1
|
Sang Y, Huang X, Li H, Hong T, Zheng M, Li Z, Jiang Z, Ni H, Li Q, Zhu Y. Improving the thermostability of Pseudoalteromonas Porphyrae κ-carrageenase by rational design and MD simulation. AMB Express 2024; 14:8. [PMID: 38245573 PMCID: PMC10799840 DOI: 10.1186/s13568-024-01661-z] [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: 12/20/2023] [Accepted: 12/28/2023] [Indexed: 01/22/2024] Open
Abstract
The industrial applications of the κ-carrageenases have been restricted by their poor thermostability. In this study, based on the folding free energy change (ΔΔG) and the flexibility analysis using molecular dynamics (MD) simulation for the alkaline κ-carrageenase KCgCD from Pseudoalteromonas porphyrae (WT), the mutant S190R was identified with improved thermostability. After incubation at 50 °C for 30 min, the residual activity of S190R was 63.7%, 25.7% higher than that of WT. The Tm values determined by differential scanning calorimetry were 66.2 °C and 64.4 °C for S190R and WT, respectively. The optimal temperature of S190R was 10 °C higher than that of WT. The κ-carrageenan hydrolysates produced by S190R showed higher xanthine oxidase inhibitory activity compared with the untreated κ-carrageenan. MD simulation analysis of S190R showed that the residues (V186-M194 and P196-G197) in F5 and the key residue R150 in F3 displayed the decreased flexibility, and residues of T169-N173 near the catalytic center displayed the increased flexibility. These changed flexibilities might be the reasons for the improved thermostability of mutant S190R. This study provides a useful rational design strategy of combination of ΔΔG calculation and MD simulation to improve the κ-carrageenase's thermostability for its better industrial applications.
Collapse
Affiliation(s)
- Yuyan Sang
- College of Ocean Food and Biological Engineering, Jimei University, 361021, Xiamen, China
| | - Xiaoyi Huang
- College of Ocean Food and Biological Engineering, Jimei University, 361021, Xiamen, China
| | - Hebin Li
- Department of Pharmacy, Xiamen Medical College, 361008, Xiamen, China
| | - Tao Hong
- College of Ocean Food and Biological Engineering, Jimei University, 361021, Xiamen, China
- Fujian Provincial Key Laboratory of Food Microbiology and Enzyme Engineering, 361021, Xiamen, China
- Research Center of Food Biotechnology of Xiamen City, 361021, Xiamen, China
| | - Mingjing Zheng
- College of Ocean Food and Biological Engineering, Jimei University, 361021, Xiamen, China
- Fujian Provincial Key Laboratory of Food Microbiology and Enzyme Engineering, 361021, Xiamen, China
- Research Center of Food Biotechnology of Xiamen City, 361021, Xiamen, China
| | - Zhipeng Li
- College of Ocean Food and Biological Engineering, Jimei University, 361021, Xiamen, China
- Fujian Provincial Key Laboratory of Food Microbiology and Enzyme Engineering, 361021, Xiamen, China
- Research Center of Food Biotechnology of Xiamen City, 361021, Xiamen, China
| | - Zedong Jiang
- College of Ocean Food and Biological Engineering, Jimei University, 361021, Xiamen, China
- Fujian Provincial Key Laboratory of Food Microbiology and Enzyme Engineering, 361021, Xiamen, China
- Research Center of Food Biotechnology of Xiamen City, 361021, Xiamen, China
| | - Hui Ni
- College of Ocean Food and Biological Engineering, Jimei University, 361021, Xiamen, China
- Fujian Provincial Key Laboratory of Food Microbiology and Enzyme Engineering, 361021, Xiamen, China
- Research Center of Food Biotechnology of Xiamen City, 361021, Xiamen, China
| | - Qingbiao Li
- College of Ocean Food and Biological Engineering, Jimei University, 361021, Xiamen, China
- Fujian Provincial Key Laboratory of Food Microbiology and Enzyme Engineering, 361021, Xiamen, China
- Research Center of Food Biotechnology of Xiamen City, 361021, Xiamen, China
| | - Yanbing Zhu
- College of Ocean Food and Biological Engineering, Jimei University, 361021, Xiamen, China.
- Fujian Provincial Key Laboratory of Food Microbiology and Enzyme Engineering, 361021, Xiamen, China.
- Research Center of Food Biotechnology of Xiamen City, 361021, Xiamen, China.
| |
Collapse
|
2
|
Braz Gomes K, Zhang YN, Lee YZ, Eldad M, Lim A, Ward G, Auclair S, He L, Zhu J. Single-Component Multilayered Self-Assembling Protein Nanoparticles Displaying Extracellular Domains of Matrix Protein 2 as a Pan-influenza A Vaccine. ACS NANO 2023; 17:23545-23567. [PMID: 37988765 PMCID: PMC10722606 DOI: 10.1021/acsnano.3c06526] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/16/2023] [Revised: 11/09/2023] [Accepted: 11/15/2023] [Indexed: 11/23/2023]
Abstract
The development of a cross-protective pan-influenza A vaccine remains a significant challenge. In this study, we designed and evaluated single-component self-assembling protein nanoparticles (SApNPs) presenting the conserved extracellular domain of matrix protein 2 (M2e) as vaccine candidates against influenza A viruses. The SApNP-based vaccine strategy was first validated for human M2e (hM2e) and then applied to tandem repeats of M2e from human, avian, and swine hosts (M2ex3). Vaccination with M2ex3 displayed on SApNPs demonstrated higher survival rates and less weight loss compared to the soluble M2ex3 antigen against the lethal challenges of H1N1 and H3N2 in mice. M2ex3 I3-01v9a SApNPs formulated with a squalene-based adjuvant were retained in the lymph node follicles over 8 weeks and induced long-lived germinal center reactions. Notably, a single low dose of M2ex3 I3-01v9a SApNP formulated with a potent adjuvant, either a Toll-like receptor 9 (TLR9) agonist or a stimulator of interferon genes (STING) agonist, conferred 90% protection against a lethal H1N1 challenge in mice. With the ability to induce robust and durable M2e-specific functional antibody and T cell responses, the M2ex3-presenting I3-01v9a SApNP provides a promising pan-influenza A vaccine candidate.
Collapse
Affiliation(s)
- Keegan Braz Gomes
- Department
of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, California 92037, United States
| | - Yi-Nan Zhang
- Department
of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, California 92037, United States
| | - Yi-Zong Lee
- Department
of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, California 92037, United States
| | - Mor Eldad
- Department
of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, California 92037, United States
| | - Alexander Lim
- Department
of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, California 92037, United States
| | - Garrett Ward
- Department
of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, California 92037, United States
| | - Sarah Auclair
- Department
of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, California 92037, United States
| | - Linling He
- Department
of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, California 92037, United States
| | - Jiang Zhu
- Department
of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, California 92037, United States
- Department
of Immunology and Microbiology, The Scripps
Research Institute, La Jolla, California 92037, United States
| |
Collapse
|
3
|
Guerrib F, Ning C, Mateos-Hernandéz L, Rakotobe S, Park Y, Hajdusek O, Perner J, Vancová M, Valdés JJ, Šimo L. Dual SIFamide receptors in Ixodes salivary glands. INSECT BIOCHEMISTRY AND MOLECULAR BIOLOGY 2023:103963. [PMID: 37257628 DOI: 10.1016/j.ibmb.2023.103963] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/08/2022] [Revised: 05/05/2023] [Accepted: 05/13/2023] [Indexed: 06/02/2023]
Abstract
Salivary glands are vital to tick feeding success and also play a crucial role in tick-borne pathogen transmission. In previous studies of Ixodes scapularis salivary glands, we demonstrated that saliva-producing type II and III acini are innervated by neuropeptidergic axons which release different classes of neuropeptides via their terminals (Šimo et al., 2009b, 2013). Among these, the neuropeptide SIFamide-along with its cognate receptor-were postulated to control the basally located acinar valve via basal epithelial and myoepithelial cells (Vancová et al., 2019). Here, we functionally characterized a second SIFamide receptor (SIFa_R2) from the I. scapularis genome and proved that it senses a low nanomolar level of its corresponding ligand. Insect SIFamide paralogs, SMYamides, also activated the receptor but less effectively compared to SIFamide. Bioinformatic and molecular dynamic analyses suggested that I. scapularis SIFamide receptors are class A GPCRs where the peptide amidated carboxy-terminus is oriented within the receptor binding cavity. The receptor was found to be expressed in Ixodes ricinus salivary glands, synganglia, midguts, trachea, and ovaries, but not in Malpighian tubules. Investigation of the temporal expression patterns suggests that the receptor transcript is highly expressed in unfed I. ricinus female salivary glands and then decreases during feeding. In synganglia, a significant transcript increase was detected in replete ticks. In salivary gland acini, an antibody targeting the second SIFamide receptor recognized basal epithelial cells, myoepithelial cells, and basal granular cells in close proximity to the SIFamide-releasing axon terminals. Immunoreactivity was also detected in specific neurons distributed throughout various I. ricinus synganglion locations. The current findings, alongside previous reports from our group, indicate that the neuropeptide SIFamide acts via two different receptors that regulate distinct or common cell types in the basal region of type II and III acini in I. ricinus salivary glands. The current study investigates the peptidergic regulation of the I. ricinus salivary gland in detail, emphasizing the complexity of this system.
Collapse
Affiliation(s)
- Fetta Guerrib
- ANSES, INRAE, Ecole Nationale Vétérinaire d'Alfort, UMR BIPAR, Laboratoire de Santé Animale, 94700, Maisons-Alfort, France
| | - Caina Ning
- ANSES, INRAE, Ecole Nationale Vétérinaire d'Alfort, UMR BIPAR, Laboratoire de Santé Animale, 94700, Maisons-Alfort, France
| | - Lourdes Mateos-Hernandéz
- ANSES, INRAE, Ecole Nationale Vétérinaire d'Alfort, UMR BIPAR, Laboratoire de Santé Animale, 94700, Maisons-Alfort, France
| | - Sabine Rakotobe
- ANSES, INRAE, Ecole Nationale Vétérinaire d'Alfort, UMR BIPAR, Laboratoire de Santé Animale, 94700, Maisons-Alfort, France
| | - Yoonseong Park
- Entomolgy department, Kansas State University, 123 Waters Hall, 66506-4004, Manhattan, KS, USA
| | - Ondrej Hajdusek
- Institute of Parasitology, Biology Centre, Czech Academy of Sciences, Branišovská 31, 37005, České Budějovice, Czech Republic
| | - Jan Perner
- Institute of Parasitology, Biology Centre, Czech Academy of Sciences, Branišovská 31, 37005, České Budějovice, Czech Republic
| | - Marie Vancová
- Institute of Parasitology, Biology Centre, Czech Academy of Sciences, Branišovská 31, 37005, České Budějovice, Czech Republic; Faculty of Science, University of South Bohemia, České Budějovice, 37005, Czech Republic
| | - James J Valdés
- Institute of Parasitology, Biology Centre, Czech Academy of Sciences, Branišovská 31, 37005, České Budějovice, Czech Republic
| | - Ladislav Šimo
- ANSES, INRAE, Ecole Nationale Vétérinaire d'Alfort, UMR BIPAR, Laboratoire de Santé Animale, 94700, Maisons-Alfort, France.
| |
Collapse
|
4
|
Geng H, Chen F, Ye J, Jiang F. Applications of Molecular Dynamics Simulation in Structure Prediction of Peptides and Proteins. Comput Struct Biotechnol J 2019; 17:1162-1170. [PMID: 31462972 PMCID: PMC6709365 DOI: 10.1016/j.csbj.2019.07.010] [Citation(s) in RCA: 43] [Impact Index Per Article: 8.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2019] [Revised: 07/07/2019] [Accepted: 07/23/2019] [Indexed: 12/21/2022] Open
Abstract
Compared with rapid accumulation of protein sequences from high-throughput DNA sequencing, obtaining experimental 3D structures of proteins is still much more difficult, making protein structure prediction (PSP) potentially very useful. Currently, a vast majority of PSP efforts are based on data mining of known sequences, structures and their relationships (informatics-based). However, if closely related template is not available, these methods are usually much less reliable than experiments. They may also be problematic in predicting the structures of naturally occurring or designed peptides. On the other hand, physics-based methods including molecular dynamics (MD) can utilize our understanding of detailed atomic interactions determining biomolecular structures. In this mini-review, we show that all-atom MD can predict structures of cyclic peptides and other peptide foldamers with accuracy similar to experiments. Then, some notable successes in reproducing experimental 3D structures of small proteins through MD simulations (some with replica-exchange) of the folding were summarized. We also describe advancements of MD-based refinement of structure models, and the integration of limited experimental or bioinformatics data into MD-based structure modeling.
