151
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Chen H, Fu W, Wang Z, Wang X, Lei T, Zhu F, Li D, Chang S, Xu L, Hou T. Reliability of Docking-Based Virtual Screening for GPCR Ligands with Homology Modeled Structures: A Case Study of the Angiotensin II Type I Receptor. ACS Chem Neurosci 2019; 10:677-689. [PMID: 30265513 DOI: 10.1021/acschemneuro.8b00489] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022] Open
Abstract
The number of solved G-protein-coupled receptor (GPCR) crystal structures has expanded rapidly, but most GPCR structures remain unsolved. Therefore, computational techniques, such as homology modeling, have been widely used to produce the theoretical structures of various GPCRs for structure-based drug design (SBDD). Due to the low sequence similarity shared by the transmembrane domains of GPCRs, accurate prediction of GPCR structures by homology modeling is quite challenging. In this study, angiotensin II type I receptor (AT1R) was taken as a typical case to assess the reliability of class A GPCR homology models for SBDD. Four homology models of angiotensin II type I receptor (AT1R) at the inactive state were built based on the crystal structures of CXCR4 chemokine receptor, CCR5 chemokine receptor, and δ-opioid receptor, and refined through molecular dynamics (MD) simulations and induced-fit docking, to allow for backbone and side-chain flexibility. Then, the quality of the homology models was assessed relative to the crystal structures in terms of two criteria commonly used in SBDD: prediction accuracy of ligand-binding poses and screening power of docking-based virtual screening. It was found that the crystal structures outperformed the homology models prior to any refinement in both assessments. MD simulations could generally improve the docking results for both the crystal structures and homology models. Moreover, the optimized homology model refined by MD simulations and induced-fit docking even shows a similar performance of the docking assessment to the crystal structures. Our results indicate that it is possible to establish a reliable class A GPCR homology model for SBDD through the refinement by integrating multiple molecular modeling techniques.
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Affiliation(s)
| | | | | | | | | | | | | | - Shan Chang
- Institute of Bioinformatics and Medical Engineering, School of Electrical and Information Engineering, Jiangsu University of Technology, Changzhou 213001, P. R. China
| | - Lei Xu
- Institute of Bioinformatics and Medical Engineering, School of Electrical and Information Engineering, Jiangsu University of Technology, Changzhou 213001, P. R. China
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152
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Leishmanicidal therapy targeted to parasite proteases. Life Sci 2019; 219:163-181. [PMID: 30641084 DOI: 10.1016/j.lfs.2019.01.015] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2018] [Revised: 01/07/2019] [Accepted: 01/09/2019] [Indexed: 12/31/2022]
Abstract
Leishmaniasis is considered a serious public health problem and the current available therapy has several disadvantages, which makes the search for new therapeutic targets and alternative treatments extremely necessary. In this context, this review focuses on the importance of parasite proteases as target drugs against Leishmania parasites, as a chemotherapy approach. Initially, we discuss about the current scenario for the treatment of leishmaniasis, highlighting the main drugs used and the problems related to their use. Subsequently, we describe the inhibitors of major proteases of Leishmania already discovered, such as Compound s9 (aziridine-2,3-dicarboxylate), Compound 1c (benzophenone derivative), Au2Phen (gold complex), AubipyC (gold complex), MDL 28170 (dipeptidyl aldehyde), K11777, Hirudin, diazo-acetyl norleucine methyl ester, Nelfinavir, Saquinavir, Nelfinavir, Saquinavir, Indinavir, Saquinavir, GNF5343 (azabenzoxazole), GNF6702 (azabenzoxazole), Benzamidine and TPCK. Next, we discuss the importance of the protease gene to parasite survival and the aspects of the validation of proteases as target drugs, with emphasis on gene disruption. Then, we describe novel important strategies that can be used to support the research of new antiparasitic drugs, such as molecular modeling and nanotechnology, whose main targets are parasitic proteases. And finally, we discuss possible perspectives to improve drug development. Based on all findings, proteases could be considered potential targets against leishmaniasis.
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153
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Shultis D, Mitra P, Huang X, Johnson J, Khattak NA, Gray F, Piper C, Czajka J, Hansen L, Wan B, Chinnaswamy K, Liu L, Wang M, Pan J, Stuckey J, Cierpicki T, Borchers CH, Wang S, Lei M, Zhang Y. Changing the Apoptosis Pathway through Evolutionary Protein Design. J Mol Biol 2019; 431:825-841. [PMID: 30625288 DOI: 10.1016/j.jmb.2018.12.016] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2018] [Revised: 12/12/2018] [Accepted: 12/28/2018] [Indexed: 11/30/2022]
Abstract
One obstacle in de novo protein design is the vast sequence space that needs to be searched through to obtain functional proteins. We developed a new method using structural profiles created from evolutionarily related proteins to constrain the simulation search process, with functions specified by atomic-level ligand-protein binding interactions. The approach was applied to redesigning the BIR3 domain of the X-linked inhibitor of apoptosis protein (XIAP), whose primary function is to suppress the cell death by inhibiting caspase-9 activity; however, the function of the wild-type XIAP can be eliminated by the binding of Smac peptides. Isothermal calorimetry and luminescence assay reveal that the designed XIAP domains can bind strongly with the Smac peptides but do not significantly inhibit the caspase-9 proteolytic activity in vitro compared with the wild-type XIAP protein. Detailed mutation assay experiments suggest that the binding specificity in the designs is essentially determined by the interplay of structural profile and physical interactions, which demonstrates the potential to modify apoptosis pathways through computational design.
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Affiliation(s)
- David Shultis
- Department of Computational Medicine and Bioinformatics, University of Michigan, 100 Washtenaw Avenue, Ann Arbor, MI 48109, USA
| | - Pralay Mitra
- Department of Computational Medicine and Bioinformatics, University of Michigan, 100 Washtenaw Avenue, Ann Arbor, MI 48109, USA
| | - Xiaoqiang Huang
- Department of Computational Medicine and Bioinformatics, University of Michigan, 100 Washtenaw Avenue, Ann Arbor, MI 48109, USA
| | - Jarrett Johnson
- Department of Computational Medicine and Bioinformatics, University of Michigan, 100 Washtenaw Avenue, Ann Arbor, MI 48109, USA
| | - Naureen Aslam Khattak
- Department of Computational Medicine and Bioinformatics, University of Michigan, 100 Washtenaw Avenue, Ann Arbor, MI 48109, USA
| | - Felicia Gray
- Department of Pathology, University of Michigan, 1150 W. Medical Center Drive, Ann Arbor, MI 48109, USA
| | - Clint Piper
- Department of Computational Medicine and Bioinformatics, University of Michigan, 100 Washtenaw Avenue, Ann Arbor, MI 48109, USA
| | - Jeff Czajka
- Department of Computational Medicine and Bioinformatics, University of Michigan, 100 Washtenaw Avenue, Ann Arbor, MI 48109, USA
| | - Logan Hansen
- Department of Computational Medicine and Bioinformatics, University of Michigan, 100 Washtenaw Avenue, Ann Arbor, MI 48109, USA
| | - Bingbing Wan
- Department of Biological Chemistry, University of Michigan, 1150 West Medical Center Drive, Ann Arbor, MI 48109, USA
| | | | - Liu Liu
- Department of Internal Medicine, University of Michigan, 1500 East Medical Center Drive, Ann Arbor, MI 48109, USA
| | - Mi Wang
- Department of Internal Medicine, University of Michigan, 1500 East Medical Center Drive, Ann Arbor, MI 48109, USA
| | - Jingxi Pan
- Department of Biochemistry & Microbiology, The University of Victoria-Genome BC Proteomics Centre, Victoria, BC, Canada V8Z 7X8
| | - Jeanne Stuckey
- Life Sciences Institute, University of Michigan, 210 Washtenaw Avenue, Ann Arbor, MI 48109, USA
| | - Tomasz Cierpicki
- Department of Pathology, University of Michigan, 1150 W. Medical Center Drive, Ann Arbor, MI 48109, USA
| | - Christoph H Borchers
- Department of Biochemistry & Microbiology, The University of Victoria-Genome BC Proteomics Centre, Victoria, BC, Canada V8Z 7X8
| | - Shaomeng Wang
- Department of Internal Medicine, University of Michigan, 1500 East Medical Center Drive, Ann Arbor, MI 48109, USA
| | - Ming Lei
- Department of Biological Chemistry, University of Michigan, 1150 West Medical Center Drive, Ann Arbor, MI 48109, USA
| | - Yang Zhang
- Department of Computational Medicine and Bioinformatics, University of Michigan, 100 Washtenaw Avenue, Ann Arbor, MI 48109, USA; Department of Biological Chemistry, University of Michigan, 1150 West Medical Center Drive, Ann Arbor, MI 48109, USA.
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154
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Mass Spectrometry- and Computational Structural Biology-Based Investigation of Proteins and Peptides. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2019; 1140:265-287. [PMID: 31347053 DOI: 10.1007/978-3-030-15950-4_15] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
Recent developments of mass spectrometry (MS) allow us to identify, estimate, and characterize proteins and protein complexes. At the same time, structural biology helps to determine the protein structure and its structure-function relationship. Together, they aid to understand the protein structure, property, function, protein-complex assembly, protein-protein interaction, and dynamics. The present chapter is organized with illustrative results to demonstrate how experimental mass spectrometry can be combined with computational structural biology for detailed studies of protein's structures. We have used tumor differentiation factor protein/peptide as ligand and Hsp70/Hsp90 as receptor protein as examples to study ligand-protein interaction. To investigate possible protein conformation, we will describe two proteins-lysozyme and myoglobin. As an application of MS-based assignment of disulfide bridges, the case of the spider venom polypeptide Phα1β will also be discussed.
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155
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MacCarthy E, Perry D, Kc DB. Advances in Protein Super-Secondary Structure Prediction and Application to Protein Structure Prediction. Methods Mol Biol 2019; 1958:15-45. [PMID: 30945212 DOI: 10.1007/978-1-4939-9161-7_2] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
Due to the advancement in various sequencing technologies, the gap between the number of protein sequences and the number of experimental protein structures is ever increasing. Community-wide initiatives like CASP have resulted in considerable efforts in the development of computational methods to accurately model protein structures from sequences. Sequence-based prediction of super-secondary structure has direct application in protein structure prediction, and there have been significant efforts in the prediction of super-secondary structure in the last decade. In this chapter, we first introduce the protein structure prediction problem and highlight some of the important progress in the field of protein structure prediction. Next, we discuss recent methods for the prediction of super-secondary structures. Finally, we discuss applications of super-secondary structure prediction in structure prediction/analysis of proteins. We also discuss prediction of protein structures that are composed of simple super-secondary structure repeats and protein structures that are composed of complex super-secondary structure repeats. Finally, we also discuss the recent trends in the field.
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Affiliation(s)
- Elijah MacCarthy
- Department of Computational Science and Engineering, North Carolina A&T State University, Greensboro, NC, USA
| | - Derrick Perry
- Department of Computational Science and Engineering, North Carolina A&T State University, Greensboro, NC, USA
| | - Dukka B Kc
- Department of Computational Science and Engineering, North Carolina A&T State University, Greensboro, NC, USA.
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156
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Flot M, Mishra A, Kuchi AS, Hoque MT. StackSSSPred: A Stacking-Based Prediction of Supersecondary Structure from Sequence. Methods Mol Biol 2019; 1958:101-122. [PMID: 30945215 DOI: 10.1007/978-1-4939-9161-7_5] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
Supersecondary structure (SSS) refers to specific geometric arrangements of several secondary structure (SS) elements that are connected by loops. The SSS can provide useful information about the spatial structure and function of a protein. As such, the SSS is a bridge between the secondary structure and tertiary structure. In this chapter, we propose a stacking-based machine learning method for the prediction of two types of SSSs, namely, β-hairpins and β-α-β, from the protein sequence based on comprehensive feature encoding. To encode protein residues, we utilize key features such as solvent accessibility, conservation profile, half surface exposure, torsion angle fluctuation, disorder probabilities, and more. The usefulness of the proposed approach is assessed using a widely used threefold cross-validation technique. The obtained empirical result shows that the proposed approach is useful and prediction can be improved further.
