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Wang Q, Liu X, Zhang H, Chu H, Shi C, Zhang L, Bai J, Liu P, Li J, Zhu X, Liu Y, Chen Z, Huang R, Chang H, Liu T, Chang Z, Cheng J, Jiang H. Cytochrome P450 Enzyme Design by Constraining the Catalytic Pocket in a Diffusion Model. RESEARCH (WASHINGTON, D.C.) 2024; 7:0413. [PMID: 38979516 PMCID: PMC11227911 DOI: 10.34133/research.0413] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/25/2024] [Accepted: 05/27/2024] [Indexed: 07/10/2024]
Abstract
Although cytochrome P450 enzymes are the most versatile biocatalysts in nature, there is insufficient comprehension of the molecular mechanism underlying their functional innovation process. Here, by combining ancestral sequence reconstruction, reverse mutation assay, and progressive forward accumulation, we identified 5 founder residues in the catalytic pocket of flavone 6-hydroxylase (F6H) and proposed a "3-point fixation" model to elucidate the functional innovation mechanisms of P450s in nature. According to this design principle of catalytic pocket, we further developed a de novo diffusion model (P450Diffusion) to generate artificial P450s. Ultimately, among the 17 non-natural P450s we generated, 10 designs exhibited significant F6H activity and 6 exhibited a 1.3- to 3.5-fold increase in catalytic capacity compared to the natural CYP706X1. This work not only explores the design principle of catalytic pockets of P450s, but also provides an insight into the artificial design of P450 enzymes with desired functions.
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Affiliation(s)
- Qian Wang
- Key Laboratory of Engineering Biology for Low-Carbon Manufacturing, Tianjin Institute of Industrial Biotechnology,
Chinese Academy of Sciences, Tianjin 300308, China
- University of Chinese Academy of Sciences, Beijing 100049, China
- National Center of Technology Innovation for Synthetic Biology, Tianjin 300308, China
| | - Xiaonan Liu
- Key Laboratory of Engineering Biology for Low-Carbon Manufacturing, Tianjin Institute of Industrial Biotechnology,
Chinese Academy of Sciences, Tianjin 300308, China
- University of Chinese Academy of Sciences, Beijing 100049, China
- National Center of Technology Innovation for Synthetic Biology, Tianjin 300308, China
| | - Hejian Zhang
- Key Laboratory of Engineering Biology for Low-Carbon Manufacturing, Tianjin Institute of Industrial Biotechnology,
Chinese Academy of Sciences, Tianjin 300308, China
- National Center of Technology Innovation for Synthetic Biology, Tianjin 300308, China
- College of Biotechnology,
Tianjin University of Science and Technology, Tianjin 300457, China
| | - Huanyu Chu
- Key Laboratory of Engineering Biology for Low-Carbon Manufacturing, Tianjin Institute of Industrial Biotechnology,
Chinese Academy of Sciences, Tianjin 300308, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Chao Shi
- Department of Biochemistry and Biophysics, School of Basic Medical Sciences,
Peking University, Beijing 100191, China
| | - Lei Zhang
- Key Laboratory of Engineering Biology for Low-Carbon Manufacturing, Tianjin Institute of Industrial Biotechnology,
Chinese Academy of Sciences, Tianjin 300308, China
- College of Life Science and Technology,
Wuhan Polytechnic University, Wuhan, Hubei 430023, China
| | - Jie Bai
- Key Laboratory of Engineering Biology for Low-Carbon Manufacturing, Tianjin Institute of Industrial Biotechnology,
Chinese Academy of Sciences, Tianjin 300308, China
- National Center of Technology Innovation for Synthetic Biology, Tianjin 300308, China
| | - Pi Liu
- Key Laboratory of Engineering Biology for Low-Carbon Manufacturing, Tianjin Institute of Industrial Biotechnology,
Chinese Academy of Sciences, Tianjin 300308, China
- National Center of Technology Innovation for Synthetic Biology, Tianjin 300308, China
| | - Jing Li
- Key Laboratory of Engineering Biology for Low-Carbon Manufacturing, Tianjin Institute of Industrial Biotechnology,
Chinese Academy of Sciences, Tianjin 300308, China
- National Center of Technology Innovation for Synthetic Biology, Tianjin 300308, China
- State Key Laboratory of Elemento-Organic Chemistry, College of Chemistry,
Nankai University, Tianjin 300071, China
- College of Life Science,
Nankai University, Tianjin 300071, China
| | - Xiaoxi Zhu
- Key Laboratory of Engineering Biology for Low-Carbon Manufacturing, Tianjin Institute of Industrial Biotechnology,
Chinese Academy of Sciences, Tianjin 300308, China
- University of Chinese Academy of Sciences, Beijing 100049, China
- National Center of Technology Innovation for Synthetic Biology, Tianjin 300308, China
| | - Yuwan Liu
- Key Laboratory of Engineering Biology for Low-Carbon Manufacturing, Tianjin Institute of Industrial Biotechnology,
Chinese Academy of Sciences, Tianjin 300308, China
- National Center of Technology Innovation for Synthetic Biology, Tianjin 300308, China
| | - Zhangxin Chen
- Department of Biochemistry and Biophysics, School of Basic Medical Sciences,
Peking University, Beijing 100191, China
| | - Rong Huang
- Key Laboratory of Engineering Biology for Low-Carbon Manufacturing, Tianjin Institute of Industrial Biotechnology,
Chinese Academy of Sciences, Tianjin 300308, China
- National Center of Technology Innovation for Synthetic Biology, Tianjin 300308, China
| | - Hong Chang
- Key Laboratory of Engineering Biology for Low-Carbon Manufacturing, Tianjin Institute of Industrial Biotechnology,
Chinese Academy of Sciences, Tianjin 300308, China
- National Center of Technology Innovation for Synthetic Biology, Tianjin 300308, China
| | - Tian Liu
- Key Laboratory of Engineering Biology for Low-Carbon Manufacturing, Tianjin Institute of Industrial Biotechnology,
Chinese Academy of Sciences, Tianjin 300308, China
- National Center of Technology Innovation for Synthetic Biology, Tianjin 300308, China
| | - Zhenzhan Chang
- Department of Biochemistry and Biophysics, School of Basic Medical Sciences,
Peking University, Beijing 100191, China
| | - Jian Cheng
- Key Laboratory of Engineering Biology for Low-Carbon Manufacturing, Tianjin Institute of Industrial Biotechnology,
Chinese Academy of Sciences, Tianjin 300308, China
- National Center of Technology Innovation for Synthetic Biology, Tianjin 300308, China
| | - Huifeng Jiang
- Key Laboratory of Engineering Biology for Low-Carbon Manufacturing, Tianjin Institute of Industrial Biotechnology,
Chinese Academy of Sciences, Tianjin 300308, China
- National Center of Technology Innovation for Synthetic Biology, Tianjin 300308, China
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2
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Adiyaman R, McGuffin LJ. Using Local Protein Model Quality Estimates to Guide a Molecular Dynamics-Based Refinement Strategy. Methods Mol Biol 2023; 2627:119-140. [PMID: 36959445 DOI: 10.1007/978-1-0716-2974-1_7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/25/2023]
Abstract
The refinement of predicted 3D models aims to bring them closer to the native structure by fixing errors including unusual bonds and torsion angles and irregular hydrogen bonding patterns. Refinement approaches based on molecular dynamics (MD) simulations using different types of restraints have performed well since CASP10. ReFOLD, developed by the McGuffin group, was one of the many MD-based refinement approaches, which were tested in CASP 12. When the performance of the ReFOLD method in CASP12 was evaluated, it was observed that ReFOLD suffered from the absence of a reliable guidance mechanism to reach consistent improvement for the quality of predicted 3D models, particularly in the case of template-based modelling (TBM) targets. Therefore, here we propose to utilize the local quality assessment score produced by ModFOLD6 to guide the MD-based refinement approach to further increase the accuracy of the predicted 3D models. The relative performance of the new local quality assessment guided MD-based refinement protocol and the original MD-based protocol ReFOLD are compared utilizing many different official scoring methods. By using the per-residue accuracy (or local quality) score to guide the refinement process, we are able to prevent the refined models from undesired structural deviations, thereby leading to more consistent improvements. This chapter will include a detailed analysis of the performance of the local quality assessment guided MD-based protocol versus that deployed in the original ReFOLD method.
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Affiliation(s)
- Recep Adiyaman
- School of Biological Sciences, University of Reading, Reading, UK
| | - Liam J McGuffin
- School of Biological Sciences, University of Reading, Reading, UK.
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3
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Keller GLJ, Weiss LI, Baker BM. Physicochemical Heuristics for Identifying High Fidelity, Near-Native Structural Models of Peptide/MHC Complexes. Front Immunol 2022; 13:887759. [PMID: 35547730 PMCID: PMC9084917 DOI: 10.3389/fimmu.2022.887759] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2022] [Accepted: 03/29/2022] [Indexed: 11/13/2022] Open
Abstract
There is long-standing interest in accurately modeling the structural features of peptides bound and presented by class I MHC proteins. This interest has grown with the advent of rapid genome sequencing and the prospect of personalized, peptide-based cancer vaccines, as well as the development of molecular and cellular therapeutics based on T cell receptor recognition of peptide-MHC. However, while the speed and accessibility of peptide-MHC modeling has improved substantially over the years, improvements in accuracy have been modest. Accuracy is crucial in peptide-MHC modeling, as T cell receptors are highly sensitive to peptide conformation and capturing fine details is therefore necessary for useful models. Studying nonameric peptides presented by the common class I MHC protein HLA-A*02:01, here we addressed a key question common to modern modeling efforts: from a set of models (or decoys) generated through conformational sampling, which is best? We found that the common strategy of decoy selection by lowest energy can lead to substantial errors in predicted structures. We therefore adopted a data-driven approach and trained functions capable of predicting near native decoys with exceptionally high accuracy. Although our implementation is limited to nonamer/HLA-A*02:01 complexes, our results serve as an important proof of concept from which improvements can be made and, given the significance of HLA-A*02:01 and its preference for nonameric peptides, should have immediate utility in select immunotherapeutic and other efforts for which structural information would be advantageous.