Collapse
Affiliation(s)
- Hao Geng
- Lab of Computational Chemistry and Drug Design, State Key Laboratory of Chemical Oncogenomics, Peking University Shenzhen Graduate School, Shenzhen 518055, China
| | - Fangfang Chen
- Guangdong and Shenzhen Key Laboratory of Male Reproductive Medicine and Genetics, Peking University Shenzhen Hospital, Shenzhen PKU-HKUST Medical Center, Shenzhen 518036, China
| | - Jing Ye
- Guangdong and Shenzhen Key Laboratory of Male Reproductive Medicine and Genetics, Peking University Shenzhen Hospital, Shenzhen PKU-HKUST Medical Center, Shenzhen 518036, China
| | - Fan Jiang
- Lab of Computational Chemistry and Drug Design, State Key Laboratory of Chemical Oncogenomics, Peking University Shenzhen Graduate School, Shenzhen 518055, China
- NanoAI Biotech Co.,Ltd., Silicon Valley Compound, Longhua District, Shenzhen 518109, China
- Corresponding author at: Lab of Computational Chemistry and Drug Design, State Key Laboratory of Chemical Oncogenomics, Peking University Shenzhen Graduate School, Shenzhen 518055, China.
| |
Collapse
|
5
|
Methods for the Refinement of Protein Structure 3D Models. Int J Mol Sci 2019; 20:ijms20092301. [PMID: 31075942 PMCID: PMC6539982 DOI: 10.3390/ijms20092301] [Citation(s) in RCA: 36] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2019] [Revised: 04/24/2019] [Accepted: 05/07/2019] [Indexed: 12/25/2022] Open
Abstract
The refinement of predicted 3D protein models is crucial in bringing them closer towards experimental accuracy for further computational studies. Refinement approaches can be divided into two main stages: The sampling and scoring stages. Sampling strategies, such as the popular Molecular Dynamics (MD)-based protocols, aim to generate improved 3D models. However, generating 3D models that are closer to the native structure than the initial model remains challenging, as structural deviations from the native basin can be encountered due to force-field inaccuracies. Therefore, different restraint strategies have been applied in order to avoid deviations away from the native structure. For example, the accurate prediction of local errors and/or contacts in the initial models can be used to guide restraints. MD-based protocols, using physics-based force fields and smart restraints, have made significant progress towards a more consistent refinement of 3D models. The scoring stage, including energy functions and Model Quality Assessment Programs (MQAPs) are also used to discriminate near-native conformations from non-native conformations. Nevertheless, there are often very small differences among generated 3D models in refinement pipelines, which makes model discrimination and selection problematic. For this reason, the identification of the most native-like conformations remains a major challenge.
Collapse
|
6
|
Wang X, Huang SY. Integrating Bonded and Nonbonded Potentials in the Knowledge-Based Scoring Function for Protein Structure Prediction. J Chem Inf Model 2019; 59:3080-3090. [DOI: 10.1021/acs.jcim.9b00057] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
Affiliation(s)
- Xinxiang Wang
- Institute of Biophysics, School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei 430074, P. R. China
| | - Sheng-You Huang
- Institute of Biophysics, School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei 430074, P. R. China
| |
Collapse
|
7
|
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.
Collapse
|
8
|
Muhammed MT, Aki-Yalcin E. Homology modeling in drug discovery: Overview, current applications, and future perspectives. Chem Biol Drug Des 2018; 93:12-20. [PMID: 30187647 DOI: 10.1111/cbdd.13388] [Citation(s) in RCA: 169] [Impact Index Per Article: 28.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2018] [Revised: 06/29/2018] [Accepted: 08/04/2018] [Indexed: 02/06/2023]
Abstract
Homology modeling is one of the computational structure prediction methods that are used to determine protein 3D structure from its amino acid sequence. It is considered to be the most accurate of the computational structure prediction methods. It consists of multiple steps that are straightforward and easy to apply. There are many tools and servers that are used for homology modeling. There is no single modeling program or server which is superior in every aspect to others. Since the functionality of the model depends on the quality of the generated protein 3D structure, maximizing the quality of homology modeling is crucial. Homology modeling has many applications in the drug discovery process. Since drugs interact with receptors that consist mainly of proteins, protein 3D structure determination, and thus homology modeling is important in drug discovery. Accordingly, there has been the clarification of protein interactions using 3D structures of proteins that are built with homology modeling. This contributes to the identification of novel drug candidates. Homology modeling plays an important role in making drug discovery faster, easier, cheaper, and more practical. As new modeling methods and combinations are introduced, the scope of its applications widens.
Collapse
Affiliation(s)
- Muhammed Tilahun Muhammed
- Department of Pharmaceutical Chemistry, Faculty of Pharmacy, Suleyman Demirel University, Isparta, Turkey.,Department of Basic Biotechnology, Institute of Biotechnology, Ankara University, Ankara, Turkey
| | - Esin Aki-Yalcin
- Department of Pharmaceutical Chemistry, Faculty of Pharmacy, Ankara University, Ankara, Turkey
| |
Collapse
|
9
|
Dutagaci B, Heo L, Feig M. Structure refinement of membrane proteins via molecular dynamics simulations. Proteins 2018; 86:738-750. [PMID: 29675899 PMCID: PMC6013386 DOI: 10.1002/prot.25508] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2018] [Revised: 04/09/2018] [Accepted: 04/14/2018] [Indexed: 12/12/2022]
Abstract
A refinement protocol based on physics-based techniques established for water soluble proteins is tested for membrane protein structures. Initial structures were generated by homology modeling and sampled via molecular dynamics simulations in explicit lipid bilayer and aqueous solvent systems. Snapshots from the simulations were selected based on scoring with either knowledge-based or implicit membrane-based scoring functions and averaged to obtain refined models. The protocol resulted in consistent and significant refinement of the membrane protein structures similar to the performance of refinement methods for soluble proteins. Refinement success was similar between sampling in the presence of lipid bilayers and aqueous solvent but the presence of lipid bilayers may benefit the improvement of lipid-facing residues. Scoring with knowledge-based functions (DFIRE and RWplus) was found to be as good as scoring using implicit membrane-based scoring functions suggesting that differences in internal packing is more important than orientations relative to the membrane during the refinement of membrane protein homology models.
Collapse
Affiliation(s)
- Bercem Dutagaci
- Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, MI, USA
| | - Lim Heo
- Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, MI, USA
| | - Michael Feig
- Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, MI, USA
| |
Collapse
|
10
|
Wang X, Zhang D, Huang SY. New Knowledge-Based Scoring Function with Inclusion of Backbone Conformational Entropies from Protein Structures. J Chem Inf Model 2018; 58:724-732. [DOI: 10.1021/acs.jcim.7b00601] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
Affiliation(s)
- Xinxiang Wang
- School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei 430074, P. R. China
| | - Di Zhang
- School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei 430074, P. R. China
| | - Sheng-You Huang
- School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei 430074, P. R. China
| |
Collapse
|
11
|
Martínez-Archundia M, Colín-Astudillo B, Moreno-Vargas LM, Ramírez-Galicia G, Garduño-Juárez R, Deeb O, Contreras-Romo MC, Quintanar-Stephano A, Abarca-Rojano E, Correa-Basurto J. Ligand recognition properties of the vasopressin V2 receptor studied under QSAR and molecular modeling strategies. Chem Biol Drug Des 2017; 90:840-853. [DOI: 10.1111/cbdd.13005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2016] [Revised: 04/06/2017] [Accepted: 04/08/2017] [Indexed: 11/28/2022]
Affiliation(s)
- Marlet Martínez-Archundia
- Laboratorio de Modelado Molecular y Diseño de Fármacos; Escuela Superior de Medicina-Instituto Politécnico Nacional; México City Mexico
| | - Brenda Colín-Astudillo
- Laboratorio de Modelado Molecular y Diseño de Fármacos; Escuela Superior de Medicina-Instituto Politécnico Nacional; México City Mexico
| | - Liliana M. Moreno-Vargas
- Unidad de Investigación en Enfermedades Oncológicas; Hospital Infantil de México; Mexico City México
| | | | - Ramón Garduño-Juárez
- Instituto de Ciencias Físicas; Universidad Nacional Autónoma de México; Cuernavaca Morelos Mexico
| | - Omar Deeb
- Faculty of Pharmacy; Al-Quds University; Jerusalem Palestine
| | - Martha Citlalli Contreras-Romo
- Departamento de Fisiología y Farmacología; Centro de Ciencias Básicas; Universidad Autónoma de Aguascalientes; Aguascalientes Mexico
| | - Andres Quintanar-Stephano
- Departamento de Fisiología y Farmacología; Centro de Ciencias Básicas; Universidad Autónoma de Aguascalientes; Aguascalientes Mexico
| | - Edgar Abarca-Rojano
- Sección de Estudios de Posgrado e Investigación, Escuela Superior de Medicina; Instituto Politécnico Nacional; Mexico DF Mexico
| | - José Correa-Basurto
- Laboratorio de Modelado Molecular y Diseño de Fármacos; Escuela Superior de Medicina-Instituto Politécnico Nacional; México City Mexico
| |
Collapse
|
12
|
Dutagaci B, Wittayanarakul K, Mori T, Feig M. Discrimination of Native-like States of Membrane Proteins with Implicit Membrane-based Scoring Functions. J Chem Theory Comput 2017; 13:3049-3059. [PMID: 28475346 DOI: 10.1021/acs.jctc.7b00254] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
A scoring protocol based on implicit membrane-based scoring functions and a new protocol for optimizing the positioning of proteins inside the membrane was evaluated for its capacity to discriminate native-like states from misfolded decoys. A decoy set previously established by the Baker lab (Proteins: Struct., Funct., Genet. 2006, 62, 1010-1025) was used along with a second set that was generated to cover higher resolution models. The Implicit Membrane Model 1 (IMM1), IMM1 model with CHARMM 36 parameters (IMM1-p36), generalized Born with simple switching (GBSW), and heterogeneous dielectric generalized Born versions 2 (HDGBv2) and 3 (HDGBv3) were tested along with the new HDGB van der Waals (HDGBvdW) model that adds implicit van der Waals contributions to the solvation free energy. For comparison, scores were also calculated with the distance-scaled finite ideal-gas reference (DFIRE) scoring function. Z-scores for native state discrimination, energy vs root-mean-square deviation (RMSD) correlations, and the ability to select the most native-like structures as top-scoring decoys were evaluated to assess the performance of the scoring functions. Ranking of the decoys in the Baker set that were relatively far from the native state was challenging and dominated largely by packing interactions that were captured best by DFIRE with less benefit of the implicit membrane-based models. Accounting for the membrane environment was much more important in the second decoy set where especially the HDGB-based scoring functions performed very well in ranking decoys and providing significant correlations between scores and RMSD, which shows promise for improving membrane protein structure prediction and refinement applications. The new membrane structure scoring protocol was implemented in the MEMScore web server ( http://feiglab.org/memscore ).
Collapse
Affiliation(s)
- Bercem Dutagaci
- Department of Biochemistry and Molecular Biology, Michigan State University , East Lansing, Michigan, United States
| | - Kitiyaporn Wittayanarakul
- Department of Natural Resource and Environmental Management, Faculty of Applied Science and Engineering, Khon Kaen University , Nong Khai Campus, Nong Khai 43000, Thailand
| | - Takaharu Mori
- Theoretical Molecular Science Laboratory, RIKEN , Wako-shi, Japan
| | - Michael Feig
- Department of Biochemistry and Molecular Biology, Michigan State University , East Lansing, Michigan, United States
| |
Collapse
|
13
|
Quantum chemical approaches to [NiFe] hydrogenase. Essays Biochem 2017; 61:293-303. [PMID: 28487405 DOI: 10.1042/ebc20160079] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2016] [Revised: 02/22/2017] [Accepted: 03/01/2017] [Indexed: 11/17/2022]
Abstract
The mechanism by which [NiFe] hydrogenase catalyses the oxidation of molecular hydrogen is a significant yet challenging topic in bioinorganic chemistry. With far-reaching applications in renewable energy and carbon mitigation, significant effort has been invested in the study of these complexes. In particular, computational approaches offer a unique perspective on how this enzyme functions at an electronic and atomistic level. In this article, we discuss state-of-the art quantum chemical methods and how they have helped deepen our comprehension of [NiFe] hydrogenase. We outline the key strategies that can be used to compute the (i) geometry, (ii) electronic structure, (iii) thermodynamics and (iv) kinetic properties associated with the enzymatic activity of [NiFe] hydrogenase and other bioinorganic complexes.
Collapse
|
14
|
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.
