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Ilyas M, Rahman A, Khan NH, Haroon M, Hussain H, Rehman L, Alam M, Rauf A, Waggas DS, Bawazeer S. Analysis of Germin-like protein genes family in Vitis vinifera (VvGLPs) using various in silico approaches. BRAZ J BIOL 2024; 84:e256732. [DOI: 10.1590/1519-6984.256732] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2021] [Accepted: 12/28/2021] [Indexed: 12/26/2022] Open
Abstract
Abstract Germin-like proteins (GLPs) play an important role against various stresses. Vitis vinifera L. genome contains 7 GLPs; many of them are functionally unexplored. However, the computational analysis may provide important new insight into their function. Currently, physicochemical properties, subcellular localization, domain architectures, 3D structures, N-glycosylation & phosphorylation sites, and phylogeney of the VvGLPs were investigated using the latest computational tools. Their functions were predicted using the Search tool for the retrieval of interacting genes/proteins (STRING) and Blast2Go servers. Most of the VvGLPs were extracellular (43%) in nature but also showed periplasmic (29%), plasma membrane (14%), and mitochondrial- or chloroplast-specific (14%) expression. The functional analysis predicted unique enzymatic activities for these proteins including terpene synthase, isoprenoid synthase, lipoxygenase, phosphate permease, receptor kinase, and hydrolases generally mediated by Mn+ cation. VvGLPs showed similarity in the overall structure, shape, and position of the cupin domain. Functionally, VvGLPs control and regulate the production of secondary metabolites to cope with various stresses. Phylogenetically VvGLP1, -3, -4, -5, and VvGLP7 showed greater similarity due to duplication while VvGLP2 and VvGLP6 revealed a distant relationship. Promoter analysis revealed the presence of diverse cis-regulatory elements among which CAAT box, MYB, MYC, unnamed-4 were common to all of them. The analysis will help to utilize VvGLPs and their promoters in future food programs by developing resistant cultivars against various biotic (Erysiphe necator and in Powdery Mildew etc.) and abiotic (Salt, drought, heat, dehydration, etc.) stresses.
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Affiliation(s)
| | | | | | | | | | | | - M. Alam
- University of Swabi, Pakistan
| | - A. Rauf
- University of Swabi, Pakistan
| | - D. S. Waggas
- Fakeeh College of Medical Sciences, Saudi Arabia
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2
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Brown SM, Voráček V, Freeland S. What Would an Alien Amino Acid Alphabet Look Like and Why? ASTROBIOLOGY 2023; 23:536-549. [PMID: 37022727 DOI: 10.1089/ast.2022.0107] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
Life on Earth builds genetically encoded proteins by using a standard alphabet of just 20 L-α-amino acids, although many others were available to life's origins and early evolution. To better understand the causes of this foundational evolutionary outcome, we extend previous analyses which have identified a highly unusual distribution of biophysical properties within the set used by life. Specifically, we use a heuristic search algorithm to identify other sets of amino acids, from a library of plausible alternatives, that emulate life's signature. We find that a subset of amino acids seems predisposed to forming such sets. We present other examples of such alphabets under various assumptions, along with analysis and reasoning about why each might be simplistic. We do so to introduce the central, open question that remains: while fundamental biophysics related to protein folding can potentially reduce a library of 1054 possible amino acid alphabets by 7 orders of magnitude, the framework of assumptions that does so leaves a further 1045 possibilities. It is therefore tempting to ask what additional assumptions can further reduce these 45 orders of magnitude? We thus conclude with a focus on library and alphabet construction as a useful target for subsequent research that may help future science speak with more confidence about what an alien amino acid alphabet would look like and why.