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Affiliation(s)
- Grant L J Keller
- Department of Chemistry & Biochemistry and the Harper Cancer Research Institute, University of Notre Dame, Notre Dame, IN, United States
| | - Laura I Weiss
- Department of Chemistry & Biochemistry and the Harper Cancer Research Institute, University of Notre Dame, Notre Dame, IN, United States
| | - Brian M Baker
- Department of Chemistry & Biochemistry and the Harper Cancer Research Institute, University of Notre Dame, Notre Dame, IN, United States
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4
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Brennick CA, George MM, Moussa MM, Hagymasi AT, Seesi SA, Shcheglova TV, Englander RP, Keller GL, Balsbaugh JL, Baker BM, Schietinger A, Mandoiu II, Srivastava PK. An unbiased approach to defining bona fide cancer neoepitopes that elicit immune-mediated cancer rejection. J Clin Invest 2021; 131:142823. [PMID: 33320837 PMCID: PMC7843235 DOI: 10.1172/jci142823] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2020] [Accepted: 12/04/2020] [Indexed: 01/01/2023] Open
Abstract
Identification of neoepitopes that are effective in cancer therapy is a major challenge in creating cancer vaccines. Here, using an entirely unbiased approach, we queried all possible neoepitopes in a mouse cancer model and asked which of those are effective in mediating tumor rejection and, independently, in eliciting a measurable CD8 response. This analysis uncovered a large trove of effective anticancer neoepitopes that have strikingly different properties from conventional epitopes and suggested an algorithm to predict them. It also revealed that our current methods of prediction discard the overwhelming majority of true anticancer neoepitopes. These results from a single mouse model were validated in another antigenically distinct mouse cancer model and are consistent with data reported in human studies. Structural modeling showed how the MHC I-presented neoepitopes had an altered conformation, higher stability, or increased exposure to T cell receptors as compared with the unmutated counterparts. T cells elicited by the active neoepitopes identified here demonstrated a stem-like early dysfunctional phenotype associated with effective responses against viruses and tumors of transgenic mice. These abundant anticancer neoepitopes, which have not been tested in human studies thus far, can be exploited for generation of personalized human cancer vaccines.
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Affiliation(s)
- Cory A Brennick
- Department of Immunology, and Carole and Ray Neag Comprehensive Cancer Center, University of Connecticut School of Medicine, Farmington, Connecticut, USA
| | - Mariam M George
- Department of Immunology, and Carole and Ray Neag Comprehensive Cancer Center, University of Connecticut School of Medicine, Farmington, Connecticut, USA
| | - Marmar M Moussa
- Department of Immunology, and Carole and Ray Neag Comprehensive Cancer Center, University of Connecticut School of Medicine, Farmington, Connecticut, USA
| | - Adam T Hagymasi
- Department of Immunology, and Carole and Ray Neag Comprehensive Cancer Center, University of Connecticut School of Medicine, Farmington, Connecticut, USA
| | - Sahar Al Seesi
- Computer Science Department, Smith College, Northampton, Massachusetts, USA
| | - Tatiana V Shcheglova
- Department of Immunology, and Carole and Ray Neag Comprehensive Cancer Center, University of Connecticut School of Medicine, Farmington, Connecticut, USA
| | - Ryan P Englander
- Department of Immunology, and Carole and Ray Neag Comprehensive Cancer Center, University of Connecticut School of Medicine, Farmington, Connecticut, USA
| | - Grant Lj Keller
- Department of Chemistry and Biochemistry and Harper Cancer Research Institute, University of Notre Dame, Notre Dame, Indiana, USA
| | - Jeremy L Balsbaugh
- Proteomics and Metabolomics Facility, Center for Open Research Resources and Equipment, University of Connecticut, Storrs, Connecticut, USA
| | - Brian M Baker
- Department of Chemistry and Biochemistry and Harper Cancer Research Institute, University of Notre Dame, Notre Dame, Indiana, USA
| | - Andrea Schietinger
- Immunology Program, Memorial Sloan Kettering Cancer Center, New York, New York, USA.,Weill Cornell Medical College, Cornell University, New York, New York, USA
| | - Ion I Mandoiu
- Department of Computer Science and Engineering, University of Connecticut, Storrs, Connecticut, USA
| | - Pramod K Srivastava
- Department of Immunology, and Carole and Ray Neag Comprehensive Cancer Center, University of Connecticut School of Medicine, Farmington, Connecticut, USA
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5
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Frick R, Høydahl LS, Petersen J, du Pré MF, Kumari S, Berntsen G, Dewan AE, Jeliazkov JR, Gunnarsen KS, Frigstad T, Vik ES, Llerena C, Lundin KEA, Yaqub S, Jahnsen J, Gray JJ, Rossjohn J, Sollid LM, Sandlie I, Løset GÅ. A high-affinity human TCR-like antibody detects celiac disease gluten peptide-MHC complexes and inhibits T cell activation. Sci Immunol 2021; 6:6/62/eabg4925. [PMID: 34417258 DOI: 10.1126/sciimmunol.abg4925] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2021] [Accepted: 07/22/2021] [Indexed: 12/12/2022]
Abstract
Antibodies specific for peptides bound to human leukocyte antigen (HLA) molecules are valuable tools for studies of antigen presentation and may have therapeutic potential. Here, we generated human T cell receptor (TCR)-like antibodies toward the immunodominant signature gluten epitope DQ2.5-glia-α2 in celiac disease (CeD). Phage display selection combined with secondary targeted engineering was used to obtain highly specific antibodies with picomolar affinity. The crystal structure of a Fab fragment of the lead antibody 3.C11 in complex with HLA-DQ2.5:DQ2.5-glia-α2 revealed a binding geometry and interaction mode highly similar to prototypic TCRs specific for the same complex. Assessment of CeD biopsy material confirmed disease specificity and reinforced the notion that abundant plasma cells present antigen in the inflamed CeD gut. Furthermore, 3.C11 specifically inhibited activation and proliferation of gluten-specific CD4+ T cells in vitro and in HLA-DQ2.5 humanized mice, suggesting a potential for targeted intervention without compromising systemic immunity.