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Affiliation(s)
- Michael Flot
- Department of Computer Science, University of New Orleans, New Orleans, LA, USA
| | - Avdesh Mishra
- Department of Computer Science, University of New Orleans, New Orleans, LA, USA
| | - Aditi Sharma Kuchi
- Department of Computer Science, University of New Orleans, New Orleans, LA, USA
| | - Md Tamjidul Hoque
- Department of Computer Science, University of New Orleans, New Orleans, LA, USA.
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157
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Studer G, Tauriello G, Bienert S, Waterhouse AM, Bertoni M, Bordoli L, Schwede T, Lepore R. Modeling of Protein Tertiary and Quaternary Structures Based on Evolutionary Information. Methods Mol Biol 2019; 1851:301-316. [PMID: 30298405 DOI: 10.1007/978-1-4939-8736-8_17] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Proteins are subject to evolutionary forces that shape their three-dimensional structure to meet specific functional demands. The knowledge of the structure of a protein is therefore instrumental to gain information about the molecular basis of its function. However, experimental structure determination is inherently time consuming and expensive, making it impossible to follow the explosion of sequence data deriving from genome-scale projects. As a consequence, computational structural modeling techniques have received much attention and established themselves as a valuable complement to experimental structural biology efforts. Among these, comparative modeling remains the method of choice to model the three-dimensional structure of a protein when homology to a protein of known structure can be detected.The general strategy consists of using experimentally determined structures of proteins as templates for the generation of three-dimensional models of related family members (targets) of which the structure is unknown. This chapter provides a description of the individual steps needed to obtain a comparative model using SWISS-MODEL, one of the most widely used automated servers for protein structure homology modeling.
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Affiliation(s)
- Gabriel Studer
- Biozentrum, University of Basel and SIB Swiss Institute of Bioinformatics, Basel, Switzerland
| | - Gerardo Tauriello
- Biozentrum, University of Basel and SIB Swiss Institute of Bioinformatics, Basel, Switzerland
| | - Stefan Bienert
- Biozentrum, University of Basel and SIB Swiss Institute of Bioinformatics, Basel, Switzerland
| | - Andrew Mark Waterhouse
- Biozentrum, University of Basel and SIB Swiss Institute of Bioinformatics, Basel, Switzerland
| | - Martino Bertoni
- Biozentrum, University of Basel and SIB Swiss Institute of Bioinformatics, Basel, Switzerland
| | - Lorenza Bordoli
- Biozentrum, University of Basel and SIB Swiss Institute of Bioinformatics, Basel, Switzerland
| | - Torsten Schwede
- Biozentrum, University of Basel and SIB Swiss Institute of Bioinformatics, Basel, Switzerland
| | - Rosalba Lepore
- Biozentrum, University of Basel and SIB Swiss Institute of Bioinformatics, Basel, Switzerland.
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158
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Sharma P, Lioutas A, Fernandez-Fuentes N, Quilez J, Carbonell-Caballero J, Wright RHG, Di Vona C, Le Dily F, Schüller R, Eick D, Oliva B, Beato M. Arginine Citrullination at the C-Terminal Domain Controls RNA Polymerase II Transcription. Mol Cell 2018; 73:84-96.e7. [PMID: 30472187 DOI: 10.1016/j.molcel.2018.10.016] [Citation(s) in RCA: 44] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2018] [Revised: 08/31/2018] [Accepted: 10/09/2018] [Indexed: 12/21/2022]
Abstract
The post-translational modification of key residues at the C-terminal domain of RNA polymerase II (RNAP2-CTD) coordinates transcription, splicing, and RNA processing by modulating its capacity to act as a landing platform for a variety of protein complexes. Here, we identify a new modification at the CTD, the deimination of arginine and its conversion to citrulline by peptidyl arginine deiminase 2 (PADI2), an enzyme that has been associated with several diseases, including cancer. We show that, among PADI family members, only PADI2 citrullinates R1810 (Cit1810) at repeat 31 of the CTD. Depletion of PADI2 or loss of R1810 results in accumulation of RNAP2 at transcription start sites, reduced gene expression, and inhibition of cell proliferation. Cit1810 is needed for interaction with the P-TEFb (positive transcription elongation factor b) kinase complex and for its recruitment to chromatin. In this way, CTD-Cit1810 favors RNAP2 pause release and efficient transcription in breast cancer cells.
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Affiliation(s)
- Priyanka Sharma
- Gene Regulation, Stem Cells and Cancer Program, Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology (BIST), Barcelona 08003, Spain
| | - Antonios Lioutas
- Gene Regulation, Stem Cells and Cancer Program, Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology (BIST), Barcelona 08003, Spain
| | - Narcis Fernandez-Fuentes
- IBERS, Institute of Biological, Environmental and Rural Science, Aberystwyth University, Aberystwyth SY23 3EB, UK
| | - Javier Quilez
- Gene Regulation, Stem Cells and Cancer Program, Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology (BIST), Barcelona 08003, Spain
| | - José Carbonell-Caballero
- Gene Regulation, Stem Cells and Cancer Program, Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology (BIST), Barcelona 08003, Spain
| | - Roni H G Wright
- Gene Regulation, Stem Cells and Cancer Program, Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology (BIST), Barcelona 08003, Spain
| | - Chiara Di Vona
- Gene Regulation, Stem Cells and Cancer Program, Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology (BIST), Barcelona 08003, Spain
| | - François Le Dily
- Gene Regulation, Stem Cells and Cancer Program, Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology (BIST), Barcelona 08003, Spain
| | - Roland Schüller
- Department of Molecular Epigenetics, Helmholtz Center Munich, Center of Integrated Protein Science, Munich, Germany
| | - Dirk Eick
- Department of Molecular Epigenetics, Helmholtz Center Munich, Center of Integrated Protein Science, Munich, Germany
| | - Baldomero Oliva
- Universitat Pompeu Fabra (UPF), Barcelona, Spain; Structural Bioinformatics Laboratory (GRIB-IMIM), Department of Experimental and Health Sciences, Barcelona 08003, Spain
| | - Miguel Beato
- Gene Regulation, Stem Cells and Cancer Program, Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology (BIST), Barcelona 08003, Spain; Universitat Pompeu Fabra (UPF), Barcelona, Spain.
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159
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Sadeghi S, Poorebrahim M, Rahimi H, Karimipoor M, Azadmanesh K, Khorramizadeh MR, Teimoori-Toolabi L. In silico studying of the whole protein structure and dynamics of Dickkopf family members showed that N-terminal domain of Dickkopf 2 in contrary to other Dickkopfs facilitates its interaction with low density lipoprotein receptor related protein 5/6. J Biomol Struct Dyn 2018; 37:2564-2580. [DOI: 10.1080/07391102.2018.1491891] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/28/2022]
Affiliation(s)
- Solmaz Sadeghi
- Department of Medical Biotechnology, School of Advanced Technologies in Medicine, Tehran University of Medical Sciences, Tehran, Iran
- Molecular Medicine Department, Pasteur Institute of Iran, Tehran, Iran
| | - Mansour Poorebrahim
- Department of Medical Biotechnology, School of Advanced Technologies in Medicine, Tehran University of Medical Sciences, Tehran, Iran
- Molecular Medicine Department, Pasteur Institute of Iran, Tehran, Iran
| | - Hamzeh Rahimi
- Molecular Medicine Department, Pasteur Institute of Iran, Tehran, Iran
| | | | | | - Mohammad Reza Khorramizadeh
- Biosensor Research Center, Endocrinology and Metabolism Molecular-Cellular Research Institute, Tehran University of Medical Sciences, Tehran, Iran
| | - Ladan Teimoori-Toolabi
- Department of Medical Biotechnology, School of Advanced Technologies in Medicine, Tehran University of Medical Sciences, Tehran, Iran
- Molecular Medicine Department, Pasteur Institute of Iran, Tehran, Iran
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160
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Padalino G, Ferla S, Brancale A, Chalmers IW, Hoffmann KF. Combining bioinformatics, cheminformatics, functional genomics and whole organism approaches for identifying epigenetic drug targets in Schistosoma mansoni. INTERNATIONAL JOURNAL FOR PARASITOLOGY-DRUGS AND DRUG RESISTANCE 2018; 8:559-570. [PMID: 30455056 PMCID: PMC6288008 DOI: 10.1016/j.ijpddr.2018.10.005] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/07/2018] [Revised: 10/22/2018] [Accepted: 10/22/2018] [Indexed: 02/07/2023]
Abstract
Schistosomiasis endangers the lives of greater than 200 million people every year and is predominantly controlled by a single class chemotherapy, praziquantel (PZQ). Development of PZQ replacement (to combat the threat of PZQ insensitivity/resistance arising) or combinatorial (to facilitate the killing of PZQ-insensitive juvenile schistosomes) chemotherapies would help sustain this control strategy into the future. Here, we re-categorise two families of druggable epigenetic targets in Schistosoma mansoni, the histone methyltransferases (HMTs) and the histone demethylases (HDMs). Amongst these, a S. mansoni Lysine Specific Demethylase 1 (SmLSD1, Smp_150560) homolog was selected for further analyses. Homology modelling of SmLSD1 and in silico docking of greater than four thousand putative inhibitors identified seven (L1 – L7) showing more favourable binding to the target pocket of SmLSD1 vs Homo sapiens HsLSD1; six of these seven (L1 – L6) plus three structural analogues of L7 (L8 – L10) were subsequently screened against schistosomula using the Roboworm anthelmintic discovery platform. The most active compounds (L10 - pirarubicin > L8 – danunorubicin hydrochloride) were subsequently tested against juvenile (3 wk old) and mature (7 wk old) schistosome stages and found to impede motility, suppress egg production and affect tegumental surfaces. When compared to a surrogate human cell line (HepG2), a moderate window of selectivity was observed for the most active compound L10 (selectivity indices - 11 for schistosomula, 9 for juveniles, 1.5 for adults). Finally, RNA interference of SmLSD1 recapitulated the egg-laying defect of schistosomes co-cultivated in the presence of L10 and L8. These preliminary results suggest that SmLSD1 represents an attractive new target for schistosomiasis; identification of more potent and selective SmLSD1 compounds, however, is essential. Nevertheless, the approaches described herein highlight an interdisciplinary strategy for selecting and screening novel/repositioned anti-schistosomals, which can be applied to any druggable (epigenetic) target encoded by the parasite's genome. Schistosoma mansoni contains 27 histone methyltransferases (HMTs) and 14 histone demethylases (HDMs). S. mansoni lysine specific demethylase 1 (SmLSD1) is a druggable target. Schistosomes treated with the putative SmLSD1 inhibitor pirarubicin or siRNAs targeting SmLSD1 are less fecund.
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Affiliation(s)
- Gilda Padalino
- The Institute of Biological, Environmental and Rural Sciences (IBERS), Aberystwyth University, SY23 3DA, Wales, UK.
| | - Salvatore Ferla
- School of Pharmacy and Pharmaceutical Sciences, Cardiff University, Cardiff, CF10 3NB, United Kingdom.
| | - Andrea Brancale
- School of Pharmacy and Pharmaceutical Sciences, Cardiff University, Cardiff, CF10 3NB, United Kingdom.
| | - Iain W Chalmers
- The Institute of Biological, Environmental and Rural Sciences (IBERS), Aberystwyth University, SY23 3DA, Wales, UK.
| | - Karl F Hoffmann
- The Institute of Biological, Environmental and Rural Sciences (IBERS), Aberystwyth University, SY23 3DA, Wales, UK.
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161
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Schlessinger A, Welch MA, van Vlijmen H, Korzekwa K, Swaan PW, Matsson P. Molecular Modeling of Drug-Transporter Interactions-An International Transporter Consortium Perspective. Clin Pharmacol Ther 2018; 104:818-835. [PMID: 29981151 PMCID: PMC6197929 DOI: 10.1002/cpt.1174] [Citation(s) in RCA: 36] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2018] [Accepted: 06/30/2018] [Indexed: 12/31/2022]
Abstract
Membrane transporters play diverse roles in the pharmacokinetics and pharmacodynamics of small-molecule drugs. Understanding the mechanisms of drug-transporter interactions at the molecular level is, therefore, essential for the design of drugs with optimal therapeutic effects. This white paper examines recent progress, applications, and challenges of molecular modeling of membrane transporters, including modeling techniques that are centered on the structures of transporter ligands, and those focusing on the structures of the transporters. The goals of this article are to illustrate current best practices and future opportunities in using molecular modeling techniques to understand and predict transporter-mediated effects on drug disposition and efficacy.Membrane transporters from the solute carrier (SLC) and ATP-binding cassette (ABC) superfamilies regulate the cellular uptake, efflux, and homeostasis of many essential nutrients and significantly impact the pharmacokinetics of drugs; further, they may provide targets for novel therapeutics as well as facilitate prodrug approaches. Because of their often broad substrate selectivity they are also implicated in many undesirable and sometimes life-threatening drug-drug interactions (DDIs).5,6.