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Affiliation(s)
- Sean M Brown
- Department of Biological Sciences, University of Maryland, Baltimore County, Maryland, USA
| | - Václav Voráček
- Department of Computer Science, University of Tübingen, Tübingen, Germany
| | - Stephen Freeland
- Department of Biological Sciences, University of Maryland, Baltimore County, Maryland, USA
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3
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Mufassirin MMM, Newton MAH, Sattar A. Artificial intelligence for template-free protein structure prediction: a comprehensive review. Artif Intell Rev 2022. [DOI: 10.1007/s10462-022-10350-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
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4
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Kaushik R, Zhang KY. An Integrated Protein Structure Fitness Scoring Approach for Identifying Native-Like Model Structures. Comput Struct Biotechnol J 2022; 20:6467-6472. [DOI: 10.1016/j.csbj.2022.11.032] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2022] [Revised: 11/14/2022] [Accepted: 11/14/2022] [Indexed: 11/18/2022] Open
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5
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Pritam M, Singh G, Kumar R, Singh SP. Screening of potential antigens from whole proteome and development of multi-epitope vaccine against Rhizopus delemar using immunoinformatics approaches. J Biomol Struct Dyn 2022; 41:2118-2145. [PMID: 35067195 DOI: 10.1080/07391102.2022.2028676] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/14/2023]
Abstract
Mucormycosis is a deadly fungal disease mainly caused by Rhizopus oryzae (strain 99-880), also known as Rhizopus delemar. Previously, mucormycosis occurs in immunocompromised patients of diabetes mellitus, cancer, organ transplant, etc. But there was a drastic increase in mucormycosis cases in the ongoing COVID-19 pandemic. Despite several available therapies and antifungal treatments, the mortality rate of mucormycosis is about more than 50%. Currently, there is no vaccine available in the market for mucormycosis that urgently needs to develop a potential vaccine against mucormycosis with high efficacy. In the present study, we have screened 4 genome-derived predicted antigens (GDPA) through sequential filtration of the whole proteome of R. delemar using different benchmarked bioinformatics tools. These 4 GDPA along with 4 randomly selected experimentally reported antigens (ERA) were sourced for prediction of B- and T- cell epitopes and utilized in designing of two potential multi-epitope vaccine candidates which can induce both innate and adaptive immunity against R. delemar. Besides these, comparative immune simulation studies and in silico cloning were performed using L. lactis as an expression system for their possible uses as oral vaccines. This is the first multi-epitope vaccine designed against R. delemar through systematic pipelined reverse vaccinology and immunoinformatic approaches. Although the wet-lab based experimental validation of designed vaccines is required before testing in the preclinical model, the current study will significantly help in reducing the cost of experimentation as well as improving the efficacy of vaccine therapy against mucormycosis and other pathogenic diseases.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Manisha Pritam
- Amity Institute of Biotechnology, Amity University Uttar Pradesh, Lucknow, India
| | - Garima Singh
- Amity Institute of Biotechnology, Amity University Uttar Pradesh, Lucknow, India
| | - Rajnish Kumar
- Amity Institute of Biotechnology, Amity University Uttar Pradesh, Lucknow, India
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6
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Kaushik R, Zhang KYJ. ProFitFun: a protein tertiary structure fitness function for quantifying the accuracies of model structures. Bioinformatics 2022; 38:369-376. [PMID: 34542606 DOI: 10.1093/bioinformatics/btab666] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2021] [Revised: 09/06/2021] [Accepted: 09/16/2021] [Indexed: 02/03/2023] Open
Abstract
MOTIVATION An accurate estimation of the quality of protein model structures typifies as a cornerstone in protein structure prediction regimes. Despite the recent groundbreaking success in the field of protein structure prediction, there are certain prospects for the improvement in model quality estimation at multiple stages of protein structure prediction and thus, to further push the prediction accuracy. Here, a novel approach, named ProFitFun, for assessing the quality of protein models is proposed by harnessing the sequence and structural features of experimental protein structures in terms of the preferences of backbone dihedral angles and relative surface accessibility of their amino acid residues at the tripeptide level. The proposed approach leverages upon the backbone dihedral angle and surface accessibility preferences of the residues by accounting for its N-terminal and C-terminal neighbors in the protein structure. These preferences are used to evaluate protein structures through a machine learning approach and tested on an extensive dataset of diverse proteins. RESULTS The approach was extensively validated on a large test dataset (n = 25 005) of protein