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Affiliation(s)
- Rahel Frick
- Centre for Immune Regulation and Department of Immunology, University of Oslo and Oslo University Hospital-Rikshospitalet, Oslo, Norway.,Centre for Immune Regulation and Department of Biosciences, University of Oslo, Oslo, Norway
| | - Lene S Høydahl
- Centre for Immune Regulation and Department of Immunology, University of Oslo and Oslo University Hospital-Rikshospitalet, Oslo, Norway.,Centre for Immune Regulation and Department of Biosciences, University of Oslo, Oslo, Norway.,KG Jebsen Coeliac Disease Research Centre, University of Oslo, Oslo, Norway
| | - Jan Petersen
- Infection and Immunity Program, Department of Biochemistry and Molecular Biology, Biomedicine Discovery Institute, Monash University, Clayton, Victoria, Australia.,Australian Research Council Centre of Excellence for Advanced Molecular Imaging, Monash University, Clayton, Victoria, Australia
| | - M Fleur du Pré
- Centre for Immune Regulation and Department of Immunology, University of Oslo and Oslo University Hospital-Rikshospitalet, Oslo, Norway.,KG Jebsen Coeliac Disease Research Centre, University of Oslo, Oslo, Norway
| | | | | | - Alisa E Dewan
- Centre for Immune Regulation and Department of Immunology, University of Oslo and Oslo University Hospital-Rikshospitalet, Oslo, Norway.,KG Jebsen Coeliac Disease Research Centre, University of Oslo, Oslo, Norway
| | | | - Kristin S Gunnarsen
- Centre for Immune Regulation and Department of Immunology, University of Oslo and Oslo University Hospital-Rikshospitalet, Oslo, Norway.,Centre for Immune Regulation and Department of Biosciences, University of Oslo, Oslo, Norway
| | | | | | - Carmen Llerena
- Infection and Immunity Program, Department of Biochemistry and Molecular Biology, Biomedicine Discovery Institute, Monash University, Clayton, Victoria, Australia
| | - Knut E A Lundin
- Centre for Immune Regulation and Department of Immunology, University of Oslo and Oslo University Hospital-Rikshospitalet, Oslo, Norway.,KG Jebsen Coeliac Disease Research Centre, University of Oslo, Oslo, Norway.,Department of Gastroenterology, Oslo University Hospital-Rikshospitalet, Oslo, Norway
| | - Sheraz Yaqub
- Department of Gastrointestinal Surgery, Oslo University Hospital-Rikshospitalet, Oslo, Norway.,Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Jørgen Jahnsen
- Institute of Clinical Medicine, University of Oslo, Oslo, Norway.,Department of Gastroenterology, Akershus University Hospital, Lørenskog, Norway
| | - Jeffrey J Gray
- Program in Molecular Biophysics, Johns Hopkins University, Baltimore, MD, USA.,Department of Chemical and Biomolecular Engineering and Institute of NanoBioTechnology, Johns Hopkins University, Baltimore, MD, USA.,Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins School of Medicine, Baltimore, MD, USA
| | - Jamie Rossjohn
- Infection and Immunity Program, Department of Biochemistry and Molecular Biology, Biomedicine Discovery Institute, Monash University, Clayton, Victoria, Australia.,Australian Research Council Centre of Excellence for Advanced Molecular Imaging, Monash University, Clayton, Victoria, Australia.,Institute of Infection and Immunity, Cardiff University School of Medicine, Heath Park, Cardiff, UK
| | - Ludvig M Sollid
- Centre for Immune Regulation and Department of Immunology, University of Oslo and Oslo University Hospital-Rikshospitalet, Oslo, Norway.,KG Jebsen Coeliac Disease Research Centre, University of Oslo, Oslo, Norway
| | - Inger Sandlie
- Centre for Immune Regulation and Department of Immunology, University of Oslo and Oslo University Hospital-Rikshospitalet, Oslo, Norway.,Centre for Immune Regulation and Department of Biosciences, University of Oslo, Oslo, Norway
| | - Geir Åge Løset
- Centre for Immune Regulation and Department of Immunology, University of Oslo and Oslo University Hospital-Rikshospitalet, Oslo, Norway. .,Centre for Immune Regulation and Department of Biosciences, University of Oslo, Oslo, Norway.,Nextera AS, Oslo, Norway
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6
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Sohraby F, Aryapour H. Rational drug repurposing for cancer by inclusion of the unbiased molecular dynamics simulation in the structure-based virtual screening approach: Challenges and breakthroughs. Semin Cancer Biol 2020; 68:249-257. [PMID: 32360530 DOI: 10.1016/j.semcancer.2020.04.007] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2019] [Revised: 03/07/2020] [Accepted: 04/22/2020] [Indexed: 12/13/2022]
Abstract
Managing cancer is now one of the biggest concerns of health organizations. Many strategies have been developed in drug discovery pipelines to help rectify this problem and two of the best ones are drug repurposing and computational methods. The combination of these approaches can have immense impact on the course of drug discovery. In silico drug repurposing can significantly reduce the time, the cost and the effort of drug development. Computational methods such as structure-based drug design (SBDD) and virtual screening can predict the potentials of small molecule binders, such as drugs, for having favorable effect on a particular molecular target. However, the demand for accuracy and efficiency of SBDD requires more sophisticated and complicated approaches such as unbiased molecular dynamics (UMD) simulation that has been recently introduced. As a complementary strategy, the knowledge acquired from UMD simulations can increase the chance of finding the right candidates and the pipeline of its administration is introduced and discussed in this review. An elaboration of this pipeline is also made by detailing an example, the binding and unbinding pathways of dasatinib-c-Src kinase complex, which shows that how influential this method can be in rational drug repurposing in cancer treatment.
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Affiliation(s)
- Farzin Sohraby
- Department of Biology, Faculty of Science, Golestan University, Gorgan, Iran
| | - Hassan Aryapour
- Department of Biology, Faculty of Science, Golestan University, Gorgan, Iran.
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7
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Saurabh S, Sivakumar PM, Perumal V, Khosravi A, Sugumaran A, Prabhawathi V. Molecular Dynamics Simulations in Drug Discovery and Drug Delivery. ACTA ACUST UNITED AC 2020. [DOI: 10.1007/978-3-030-36260-7_10] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/09/2023]
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8
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Tahir RA, Bashir A, Yousaf MN, Ahmed A, Dali Y, Khan S, Sehgal SA. In Silico identification of angiotensin-converting enzyme inhibitory peptides from MRJP1. PLoS One 2020; 15:e0228265. [PMID: 32012183 PMCID: PMC6996805 DOI: 10.1371/journal.pone.0228265] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2019] [Accepted: 01/12/2020] [Indexed: 01/14/2023] Open
Abstract
Hypertension is considered as one of the most common diseases that affect human beings (both male and female) due to its high prevalence and also extending widely to both industrialize and developing countries. Angiotensin-converting enzyme (ACE) has a significant role in the regulation of blood pressure and ACE inhibition with inhibitory peptides is considered as a major target to prevent hypertension. In the current study, a blood pressure regulating honey protein (MRJP1) was examined to identify the ACE inhibitory peptides. The 3D structure of MRJP1 was predicted by utilizing the threading approach and further optimized by performing molecular dynamics simulation for 30 nanoseconds (ns) to improve the quality factor up to 92.43%. Root mean square deviation and root mean square fluctuations were calculated to evaluate the structural features and observed the fluctuations in the timescale of 30 ns. AHTpin server based on scoring vector machine of regression models, proteolysis and structural characterization approaches were implemented to identify the potential inhibitory peptides. The anti-hypertensive peptides were scrutinized based on the QSAR models of anti-hypertensive activity and the molecular docking analyses were performed to explore the binding affinities and potential interacting residues. The peptide "EALPHVPIFDR" showed the strong binding affinity and higher anti-hypertensive activity along with the global energy of -58.29 and docking score of 9590. The aromatic amino acids especially Tyr was observed as the key residue to design the dietary peptides and drugs like ACE inhibitors.
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Affiliation(s)
- Rana Adnan Tahir
- Key Laboratory of Molecular Medicine and Biotherapy in the Ministry of Industry and Information Technology, Department of Biology, School of Life Sciences, Beijing Institute of Technology, Beijing, China
- Department of Biosciences, COMSATS University Islamabad Sahiwal Campus, Sahiwal, Pakistan
| | - Afsheen Bashir
- Khyber Girls Medical College, Hayatabad, Peshawar, Pakistan
| | | | - Azka Ahmed
- Department of Biosciences, COMSATS University Islamabad Sahiwal Campus, Sahiwal, Pakistan
| | - Yasmine Dali
- State Key Laboratory of Membrane Biology, Institute of Zoology, Chinese Academy of Sciences; Beijing, China
| | - Sanaullah Khan
- Department of Zoology, University of Peshawar, Peshawar, Pakistan
| | - Sheikh Arslan Sehgal
- Department of Bioinformatics and Biotechnology, Government College University Faisalabad, Faisalabad, Pakistan
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9
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Ebrahimi-Nik H, Michaux J, Corwin WL, Keller GL, Shcheglova T, Pak H, Coukos G, Baker BM, Mandoiu II, Bassani-Sternberg M, Srivastava PK. Mass spectrometry driven exploration reveals nuances of neoepitope-driven tumor rejection. JCI Insight 2019; 5:129152. [PMID: 31219806 PMCID: PMC6675551 DOI: 10.1172/jci.insight.129152] [Citation(s) in RCA: 33] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022] Open
Abstract
Neoepitopes are the only truly tumor-specific antigens. Although potential neoepitopes can be readily identified using genomics, the neoepitopes that mediate tumor rejection constitute a small minority, and there is little consensus on how to identify them. Here, for the first time to our knowledge, we use a combination of genomics, unbiased discovery mass spectrometry (MS) immunopeptidomics, and targeted MS to directly identify neoepitopes that elicit actual tumor rejection in mice. We report that MS-identified neoepitopes are an astonishingly rich source of tumor rejection-mediating neoepitopes (TRMNs). MS has also demonstrated unambiguously the presentation by MHC I, of confirmed tumor rejection neoepitopes that bind weakly to MHC I; this was done using DCs exogenously loaded with long peptides containing the weakly binding neoepitopes. Such weakly MHC I–binding neoepitopes are routinely excluded from analysis, and our demonstration of their presentation, and their activity in tumor rejection, reveals a broader universe of tumor-rejection neoepitopes than presently imagined. Modeling studies show that a mutation in the active neoepitope alters its conformation such that its T cell receptor–facing surface is substantially altered, increasing its exposed hydrophobicity. No such changes are observed in the inactive neoepitope. These results broaden our understanding of antigen presentation and help prioritize neoepitopes for personalized cancer immunotherapy. Neoepitopes identified by mass spectrometry are a rich source of tumor rejection antigens, including those with a weak binding to MHC I.