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Affiliation(s)
- Avner Schlessinger
- Department of Pharmacological Sciences, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Matthew A. Welch
- Department of Pharmaceutical Sciences, University of Maryland, Baltimore, MD
| | - Herman van Vlijmen
- Computational Chemistry, Discovery Sciences, Janssen Research & Development, Beerse, Belgium
| | - Ken Korzekwa
- Department of Pharmaceutical Sciences, Temple University, Philadelphia, PA
| | - Peter W. Swaan
- Department of Pharmaceutical Sciences, University of Maryland, Baltimore, MD
| | - Pär Matsson
- Department of Pharmacy, Uppsala University, Sweden
,Address correspondence to: Pär Matsson, Department of Pharmacy, Uppsala University, Box 580, SE-75123 Uppsala, Sweden, Phone: +46-(0)18-471 46 30, Fax: +46-(0)18-471 42 23,
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162
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Song S, Ji J, Chen X, Gao S, Tang Z, Todo Y. Adoption of an improved PSO to explore a compound multi-objective energy function in protein structure prediction. Appl Soft Comput 2018. [DOI: 10.1016/j.asoc.2018.07.042] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
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163
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Rajendran BK, Xavier Suresh M, Bhaskaran SP, Harshitha Y, Gaur U, Kwok HF. Pharmacoinformatic Approach to Explore the Antidote Potential of Phytochemicals on Bungarotoxin from Indian Krait, Bungarus caeruleus. Comput Struct Biotechnol J 2018; 16:450-461. [PMID: 30455855 PMCID: PMC6231056 DOI: 10.1016/j.csbj.2018.10.005] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2018] [Revised: 10/08/2018] [Accepted: 10/12/2018] [Indexed: 12/25/2022] Open
Abstract
Venomous reptiles especially serpents are well known for their adverse effects after accidental conflicts with humans. Upon biting humans these serpents transmit arrays of detrimental toxins with diverse physiological activities that may either lead to minor symptoms such as dermatitis and allergic response or highly severe symptoms such as blood coagulation, disseminated intravascular coagulation, tissue injury, and hemorrhage. Other complications like respiratory arrest and necrosis may also occur. Bungarotoxins are a group of closely related neurotoxic proteins derived from the venom of kraits (Bungarus caeruleus) one of the six most poisonous snakes in India whose bite causes respiratory paralysis and mortality without showing any local symptoms. In the current study, by employing various pharmacoinformatic approaches, we have explored the antidote properties of 849 bioactive phytochemicals from 82 medicinal plants which have already shown antidote properties against various venomous toxins. These herbal compounds were taken and pharmacoinformatic approaches such as ADMET, docking and molecular dynamics were employed. The three-dimensional modelling approach provides structural insights on the interaction between bungarotoxin and phytochemicals. In silico simulations proved to be an effective analytical tools to investigate the toxin-ligand interaction, correlating with the affinity of binding. By analyzing the results from the present study, we proposed nine bioactive phytochemical compounds which are, 2-dodecanol, 7-hydroxycadalene, indole-3-(4'-oxo)butyric acid, nerolidol-2, trans-nerolidol, eugenol, benzene propanoic acid, 2-methyl-1-undecanol, germacren-4-ol can be used as antidotes for bungarotoxin.
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Affiliation(s)
- Barani Kumar Rajendran
- Institute of Translational Medicine, Faculty of Health Sciences, University of Macau, Avenida de Universidade, Taipa, Macau
| | - M. Xavier Suresh
- Department of Physics, Sathyabama Institute of Science and Technology, Deemed to be University, Chennai 600119, India
| | - Shanmuga Priya Bhaskaran
- Institute of Translational Medicine, Faculty of Health Sciences, University of Macau, Avenida de Universidade, Taipa, Macau
| | - Yarradoddi Harshitha
- Department of Physics, Sathyabama Institute of Science and Technology, Deemed to be University, Chennai 600119, India
| | - Uma Gaur
- Institute of Translational Medicine, Faculty of Health Sciences, University of Macau, Avenida de Universidade, Taipa, Macau
| | - Hang Fai Kwok
- Institute of Translational Medicine, Faculty of Health Sciences, University of Macau, Avenida de Universidade, Taipa, Macau
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164
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de Brevern AG, Floch A, Barrault A, Martret J, Bodivit G, Djoudi R, Pirenne F, Tournamille C. Alloimmunization risk associated with amino acid 223 substitution in the RhD protein: analysis in the light of molecular modeling. Transfusion 2018; 58:2683-2692. [DOI: 10.1111/trf.14809] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2017] [Revised: 04/09/2018] [Accepted: 04/21/2018] [Indexed: 12/26/2022]
Affiliation(s)
- Alexandre G. de Brevern
- INSERM UMR_S 1134; Univ. Paris Diderot, Sorbonne Paris Cité, Univ. de la Réunion, Univ. Antilles; Paris
- Laboratory of Excellence GR-Ex; Paris
- Institut National de la Transfusion Sanguine (INTS); Paris
| | - Aline Floch
- Laboratory of Excellence GR-Ex; Paris
- Etablissement Français du Sang Ile de France; Créteil France
- IMRB-INSERM U955 Team 2 “Transfusion et Maladies du Globule Rouge”; Créteil France
- UPEC; Université Paris Est-Créteil; Créteil France
| | | | | | - Gwellaouen Bodivit
- Laboratory of Excellence GR-Ex; Paris
- Etablissement Français du Sang Ile de France; Créteil France
- IMRB-INSERM U955 Team 2 “Transfusion et Maladies du Globule Rouge”; Créteil France
| | - Rachid Djoudi
- Etablissement Français du Sang Ile de France; Créteil France
| | - France Pirenne
- Laboratory of Excellence GR-Ex; Paris
- Etablissement Français du Sang Ile de France; Créteil France
- IMRB-INSERM U955 Team 2 “Transfusion et Maladies du Globule Rouge”; Créteil France
- UPEC; Université Paris Est-Créteil; Créteil France
| | - Christophe Tournamille
- Laboratory of Excellence GR-Ex; Paris
- Etablissement Français du Sang Ile de France; Créteil France
- IMRB-INSERM U955 Team 2 “Transfusion et Maladies du Globule Rouge”; Créteil France
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165
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Embryogenic remodeling of global chromatin and its role on structure of corresponding lattice representation. Biosystems 2018; 173:273-280. [PMID: 30268924 DOI: 10.1016/j.biosystems.2018.09.013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2018] [Revised: 09/23/2018] [Accepted: 09/24/2018] [Indexed: 11/21/2022]
Abstract
Recent findings demonstrate that chromatin is functionally compartmentalized and packed into three dimensional multi-hierarchical structures that influence gene expression patterns. Using lattice theory tools we compare lattice representation of unstructured DNA, as identified in early-embryo phase, with compartmentalized DNA, as found in later embryo phases and in differentiated cells. By comparing these two representations we show that corresponding contact maps have elementary diagonal structure (unstructured DNA) and block diagonal structure (compartmentalized DNA). We show that a lattice corresponding to the binary relation with block diagonal structure is a product of so called Chinese lantern lattice. In contrast to Boolean lattice, formation of words in Chinese lantern lattice is highly restricted which is comparable to emergence of DNA compartmentalization during embryogenesis.
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166
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Agrawal N, Hossain MS, Skelton AA, Muralidhar K, Kaushik S. Unraveling the mechanism of l-gulonate-3-dehydrogenase inhibition by ascorbic acid: Insights from molecular modeling. Comput Biol Chem 2018; 77:146-153. [PMID: 30316191 DOI: 10.1016/j.compbiolchem.2018.09.015] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2018] [Revised: 09/06/2018] [Accepted: 09/23/2018] [Indexed: 10/28/2022]
Abstract
l-Gulonate dehydrogenase (GuDH) is a crucial enzyme in the non-phosphorylated sugar metabolism or glucuronate-xylulose (GX) pathway. Some naturally occurring compounds inhibit GuDH. Ascorbic acid is one of such inhibitors for GuDH. However, the exact mechanism by which ascorbic acid inhibits GuDH is still unknown. In this study, we try to investigate GuDH inhibition using computational approaches by generating a model for buffalo GuDH. We used this model to perform blind dockings of ascorbic acid to GuDH. Some docked conformations of ascorbic acid bind near Asp39 and have steric clashes with crystal structure conformation of NADH. To assess the dynamic stability of the GuDH-ascorbic acid complex, we performed six molecular dynamics simulations for GuDH, three each in its free form and in complex with ascorbic acid for 50 ns, to obtain 300 ns of trajectories in total. During the simulations, ascorbic acid interacted with several residues nearby Asp39. As Asp39 is an important residue for NADH binding and specificity, the interaction of ascorbic acid near Asp39 hinders further NADH binding and ultimately affects the enzymatic functioning of GuDH. In this study, we analyze these interactions between ascorbic acid and GuDH. Our analysis reveals novel details on the mechanism of GuDH inhibition by ascorbic acid.
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Affiliation(s)
- Nikhil Agrawal
- School of Pharmacy and Pharmacology, University of KwaZulu-Natal, Durban, South Africa
| | | | - Adam A Skelton
- School of Pharmacy and Pharmacology, University of KwaZulu-Natal, Durban, South Africa
| | | | - Sandeep Kaushik
- 3B's Research Group, Head Quarters of European Institute of Excellence on Tissue Engineering and Regenerative Medicine, AvePark - Parque de Ciência e Tecnologia, Zona Industrial da Gandra, Barco, 4805-017, Guimarães, Portugal.
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167
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Pfeiffenberger E, Bates PA. Predicting improved protein conformations with a temporal deep recurrent neural network. PLoS One 2018; 13:e0202652. [PMID: 30180164 PMCID: PMC6122789 DOI: 10.1371/journal.pone.0202652] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2018] [Accepted: 08/07/2018] [Indexed: 02/03/2023] Open
Abstract
Accurate protein structure prediction from amino acid sequence is still an unsolved problem. The most reliable methods centre on template based modelling. However, the accuracy of these models entirely depends on the availability of experimentally resolved homologous template structures. In order to generate more accurate models, extensive physics based molecular dynamics (MD) refinement simulations are performed to sample many different conformations to find improved conformational states. In this study, we propose a deep recurrent network model, called DeepTrajectory, that is able to identify these improved conformational states, with high precision, from a variety of different MD based sampling protocols. The proposed model learns the temporal patterns of features computed from MD trajectory data in order to classify whether each recorded simulation snapshot is an improved quality conformational state, decreased quality conformational state or whether there is no perceivable change in state with respect to the starting conformation. The model was trained and tested on 904 trajectories from 42 different protein systems with a cumulative number of more than 1.7 million snapshots. We show that our model outperforms other state of the art machine-learning algorithms that do not consider temporal dependencies. To our knowledge, DeepTrajectory is the first implementation of a time-dependent deep-learning protocol that is re-trainable and able to adapt to any new MD based sampling procedure, thereby demonstrating how a neural network can be used to learn the latter part of the protein folding funnel.