structures, comprising 23 661 models of 82 non-homologous proteins and 1344 non-homologous experimental structures. In addition, an external dataset of 40 000 models of 200 non-homologous proteins was also used for the validation of the proposed method. Both datasets were further used for benchmarking the proposed method with four different state-of-the-art methods for protein structure quality assessment. In the benchmarking, the proposed method outperformed some state-of-the-art methods in terms of Spearman's and Pearson's correlation coefficients, average GDT-TS loss, sum of z-scores and average absolute difference of predictions over corresponding observed values. The high accuracy of the proposed approach promises a potential use of the sequence and structural features in computational protein design. AVAILABILITY AND IMPLEMENTATION http://github.com/KYZ-LSB/ProTerS-FitFun. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Rahul Kaushik
- Laboratory for Structural Bioinformatics, Center for Biosystems Dynamics Research, RIKEN, Yokohama, Kanagawa 230-0045, Japan
| | - Kam Y J Zhang
- Laboratory for Structural Bioinformatics, Center for Biosystems Dynamics Research, RIKEN, Yokohama, Kanagawa 230-0045, Japan
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7
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McTiernan TJ, Diaz DB, Saunders GJ, Sprang F, Yudin AK. Navigating complex peptide structures using macrocycle conformational maps. RSC Chem Biol 2022; 3:739-747. [PMID: 35755184 PMCID: PMC9175111 DOI: 10.1039/d2cb00016d] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2022] [Accepted: 04/14/2022] [Indexed: 11/21/2022] Open
Abstract
Identification of turn motifs that are stabilized by intramolecular hydrogen bonds can be useful in describing the conformation of peptide systems. However, this approach is somewhat insufficient for cyclic peptides because peptide regions that are not positioned within a hydrogen bond can be left with no description. Furthermore, non-regular secondary structures and other rarely-observed conformations can be left without detailed evaluation. Herein, we describe “higher-order” ϕ/ψ plots termed macrocycle conformational maps (MCMs) as a tool for evaluating and comparing the conformations of a series of structurally related macrocyclic peptides. Identification of turn motifs that are stabilized by hydrogen bonds can be useful in describing the conformation of peptides. Herein, we describe “higher-order” ϕ/ψ plots termed macrocycle conformational maps (MCMs) as a tool to evaluate and compare the conformations of related macrocycles.![]()
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Affiliation(s)
- Timothy J McTiernan
- Davenport Research Laboratories, Department of Chemistry, University of Toronto Toronto ON M5S 3H6 Canada
| | - Diego B Diaz
- Davenport Research Laboratories, Department of Chemistry, University of Toronto Toronto ON M5S 3H6 Canada
| | - George J Saunders
- Davenport Research Laboratories, Department of Chemistry, University of Toronto Toronto ON M5S 3H6 Canada
| | - Fiona Sprang
- Davenport Research Laboratories, Department of Chemistry, University of Toronto Toronto ON M5S 3H6 Canada
| | - Andrei K Yudin
- Davenport Research Laboratories, Department of Chemistry, University of Toronto Toronto ON M5S 3H6 Canada
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8
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Zhang R, Stahr MC, Kennedy MA. Introduction of a new scheme for classifying β-turns in protein structures. Proteins 2021; 90:110-122. [PMID: 34322903 DOI: 10.1002/prot.26190] [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: 01/05/2021] [Accepted: 07/11/2021] [Indexed: 11/09/2022]
Abstract
Protein β-turn classification remains an area of ongoing development in structural biology research. While the commonly used nomenclature defining type I, type II and type IV β-turns was introduced in the 1970s and 1980s, refinements of β-turn type definitions have been introduced as recently as 2019 by Dunbrack, Jr and co-workers who expanded the number of β-turn types to 18 (Shapovalov et al, PLOS Computat. Biol., 15, e1006844, 2019). Based on their analysis of 13 030 turns from 1074 ultrahigh resolution (≤1.2 Å) protein structures, they used a new clustering algorithm to expand the definitions used to classify protein β-turns and introduced a new nomenclature system. We recently encountered a specific problem when classifying β-turns in crystal structures of pentapeptide repeat proteins (PRPs) determined in our lab that are largely composed of β-turns that often lie close to, but just outside of, canonical β-turn regions. To address this problem, we devised a new scheme that merges the Klyne-Prelog stereochemistry nomenclature and definitions with the Ramachandran plot. The resulting Klyne-Prelog-modified Ramachandran plot scheme defines 1296 distinct potential β-turn classifications that cover all possible protein β-turn space with a nomenclature that indicates the stereochemistry of i + 1 and i + 2 backbone dihedral angles. The utility of the new classification scheme was illustrated by re-classification of the β-turns in all known protein structures in the PRP superfamily and further assessed using a database of 16 657 high-resolution protein structures (≤1.5 Å) from which 522 776 β-turns were identified and classified.