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Affiliation(s)
- Hakimeh Ebrahimi-Nik
- Department of Immunology and Carole and Ray Neag Comprehensive Cancer Center, University of Connecticut School of Medicine, Farmington, Connecticut, USA
| | - Justine Michaux
- University of Lausanne, Lausanne, Switzerland.,Department of Oncology, University Hospital of Lausanne, Lausanne, Switzerland
| | - William L Corwin
- Department of Immunology and Carole and Ray Neag Comprehensive Cancer Center, University of Connecticut School of Medicine, Farmington, Connecticut, USA
| | - Grant Lj Keller
- Department of Chemistry and Biochemistry and Harper Cancer Research Institute, University of Notre Dame, Notre Dame, Indiana, USA
| | - Tatiana Shcheglova
- Department of Immunology and Carole and Ray Neag Comprehensive Cancer Center, University of Connecticut School of Medicine, Farmington, Connecticut, USA
| | - HuiSong Pak
- University of Lausanne, Lausanne, Switzerland.,Department of Oncology, University Hospital of Lausanne, Lausanne, Switzerland
| | - George Coukos
- University of Lausanne, Lausanne, Switzerland.,Department of Oncology, University Hospital of Lausanne, Lausanne, Switzerland
| | - Brian M Baker
- Department of Chemistry and Biochemistry and Harper Cancer Research Institute, University of Notre Dame, Notre Dame, Indiana, USA
| | - Ion I Mandoiu
- Department of Computer Sciences, University of Connecticut School of Engineering, Storrs, Connecticut, USA
| | - Michal Bassani-Sternberg
- University of Lausanne, Lausanne, Switzerland.,Department of Oncology, University Hospital of Lausanne, Lausanne, Switzerland
| | - Pramod K Srivastava
- Department of Immunology and Carole and Ray Neag Comprehensive Cancer Center, University of Connecticut School of Medicine, Farmington, Connecticut, USA
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10
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Yu Z, Yao Y, Deng H, Yi M. ANDIS: an atomic angle- and distance-dependent statistical potential for protein structure quality assessment. BMC Bioinformatics 2019; 20:299. [PMID: 31159742 PMCID: PMC6547486 DOI: 10.1186/s12859-019-2898-y] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2018] [Accepted: 05/13/2019] [Indexed: 01/05/2023] Open
Abstract
Background The knowledge-based statistical potential has been widely used in protein structure modeling and model quality assessment. They are commonly evaluated based on their abilities of native recognition as well as decoy discrimination. However, these two aspects are found to be mutually exclusive in many statistical potentials. Results We developed an atomic ANgle- and DIStance-dependent (ANDIS) statistical potential for protein structure quality assessment with distance cutoff being a tunable parameter. When distance cutoff is ≤9.0 Å, “effective atomic interaction” is employed to enhance the ability of native recognition. For a distance cutoff of ≥10 Å, the distance-dependent atom-pair potential with random-walk reference state is combined to strengthen the ability of decoy discrimination. Benchmark tests on 632 structural decoy sets from diverse sources demonstrate that ANDIS outperforms other state-of-the-art potentials in both native recognition and decoy discrimination. Conclusions Distance cutoff is a crucial parameter for distance-dependent statistical potentials. A lower distance cutoff is better for native recognition, while a higher one is favorable for decoy discrimination. The ANDIS potential is freely available as a standalone application at http://qbp.hzau.edu.cn/ANDIS/. Electronic supplementary material The online version of this article (10.1186/s12859-019-2898-y) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Zhongwang Yu
- Department of Physics, College of Science, Huazhong Agricultural University, Wuhan, 430070, China
| | - Yuangen Yao
- Department of Physics, College of Science, Huazhong Agricultural University, Wuhan, 430070, China
| | - Haiyou Deng
- Department of Physics, College of Science, Huazhong Agricultural University, Wuhan, 430070, China. .,Institute of Applied Physics, Huazhong Agricultural University, Wuhan, 430070, China.
| | - Ming Yi
- Department of Physics, College of Science, Huazhong Agricultural University, Wuhan, 430070, China. .,Institute of Applied Physics, Huazhong Agricultural University, Wuhan, 430070, China.
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11
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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.
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12
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Yasuike N, Blacklock KM, Lu H, Jaikaran ASI, McDonald S, Uppalapati M, Khare SD, Woolley GA. Photoswitchable affinity reagents: Computational design and efficient red-light switching. CHEMPHOTOCHEM 2019; 3:431-440. [PMID: 32856001 DOI: 10.1002/cptc.201900016] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
Abstract
Photo-controlled affinity reagents seek to provide modular spatiotemporal control of bioactivity by conferring photo-switchability of function on an affinity reagent scaffold. Here we used Rosetta-based computational methods to screen for sites on the Fynomer affinity reagent structure for attachment of photoswitchable cross-linkers. Both established UV-based cross-linkers (azobenzene-iodoacetamide (IAC)) and an azonium-based efficient red light switchable cross-linker, piperazino-tetra-ortho-methoxy azobenzene (PIP), were then tested experimentally. Several sites compatible with Fynomer function were identified, including sites showing rapid (<10s) red light (633 nm) modulation of function. While a range of overall target binding affinities were observed, the degree of photo-switchability of Fynomer function was generally small (<2-fold). Computational models suggest that local flexibility limits the degree of switching seen in these designs.
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Affiliation(s)
- Nobuo Yasuike
- Department of Chemistry, University of Toronto, 80 St. George St., Toronto, M5S 3H6, Canada.,JSR Corporation, 1-9-2, Higashi-Shinbashi, Minato-ku, Tokyo, 105-8640, Japan
| | - Kristin M Blacklock
- Department of Pathology and Laboratory Medicine, University of Saskatchewan, Saskatoon, Saskatchewan S7N 5A2, Canada
| | - Huixin Lu
- Department of Chemistry, University of Toronto, 80 St. George St., Toronto, M5S 3H6, Canada
| | - Anna S I Jaikaran
- Department of Chemistry, University of Toronto, 80 St. George St., Toronto, M5S 3H6, Canada
| | - Sherin McDonald
- Department of Chemistry and Chemical Biology, Center for Integrative Proteomics Research, Rutgers University, New Brunswick, New Jersey 08854, U.S.A
| | - Maruti Uppalapati
- Department of Chemistry and Chemical Biology, Center for Integrative Proteomics Research, Rutgers University, New Brunswick, New Jersey 08854, U.S.A
| | - Sagar D Khare
- Department of Pathology and Laboratory Medicine, University of Saskatchewan, Saskatoon, Saskatchewan S7N 5A2, Canada
| | - G Andrew Woolley
- Department of Chemistry, University of Toronto, 80 St. George St., Toronto, M5S 3H6, Canada
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13
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Ochoa R, Soler MA, Laio A, Cossio P. Assessing the capability of in silico mutation protocols for predicting the finite temperature conformation of amino acids. Phys Chem Chem Phys 2018; 20:25901-25909. [PMID: 30289133 DOI: 10.1039/c8cp03826k] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
Mutation protocols are a key tool in computational biophysics for modelling unknown side chain conformations. In particular, these protocols are used to generate the starting structures for molecular dynamics simulations. The accuracy of the initial side chain and backbone placement is crucial to obtain a stable and quickly converging simulation. In this work, we assessed the performance of several mutation protocols in predicting the most probable conformer observed in finite temperature molecular dynamics simulations for a set of protein-peptide crystals differing only by single-point mutations in the peptide sequence. Our results show that several programs which predict well the crystal conformations fail to predict the most probable finite temperature configuration. Methods relying on backbone-dependent rotamer libraries have, in general, a better performance, but even the best protocol fails in predicting approximately 30% of the mutations.
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Affiliation(s)
- Rodrigo Ochoa
- Biophysics of Tropical Diseases, Max Planck Tandem Group, University of Antioquia, Medellin, Colombia.
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14
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Kandathil SM, Garza-Fabre M, Handl J, Lovell SC. Improved fragment-based protein structure prediction by redesign of search heuristics. Sci Rep 2018; 8:13694. [PMID: 30209258 PMCID: PMC6135816 DOI: 10.1038/s41598-018-31891-8] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2018] [Accepted: 08/22/2018] [Indexed: 11/09/2022] Open
Abstract
Difficulty in sampling large and complex conformational spaces remains a key limitation in fragment-based de novo prediction of protein structure. Our previous work has shown that even for small-to-medium-sized proteins, some current methods inadequately sample alternative structures. We have developed two new conformational sampling techniques, one employing a bilevel optimisation framework and the other employing iterated local search. We combine strategies of forced structural perturbation (where some fragment insertions are accepted regardless of their impact on scores) and greedy local optimisation, allowing greater exploration of the available conformational space. Comparisons against the Rosetta Abinitio method indicate that our protocols more frequently generate native-like predictions for many targets, even following the low-resolution phase, using a given set of fragment libraries. By contrasting results across two different fragment sets, we show that our methods are able to better take advantage of high-quality fragments. These improvements can also translate into more reliable identification of near-native structures in a simple clustering-based model selection procedure. We show that when fragment libraries are sufficiently well-constructed, improved breadth of exploration within runs improves prediction accuracy. Our results also suggest that in benchmarking scenarios, a total exclusion of fragments drawn from homologous templates can make performance differences between methods appear less pronounced.
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Affiliation(s)
- Shaun M Kandathil
- Division of Evolution and Genomic Sciences, School of Biological Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, M13 9PL, United Kingdom. .,Department of Computer Science, University College London, Gower Street, London, WC1E 6BT, United Kingdom.
| | - Mario Garza-Fabre
- Decision and Cognitive Sciences Research Centre, University of Manchester, Manchester, M13 9PL, United Kingdom.,Center for Research and Advanced Studies of the National Polytechnic Institute (CINVESTAV-IPN), Km. 5.5 Carretera Cd. Victoria-Soto La Marina, Cd. Victoria, Tamaulipas, 87130, Mexico
| | - Julia Handl
- Decision and Cognitive Sciences Research Centre, University of Manchester, Manchester, M13 9PL, United Kingdom
| | - Simon C Lovell
- Division of Evolution and Genomic Sciences, School of Biological Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, M13 9PL, United Kingdom
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15
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Barrows RD, Blacklock KM, Rablen PR, Khare SD, Knapp S. Computational assessment of thioether isosteres. J Mol Graph Model 2018; 80:282-292. [PMID: 29414047 DOI: 10.1016/j.jmgm.2018.01.018] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2017] [Revised: 01/25/2018] [Accepted: 01/29/2018] [Indexed: 11/16/2022]
Abstract
Replacement of the sulfur atom in biologically active diaryl and heteroaryl thioethers (Ar-S-Ar', HAr-S-Ar, and HAr-S-HAr') with any of several one-atom or two-atom linkers can be expected to reduce the susceptibility of the analogue to metabolic oxidation, a well-documented problem for thioethers intended for medicinal chemistry applications. Ab initio calculations indicate how well various proposed thioether isosteric groups, including some new and unusual ones, may perform structurally and electronically in replacing the bridging sulfur atom. Four of these are calculationally evaluated as proposed substructures in Axitinib analogues. The predicted binding behavior of the latter within two different previously crystallographically characterized protein-Axitinib binding sites (VEGFR2 kinase and ABL1 T315I gatekeeper mutant kinase), and an assessment of their suitability and anticipated shortcomings, are presented.