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Affiliation(s)
- Erik Pfeiffenberger
- Biomolecular Modelling Laboratory, The Francis Crick Institute, 1 Midland Road, London NW1 1AT, United Kingdom
| | - Paul A. Bates
- Biomolecular Modelling Laboratory, The Francis Crick Institute, 1 Midland Road, London NW1 1AT, United Kingdom
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168
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Travers T, Wang KJ, López CA, Gnanakaran S. Sequence- and structure-based computational analyses of Gram-negative tripartite efflux pumps in the context of bacterial membranes. Res Microbiol 2018; 169:414-424. [DOI: 10.1016/j.resmic.2018.01.002] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2017] [Revised: 12/28/2017] [Accepted: 01/21/2018] [Indexed: 01/12/2023]
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169
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Kc DB. Recent advances in sequence-based protein structure prediction. Brief Bioinform 2018; 18:1021-1032. [PMID: 27562963 DOI: 10.1093/bib/bbw070] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2016] [Indexed: 11/13/2022] Open
Abstract
The most accurate characterizations of the structure of proteins are provided by structural biology experiments. However, because of the high cost and labor-intensive nature of the structural experiments, the gap between the number of protein sequences and solved structures is widening rapidly. Development of computational methods to accurately model protein structures from sequences is becoming increasingly important to the biological community. In this article, we highlight some important progress in the field of protein structure prediction, especially those related to free modeling (FM) methods that generate structure models without using homologous templates. We also provide a short synopsis of some of the recent advances in FM approaches as demonstrated in the recent Computational Assessment of Structure Prediction competition as well as recent trends and outlook for FM approaches in protein structure prediction.
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170
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Ranbhor R, Kumar A, Tendulkar A, Patel K, Ramakrishnan V, Durani S. IDeAS: automated design tool for hetero-chiral protein folds. Phys Biol 2018; 15:066005. [PMID: 29923499 DOI: 10.1088/1478-3975/aacdc3] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/25/2023]
Abstract
Incorporating D amino acids in the protein design alphabet can in principle multiply the design space by many orders of magnitude. All native proteins are polymers composed of L chiral amino acids. Practically limitless in diversity over amino acid sequences, protein structure is limited in folds and thus shapes, principally due to the poly L stereochemistry of their backbone. To diversify shapes, we introduced both L- and D α-amino acids as design alphabets to explore the possibility of generating novel folds, varied in chemical as well as stereo-chemical sequence. Now, to have stereochemically-defined proteins tuned chemically, we present the Inverse Design and Automation Software, IDeAS. Retro-fitting side chains on a backbone with L and D stereochemistry, the software demonstrate functional fits over stereo-chemically diverse folds in a range of applications of interest in protein design.
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Affiliation(s)
- Ranjit Ranbhor
- Department of Biosciences and Bioengineering, Indian Institute of Technology Bombay, Mumbai 400076, India
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171
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Delarue M, Koehl P. Combined approaches from physics, statistics, and computer science for ab initio protein structure prediction: ex unitate vires (unity is strength)? F1000Res 2018; 7. [PMID: 30079234 PMCID: PMC6058471 DOI: 10.12688/f1000research.14870.1] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 07/19/2018] [Indexed: 11/20/2022] Open
Abstract
Connecting the dots among the amino acid sequence of a protein, its structure, and its function remains a central theme in molecular biology, as it would have many applications in the treatment of illnesses related to misfolding or protein instability. As a result of high-throughput sequencing methods, biologists currently live in a protein sequence-rich world. However, our knowledge of protein structure based on experimental data remains comparatively limited. As a consequence, protein structure prediction has established itself as a very active field of research to fill in this gap. This field, once thought to be reserved for theoretical biophysicists, is constantly reinventing itself, borrowing ideas informed by an ever-increasing assembly of scientific domains, from biology, chemistry, (statistical) physics, mathematics, computer science, statistics, bioinformatics, and more recently data sciences. We review the recent progress arising from this integration of knowledge, from the development of specific computer architecture to allow for longer timescales in physics-based simulations of protein folding to the recent advances in predicting contacts in proteins based on detection of coevolution using very large data sets of aligned protein sequences.
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Affiliation(s)
- Marc Delarue
- Unité Dynamique Structurale des Macromolécules, Institut Pasteur, and UMR 3528 du CNRS, Paris, France
| | - Patrice Koehl
- Department of Computer Science, Genome Center, University of California, Davis, Davis, California, USA
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172
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Abstract
Since the 1980s, deep learning and biomedical data have been coevolving and feeding each other. The breadth, complexity, and rapidly expanding size of biomedical data have stimulated the development of novel deep learning methods, and application of these methods to biomedical data have led to scientific discoveries and practical solutions. This overview provides technical and historical pointers to the field, and surveys current applications of deep learning to biomedical data organized around five subareas, roughly of increasing spatial scale: chemoinformatics, proteomics, genomics and transcriptomics, biomedical imaging, and health care. The black box problem of deep learning methods is also briefly discussed.
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Affiliation(s)
- Pierre Baldi
- Department of Computer Science, Institute for Genomics and Bioinformatics, and Center for Machine Learning and Intelligent Systems, University of California, Irvine, California 92697, USA
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173
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Wu H, Zeng W, Chen L, Yu B, Guo Y, Chen G, Liang Z. Integrated multi-spectroscopic and molecular docking techniques to probe the interaction mechanism between maltase and 1-deoxynojirimycin, an α-glucosidase inhibitor. Int J Biol Macromol 2018; 114:1194-1202. [DOI: 10.1016/j.ijbiomac.2018.04.024] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2018] [Revised: 04/03/2018] [Accepted: 04/05/2018] [Indexed: 12/16/2022]
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174
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Dutagaci B, Heo L, Feig M. Structure refinement of membrane proteins via molecular dynamics simulations. Proteins 2018; 86:738-750. [PMID: 29675899 PMCID: PMC6013386 DOI: 10.1002/prot.25508] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2018] [Revised: 04/09/2018] [Accepted: 04/14/2018] [Indexed: 12/12/2022]
Abstract
A refinement protocol based on physics-based techniques established for water soluble proteins is tested for membrane protein structures. Initial structures were generated by homology modeling and sampled via molecular dynamics simulations in explicit lipid bilayer and aqueous solvent systems. Snapshots from the simulations were selected based on scoring with either knowledge-based or implicit membrane-based scoring functions and averaged to obtain refined models. The protocol resulted in consistent and significant refinement of the membrane protein structures similar to the performance of refinement methods for soluble proteins. Refinement success was similar between sampling in the presence of lipid bilayers and aqueous solvent but the presence of lipid bilayers may benefit the improvement of lipid-facing residues. Scoring with knowledge-based functions (DFIRE and RWplus) was found to be as good as scoring using implicit membrane-based scoring functions suggesting that differences in internal packing is more important than orientations relative to the membrane during the refinement of membrane protein homology models.
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Affiliation(s)
- Bercem Dutagaci
- Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, MI, USA
| | - Lim Heo
- Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, MI, USA
| | - Michael Feig
- Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, MI, USA
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175
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Shirvanizadeh N, Vriend G, Arab SS. Loop modelling 1.0. J Mol Graph Model 2018; 84:64-68. [PMID: 29920424 DOI: 10.1016/j.jmgm.2018.06.001] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2018] [Revised: 05/28/2018] [Accepted: 06/01/2018] [Indexed: 10/14/2022]
Abstract
Engineering surface loops is a sub-topic of protein engineering that is used routinely in many research fields in academia and industry alike. We provide some tools that search in the PDB for loops satisfying a wide variety of constraints. We illustrate the usefulness of these tools by applying them to a series of recently published studies that included loop engineering or loop modelling. LoopFinder finds loops that fit between two anchor stretches of typically 2, 3, or 4 amino acids each. ProDA find loops of a given length with predefined secondary structure, residue types, hydrophobicity, etc. WHAT IF has gotten a series of new options to scan the whole PDB for loops combining the LoopFinder and ProDA techniques. The open nature of these tools will allow bioinformaticians in this field to easily design their own loop modelling software around our tools. AVAILABILITY AND IMPLEMENTATION LoopFinder is a stand-alone Fortran program that is likely to compile and run on every computer. The LoopFinder source code, data files, and documentation are freely available from swift.cmbi.ru.nl/gv/loops/. ProDA is free to all users. There is no login requirement. It is available at: http://bioinf.modares.ac.ir/software/linda/. WHAT IF is shareware that is available from https://swift.cmbi.ru.nl/whatif/.
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Affiliation(s)
- Niloofar Shirvanizadeh
- Department of Biophysics, Faculty of Biological Sciences, Tarbiat Modares University, 14115-175, Tehran, Iran
| | - Gert Vriend
- CMBI, Radboudumc, 260 Nijmegen, the Netherlands
| | - Seyed Shahriar Arab
- Department of Biophysics, Faculty of Biological Sciences, Tarbiat Modares University, 14115-175, Tehran, Iran.
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176
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Geyer KK, Munshi SE, Whiteland HL, Fernandez-Fuentes N, Phillips DW, Hoffmann KF. Methyl-CpG-binding (SmMBD2/3) and chromobox (SmCBX) proteins are required for neoblast proliferation and oviposition in the parasitic blood fluke Schistosoma mansoni. PLoS Pathog 2018; 14:e1007107. [PMID: 29953544 PMCID: PMC6023120 DOI: 10.1371/journal.ppat.1007107] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2018] [Accepted: 05/17/2018] [Indexed: 12/11/2022] Open
Abstract
While schistosomiasis remains a significant health problem in low to middle income countries, it also represents a recently recognised threat to more economically-developed regions. Until a vaccine is developed, this neglected infectious disease is primarily controlled by praziquantel, a drug with a currently unknown mechanism of action. By further elucidating how Schistosoma molecular components cooperate to regulate parasite developmental processes, next generation targets will be identified. Here, we continue our studies on schistosome epigenetic participants and characterise the function of a DNA methylation reader, the Schistosoma mansoni methyl-CpG-binding domain protein (SmMBD2/3). Firstly, we demonstrate that SmMBD2/3 contains amino acid features essential for 5-methyl cytosine (5mC) binding and illustrate that adult schistosome nuclear extracts (females > males) contain this activity. We subsequently show that SmMBD2/3 translocates into nuclear compartments of transfected murine NIH-3T3 fibroblasts and recombinant SmMBD2/3 exhibits 5mC binding activity. Secondly, using a yeast-two hybrid (Y2H) screen, we show that SmMBD2/3 interacts with the chromo shadow domain (CSD) of an epigenetic adaptor, S. mansoni chromobox protein (SmCBX). Moreover, fluorescent in situ hybridisation (FISH) mediated co-localisation of Smmbd2/3 and Smcbx to mesenchymal cells as well as somatic- and reproductive- stem cells confirms the Y2H results and demonstrates that these interacting partners are ubiquitously expressed and found within both differentiated as well as proliferating cells. Finally, using RNA interference, we reveal that depletion of Smmbd2/3 or Smcbx in adult females leads to significant reductions (46-58%) in the number of proliferating somatic stem cells (PSCs or neoblasts) as well as in the quantity of in vitro laid eggs. Collectively, these results further expand upon the schistosome components involved in epigenetic processes and suggest that pharmacological inhibition of SmMBD2/3 and/or SmCBX biology could prove useful in the development of future schistosomiasis control strategies.
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Affiliation(s)
- Kathrin K. Geyer
- Institute of Biological, Environmental and Rural Sciences, Aberystwyth University, Aberystwyth, United Kingdom
| | - Sabrina E. Munshi
- Institute of Biological, Environmental and Rural Sciences, Aberystwyth University, Aberystwyth, United Kingdom
| | - Helen L. Whiteland
- Institute of Biological, Environmental and Rural Sciences, Aberystwyth University, Aberystwyth, United Kingdom
| | - Narcis Fernandez-Fuentes
- Institute of Biological, Environmental and Rural Sciences, Aberystwyth University, Aberystwyth, United Kingdom
| | - Dylan W. Phillips
- Institute of Biological, Environmental and Rural Sciences, Aberystwyth University, Aberystwyth, United Kingdom
| | - Karl F. Hoffmann
- Institute of Biological, Environmental and Rural Sciences, Aberystwyth University, Aberystwyth, United Kingdom
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177
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Ahmed MS, Shahjaman M, Kabir E, Kamruzzaman M. Structure modeling to function prediction of Uncharacterized Human Protein C15orf41. Bioinformation 2018; 14:206-212. [PMID: 30108417 PMCID: PMC6077826 DOI: 10.6026/97320630014206] [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] [Received: 03/17/2018] [Revised: 04/29/2018] [Accepted: 04/29/2018] [Indexed: 01/18/2023] Open
Abstract
The dyserythropoietic anemia disease is a genetic disorder of erythropoiesis characterized by morphological abnormalities of erythroblasts. This is caused by human gene C15orf41 mutation. The uncharacterized C15orf41 protein is involved in the formation of a functional complex structure. The uncharacterized C15orf41 protein is thermostable, unstable and acidic. This is associated with TPD (Treponema Pallidum) domain (135 to 265 residue position) and three PTM sites such as K50 (Acetylation), T114 (Phosphorylation) and K176 (Ubiquitination). C15orf41 is paralogous to isoform-1 (gi|194018542|) and open reading frame isoform-CRA_c (gi|119612744|) of Homo sapiens located at chromosome 15. It interacts with the human ATP (Adenosine Triphosphate) binding domain 4 (ATPBD4) having similarity score 0.725 as per protein-protein interaction (PPI) network analysis. This data provides valuable insights towards the functional characterization of human gene C15orf41.
