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Almanaa TN. Design of a novel multi-epitopes vaccine against Escherichia fergusonii: a pan-proteome based in- silico approach. Front Immunol 2023; 14:1332378. [PMID: 38143752 PMCID: PMC10739491 DOI: 10.3389/fimmu.2023.1332378] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2023] [Accepted: 11/23/2023] [Indexed: 12/26/2023] Open
Abstract
Escherichia fergusonii a gram-negative rod-shaped bacterium in the Enterobacteriaceae family, infect humans, causing serious illnesses such as urinary tract infection, cystitis, biliary tract infection, pneumonia, meningitis, hemolytic uremic syndrome, and death. Initially treatable with penicillin, antibiotic misuse led to evolving resistance, including resistance to colistin, a last-resort drug. With no licensed vaccine, the study aimed to design a multi-epitope vaccine against E. fergusonii. The study started with the retrieval of the complete proteome of all known strains and proceeded to filter the surface exposed virulent proteins. Seventeen virulent proteins (4 extracellular, 4 outer membranes, 9 periplasmic) with desirable physicochemical properties were identified from the complete proteome of known strains. Further, these proteins were processed for B-cell and T-cell epitope mapping. Obtained epitopes were evaluated for antigenicity, allergenicity, solubility, MHC-binding, and toxicity and the filtered epitopes were fused by specific linkers and an adjuvant into a vaccine construct. Structure of the vaccine candidate was predicted and refined resulting in 78.1% amino acids in allowed regions and VERIFY3D score of 81%. Vaccine construct was docked with TLR-4, MHC-I, and MHC-II, showing binding energies of -1040.8 kcal/mol, -871.4 kcal/mol, and -1154.6 kcal/mol and maximum interactions. Further, molecular dynamic simulation of the docked complexes was carried out resulting in a significant stable nature of the docked complexes (high B-factor and deformability values, lower Eigen and high variance values) in terms of intermolecular binding conformation and interactions. The vaccine was also reported to stimulate a variety of immunological pathways after administration. In short, the designed vaccine revealed promising predictions about its immune protective potential against E. fergusonii infections however experimental validation is needed to validate the results.
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Affiliation(s)
- Taghreed N. Almanaa
- Department of Botany and Microbiology, College of Science, King Saud University, Riyadh, Saudi Arabia
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2
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Kunamneni A, Montera MA, Durvasula R, Alles SRA, Goyal S, Westlund KN. Rapid Generation and Molecular Docking Analysis of Single-Chain Fragment Variable (scFv) Antibody Selected by Ribosome Display Targeting Cholecystokinin B Receptor (CCK-BR) for Reduction of Chronic Neuropathic Pain. Int J Mol Sci 2023; 24:11035. [PMID: 37446213 DOI: 10.3390/ijms241311035] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2023] [Revised: 06/06/2023] [Accepted: 06/22/2023] [Indexed: 07/15/2023] Open
Abstract
A robust cell-free platform technology, ribosome display in combination with cloning, expression, and purification was utilized to develop single chain Fragment variable (scFv) antibody variants as pain therapy directed at the mouse cholecystokinin B (CCK-B) receptor. Three effective CCK-B peptide-specific scFvs were generated through ribosomal display technology. Soluble expression and ELISA analysis showed that one antibody, scFv77-2 had the highest binding and could be purified from bacterial cells in large quantities. Octet measurements further revealed that the CCK-B scFv77-2 antibody had binding kinetics of KD = 1.794 × 10-8 M. Molecular modeling and docking analyses suggested that the scFv77-2 antibody shaped a proper cavity to embed the whole CCK-B peptide molecule and that a steady-state complex was formed relying on intermolecular forces, including hydrogen bonding, electrostatic force, and hydrophobic interactions. Thus, the scFv antibody can be applied for mechanistic intermolecular interactions and functional in vivo studies of CCK-BR. The high affinity scFv77-2 antibody showed good efficacy with binding to CCK-BR tested in a chronic pain model. In vivo studies validated the efficacy of the CCK-B receptor (CCK-BR) scFv77-2 antibody as a potential therapy for chronic trigeminal nerve injury-induced pain. Mice were given a single dose of the CCK-B receptor (CCK-BR) scFv antibody 3 weeks after induction of a chronic trigeminal neuropathic pain model, during the transition from acute to chronic pain. The long-term effectiveness for the reduction of mechanical hypersensitivity was evident, persisting for months. The anxiety- and depression-related behaviors typically accompanying persisting hypersensitivity subsequently never developed in the mice given CCK-BR scFv. The effectiveness of the antibody is the basis for further development of the lead CCK-BR scFv as a promising non-opioid therapeutic for chronic pain and the long-term reduction of chronic pain- and anxiety-related behaviors.
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Affiliation(s)
- Adinarayana Kunamneni
- Department of Internal Medicine, Mayo Clinic, Jacksonville, FL 32224-1865, USA
- Department of Medicine, Loyola University Medical Center, Maywood, IL 60153-3328, USA
| | - Marena A Montera
- Department of Anesthesiology & Critical Care Medicine, University of New Mexico Health Sciences Center, Albuquerque, NM 87131-0001, USA
| | - Ravi Durvasula
- Department of Internal Medicine, Mayo Clinic, Jacksonville, FL 32224-1865, USA
- Department of Medicine, Loyola University Medical Center, Maywood, IL 60153-3328, USA
| | - Sascha R A Alles
- Department of Anesthesiology & Critical Care Medicine, University of New Mexico Health Sciences Center, Albuquerque, NM 87131-0001, USA
| | - Sachin Goyal
- Department of Anesthesiology & Critical Care Medicine, University of New Mexico Health Sciences Center, Albuquerque, NM 87131-0001, USA
| | - Karin N Westlund
- Department of Anesthesiology & Critical Care Medicine, University of New Mexico Health Sciences Center, Albuquerque, NM 87131-0001, USA
- Biomedical Laboratory Research & Development (121F), New Mexico VA Health Care System, Albuquerque, NM 87108-5153, USA
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Yu Y, Xu S, He R, Liang G. Application of Molecular Simulation Methods in Food Science: Status and Prospects. JOURNAL OF AGRICULTURAL AND FOOD CHEMISTRY 2023; 71:2684-2703. [PMID: 36719790 DOI: 10.1021/acs.jafc.2c06789] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/18/2023]
Abstract
Molecular simulation methods, such as molecular docking, molecular dynamic (MD) simulation, and quantum chemical (QC) calculation, have become popular as characterization and/or virtual screening tools because they can visually display interaction details that in vitro experiments can not capture and quickly screen bioactive compounds from large databases with millions of molecules. Currently, interdisciplinary research has expanded molecular simulation technology from computer aided drug design (CADD) to food science. More food scientists are supporting their hypotheses/results with this technology. To understand better the use of molecular simulation methods, it is necessary to systematically summarize the latest applications and usage trends of molecular simulation methods in the research field of food science. However, this type of review article is rare. To bridge this gap, we have comprehensively summarized the principle, combination usage, and application of molecular simulation methods in food science. We also analyzed the limitations and future trends and offered valuable strategies with the latest technologies to help food scientists use molecular simulation methods.
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Affiliation(s)
- Yuandong Yu
- Key Laboratory of Biorheological Science and Technology, Ministry of Education, Bioengineering College, Chongqing University, Chongqing400030, China
| | - Shiqi Xu
- Key Laboratory of Biorheological Science and Technology, Ministry of Education, Bioengineering College, Chongqing University, Chongqing400030, China
| | - Ran He
- Key Laboratory of Biorheological Science and Technology, Ministry of Education, Bioengineering College, Chongqing University, Chongqing400030, China
| | - Guizhao Liang
- Key Laboratory of Biorheological Science and Technology, Ministry of Education, Bioengineering College, Chongqing University, Chongqing400030, China
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Combination of Docking-Based and Pharmacophore-Based Virtual Screening Identifies Novel Agonists That Target the Urotensin Receptor. MOLECULES (BASEL, SWITZERLAND) 2022; 27:molecules27248692. [PMID: 36557826 PMCID: PMC9788431 DOI: 10.3390/molecules27248692] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/08/2022] [Revised: 12/01/2022] [Accepted: 12/06/2022] [Indexed: 12/13/2022]
Abstract
The urotensin receptor (UT receptor), a G-protein-coupled receptor mediating urotensin-II and urotensin-II-related peptide signaling in the urotensinergic system, has multiple pharmacological activities. However, there is no drug targeting the UT receptor currently in clinical use, and the discovery of new leads is still important. The complete crystal structure of the UT receptor has not yet been resolved and a screening strategy combining multiple methods can improve the accuracy and efficiency of drug screening. This study aimed to identify novel UT receptor agonists using a combination of docking-based, pharmacophore-based, and cell-based drug screening. First, the three-dimensional structures of the UT receptor were constructed through single-template, multi-template homologous modeling and threading strategies. After structure evaluation and ligand enrichment analysis, a model from the threading modeling was selected for docking-based virtual screening based on stepwise filtering, and 1368 positive compounds were obtained from our compound library. Second, the pharmacophore models were constructed using known ligands targeting the UT receptor for pharmacophore-based virtual screening. A model was selected after model validation, and 300 positive compounds were retrieved. Then, after intersecting the results of two different virtual screening methods with 570 compound entities from our primary screening, 14 compounds were obtained. Finally, three hits were obtained after in vitro confirmation. Furthermore, preliminary evaluation of the hits showed that they influenced glucose consumption. In summary, by integrating docking-based, pharmacophore-based, and in vitro drug screening, three new agonists targeting the UT receptor were identified which may serve as promising therapeutic agents for urotensinergic system disorders.
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5
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Overhoff B, Falls Z, Mangione W, Samudrala R. A Deep-Learning Proteomic-Scale Approach for Drug Design. Pharmaceuticals (Basel) 2021; 14:1277. [PMID: 34959678 PMCID: PMC8709297 DOI: 10.3390/ph14121277] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2021] [Revised: 11/27/2021] [Accepted: 11/29/2021] [Indexed: 12/26/2022] Open
Abstract
Computational approaches have accelerated novel therapeutic discovery in recent decades. The Computational Analysis of Novel Drug Opportunities (CANDO) platform for shotgun multitarget therapeutic discovery, repurposing, and design aims to improve their efficacy and safety by employing a holistic approach that computes interaction signatures between every drug/compound and a large library of non-redundant protein structures corresponding to the human proteome fold space. These signatures are compared and analyzed to determine if a given drug/compound is efficacious and safe for a given indication/disease. In this study, we used a deep learning-based autoencoder to first reduce the dimensionality of CANDO-computed drug-proteome interaction signatures. We then employed a reduced conditional variational autoencoder to generate novel drug-like compounds when given a target encoded "objective" signature. Using this approach, we designed compounds to recreate the interaction signatures for twenty approved and experimental drugs and showed that 16/20 designed compounds were predicted to be significantly (p-value ≤ 0.05) more behaviorally similar relative to all corresponding controls, and 20/20 were predicted to be more behaviorally similar relative to a random control. We further observed that redesigns of objectives developed via rational drug design performed significantly better than those derived from natural sources (p-value ≤ 0.05), suggesting that the model learned an abstraction of rational drug design. We also show that the designed compounds are structurally diverse and synthetically feasible when compared to their respective objective drugs despite consistently high predicted behavioral similarity. Finally, we generated new designs that enhanced thirteen drugs/compounds associated with non-small cell lung cancer and anti-aging properties using their predicted proteomic interaction signatures. his study represents a significant step forward in automating holistic therapeutic design with machine learning, enabling the rapid generation of novel, effective, and safe drug leads for any indication.