Collapse
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
| |
Collapse
|
15
|
Pang YP. FF12MC: A revised AMBER forcefield and new protein simulation protocol. Proteins 2016; 84:1490-516. [PMID: 27348292 PMCID: PMC5129589 DOI: 10.1002/prot.25094] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2016] [Revised: 06/16/2016] [Accepted: 06/18/2016] [Indexed: 12/25/2022]
Abstract
Specialized to simulate proteins in molecular dynamics (MD) simulations with explicit solvation, FF12MC is a combination of a new protein simulation protocol employing uniformly reduced atomic masses by tenfold and a revised AMBER forcefield FF99 with (i) shortened CH bonds, (ii) removal of torsions involving a nonperipheral sp(3) atom, and (iii) reduced 1-4 interaction scaling factors of torsions ϕ and ψ. This article reports that in multiple, distinct, independent, unrestricted, unbiased, isobaric-isothermal, and classical MD simulations FF12MC can (i) simulate the experimentally observed flipping between left- and right-handed configurations for C14-C38 of BPTI in solution, (ii) autonomously fold chignolin, CLN025, and Trp-cage with folding times that agree with the experimental values, (iii) simulate subsequent unfolding and refolding of these miniproteins, and (iv) achieve a robust Z score of 1.33 for refining protein models TMR01, TMR04, and TMR07. By comparison, the latest general-purpose AMBER forcefield FF14SB locks the C14-C38 bond to the right-handed configuration in solution under the same protein simulation conditions. Statistical survival analysis shows that FF12MC folds chignolin and CLN025 in isobaric-isothermal MD simulations 2-4 times faster than FF14SB under the same protein simulation conditions. These results suggest that FF12MC may be used for protein simulations to study kinetics and thermodynamics of miniprotein folding as well as protein structure and dynamics. Proteins 2016; 84:1490-1516. © 2016 The Authors Proteins: Structure, Function, and Bioinformatics Published by Wiley Periodicals, Inc.
Collapse
Affiliation(s)
- Yuan-Ping Pang
- Computer-Aided Molecular Design Laboratory, Mayo Clinic, Rochester, MN, 55905, USA.
| |
Collapse
|
16
|
Pang YP. Use of multiple picosecond high-mass molecular dynamics simulations to predict crystallographic B-factors of folded globular proteins. Heliyon 2016; 2:e00161. [PMID: 27699282 PMCID: PMC5035356 DOI: 10.1016/j.heliyon.2016.e00161] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2016] [Revised: 08/18/2016] [Accepted: 09/12/2016] [Indexed: 12/22/2022] Open
Abstract
Predicting crystallographic B-factors of a protein from a conventional molecular dynamics simulation is challenging, in part because the B-factors calculated through sampling the atomic positional fluctuations in a picosecond molecular dynamics simulation are unreliable, and the sampling of a longer simulation yields overly large root mean square deviations between calculated and experimental B-factors. This article reports improved B-factor prediction achieved by sampling the atomic positional fluctuations in multiple picosecond molecular dynamics simulations that use uniformly increased atomic masses by 100-fold to increase time resolution. Using the third immunoglobulin-binding domain of protein G, bovine pancreatic trypsin inhibitor, ubiquitin, and lysozyme as model systems, the B-factor root mean square deviations (mean ± standard error) of these proteins were 3.1 ± 0.2–9 ± 1 Å2 for Cα and 7.3 ± 0.9–9.6 ± 0.2 Å2 for Cγ, when the sampling was done for each of these proteins over 20 distinct, independent, and 50-picosecond high-mass molecular dynamics simulations with AMBER forcefield FF12MC or FF14SB. These results suggest that sampling the atomic positional fluctuations in multiple picosecond high-mass molecular dynamics simulations may be conducive to a priori prediction of crystallographic B-factors of a folded globular protein.
Collapse
Affiliation(s)
- Yuan-Ping Pang
- Computer-Aided Molecular Design Laboratory, Mayo Clinic, Rochester, MN 55905, USA
| |
Collapse
|
17
|
Kong L, He L, de Val N, Vora N, Morris CD, Azadnia P, Sok D, Zhou B, Burton DR, Ward AB, Wilson IA, Zhu J. Uncleaved prefusion-optimized gp140 trimers derived from analysis of HIV-1 envelope metastability. Nat Commun 2016; 7:12040. [PMID: 27349805 PMCID: PMC4931249 DOI: 10.1038/ncomms12040] [Citation(s) in RCA: 96] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2016] [Accepted: 05/24/2016] [Indexed: 11/17/2022] Open
Abstract
The trimeric HIV-1 envelope glycoprotein (Env) is critical for host immune recognition and neutralization. Despite advances in trimer design, the roots of Env trimer metastability remain elusive. Here we investigate the contribution of two Env regions to metastability. First, we computationally redesign a largely disordered bend in heptad region 1 (HR1) of SOSIP trimers that connects the long, central HR1 helix to the fusion peptide, substantially improving the yield of soluble, well-folded trimers. Structural and antigenic analyses of two distinct HR1 redesigns confirm that redesigned Env closely mimics the native, prefusion trimer with a more stable gp41. Next, we replace the cleavage site between gp120 and gp41 with various linkers in the context of an HR1 redesign. Electron microscopy reveals a potential fusion intermediate state for uncleaved trimers containing short but not long linkers. Together, these results outline a general approach for stabilization of Env trimers from diverse HIV-1 strains.
Collapse
Affiliation(s)
- Leopold Kong
- Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, California 92037, USA
- International AIDS Vaccine Initiative Neutralizing Antibody Center and the Collaboration for AIDS Vaccine Discovery, The Scripps Research Institute, La Jolla, California 92037, USA
- Scripps Center for HIV/AIDS Vaccine Immunology & Immunogen Discovery, The Scripps Research Institute, La Jolla, California 92037, USA
| | - Linling He
- Department of Immunology and Microbial Science, The Scripps Research Institute, La Jolla, California 92037, USA
| | - Natalia de Val
- Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, California 92037, USA
- International AIDS Vaccine Initiative Neutralizing Antibody Center and the Collaboration for AIDS Vaccine Discovery, The Scripps Research Institute, La Jolla, California 92037, USA
- Scripps Center for HIV/AIDS Vaccine Immunology & Immunogen Discovery, The Scripps Research Institute, La Jolla, California 92037, USA
| | - Nemil Vora
- Department of Immunology and Microbial Science, The Scripps Research Institute, La Jolla, California 92037, USA
| | - Charles D. Morris
- Department of Immunology and Microbial Science, The Scripps Research Institute, La Jolla, California 92037, USA
| | - Parisa Azadnia
- Department of Immunology and Microbial Science, The Scripps Research Institute, La Jolla, California 92037, USA
| | - Devin Sok
- International AIDS Vaccine Initiative Neutralizing Antibody Center and the Collaboration for AIDS Vaccine Discovery, The Scripps Research Institute, La Jolla, California 92037, USA
- Scripps Center for HIV/AIDS Vaccine Immunology & Immunogen Discovery, The Scripps Research Institute, La Jolla, California 92037, USA
- Department of Immunology and Microbial Science, The Scripps Research Institute, La Jolla, California 92037, USA
| | - Bin Zhou
- Department of Chemistry, The Scripps Research Institute, La Jolla, California 92037, USA
| | - Dennis R. Burton
- International AIDS Vaccine Initiative Neutralizing Antibody Center and the Collaboration for AIDS Vaccine Discovery, The Scripps Research Institute, La Jolla, California 92037, USA
- Scripps Center for HIV/AIDS Vaccine Immunology & Immunogen Discovery, The Scripps Research Institute, La Jolla, California 92037, USA
- Department of Immunology and Microbial Science, The Scripps Research Institute, La Jolla, California 92037, USA
- Ragon Institute of Massachusetts General Hospital, Massachusetts Institute of Technology and Harvard, Cambridge, Massachusetts 02139-3583, USA
| | - Andrew B. Ward
- Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, California 92037, USA
- International AIDS Vaccine Initiative Neutralizing Antibody Center and the Collaboration for AIDS Vaccine Discovery, The Scripps Research Institute, La Jolla, California 92037, USA
- Scripps Center for HIV/AIDS Vaccine Immunology & Immunogen Discovery, The Scripps Research Institute, La Jolla, California 92037, USA
| | - Ian A. Wilson
- Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, California 92037, USA
- International AIDS Vaccine Initiative Neutralizing Antibody Center and the Collaboration for AIDS Vaccine Discovery, The Scripps Research Institute, La Jolla, California 92037, USA
- Scripps Center for HIV/AIDS Vaccine Immunology & Immunogen Discovery, The Scripps Research Institute, La Jolla, California 92037, USA
- The Joint Center for Structural Genomics, The Scripps Research Institute, La Jolla, California 92037, USA
- Skaggs Institute for Chemical Biology, The Scripps Research Institute, La Jolla, California 92037, USA
| | - Jiang Zhu
- Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, California 92037, USA
- Scripps Center for HIV/AIDS Vaccine Immunology & Immunogen Discovery, The Scripps Research Institute, La Jolla, California 92037, USA
- Department of Immunology and Microbial Science, The Scripps Research Institute, La Jolla, California 92037, USA
| |
Collapse
|
18
|
Subramanian N, Scopelitti AJ, Carland JE, Ryan RM, O’Mara ML, Vandenberg RJ. Identification of a 3rd Na+ Binding Site of the Glycine Transporter, GlyT2. PLoS One 2016; 11:e0157583. [PMID: 27337045 PMCID: PMC4919009 DOI: 10.1371/journal.pone.0157583] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2016] [Accepted: 06/01/2016] [Indexed: 12/25/2022] Open
Abstract
The Na+/Cl- dependent glycine transporters GlyT1 and GlyT2 regulate synaptic glycine concentrations. Glycine transport by GlyT2 is coupled to the co-transport of three Na+ ions, whereas transport by GlyT1 is coupled to the co-transport of only two Na+ ions. These differences in ion-flux coupling determine their respective concentrating capacities and have a direct bearing on their functional roles in synaptic transmission. The crystal structures of the closely related bacterial Na+-dependent leucine transporter, LeuTAa, and the Drosophila dopamine transporter, dDAT, have allowed prediction of two Na+ binding sites in GlyT2, but the physical location of the third Na+ site in GlyT2 is unknown. A bacterial betaine transporter, BetP, has also been crystallized and shows structural similarity to LeuTAa. Although betaine transport by BetP is coupled to the co-transport of two Na+ ions, the first Na+ site is not conserved between BetP and LeuTAa, the so called Na1' site. We hypothesized that the third Na+ binding site (Na3 site) of GlyT2 corresponds to the BetP Na1' binding site. To identify the Na3 binding site of GlyT2, we performed molecular dynamics (MD) simulations. Surprisingly, a Na+ placed at the location consistent with the Na1' site of BetP spontaneously dissociated from its initial location and bound instead to a novel Na3 site. Using a combination of MD simulations of a comparative model of GlyT2 together with an analysis of the functional properties of wild type and mutant GlyTs we have identified an electrostatically favorable novel third Na+ binding site in GlyT2 formed by Trp263 and Met276 in TM3, Ala481 in TM6 and Glu648 in TM10.
Collapse
Affiliation(s)
- Nandhitha Subramanian
- Research School of Chemistry, The Australian National University, Canberra, ACT, 2601, Australia
| | - Amanda J. Scopelitti
- Discipline of Pharmacology, School of Medical Sciences, University of Sydney, Sydney, NSW, 2006, Australia
- Department of Physiology and Biophysics, Weill Cornell Medical College, New York, NY, 10021, United States of America
| | - Jane E. Carland
- Discipline of Pharmacology, School of Medical Sciences, University of Sydney, Sydney, NSW, 2006, Australia
| | - Renae M. Ryan
- Discipline of Pharmacology, School of Medical Sciences, University of Sydney, Sydney, NSW, 2006, Australia
| | - Megan L. O’Mara
- Research School of Chemistry, The Australian National University, Canberra, ACT, 2601, Australia
| | - Robert J. Vandenberg
- Discipline of Pharmacology, School of Medical Sciences, University of Sydney, Sydney, NSW, 2006, Australia
| |
Collapse
|
19
|
|
20
|
Wildberg A, Della Corte D, Schröder GF. Coupling an Ensemble of Homologues Improves Refinement of Protein Homology Models. J Chem Theory Comput 2015; 11:5578-82. [PMID: 26642980 DOI: 10.1021/acs.jctc.5b00942] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
Atomic models of proteins built by homology modeling or from low-resolution experimental data may contain considerable local errors. The refinement success of molecular dynamics simulations is usually limited by both force field accuracy and by the substantial width of the conformational distribution at physiological temperatures. We propose a method to overcome both these problems by coupling homologous replicas during a molecular dynamics simulation, which narrows the conformational distribution, and smoothens and even improves the energy landscape by adding evolutionary information.