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Affiliation(s)
- Md. Shakil Ahmed
- Department of Statistics, University of Rajshahi, Rajshahi-6205, Bangladesh
| | - Md. Shahjaman
- Department of Statistics, Begum Rokeya University, Rangpur-5400, Bangladesh
| | - Enamul Kabir
- School of Agricultural, Computational and Environmental Sciences, University of Southern Queensland, Australia
| | - Md. Kamruzzaman
- Data Science for Knowledge Creation Research Center, Seoul National University, Korea
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178
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Huang YT, Chen JM, Ho BC, Wu ZY, Kuo RC, Liu PY. Genome Sequencing and Comparative Analysis of Stenotrophomonas acidaminiphila Reveal Evolutionary Insights Into Sulfamethoxazole Resistance. Front Microbiol 2018; 9:1013. [PMID: 29867899 PMCID: PMC5966563 DOI: 10.3389/fmicb.2018.01013] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2018] [Accepted: 04/30/2018] [Indexed: 11/23/2022] Open
Abstract
Stenotrophomonas acidaminiphila is an aerobic, glucose non-fermentative, Gram-negative bacterium that been isolated from various environmental sources, particularly aquatic ecosystems. Although resistance to multiple antimicrobial agents has been reported in S. acidaminiphila, the mechanisms are largely unknown. Here, for the first time, we report the complete genome and antimicrobial resistome analysis of a clinical isolate S. acidaminiphila SUNEO which is resistant to sulfamethoxazole. Comparative analysis among closely related strains identified common and strain-specific genes. In particular, comparison with a sulfamethoxazole-sensitive strain identified a mutation within the sulfonamide-binding site of folP in SUNEO, which may reduce the binding affinity of sulfamethoxazole. Selection pressure analysis indicated folP in SUNEO is under purifying selection, which may be owing to long-term administration of sulfonamide against Stenotrophomonas.
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Affiliation(s)
- Yao-Ting Huang
- Department of Computer Science and Information Engineering, National Chung Cheng University, Chiayi, Taiwan
| | - Jia-Min Chen
- Department of Computer Science and Information Engineering, National Chung Cheng University, Chiayi, Taiwan
| | - Bing-Ching Ho
- Department of Clinical Laboratory Sciences and Medical Biotechnology, National Taiwan University Hospital, Taipei, Taiwan
| | - Zong-Yen Wu
- DOE Joint Genome Institute, Walnut Creek, CA, United States.,Department of Veterinary Medicine, National Chung Hsing University, Taichung, Taiwan
| | - Rita C Kuo
- DOE Joint Genome Institute, Walnut Creek, CA, United States
| | - Po-Yu Liu
- The Department of Nursing, Shu-Zen Junior College of Medicine and Management, Kaohsiung, Taiwan.,Rong Hsing Research Center for Translational Medicine, College of Life Sciences, National Chung Hsing University, Taichung, Taiwan.,Division of Infectious Diseases, Department of Internal Medicine, Taichung Veterans General Hospital, Taichung, Taiwan
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179
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Setiawan D, Brender J, Zhang Y. Recent advances in automated protein design and its future challenges. Expert Opin Drug Discov 2018; 13:587-604. [PMID: 29695210 DOI: 10.1080/17460441.2018.1465922] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
Abstract
INTRODUCTION Protein function is determined by protein structure which is in turn determined by the corresponding protein sequence. If the rules that cause a protein to adopt a particular structure are understood, it should be possible to refine or even redefine the function of a protein by working backwards from the desired structure to the sequence. Automated protein design attempts to calculate the effects of mutations computationally with the goal of more radical or complex transformations than are accessible by experimental techniques. Areas covered: The authors give a brief overview of the recent methodological advances in computer-aided protein design, showing how methodological choices affect final design and how automated protein design can be used to address problems considered beyond traditional protein engineering, including the creation of novel protein scaffolds for drug development. Also, the authors address specifically the future challenges in the development of automated protein design. Expert opinion: Automated protein design holds potential as a protein engineering technique, particularly in cases where screening by combinatorial mutagenesis is problematic. Considering solubility and immunogenicity issues, automated protein design is initially more likely to make an impact as a research tool for exploring basic biology in drug discovery than in the design of protein biologics.
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Affiliation(s)
- Dani Setiawan
- a Department of Computational Medicine and Bioinformatics , University of Michigan , Ann Arbor , MI , USA
| | - Jeffrey Brender
- b Radiation Biology Branch , Center for Cancer Research, National Cancer Institute - NIH , Bethesda , MD , USA
| | - Yang Zhang
- a Department of Computational Medicine and Bioinformatics , University of Michigan , Ann Arbor , MI , USA.,c Department of Biological Chemistry , University of Michigan , Ann Arbor , MI , USA
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180
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AIMOES: Archive information assisted multi-objective evolutionary strategy for ab initio protein structure prediction. Knowl Based Syst 2018. [DOI: 10.1016/j.knosys.2018.01.028] [Citation(s) in RCA: 40] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023]
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181
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Nicoludis JM, Gaudet R. Applications of sequence coevolution in membrane protein biochemistry. BIOCHIMICA ET BIOPHYSICA ACTA. BIOMEMBRANES 2018; 1860:895-908. [PMID: 28993150 PMCID: PMC5807202 DOI: 10.1016/j.bbamem.2017.10.004] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/19/2017] [Revised: 09/28/2017] [Accepted: 10/02/2017] [Indexed: 12/22/2022]
Abstract
Recently, protein sequence coevolution analysis has matured into a predictive powerhouse for protein structure and function. Direct methods, which use global statistical models of sequence coevolution, have enabled the prediction of membrane and disordered protein structures, protein complex architectures, and the functional effects of mutations in proteins. The field of membrane protein biochemistry and structural biology has embraced these computational techniques, which provide functional and structural information in an otherwise experimentally-challenging field. Here we review recent applications of protein sequence coevolution analysis to membrane protein structure and function and highlight the promising directions and future obstacles in these fields. We provide insights and guidelines for membrane protein biochemists who wish to apply sequence coevolution analysis to a given experimental system.
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Affiliation(s)
- John M Nicoludis
- Department of Chemistry and Chemical Biology, Harvard University, Cambridge, MA 02138, United States
| | - Rachelle Gaudet
- Department of Molecular and Cellular Biology, Harvard University, Cambridge, MA, 02138, United States.
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182
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Manavalan B, Lee J. SVMQA: support-vector-machine-based protein single-model quality assessment. Bioinformatics 2018; 33:2496-2503. [PMID: 28419290 DOI: 10.1093/bioinformatics/btx222] [Citation(s) in RCA: 130] [Impact Index Per Article: 21.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2016] [Accepted: 04/12/2017] [Indexed: 01/03/2023] Open
Abstract
Motivation The accurate ranking of predicted structural models and selecting the best model from a given candidate pool remain as open problems in the field of structural bioinformatics. The quality assessment (QA) methods used to address these problems can be grouped into two categories: consensus methods and single-model methods. Consensus methods in general perform better and attain higher correlation between predicted and true quality measures. However, these methods frequently fail to generate proper quality scores for native-like structures which are distinct from the rest of the pool. Conversely, single-model methods do not suffer from this drawback and are better suited for real-life applications where many models from various sources may not be readily available. Results In this study, we developed a support-vector-machine-based single-model global quality assessment (SVMQA) method. For a given protein model, the SVMQA method predicts TM-score and GDT_TS score based on a feature vector containing statistical potential energy terms and consistency-based terms between the actual structural features (extracted from the three-dimensional coordinates) and predicted values (from primary sequence). We trained SVMQA using CASP8, CASP9 and CASP10 targets and determined the machine parameters by 10-fold cross-validation. We evaluated the performance of our SVMQA method on various benchmarking datasets. Results show that SVMQA outperformed the existing best single-model QA methods both in ranking provided protein models and in selecting the best model from the pool. According to the CASP12 assessment, SVMQA was the best method in selecting good-quality models from decoys in terms of GDTloss. Availability and implementation SVMQA method can be freely downloaded from http://lee.kias.re.kr/SVMQA/SVMQA_eval.tar.gz. Contact jlee@kias.re.kr. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Balachandran Manavalan
- Center for In Silico Protein Science and School of Computational Sciences, Korea Institute for Advanced Study, Seoul 130-722, Korea
| | - Jooyoung Lee
- Center for In Silico Protein Science and School of Computational Sciences, Korea Institute for Advanced Study, Seoul 130-722, Korea
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183
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Fanuel S, Tabesh S, Mokhtarian K, Saroddiny E, Fazlollahi MR, Pourpak Z, Falak R, Kardar GA. Construction of a recombinant B-cell epitope vaccine based on a Der p1-derived hypoallergen: a bioinformatics approach. Immunotherapy 2018; 10:537-553. [PMID: 29569512 DOI: 10.2217/imt-2017-0163] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022] Open
Abstract
AIM House dust mite (HDM) allergens are important elicitors of IgE-mediated allergies. This study was aimed at constructing and characterizing a recombinant fusion protein, DpTTDp, which was based on carrier-bound Der p 1-derived peptides for HDM allergen immunotherapy. METHODS Using the Immune Epitope Database (IEDB), we identified from Der p 1, a 34-mer hypoallergenic peptide. Two copies of the hypoallergen were then fused to a partial fragment of a tetanus toxoid molecule's N-and C terminus and expressed in Escherichia coli. After purification to homogeneity, the protein was evaluated for allergenicity and its ability to induce blocking antibodies upon immunization. RESULTS Upon immunization of mice, DpTTDp induced high levels of protective IgG-antibodies that blocked allergic patients' IgE reactivity to HDM. In addition, DpTTDp lacked relevant IgE-reactivity, induced low T-cell proliferation and IFN-γ in peripheral blood mononuclear cells of HDM-allergic patients' sera. CONCLUSION The protein represents a promising HDM-allergy immunotherapy candidate vaccine.