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Affiliation(s)
- Robert D Barrows
- Department of Chemistry & Chemical Biology, Rutgers The State University of New Jersey, 610 Taylor Rd., Piscataway, NJ 08854 USA
| | - Kristin M Blacklock
- Department of Chemistry & Chemical Biology, Rutgers The State University of New Jersey, 610 Taylor Rd., Piscataway, NJ 08854 USA
| | - Paul R Rablen
- Department of Chemistry & Biochemistry, Swarthmore College, 500 College Ave., Swarthmore, PA 19081 USA
| | - Sagar D Khare
- Department of Chemistry & Chemical Biology, Rutgers The State University of New Jersey, 610 Taylor Rd., Piscataway, NJ 08854 USA
| | - Spencer Knapp
- Department of Chemistry & Chemical Biology, Rutgers The State University of New Jersey, 610 Taylor Rd., Piscataway, NJ 08854 USA.
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16
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Blacklock KM, Yang L, Mulligan VK, Khare SD. A computational method for the design of nested proteins by loop-directed domain insertion. Proteins 2018; 86:354-369. [PMID: 29250820 DOI: 10.1002/prot.25445] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2017] [Revised: 12/04/2017] [Accepted: 12/15/2017] [Indexed: 12/23/2022]
Abstract
The computational design of novel nested proteins-in which the primary structure of one protein domain (insert) is flanked by the primary structure segments of another (parent)-would enable the generation of multifunctional proteins. Here we present a new algorithm, called Loop-Directed Domain Insertion (LooDo), implemented within the Rosetta software suite, for the purpose of designing nested protein domain combinations connected by flexible linker regions. Conformational space for the insert domain is sampled using large libraries of linker fragments for linker-to-parent domain superimposition followed by insert-to-linker superimposition. The relative positioning of the two domains (treated as rigid bodies) is sampled efficiently by a grid-based, mutual placement compatibility search. The conformations of the loop residues, and the identities of loop as well as interface residues, are simultaneously optimized using a generalized kinematic loop closure algorithm and Rosetta EnzymeDesign, respectively, to minimize interface energy. The algorithm was found to consistently sample near-native conformations and interface sequences for a benchmark set of structurally similar but functionally divergent domain-inserted enzymes from the α/β hydrolase superfamily, and discriminates well between native and nonnative conformations and sequences, although loop conformations tended to deviate from the native conformations. Furthermore, in cross-domain placement tests, native insert-parent domain combinations were ranked as the best-scoring structures compared to nonnative domain combinations. This algorithm should be broadly applicable to the design of multi-domain protein complexes with any combination of inserted or tandem domain connections.
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Affiliation(s)
- Kristin M Blacklock
- Institute for Quantitative Biomedicine, Rutgers The State University of New Jersey, Piscataway, New Jersey.,Department of Chemistry and Chemical Biology, Rutgers The State University of New Jersey, Piscataway, New Jersey.,Center for Integrative Proteomics Research, Rutgers The State University of New Jersey, Piscataway, New Jersey
| | - Lu Yang
- Department of Chemistry and Chemical Biology, Rutgers The State University of New Jersey, Piscataway, New Jersey.,Center for Integrative Proteomics Research, Rutgers The State University of New Jersey, Piscataway, New Jersey
| | - Vikram K Mulligan
- Institute for Protein Design and Department of Biochemistry, University of Washington, Seattle, Washington
| | - Sagar D Khare
- Institute for Quantitative Biomedicine, Rutgers The State University of New Jersey, Piscataway, New Jersey.,Department of Chemistry and Chemical Biology, Rutgers The State University of New Jersey, Piscataway, New Jersey.,Center for Integrative Proteomics Research, Rutgers The State University of New Jersey, Piscataway, New Jersey
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17
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Computational Modeling of the Staphylococcal Enterotoxins and Their Interaction with Natural Antitoxin Compounds. Int J Mol Sci 2018; 19:ijms19010133. [PMID: 29301344 PMCID: PMC5796082 DOI: 10.3390/ijms19010133] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2017] [Revised: 12/26/2017] [Accepted: 12/27/2017] [Indexed: 01/08/2023] Open
Abstract
Staphylococcus aureus is an opportunistic bacterium that produces various types of toxins, resulting in serious food poisoning. Staphylococcal enterotoxins (SEs) are heat-stable and resistant to hydrolysis by digestive enzymes, representing a potential hazard for consumers worldwide. In the present study, we used amino-acid sequences encoding SEA and SEB-like to identify their respective template structure and build the three-dimensional (3-D) models using homology modeling method. Two natural compounds, Betulin and 28-Norolean-12-en-3-one, were selected for docking study on the basis of the criteria that they satisfied the Lipinski’s Rule-of-Five. A total of 14 and 13 amino-acid residues were present in the best binding site predicted in the SEA and SEB-like, respectively, using the Computer Atlas of Surface Topology of Proteins (CASTp). Among these residues, the docking study with natural compounds Betulin and 28-Norolean-12-en-3-one revealed that GLN43 and GLY227 in the binding site of the SEA, each formed a hydrogen-bond interaction with 28-Norolean-12-en-3-one; while GLY227 residue established a hydrogen bond with Betulin. In the case of SEB-like, the docking study demonstrated that ASN87 and TYR88 residues in its binding site formed hydrogen bonds with Betulin; whereas HIS59 in the binding site formed a hydrogen-bond interaction with 28-Norolean-12-en-3-one. Our results demonstrate that the toxic effects of these two SEs can be effectively treated with antitoxins like Betulin and 28-Norolean-12-en-3-one, which could provide an effective drug therapy for this pathogen.
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18
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Blacklock KM, Yachnin BJ, Woolley GA, Khare SD. Computational Design of a Photocontrolled Cytosine Deaminase. J Am Chem Soc 2017; 140:14-17. [PMID: 29251923 DOI: 10.1021/jacs.7b08709] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
There is growing interest in designing spatiotemporal control over enzyme activities using noninvasive stimuli such as light. Here, we describe a structure-based, computation-guided predictive method for reversibly controlling enzyme activity using covalently attached photoresponsive azobenzene groups. Applying the method to the therapeutically useful enzyme yeast cytosine deaminase, we obtained a ∼3-fold change in enzyme activity by the photocontrolled modulation of the enzyme's active site lid structure, while fully maintaining thermostability. Multiple cycles of switching, controllable in real time, are possible. The predictiveness of the method is demonstrated by the construction of a variant that does not photoswitch as expected from computational modeling. Our design approach opens new avenues for optically controlling enzyme function. The designed photocontrolled cytosine deaminases may also aid in improving chemotherapy approaches that utilize this enzyme.
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Affiliation(s)
- Kristin M Blacklock
- Department of Chemistry and Chemical Biology, Center for Integrative Proteomics Research, Rutgers University , New Brunswick, New Jersey 08854, United States
| | - Brahm J Yachnin
- Department of Chemistry and Chemical Biology, Center for Integrative Proteomics Research, Rutgers University , New Brunswick, New Jersey 08854, United States
| | - G Andrew Woolley
- Department of Chemistry, University of Toronto , Toronto, Ontario M5S 3H6, Canada
| | - Sagar D Khare
- Department of Chemistry and Chemical Biology, Center for Integrative Proteomics Research, Rutgers University , New Brunswick, New Jersey 08854, United States
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19
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Yao Y, Gui R, Liu Q, Yi M, Deng H. Diverse effects of distance cutoff and residue interval on the performance of distance-dependent atom-pair potential in protein structure prediction. BMC Bioinformatics 2017; 18:542. [PMID: 29221443 PMCID: PMC5723101 DOI: 10.1186/s12859-017-1983-3] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2017] [Accepted: 12/04/2017] [Indexed: 12/27/2022] Open
Abstract
BACKGROUND As one of the most successful knowledge-based energy functions, the distance-dependent atom-pair potential is widely used in all aspects of protein structure prediction, including conformational search, model refinement, and model assessment. During the last two decades, great efforts have been made to improve the reference state of the potential, while other factors that also strongly affect the performance of the potential have been relatively less investigated. RESULTS Based on different distance cutoffs (from 5 to 22 Å) and residue intervals (from 0 to 15) as well as six different reference states, we constructed a series of distance-dependent atom-pair potentials and tested them on several groups of structural decoy sets collected from diverse sources. A comprehensive investigation has been performed to clarify the effects of distance cutoff and residue interval on the potential's performance. Our results provide a new perspective as well as a practical guidance for optimizing distance-dependent statistical potentials. CONCLUSIONS The optimal distance cutoff and residue interval are highly related with the reference state that the potential is based on, the measurements of the potential's performance, and the decoy sets that the potential is applied to. The performance of distance-dependent statistical potential can be significantly improved when the best statistical parameters for the specific application environment are adopted.