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Affiliation(s)
- Ruojing Zhang
- Department of Chemistry and Biochemistry, Miami University, Oxford, Ohio, USA
| | - Michael C Stahr
- Department of Computer Science and Software Engineering, Miami University, Oxford, Ohio, USA
| | - Michael A Kennedy
- Department of Chemistry and Biochemistry, Miami University, Oxford, Ohio, USA
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9
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Bhat R, Kaushik R, Singh A, DasGupta D, Jayaraj A, Soni A, Shandilya A, Shekhar V, Shekhar S, Jayaram B. A comprehensive automated computer-aided discovery pipeline from genomes to hit molecules. Chem Eng Sci 2020. [DOI: 10.1016/j.ces.2020.115711] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
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10
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Ghavimi R, Mohammadi E, Akbari V, Shafiee F, Jahanian-Najafabadi A. In silico design of two novel fusion proteins, p28-IL-24 and p28-M4, targeted to breast cancer cells. Res Pharm Sci 2020; 15:200-208. [PMID: 32582360 PMCID: PMC7306244 DOI: 10.4103/1735-5362.283820] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2019] [Revised: 01/20/2020] [Accepted: 04/18/2020] [Indexed: 01/29/2023] Open
Abstract
Background and purpose: An anticancer peptide P28, has shown to be cytolethal on various cancer cells including breast cancer. Moreover, p28 can be also used as a targeting moiety in the structure of fusion proteins. IL-24 (or its truncated form, M4) is a cytokine with anticancer activity against a wide range of tumor cells. We aimed at production of a fusion protein consisted of p28 and either IL-24 or M4 to target breast cancer. However, selection of a proper linker to join the two moieties without intervening each other’s function is a key factor in the construction of fusion proteins. In the present study, the impact of different linkers on construction of the two chimeric proteins (p28-IL-24 and p28-M4) was assessed in silico. Experimental approach: After selection of some linkers with different lengths and characteristics, a small library of the chimeric proteins was created and assessed. Furthermore, following selection of the most suitable linker, the three-dimensional structures and dynamic behavior of both fusion proteins were evaluated by homology modeling and molecular dynamic simulation, respectively. Findings / Results: Based on the results, a rigid linker having the peptide sequences of AEAAAKEAAAKA showed highest freedom of action for both moieties. Conclusion and implications: Between the p28-IL-24 and p28-M4 fusion proteins, the former showed better stability as well as solubility and might show stronger anticancer effects in vitro and in vivo, because its peptide moieties showed to exert their activities freely.