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Affiliation(s)
- Songwe Fanuel
- Department of Medical Biotechnology, School of Advanced Technologies in Medicine, Tehran University of Medical Sciences - International Campus (IC-TUMS) Tehran, Iran.,Immunology, Asthma & Allergy Research Institute (IAARI), Tehran University of Medical Science, Tehran, Iran
| | - Saeideh Tabesh
- Department of Immunology, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran
| | - Kobra Mokhtarian
- Medicinal Plant Research Center, Shahrekord University of Medical Sciences, Shahrekord, Iran
| | - Esmaeil Saroddiny
- Department of Medical Biotechnology, School of Advanced Technologies in Medicine, Tehran University of Medical Sciences - International Campus (IC-TUMS) Tehran, Iran
| | - Mohammad Reza Fazlollahi
- Immunology, Asthma & Allergy Research Institute (IAARI), Tehran University of Medical Science, Tehran, Iran
| | - Zahra Pourpak
- Immunology, Asthma & Allergy Research Institute (IAARI), Tehran University of Medical Science, Tehran, Iran
| | - Reza Falak
- Department of Immunology, School of Medicine, Iran University of Medical Sciences, Tehran, Iran
| | - Gholam Ali Kardar
- Department of Medical Biotechnology, School of Advanced Technologies in Medicine, Tehran University of Medical Sciences - International Campus (IC-TUMS) Tehran, Iran.,Immunology, Asthma & Allergy Research Institute (IAARI), Tehran University of Medical Science, Tehran, Iran
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184
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Kato Y, Kihara H, Fukui K, Kojima M. A ternary complex model of Sirtuin4-NAD +-Glutamate dehydrogenase. Comput Biol Chem 2018; 74:94-104. [PMID: 29571013 DOI: 10.1016/j.compbiolchem.2018.03.006] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2016] [Revised: 11/09/2017] [Accepted: 03/08/2018] [Indexed: 10/17/2022]
Abstract
Sirtuin4 (Sirt4) is one of the mammalian homologues of Silent information regulator 2 (Sir2), which promotes the longevity of yeast, C. elegans, fruit flies and mice. Sirt4 is localized in the mitochondria, where it contributes to preventing the development of cancers and ischemic heart disease through regulating energy metabolism. The ADP-ribosylation of glutamate dehydrogenase (GDH), which is catalyzed by Sirt4, downregulates the TCA cycle. However, this reaction mechanism is obscure, because the structure of Sirt4 is unknown. We here constructed structural models of Sirt4 by homology modeling and threading, and docked nicotinamide adenine dinucleotide+ (NAD+) to Sirt4. In addition, a partial GDH structure was docked to the Sirt4-NAD+ complex model. In the ternary complex model of Sirt4-NAD+-GDH, the acetylated lysine 171 of GDH is located close to NAD+. This suggests a possible mechanism underlying the ADP-ribosylation at cysteine 172, which may occur through a transient intermediate with ADP-ribosylation at the acetylated lysine 171. These results may be useful in designing drugs for the treatment of cancers and ischemic heart disease.
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Affiliation(s)
- Yusuke Kato
- School of Life Sciences, Tokyo University of Pharmacy and Life Sciences, 1432-1 Horinouchi, Hachioji 192-0392, Japan; Himeji Hinomoto College, 890 Koro, Himeji 679-2151, Japan; Institute for Enzyme Research, Tokushima University, 3-18-15 Kuramoto, Tokushima 770-8503, Japan.
| | - Hiroshi Kihara
- Himeji Hinomoto College, 890 Koro, Himeji 679-2151, Japan
| | - Kiyoshi Fukui
- Institute for Enzyme Research, Tokushima University, 3-18-15 Kuramoto, Tokushima 770-8503, Japan
| | - Masaki Kojima
- School of Life Sciences, Tokyo University of Pharmacy and Life Sciences, 1432-1 Horinouchi, Hachioji 192-0392, Japan
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185
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Correnti CE, Gewe MM, Mehlin C, Bandaranayake AD, Johnsen WA, Rupert PB, Brusniak MY, Clarke M, Burke SE, De Van Der Schueren W, Pilat K, Turnbaugh SM, May D, Watson A, Chan MK, Bahl CD, Olson JM, Strong RK. Screening, large-scale production and structure-based classification of cystine-dense peptides. Nat Struct Mol Biol 2018; 25:270-278. [PMID: 29483648 PMCID: PMC5840021 DOI: 10.1038/s41594-018-0033-9] [Citation(s) in RCA: 36] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2017] [Accepted: 01/23/2018] [Indexed: 12/04/2022]
Abstract
Peptides folded through interwoven disulfides display extreme biochemical properties and unique medicinal potential. However, their exploitation has been hampered by the limited amounts isolatable from natural sources and the expense of chemical synthesis. We developed reliable biological methods for high-throughput expression, screening and large-scale production of these peptides: 46 were successfully produced in multimilligram quantities, and >600 more were deemed expressible through stringent screening criteria. Many showed extreme resistance to temperature, proteolysis and/or reduction, and all displayed inhibitory activity against at least 1 of 20 ion channels tested, thus confirming their biological functionality. Crystal structures of 12 confirmed proper cystine topology and the utility of crystallography to study these molecules but also highlighted the need for rational classification. Previous categorization attempts have focused on limited subsets featuring distinct motifs. Here we present a global definition, classification and analysis of >700 structures of cystine-dense peptides, providing a unifying framework for these molecules.
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Affiliation(s)
- Colin E Correnti
- Clinical Research Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Mesfin M Gewe
- Clinical Research Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Christopher Mehlin
- Clinical Research Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Ashok D Bandaranayake
- Clinical Research Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - William A Johnsen
- Clinical Research Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Peter B Rupert
- Basic Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Mi-Youn Brusniak
- Clinical Research Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Midori Clarke
- Clinical Research Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Skyler E Burke
- Clinical Research Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | | | - Kristina Pilat
- Clinical Research Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Shanon M Turnbaugh
- Clinical Research Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Damon May
- Clinical Research Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
| | - Alex Watson
- Clinical Research Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Man Kid Chan
- Clinical Research Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | | | - James M Olson
- Clinical Research Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA.
| | - Roland K Strong
- Basic Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA.
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186
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Nielsen JT, Mulder FAA. POTENCI: prediction of temperature, neighbor and pH-corrected chemical shifts for intrinsically disordered proteins. JOURNAL OF BIOMOLECULAR NMR 2018; 70:141-165. [PMID: 29399725 DOI: 10.1007/s10858-018-0166-5] [Citation(s) in RCA: 94] [Impact Index Per Article: 15.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/29/2017] [Accepted: 01/25/2018] [Indexed: 05/04/2023]
Abstract
Chemical shifts contain important site-specific information on the structure and dynamics of proteins. Deviations from statistical average values, known as random coil chemical shifts (RCCSs), are extensively used to infer these relationships. Unfortunately, the use of imprecise reference RCCSs leads to biased inference and obstructs the detection of subtle structural features. Here we present a new method, POTENCI, for the prediction of RCCSs that outperforms the currently most authoritative methods. POTENCI is parametrized using a large curated database of chemical shifts for protein segments with validated disorder; It takes pH and temperature explicitly into account, and includes sequence-dependent nearest and next-nearest neighbor corrections as well as second-order corrections. RCCS predictions with POTENCI show root-mean-square values that are lower by 25-78%, with the largest improvements observed for 1Hα and 13C'. It is demonstrated how POTENCI can be applied to analyze subtle deviations from RCCSs to detect small populations of residual structure in intrinsically disorder proteins that were not discernible before. POTENCI source code is available for download, or can be deployed from the URL http://www.protein-nmr.org .
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Affiliation(s)
- Jakob Toudahl Nielsen
- Interdisciplinary Nanoscience Center (iNANO), Aarhus University, Gustav Wieds Vej 14, 8000, Aarhus C, Denmark.
- Department of Chemistry, Aarhus University, Langelandsgade 140, 8000, Aarhus C, Denmark.
| | - Frans A A Mulder
- Interdisciplinary Nanoscience Center (iNANO), Aarhus University, Gustav Wieds Vej 14, 8000, Aarhus C, Denmark.
- Department of Chemistry, Aarhus University, Langelandsgade 140, 8000, Aarhus C, Denmark.
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187
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Moult J, Fidelis K, Kryshtafovych A, Schwede T, Tramontano A. Critical assessment of methods of protein structure prediction (CASP)-Round XII. Proteins 2018; 86 Suppl 1:7-15. [PMID: 29082672 PMCID: PMC5897042 DOI: 10.1002/prot.25415] [Citation(s) in RCA: 245] [Impact Index Per Article: 40.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2017] [Revised: 10/25/2017] [Accepted: 10/27/2017] [Indexed: 12/24/2022]
Abstract
This article reports the outcome of the 12th round of Critical Assessment of Structure Prediction (CASP12), held in 2016. CASP is a community experiment to determine the state of the art in modeling protein structure from amino acid sequence. Participants are provided sequence information and in turn provide protein structure models and related information. Analysis of the submitted structures by independent assessors provides a comprehensive picture of the capabilities of current methods, and allows progress to be identified. This was again an exciting round of CASP, with significant advances in 4 areas: (i) The use of new methods for predicting three-dimensional contacts led to a two-fold improvement in contact accuracy. (ii) As a consequence, model accuracy for proteins where no template was available improved dramatically. (iii) Models based on a structural template showed overall improvement in accuracy. (iv) Methods for estimating the accuracy of a model continued to improve. CASP continued to develop new areas: (i) Assessing methods for building quaternary structure models, including an expansion of the collaboration between CASP and CAPRI. (ii) Modeling with the aid of experimental data was extended to include SAXS data, as well as again using chemical cross-linking information. (iii) A team of assessors evaluated the suitability of models for a range of applications, including mutation interpretation, analysis of ligand binding properties, and identification of interfaces. This article describes the experiment and summarizes the results. The rest of this special issue of PROTEINS contains papers describing CASP12 results and assessments in more detail.
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Affiliation(s)
- John Moult
- Institute for Bioscience and Biotechnology Research and Department of Cell Biology and Molecular Genetics, University of Maryland, 9600 Gudelsky Drive, Rockville, MD 20850, USA
| | - Krzysztof Fidelis
- Genome Center, University of California, Davis, 451 Health Sciences Drive, Davis, CA 95616, USA
| | - Andriy Kryshtafovych
- Genome Center, University of California, Davis, 451 Health Sciences Drive, Davis, CA 95616, USA
| | - Torsten Schwede
- University of Basel, Biozentrum & SIB Swiss Institute of Bioinformatics, Basel, Switzerland
| | - Anna Tramontano
- Department of Physics and Istituto Pasteur - Fondazione Cenci Bolognetti, Sapienza University of Rome, P.le Aldo Moro, 5, 00185 Rome, Italy
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188
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Álvarez Ó, Fernández-Martínez JL, Fernández-Brillet C, Cernea A, Fernández-Muñiz Z, Kloczkowski A. Principal component analysis in protein tertiary structure prediction. J Bioinform Comput Biol 2018; 16:1850005. [PMID: 29566640 DOI: 10.1142/s0219720018500051] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
We discuss applicability of principal component analysis (PCA) for protein tertiary structure prediction from amino acid sequence. The algorithm presented in this paper belongs to the category of protein refinement models and involves establishing a low-dimensional space where the sampling (and optimization) is carried out via particle swarm optimizer (PSO). The reduced space is found via PCA performed for a set of low-energy protein models previously found using different optimization techniques. A high frequency term is added into this expansion by projecting the best decoy into the PCA basis set and calculating the residual model. This term is aimed at providing high frequency details in the energy optimization. The goal of this research is to analyze how the dimensionality reduction affects the prediction capability of the PSO procedure. For that purpose, different proteins from the Critical Assessment of Techniques for Protein Structure Prediction experiments were modeled. In all the cases, both the energy of the best decoy and the distance to the native structure have decreased. Our analysis also shows how the predicted backbone structure of native conformation and of alternative low energy states varies with respect to the PCA dimensionality. Generally speaking, the reconstruction can be successfully achieved with 10 principal components and the high frequency term. We also provide a computational analysis of protein energy landscape for the inverse problem of reconstructing structure from the reduced number of principal components, showing that the dimensionality reduction alleviates the ill-posed character of this high-dimensional energy optimization problem. The procedure explained in this paper is very fast and allows testing different PCA expansions. Our results show that PSO improves the energy of the best decoy used in the PCA when the adequate number of PCA terms is considered.