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Affiliation(s)
- Yuangen Yao
- Department of Physics, College of Science, Huazhong Agricultural University, Wuhan, 430070 China
| | - Rong Gui
- Department of Physics, College of Science, Huazhong Agricultural University, Wuhan, 430070 China
| | - Quan Liu
- Department of Physics, College of Science, Huazhong Agricultural University, Wuhan, 430070 China
| | - Ming Yi
- Department of Physics, College of Science, Huazhong Agricultural University, Wuhan, 430070 China
| | - Haiyou Deng
- Department of Physics, College of Science, Huazhong Agricultural University, Wuhan, 430070 China
- Institute of Applied Physics, Huazhong Agricultural University, Wuhan, 430070 China
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20
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Alexander NS, Palczewski K. Crowd sourcing difficult problems in protein science . Protein Sci 2017; 26:2118-2125. [PMID: 28762619 DOI: 10.1002/pro.3247] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2017] [Accepted: 07/21/2017] [Indexed: 11/08/2022]
Abstract
Dedicated computing resources are expensive to develop, maintain, and administrate. Frequently, research groups require bursts of computing power, during which progress is still limited by available computing resources. One way to alleviate this bottleneck would be to use additional computing resources. Today, many computing devices remain idle most of the time. Passive volunteer computing exploits this unemployed reserve of computing power by allowing device-owners to donate computing time on their own devices. Another complementary way to alleviate bottlenecks in computing resources is to use more efficient algorithms. Engaging volunteer computing employs human intuition to help solve challenging problems for which efficient algorithms are difficult to develop or unavailable. Designing engaging volunteer computing projects is challenging but can result in high-quality solutions. Here, we highlight four examples.
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Affiliation(s)
- Nathan S Alexander
- Department of Pharmacology, School of Medicine, Case Western Reserve University, Cleveland, Ohio, 44106
| | - Krzysztof Palczewski
- Department of Pharmacology, School of Medicine, Case Western Reserve University, Cleveland, Ohio, 44106.,Cleveland Center for Membrane and Structural Biology, Case Western Reserve University, Cleveland, Ohio, 44106
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21
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Feig M. Computational protein structure refinement: Almost there, yet still so far to go. WILEY INTERDISCIPLINARY REVIEWS. COMPUTATIONAL MOLECULAR SCIENCE 2017; 7:e1307. [PMID: 30613211 PMCID: PMC6319934 DOI: 10.1002/wcms.1307] [Citation(s) in RCA: 46] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
Protein structures are essential in modern biology yet experimental methods are far from being able to catch up with the rapid increase in available genomic data. Computational protein structure prediction methods aim to fill the gap while the role of protein structure refinement is to take approximate initial template-based models and bring them closer to the true native structure. Current methods for computational structure refinement rely on molecular dynamics simulations, related sampling methods, or iterative structure optimization protocols. The best methods are able to achieve moderate degrees of refinement but consistent refinement that can reach near-experimental accuracy remains elusive. Key issues revolve around the accuracy of the energy function, the inability to reliably rank multiple models, and the use of restraints that keep sampling close to the native state but also limit the degree of possible refinement. A different aspect is the question of what exactly the target of high-resolution refinement should be as experimental structures are affected by experimental conditions and different biological questions require varying levels of accuracy. While improvement of the global protein structure is a difficult problem, high-resolution refinement methods that improves local structural quality such as favorable stereochemistry and the avoidance of atomic clashes are much more successful.
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Affiliation(s)
- Michael Feig
- Department of Biochemistry and Molecular Biology, Michigan State University, 603 Wilson Rd., Room 218 BCH, East Lansing, MI, USA, ; 517-432-7439
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22
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Marze NA, Jeliazkov JR, Roy Burman SS, Boyken SE, DiMaio F, Gray JJ. Modeling oblong proteins and water-mediated interfaces with RosettaDock in CAPRI rounds 28-35. Proteins 2017; 85:479-486. [PMID: 27667482 PMCID: PMC5710743 DOI: 10.1002/prot.25168] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2016] [Revised: 09/01/2016] [Accepted: 09/26/2016] [Indexed: 12/27/2022]
Abstract
The 28th-35th rounds of the Critical Assessment of PRotein Interactions (CAPRI) served as a practical benchmark for our RosettaDock protein-protein docking protocols, highlighting strengths and weaknesses of the approach. We achieved acceptable or better quality models in three out of 11 targets. For the two α-repeat protein-green fluorescent protein (αrep-GFP) complexes, we used a novel ellipsoidal partial-global docking method (Ellipsoidal Dock) to generate models with 2.2 Å/1.5 Å interface RMSD, capturing 49%/42% of the native contacts, for the 7-/5-repeat αrep complexes. For the DNase-immunity protein complex, we used a new predictor of hydrogen-bonding networks, HBNet with Bridging Waters, to place individual water models at the complex interface; models were generated with 1.8 Å interface RMSD and 12% native water contacts recovered. The targets for which RosettaDock failed to create an acceptable model were typically difficult in general, as six had no acceptable models submitted by any CAPRI predictor. The UCH-L5-RPN13 and UCH-L5-INO80G de-ubiquitinating enzyme-inhibitor complexes comprised inhibitors undergoing significant structural changes upon binding, with the partners being highly interwoven in the docked complexes. Our failure to predict the nucleosome-enzyme complex in Target 95 was largely due to tight constraints we placed on our model based on sparse biochemical data suggesting two specific cross-interface interactions, preventing the correct structure from being sampled. While RosettaDock's three successes show that it is a state-of-the-art docking method, the difficulties with highly flexible and multi-domain complexes highlight the need for better flexible docking and domain-assembly methods. Proteins 2017; 85:479-486. © 2016 Wiley Periodicals, Inc.
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Affiliation(s)
- Nicholas A. Marze
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, Maryland
| | - Jeliazko R. Jeliazkov
- T.C. Jenkins Department of Biophysics, Johns Hopkins University, Baltimore, Maryland
- Program in Molecular Biophysics, Johns Hopkins University, Baltimore, Maryland
| | - Shourya S. Roy Burman
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, Maryland
| | - Scott E. Boyken
- Department of Biochemistry, University of Washington, Seattle, Washington
- Institute for Protein Design, University of Washington, Seattle, Washington
| | - Frank DiMaio
- Department of Biochemistry, University of Washington, Seattle, Washington
- Institute for Protein Design, University of Washington, Seattle, Washington
| | - Jeffrey J. Gray
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, Maryland
- Program in Molecular Biophysics, Johns Hopkins University, Baltimore, Maryland
- Institute for NanoBioTechnology, Johns Hopkins University, Baltimore, Maryland
- Johns Hopkins School of Medicine, Sidney Kimmel Comprehensive Cancer Center, Baltimore, Maryland
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23
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Weitzner BD, Jeliazkov JR, Lyskov S, Marze N, Kuroda D, Frick R, Adolf-Bryfogle J, Biswas N, Dunbrack RL, Gray JJ. Modeling and docking of antibody structures with Rosetta. Nat Protoc 2017; 12:401-416. [PMID: 28125104 DOI: 10.1038/nprot.2016.180] [Citation(s) in RCA: 180] [Impact Index Per Article: 25.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
We describe Rosetta-based computational protocols for predicting the 3D structure of an antibody from sequence (RosettaAntibody) and then docking the antibody to protein antigens (SnugDock). Antibody modeling leverages canonical loop conformations to graft large segments from experimentally determined structures, as well as offering (i) energetic calculations to minimize loops, (ii) docking methodology to refine the VL-VH relative orientation and (iii) de novo prediction of the elusive complementarity determining region (CDR) H3 loop. To alleviate model uncertainty, antibody-antigen docking resamples CDR loop conformations and can use multiple models to represent an ensemble of conformations for the antibody, the antigen or both. These protocols can be run fully automated via the ROSIE web server (http://rosie.rosettacommons.org/) or manually on a computer with user control of individual steps. For best results, the protocol requires roughly 1,000 CPU-hours for antibody modeling and 250 CPU-hours for antibody-antigen docking. Tasks can be completed in under a day by using public supercomputers.
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Affiliation(s)
- Brian D Weitzner
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, Maryland, USA
| | - Jeliazko R Jeliazkov
- Program in Molecular Biophysics, Johns Hopkins University, Baltimore, Maryland, USA
| | - Sergey Lyskov
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, Maryland, USA
| | - Nicholas Marze
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, Maryland, USA
| | - Daisuke Kuroda
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, Maryland, USA.,Department of Analytical and Physical Chemistry, Showa University School of Pharmacy, Tokyo, Japan
| | - Rahel Frick
- Centre for Immune Regulation, Department of Biosciences, University of Oslo, Oslo, Norway.,Centre for Immune Regulation, Department of Immunology, Oslo University Hospital Rikshospitalet, Oslo, Norway
| | - Jared Adolf-Bryfogle
- Department of Immunology and Microbial Science, The Scripps Research Institute, La Jolla, California, USA.,Institute for Cancer Research, Fox Chase Cancer Center, Philadelphia, Pennsylvania, USA
| | - Naireeta Biswas
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, Maryland, USA
| | - Roland L Dunbrack
- Institute for Cancer Research, Fox Chase Cancer Center, Philadelphia, Pennsylvania, USA
| | - Jeffrey J Gray
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, Maryland, USA.,Program in Molecular Biophysics, Johns Hopkins University, Baltimore, Maryland, USA.,Institute for NanoBioTechnology, Johns Hopkins University, Baltimore, Maryland, USA.,Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins School of Medicine, Baltimore, Maryland, USA
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24
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Abstract
Comparative protein structure modeling predicts the three-dimensional structure of a given protein sequence (target) based primarily on its alignment to one or more proteins of known structure (templates). The prediction process consists of fold assignment, target-template alignment, model building, and model evaluation. This unit describes how to calculate comparative models using the program MODELLER and how to use the ModBase database of such models, and discusses all four steps of comparative modeling, frequently observed errors, and some applications. Modeling lactate dehydrogenase from Trichomonas vaginalis (TvLDH) is described as an example. The download and installation of the MODELLER software is also described. © 2016 by John Wiley & Sons, Inc.