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Affiliation(s)
| | | | | | - Ram Samudrala
- Department of Biomedical Informatics, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, NY 14203, USA; (B.O.); (Z.F.); (W.M.)
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6
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Cueno ME, Imai K. Structural Insights on the SARS-CoV-2 Variants of Concern Spike Glycoprotein: A Computational Study With Possible Clinical Implications. Front Genet 2021; 12:773726. [PMID: 34745235 PMCID: PMC8568765 DOI: 10.3389/fgene.2021.773726] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2021] [Accepted: 10/07/2021] [Indexed: 12/31/2022] Open
Abstract
Coronavirus disease 2019 (COVID-19) pandemic has been attributed to SARS-CoV-2 (SARS2) and, consequently, SARS2 has evolved into multiple SARS2 variants driving subsequent waves of infections. In particular, variants of concern (VOC) were identified to have both increased transmissibility and virulence ascribable to mutational changes occurring within the spike protein resulting to modifications in the protein structural orientation which in-turn may affect viral pathogenesis. However, this was never fully elucidated. Here, we generated spike models of endemic HCoVs (HCoV 229E, HCoV OC43, HCoV NL63, HCoV HKU1, SARS CoV, MERS CoV), original SARS2, and VOC (alpha, beta, gamma, delta). Model quality check, structural superimposition, and structural comparison based on RMSD values, TM scores, and contact mapping were all performed. We found that: 1) structural comparison between the original SARS2 and VOC whole spike protein model have minor structural differences (TM > 0.98); 2) the whole VOC spike models putatively have higher structural similarity (TM > 0.70) to spike models from endemic HCoVs coming from the same phylogenetic cluster; 3) original SARS2 S1-CTD and S1-NTD models are structurally comparable to VOC S1-CTD (TM = 1.0) and S1-NTD (TM > 0.96); and 4) endemic HCoV S1-CTD and S1-NTD models are structurally comparable to VOC S1-CTD (TM > 0.70) and S1-NTD (TM > 0.70) models belonging to the same phylogenetic cluster. Overall, we propose that structural similarities (possibly ascribable to similar conformational epitopes) may help determine immune cross-reactivity, whereas, structural differences (possibly associated with varying conformational epitopes) may lead to viral infection (either reinfection or breakthrough infection).
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Affiliation(s)
- Marni E Cueno
- Department of Microbiology, Nihon University School of Dentistry, Tokyo, Japan
| | - Kenichi Imai
- Department of Microbiology, Nihon University School of Dentistry, Tokyo, Japan
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7
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Skolnick J, Gao M, Zhou H, Singh S. AlphaFold 2: Why It Works and Its Implications for Understanding the Relationships of Protein Sequence, Structure, and Function. J Chem Inf Model 2021; 61:4827-4831. [PMID: 34586808 DOI: 10.1021/acs.jcim.1c01114] [Citation(s) in RCA: 89] [Impact Index Per Article: 29.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
AlphaFold 2 (AF2) was the star of CASP14, the last biannual structure prediction experiment. Using novel deep learning, AF2 predicted the structures of many difficult protein targets at or near experimental resolution. Here, we present our perspective of why AF2 works and show that it is a very sophisticated fold recognition algorithm that exploits the completeness of the library of single domain PDB structures. It has also learned local side chain packing rearrangements that enable it to refine proteins to high resolution. The benefits and limitations of its ability to predict the structures of many more proteins at or close to atomic detail are discussed.
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Affiliation(s)
- Jeffrey Skolnick
- Center for the Study of Systems Biology, School of Biological Sciences, Georgia Institute of Technology, Atlanta, Georgia 30332, United States
| | - Mu Gao
- Center for the Study of Systems Biology, School of Biological Sciences, Georgia Institute of Technology, Atlanta, Georgia 30332, United States
| | - Hongyi Zhou
- Center for the Study of Systems Biology, School of Biological Sciences, Georgia Institute of Technology, Atlanta, Georgia 30332, United States
| | - Suresh Singh
- Twilight Design, 4 Adams Road, Kendall Park, New Jersey 08824, United States
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8
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Bender BJ, Gahbauer S, Luttens A, Lyu J, Webb CM, Stein RM, Fink EA, Balius TE, Carlsson J, Irwin JJ, Shoichet BK. A practical guide to large-scale docking. Nat Protoc 2021; 16:4799-4832. [PMID: 34561691 PMCID: PMC8522653 DOI: 10.1038/s41596-021-00597-z] [Citation(s) in RCA: 184] [Impact Index Per Article: 61.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2021] [Accepted: 06/22/2021] [Indexed: 02/08/2023]
Abstract
Structure-based docking screens of large compound libraries have become common in early drug and probe discovery. As computer efficiency has improved and compound libraries have grown, the ability to screen hundreds of millions, and even billions, of compounds has become feasible for modest-sized computer clusters. This allows the rapid and cost-effective exploration and categorization of vast chemical space into a subset enriched with potential hits for a given target. To accomplish this goal at speed, approximations are used that result in undersampling of possible configurations and inaccurate predictions of absolute binding energies. Accordingly, it is important to establish controls, as are common in other fields, to enhance the likelihood of success in spite of these challenges. Here we outline best practices and control docking calculations that help evaluate docking parameters for a given target prior to undertaking a large-scale prospective screen, with exemplification in one particular target, the melatonin receptor, where following this procedure led to direct docking hits with activities in the subnanomolar range. Additional controls are suggested to ensure specific activity for experimentally validated hit compounds. These guidelines should be useful regardless of the docking software used. Docking software described in the outlined protocol (DOCK3.7) is made freely available for academic research to explore new hits for a range of targets.
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Affiliation(s)
- Brian J Bender
- Department of Pharmaceutical Chemistry, University of California-San Francisco, San Francisco, CA, USA
| | - Stefan Gahbauer
- Department of Pharmaceutical Chemistry, University of California-San Francisco, San Francisco, CA, USA
| | - Andreas Luttens
- Science for Life Laboratory, Department of Cell and Molecular Biology, Uppsala University, Uppsala, Sweden
| | - Jiankun Lyu
- Department of Pharmaceutical Chemistry, University of California-San Francisco, San Francisco, CA, USA
| | - Chase M Webb
- Department of Pharmaceutical Chemistry, University of California-San Francisco, San Francisco, CA, USA
| | - Reed M Stein
- Department of Pharmaceutical Chemistry, University of California-San Francisco, San Francisco, CA, USA
| | - Elissa A Fink
- Department of Pharmaceutical Chemistry, University of California-San Francisco, San Francisco, CA, USA
| | - Trent E Balius
- NCI RAS Initiative, Cancer Research Technology Program, Frederick National Laboratory for Cancer Research, Leidos Biomedical Research, Inc, Frederick, MD, USA
| | - Jens Carlsson
- Science for Life Laboratory, Department of Cell and Molecular Biology, Uppsala University, Uppsala, Sweden
| | - John J Irwin
- Department of Pharmaceutical Chemistry, University of California-San Francisco, San Francisco, CA, USA
| | - Brian K Shoichet
- Department of Pharmaceutical Chemistry, University of California-San Francisco, San Francisco, CA, USA.
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9
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Possible therapeutic targets and promising drugs based on unsymmetrical hetaryl-substituted porphyrins to combat SARS-CoV-2. J Pharm Anal 2021; 11:691-698. [PMID: 34377564 PMCID: PMC8340555 DOI: 10.1016/j.jpha.2021.08.003] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2021] [Revised: 07/15/2021] [Accepted: 08/03/2021] [Indexed: 01/10/2023] Open
Abstract
Coronavirus disease 2019 is a serious disease that causes acute respiratory syndrome and negatively affects the central nervous system. SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) crosses the blood-brain barrier due to the S protein on the surface of the viral particles. Thus, it is important to develop compounds that not only have an inhibitory effect but are also capable of completely deactivating the S-protein function. This study describes the purposeful modification of porphyrins and proposes compounds, asymmetrically hetaryl-substituted porphyrins with benzothiazole, benzoxazole, and N-methylbenzimidazole residues, to deactivate the S-protein functions. Molecular docking of SARS-CoV-2 proteins with hetaryl-substituted porphyrins showed that the viral proteins S-, N-, and nsp13 exhibited the highest binding affinity. Hetaryl-substituted porphyrins form strong complexes (13-14 kcal/mol) with the receptor-binding domain of the S protein, while the distance from the porphyrins to the receptor-binding motif (RBM) does not exceed 20 Å; therefore, RBM can be oxidized by 1O2, which is generated by porphyrin. Hetaryl-substituted porphyrins interact with the nucleocapsid protein in the serine/arginine-rich region, and a number of vulnerable amino acid residues are located in the photooxidation zone. This damage complicates the packaging of viral RNA into new virions. High-energy binding of hetaryl-substituted porphyrins with the N- and C-terminal domains of nsp13 was observed. This binding blocks the action of nsp13 as an enzyme of exoribonuclease and methyltransferase, thereby preventing RNA replication and processing. A procedure for the synthesis of hetaryl-substituted porphyrins was developed, new compounds were obtained, their structures were identified, and their photocatalytic properties were studied.