Collapse
Affiliation(s)
- André Wildberg
- ICS-6: Structural Biochemistry, Institute of Complex Systems, Forschungszentrum Jülich , 52425 Jülich, Germany
| | - Dennis Della Corte
- ICS-6: Structural Biochemistry, Institute of Complex Systems, Forschungszentrum Jülich , 52425 Jülich, Germany
| | - Gunnar F Schröder
- ICS-6: Structural Biochemistry, Institute of Complex Systems, Forschungszentrum Jülich , 52425 Jülich, Germany.,Physics Department, Heinrich-Heine Universität Düsseldorf , 40225 Düsseldorf, Germany
| |
Collapse
|
21
|
Della Corte D, Wildberg A, Schröder GF. Protein structure refinement with adaptively restrained homologous replicas. Proteins 2015; 84 Suppl 1:302-13. [PMID: 26441154 DOI: 10.1002/prot.24939] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2015] [Revised: 09/02/2015] [Accepted: 09/29/2015] [Indexed: 12/27/2022]
Abstract
A novel protein refinement protocol is presented which utilizes molecular dynamics (MD) simulations of an ensemble of adaptively restrained homologous replicas. This approach adds evolutionary information to the force field and reduces random conformational fluctuations by coupling of several replicas. It is shown that this protocol refines the majority of models from the CASP11 refinement category and that larger conformational changes of the starting structure are possible than with current state of the art methods. The performance of this protocol in the CASP11 experiment is discussed. We found that the quality of the refined model is correlated with the structural variance of the coupled replicas, which therefore provides a good estimator of model quality. Furthermore, some remarkable refinement results are discussed in detail. Proteins 2016; 84(Suppl 1):302-313. © 2015 Wiley Periodicals, Inc.
Collapse
Affiliation(s)
- Dennis Della Corte
- Institute of Complex Systems (ICS-6), Forschungszentrum Jülich, Jülich, 52425, Germany
| | - André Wildberg
- Institute of Complex Systems (ICS-6), Forschungszentrum Jülich, Jülich, 52425, Germany
| | - Gunnar F Schröder
- Institute of Complex Systems (ICS-6), Forschungszentrum Jülich, Jülich, 52425, Germany. .,Physics Department, University of Düsseldorf, Düsseldorf, 40225, Germany.
| |
Collapse
|
22
|
Approaching rational epitope vaccine design for hepatitis C virus with meta-server and multivalent scaffolding. Sci Rep 2015; 5:12501. [PMID: 26238798 PMCID: PMC4533164 DOI: 10.1038/srep12501] [Citation(s) in RCA: 51] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2015] [Accepted: 06/18/2015] [Indexed: 02/06/2023] Open
Abstract
Development of a prophylactic vaccine against hepatitis C virus (HCV) has been hampered by the extraordinary viral diversity and the poor host immune response. Scaffolding, by grafting an epitope onto a heterologous protein scaffold, offers a possible solution to epitope vaccine design. In this study, we designed and characterized epitope vaccine antigens for the antigenic sites of HCV envelope glycoproteins E1 (residues 314–324) and E2 (residues 412–423), for which neutralizing antibody-bound structures are available. We first combined six structural alignment algorithms in a “scaffolding meta-server” to search for diverse scaffolds that can structurally accommodate the HCV epitopes. For each antigenic site, ten scaffolds were selected for computational design, and the resulting epitope scaffolds were analyzed using structure-scoring functions and molecular dynamics simulation. We experimentally confirmed that three E1 and five E2 epitope scaffolds bound to their respective neutralizing antibodies, but with different kinetics. We then investigated a “multivalent scaffolding” approach by displaying 24 copies of an epitope scaffold on a self-assembling nanoparticle, which markedly increased the avidity of antibody binding. Our study thus demonstrates the utility of a multi-scale scaffolding strategy in epitope vaccine design and provides promising HCV immunogens for further assessment in vivo.
Collapse
|
23
|
Lee GR, Heo L, Seok C. Effective protein model structure refinement by loop modeling and overall relaxation. Proteins 2015; 84 Suppl 1:293-301. [PMID: 26172288 DOI: 10.1002/prot.24858] [Citation(s) in RCA: 60] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2015] [Revised: 06/29/2015] [Accepted: 07/06/2015] [Indexed: 12/25/2022]
Abstract
Protein structures predicted by state-of-the-art template-based methods may still have errors when the template proteins are not similar enough to the target protein. Overall target structure may deviate from the template structures owing to differences in sequences. Structural information for some local regions such as loops may not be available when there are sequence insertions or deletions. Those structural aspects that originate from deviations from templates can be dealt with by ab initio structure refinement methods to further improve model accuracy. In the CASP11 refinement experiment, we tested three different refinement methods that utilize overall structure relaxation, loop modeling, and quality assessment of multiple initial structures. From this experiment, we conclude that the overall relaxation method can consistently improve model quality. Loop modeling is the most useful when the initial model structure is high quality, with GDT-HA >60. The method that used multiple initial structures further refined the already refined models; the minor improvements with this method raise the issue of problem with the current energy function. Future research directions are also discussed. Proteins 2016; 84(Suppl 1):293-301. © 2015 Wiley Periodicals, Inc.
Collapse
Affiliation(s)
- Gyu Rie Lee
- Department of Chemistry, Seoul National University, Seoul, 151-747, Republic of Korea
| | - Lim Heo
- Department of Chemistry, Seoul National University, Seoul, 151-747, Republic of Korea
| | - Chaok Seok
- Department of Chemistry, Seoul National University, Seoul, 151-747, Republic of Korea.
| |
Collapse
|
24
|
Ke YY, Singh VK, Coumar MS, Hsu YC, Wang WC, Song JS, Chen CH, Lin WH, Wu SH, Hsu JTA, Shih C, Hsieh HP. Homology modeling of DFG-in FMS-like tyrosine kinase 3 (FLT3) and structure-based virtual screening for inhibitor identification. Sci Rep 2015; 5:11702. [PMID: 26118648 PMCID: PMC4483777 DOI: 10.1038/srep11702] [Citation(s) in RCA: 57] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2014] [Accepted: 06/02/2015] [Indexed: 12/23/2022] Open
Abstract
The inhibition of FMS-like tyrosine kinase 3 (FLT3) activity using small-molecule inhibitors has emerged as a target-based alternative to traditional chemotherapy for the treatment of acute myeloid leukemia (AML). In this study, we report the use of structure-based virtual screening (SBVS), a computer-aided drug design technique for the identification of new chemotypes for FLT3 inhibition. For this purpose, homology modeling (HM) of the DFG-in FLT3 structure was carried using two template structures, including PDB ID: 1RJB (DFG-out FLT3 kinase domain) and PDB ID: 3LCD (DFG-in CSF-1 kinase domain). The modeled structure was able to correctly identify known DFG-in (SU11248, CEP-701, and PKC-412) and DFG-out (sorafenib, ABT-869 and AC220) FLT3 inhibitors, in docking studies. The modeled structure was then used to carry out SBVS of an HTS library of 125,000 compounds. The top scoring 97 compounds were tested for FLT3 kinase inhibition, and two hits (BPR056, IC50 = 2.3 and BPR080, IC50 = 10.7 μM) were identified. Molecular dynamics simulation and density functional theory calculation suggest that BPR056 (MW: 325.32; cLogP: 2.48) interacted with FLT3 in a stable manner and could be chemically optimized to realize a drug-like lead in the future.
Collapse
Affiliation(s)
- Yi-Yu Ke
- Institute of Biotechnology and Pharmaceutical Research, National Health Research Institutes, 35 Keyan Road, Zhunan, Miaoli County 350, Taiwan, ROC
| | - Vivek Kumar Singh
- Centre for Bioinformatics, School of Life Sciences, Pondicherry University, Kalapet, Puducherry 605014, India
| | - Mohane Selvaraj Coumar
- Centre for Bioinformatics, School of Life Sciences, Pondicherry University, Kalapet, Puducherry 605014, India
| | - Yung Chang Hsu
- Institute of Biotechnology and Pharmaceutical Research, National Health Research Institutes, 35 Keyan Road, Zhunan, Miaoli County 350, Taiwan, ROC
| | - Wen-Chieh Wang
- Institute of Biotechnology and Pharmaceutical Research, National Health Research Institutes, 35 Keyan Road, Zhunan, Miaoli County 350, Taiwan, ROC
| | - Jen-Shin Song
- Institute of Biotechnology and Pharmaceutical Research, National Health Research Institutes, 35 Keyan Road, Zhunan, Miaoli County 350, Taiwan, ROC
| | - Chun-Hwa Chen
- Institute of Biotechnology and Pharmaceutical Research, National Health Research Institutes, 35 Keyan Road, Zhunan, Miaoli County 350, Taiwan, ROC
| | - Wen-Hsing Lin
- Institute of Biotechnology and Pharmaceutical Research, National Health Research Institutes, 35 Keyan Road, Zhunan, Miaoli County 350, Taiwan, ROC
| | - Szu-Huei Wu
- Institute of Biotechnology and Pharmaceutical Research, National Health Research Institutes, 35 Keyan Road, Zhunan, Miaoli County 350, Taiwan, ROC
| | - John T A Hsu
- Institute of Biotechnology and Pharmaceutical Research, National Health Research Institutes, 35 Keyan Road, Zhunan, Miaoli County 350, Taiwan, ROC
| | - Chuan Shih
- Institute of Biotechnology and Pharmaceutical Research, National Health Research Institutes, 35 Keyan Road, Zhunan, Miaoli County 350, Taiwan, ROC
| | - Hsing-Pang Hsieh
- Institute of Biotechnology and Pharmaceutical Research, National Health Research Institutes, 35 Keyan Road, Zhunan, Miaoli County 350, Taiwan, ROC
| |
Collapse
|
25
|
Xun S, Jiang F, Wu YD. Significant Refinement of Protein Structure Models Using a Residue-Specific Force Field. J Chem Theory Comput 2015; 11:1949-56. [PMID: 26574396 DOI: 10.1021/acs.jctc.5b00029] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/12/2023]
Abstract
An important application of all-atom explicit-solvent molecular dynamics (MD) simulations is the refinement of protein structures from low-resolution experiments or template-based modeling. A critical requirement is that the native structure is stable with the force field. We have applied a recently developed residue-specific force field, RSFF1, to a set of 30 refinement targets from recent CASP experiments. Starting from their experimental structures, 1.0 μs unrestrained simulations at 298 K retain most of the native structures quite well except for a few flexible terminals and long internal loops. Starting from each homology model, a 150 ns MD simulation at 380 K generates the best RMSD improvement of 0.85 Å on average. The structural improvements roughly correlate with the RMSD of the initial homology models, indicating possible consistent structure refinement. Finally, targets TR614 and TR624 have been subjected to long-time replica-exchange MD simulations. Significant structural improvements are generated, with RMSD of 1.91 and 1.36 Å with respect to their crystal structures. Thus, it is possible to achieve realistic refinement of protein structure models to near-experimental accuracy, using accurate force field with sufficient conformational sampling.
Collapse
Affiliation(s)
- Sangni Xun
- Laboratory of Computational Chemistry and Drug Design, Laboratory of Chemical Genomics, Peking University Shenzhen Graduate School , Shenzhen, 518055, China
| | - Fan Jiang
- Laboratory of Computational Chemistry and Drug Design, Laboratory of Chemical Genomics, Peking University Shenzhen Graduate School , Shenzhen, 518055, China
| | - Yun-Dong Wu
- Laboratory of Computational Chemistry and Drug Design, Laboratory of Chemical Genomics, Peking University Shenzhen Graduate School , Shenzhen, 518055, China.,College of Chemistry and Molecular Engineering, Peking University , Beijing, 100871, China
| |
Collapse
|
26
|
Shahlaei M, Mousavi A. A Conformational Analysis Study on the Melanocortin 4 Receptor Using Multiple Molecular Dynamics Simulations. Chem Biol Drug Des 2015; 86:309-21. [DOI: 10.1111/cbdd.12495] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2014] [Revised: 05/29/2014] [Accepted: 06/13/2014] [Indexed: 12/28/2022]
Affiliation(s)
- Mohsen Shahlaei
- Novel Drug Delivery Research Center; School of Pharmacy; Kermanshah University of Medical Sciences; Parastar Bolvar 6734667149 Kermanshah Iran
| | - Atefeh Mousavi
- Student Research Committee; School of Pharmacy; Kermanshah University of Medical Sciences; Parastar Bolvar 6734667149 Kermanshah Iran
| |
Collapse
|
27
|
Jayaram B, Dhingra P, Mishra A, Kaushik R, Mukherjee G, Singh A, Shekhar S. Bhageerath-H: a homology/ab initio hybrid server for predicting tertiary structures of monomeric soluble proteins. BMC Bioinformatics 2014; 15 Suppl 16:S7. [PMID: 25521245 PMCID: PMC4290660 DOI: 10.1186/1471-2105-15-s16-s7] [Citation(s) in RCA: 29] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023] Open
Abstract
BACKGROUND The advent of human genome sequencing project has led to a spurt in the number of protein sequences in the databanks. Success of structure based drug discovery severely hinges on the availability of structures. Despite significant progresses in the area of experimental protein structure determination, the sequence-structure gap is continually widening. Data driven homology based computational methods have proved successful in predicting tertiary structures for sequences sharing medium to high sequence similarities. With dwindling similarities of query sequences, advanced homology/ ab initio hybrid approaches are being explored to solve structure prediction problem. Here we describe Bhageerath-H, a homology/ ab initio hybrid software/server for predicting protein tertiary structures with advancing drug design attempts as one of the goals. RESULTS Bhageerath-H web-server was validated on 75 CASP10 targets which showed TM-scores ≥ 0.5 in 91% of the cases and Cα RMSDs ≤ 5 Å from the native in 58% of the targets, which is well above the CASP10 water mark. Comparison with some leading servers demonstrated the uniqueness of the hybrid methodology in effectively sampling conformational space, scoring best decoys and refining low resolution models to high and medium resolution. CONCLUSION Bhageerath-H methodology is web enabled for the scientific community as a freely accessible web server. The methodology is fielded in the on-going CASP11 experiment.