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Affiliation(s)
- Benjamin Webb
- University of California at San Francisco, San Francisco, California
| | - Andrej Sali
- University of California at San Francisco, San Francisco, California
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25
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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.
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Affiliation(s)
- Yuan-Ping Pang
- Computer-Aided Molecular Design Laboratory, Mayo Clinic, Rochester, MN, 55905, USA.
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26
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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.
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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
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27
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Webb B, Sali A. Comparative Protein Structure Modeling Using MODELLER. CURRENT PROTOCOLS IN BIOINFORMATICS 2016; 54:5.6.1-5.6.37. [PMID: 27322406 PMCID: PMC5031415 DOI: 10.1002/cpbi.3] [Citation(s) in RCA: 1890] [Impact Index Per Article: 236.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
Comparative protein structure modeling predicts the three-dimensional structure of a given protein sequence (target) based primarily on its alignment to one or more proteins of known structure (templates). The prediction process consists of fold assignment, target-template alignment, model building, and model evaluation. This unit describes how to calculate comparative models using the program MODELLER and how to use the ModBase database of such models, and discusses all four steps of comparative modeling, frequently observed errors, and some applications. Modeling lactate dehydrogenase from Trichomonas vaginalis (TvLDH) is described as an example. The download and installation of the MODELLER software is also described. © 2016 by John Wiley & Sons, Inc.
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Affiliation(s)
- Benjamin Webb
- University of California at San Francisco, San Francisco, California
| | - Andrej Sali
- University of California at San Francisco, San Francisco, California
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28
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Arora B, Coudrat T, Wootten D, Christopoulos A, Noronha SB, Sexton PM. Prediction of Loops in G Protein-Coupled Receptor Homology Models: Effect of Imprecise Surroundings and Constraints. J Chem Inf Model 2016; 56:671-86. [DOI: 10.1021/acs.jcim.5b00554] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Bhumika Arora
- Department
of Chemical Engineering, Indian Institute of Technology Bombay, Mumbai 400076, India
- Department
of Pharmacology, Monash University, Clayton, Victoria 3800, Australia
- IITB−Monash
Research Academy, IIT Bombay, Mumbai 400076, India
| | - Thomas Coudrat
- Drug
Discovery Biology, Monash Institute of Pharmaceutical Sciences, and
Department of Pharmacology, Monash University, Parkville, Victoria 3052, Australia
| | - Denise Wootten
- Drug
Discovery Biology, Monash Institute of Pharmaceutical Sciences, and
Department of Pharmacology, Monash University, Parkville, Victoria 3052, Australia
| | - Arthur Christopoulos
- Drug
Discovery Biology, Monash Institute of Pharmaceutical Sciences, and
Department of Pharmacology, Monash University, Parkville, Victoria 3052, Australia
| | - Santosh B. Noronha
- Department
of Chemical Engineering, Indian Institute of Technology Bombay, Mumbai 400076, India
| | - Patrick M. Sexton
- Drug
Discovery Biology, Monash Institute of Pharmaceutical Sciences, and
Department of Pharmacology, Monash University, Parkville, Victoria 3052, Australia
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29
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Garza-Fabre M, Kandathil SM, Handl J, Knowles J, Lovell SC. Generating, Maintaining, and Exploiting Diversity in a Memetic Algorithm for Protein Structure Prediction. EVOLUTIONARY COMPUTATION 2016; 24:577-607. [PMID: 26908350 DOI: 10.1162/evco_a_00176] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/05/2023]
Abstract
Computational approaches to de novo protein tertiary structure prediction, including those based on the preeminent "fragment-assembly" technique, have failed to scale up fully to larger proteins (on the order of 100 residues and above). A number of limiting factors are thought to contribute to the scaling problem over and above the simple combinatorial explosion, but the key ones relate to the lack of exploration of properly diverse protein folds, and to an acute form of "deception" in the energy function, whereby low-energy conformations do not reliably equate with native structures. In this article, solutions to both of these problems are investigated through a multistage memetic algorithm incorporating the successful Rosetta method as a local search routine. We found that specialised genetic operators significantly add to structural diversity and that this translates well to reaching low energies. The use of a generalised stochastic ranking procedure for selection enables the memetic algorithm to handle and traverse deep energy wells that can be considered deceptive, which further adds to the ability of the algorithm to obtain a much-improved diversity of folds. The results should translate to a tangible improvement in the performance of protein structure prediction algorithms in blind experiments such as CASP, and potentially to a further step towards the more challenging problem of predicting the three-dimensional shape of large proteins.
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Affiliation(s)
- Mario Garza-Fabre
- Decision and Cognitive Sciences Research Centre, University of Manchester, Manchester, M15 6PB, UK
| | - Shaun M Kandathil
- Faculty of Life Sciences, University of Manchester, Manchester, M13 9PT, UK
| | - Julia Handl
- Decision and Cognitive Sciences Research Centre, University of Manchester, Manchester, M15 6PB, UK
| | - Joshua Knowles
- School of Computer Science, University of Birmingham, Birmingham, B15 2TT, UK
| | - Simon C Lovell
- Faculty of Life Sciences, University of Manchester, Manchester, M13 9PT, UK
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30
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Kandathil SM, Handl J, Lovell SC. Toward a detailed understanding of search trajectories in fragment assembly approaches to protein structure prediction. Proteins 2016; 84:411-26. [PMID: 26799916 PMCID: PMC4982100 DOI: 10.1002/prot.24987] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2015] [Revised: 12/03/2015] [Accepted: 12/31/2015] [Indexed: 11/30/2022]
Abstract
Energy functions, fragment libraries, and search methods constitute three key components of fragment‐assembly methods for protein structure prediction, which are all crucial for their ability to generate high‐accuracy predictions. All of these components are tightly coupled; efficient searching becomes more important as the quality of fragment libraries decreases. Given these relationships, there is currently a poor understanding of the strengths and weaknesses of the sampling approaches currently used in fragment‐assembly techniques. Here, we determine how the performance of search techniques can be assessed in a meaningful manner, given the above problems. We describe a set of techniques that aim to reduce the impact of the energy function, and assess exploration in view of the search space defined by a given fragment library. We illustrate our approach using Rosetta and EdaFold, and show how certain features of these methods encourage or limit conformational exploration. We demonstrate that individual trajectories of Rosetta are susceptible to local minima in the energy landscape, and that this can be linked to non‐uniform sampling across the protein chain. We show that EdaFold's novel approach can help balance broad exploration with locating good low‐energy conformations. This occurs through two mechanisms which cannot be readily differentiated using standard performance measures: exclusion of false minima, followed by an increasingly focused search in low‐energy regions of conformational space. Measures such as ours can be helpful in characterizing new fragment‐based methods in terms of the quality of conformational exploration realized. Proteins 2016; 84:411–426. © 2016 The Authors Proteins: Structure, Function, and Bioinformatics Published by Wiley Periodicals, Inc.
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Affiliation(s)
- Shaun M Kandathil
- Faculty of Life Sciences, the University of Manchester, Manchester, M13 9PL, United Kingdom
| | - Julia Handl
- Alliance Manchester Business School, Faculty of Humanities, the University of Manchester, Manchester, M13 9PL, United Kingdom
| | - Simon C Lovell
- Faculty of Life Sciences, the University of Manchester, Manchester, M13 9PL, United Kingdom
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31
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Lee MS, Olson MA. Assessment of Detection and Refinement Strategies for de novo Protein Structures Using Force Field and Statistical Potentials. J Chem Theory Comput 2015; 3:312-24. [PMID: 26627174 DOI: 10.1021/ct600195f] [Citation(s) in RCA: 25] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
De novo predictions of protein structures at high resolution are plagued by the problem of detecting the native conformation from false energy minima. In this work, we provide an assessment of various detection and refinement protocols on a small subset of the second-generation all-atom Rosetta decoy set (Tsai et al. Proteins 2003, 53, 76-87) using two potentials: the all-atom CHARMM PARAM22 force field combined with generalized Born/surface-area (GB-SA) implicit solvation and the DFIRE-AA statistical potential. Detection schemes included DFIRE-AA conformational scoring and energy minimization followed by scoring with both GB-SA and DFIRE-AA potentials. Refinement methods included short-time (1-ps) molecular dynamics simulations, temperature-based replica exchange molecular dynamics, and a new computational unfold/refold procedure. Refinement methods include temperature-based replica exchange molecular dynamics and a new computational unfold/refold procedure. Our results indicate that simple detection with only minimization is the best protocol for finding the most nativelike structures in the decoy set. The refinement techniques that we tested are generally unsuccessful in improving detection; however, they provide marginal improvements to some of the decoy structures. Future directions in the development of refinement techniques are discussed in the context of the limitations of the protocols evaluated in this study.