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10
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Pakhrin SC, Shrestha B, Adhikari B, KC DB. Deep Learning-Based Advances in Protein Structure Prediction. Int J Mol Sci 2021; 22:5553. [PMID: 34074028 PMCID: PMC8197379 DOI: 10.3390/ijms22115553] [Citation(s) in RCA: 47] [Impact Index Per Article: 15.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2021] [Revised: 05/12/2021] [Accepted: 05/18/2021] [Indexed: 12/29/2022] Open
Abstract
Obtaining an accurate description of protein structure is a fundamental step toward understanding the underpinning of biology. Although recent advances in experimental approaches have greatly enhanced our capabilities to experimentally determine protein structures, the gap between the number of protein sequences and known protein structures is ever increasing. Computational protein structure prediction is one of the ways to fill this gap. Recently, the protein structure prediction field has witnessed a lot of advances due to Deep Learning (DL)-based approaches as evidenced by the success of AlphaFold2 in the most recent Critical Assessment of protein Structure Prediction (CASP14). In this article, we highlight important milestones and progresses in the field of protein structure prediction due to DL-based methods as observed in CASP experiments. We describe advances in various steps of protein structure prediction pipeline viz. protein contact map prediction, protein distogram prediction, protein real-valued distance prediction, and Quality Assessment/refinement. We also highlight some end-to-end DL-based approaches for protein structure prediction approaches. Additionally, as there have been some recent DL-based advances in protein structure determination using Cryo-Electron (Cryo-EM) microscopy based, we also highlight some of the important progress in the field. Finally, we provide an outlook and possible future research directions for DL-based approaches in the protein structure prediction arena.
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Affiliation(s)
- Subash C. Pakhrin
- Department of Electrical Engineering and Computer Science, Wichita State University, Wichita, KS 67260, USA;
| | - Bikash Shrestha
- Department of Computer Science, University of Missouri-St. Louis, St. Louis, MO 63121, USA;
| | - Badri Adhikari
- Department of Computer Science, University of Missouri-St. Louis, St. Louis, MO 63121, USA;
| | - Dukka B. KC
- Department of Electrical Engineering and Computer Science, Wichita State University, Wichita, KS 67260, USA;
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Prediction of LncRNA-encoded small peptides in glioma and oligomer channel functional analysis using in silico approaches. PLoS One 2021; 16:e0248634. [PMID: 33735310 PMCID: PMC7971536 DOI: 10.1371/journal.pone.0248634] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2020] [Accepted: 03/03/2021] [Indexed: 12/13/2022] Open
Abstract
Glioma is a lethal malignant brain cancer, and many reports have shown that abnormalities in the behavior of water and ion channels play an important role in regulating tumor proliferation, migration, apoptosis, and differentiation. Recently, new studies have suggested that some long noncoding RNAs containing small open reading frames can encode small peptides and form oligomers for water or ion regulation. However, because the peptides are difficult to identify, their functional mechanisms are far from being clearly understood. In this study, we used bioinformatics methods to identify and evaluate lncRNAs, which may encode small transmembrane peptides in gliomas. Combining ab initio homology modeling, molecular dynamics simulations, and free energy calculations, we constructed a predictive model and predicted the oligomer channel activity of peptides by identifying the lncRNA ORFs. We found that one key hub lncRNA, namely, DLEU1, which contains two smORFs (ORF1 and ORF8), encodes small peptides that form pentameric channels. The mechanics of water and ion (Na+ and Cl-) transport through this pentameric channel were simulated. The potential mean force of the H2O molecules along the two ORF-encoded peptide channels indicated that the energy barrier was different between ORF1 and ORF8. The ORF1-encoded peptide pentamer acted as a self-assembled water channel but not as an ion channel, and the ORF8 permeated neither ions nor water. This work provides new methods and theoretical support for further elucidation of the function of lncRNA-encoded small peptides and their role in cancer. Additionally, this study provides a theoretical basis for drug development.
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12
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Cueno ME, Imai K. Structural Comparison of the SARS CoV 2 Spike Protein Relative to Other Human-Infecting Coronaviruses. Front Med (Lausanne) 2021; 7:594439. [PMID: 33585502 PMCID: PMC7874069 DOI: 10.3389/fmed.2020.594439] [Citation(s) in RCA: 31] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2020] [Accepted: 12/14/2020] [Indexed: 12/19/2022] Open
Abstract
Coronaviruses (CoV) are enveloped positive-stranded RNA viruses and, historically, there are seven known human-infecting CoVs with varying degrees of virulence. CoV attachment to the host is the first step of viral pathogenesis and mainly relies on the spike glycoprotein located on the viral surface. Among the human-infecting CoVs, only the infection of SARS CoV 2 (SARS2) among humans resulted to a pandemic which would suggest that the protein structural conformation of SARS2 spike protein is distinct as compared to other human-infecting CoVs. Surprisingly, the possible differences and similarities in the protein structural conformation between the various human-infecting CoV spike proteins have not been fully elucidated. In this study, we utilized a computational approach to generate models and analyze the seven human-infecting CoV spike proteins, namely: HCoV 229E, HCoV OC43, HCoV NL63, HCoV HKU1, SARS CoV, MERS CoV, and SARS2. Model quality assessment of all CoV models generated, structural superimposition of the whole protein model and selected S1 domains (S1-CTD and S1-NTD), and structural comparison based on RMSD values, Tm scores, and contact mapping were all performed. We found that the structural orientation of S1-CTD is a potential structural feature associated to both the CoV phylogenetic cluster and lineage. Moreover, we observed that spike models in the same phylogenetic cluster or lineage could potentially have similar protein structure. Additionally, we established that there are potentially three distinct S1-CTD orientation (Pattern I, Pattern II, Pattern III) among the human-infecting CoVs. Furthermore, we postulate that human-infecting CoVs in the same phylogenetic cluster may have similar S1-CTD and S1-NTD structural orientation. Taken together, we propose that the SARS2 spike S1-CTD follows a Pattern III orientation which has a higher degree of similarity with SARS1 and some degree of similarity with both OC43 and HKU1 which coincidentally are in the same phylogenetic cluster and lineage, whereas, the SARS2 spike S1-NTD has some degree of similarity among human-infecting CoVs that are either in the same phylogenetic cluster or lineage.
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Affiliation(s)
- Marni E Cueno
- Department of Microbiology, Nihon University School of Dentistry, Tokyo, Japan
| | - Kenichi Imai
- Department of Microbiology, Nihon University School of Dentistry, Tokyo, Japan
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13
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Bender BJ, Marlow B, Meiler J. Improving homology modeling from low-sequence identity templates in Rosetta: A case study in GPCRs. PLoS Comput Biol 2020; 16:e1007597. [PMID: 33112852 PMCID: PMC7652349 DOI: 10.1371/journal.pcbi.1007597] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2019] [Revised: 11/09/2020] [Accepted: 08/11/2020] [Indexed: 01/16/2023] Open
Abstract
As sequencing methodologies continue to advance, the availability of protein sequences far outpaces the ability of structure determination. Homology modeling is used to bridge this gap but relies on high-identity templates for accurate model building. G-protein coupled receptors (GPCRs) represent a significant target class for pharmaceutical therapies in which homology modeling could fill the knowledge gap for structure-based drug design. To date, only about 17% of druggable GPCRs have had their structures characterized at atomic resolution. However, modeling of the remaining 83% is hindered by the low sequence identity between receptors. Here we test key inputs in the model building process using GPCRs as a focus to improve the pipeline in two critical ways: Firstly, we use a blended sequence- and structure-based alignment that accounts for structure conservation in loop regions. Secondly, by merging multiple template structures into one comparative model, the best possible template for every region of a target can be used expanding the conformational space sampled in a meaningful way. This optimization allows for accurate modeling of receptors using templates as low as 20% sequence identity, which accounts for nearly the entire druggable space of GPCRs. A model database of all non-odorant GPCRs is made available at www.rosettagpcr.org. Additionally, all protocols are made available with insights into modifications that may improve accuracy at new targets.
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Affiliation(s)
- Brian Joseph Bender
- Department of Pharmacology, Department of Chemistry, and Center for Structural Biology, Vanderbilt University, Nashville, Tennessee, United States of America
| | - Brennica Marlow
- Department of Pharmacology, Department of Chemistry, and Center for Structural Biology, Vanderbilt University, Nashville, Tennessee, United States of America
| | - Jens Meiler
- Department of Pharmacology, Department of Chemistry, and Center for Structural Biology, Vanderbilt University, Nashville, Tennessee, United States of America
- Institute for Drug Discovery, Leipzig University Medical School, Leipzig, SAC, Germany
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14
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Structure-based screening for discovery of sweet compounds. Food Chem 2020; 315:126286. [DOI: 10.1016/j.foodchem.2020.126286] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2019] [Revised: 01/10/2020] [Accepted: 01/21/2020] [Indexed: 02/06/2023]
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15
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Bendahou MA, Arrouchi H, Lakhlili W, Allam L, Aanniz T, Cherradi N, Ibrahimi A, Boutarbouch M. Computational Analysis of IDH1, IDH2, and TP53 Mutations in Low-Grade Gliomas Including Oligodendrogliomas and Astrocytomas. Cancer Inform 2020; 19:1176935120915839. [PMID: 32313423 PMCID: PMC7160765 DOI: 10.1177/1176935120915839] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2019] [Accepted: 03/09/2020] [Indexed: 12/18/2022] Open
Abstract
Introduction: The emergence of new omics approaches, such as genomic algorithms to identify
tumor mutations and molecular modeling tools to predict the
three-dimensional structure of proteins, has facilitated the understanding
of the dynamic mechanisms involved in the pathogenesis of low-grade gliomas
including oligodendrogliomas and astrocytomas. Methods: In this study, we targeted known mutations involved in low-grade gliomas,
starting with the sequencing of genomic regions encompassing exon 4 of
isocitrate dehydrogenase 1 (IDH1) and isocitrate
dehydrogenase 2 (IDH2) and the four exons (5-6 and 7-8) of
TP53 from 32 samples, followed by computational
analysis to study the impact of these mutations on the structure and
function of 3 proteins IDH1, IDH2, and
p53. Results: We obtain a mutation that has an effect on the catalytic site of the protein
IDH1 as R132H and on the catalytic site of the protein
IDH2 as R172M. Other mutations at p53
have been identified as K305N, which is a pathogenic mutation; R175 H, which
is a benign mutation; and R158G, which disrupts the structural conformation
of the tumor suppressor protein. Conclusion: In low-grade gliomas, mutations in IDH1, IDH2, and
TP53 may be the key to tumor progression because they
have an effect on the function of the protein such as mutations R132H in
IDH1 and R172M in IDH2, which change
the function of the enzyme alpha-ketoglutarate, or R158G in
TP53, which affects the structure of the generated
protein, thus their importance in understanding gliomagenesis and for more
accurate diagnosis complementary to the anatomical pathology tests.