Collapse
|
28
|
Carlsen M, Koehl P, Røgen P. On the importance of the distance measures used to train and test knowledge-based potentials for proteins. PLoS One 2014; 9:e109335. [PMID: 25411785 PMCID: PMC4239004 DOI: 10.1371/journal.pone.0109335] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2014] [Accepted: 08/31/2014] [Indexed: 12/15/2022] Open
Abstract
Knowledge-based potentials are energy functions derived from the analysis of databases of protein structures and sequences. They can be divided into two classes. Potentials from the first class are based on a direct conversion of the distributions of some geometric properties observed in native protein structures into energy values, while potentials from the second class are trained to mimic quantitatively the geometric differences between incorrectly folded models and native structures. In this paper, we focus on the relationship between energy and geometry when training the second class of knowledge-based potentials. We assume that the difference in energy between a decoy structure and the corresponding native structure is linearly related to the distance between the two structures. We trained two distance-based knowledge-based potentials accordingly, one based on all inter-residue distances (PPD), while the other had the set of all distances filtered to reflect consistency in an ensemble of decoys (PPE). We tested four types of metric to characterize the distance between the decoy and the native structure, two based on extrinsic geometry (RMSD and GTD-TS*), and two based on intrinsic geometry (Q* and MT). The corresponding eight potentials were tested on a large collection of decoy sets. We found that it is usually better to train a potential using an intrinsic distance measure. We also found that PPE outperforms PPD, emphasizing the benefits of capturing consistent information in an ensemble. The relevance of these results for the design of knowledge-based potentials is discussed.
Collapse
Affiliation(s)
- Martin Carlsen
- Department of Applied Mathematics and Computer Science, Technical University of Denmark, Kongens Lyngby, Denmark
| | - Patrice Koehl
- Department of Computer Science and Genome Center, University of California Davis, Davis, CA, United States of America
| | - Peter Røgen
- Department of Applied Mathematics and Computer Science, Technical University of Denmark, Kongens Lyngby, Denmark
- * E-mail:
| |
Collapse
|
29
|
Ryu H, Kim TR, Ahn S, Ji S, Lee J. Protein NMR structures refined without NOE data. PLoS One 2014; 9:e108888. [PMID: 25279564 PMCID: PMC4184813 DOI: 10.1371/journal.pone.0108888] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2014] [Accepted: 09/04/2014] [Indexed: 12/31/2022] Open
Abstract
The refinement of low-quality structures is an important challenge in protein structure prediction. Many studies have been conducted on protein structure refinement; the refinement of structures derived from NMR spectroscopy has been especially intensively studied. In this study, we generated flat-bottom distance potential instead of NOE data because NOE data have ambiguity and uncertainty. The potential was derived from distance information from given structures and prevented structural dislocation during the refinement process. A simulated annealing protocol was used to minimize the potential energy of the structure. The protocol was tested on 134 NMR structures in the Protein Data Bank (PDB) that also have X-ray structures. Among them, 50 structures were used as a training set to find the optimal "width" parameter in the flat-bottom distance potential functions. In the validation set (the other 84 structures), most of the 12 quality assessment scores of the refined structures were significantly improved (total score increased from 1.215 to 2.044). Moreover, the secondary structure similarity of the refined structure was improved over that of the original structure. Finally, we demonstrate that the combination of two energy potentials, statistical torsion angle potential (STAP) and the flat-bottom distance potential, can drive the refinement of NMR structures.
Collapse
Affiliation(s)
- Hyojung Ryu
- Korean Bioinformation Center (KOBIC), Korea Research Institute of Bioscience and Biotechnology, Daejeon, The Republic of Korea
- Department of Bioinformatics, University of Science and Technology, Daejeon, The Republic of Korea
| | - Tae-Rae Kim
- Department of Chemistry, Seoul National University, Seoul, The Republic of Korea
| | - SeonJoo Ahn
- Korean Bioinformation Center (KOBIC), Korea Research Institute of Bioscience and Biotechnology, Daejeon, The Republic of Korea
| | - Sunyoung Ji
- Korean Bioinformation Center (KOBIC), Korea Research Institute of Bioscience and Biotechnology, Daejeon, The Republic of Korea
- Department of Bioinformatics, University of Science and Technology, Daejeon, The Republic of Korea
| | - Jinhyuk Lee
- Korean Bioinformation Center (KOBIC), Korea Research Institute of Bioscience and Biotechnology, Daejeon, The Republic of Korea
- Department of Bioinformatics, University of Science and Technology, Daejeon, The Republic of Korea
| |
Collapse
|
30
|
Zhou T, Zhu J, Yang Y, Gorman J, Ofek G, Srivatsan S, Druz A, Lees CR, Lu G, Soto C, Stuckey J, Burton DR, Koff WC, Connors M, Kwon PD. Transplanting supersites of HIV-1 vulnerability. PLoS One 2014; 9:e99881. [PMID: 24992528 PMCID: PMC4084637 DOI: 10.1371/journal.pone.0099881] [Citation(s) in RCA: 47] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2014] [Accepted: 05/19/2014] [Indexed: 11/24/2022] Open
Abstract
One strategy for isolating or eliciting antibodies against a specific target region on the envelope glycoprotein trimer (Env) of the human immunodeficiency virus type 1 (HIV-1) involves the creation of site transplants, which present the target region on a heterologous protein scaffold with preserved antibody-binding properties. If the target region is a supersite of HIV-1 vulnerability, recognized by a collection of broadly neutralizing antibodies, this strategy affords the creation of “supersite transplants”, capable of binding (and potentially eliciting) antibodies similar to the template collection of effective antibodies. Here we transplant three supersites of HIV-1 vulnerability, each targeted by effective neutralizing antibodies from multiple donors. To implement our strategy, we chose a single representative antibody against each of the target supersites: antibody 10E8, which recognizes the membrane-proximal external region (MPER) on the HIV-1 gp41 glycoprotein; antibody PG9, which recognizes variable regions one and two (V1V2) on the HIV-1 gp120 glycoprotein; and antibody PGT128 which recognizes a glycopeptide supersite in variable region 3 (glycan V3) on gp120. We used a structural alignment algorithm to identify suitable acceptor proteins, and then designed, expressed, and tested antigenically over 100-supersite transplants in a 96-well microtiter-plate format. The majority of the supersite transplants failed to maintain the antigenic properties of their respective template supersite. However, seven of the glycan V3-supersite transplants exhibited nanomolar affinity to effective neutralizing antibodies from at least three donors and recapitulated the mannose9-N-linked glycan requirement of the template supersite. The binding of these transplants could be further enhanced by placement into self-assembling nanoparticles. Essential elements of the glycan V3 supersite, embodied by as few as 3 N-linked glycans and ∼25 Env residues, can be segregated into acceptor scaffolds away from the immune-evading capabilities of the rest of HIV-1 Env, thereby providing a means to focus the immune response on the scaffolded supersite.
Collapse
Affiliation(s)
- Tongqing Zhou
- Vaccine Research Center, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, Maryland, United States of America
| | - Jiang Zhu
- Vaccine Research Center, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, Maryland, United States of America
| | - Yongping Yang
- Vaccine Research Center, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, Maryland, United States of America
| | - Jason Gorman
- Vaccine Research Center, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, Maryland, United States of America
| | - Gilad Ofek
- Vaccine Research Center, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, Maryland, United States of America
| | - Sanjay Srivatsan
- Vaccine Research Center, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, Maryland, United States of America
| | - Aliaksandr Druz
- Vaccine Research Center, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, Maryland, United States of America
| | - Christopher R. Lees
- Vaccine Research Center, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, Maryland, United States of America
| | - Gabriel Lu
- Vaccine Research Center, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, Maryland, United States of America
| | - Cinque Soto
- Vaccine Research Center, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, Maryland, United States of America
| | - Jonathan Stuckey
- Vaccine Research Center, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, Maryland, United States of America
| | - Dennis R. Burton
- Department of Immunology and Microbial Science and IAVI Neutralizing Antibody Center, and Center for HIV/AIDS Vaccine Immunology and Immunogen Design, The Scripps Research Institute, La Jolla, California, United States of America
- Ragon Institute of MGH, MIT and Harvard, Cambridge, Massachusetts, United States of America
| | - Wayne C. Koff
- International AIDS Vaccine Initiative (IAVI), New York, New York, United States of America
| | - Mark Connors
- HIV-Specific Immunity Section, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, Maryland, United States of America
| | - Peter D. Kwon
- Vaccine Research Center, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, Maryland, United States of America
- * E-mail:
| |
Collapse
|
31
|
Mirjalili V, Noyes K, Feig M. Physics-based protein structure refinement through multiple molecular dynamics trajectories and structure averaging. Proteins 2014; 82 Suppl 2:196-207. [PMID: 23737254 PMCID: PMC4212311 DOI: 10.1002/prot.24336] [Citation(s) in RCA: 87] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2013] [Revised: 04/30/2013] [Accepted: 05/09/2013] [Indexed: 12/26/2022]
Abstract
We used molecular dynamics (MD) simulations for structure refinement of Critical Assessment of Techniques for Protein Structure Prediction 10 (CASP10) targets. Refinement was achieved by selecting structures from the MD-based ensembles followed by structural averaging. The overall performance of this method in CASP10 is described, and specific aspects are analyzed in detail to provide insight into key components. In particular, the use of different restraint types, sampling from multiple short simulations versus a single long simulation, the success of a quality assessment criterion, the application of scoring versus averaging, and the impact of a final refinement step are discussed in detail.
Collapse
Affiliation(s)
- Vahid Mirjalili
- Department of Mechanical Engineering Michigan State University East Lansing, MI 48824; USA
- Department of Biochemistry and Molecular Biology Michigan State University East Lansing, MI 48824; USA
| | - Keenan Noyes
- Department of Chemistry Michigan State University East Lansing, MI 48824; USA
| | - Michael Feig
- Department of Biochemistry and Molecular Biology Michigan State University East Lansing, MI 48824; USA
- Department of Chemistry Michigan State University East Lansing, MI 48824; USA
| |
Collapse
|
32
|
|
33
|
Dong GQ, Fan H, Schneidman-Duhovny D, Webb B, Sali A. Optimized atomic statistical potentials: assessment of protein interfaces and loops. Bioinformatics 2013; 29:3158-66. [PMID: 24078704 PMCID: PMC3842762 DOI: 10.1093/bioinformatics/btt560] [Citation(s) in RCA: 98] [Impact Index Per Article: 8.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2013] [Revised: 08/13/2013] [Accepted: 09/22/2013] [Indexed: 01/16/2023] Open
Abstract
MOTIVATION Statistical potentials have been widely used for modeling whole proteins and their parts (e.g. sidechains and loops) as well as interactions between proteins, nucleic acids and small molecules. Here, we formulate the statistical potentials entirely within a statistical framework, avoiding questionable statistical mechanical assumptions and approximations, including a definition of the reference state. RESULTS We derive a general Bayesian framework for inferring statistically optimized atomic potentials (SOAP) in which the reference state is replaced with data-driven 'recovery' functions. Moreover, we restrain the relative orientation between two covalent bonds instead of a simple distance between two atoms, in an effort to capture orientation-dependent interactions such as hydrogen bonds. To demonstrate this general approach, we computed statistical potentials for protein-protein docking (SOAP-PP) and loop modeling (SOAP-Loop). For docking, a near-native model is within the top 10 scoring models in 40% of the PatchDock benchmark cases, compared with 23 and 27% for the state-of-the-art ZDOCK and FireDock scoring functions, respectively. Similarly, for modeling 12-residue loops in the PLOP benchmark, the average main-chain root mean square deviation of the best scored conformations by SOAP-Loop is 1.5 Å, close to the average root mean square deviation of the best sampled conformations (1.2 Å) and significantly better than that selected by Rosetta (2.1 Å), DFIRE (2.3 Å), DOPE (2.5 Å) and PLOP scoring functions (3.0 Å). Our Bayesian framework may also result in more accurate statistical potentials for additional modeling applications, thus affording better leverage of the experimentally determined protein structures. AVAILABILITY AND IMPLEMENTATION SOAP-PP and SOAP-Loop are available as part of MODELLER (http://salilab.org/modeller).