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Affiliation(s)
- Óscar Álvarez
- * Group of Inverse Problems, Optimization and Machine Learning, Department of Mathematics, University of Oviedo, C. Federico García Lorca, 18, 33007 Oviedo, Spain
| | - Juan Luis Fernández-Martínez
- * Group of Inverse Problems, Optimization and Machine Learning, Department of Mathematics, University of Oviedo, C. Federico García Lorca, 18, 33007 Oviedo, Spain
| | - Celia Fernández-Brillet
- * Group of Inverse Problems, Optimization and Machine Learning, Department of Mathematics, University of Oviedo, C. Federico García Lorca, 18, 33007 Oviedo, Spain
| | - Ana Cernea
- * Group of Inverse Problems, Optimization and Machine Learning, Department of Mathematics, University of Oviedo, C. Federico García Lorca, 18, 33007 Oviedo, Spain
| | - Zulima Fernández-Muñiz
- * Group of Inverse Problems, Optimization and Machine Learning, Department of Mathematics, University of Oviedo, C. Federico García Lorca, 18, 33007 Oviedo, Spain
| | - Andrzej Kloczkowski
- † Batelle Center for Mathematical Medicine, Nationwide Children's Hospital, Columbus, OH, USA.,‡ Department of Pediatrics, The Ohio State University, Columbus, OH, USA
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189
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Wang X, Zhang D, Huang SY. New Knowledge-Based Scoring Function with Inclusion of Backbone Conformational Entropies from Protein Structures. J Chem Inf Model 2018; 58:724-732. [DOI: 10.1021/acs.jcim.7b00601] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
Affiliation(s)
- Xinxiang Wang
- School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei 430074, P. R. China
| | - Di Zhang
- School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei 430074, P. R. China
| | - Sheng-You Huang
- School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei 430074, P. R. China
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190
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dos Santos RN, Ferrari AJR, de Jesus HCR, Gozzo FC, Morcos F, Martínez L. Enhancing protein fold determination by exploring the complementary information of chemical cross-linking and coevolutionary signals. Bioinformatics 2018; 34:2201-2208. [DOI: 10.1093/bioinformatics/bty074] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2017] [Accepted: 02/10/2018] [Indexed: 11/13/2022] Open
Affiliation(s)
- Ricardo N dos Santos
- Institute of Chemistry, University of Campinas, Campinas, Brazil
- Center for Computational Engineering and Sciences, University of Campinas, Campinas, Brazil
| | | | | | - Fábio C Gozzo
- Institute of Chemistry, University of Campinas, Campinas, Brazil
| | - Faruck Morcos
- Department of Biological Sciences, University of Texas at Dallas, Richardson, USA
| | - Leandro Martínez
- Institute of Chemistry, University of Campinas, Campinas, Brazil
- Center for Computational Engineering and Sciences, University of Campinas, Campinas, Brazil
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191
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Guardiani C, Fedorenko OA, Roberts SK, Khovanov IA. On the selectivity of the NaChBac channel: an integrated computational and experimental analysis of sodium and calcium permeation. Phys Chem Chem Phys 2018; 19:29840-29854. [PMID: 29090695 DOI: 10.1039/c7cp05928k] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
Ion channel selectivity is essential for their function, yet the molecular basis of a channel's ability to select between ions is still rather controversial. In this work, using a combination of molecular dynamics simulations and electrophysiological current measurements we analyze the ability of the NaChBac channel to discriminate between calcium and sodium. Our simulations show that a single calcium ion can access the Selectivity Filter (SF) interacting so strongly with the glutamate ring so as to remain blocked inside. This is consistent with the tiny calcium currents recorded in our patch-clamp experiments. Two reasons explain this scenario. The first is the higher free energy of ion/SF binding of Ca2+ with respect to Na+. The second is the strong electrostatic repulsion exerted by the resident ion that turns back a second potentially incoming Ca2+, preventing the knock-on permeation mechanism. Finally, we analyzed the possibility of the Anomalous Mole Fraction Effect (AMFE), i.e. the ability of micromolar Ca2+ concentrations to block Na+ currents. Current measurements in Na+/Ca2+ mixed solutions excluded the AMFE, in agreement with metadynamics simulations showing the ability of a sodium ion to by-pass and partially displace the resident calcium. Our work supports a new scenario for Na+/Ca2+ selectivity in the bacterial sodium channel, challenging the traditional notion of an exclusion mechanism strictly confining Ca2+ ions outside the channel.
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Affiliation(s)
- Carlo Guardiani
- School of Engineering, University of Warwick, Coventry CV4 7AL, UK.
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192
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Schneider J, Korshunova K, Musiani F, Alfonso-Prieto M, Giorgetti A, Carloni P. Predicting ligand binding poses for low-resolution membrane protein models: Perspectives from multiscale simulations. Biochem Biophys Res Commun 2018; 498:366-374. [PMID: 29409902 DOI: 10.1016/j.bbrc.2018.01.160] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2017] [Revised: 01/24/2018] [Accepted: 01/25/2018] [Indexed: 12/21/2022]
Abstract
Membrane receptors constitute major targets for pharmaceutical intervention. Drug design efforts rely on the identification of ligand binding poses. However, the limited experimental structural information available may make this extremely challenging, especially when only low-resolution homology models are accessible. In these cases, the predictions may be improved by molecular dynamics simulation approaches. Here we review recent developments of multiscale, hybrid molecular mechanics/coarse-grained (MM/CG) methods applied to membrane proteins. In particular, we focus on our in-house MM/CG approach. It is especially tailored for G-protein coupled receptors, the largest membrane receptor family in humans. We show that our MM/CG approach is able to capture the atomistic details of the receptor/ligand binding interactions, while keeping the computational cost low by representing the protein frame and the membrane environment in a highly simplified manner. We close this review by discussing ongoing improvements and challenges of the current implementation of our MM/CG code.
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Affiliation(s)
- Jakob Schneider
- Computational Biomedicine, Institute for Advanced Simulations IAS-5 and Institute of Neuroscience and Medicine INM-9, Forschungszentrum Jülich GmbH, Jülich, Germany; Department of Physics, Rheinisch-Westfälische Technische Hochschule Aachen, Aachen, Germany; JARA Institute Molecular Neuroscience and Neuroimaging (INM-11), Forschungszentrum Jülich GmbH, Jülich, Germany
| | - Ksenia Korshunova
- Computational Biomedicine, Institute for Advanced Simulations IAS-5 and Institute of Neuroscience and Medicine INM-9, Forschungszentrum Jülich GmbH, Jülich, Germany; Department of Physics, Rheinisch-Westfälische Technische Hochschule Aachen, Aachen, Germany
| | - Francesco Musiani
- Laboratory of Bioinorganic Chemistry, Department of Pharmacy and Biotechnology, University of Bologna, Bologna, Italy
| | - Mercedes Alfonso-Prieto
- Computational Biomedicine, Institute for Advanced Simulations IAS-5 and Institute of Neuroscience and Medicine INM-9, Forschungszentrum Jülich GmbH, Jülich, Germany; Cécile and Oskar Vogt Institute for Brain Research, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany.
| | - Alejandro Giorgetti
- Computational Biomedicine, Institute for Advanced Simulations IAS-5 and Institute of Neuroscience and Medicine INM-9, Forschungszentrum Jülich GmbH, Jülich, Germany; Department of Biotechnology, University of Verona, Verona, Italy
| | - Paolo Carloni
- Computational Biomedicine, Institute for Advanced Simulations IAS-5 and Institute of Neuroscience and Medicine INM-9, Forschungszentrum Jülich GmbH, Jülich, Germany; Department of Physics, Rheinisch-Westfälische Technische Hochschule Aachen, Aachen, Germany; JARA Institute Molecular Neuroscience and Neuroimaging (INM-11), Forschungszentrum Jülich GmbH, Jülich, Germany; VNU Key Laboratory "Multiscale Simulation of Complex Systems", VNU University of Science, Vietnam National University, Hanoi, Viet Nam.
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193
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Li B, Fooksa M, Heinze S, Meiler J. Finding the needle in the haystack: towards solving the protein-folding problem computationally. Crit Rev Biochem Mol Biol 2018; 53:1-28. [PMID: 28976219 PMCID: PMC6790072 DOI: 10.1080/10409238.2017.1380596] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2017] [Revised: 08/22/2017] [Accepted: 09/13/2017] [Indexed: 12/22/2022]
Abstract
Prediction of protein tertiary structures from amino acid sequence and understanding the mechanisms of how proteins fold, collectively known as "the protein folding problem," has been a grand challenge in molecular biology for over half a century. Theories have been developed that provide us with an unprecedented understanding of protein folding mechanisms. However, computational simulation of protein folding is still difficult, and prediction of protein tertiary structure from amino acid sequence is an unsolved problem. Progress toward a satisfying solution has been slow due to challenges in sampling the vast conformational space and deriving sufficiently accurate energy functions. Nevertheless, several techniques and algorithms have been adopted to overcome these challenges, and the last two decades have seen exciting advances in enhanced sampling algorithms, computational power and tertiary structure prediction methodologies. This review aims at summarizing these computational techniques, specifically conformational sampling algorithms and energy approximations that have been frequently used to study protein-folding mechanisms or to de novo predict protein tertiary structures. We hope that this review can serve as an overview on how the protein-folding problem can be studied computationally and, in cases where experimental approaches are prohibitive, help the researcher choose the most relevant computational approach for the problem at hand. We conclude with a summary of current challenges faced and an outlook on potential future directions.
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Affiliation(s)
- Bian Li
- Department of Chemistry, Vanderbilt University, Nashville, TN, USA
- Center for Structural Biology, Vanderbilt University, Nashville, TN, USA
| | - Michaela Fooksa
- Center for Structural Biology, Vanderbilt University, Nashville, TN, USA
- Chemical and Physical Biology Graduate Program, Vanderbilt University, Nashville, TN, USA
| | - Sten Heinze
- Department of Chemistry, Vanderbilt University, Nashville, TN, USA
- Center for Structural Biology, Vanderbilt University, Nashville, TN, USA
| | - Jens Meiler
- Department of Chemistry, Vanderbilt University, Nashville, TN, USA
- Center for Structural Biology, Vanderbilt University, Nashville, TN, USA
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194
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Antunes D, Jorge NAN, Caffarena ER, Passetti F. Using RNA Sequence and Structure for the Prediction of Riboswitch Aptamer: A Comprehensive Review of Available Software and Tools. Front Genet 2018; 8:231. [PMID: 29403526 PMCID: PMC5780412 DOI: 10.3389/fgene.2017.00231] [Citation(s) in RCA: 25] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2017] [Accepted: 12/21/2017] [Indexed: 12/14/2022] Open
Abstract
RNA molecules are essential players in many fundamental biological processes. Prokaryotes and eukaryotes have distinct RNA classes with specific structural features and functional roles. Computational prediction of protein structures is a research field in which high confidence three-dimensional protein models can be proposed based on the sequence alignment between target and templates. However, to date, only a few approaches have been developed for the computational prediction of RNA structures. Similar to proteins, RNA structures may be altered due to the interaction with various ligands, including proteins, other RNAs, and metabolites. A riboswitch is a molecular mechanism, found in the three kingdoms of life, in which the RNA structure is modified by the binding of a metabolite. It can regulate multiple gene expression mechanisms, such as transcription, translation initiation, and mRNA splicing and processing. Due to their nature, these entities also act on the regulation of gene expression and detection of small metabolites and have the potential to helping in the discovery of new classes of antimicrobial agents. In this review, we describe software and web servers currently available for riboswitch aptamer identification and secondary and tertiary structure prediction, including applications.