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Affiliation(s)
- Mohammed Amine Bendahou
- Medical Biotechnology Laboratory (MedBiotech), BioInova Research Center, Medical and Pharmacy School, Mohammed V University Rabat, Morocco
| | - Housna Arrouchi
- Medical Biotechnology Laboratory (MedBiotech), BioInova Research Center, Medical and Pharmacy School, Mohammed V University Rabat, Morocco
| | - Wiame Lakhlili
- Medical Biotechnology Laboratory (MedBiotech), BioInova Research Center, Medical and Pharmacy School, Mohammed V University Rabat, Morocco
| | - Loubna Allam
- Medical Biotechnology Laboratory (MedBiotech), BioInova Research Center, Medical and Pharmacy School, Mohammed V University Rabat, Morocco
| | - Tarik Aanniz
- Medical Biotechnology Laboratory (MedBiotech), BioInova Research Center, Medical and Pharmacy School, Mohammed V University Rabat, Morocco
| | - Nadia Cherradi
- Department of Pathological Anatomy, Hospital of Specialties, CHU Ibn Sina, Rabat, Medical and Pharmacy School, Mohammed V University Rabat, Morocco
| | - Azeddine Ibrahimi
- Medical Biotechnology Laboratory (MedBiotech), BioInova Research Center, Medical and Pharmacy School, Mohammed V University Rabat, Morocco
| | - Mahjouba Boutarbouch
- Department of Neurosurgery, Hospital of Specialties, CHU Ibn Sina, Rabat, Medical and Pharmacy School, Mohammed V University Rabat, Morocco
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16
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Googins MR, Woghiren-Afegbua AO, Calderon M, St Croix CM, Kiselyov KI, VanDemark AP. Structural and functional divergence of GDAP1 from the glutathione S-transferase superfamily. FASEB J 2020; 34:7192-7207. [PMID: 32274853 DOI: 10.1096/fj.202000110r] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2020] [Revised: 03/12/2020] [Accepted: 03/24/2020] [Indexed: 11/11/2022]
Abstract
Mutations in ganglioside-induced differentiation-associated protein 1 (GDAP1) alter mitochondrial morphology and result in several subtypes of the inherited peripheral neuropathy Charcot-Marie-Tooth disease; however, the mechanism by which GDAP1 functions has remained elusive. GDAP1 contains primary sequence homology to the GST superfamily; however, the question of whether GDAP1 is an active GST has not been clearly resolved. Here, we present biochemical evidence, suggesting that GDAP1 has lost the ability to bind glutathione without a loss of substrate binding activity. We have revealed that the α-loop, located within the H-site motif is the primary determinant for substrate binding. Using structural data of GDAP1, we have found that critical residues and configurations in the G-site which canonically interact with glutathione are altered in GDAP1, rendering it incapable of binding glutathione. Last, we have found that the overexpression of GDAP1 in HeLa cells results in a mitochondrial phenotype which is distinct from oxidative stress-induced mitochondrial fragmentation. This phenotype is dependent on the presence of the transmembrane domain, as well as a unique hydrophobic domain that is not found in canonical GSTs. Together, we data point toward a non-enzymatic role for GDAP1, such as a sensor or receptor.
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Affiliation(s)
- Matthew R Googins
- Department of Biological Sciences, University of Pittsburgh, Pittsburgh, PA, USA
| | | | - Michael Calderon
- Center for Biologic Imaging, University of Pittsburgh, Pittsburgh, PA, USA
| | | | - Kirill I Kiselyov
- Department of Biological Sciences, University of Pittsburgh, Pittsburgh, PA, USA
| | - Andrew P VanDemark
- Department of Biological Sciences, University of Pittsburgh, Pittsburgh, PA, USA
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17
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Wu H, Huang H, Lu W, Fu Q, Ding Y, Qiu J, Li H. Ranking near-native candidate protein structures via random forest classification. BMC Bioinformatics 2019; 20:683. [PMID: 31874596 PMCID: PMC6929337 DOI: 10.1186/s12859-019-3257-8] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/12/2023] Open
Abstract
Background In ab initio protein-structure predictions, a large set of structural decoys are often generated, with the requirement to select best five or three candidates from the decoys. The clustered central structures with the most number of neighbors are frequently regarded as the near-native protein structures with the lowest free energy; however, limitations in clustering methods and three-dimensional structural-distance assessments make identifying exact order of the best five or three near-native candidate structures difficult. Results To address this issue, we propose a method that re-ranks the candidate structures via random forest classification using intra- and inter-cluster features from the results of the clustering. Comparative analysis indicated that our method was better able to identify the order of the candidate structures as comparing with current methods SPICKR, Calibur, and Durandal. The results confirmed that the identification of the first model were closer to the native structure in 12 of 43 cases versus four for SPICKER, and the same as the native structure in up to 27 of 43 cases versus 14 for Calibur and up to eight of 43 cases versus two for Durandal. Conclusions In this study, we presented an improved method based on random forest classification to transform the problem of re-ranking the candidate structures by an binary classification. Our results indicate that this method is a powerful method for the problem and the effect of this method is better than other methods.
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Affiliation(s)
- Hongjie Wu
- School of Electronic and Information Engineering, Suzhou University of Science and Technology, Suzhou, 215009, China
| | - Hongmei Huang
- School of Electronic and Information Engineering, Suzhou University of Science and Technology, Suzhou, 215009, China
| | - Weizhong Lu
- School of Electronic and Information Engineering, Suzhou University of Science and Technology, Suzhou, 215009, China.
| | - Qiming Fu
- School of Electronic and Information Engineering, Suzhou University of Science and Technology, Suzhou, 215009, China.,Jiangsu Province Key Laboratory of Intelligent Building Energy Efficiency, Suzhou University of Science and Technology, Suzhou, 215009, China
| | - Yijie Ding
- School of Electronic and Information Engineering, Suzhou University of Science and Technology, Suzhou, 215009, China
| | - Jing Qiu
- School of Electronic and Information Engineering, Suzhou University of Science and Technology, Suzhou, 215009, China
| | - Haiou Li
- School of Electronic and Information Engineering, Suzhou University of Science and Technology, Suzhou, 215009, China
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18
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Felix CR, Siedler BS, Barbosa LN, Timm GR, McFadden J, McBride AJA. An overview of human leptospirosis vaccine design and future perspectives. Expert Opin Drug Discov 2019; 15:179-188. [PMID: 31777290 DOI: 10.1080/17460441.2020.1694508] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
Introduction: It's been 20 years since the first report of a recombinant vaccine that protected against leptospirosis. Since then, numerous recombinant vaccines have been evaluated; however, no recombinant vaccine candidate has advanced to clinical trials. With the ever-increasing burden of leptospirosis, there is an urgent need for a universal vaccine against leptospirosis.Areas covered: This review covers the most promising vaccine candidates that induced significant, reproducible, protection and how advances in the field of bioinformatics has led to the discovery of hundreds of novel protein targets. The authors also discuss the most recent findings regarding the innate immune response and host-pathogen interactions and their impact on the discovery of novel vaccine candidates. In addition, the authors have identified what they believe are the most challenging problems for the discovery and development of a universal vaccine and their potential solutions.Expert opinion: A universal vaccine for leptospirosis will likely only be achieved using a recombinant vaccine as the bacterins are of limited use due to the lack of a cross-protective immune response. Although there are hundreds of novel targets, due to the lack of immune correlates and the need for more research into the basic microbiology of Leptospira spp., a universal vaccine is 10-15 years away.
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Affiliation(s)
- Carolina R Felix
- Biotechnology Department, Centre for Technological Development, Federal University of Pelotas, Pelotas, Brazil
| | - Bianca S Siedler
- Biotechnology Department, Centre for Technological Development, Federal University of Pelotas, Pelotas, Brazil.,School of Biosciences and Medicine, Faculty of Health & Medical Sciences, University of Surrey, Guildford, UK
| | - Liana N Barbosa
- Biotechnology Department, Centre for Technological Development, Federal University of Pelotas, Pelotas, Brazil
| | - Gabriana R Timm
- Biotechnology Department, Centre for Technological Development, Federal University of Pelotas, Pelotas, Brazil
| | - Johnjoe McFadden
- School of Biosciences and Medicine, Faculty of Health & Medical Sciences, University of Surrey, Guildford, UK
| | - Alan J A McBride
- Biotechnology Department, Centre for Technological Development, Federal University of Pelotas, Pelotas, Brazil
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19
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Kumawat M, Singh PK, Rananaware SR, Ahlawat S. Comparative evaluation of structure and characteristic of peptidyl-prolyl cis-trans isomerase proteins and their function in Salmonella Typhimurium stress responses and virulence. Folia Microbiol (Praha) 2019; 65:161-171. [PMID: 31111418 DOI: 10.1007/s12223-019-00717-z] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2018] [Accepted: 04/30/2019] [Indexed: 01/19/2023]
Abstract
Peptidyl-prolyl cis-trans isomerases (PPIase) exhibit chaperone activity and assist in protein folding by increasing the rate of cis-trans transition on proline-peptide bonds. The current study aimed to identify and characterize three genes, ppiA, ppiB, and ppiC, which encode proteins of the PPIase family in the bacterium Salmonella enterica serovar Typhimurium. Salmonella Typhimurium is a facultative intracellular zoonotic pathogen that causes food- and water-borne gastroenteritis in humans (leading to bacteremia in immune-compromised subjects). Recombinant clones for the three genes were constructed and sequenced and the sequences submitted to NCBI GenBank. Three-dimensional structures for the corresponding proteins were predicted by comparative modeling. A maximum-likelihood phylogenetic gene tree constructed for the three genes showed a low evolutionary mean diversity, indicating strong evolutionary conservation. Further, single-gene deletion mutant strains, generated for the respective genes, were observed to be more susceptible to the stationary phase of growth and heat stress conditions and showed reduced survival within macrophage cells line. The present study thus indicates that ppiA, ppiB, and ppiC genes are conserved among Salmonella genome, are critical for the growth of Salmonella Typhimurium in the examined stress conditions, and may play a role in its responses and virulence.
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Affiliation(s)
- Manoj Kumawat
- Department of Biological Sciences, Indian Institute of Science Education and Research, Bhopal, 462066, India. .,Department of Biochemistry & Biochemical Engineering, Sam Higginbottom University of Agriculture, Technology and Sciences, Allahabad, 211007, India.
| | - Piyush Kumar Singh
- Division of Veterinary Biotechnology, Indian Veterinary Research Institute, Bareilly, 243122, India
| | | | - Sushma Ahlawat
- Department of Biochemistry & Biochemical Engineering, Sam Higginbottom University of Agriculture, Technology and Sciences, Allahabad, 211007, India.