Collapse
Affiliation(s)
- Guang Qiang Dong
- Department of Bioengineering and Therapeutic Sciences, Department of Pharmaceutical Chemistry and California Institute for Quantitative Biosciences (QB3), University of California, San Francisco, CA 94158, USA
| | | | | | | | | |
Collapse
|
34
|
Petrella RJ. OPTIMIZATION BIAS IN ENERGY-BASED STRUCTURE PREDICTION. JOURNAL OF THEORETICAL & COMPUTATIONAL CHEMISTRY 2013; 12:1341014. [PMID: 25552783 PMCID: PMC4278582 DOI: 10.1142/s0219633613410149] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Physics-based computational approaches to predicting the structure of macromolecules such as proteins are gaining increased use, but there are remaining challenges. In the current work, it is demonstrated that in energy-based prediction methods, the degree of optimization of the sampled structures can influence the prediction results. In particular, discrepancies in the degree of local sampling can bias the predictions in favor of the oversampled structures by shifting the local probability distributions of the minimum sampled energies. In simple systems, it is shown that the magnitude of the errors can be calculated from the energy surface, and for certain model systems, derived analytically. Further, it is shown that for energy wells whose forms differ only by a randomly assigned energy shift, the optimal accuracy of prediction is achieved when the sampling around each structure is equal. Energy correction terms can be used in cases of unequal sampling to reproduce the total probabilities that would occur under equal sampling, but optimal corrections only partially restore the prediction accuracy lost to unequal sampling. For multiwell systems, the determination of the correction terms is a multibody problem; it is shown that the involved cross-correlation multiple integrals can be reduced to simpler integrals. The possible implications of the current analysis for macromolecular structure prediction are discussed.
Collapse
Affiliation(s)
- Robert J. Petrella
- Department of Chemistry and Chemical Biology, Harvard University, 12 Oxford Street, Cambridge, MA 02138, USA
- Department of Medicine, Harvard Medical School, 25 Shattuck Street, Boston, MA 02115, USA
| |
Collapse
|
35
|
Chakraborty S, Venkatramani R, Rao BJ, Asgeirsson B, Dandekar AM. The electrostatic profile of consecutive Cβ atoms applied to protein structure quality assessment. F1000Res 2013; 2:243. [PMID: 25506420 PMCID: PMC4257144 DOI: 10.12688/f1000research.2-243.v1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 09/16/2014] [Indexed: 02/10/2024] Open
Abstract
The structure of a protein provides insight into its physiological interactions with other components of the cellular soup. Methods that predict putative structures from sequences typically yield multiple, closely-ranked possibilities. A critical component in the process is the model quality assessing program (MQAP), which selects the best candidate from this pool of structures. Here, we present a novel MQAP based on the physical properties of sidechain atoms. We propose a method for assessing the quality of protein structures based on the electrostatic potential difference (EPD) of Cβ atoms in consecutive residues. We demonstrate that the EPDs of Cβ atoms on consecutive residues provide unique signatures of the amino acid types. The EPD of Cβ atoms are learnt from a set of 1000 non-homologous protein structures with a resolution cuto of 1.6 Å obtained from the PISCES database. Based on the Boltzmann hypothesis that lower energy conformations are proportionately sampled more, and on Annsen's thermodynamic hypothesis that the native structure of a protein is the minimum free energy state, we hypothesize that the deviation of observed EPD values from the mean values obtained in the learning phase is minimized in the native structure. We achieved an average specificity of 0.91, 0.94 and 0.93 on hg_structal, 4state_reduced and ig_structal decoy sets, respectively, taken from the Decoys `R' Us database. The source code and manual is made available at https://github.com/sanchak/mqap and permanently available on 10.5281/zenodo.7134.
Collapse
Affiliation(s)
- Sandeep Chakraborty
- Department of Biological Sciences, Tata Institute of Fundamental Research, Mumbai, 400 005, India
| | - Ravindra Venkatramani
- Department of Chemical Sciences, Tata Institute of Fundamental Research, Mumbai, 400 005, India
| | - Basuthkar J. Rao
- Department of Biological Sciences, Tata Institute of Fundamental Research, Mumbai, 400 005, India
| | - Bjarni Asgeirsson
- Science Institute, Department of Biochemistry, University of Iceland, IS-107 Reykjavik, Iceland
| | - Abhaya M. Dandekar
- Plant Sciences Department, University of California,, Davis, CA, 95616, USA
| |
Collapse
|
36
|
Chakraborty S, Venkatramani R, Rao BJ, Asgeirsson B, Dandekar AM. Protein structure quality assessment based on the distance profiles of consecutive backbone Cα atoms. F1000Res 2013; 2:211. [PMID: 24555103 PMCID: PMC3892923 DOI: 10.12688/f1000research.2-211.v1#sthash.lfll9fko.snt845h1.dpuf] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 12/16/2013] [Indexed: 06/29/2024] Open
Abstract
Predicting the three dimensional native state structure of a protein from its primary sequence is an unsolved grand challenge in molecular biology. Two main computational approaches have evolved to obtain the structure from the protein sequence - ab initio/de novo methods and template-based modeling - both of which typically generate multiple possible native state structures. Model quality assessment programs (MQAP) validate these predicted structures in order to identify the correct native state structure. Here, we propose a MQAP for assessing the quality of protein structures based on the distances of consecutive Cα atoms. We hypothesize that the root-mean-square deviation of the distance of consecutive Cα (RDCC) atoms from the ideal value of 3.8 Å, derived from a statistical analysis of high quality protein structures (top100H database), is minimized in native structures. Based on tests with the top100H set, we propose a RDCC cutoff value of 0.012 Å, above which a structure can be filtered out as a non-native structure. We applied the RDCC discriminator on decoy sets from the Decoys 'R' Us database to show that the native structures in all decoy sets tested have RDCC below the 0.012 Å cutoff. While most decoy sets were either indistinguishable using this discriminator or had very few violations, all the decoy structures in the fisa decoy set were discriminated by applying the RDCC criterion. This highlights the physical non-viability of the fisa decoy set, and possible issues in benchmarking other methods using this set. The source code and manual is made available at https://github.com/sanchak/mqap and permanently available on 10.5281/zenodo.7134.
Collapse
Affiliation(s)
- Sandeep Chakraborty
- Department of Biological Sciences, Tata Institute of Fundamental Research, Mumbai, 400 005, India
| | - Ravindra Venkatramani
- Department of Chemical Sciences, Tata Institute of Fundamental Research, Mumbai, 400 005, India
| | - Basuthkar J. Rao
- Department of Biological Sciences, Tata Institute of Fundamental Research, Mumbai, 400 005, India
| | - Bjarni Asgeirsson
- Science Institute, Department of Biochemistry, University of Iceland, Reykjavik, IS-107, Iceland
| | - Abhaya M. Dandekar
- Plant Sciences Department, University of California, Davis, CA 95616, USA
| |
Collapse
|
37
|
Chakraborty S, Venkatramani R, Rao BJ, Asgeirsson B, Dandekar AM. Protein structure quality assessment based on the distance profiles of consecutive backbone Cα atoms. F1000Res 2013; 2:211. [PMID: 24555103 DOI: 10.12688/f1000research.2-211.v1] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 10/10/2013] [Indexed: 01/22/2023] Open
Abstract
Predicting the three dimensional native state structure of a protein from its primary sequence is an unsolved grand challenge in molecular biology. Two main computational approaches have evolved to obtain the structure from the protein sequence - ab initio/de novo methods and template-based modeling - both of which typically generate multiple possible native state structures. Model quality assessment programs (MQAP) validate these predicted structures in order to identify the correct native state structure. Here, we propose a MQAP for assessing the quality of protein structures based on the distances of consecutive Cα atoms. We hypothesize that the root-mean-square deviation of the distance of consecutive Cα (RDCC) atoms from the ideal value of 3.8 Å, derived from a statistical analysis of high quality protein structures (top100H database), is minimized in native structures. Based on tests with the top100H set, we propose a RDCC cutoff value of 0.012 Å, above which a structure can be filtered out as a non-native structure. We applied the RDCC discriminator on decoy sets from the Decoys 'R' Us database to show that the native structures in all decoy sets tested have RDCC below the 0.012 Å cutoff. While most decoy sets were either indistinguishable using this discriminator or had very few violations, all the decoy structures in the fisa decoy set were discriminated by applying the RDCC criterion. This highlights the physical non-viability of the fisa decoy set, and possible issues in benchmarking other methods using this set. The source code and manual is made available at https://github.com/sanchak/mqap and permanently available on 10.5281/zenodo.7134.
Collapse
Affiliation(s)
- Sandeep Chakraborty
- Department of Biological Sciences, Tata Institute of Fundamental Research, Mumbai, 400 005, India
| | - Ravindra Venkatramani
- Department of Chemical Sciences, Tata Institute of Fundamental Research, Mumbai, 400 005, India
| | - Basuthkar J Rao
- Department of Biological Sciences, Tata Institute of Fundamental Research, Mumbai, 400 005, India
| | - Bjarni Asgeirsson
- Science Institute, Department of Biochemistry, University of Iceland, Reykjavik, IS-107, Iceland
| | - Abhaya M Dandekar
- Plant Sciences Department, University of California, Davis, CA 95616, USA
| |
Collapse
|
38
|
Bhattacharya D, Cheng J. i3Drefine software for protein 3D structure refinement and its assessment in CASP10. PLoS One 2013; 8:e69648. [PMID: 23894517 PMCID: PMC3716612 DOI: 10.1371/journal.pone.0069648] [Citation(s) in RCA: 41] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2013] [Accepted: 06/13/2013] [Indexed: 12/25/2022] Open
Abstract
Protein structure refinement refers to the process of improving the qualities of protein structures during structure modeling processes to bring them closer to their native states. Structure refinement has been drawing increasing attention in the community-wide Critical Assessment of techniques for Protein Structure prediction (CASP) experiments since its addition in 8th CASP experiment. During the 9th and recently concluded 10th CASP experiments, a consistent growth in number of refinement targets and participating groups has been witnessed. Yet, protein structure refinement still remains a largely unsolved problem with majority of participating groups in CASP refinement category failed to consistently improve the quality of structures issued for refinement. In order to alleviate this need, we developed a completely automated and computationally efficient protein 3D structure refinement method, i3Drefine, based on an iterative and highly convergent energy minimization algorithm with a powerful all-atom composite physics and knowledge-based force fields and hydrogen bonding (HB) network optimization technique. In the recent community-wide blind experiment, CASP10, i3Drefine (as ‘MULTICOM-CONSTRUCT’) was ranked as the best method in the server section as per the official assessment of CASP10 experiment. Here we provide the community with free access to i3Drefine software and systematically analyse the performance of i3Drefine in strict blind mode on the refinement targets issued in CASP10 refinement category and compare with other state-of-the-art refinement methods participating in CASP10. Our analysis demonstrates that i3Drefine is only fully-automated server participating in CASP10 exhibiting consistent improvement over the initial structures in both global and local structural quality metrics. Executable version of i3Drefine is freely available at http://protein.rnet.missouri.edu/i3drefine/.
