathidasan University; Tiruchirappalli; 620024; Tamil Nadu; India
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14
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Saravanan KM, Selvaraj S. Search and analysis of identical reverse octapeptides in unrelated proteins. GENOMICS, PROTEOMICS & BIOINFORMATICS 2013; 11:114-21. [PMID: 23523652 PMCID: PMC4357837 DOI: 10.1016/j.gpb.2012.11.005] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/14/2012] [Revised: 11/20/2012] [Accepted: 11/21/2012] [Indexed: 11/19/2022]
Abstract
For the past few decades, intensive studies have been carried out in an attempt to understand how the amino acid sequences of proteins encode their three dimensional structures to perform their specific functions. In order to understand the sequence-structure relationship of proteins, several sub-sequence search studies in non-redundant sequence-structure databases have been undertaken which have given some fruitful clues. In our earlier work, we analyzed a set of 3124 non-redundant protein sequences from the Protein Data Bank (PDB) and retrieved 30 identical octapeptides having different secondary structures. These octapeptides were characterized by using different computational procedures. This prompted us to explore the presence of octapeptides with reverse sequences and to analyze whether these octapeptides would adopt similar structures as that of their parent octapeptides. Our identical reverse octapeptide search resulted in the finding of eight octapeptide pairs (octapeptide and reverse octapeptide) with similar secondary structure and 23 octapeptide pairs with different secondary structures. In the present work, the geometrical and biophysical characteristics of identical reverse octapeptides were explored and compared with unrelated octapeptide pairs by using various computational tools. We thus conclude that proteins containing identical reverse octapeptides are not very abundant and residues in the octapeptide pairs do not contribute to the stability of the protein. Furthermore, compared to unrelated octapeptides, identical reverse octapeptides do not show certain biophysical and geometrical properties.
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