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Affiliation(s)
- Reza Ghavimi
- Department of Pharmaceutical Biotechnology, School of Pharmacy and Pharmaceutical Sciences, Isfahan University of Medical Sciences, Isfahan, I.R. Iran
| | - Elmira Mohammadi
- Department of Pharmaceutical Biotechnology, School of Pharmacy and Pharmaceutical Sciences, Isfahan University of Medical Sciences, Isfahan, I.R. Iran
| | - Vajihe Akbari
- Department of Pharmaceutical Biotechnology, School of Pharmacy and Pharmaceutical Sciences, Isfahan University of Medical Sciences, Isfahan, I.R. Iran
| | - Fatemeh Shafiee
- Department of Pharmaceutical Biotechnology, School of Pharmacy and Pharmaceutical Sciences, Isfahan University of Medical Sciences, Isfahan, I.R. Iran
| | - Ali Jahanian-Najafabadi
- Department of Pharmaceutical Biotechnology, School of Pharmacy and Pharmaceutical Sciences, Isfahan University of Medical Sciences, Isfahan, I.R. Iran
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11
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Kaushik R, Zhang KYJ. A protein sequence fitness function for identifying natural and nonnatural proteins. Proteins 2020; 88:1271-1284. [PMID: 32415863 DOI: 10.1002/prot.25900] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2020] [Revised: 03/26/2020] [Accepted: 05/07/2020] [Indexed: 12/17/2022]
Abstract
The infinitesimally small sequence space naturally scouted in the millions of years of evolution suggests that the natural proteins are constrained by some functional prerequisites and should differ from randomly generated sequences. We have developed a protein sequence fitness scoring function that implements sequence and corresponding secondary structural information at tripeptide levels to differentiate natural and nonnatural proteins. The proposed fitness function is extensively validated on a dataset of about 210 000 natural and nonnatural protein sequences and benchmarked with existing methods for differentiating natural and nonnatural proteins. The high sensitivity, specificity, and percentage accuracy (0.81%, 0.95%, and 91% respectively) of the fitness function demonstrates its potential application for sampling the protein sequences with higher probability of mimicking natural proteins. Moreover, the four major classes of proteins (α proteins, β proteins, α/β proteins, and α + β proteins) are separately analyzed and β proteins are found to score slightly lower as compared to other classes. Further, an analysis of about 250 designed proteins (adopted from previously reported cases) helped to define the boundaries for sampling the ideal protein sequences. The protein sequence characterization aided by the proposed fitness function could facilitate the exploration of new perspectives in the design of novel functional proteins.
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Affiliation(s)
- Rahul Kaushik
- Laboratory for Structural Bioinformatics, Center for Biosystems Dynamics Research, Yokohama, Kanagawa, Japan
| | - Kam Y J Zhang
- Laboratory for Structural Bioinformatics, Center for Biosystems Dynamics Research, Yokohama, Kanagawa, Japan
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12
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Abstract
AbstractDuring three decades, only about 20 new drugs have been developed for malaria, tuberculosis and all neglected tropical diseases (NTDs). This critical situation was reached because NTDs represent only 10% of health research investments; however, they comprise about 90% of the global disease burden. Computational simulations applied in virtual screening (VS) strategies are very efficient tools to identify pharmacologically active compounds or new indications for drugs already administered for other diseases. One of the advantages of this approach is the low time-consuming and low-budget first stage, which filters for testing experimentally a group of candidate compounds with high chances of binding to the target and present trypanocidal activity. In this work, we review the most common VS strategies that have been used for the identification of new drugs with special emphasis on those applied to trypanosomiasis and leishmaniasis. Computational simulations based on the selected protein targets or their ligands are explained, including the method selection criteria, examples of successful VS campaigns applied to NTDs, a list of validated molecular targets for drug development and repositioned drugs for trypanosomatid-caused diseases. Thereby, here we present the state-of-the-art of VS and drug repurposing to conclude pointing out the future perspectives in the field.
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13
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Ravikumar A, Ramakrishnan C, Srinivasan N. Stereochemical Assessment of (φ,ψ) Outliers in Protein Structures Using Bond Geometry-Specific Ramachandran Steric-Maps. Structure 2019; 27:1875-1884.e2. [PMID: 31607615 DOI: 10.1016/j.str.2019.09.009] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2019] [Revised: 08/23/2019] [Accepted: 09/20/2019] [Indexed: 10/25/2022]
Abstract
Ramachandran validation of protein structures is commonly performed using developments, such as MolProbity. We suggest tailoring such analyses by position-wise, geometry-specific steric-maps, which show (φ,ψ) regions with steric-clash at every residue position. These maps are different from the classical steric-map because they are highly sensitive to bond length and angle values that are used, in our steric-maps, as observed in the residue positions in super-high-resolution peptide and protein structures. (φ,ψ) outliers observed in such structures seldom have steric-clash. Therefore, we propose that a (φ,ψ) outlier is unacceptable if it is located within the steric-clash region of a bond geometry-specific steric-map for a residue position. These steric-maps also suggest position-specific accessible (φ,ψ) space. The PARAMA web resource performs in-depth position-wise analysis of protein structures using bond geometry-specific steric-maps.