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20
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Nash A, Rhodes J. Simulations of CYP51A from Aspergillus fumigatus in a model bilayer provide insights into triazole drug resistance. Med Mycol 2019; 56:361-373. [PMID: 28992260 PMCID: PMC5895076 DOI: 10.1093/mmy/myx056] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2016] [Accepted: 07/14/2017] [Indexed: 11/26/2022] Open
Abstract
Azole antifungal drugs target CYP51A in Aspergillus fumigatus by binding with the active site of the protein, blocking ergosterol biosynthesis. Resistance to azole antifungal drugs is now common, with a leucine to histidine amino acid substitution at position 98 the most frequent, predominantly conferring resistance to itraconazole, although cross-resistance has been reported in conjunction with other mutations. In this study, we create a homology model of CYP51A using a recently published crystal structure of the paralog protein CYP51B. The derived structures, wild type, and L98H mutant are positioned within a lipid membrane bilayer and subjected to molecular dynamics simulations in order improve the accuracy of both models. The structural analysis from our simulations suggests a decrease in active site surface from the formation of hydrogen bonds between the histidine substitution and neighboring polar side chains, potentially preventing the binding of azole drugs. This study yields a biologically relevant structure and set of dynamics of the A. fumigatus Lanosterol 14 alpha-demethylase enzyme and provides further insight into azole antifungal drug resistance.
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Affiliation(s)
- Anthony Nash
- Department of Chemistry, University College London, London, United Kingdom
| | - Johanna Rhodes
- Department of Infectious Disease Epidemiology, Imperial College London, London, United Kingdom
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21
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Téletchéa S, Santuz H, Léonard S, Etchebest C. Repository of Enriched Structures of Proteins Involved in the Red Blood Cell Environment (RESPIRE). PLoS One 2019; 14:e0211043. [PMID: 30794542 PMCID: PMC6386447 DOI: 10.1371/journal.pone.0211043] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2018] [Accepted: 01/07/2019] [Indexed: 12/25/2022] Open
Abstract
The Red Blood Cell (RBC) is a metabolically-driven cell vital for processes such a gas transport and homeostasis. RBC possesses at its surface exposing antigens proteins that are critical in blood transfusion. Due to their importance, numerous studies address the cell function as a whole but more and more details of RBC structure and protein content are now studied using massive state-of-the art characterisation techniques. Yet, the resulting information is frequently scattered in many scientific articles, in many databases and specialized web servers. To provide a more compendious view of erythrocytes and of their protein content, we developed a dedicated database called RESPIRE that aims at gathering a comprehensive and coherent ensemble of information and data about proteins in RBC. This cell-driven database lists proteins found in erythrocytes. For a given protein entry, initial data are processed from external portals and enriched by using state-of-the-art bioinformatics methods. As structural information is extremely useful to understand protein function and predict the impact of mutations, a strong effort has been put on the prediction of protein structures with a special treatment for membrane proteins. Browsing the database is available through text search for reference gene names or protein identifiers, through pre-defined queries or via hyperlinks. The RESPIRE database provides valuable information and unique annotations that should be useful to a wide audience of biologists, clinicians and structural biologists. Database URL:http://www.dsimb.inserm.fr/respire
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Affiliation(s)
- S. Téletchéa
- Institut National de la Transfusion Sanguine, Paris, France
- Inserm, UMR_S 1134, Paris, France
- Université Paris Diderot, Sorbonne Paris Cité, Paris, France
- Laboratory of Excellence GR-Ex., Paris, France
- UFIP, University of Nantes, CNRS UMR 6286, Nantes, France
| | - H. Santuz
- Institut National de la Transfusion Sanguine, Paris, France
- Inserm, UMR_S 1134, Paris, France
- Université Paris Diderot, Sorbonne Paris Cité, Paris, France
- Laboratory of Excellence GR-Ex., Paris, France
| | - S. Léonard
- Institut National de la Transfusion Sanguine, Paris, France
- Inserm, UMR_S 1134, Paris, France
- Université Paris Diderot, Sorbonne Paris Cité, Paris, France
- Laboratory of Excellence GR-Ex., Paris, France
| | - C. Etchebest
- Institut National de la Transfusion Sanguine, Paris, France
- Inserm, UMR_S 1134, Paris, France
- Université Paris Diderot, Sorbonne Paris Cité, Paris, France
- Laboratory of Excellence GR-Ex., Paris, France
- * E-mail:
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22
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In silico prediction of prolactin molecules as a tool for equine genomics reproduction. Mol Divers 2019; 23:1019-1028. [PMID: 30740642 DOI: 10.1007/s11030-018-09914-3] [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: 11/06/2018] [Accepted: 12/31/2018] [Indexed: 10/27/2022]
Abstract
The prolactin hormone is involved in several biological functions, although its main role resides on reproduction. As it interferes on fertility changes, studies focused on human health have established a linkage of this hormone to fertility losses. Regarding animal research, there is still a lack of information about the structure of prolactin. In case of horse breeding, prolactin has a particular influence; once there is an individualization of these animals and equines are known for presenting several reproductive disorders. As there is no molecular structure available for the prolactin hormone and receptor, we performed several bioinformatics analyses through prediction and refinement softwares, as well as manual modifications. Aiming to elucidate the first computational structure of both molecules and analyse structural and functional aspects related to these proteins, here we provide the first known equine model for prolactin and prolactin receptor, which obtained high global quality scores in diverse software's for quality assessment. QMEAN overall score obtained for ePrl was (- 4.09) and QMEANbrane for ePrlr was (- 8.45), which proves the structures' reliability. This study will implement another tool in equine genomics in order to give light to interactions of these molecules, structural and functional alterations and therefore help diagnosing fertility problems, contributing in the selection of a high genetic herd.
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23
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Ciemny MP, Badaczewska-Dawid AE, Pikuzinska M, Kolinski A, Kmiecik S. Modeling of Disordered Protein Structures Using Monte Carlo Simulations and Knowledge-Based Statistical Force Fields. Int J Mol Sci 2019; 20:E606. [PMID: 30708941 PMCID: PMC6386871 DOI: 10.3390/ijms20030606] [Citation(s) in RCA: 33] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2018] [Revised: 01/23/2019] [Accepted: 01/29/2019] [Indexed: 12/20/2022] Open
Abstract
The description of protein disordered states is important for understanding protein folding mechanisms and their functions. In this short review, we briefly describe a simulation approach to modeling protein interactions, which involve disordered peptide partners or intrinsically disordered protein regions, and unfolded states of globular proteins. It is based on the CABS coarse-grained protein model that uses a Monte Carlo (MC) sampling scheme and a knowledge-based statistical force field. We review several case studies showing that description of protein disordered states resulting from CABS simulations is consistent with experimental data. The case studies comprise investigations of protein⁻peptide binding and protein folding processes. The CABS model has been recently made available as the simulation engine of multiscale modeling tools enabling studies of protein⁻peptide docking and protein flexibility. Those tools offer customization of the modeling process, driving the conformational search using distance restraints, reconstruction of selected models to all-atom resolution, and simulation of large protein systems in a reasonable computational time. Therefore, CABS can be combined in integrative modeling pipelines incorporating experimental data and other modeling tools of various resolution.
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Affiliation(s)
- Maciej Pawel Ciemny
- Faculty of Chemistry, Biological and Chemical Research Center, University of Warsaw, Pasteura 1, 02-093 Warsaw, Poland.
- Faculty of Physics, University of Warsaw, Pasteura 5, 02-093 Warsaw, Poland.
| | | | - Monika Pikuzinska
- Faculty of Chemistry, Biological and Chemical Research Center, University of Warsaw, Pasteura 1, 02-093 Warsaw, Poland.
| | - Andrzej Kolinski
- Faculty of Chemistry, Biological and Chemical Research Center, University of Warsaw, Pasteura 1, 02-093 Warsaw, Poland.
| | - Sebastian Kmiecik
- Faculty of Chemistry, Biological and Chemical Research Center, University of Warsaw, Pasteura 1, 02-093 Warsaw, Poland.
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24
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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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25
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Experimental accuracy in protein structure refinement via molecular dynamics simulations. Proc Natl Acad Sci U S A 2018; 115:13276-13281. [PMID: 30530696 DOI: 10.1073/pnas.1811364115] [Citation(s) in RCA: 52] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022] Open
Abstract
Refinement is the last step in protein structure prediction pipelines to convert approximate homology models to experimental accuracy. Protocols based on molecular dynamics (MD) simulations have shown promise, but current methods are limited to moderate levels of consistent refinement. To explore the energy landscape between homology models and native structures and analyze the challenges of MD-based refinement, eight test cases were studied via extensive simulations followed by Markov state modeling. In all cases, native states were found very close to the experimental structures and at the lowest free energies, but refinement was hindered by a rough energy landscape. Transitions from the homology model to the native states require the crossing of significant kinetic barriers on at least microsecond time scales. A significant energetic driving force toward the native state was lacking until its immediate vicinity, and there was significant sampling of off-pathway states competing for productive refinement. The role of recent force field improvements is discussed and transition paths are analyzed in detail to inform which key transitions have to be overcome to achieve successful refinement.
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26
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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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27
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Heo L, Feig M. PREFMD: a web server for protein structure refinement via molecular dynamics simulations. Bioinformatics 2018; 34:1063-1065. [PMID: 29126101 PMCID: PMC5860225 DOI: 10.1093/bioinformatics/btx726] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2017] [Revised: 10/04/2017] [Accepted: 11/07/2017] [Indexed: 11/13/2022] Open
Abstract
Summary Refinement of protein structure models is a long-standing problem in structural bioinformatics. Molecular dynamics-based methods have emerged as an avenue to achieve consistent refinement. The PREFMD web server implements an optimized protocol based on the method successfully tested in CASP11. Validation with recent CASP refinement targets shows consistent and more significant improvement in global structure accuracy over other state-of-the-art servers. Availability and implementation PREFMD is freely available as a web server at http://feiglab.org/prefmd. Scripts for running PREFMD as a stand-alone package are available at https://github.com/feiglab/prefmd.git. Contact feig@msu.edu. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Lim Heo
- Biochemistry and Molecular Biology, Michigan State University, East Lansing, MI, USA
| | - Michael Feig
- Biochemistry and Molecular Biology, Michigan State University, East Lansing, MI, USA
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Frigori RB. Be positive: optimizing pramlintide from microcanonical analysis of amylin isoforms. Phys Chem Chem Phys 2018; 19:25617-25633. [PMID: 28905065 DOI: 10.1039/c7cp04074a] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
Amylin, or human islet amyloid polypeptide (hIAPP), is a 37-residue hormone synergistic to insulin and co-secreted with it by β-cells in the pancreas. The deposition of its cytotoxic amyloid fibrils is strongly related to the progression of Type II diabetes (T2D) and islet graft failures. Notably, isoforms from some mammalian species, such as rats (rIAPP) and porcine (pIAPP), present a few key mutations preventing aggregation. This has lead to biotechnological development of drugs for adjunct therapies of T2D, such as pramlintide, a variant of hIAPP inspired by rIAPP whose proline substitutions have β-strand fibril-breaking properties. Ideally, such a drug should be formulated with insulin and co-administered, but this has been prevented by a poor solubility profile at the appropriate pH. Hopefully, this could be improved with appropriate point mutations, increasing the molecular net charge. Despite experimental progress, preliminary screening during rational drug design can greatly benefit from thermodynamic insight derived from molecular simulations. So we introduce microcanonical thermostatistics analysis of multicanonical (MUCA) simulations of wild-type amylin isoforms as a systematic assessment of protein thermostability. As a consequence of this comprehensive investigation, the most suitable single-point mutations able to optimize pramlintide are located among the wild-type amylin isoforms. In particular, we find that aggregation inhibition and increased solubility are inherited by pramlintide through further S20R substitution typical of pIAPP. Thus, we provide a consistent thermostatistical methodology to aid the design of improved adjunct therapies for T2D according to current clinical knowledge.