Collapse
Affiliation(s)
- Debswapna Bhattacharya
- Department of Computer Science, University of Missouri, Columbia, Missouri, United States of America
| | - Jianlin Cheng
- Department of Computer Science, Informatics Institute, Bond Life Science Center, University of Missouri, Columbia, Missouri, United States of America
- * E-mail:
| |
Collapse
|
39
|
Olson MA, Lee MS. Application of replica exchange umbrella sampling to protein structure refinement of nontemplate models. J Comput Chem 2013; 34:1785-93. [PMID: 23703032 DOI: 10.1002/jcc.23325] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2012] [Revised: 03/12/2013] [Accepted: 04/21/2013] [Indexed: 12/30/2022]
Abstract
We provide an assessment of a computational strategy for protein structure refinement that combines self-guided Langevin dynamics with umbrella-potential biasing replica exchange using the radius of gyration as a coordinate (Rg -ReX). Eight structurally nonredundant proteins and their decoys were examined by sampling conformational space at room temperature using the CHARMM22/GBMV2 force field to generate the ensemble of structures. Two atomic statistical potentials (RWplus and DFIRE) were analyzed for structure identification and compared to the simulation force-field potential. The results show that, while the Rg -ReX simulations were able to sample conformational basins that were more structurally similar to the X-ray crystallographic structures than the starting first-order ranked decoys, the potentials failed to detect these basins from refinement. Of the three potential functions, RWplus yielded the highest accuracy for recognition of structures that refined to an average of nearly 20% increase in native contacts relative to the starting decoys. The overall performance of Rg -ReX is compared to an earlier study of applying temperature-based replica exchange to refine the same decoy sets and highlights the general challenge of achieving consistently the sampling and detection threshold of 70% fraction of native contacts.
Collapse
Affiliation(s)
- Mark A Olson
- Department of Cell Biology and Biochemistry, USAMRIID, Fredrick, Maryland 21702, USA.
| | | |
Collapse
|
40
|
Mirjalili V, Feig M. Protein Structure Refinement through Structure Selection and Averaging from Molecular Dynamics Ensembles. J Chem Theory Comput 2013; 9:1294-1303. [PMID: 23526422 PMCID: PMC3603382 DOI: 10.1021/ct300962x] [Citation(s) in RCA: 70] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
Abstract
A molecular dynamics (MD) simulation based protocol for structure refinement of template-based model predictions is described. The protocol involves the application of restraints, ensemble averaging of selected subsets, interpolation between initial and refined structures, and assessment of refinement success. It is found that sub-microsecond MD-based sampling when combined with ensemble averaging can produce moderate but consistent refinement for most systems in the CASP targets considered here.
Collapse
Affiliation(s)
- Vahid Mirjalili
- Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, MI 48824; USA
- Department of Mechanical Engineering, Michigan State University, East Lansing, MI 48824; USA
| | - Michael Feig
- Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, MI 48824; USA
- Department of Chemistry, Michigan State University, East Lansing, MI 48824; USA
| |
Collapse
|
41
|
Srivastava A, Meena SK, Alam M, Nayeem SM, Deep S, Sau AK. Structural and Functional Insights into the Regulation of Helicobacter pylori Arginase Activity by an Evolutionary Nonconserved Motif. Biochemistry 2013; 52:508-19. [DOI: 10.1021/bi301421v] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023]
Affiliation(s)
- Abhishek Srivastava
- National Institute of Immunology, Aruna Asaf Ali Marg, New Delhi 110 067,
India
| | - Shiv Kumar Meena
- National Institute of Immunology, Aruna Asaf Ali Marg, New Delhi 110 067,
India
| | - Mashkoor Alam
- National Institute of Immunology, Aruna Asaf Ali Marg, New Delhi 110 067,
India
| | - Shahid M. Nayeem
- Department of Chemistry, Indian Institute of Technology, New Delhi 110 016,
India
| | - Shashank Deep
- Department of Chemistry, Indian Institute of Technology, New Delhi 110 016,
India
| | - Apurba Kumar Sau
- National Institute of Immunology, Aruna Asaf Ali Marg, New Delhi 110 067,
India
| |
Collapse
|
42
|
Chakraborty S, Venkatramani R, Rao BJ, Asgeirsson B, Dandekar AM. The electrostatic profile of consecutive Cβ atoms applied to protein structure quality assessment. F1000Res 2013; 2:243. [PMID: 25506420 PMCID: PMC4257144 DOI: 10.12688/f1000research.2-243.v3] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 09/16/2014] [Indexed: 12/23/2022] Open
Abstract
The structure of a protein provides insight into its physiological interactions with other components of the cellular soup. Methods that predict putative structures from sequences typically yield multiple, closely-ranked possibilities. A critical component in the process is the model quality assessing program (MQAP), which selects the best candidate from this pool of structures. Here, we present a novel MQAP based on the physical properties of sidechain atoms. We propose a method for assessing the quality of protein structures based on the electrostatic potential difference (EPD) of Cβ atoms in consecutive residues. We demonstrate that the EPDs of Cβ atoms on consecutive residues provide unique signatures of the amino acid types. The EPD of Cβ atoms are learnt from a set of 1000 non-homologous protein structures with a resolution cuto of 1.6 Å obtained from the PISCES database. Based on the Boltzmann hypothesis that lower energy conformations are proportionately sampled more, and on Annsen's thermodynamic hypothesis that the native structure of a protein is the minimum free energy state, we hypothesize that the deviation of observed EPD values from the mean values obtained in the learning phase is minimized in the native structure. We achieved an average specificity of 0.91, 0.94 and 0.93 on hg_structal, 4state_reduced and ig_structal decoy sets, respectively, taken from the Decoys `R' Us database. The source code and manual is made available at
https://github.com/sanchak/mqap and permanently available on 10.5281/zenodo.7134.
Collapse
Affiliation(s)
- Sandeep Chakraborty
- Department of Biological Sciences, Tata Institute of Fundamental Research, Mumbai, 400 005, India
| | - Ravindra Venkatramani
- Department of Chemical Sciences, Tata Institute of Fundamental Research, Mumbai, 400 005, India
| | - Basuthkar J Rao
- Department of Biological Sciences, Tata Institute of Fundamental Research, Mumbai, 400 005, India
| | - Bjarni Asgeirsson
- Science Institute, Department of Biochemistry, University of Iceland, IS-107 Reykjavik, Iceland
| | - Abhaya M Dandekar
- Plant Sciences Department, University of California,, Davis, CA, 95616, USA
| |
Collapse
|
43
|
Vyas VK, Ukawala RD, Ghate M, Chintha C. Homology modeling a fast tool for drug discovery: current perspectives. Indian J Pharm Sci 2012. [PMID: 23204616 PMCID: PMC3507339 DOI: 10.4103/0250-474x.102537] [Citation(s) in RCA: 139] [Impact Index Per Article: 11.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022] Open
Abstract
Major goal of structural biology involve formation of protein-ligand complexes; in which the protein molecules act energetically in the course of binding. Therefore, perceptive of protein-ligand interaction will be very important for structure based drug design. Lack of knowledge of 3D structures has hindered efforts to understand the binding specificities of ligands with protein. With increasing in modeling software and the growing number of known protein structures, homology modeling is rapidly becoming the method of choice for obtaining 3D coordinates of proteins. Homology modeling is a representation of the similarity of environmental residues at topologically corresponding positions in the reference proteins. In the absence of experimental data, model building on the basis of a known 3D structure of a homologous protein is at present the only reliable method to obtain the structural information. Knowledge of the 3D structures of proteins provides invaluable insights into the molecular basis of their functions. The recent advances in homology modeling, particularly in detecting and aligning sequences with template structures, distant homologues, modeling of loops and side chains as well as detecting errors in a model contributed to consistent prediction of protein structure, which was not possible even several years ago. This review focused on the features and a role of homology modeling in predicting protein structure and described current developments in this field with victorious applications at the different stages of the drug design and discovery.
Collapse
Affiliation(s)
- V K Vyas
- Department of Pharmaceutical Chemistry, Institute of Pharmacy, Nirma University, Ahmedabad-382 481, India
| | | | | | | |
Collapse
|
44
|
Olson MA, Lee MS. Structure refinement of protein model decoys requires accurate side-chain placement. Proteins 2012; 81:469-78. [PMID: 23070940 DOI: 10.1002/prot.24204] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2012] [Revised: 09/18/2012] [Accepted: 10/02/2012] [Indexed: 11/10/2022]
Abstract
In this study, the application of temperature-based replica-exchange (T-ReX) simulations for structure refinement of decoys taken from the I-TASSER dataset was examined. A set of eight nonredundant proteins was investigated using self-guided Langevin dynamics (SGLD) with a generalized Born implicit solvent model to sample conformational space. For two of the protein test cases, a comparison of the SGLD/T-ReX method with that of a hybrid explicit/implicit solvent molecular dynamics T-ReX simulation model is provided. Additionally, the effect of side-chain placement among the starting decoy structures, using alternative rotamer conformations taken from the SCWRL4 modeling program, was investigated. The simulation results showed that, despite having near-native backbone conformations among the starting decoys, the determinant of their refinement is side-chain packing to a level that satisfies a minimum threshold of native contacts to allow efficient excursions toward the downhill refinement regime on the energy landscape. By repacking using SCWRL4 and by applying the RWplus statistical potential for structure identification, the SGLD/T-ReX simulations achieved refinement to an average of 38% increase in the number of native contacts relative to the original I-TASSER decoy sets and a 25% reduction in values of C(α) root-mean-square deviation. The hybrid model succeeded in obtaining a sharper funnel to low-energy states for a modeled target than the implicit solvent SGLD model; yet, structure identification remained roughly the same. Without meeting a threshold of near-native packing of side chains, the T-ReX simulations degrade the accuracy of the decoys, and subsequently, refinement becomes tantamount to the protein folding problem.
Collapse
Affiliation(s)
- Mark A Olson
- Department of Cell Biology and Biochemistry, USAMRIID, Frederick, Maryland 21702, USA.
| | | |
Collapse
|
45
|
Raval A, Piana S, Eastwood MP, Dror RO, Shaw DE. Refinement of protein structure homology models via long, all-atom molecular dynamics simulations. Proteins 2012; 80:2071-9. [PMID: 22513870 DOI: 10.1002/prot.24098] [Citation(s) in RCA: 184] [Impact Index Per Article: 15.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2012] [Revised: 04/03/2012] [Accepted: 04/11/2012] [Indexed: 11/07/2022]
Abstract
Accurate computational prediction of protein structure represents a longstanding challenge in molecular biology and structure-based drug design. Although homology modeling techniques are widely used to produce low-resolution models, refining these models to high resolution has proven difficult. With long enough simulations and sufficiently accurate force fields, molecular dynamics (MD) simulations should in principle allow such refinement, but efforts to refine homology models using MD have for the most part yielded disappointing results. It has thus far been unclear whether MD-based refinement is limited primarily by accessible simulation timescales, force field accuracy, or both. Here, we examine MD as a technique for homology model refinement using all-atom simulations, each at least 100 μs long-more than 100 times longer than previous refinement simulations-and a physics-based force field that was recently shown to successfully fold a structurally diverse set of fast-folding proteins. In MD simulations of 24 proteins chosen from the refinement category of recent Critical Assessment of Structure Prediction (CASP) experiments, we find that in most cases, simulations initiated from homology models drift away from the native structure. Comparison with simulations initiated from the native structure suggests that force field accuracy is the primary factor limiting MD-based refinement. This problem can be mitigated to some extent by restricting sampling to the neighborhood of the initial model, leading to structural improvement that, while limited, is roughly comparable to the leading alternative methods.
Collapse
Affiliation(s)
- Alpan Raval
- D E Shaw Research, New York, New York 10036, USA
| | | | | | | | | |
Collapse
|
46
|
Fan H, Periole X, Mark AE. Mimicking the action of folding chaperones by Hamiltonian replica-exchange molecular dynamics simulations: application in the refinement of de novo models. Proteins 2012; 80:1744-54. [PMID: 22411697 DOI: 10.1002/prot.24068] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2011] [Revised: 02/11/2012] [Accepted: 03/03/2012] [Indexed: 12/25/2022]
Abstract
The efficiency of using a variant of Hamiltonian replica-exchange molecular dynamics (Chaperone H-replica-exchange molecular dynamics [CH-REMD]) for the refinement of protein structural models generated de novo is investigated. In CH-REMD, the interaction between the protein and its environment, specifically, the electrostatic interaction between the protein and the solvating water, is varied leading to cycles of partial unfolding and refolding mimicking some aspects of folding chaperones. In 10 of the 15 cases examined, the CH-REMD approach sampled structures in which the root-mean-square deviation (RMSD) of secondary structure elements (SSE-RMSD) with respect to the experimental structure was more than 1.0 Å lower than the initial de novo model. In 14 of the 15 cases, the improvement was more than 0.5 Å. The ability of three different statistical potentials to identify near-native conformations was also examined. Little correlation between the SSE-RMSD of the sampled structures with respect to the experimental structure and any of the scoring functions tested was found. The most effective scoring function tested was the DFIRE potential. Using the DFIRE potential, the SSE-RMSD of the best scoring structures was on average 0.3 Å lower than the initial model. Overall the work demonstrates that targeted enhanced-sampling techniques such as CH-REMD can lead to the systematic refinement of protein structural models generated de novo but that improved potentials for the identification of near-native structures are still needed.