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Affiliation(s)
- Ashraya Ravikumar
- Molecular Biophysics Unit, Indian Institute of Science, Bangalore 560012, India
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14
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Mehmankhah M, Bhat R, Anvar MS, Ali S, Alam A, Farooqui A, Amir F, Anwer A, Khan S, Azmi I, Ali R, Ishrat R, Hassan MI, Minuchehr Z, Kazim SN. Structure-Guided Approach to Identify Potential Inhibitors of Large Envelope Protein to Prevent Hepatitis B Virus Infection. OXIDATIVE MEDICINE AND CELLULAR LONGEVITY 2019; 2019:1297484. [PMID: 31772697 PMCID: PMC6854180 DOI: 10.1155/2019/1297484] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/05/2019] [Revised: 06/10/2019] [Accepted: 07/02/2019] [Indexed: 01/05/2023]
Abstract
Hepatitis B virus (HBV) infection is one of the major causes of liver diseases, which can lead to hepatocellular carcinoma. The role of HBV envelope proteins is crucial in viral morphogenesis, infection, and propagation. Thus, blocking the pleiotropic functions of these proteins especially the PreS1 and PreS2 domains of the large surface protein (LHBs) is a promising strategy for designing efficient antivirals against HBV infection. Unfortunately, the structure of the LHBs protein has not been elucidated yet, and it seems that any structure-based drug discovery is critically dependent on this. To find effective inhibitors of LHBs, we have modeled and validated its three-dimensional structure and subsequently performed a virtual high-throughput screening against the ZINC database using RASPD and ParDOCK tools. We have identified four compounds, ZINC11882026, ZINC19741044, ZINC00653293, and ZINC15000762, showing appreciable binding affinity with the LHBs protein. The drug likeness was further validated using ADME screening and toxicity analysis. Interestingly, three of the four compounds showed the formation of hydrogen bonds with amino acid residues lying in the capsid binding region of the PreS1 domain of LHBs, suggesting the possibility of inhibiting the viral assembly and maturation process. The identification of potential lead molecules will help to discover more potent inhibitors with significant antiviral activities.
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Affiliation(s)
- Mahboubeh Mehmankhah
- Center for Interdisciplinary Research in Basic Sciences, Jamia Millia Islamia, New Delhi 110025, India
| | - Ruchika Bhat
- Department of Chemistry & School of Biological Sciences, Indian Institute of Technology Delhi, New Delhi 110016, India
| | - Mohammad Sabery Anvar
- Systems Biotechnology Department, National Institute of Genetic Engineering and Biotechnology, Tehran, Iran
| | - Shahnawaz Ali
- Center for Interdisciplinary Research in Basic Sciences, Jamia Millia Islamia, New Delhi 110025, India
| | - Aftab Alam
- Center for Interdisciplinary Research in Basic Sciences, Jamia Millia Islamia, New Delhi 110025, India
| | - Anam Farooqui
- Center for Interdisciplinary Research in Basic Sciences, Jamia Millia Islamia, New Delhi 110025, India
| | - Fatima Amir
- Center for Interdisciplinary Research in Basic Sciences, Jamia Millia Islamia, New Delhi 110025, India
| | - Ayesha Anwer
- Center for Interdisciplinary Research in Basic Sciences, Jamia Millia Islamia, New Delhi 110025, India
| | - Saniya Khan
- Center for Interdisciplinary Research in Basic Sciences, Jamia Millia Islamia, New Delhi 110025, India
| | - Iqbal Azmi
- Multidisciplinary Center for Advanced Research and Studies, Jamia Millia Islamia, New Delhi 110025, India
| | - Rafat Ali
- Center for Interdisciplinary Research in Basic Sciences, Jamia Millia Islamia, New Delhi 110025, India
| | - Romana Ishrat
- Center for Interdisciplinary Research in Basic Sciences, Jamia Millia Islamia, New Delhi 110025, India
| | - Md. Imtaiyaz Hassan
- Center for Interdisciplinary Research in Basic Sciences, Jamia Millia Islamia, New Delhi 110025, India
| | - Zarrin Minuchehr
- Systems Biotechnology Department, National Institute of Genetic Engineering and Biotechnology, Tehran, Iran
| | - Syed Naqui Kazim
- Center for Interdisciplinary Research in Basic Sciences, Jamia Millia Islamia, New Delhi 110025, India
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15
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Affiliation(s)
- Rahul Kaushik
- Kusuma
School of Biological Sciences, Indian Institute of Technology, Delhi, India
- Supercomputing Facility for Bioinformatics & Computational Biology, Indian Institute of Technology, Delhi, India
| | - Ankita Singh