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Affiliation(s)
- Rafael B Frigori
- Universidade Tecnológica Federal do Paraná, Rua Cristo Rei 19, 85902-490, Toledo, PR, Brazil.
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29
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Sharma S, Hooda V. Identification of coding sequence and its use for functional and structural characterization of catalase from Phyllanthus emblica. Bioinformation 2018; 14:8-14. [PMID: 29497254 PMCID: PMC5818641 DOI: 10.6026/97320630014008] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2017] [Revised: 12/31/2017] [Accepted: 01/16/2018] [Indexed: 11/23/2022] Open
Abstract
Catalase is an essential antioxidant enzyme that is well characterized from microbial and animal sources. The structure of plant catalase is unknown. Therefore, it is of interest to understand the functional and structural characteristics of catalase from an Indian gooseberry, Phyllanthus emblica (or Emblica officinalis). Hence, catalase from P. emblica was cloned in pUC18 plasmid, sequenced and submitted to GenBank with the accession numbers "MF979112" and "ATO98311.1". InterProScan showed that the coding sequence is monofunctional and haem-dependent catalase-like superfamily. Multiple sequence alignment (MSA) followed by phylogenetic analysis showed that the P. emblica catalase groups with soybean catalase. We further report the characteristics of structural model of the enzyme for functional characterization.
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Affiliation(s)
- Swati Sharma
- Department of Botany, Faculty of Life Sciences, Maharshi Dayanand University, Rohtak-124001, India
| | - Vinita Hooda
- Department of Botany, Faculty of Life Sciences, Maharshi Dayanand University, Rohtak-124001, India
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30
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Abstract
[Formula: see text]-Barrel membrane proteins ([Formula: see text]MPs) play important roles, but knowledge of their structures is limited. We have developed a method to predict their 3D structures. We predict strand registers and construct transmembrane (TM) domains of [Formula: see text]MPs accurately, including proteins for which no prediction has been attempted before. Our method also accurately predicts structures from protein families with a limited number of sequences and proteins with novel folds. An average main-chain rmsd of 3.48 Å is achieved between predicted and experimentally resolved structures of TM domains, which is a significant improvement ([Formula: see text]3 Å) over a recent study. For [Formula: see text]MPs with NMR structures, the deviation between predictions and experimentally solved structures is similar to the difference among the NMR structures, indicating excellent prediction accuracy. Moreover, we can now accurately model the extended [Formula: see text]-barrels and loops in non-TM domains, increasing the overall coverage of structure prediction by [Formula: see text]%. Our method is general and can be applied to genome-wide structural prediction of [Formula: see text]MPs.
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31
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Jung SH, Kim CK, Lee G, Yoon J, Lee M. Structural Analysis of Recombinant Human Preproinsulins by Structure Prediction, Molecular Dynamics, and Protein-Protein Docking. Genomics Inform 2017; 15:142-146. [PMID: 29307140 PMCID: PMC5769858 DOI: 10.5808/gi.2017.15.4.142] [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: 11/21/2017] [Revised: 11/28/2017] [Accepted: 11/28/2017] [Indexed: 12/31/2022] Open
Abstract
More effective production of human insulin is important, because insulin is the main medication that is used to treat multiple types of diabetes and because many people are suffering from diabetes. The current system of insulin production is based on recombinant DNA technology, and the expression vector is composed of a preproinsulin sequence that is a fused form of an artificial leader peptide and the native proinsulin. It has been reported that the sequence of the leader peptide affects the production of insulin. To analyze how the leader peptide affects the maturation of insulin structurally, we adapted several in silico simulations using 13 artificial proinsulin sequences. Three-dimensional structures of models were predicted and compared. Although their sequences had few differences, the predicted structures were somewhat different. The structures were refined by molecular dynamics simulation, and the energy of each model was estimated. Then, protein-protein docking between the models and trypsin was carried out to compare how efficiently the protease could access the cleavage sites of the proinsulin models. The results showed some concordance with experimental results that have been reported; so, we expect our analysis will be used to predict the optimized sequence of artificial proinsulin for more effective production.
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Affiliation(s)
- Sung Hun Jung
- Department of Biological Science, Sangji University, Wonju 26339, Korea
- Theragen Etex Bio Institute, Suwon 16229, Korea
| | | | - Gunhee Lee
- Department of Biomedicine & Health Sciences, Graduate School, The Catholic University of Korea, Seoul 06591, Korea
| | - Jonghwan Yoon
- Department of Biomedicine & Health Sciences, Graduate School, The Catholic University of Korea, Seoul 06591, Korea
| | - Minho Lee
- Catholic Precision Medicine Research Center, College of Medicine, The Catholic University of Korea, Seoul 06591, Korea
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32
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Zhang C, Mortuza SM, He B, Wang Y, Zhang Y. Template-based and free modeling of I-TASSER and QUARK pipelines using predicted contact maps in CASP12. Proteins 2017; 86 Suppl 1:136-151. [PMID: 29082551 DOI: 10.1002/prot.25414] [Citation(s) in RCA: 64] [Impact Index Per Article: 9.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2017] [Revised: 10/09/2017] [Accepted: 10/27/2017] [Indexed: 12/26/2022]
Abstract
We develop two complementary pipelines, "Zhang-Server" and "QUARK", based on I-TASSER and QUARK pipelines for template-based modeling (TBM) and free modeling (FM), and test them in the CASP12 experiment. The combination of I-TASSER and QUARK successfully folds three medium-size FM targets that have more than 150 residues, even though the interplay between the two pipelines still awaits further optimization. Newly developed sequence-based contact prediction by NeBcon plays a critical role to enhance the quality of models, particularly for FM targets, by the new pipelines. The inclusion of NeBcon predicted contacts as restraints in the QUARK simulations results in an average TM-score of 0.41 for the best in top five predicted models, which is 37% higher than that by the QUARK simulations without contacts. In particular, there are seven targets that are converted from non-foldable to foldable (TM-score >0.5) due to the use of contact restraints in the simulations. Another additional feature in the current pipelines is the local structure quality prediction by ResQ, which provides a robust residue-level modeling error estimation. Despite the success, significant challenges still remain in ab initio modeling of multi-domain proteins and folding of β-proteins with complicated topologies bound by long-range strand-strand interactions. Improvements on domain boundary and long-range contact prediction, as well as optimal use of the predicted contacts and multiple threading alignments, are critical to address these issues seen in the CASP12 experiment.
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Affiliation(s)
- Chengxin Zhang
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan
| | - S M Mortuza
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan
| | - Baoji He
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan.,Institute of Theoretical Physics, Chinese Academy of Sciences, Beijing, China
| | - Yanting Wang
- Institute of Theoretical Physics, Chinese Academy of Sciences, Beijing, China
| | - Yang Zhang
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan.,Department of Biological Chemistry, University of Michigan, Ann Arbor, Michigan
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33
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Dawid AE, Gront D, Kolinski A. SURPASS Low-Resolution Coarse-Grained Protein Modeling. J Chem Theory Comput 2017; 13:5766-5779. [PMID: 28992694 DOI: 10.1021/acs.jctc.7b00642] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Coarse-grained modeling of biomolecules has a very important role in molecular biology. In this work we present a novel SURPASS (Single United Residue per Pre-Averaged Secondary Structure fragment) model of proteins that can be an interesting alternative for existing coarse-grained models. The design of the model is unique and strongly supported by the statistical analysis of structural regularities characteristic for protein systems. Coarse-graining of protein chain structures assumes a single center of interactions per residue and accounts for preaveraged effects of four adjacent residue fragments. Knowledge-based statistical potentials encode complex interaction patterns of these fragments. Using the Replica Exchange Monte Carlo sampling scheme and a generic version of the SURPASS force field we performed test simulations of a representative set of single-domain globular proteins. The method samples a significant part of conformational space and reproduces protein structures, including native-like, with surprisingly good accuracy. Future extension of the SURPASS model on large biomacromolecular systems is briefly discussed.
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Affiliation(s)
- Aleksandra E Dawid
- Faculty of Chemistry, Biological and Chemical Research Center, University of Warsaw , Pasteura 1, 02-093 Warsaw, Poland
| | - Dominik Gront
- Faculty of Chemistry, Biological and Chemical Research Center, University of Warsaw , Pasteura 1, 02-093 Warsaw, Poland
| | - Andrzej Kolinski
- Faculty of Chemistry, Biological and Chemical Research Center, University of Warsaw , Pasteura 1, 02-093 Warsaw, Poland
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34
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Lam SD, Das S, Sillitoe I, Orengo C. An overview of comparative modelling and resources dedicated to large-scale modelling of genome sequences. Acta Crystallogr D Struct Biol 2017; 73:628-640. [PMID: 28777078 PMCID: PMC5571743 DOI: 10.1107/s2059798317008920] [Citation(s) in RCA: 33] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2016] [Accepted: 06/14/2017] [Indexed: 12/02/2022] Open
Abstract
Computational modelling of proteins has been a major catalyst in structural biology. Bioinformatics groups have exploited the repositories of known structures to predict high-quality structural models with high efficiency at low cost. This article provides an overview of comparative modelling, reviews recent developments and describes resources dedicated to large-scale comparative modelling of genome sequences. The value of subclustering protein domain superfamilies to guide the template-selection process is investigated. Some recent cases in which structural modelling has aided experimental work to determine very large macromolecular complexes are also cited.