Collapse
Affiliation(s)
- Hao Fan
- Department of Pharmaceutical Chemistry, University of California at San Francisco, San Francisco, California 94158-2330, USA
| | | | | |
Collapse
|
47
|
Atomic-level protein structure refinement using fragment-guided molecular dynamics conformation sampling. Structure 2012; 19:1784-95. [PMID: 22153501 DOI: 10.1016/j.str.2011.09.022] [Citation(s) in RCA: 248] [Impact Index Per Article: 20.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2011] [Revised: 09/19/2011] [Accepted: 09/24/2011] [Indexed: 11/22/2022]
Abstract
One of critical difficulties of molecular dynamics (MD) simulations in protein structure refinement is that the physics-based energy landscape lacks a middle-range funnel to guide nonnative conformations toward near-native states. We propose to use the target model as a probe to identify fragmental analogs from PDB. The distance maps are then used to reshape the MD energy funnel. The protocol was tested on 181 benchmarking and 26 CASP targets. It was found that structure models of correct folds with TM-score >0.5 can be often pulled closer to native with higher GDT-HA score, but improvement for the models of incorrect folds (TM-score <0.5) are much less pronounced. These data indicate that template-based fragmental distance maps essentially reshaped the MD energy landscape from golf-course-like to funnel-like ones in the successfully refined targets with a radius of TM-score ∼0.5. These results demonstrate a new avenue to improve high-resolution structures by combining knowledge-based template information with physics-based MD simulations.
Collapse
|
48
|
Chaudhury S, Olson MA, Tawa G, Wallqvist A, Lee MS. Efficient Conformational Sampling in Explicit Solvent Using a Hybrid Replica Exchange Molecular Dynamics Method. J Chem Theory Comput 2012; 8:677-87. [DOI: 10.1021/ct200529b] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Sidhartha Chaudhury
- Biotechnology High Performance Computing Software Applications Institute, Telemedicine and Advanced Technology Research Center, U.S Army Medical Research and Materiel Command, Fort Detrick, Maryland
| | - Mark A. Olson
- Department of Cell Biology and Biochemistry, U.S. Army Medical Research Institute of Infectious Diseases, Fort Detrick, Maryland
| | - Gregory Tawa
- Biotechnology High Performance Computing Software Applications Institute, Telemedicine and Advanced Technology Research Center, U.S Army Medical Research and Materiel Command, Fort Detrick, Maryland
| | - Anders Wallqvist
- Biotechnology High Performance Computing Software Applications Institute, Telemedicine and Advanced Technology Research Center, U.S Army Medical Research and Materiel Command, Fort Detrick, Maryland
| | - Michael S. Lee
- Computational Sciences and Engineering Branch, U.S. Army Research Laboratory, Aberdeen Proving Ground, Maryland
| |
Collapse
|
49
|
Du S, Harano Y, Kinoshita M, Sakurai M. A scoring function based on solvation thermodynamics for protein structure prediction. Biophysics (Nagoya-shi) 2012; 8:127-38. [PMID: 27493529 PMCID: PMC4629643 DOI: 10.2142/biophysics.8.127] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2012] [Accepted: 07/31/2012] [Indexed: 12/01/2022] Open
Abstract
We predict protein structure using our recently developed free energy function for describing protein stability, which is focused on solvation thermodynamics. The function is combined with the current most reliable sampling methods, i.e., fragment assembly (FA) and comparative modeling (CM). The prediction is tested using 11 small proteins for which high-resolution crystal structures are available. For 8 of these proteins, sequence similarities are found in the database, and the prediction is performed with CM. Fairly accurate models with average Cα root mean square deviation (RMSD) ∼ 2.0 Å are successfully obtained for all cases. For the rest of the target proteins, we perform the prediction following FA protocols. For 2 cases, we obtain predicted models with an RMSD ∼ 3.0 Å as the best-scored structures. For the other case, the RMSD remains larger than 7 Å. For all the 11 target proteins, our scoring function identifies the experimentally determined native structure as the best structure. Starting from the predicted structure, replica exchange molecular dynamics is performed to further refine the structures. However, we are unable to improve its RMSD toward the experimental structure. The exhaustive sampling by coarse-grained normal mode analysis around the native structures reveals that our function has a linear correlation with RMSDs < 3.0 Å. These results suggest that the function is quite reliable for the protein structure prediction while the sampling method remains one of the major limiting factors in it. The aspects through which the methodology could further be improved are discussed.
Collapse
Affiliation(s)
- Shiqiao Du
- Center for Biological Resources and Informatics, Tokyo Institute of Technology, Yokohama 226-8501, Japan
| | - Yuichi Harano
- Institute for Protein Research, Osaka University, Suita, Osaka 565-0871, Japan
| | - Masahiro Kinoshita
- Institute of Advanced Energy, Kyoto University, Uji, Kyoto 611-0011, Japan
| | - Minoru Sakurai
- Center for Biological Resources and Informatics, Tokyo Institute of Technology, Yokohama 226-8501, Japan
| |
Collapse
|
50
|
Wu X, Zhou T, Zhu J, Zhang B, Georgiev I, Wang C, Chen X, Longo NS, Louder M, McKee K, O’Dell S, Perfetto S, Schmidt SD, Shi W, Wu L, Yang Y, Yang ZY, Yang Z, Zhang Z, Bonsignori M, Crump JA, Kapiga SH, Sam NE, Haynes BF, Simek M, Burton DR, Koff WC, Doria-Rose NA, Connors M, Mullikin JC, Nabel GJ, Roederer M, Shapiro L, Kwong PD, Mascola JR. Focused evolution of HIV-1 neutralizing antibodies revealed by structures and deep sequencing. Science 2011; 333:1593-602. [PMID: 21835983 PMCID: PMC3516815 DOI: 10.1126/science.1207532] [Citation(s) in RCA: 694] [Impact Index Per Article: 53.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Abstract
Antibody VRC01 is a human immunoglobulin that neutralizes about 90% of HIV-1 isolates. To understand how such broadly neutralizing antibodies develop, we used x-ray crystallography and 454 pyrosequencing to characterize additional VRC01-like antibodies from HIV-1-infected individuals. Crystal structures revealed a convergent mode of binding for diverse antibodies to the same CD4-binding-site epitope. A functional genomics analysis of expressed heavy and light chains revealed common pathways of antibody-heavy chain maturation, confined to the IGHV1-2*02 lineage, involving dozens of somatic changes, and capable of pairing with different light chains. Broadly neutralizing HIV-1 immunity associated with VRC01-like antibodies thus involves the evolution of antibodies to a highly affinity-matured state required to recognize an invariant viral structure, with lineages defined from thousands of sequences providing a genetic roadmap of their development.
Collapse
MESH Headings
- AIDS Vaccines
- Amino Acid Sequence
- Antibodies, Neutralizing/chemistry
- Antibodies, Neutralizing/genetics
- Antibodies, Neutralizing/immunology
- Antibodies, Neutralizing/isolation & purification
- Antibody Affinity
- Antibody Specificity
- Base Sequence
- Binding Sites
- Binding Sites, Antibody
- CD4 Antigens/metabolism
- Complementarity Determining Regions/genetics
- Crystallography, X-Ray
- Epitopes
- Evolution, Molecular
- Genes, Immunoglobulin Heavy Chain
- HIV Antibodies/chemistry
- HIV Antibodies/genetics
- HIV Antibodies/immunology
- HIV Antibodies/isolation & purification
- HIV Envelope Protein gp120/chemistry
- HIV Envelope Protein gp120/immunology
- HIV Envelope Protein gp120/metabolism
- HIV Infections/immunology
- HIV-1/chemistry
- HIV-1/immunology
- High-Throughput Nucleotide Sequencing
- Humans
- Immunoglobulin Fab Fragments/chemistry
- Immunoglobulin Fab Fragments/immunology
- Immunoglobulin Heavy Chains/chemistry
- Immunoglobulin Heavy Chains/immunology
- Immunoglobulin J-Chains/genetics
- Immunoglobulin Light Chains/chemistry
- Immunoglobulin Light Chains/immunology
- Models, Molecular
- Molecular Sequence Data
- Mutation
- Sequence Analysis, DNA
Collapse
Affiliation(s)
- Xueling Wu
- Vaccine Research Center, National Institutes of Health, Bethesda, MD 20892, USA
| | - Tongqing Zhou
- Vaccine Research Center, National Institutes of Health, Bethesda, MD 20892, USA
| | - Jiang Zhu
- Vaccine Research Center, National Institutes of Health, Bethesda, MD 20892, USA
| | - Baoshan Zhang
- Vaccine Research Center, National Institutes of Health, Bethesda, MD 20892, USA
| | - Ivelin Georgiev
- Vaccine Research Center, National Institutes of Health, Bethesda, MD 20892, USA
| | - Charlene Wang
- Vaccine Research Center, National Institutes of Health, Bethesda, MD 20892, USA
| | - Xuejun Chen
- Vaccine Research Center, National Institutes of Health, Bethesda, MD 20892, USA
| | - Nancy S. Longo
- Vaccine Research Center, National Institutes of Health, Bethesda, MD 20892, USA
| | - Mark Louder
- Vaccine Research Center, National Institutes of Health, Bethesda, MD 20892, USA
| | - Krisha McKee
- Vaccine Research Center, National Institutes of Health, Bethesda, MD 20892, USA
| | - Sijy O’Dell
- Vaccine Research Center, National Institutes of Health, Bethesda, MD 20892, USA
| | - Stephen Perfetto
- Vaccine Research Center, National Institutes of Health, Bethesda, MD 20892, USA
| | - Stephen D. Schmidt
- Vaccine Research Center, National Institutes of Health, Bethesda, MD 20892, USA
| | - Wei Shi
- Vaccine Research Center, National Institutes of Health, Bethesda, MD 20892, USA
| | - Lan Wu
- Vaccine Research Center, National Institutes of Health, Bethesda, MD 20892, USA
| | - Yongping Yang
- Vaccine Research Center, National Institutes of Health, Bethesda, MD 20892, USA
| | - Zhi-Yong Yang
- Vaccine Research Center, National Institutes of Health, Bethesda, MD 20892, USA
| | - Zhongjia Yang
- Vaccine Research Center, National Institutes of Health, Bethesda, MD 20892, USA
| | - Zhenhai Zhang
- Vaccine Research Center, National Institutes of Health, Bethesda, MD 20892, USA
- Department of Biochemistry and Molecular Biophysics, Columbia University, New York, NY 10032, USA
| | - Mattia Bonsignori
- Duke Human Vaccine Institute, Duke University School of Medicine, and Duke University Medical Center, Durham, NC 27710, USA
| | - John A. Crump
- Division of Infectious Diseases and International Health, Department of Medicine, and Department of Pathology, Duke University Medical Center, Durham, NC 27710, USA
- Kilimanjaro Christian Medical Centre and Kilimanjaro Christian Medical College, Tumaini University, Moshi, Tanzania
| | | | - Noel E. Sam
- Kilimanjaro Christian Medical Centre and Kilimanjaro Christian Medical College, Tumaini University, Moshi, Tanzania
- Kilimanjaro Reproductive Health Programme, Moshi, Tanzania
| | - Barton F. Haynes
- Duke Human Vaccine Institute, Duke University School of Medicine, and Duke University Medical Center, Durham, NC 27710, USA
| | - Melissa Simek
- International AIDS Vaccine Initiative (IAVI), New York, NY 10038, USA
| | - Dennis R. Burton
- Department of Immunology and Microbial Science and IAVI Neutralizing Antibody Center, The Scripps Research Institute, La Jolla, CA 92037, USA
- Ragon Institute of Massachusetts General Hospital, Massachusetts Institute of Technology, and Harvard, Cambridge, MA 02129, USA
| | - Wayne C. Koff
- International AIDS Vaccine Initiative (IAVI), New York, NY 10038, USA
| | - Nicole A. Doria-Rose
- Laboratory of Immunoregulation, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD 20892, USA
| | - Mark Connors
- Laboratory of Immunoregulation, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD 20892, USA
| | | | - James C. Mullikin
- NIH Intramural Sequencing Center (NISC), National Human Genome Research Institute, National Institutes of Health, Bethesda, MD 20892, USA
| | - Gary J. Nabel
- Vaccine Research Center, National Institutes of Health, Bethesda, MD 20892, USA
| | - Mario Roederer
- Vaccine Research Center, National Institutes of Health, Bethesda, MD 20892, USA
| | - Lawrence Shapiro
- Vaccine Research Center, National Institutes of Health, Bethesda, MD 20892, USA
- Department of Biochemistry and Molecular Biophysics, Columbia University, New York, NY 10032, USA
| | - Peter D. Kwong
- Vaccine Research Center, National Institutes of Health, Bethesda, MD 20892, USA
| | - John R. Mascola
- Vaccine Research Center, National Institutes of Health, Bethesda, MD 20892, USA
| |
Collapse
|