- Supercomputing Facility for Bioinformatics & Computational Biology, Indian Institute of Technology, Delhi, India
- Department
of Bioinformatics, Banasthali Vidyapith, Banasthali, India
| | - B. Jayaram
- Kusuma
School of Biological Sciences, Indian Institute of Technology, Delhi, India
- Supercomputing Facility for Bioinformatics & Computational Biology, Indian Institute of Technology, Delhi, India
- Department
of Chemistry, Indian Institute of Technology, Delhi, India
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16
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DasGupta D, Mandalaparthy V, Jayaram B. A component analysis of the free energies of folding of 35 proteins: A consensus view on the thermodynamics of folding at the molecular level. J Comput Chem 2017; 38:2791-2801. [PMID: 28940242 DOI: 10.1002/jcc.25072] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2017] [Revised: 07/27/2017] [Accepted: 09/01/2017] [Indexed: 02/05/2023]
Abstract
What factors favor protein folding? This is a textbook question. Parsing the experimental free energies of folding/unfolding into diverse enthalpic and entropic components of solute and solvent favoring or disfavoring folding is not an easy task. In this study, we present a computational protocol for estimating the free energy contributors to protein folding semi-quantitatively using ensembles of unfolded and native states generated via molecular dynamics simulations. We tested the methodology on 35 proteins with diverse structural motifs and sizes and found that the calculated free energies correlate well with experiment (correlation coefficient ∼ 0.85), enabling us to develop a consensus view of the energetics of folding. As a more sensitive test of the methodology, we also investigated the free energies of folding of an additional 33 single point mutants and obtained a correlation coefficient of 0.8. A notable observation is that the folding free energy components appear to carry signatures of the fold (SCOP classification) of the protein. © 2017 Wiley Periodicals, Inc.
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Affiliation(s)
- Debarati DasGupta
- Department of Chemistry, Indian Institute of Technology, New Delhi, 110016, India.,Supercomputing Facility for Bioinformatics and Computational Biology, Indian Institute of Technology, New Delhi, 110016, India
| | - Varun Mandalaparthy
- Department of Chemistry, Indian Institute of Technology, New Delhi, 110016, India
| | - Bhyravabhotla Jayaram
- Department of Chemistry, Indian Institute of Technology, New Delhi, 110016, India.,Supercomputing Facility for Bioinformatics and Computational Biology, Indian Institute of Technology, New Delhi, 110016, India.,Kusuma School of Biological Sciences, Indian Institute of Technology, New Delhi, 110016, India
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17
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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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Ponnuraj K, Saravanan KM. Dihedral angle preferences of DNA and RNA binding amino acid residues in proteins. Int J Biol Macromol 2017; 97:434-439. [PMID: 28099891 DOI: 10.1016/j.ijbiomac.2017.01.068] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2016] [Revised: 01/12/2017] [Accepted: 01/13/2017] [Indexed: 11/30/2022]
Abstract
A protein can interact with DNA or RNA molecules to perform various cellular processes. Identifying or analyzing DNA/RNA binding site amino acid residues is important to understand molecular recognition process. It is quite possible to accurately model DNA/RNA binding amino acid residues in experimental protein-DNA/RNA complex by using the electron density map whereas, locating/modeling the binding site amino acid residues in the predicted three dimensional structures of DNA/RNA binding proteins is still a difficult task. Considering the above facts, in the present work, we have carried out a comprehensive analysis of dihedral angle preferences of DNA and RNA binding site amino acid residues by using a classical Ramachandran map. We have computed backbone dihedral angles of non-DNA/RNA binding residues and used as control dataset to make a comparative study. The dihedral angle preference of DNA and RNA binding site residues of twenty amino acid type is presented. Our analysis clearly revealed that the dihedral angles (φ, ψ) of DNA/RNA binding amino acid residues prefer to occupy (-89° to -60°, -59° to -30°) bins. The results presented in this paper will help to model/locate DNA/RNA binding amino acid residues with better accuracy.