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Affiliation(s)
- Su Datt Lam
- Institute of Structural and Molecular Biology, UCL, Darwin Building, Gower Street, London WC1E 6BT, England
- School of Biosciences and Biotechnology, Faculty of Science and Technology, University Kebangsaan Malaysia, 43600 Bangi, Selangor, Malaysia
| | - Sayoni Das
- Institute of Structural and Molecular Biology, UCL, Darwin Building, Gower Street, London WC1E 6BT, England
| | - Ian Sillitoe
- Institute of Structural and Molecular Biology, UCL, Darwin Building, Gower Street, London WC1E 6BT, England
| | - Christine Orengo
- Institute of Structural and Molecular Biology, UCL, Darwin Building, Gower Street, London WC1E 6BT, England
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35
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Feig M. Computational protein structure refinement: Almost there, yet still so far to go. WILEY INTERDISCIPLINARY REVIEWS. COMPUTATIONAL MOLECULAR SCIENCE 2017; 7:e1307. [PMID: 30613211 PMCID: PMC6319934 DOI: 10.1002/wcms.1307] [Citation(s) in RCA: 46] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
Protein structures are essential in modern biology yet experimental methods are far from being able to catch up with the rapid increase in available genomic data. Computational protein structure prediction methods aim to fill the gap while the role of protein structure refinement is to take approximate initial template-based models and bring them closer to the true native structure. Current methods for computational structure refinement rely on molecular dynamics simulations, related sampling methods, or iterative structure optimization protocols. The best methods are able to achieve moderate degrees of refinement but consistent refinement that can reach near-experimental accuracy remains elusive. Key issues revolve around the accuracy of the energy function, the inability to reliably rank multiple models, and the use of restraints that keep sampling close to the native state but also limit the degree of possible refinement. A different aspect is the question of what exactly the target of high-resolution refinement should be as experimental structures are affected by experimental conditions and different biological questions require varying levels of accuracy. While improvement of the global protein structure is a difficult problem, high-resolution refinement methods that improves local structural quality such as favorable stereochemistry and the avoidance of atomic clashes are much more successful.
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Affiliation(s)
- Michael Feig
- Department of Biochemistry and Molecular Biology, Michigan State University, 603 Wilson Rd., Room 218 BCH, East Lansing, MI, USA, ; 517-432-7439
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36
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Identify High-Quality Protein Structural Models by Enhanced K-Means. BIOMED RESEARCH INTERNATIONAL 2017; 2017:7294519. [PMID: 28421198 PMCID: PMC5381204 DOI: 10.1155/2017/7294519] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/23/2016] [Revised: 02/09/2017] [Accepted: 02/19/2017] [Indexed: 01/01/2023]
Abstract
Background. One critical issue in protein three-dimensional structure prediction using either ab initio or comparative modeling involves identification of high-quality protein structural models from generated decoys. Currently, clustering algorithms are widely used to identify near-native models; however, their performance is dependent upon different conformational decoys, and, for some algorithms, the accuracy declines when the decoy population increases. Results. Here, we proposed two enhanced K-means clustering algorithms capable of robustly identifying high-quality protein structural models. The first one employs the clustering algorithm SPICKER to determine the initial centroids for basic K-means clustering (SK-means), whereas the other employs squared distance to optimize the initial centroids (K-means++). Our results showed that SK-means and K-means++ were more robust as compared with SPICKER alone, detecting 33 (59%) and 42 (75%) of 56 targets, respectively, with template modeling scores better than or equal to those of SPICKER. Conclusions. We observed that the classic K-means algorithm showed a similar performance to that of SPICKER, which is a widely used algorithm for protein-structure identification. Both SK-means and K-means++ demonstrated substantial improvements relative to results from SPICKER and classical K-means.
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37
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Skolnick J, Zhou H. Why Is There a Glass Ceiling for Threading Based Protein Structure Prediction Methods? J Phys Chem B 2016; 121:3546-3554. [PMID: 27748116 DOI: 10.1021/acs.jpcb.6b09517] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
Abstract
Despite their different implementations, comparison of the best threading approaches to the prediction of evolutionary distant protein structures reveals that they tend to succeed or fail on the same protein targets. This is true despite the fact that the structural template library has good templates for all cases. Thus, a key question is why are certain protein structures threadable while others are not. Comparison with threading results on a set of artificial sequences selected for stability further argues that the failure of threading is due to the nature of the protein structures themselves. Using a new contact map based alignment algorithm, we demonstrate that certain folds are highly degenerate in that they can have very similar coarse grained fractions of native contacts aligned and yet differ significantly from the native structure. For threadable proteins, this is not the case. Thus, contemporary threading approaches appear to have reached a plateau, and new approaches to structure prediction are required.
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Affiliation(s)
- Jeffrey Skolnick
- Center for the Study of Systems Biology, School of Biological Sciences, Georgia Institute of Technology , 950 Atlantic Drive Northwest, Atlanta, Georgia 30318, United States
| | - Hongyi Zhou
- Center for the Study of Systems Biology, School of Biological Sciences, Georgia Institute of Technology , 950 Atlantic Drive Northwest, Atlanta, Georgia 30318, United States
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38
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Mapping the Geometric Evolution of Protein Folding Motor. PLoS One 2016; 11:e0163993. [PMID: 27716851 PMCID: PMC5055333 DOI: 10.1371/journal.pone.0163993] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2016] [Accepted: 09/19/2016] [Indexed: 11/19/2022] Open
Abstract
Polypeptide chain has an invariant main-chain and a variant side-chain sequence. How the side-chain sequence determines fold in terms of its chemical constitution has been scrutinized extensively and verified periodically. However, a focussed investigation on the directive effect of side-chain geometry may provide important insights supplementing existing algorithms in mapping the geometrical evolution of protein chains and its structural preferences. Geometrically, folding of protein structure may be envisaged as the evolution of its geometric variables: ϕ, and ψ dihedral angles of polypeptide main-chain directed by χ1, and χ2 of side chain. In this work, protein molecule is metaphorically modelled as a machine with 4 rotors ϕ, ψ, χ1 and χ2, with its evolution to the functional fold is directed by combinations of its rotor directions. We observe that differential rotor motions lead to different secondary structure formations and the combinatorial pattern is unique and consistent for particular secondary structure type. Further, we found that combination of rotor geometries of each amino acid is unique which partly explains how different amino acid sequence combinations have unique structural evolution and functional adaptation. Quantification of these amino acid rotor preferences, resulted in the generation of 3 substitution matrices, which later on plugged in the BLAST tool, for evaluating their efficiency in aligning sequences. We have employed BLOSUM62 and PAM30 as standard for primary evaluation. Generation of substitution matrices is a logical extension of the conceptual framework we attempted to build during the development of this work. Optimization of matrices following the conventional routines and possible application with biologically relevant data sets are beyond the scope of this manuscript, though it is a part of the larger project design.
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Krupa P, Mozolewska MA, Wiśniewska M, Yin Y, He Y, Sieradzan AK, Ganzynkowicz R, Lipska AG, Karczyńska A, Ślusarz M, Ślusarz R, Giełdoń A, Czaplewski C, Jagieła D, Zaborowski B, Scheraga HA, Liwo A. Performance of protein-structure predictions with the physics-based UNRES force field in CASP11. Bioinformatics 2016; 32:3270-3278. [PMID: 27378298 DOI: 10.1093/bioinformatics/btw404] [Citation(s) in RCA: 37] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2016] [Accepted: 06/20/2016] [Indexed: 12/20/2022] Open
Abstract
Participating as the Cornell-Gdansk group, we have used our physics-based coarse-grained UNited RESidue (UNRES) force field to predict protein structure in the 11th Community Wide Experiment on the Critical Assessment of Techniques for Protein Structure Prediction (CASP11). Our methodology involved extensive multiplexed replica exchange simulations of the target proteins with a recently improved UNRES force field to provide better reproductions of the local structures of polypeptide chains. All simulations were started from fully extended polypeptide chains, and no external information was included in the simulation process except for weak restraints on secondary structure to enable us to finish each prediction within the allowed 3-week time window. Because of simplified UNRES representation of polypeptide chains, use of enhanced sampling methods, code optimization and parallelization and sufficient computational resources, we were able to treat, for the first time, all 55 human prediction targets with sizes from 44 to 595 amino acid residues, the average size being 251 residues. Complete structures of six single-domain proteins were predicted accurately, with the highest accuracy being attained for the T0769, for which the CαRMSD was 3.8 Å for 97 residues of the experimental structure. Correct structures were also predicted for 13 domains of multi-domain proteins with accuracy comparable to that of the best template-based modeling methods. With further improvements of the UNRES force field that are now underway, our physics-based coarse-grained approach to protein-structure prediction will eventually reach global prediction capacity and, consequently, reliability in simulating protein structure and dynamics that are important in biochemical processes. AVAILABILITY AND IMPLEMENTATION Freely available on the web at http://www.unres.pl/ CONTACT: has5@cornell.edu.