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Affiliation(s)
- Deborah Antunes
- Scientific Computing Program (PROCC), Computational Biophysics and Molecular Modeling Group, Fundação Oswaldo Cruz, Rio de Janeiro, Brazil
| | - Natasha A N Jorge
- Laboratory of Functional Genomics and Bioinformatics, Oswaldo Cruz Institute, Fundação Oswaldo Cruz, Rio de Janeiro, Brazil.,Laboratory of Gene Expression Regulation, Carlos Chagas Institute, Fundação Oswaldo Cruz, Curitiba, Brazil
| | - Ernesto R Caffarena
- Scientific Computing Program (PROCC), Computational Biophysics and Molecular Modeling Group, Fundação Oswaldo Cruz, Rio de Janeiro, Brazil
| | - Fabio Passetti
- Laboratory of Functional Genomics and Bioinformatics, Oswaldo Cruz Institute, Fundação Oswaldo Cruz, Rio de Janeiro, Brazil.,Laboratory of Gene Expression Regulation, Carlos Chagas Institute, Fundação Oswaldo Cruz, Curitiba, Brazil
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195
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Marks C, Nowak J, Klostermann S, Georges G, Dunbar J, Shi J, Kelm S, Deane CM. Sphinx: merging knowledge-based and ab initio approaches to improve protein loop prediction. Bioinformatics 2018; 33:1346-1353. [PMID: 28453681 PMCID: PMC5408792 DOI: 10.1093/bioinformatics/btw823] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2016] [Accepted: 01/09/2017] [Indexed: 01/31/2023] Open
Abstract
Motivation Loops are often vital for protein function, however, their irregular structures make them difficult to model accurately. Current loop modelling algorithms can mostly be divided into two categories: knowledge-based, where databases of fragments are searched to find suitable conformations and ab initio, where conformations are generated computationally. Existing knowledge-based methods only use fragments that are the same length as the target, even though loops of slightly different lengths may adopt similar conformations. Here, we present a novel method, Sphinx, which combines ab initio techniques with the potential extra structural information contained within loops of a different length to improve structure prediction. Results We show that Sphinx is able to generate high-accuracy predictions and decoy sets enriched with near-native loop conformations, performing better than the ab initio algorithm on which it is based. In addition, it is able to provide predictions for every target, unlike some knowledge-based methods. Sphinx can be used successfully for the difficult problem of antibody H3 prediction, outperforming RosettaAntibody, one of the leading H3-specific ab initio methods, both in accuracy and speed. Availability and Implementation Sphinx is available at http://opig.stats.ox.ac.uk/webapps/sphinx. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Claire Marks
- Department of Statistics, University of Oxford, Oxford, UK
| | - Jaroslaw Nowak
- Department of Statistics, University of Oxford, Oxford, UK
| | | | - Guy Georges
- Pharma Research and Early Development, Large Molecule Research, Roche Innovation Center Munich, Penzberg, DE, Germany
| | - James Dunbar
- Pharma Research and Early Development, Large Molecule Research, Roche Innovation Center Munich, Penzberg, DE, Germany
| | - Jiye Shi
- Department of Informatics, UCB Pharma, Slough, UK
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196
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Assessing Exhaustiveness of Stochastic Sampling for Integrative Modeling of Macromolecular Structures. Biophys J 2018; 113:2344-2353. [PMID: 29211988 DOI: 10.1016/j.bpj.2017.10.005] [Citation(s) in RCA: 53] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2017] [Revised: 09/22/2017] [Accepted: 10/02/2017] [Indexed: 12/22/2022] Open
Abstract
Modeling of macromolecular structures involves structural sampling guided by a scoring function, resulting in an ensemble of good-scoring models. By necessity, the sampling is often stochastic, and must be exhaustive at a precision sufficient for accurate modeling and assessment of model uncertainty. Therefore, the very first step in analyzing the ensemble is an estimation of the highest precision at which the sampling is exhaustive. Here, we present an objective and automated method for this task. As a proxy for sampling exhaustiveness, we evaluate whether two independently and stochastically generated sets of models are sufficiently similar. The protocol includes testing 1) convergence of the model score, 2) whether model scores for the two samples were drawn from the same parent distribution, 3) whether each structural cluster includes models from each sample proportionally to its size, and 4) whether there is sufficient structural similarity between the two model samples in each cluster. The evaluation also provides the sampling precision, defined as the smallest clustering threshold that satisfies the third, most stringent test. We validate the protocol with the aid of enumerated good-scoring models for five illustrative cases of binary protein complexes. Passing the proposed four tests is necessary, but not sufficient for thorough sampling. The protocol is general in nature and can be applied to the stochastic sampling of any set of models, not just structural models. In addition, the tests can be used to stop stochastic sampling as soon as exhaustiveness at desired precision is reached, thereby improving sampling efficiency; they may also help in selecting a model representation that is sufficiently detailed to be informative, yet also sufficiently coarse for sampling to be exhaustive.
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197
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Slater PG, Gutierrez-Maldonado SE, Gysling K, Lagos CF. Molecular Modeling of Structures and Interaction of Human Corticotropin-Releasing Factor (CRF) Binding Protein and CRF Type-2 Receptor. Front Endocrinol (Lausanne) 2018; 9:43. [PMID: 29515519 PMCID: PMC5826306 DOI: 10.3389/fendo.2018.00043] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
The corticotropin-releasing factor (CRF) system is a key mediator of the stress response and addictive behavior. The CRF system includes four peptides: The CRF system includes four peptides: CRF, urocortins I-III, CRF binding protein (CRF-BP) that binds CRF with high affinity, and two class B G-protein coupled receptors CRF1R and CRF2R. CRF-BP is a secreted protein without significant sequence homology to CRF receptors or to any other known class of protein. Recently, it has been described a potentiation role of CRF-BP over CRF signaling through CRF2R in addictive-related neuronal plasticity and behavior. In addition, it has been described that CRF-BP is capable to physically interact specifically with the α isoform of CRF2R and acts like an escort protein increasing the amount of the receptor in the plasma membrane. At present, there are no available structures for CRF-BP or for full-length CRFR. Knowing and studying the structure of these proteins could be beneficial in order to characterize the CRF-BP/CRF2αR interaction. In this work, we report the modeling of CRF-BP and of full-length CRF2αR and CRF2βR based on the recently solved crystal structures of the transmembrane domains of the human glucagon receptor and human CRF1R, in addition with the resolved N-terminal extracellular domain of CRFRs. These models were further studied using molecular dynamics simulations and protein-protein docking. The results predicted a higher possibility of interaction of CRF-BP with CRF2αR than CRF2βR and yielded the possible residues conforming the interacting interface. Thus, the present study provides a framework for further investigation of the CRF-BP/CRF2αR interaction.
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Affiliation(s)
- Paula G. Slater
- Department of Cellular and Molecular Biology, Faculty of Biological Sciences, Pontificia Universidad Católica de Chile, Santiago, Chile
| | | | - Katia Gysling
- Department of Cellular and Molecular Biology, Faculty of Biological Sciences, Pontificia Universidad Católica de Chile, Santiago, Chile
- *Correspondence: Katia Gysling, ; Carlos F. Lagos,
| | - Carlos F. Lagos
- Department of Endocrinology, School of Medicine, Pontificia Universidad Católica de Chile, Santiago, Chile
- *Correspondence: Katia Gysling, ; Carlos F. Lagos,
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198
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Abstract
Functional genomics encompasses diverse disciplines in molecular biology and bioinformatics to comprehend the blueprint, regulation, and expression of genetic elements that define the physiology of an organism. The deluge of sequencing data in the postgenomics era has demanded the involvement of computer scientists and mathematicians to create algorithms, analytical software, and databases for the storage, curation, and analysis of biological big data. In this chapter, we discuss on the concept of functional genomics in the context of systems biology and provide examples of its application in human genetic disease studies, molecular crop improvement, and metagenomics for antibiotic discovery. An overview of transcriptomics workflow and experimental considerations is also introduced. Lastly, we present an in-house case study of transcriptomics analysis of an aromatic herbal plant to understand the effect of elicitation on the biosynthesis of volatile organic compounds.
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Affiliation(s)
- Hoe-Han Goh
- Institute of Systems Biology, Universiti Kebangsaan Malaysia (UKM), Bangi, Malaysia.
| | - Chyan Leong Ng
- Institute of Systems Biology, Universiti Kebangsaan Malaysia (UKM), Bangi, Malaysia
| | - Kok-Keong Loke
- Institute of Systems Biology, Universiti Kebangsaan Malaysia (UKM), Bangi, Malaysia
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199
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Loo JSE, Emtage AL, Ng KW, Yong ASJ, Doughty SW. Assessing GPCR homology models constructed from templates of various transmembrane sequence identities: Binding mode prediction and docking enrichment. J Mol Graph Model 2017; 80:38-47. [PMID: 29306746 DOI: 10.1016/j.jmgm.2017.12.017] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2017] [Revised: 11/27/2017] [Accepted: 12/26/2017] [Indexed: 11/15/2022]
Abstract
GPCR crystal structures have become more readily accessible in recent years. However, homology models of GPCRs continue to play an important role as many GPCR structures remain unsolved. The new crystal structures now available provide not only additional templates for homology modelling but also the opportunity to assess the performance of homology models against their respective crystal structures and gain insight into the performance of such models. In this study we have constructed homology models from templates of various transmembrane sequence identities for eight GPCR targets to better understand the relationship between transmembrane sequence identity and model quality. Model quality was assessed relative to the crystal structure in terms of structural accuracy as well as performance in two typical structure-based drug design applications: ligand binding pose prediction and docking enrichment in virtual screening. Crystal structures significantly outperformed homology models in both assessments. Accurate ligand binding pose prediction was possible but difficult to achieve using homology models, even with the use of induced fit docking. In virtual screening using homology models still conferred significant enrichment compared to random selection, with a clear benefit also observed in using models optimized through induced fit docking. Our results indicate that while homology models that are reasonably accurate structurally can be constructed, without significant refinement homology models will be outperformed by crystal structures in ligand binding pose prediction and docking enrichment regardless of the template used, primarily due to the extremely high level of structural accuracy needed for such applications.
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Affiliation(s)
- Jason S E Loo
- School of Pharmacy, Taylor's University, No.1 Jalan Taylor's, 47500 Subang Jaya, Selangor, Malaysia.
| | - Abigail L Emtage
- School of Pharmacy, The University of Nottingham Malaysia Campus, Jalan Broga, 43500 Semenyih, Selangor, Malaysia
| | - Kar Weng Ng
- School of Pharmacy, Taylor's University, No.1 Jalan Taylor's, 47500 Subang Jaya, Selangor, Malaysia
| | - Alene S J Yong
- School of Pharmacy, Taylor's University, No.1 Jalan Taylor's, 47500 Subang Jaya, Selangor, Malaysia
| | - Stephen W Doughty
- Penang Medical College, No. 4 Jalan Sepoy Lines, 10450 George Town, Penang, Malaysia
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200
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Suplatov D, Sharapova Y, Timonina D, Kopylov K, Švedas V. The visualCMAT: A web-server to select and interpret correlated mutations/co-evolving residues in protein families. J Bioinform Comput Biol 2017; 16:1840005. [PMID: 29361894 DOI: 10.1142/s021972001840005x] [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] [Indexed: 11/18/2022]
Abstract
The visualCMAT web-server was designed to assist experimental research in the fields of protein/enzyme biochemistry, protein engineering, and drug discovery by providing an intuitive and easy-to-use interface to the analysis of correlated mutations/co-evolving residues. Sequence and structural information describing homologous proteins are used to predict correlated substitutions by the Mutual information-based CMAT approach, classify them into spatially close co-evolving pairs, which either form a direct physical contact or interact with the same ligand (e.g. a substrate or a crystallographic water molecule), and long-range correlations, annotate and rank binding sites on the protein surface by the presence of statistically significant co-evolving positions. The results of the visualCMAT are organized for a convenient visual analysis and can be downloaded to a local computer as a content-rich all-in-one PyMol session file with multiple layers of annotation corresponding to bioinformatic, statistical and structural analyses of the predicted co-evolution, or further studied online using the built-in interactive analysis tools. The online interactivity is implemented in HTML5 and therefore neither plugins nor Java are required. The visualCMAT web-server is integrated with the Mustguseal web-server capable of constructing large structure-guided sequence alignments of protein families and superfamilies using all available information about their structures and sequences in public databases. The visualCMAT web-server can be used to understand the relationship between structure and function in proteins, implemented at selecting hotspots and compensatory mutations for rational design and directed evolution experiments to produce novel enzymes with improved properties, and employed at studying the mechanism of selective ligand's binding and allosteric communication between topologically independent sites in protein structures. The web-server is freely available at https://biokinet.belozersky.msu.ru/visualcmat and there are no login requirements.
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Affiliation(s)
- Dmitry Suplatov
- 1 Belozersky Institute of Physicochemical Biology, Faculty of Bioengineering and Bioinformatics, Lomonosov Moscow State University, Leninskiye Gory 1-73, Moscow 119991, Russia
| | - Yana Sharapova
- 1 Belozersky Institute of Physicochemical Biology, Faculty of Bioengineering and Bioinformatics, Lomonosov Moscow State University, Leninskiye Gory 1-73, Moscow 119991, Russia
| | - Daria Timonina
- 1 Belozersky Institute of Physicochemical Biology, Faculty of Bioengineering and Bioinformatics, Lomonosov Moscow State University, Leninskiye Gory 1-73, Moscow 119991, Russia
| | - Kirill Kopylov
- 1 Belozersky Institute of Physicochemical Biology, Faculty of Bioengineering and Bioinformatics, Lomonosov Moscow State University, Leninskiye Gory 1-73, Moscow 119991, Russia
| | - Vytas Švedas
- 1 Belozersky Institute of Physicochemical Biology, Faculty of Bioengineering and Bioinformatics, Lomonosov Moscow State University, Leninskiye Gory 1-73, Moscow 119991, Russia
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