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Affiliation(s)
- Karthe Ponnuraj
- Centre of Advanced Study in Crystallography & Biophysics, University of Madras, Guindy Campus, Chennai 600 025, Tamilnadu, India
| | - Konda Mani Saravanan
- Centre of Advanced Study in Crystallography & Biophysics, University of Madras, Guindy Campus, Chennai 600 025, Tamilnadu, India.
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Das Gupta D, Kaushik R, Jayaram B. Protein folding is a convergent problem! Biochem Biophys Res Commun 2016; 480:741-744. [DOI: 10.1016/j.bbrc.2016.10.119] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2016] [Accepted: 10/27/2016] [Indexed: 11/25/2022]
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Kaushik R, Jayaram B. Structural difficulty index: a reliable measure for modelability of protein tertiary structures. Protein Eng Des Sel 2016; 29:391-7. [PMID: 27334454 DOI: 10.1093/protein/gzw025] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2016] [Accepted: 05/27/2016] [Indexed: 11/13/2022] Open
Abstract
The success in protein tertiary-structure prediction is considered to be a function of coverage and similarity/identity of their sequences with suitable templates in the structural databases. However, this measure of modelability of a protein sequence into its structure may be misleading. Addressing this limitation, we propose here a 'structural difficulty (SD)' index, which is derived from secondary structures, homology and physicochemical features of protein sequences. The SD index reflects the capability of predicting accurate structures and helps to assess the potential for developing proteome level structural databases for various organisms with some of the best methodologies available currently. For instance, the plausibility of populating the structural database of human proteome with reliable quality structures under 3 Å root mean square deviation from the corresponding natives is found to be ∼37% of a total of 11 084 manually curated soluble proteins and ∼64% for all annotated and reviewed unique soluble protein (344 661 sequences) of UniProtKB. Also for 77 human pathogenic viruses comprising 2365 globular viral proteins out of which only 162 structures are solved experimentally, SD index scores 1336 proteins in the modelable zone. Availability of reliable protein structures may prove a crucial aid in developing species-wise structural proteomic databases for accelerating function annotation and for drug development endeavors.
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Affiliation(s)
- Rahul Kaushik
- Kusuma School of Biological Sciences, Indian Institute of Technology, Hauz Khas, New Delhi 110016, India Supercomputing Facility for Bioinformatics & Computational Biology, Indian Institute of Technology, Hauz Khas, New Delhi 110016, India
| | - B Jayaram
- Kusuma School of Biological Sciences, Indian Institute of Technology, Hauz Khas, New Delhi 110016, India Supercomputing Facility for Bioinformatics & Computational Biology, Indian Institute of Technology, Hauz Khas, New Delhi 110016, India Department of Chemistry, Indian Institute of Technology, Hauz Khas, New Delhi 110016, India
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Borguesan B, e Silva MB, Grisci B, Inostroza-Ponta M, Dorn M. APL: An angle probability list to improve knowledge-based metaheuristics for the three-dimensional protein structure prediction. Comput Biol Chem 2015; 59 Pt A:142-57. [DOI: 10.1016/j.compbiolchem.2015.08.006] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2015] [Revised: 08/05/2015] [Accepted: 08/17/2015] [Indexed: 10/23/2022]
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ProTSAV: A protein tertiary structure analysis and validation server. BIOCHIMICA ET BIOPHYSICA ACTA-PROTEINS AND PROTEOMICS 2015; 1864:11-9. [PMID: 26478257 DOI: 10.1016/j.bbapap.2015.10.004] [Citation(s) in RCA: 46] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/15/2015] [Revised: 09/26/2015] [Accepted: 10/14/2015] [Indexed: 01/06/2023]
Abstract
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.
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