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Affiliation(s)
- Paweł Krupa
- Faculty of Chemistry, University of Gdańsk, Wita Stwosza 63, Gdańsk 80-308, Poland Baker Laboratory of Chemistry and Chemical Biology, Cornell University, Ithaca, NY 14853-1301, USA
| | - Magdalena A Mozolewska
- Faculty of Chemistry, University of Gdańsk, Wita Stwosza 63, Gdańsk 80-308, Poland Baker Laboratory of Chemistry and Chemical Biology, Cornell University, Ithaca, NY 14853-1301, USA
| | - Marta Wiśniewska
- Faculty of Chemistry, University of Gdańsk, Wita Stwosza 63, Gdańsk 80-308, Poland Baker Laboratory of Chemistry and Chemical Biology, Cornell University, Ithaca, NY 14853-1301, USA
| | - Yanping Yin
- Baker Laboratory of Chemistry and Chemical Biology, Cornell University, Ithaca, NY 14853-1301, USA
| | - Yi He
- Baker Laboratory of Chemistry and Chemical Biology, Cornell University, Ithaca, NY 14853-1301, USA
| | - Adam K Sieradzan
- Faculty of Chemistry, University of Gdańsk, Wita Stwosza 63, Gdańsk 80-308, Poland Baker Laboratory of Chemistry and Chemical Biology, Cornell University, Ithaca, NY 14853-1301, USA
| | - Robert Ganzynkowicz
- Faculty of Chemistry, University of Gdańsk, Wita Stwosza 63, Gdańsk 80-308, Poland
| | - Agnieszka G Lipska
- Faculty of Chemistry, University of Gdańsk, Wita Stwosza 63, Gdańsk 80-308, Poland Baker Laboratory of Chemistry and Chemical Biology, Cornell University, Ithaca, NY 14853-1301, USA
| | - Agnieszka Karczyńska
- Faculty of Chemistry, University of Gdańsk, Wita Stwosza 63, Gdańsk 80-308, Poland
| | - Magdalena Ślusarz
- Faculty of Chemistry, University of Gdańsk, Wita Stwosza 63, Gdańsk 80-308, Poland
| | - Rafał Ślusarz
- Faculty of Chemistry, University of Gdańsk, Wita Stwosza 63, Gdańsk 80-308, Poland
| | - Artur Giełdoń
- Faculty of Chemistry, University of Gdańsk, Wita Stwosza 63, Gdańsk 80-308, Poland
| | - Cezary Czaplewski
- Faculty of Chemistry, University of Gdańsk, Wita Stwosza 63, Gdańsk 80-308, Poland
| | - Dawid Jagieła
- Faculty of Chemistry, University of Gdańsk, Wita Stwosza 63, Gdańsk 80-308, Poland
| | | | - Harold A Scheraga
- Baker Laboratory of Chemistry and Chemical Biology, Cornell University, Ithaca, NY 14853-1301, USA
| | - Adam Liwo
- Faculty of Chemistry, University of Gdańsk, Wita Stwosza 63, Gdańsk 80-308, Poland
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Ghouzam Y, Postic G, Guerin PE, de Brevern AG, Gelly JC. ORION: a web server for protein fold recognition and structure prediction using evolutionary hybrid profiles. Sci Rep 2016; 6:28268. [PMID: 27319297 PMCID: PMC4913311 DOI: 10.1038/srep28268] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2016] [Accepted: 06/01/2016] [Indexed: 11/09/2022] Open
Abstract
Protein structure prediction based on comparative modeling is the most efficient way to produce structural models when it can be performed. ORION is a dedicated webserver based on a new strategy that performs this task. The identification by ORION of suitable templates is performed using an original profile-profile approach that combines sequence and structure evolution information. Structure evolution information is encoded into profiles using structural features, such as solvent accessibility and local conformation -with Protein Blocks-, which give an accurate description of the local protein structure. ORION has recently been improved, increasing by 5% the quality of its results. The ORION web server accepts a single protein sequence as input and searches homologous protein structures within minutes. Various databases such as PDB, SCOP and HOMSTRAD can be mined to find an appropriate structural template. For the modeling step, a protein 3D structure can be directly obtained from the selected template by MODELLER and displayed with global and local quality model estimation measures. The sequence and the predicted structure of 4 examples from the CAMEO server and a recent CASP11 target from the 'Hard' category (T0818-D1) are shown as pertinent examples. Our web server is accessible at http://www.dsimb.inserm.fr/ORION/.
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Affiliation(s)
- Yassine Ghouzam
- INSERM, U 1134, DSIMB, F-75739 Paris, France
- Univ. Paris Diderot, Sorbonne Paris Cité, UMR_S 1134, F-75739 Paris, France
- Institut National de la Transfusion Sanguine (INTS), F-75739 Paris, France
- Laboratoire d’Excellence GR-Ex, F-75739 Paris, France
| | - Guillaume Postic
- INSERM, U 1134, DSIMB, F-75739 Paris, France
- Univ. Paris Diderot, Sorbonne Paris Cité, UMR_S 1134, F-75739 Paris, France
- Institut National de la Transfusion Sanguine (INTS), F-75739 Paris, France
- Laboratoire d’Excellence GR-Ex, F-75739 Paris, France
| | - Pierre-Edouard Guerin
- INSERM, U 1134, DSIMB, F-75739 Paris, France
- Univ. Paris Diderot, Sorbonne Paris Cité, UMR_S 1134, F-75739 Paris, France
- Institut National de la Transfusion Sanguine (INTS), F-75739 Paris, France
- Laboratoire d’Excellence GR-Ex, F-75739 Paris, France
| | - Alexandre G. de Brevern
- INSERM, U 1134, DSIMB, F-75739 Paris, France
- Univ. Paris Diderot, Sorbonne Paris Cité, UMR_S 1134, F-75739 Paris, France
- Institut National de la Transfusion Sanguine (INTS), F-75739 Paris, France
- Laboratoire d’Excellence GR-Ex, F-75739 Paris, France
| | - Jean-Christophe Gelly
- INSERM, U 1134, DSIMB, F-75739 Paris, France
- Univ. Paris Diderot, Sorbonne Paris Cité, UMR_S 1134, F-75739 Paris, France
- Institut National de la Transfusion Sanguine (INTS), F-75739 Paris, France
- Laboratoire d’Excellence GR-Ex, F-75739 Paris, France
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Nie L, Grell E, Malviya VN, Xie H, Wang J, Michel H. Identification of the High-affinity Substrate-binding Site of the Multidrug and Toxic Compound Extrusion (MATE) Family Transporter from Pseudomonas stutzeri. J Biol Chem 2016; 291:15503-14. [PMID: 27235402 DOI: 10.1074/jbc.m116.728618] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2016] [Indexed: 01/05/2023] Open
Abstract
Multidrug and toxic compound extrusion (MATE) transporters exist in all three domains of life. They confer multidrug resistance by utilizing H(+) or Na(+) electrochemical gradients to extrude various drugs across the cell membranes. The substrate binding and the transport mechanism of MATE transporters is a fundamental process but so far not fully understood. Here we report a detailed substrate binding study of NorM_PS, a representative MATE transporter from Pseudomonas stutzeri Our results indicate that NorM_PS is a proton-dependent multidrug efflux transporter. Detailed binding studies between NorM_PS and 4',6-diamidino-2-phenylindole (DAPI) were performed by isothermal titration calorimetry (ITC), differential scanning calorimetry (DSC), and spectrofluorometry. Two exothermic binding events were observed from ITC data, and the high-affinity event was directly correlated with the extrusion of DAPI. The affinities are about 1 μm and 0.1 mm for the high and low affinity binding, respectively. Based on our homology model of NorM_PS, variants with mutations of amino acids that are potentially involved in substrate binding, were constructed. By carrying out the functional characterization of these variants, the critical amino acid residues (Glu-257 and Asp-373) for high-affinity DAPI binding were determined. Taken together, our results suggest a new substrate-binding site for MATE transporters.
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Affiliation(s)
- Laiyin Nie
- From the Tianjin University, School of Chemical Engineering and Technology, State Key Laboratory for Chemical Engineering, Collaborative Innovation Center of Chemical Science and Chemical Engineering, 300072 Tianjin, China and the Department of Molecular Membrane Biology, Max Planck Institute of Biophysics, Max-von-Laue-Strasse 3, 60438 Frankfurt am Main, Germany
| | - Ernst Grell
- the Department of Molecular Membrane Biology, Max Planck Institute of Biophysics, Max-von-Laue-Strasse 3, 60438 Frankfurt am Main, Germany
| | - Viveka Nand Malviya
- the Department of Molecular Membrane Biology, Max Planck Institute of Biophysics, Max-von-Laue-Strasse 3, 60438 Frankfurt am Main, Germany
| | - Hao Xie
- the Department of Molecular Membrane Biology, Max Planck Institute of Biophysics, Max-von-Laue-Strasse 3, 60438 Frankfurt am Main, Germany
| | - Jingkang Wang
- From the Tianjin University, School of Chemical Engineering and Technology, State Key Laboratory for Chemical Engineering, Collaborative Innovation Center of Chemical Science and Chemical Engineering, 300072 Tianjin, China and
| | - Hartmut Michel
- the Department of Molecular Membrane Biology, Max Planck Institute of Biophysics, Max-von-Laue-Strasse 3, 60438 Frankfurt am Main, Germany
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Fischer AW, Heinze S, Putnam DK, Li B, Pino JC, Xia Y, Lopez CF, Meiler J. CASP11--An Evaluation of a Modular BCL::Fold-Based Protein Structure Prediction Pipeline. PLoS One 2016; 11:e0152517. [PMID: 27046050 PMCID: PMC4821492 DOI: 10.1371/journal.pone.0152517] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2015] [Accepted: 03/15/2016] [Indexed: 11/18/2022] Open
Abstract
In silico prediction of a protein's tertiary structure remains an unsolved problem. The community-wide Critical Assessment of Protein Structure Prediction (CASP) experiment provides a double-blind study to evaluate improvements in protein structure prediction algorithms. We developed a protein structure prediction pipeline employing a three-stage approach, consisting of low-resolution topology search, high-resolution refinement, and molecular dynamics simulation to predict the tertiary structure of proteins from the primary structure alone or including distance restraints either from predicted residue-residue contacts, nuclear magnetic resonance (NMR) nuclear overhauser effect (NOE) experiments, or mass spectroscopy (MS) cross-linking (XL) data. The protein structure prediction pipeline was evaluated in the CASP11 experiment on twenty regular protein targets as well as thirty-three 'assisted' protein targets, which also had distance restraints available. Although the low-resolution topology search module was able to sample models with a global distance test total score (GDT_TS) value greater than 30% for twelve out of twenty proteins, frequently it was not possible to select the most accurate models for refinement, resulting in a general decay of model quality over the course of the prediction pipeline. In this study, we provide a detailed overall analysis, study one target protein in more detail as it travels through the protein structure prediction pipeline, and evaluate the impact of limited experimental data.
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Affiliation(s)
- Axel W. Fischer
- Department of Chemistry, Vanderbilt University, Nashville, TN, 37232, United States of America
- Center for Structural Biology, Vanderbilt University, Nashville, TN, 37232, United States of America
| | - Sten Heinze
- Department of Chemistry, Vanderbilt University, Nashville, TN, 37232, United States of America
- Center for Structural Biology, Vanderbilt University, Nashville, TN, 37232, United States of America
| | - Daniel K. Putnam
- Department of Biomedical Informatics, Vanderbilt University, Nashville, TN, 37232, United States of America
| | - Bian Li
- Department of Chemistry, Vanderbilt University, Nashville, TN, 37232, United States of America
- Center for Structural Biology, Vanderbilt University, Nashville, TN, 37232, United States of America
| | - James C. Pino
- Chemical and Physical Biology Graduate Program, Vanderbilt University, Nashville, TN, 37232, United States of America
| | - Yan Xia
- Department of Chemistry, Vanderbilt University, Nashville, TN, 37232, United States of America
- Center for Structural Biology, Vanderbilt University, Nashville, TN, 37232, United States of America
| | - Carlos F. Lopez
- Center for Structural Biology, Vanderbilt University, Nashville, TN, 37232, United States of America
- Department of Cancer Biology and Center for Quantitative Sciences, Vanderbilt University, Nashville, TN, 37232, United States of America
| | - Jens Meiler
- Department of Chemistry, Vanderbilt University, Nashville, TN, 37232, United States of America
- Center for Structural Biology, Vanderbilt University, Nashville, TN, 37232, United States of America
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