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Affiliation(s)
- Michael S Lee
- Computational and Information Sciences Directorate, U.S. Army Research Laboratory, Aberdeen Proving Ground, Maryland 21005, and Department of Cell Biology and Biochemistry, U.S. Army Medical Research Institute of Infectious Diseases, Frederick, Maryland 21702
| | - Mark A Olson
- Computational and Information Sciences Directorate, U.S. Army Research Laboratory, Aberdeen Proving Ground, Maryland 21005, and Department of Cell Biology and Biochemistry, U.S. Army Medical Research Institute of Infectious Diseases, Frederick, Maryland 21702
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32
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Hospital A, Goñi JR, Orozco M, Gelpí JL. Molecular dynamics simulations: advances and applications. Adv Appl Bioinform Chem 2015; 8:37-47. [PMID: 26604800 PMCID: PMC4655909 DOI: 10.2147/aabc.s70333] [Citation(s) in RCA: 240] [Impact Index Per Article: 26.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022] Open
Abstract
Molecular dynamics simulations have evolved into a mature technique that can be used effectively to understand macromolecular structure-to-function relationships. Present simulation times are close to biologically relevant ones. Information gathered about the dynamic properties of macromolecules is rich enough to shift the usual paradigm of structural bioinformatics from studying single structures to analyze conformational ensembles. Here, we describe the foundations of molecular dynamics and the improvements made in the direction of getting such ensemble. Specific application of the technique to three main issues (allosteric regulation, docking, and structure refinement) is discussed.
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Affiliation(s)
- Adam Hospital
- Institute for Research in Biomedicine, The Barcelona Institute of Science and Technology, University of Barcelona, Barcelona, Spain
| | - Josep Ramon Goñi
- Joint BSC-IRB Research Program in Computational Biology, University of Barcelona, Barcelona, Spain ; Barcelona Supercomputing Center, University of Barcelona, Barcelona, Spain
| | - Modesto Orozco
- Institute for Research in Biomedicine, The Barcelona Institute of Science and Technology, University of Barcelona, Barcelona, Spain ; Joint BSC-IRB Research Program in Computational Biology, University of Barcelona, Barcelona, Spain ; Barcelona Supercomputing Center, University of Barcelona, Barcelona, Spain ; Department of Biochemistry and Molecular Biology, University of Barcelona, Barcelona, Spain
| | - Josep L Gelpí
- Joint BSC-IRB Research Program in Computational Biology, University of Barcelona, Barcelona, Spain ; Barcelona Supercomputing Center, University of Barcelona, Barcelona, Spain ; Department of Biochemistry and Molecular Biology, University of Barcelona, Barcelona, Spain
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33
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H. DeLuca S, L. DeLuca S, Leaver-Fay A, Meiler J. RosettaTMH: a method for membrane protein structure elucidation combining EPR distance restraints with assembly of transmembrane helices. AIMS BIOPHYSICS 2015. [DOI: 10.3934/biophy.2016.1.1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
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34
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Protein-protein docking with dynamic residue protonation states. PLoS Comput Biol 2014; 10:e1004018. [PMID: 25501663 PMCID: PMC4263365 DOI: 10.1371/journal.pcbi.1004018] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2014] [Accepted: 11/02/2014] [Indexed: 12/19/2022] Open
Abstract
Protein-protein interactions depend on a host of environmental factors. Local pH conditions influence the interactions through the protonation states of the ionizable residues that can change upon binding. In this work, we present a pH-sensitive docking approach, pHDock, that can sample side-chain protonation states of five ionizable residues (Asp, Glu, His, Tyr, Lys) on-the-fly during the docking simulation. pHDock produces successful local docking funnels in approximately half (79/161) the protein complexes, including 19 cases where standard RosettaDock fails. pHDock also performs better than the two control cases comprising docking at pH 7.0 or using fixed, predetermined protonation states. On average, the top-ranked pHDock structures have lower interface RMSDs and recover more native interface residue-residue contacts and hydrogen bonds compared to RosettaDock. Addition of backbone flexibility using a computationally-generated conformational ensemble further improves native contact and hydrogen bond recovery in the top-ranked structures. Although pHDock is designed to improve docking, it also successfully predicts a large pH-dependent binding affinity change in the Fc–FcRn complex, suggesting that it can be exploited to improve affinity predictions. The approaches in the study contribute to the goal of structural simulations of whole-cell protein-protein interactions including all the environmental factors, and they can be further expanded for pH-sensitive protein design. Protein-protein interactions are fundamental for biological function and are strongly influenced by their local environment. Cellular pH is tightly controlled and is one of the critical environmental factors that regulates protein-protein interactions. Three-dimensional structures of the protein complexes can help us understand the mechanism of the interactions. Since experimental determination of the structures of protein-protein complexes is expensive and time-consuming, computational docking algorithms are helpful to predict the structures. However, none of the current protein-protein docking algorithms account for the critical environmental pH effects. So we developed a pH-sensitive docking algorithm that can dynamically pick the favorable protonation states of the ionizable amino-acid residues. Compared to our previous standard docking algorithm, the new algorithm improves docking accuracy and generates higher-quality predictions over a large dataset of protein-protein complexes. We also use a case study to demonstrate efficacy of the algorithm in predicting a large pH-dependent binding affinity change that cannot be captured by the other methods that neglect pH effects. In principle, the approaches in the study can be used for rational design of pH-dependent protein inhibitors or industrial enzymes that are active over a wide range of pH values.
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35
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Hansen N, Heller F, Schmid N, van Gunsteren WF. Time-averaged order parameter restraints in molecular dynamics simulations. JOURNAL OF BIOMOLECULAR NMR 2014; 60:169-187. [PMID: 25312596 DOI: 10.1007/s10858-014-9866-7] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/13/2014] [Accepted: 09/25/2014] [Indexed: 06/04/2023]
Abstract
A method is described that allows experimental S(2) order parameters to be enforced as a time-averaged quantity in molecular dynamics simulations. The two parameters that characterize time-averaged restraining, the memory relaxation time and the weight of the restraining potential energy term in the potential energy function used in the simulation, are systematically investigated based on two model systems, a vector with one end restrained in space and a pentapeptide. For the latter it is shown that the backbone N-H order parameter of individual residues can be enforced such that the spatial fluctuations of quantities depending on atomic coordinates are not significantly perturbed. The applicability to realistic systems is illustrated for the B3 domain of protein G in aqueous solution.
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Affiliation(s)
- Niels Hansen
- Laboratory of Physical Chemistry, Swiss Federal Institute of Technology, ETH, 8093, Zurich, Switzerland,
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36
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Abstract
Functional characterization of a protein sequence is one of the most frequent problems in biology. This task is usually facilitated by accurate three-dimensional (3-D) structure of the studied protein. In the absence of an experimentally determined structure, comparative or homology modeling can sometimes provide a useful 3-D model for a protein that is related to at least one known protein structure. Comparative modeling predicts the 3-D structure of a given protein sequence (target) based primarily on its alignment to one or more proteins of known structure (templates). The prediction process consists of fold assignment, target-template alignment, model building, and model evaluation. This unit describes how to calculate comparative models using the program MODELLER and discusses all four steps of comparative modeling, frequently observed errors, and some applications. Modeling lactate dehydrogenase from Trichomonas vaginalis (TvLDH) is described as an example. The download and installation of the MODELLER software is also described.
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Affiliation(s)
- Benjamin Webb
- University of California at San Francisco, San Francisco, California
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37
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Pedragosa-Badia X, Sliwoski GR, Dong Nguyen E, Lindner D, Stichel J, Kaufmann KW, Meiler J, Beck-Sickinger AG. Pancreatic polypeptide is recognized by two hydrophobic domains of the human Y4 receptor binding pocket. J Biol Chem 2014; 289:5846-59. [PMID: 24375409 PMCID: PMC3937655 DOI: 10.1074/jbc.m113.502021] [Citation(s) in RCA: 24] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2013] [Revised: 12/21/2013] [Indexed: 12/12/2022] Open
Abstract
Structural characterization of the human Y4 receptor (hY4R) interaction with human pancreatic polypeptide (hPP) is crucial, not only for understanding its biological function but also for testing treatment strategies for obesity that target this interaction. Here, the interaction of receptor mutants with pancreatic polypeptide analogs was studied through double-cycle mutagenesis. To guide mutagenesis and interpret results, a three-dimensional comparative model of the hY4R-hPP complex was constructed based on all available class A G protein-coupled receptor crystal structures and refined using experimental data. Our study reveals that residues of the hPP and the hY4R form a complex network consisting of ionic interactions, hydrophobic interactions, and hydrogen binding. Residues Tyr(2.64), Asp(2.68), Asn(6.55), Asn(7.32), and Phe(7.35) of Y4R are found to be important in receptor activation by hPP. Specifically, Tyr(2.64) interacts with Tyr(27) of hPP through hydrophobic contacts. Asn(7.32) is affected by modifications on position Arg(33) of hPP, suggesting a hydrogen bond between these two residues. Likewise, we find that Phe(7.35) is affected by modifications of hPP at positions 33 and 36, indicating interactions between these three amino acids. Taken together, we demonstrate that the top of transmembrane helix 2 (TM2) and the top of transmembrane helices 6 and 7 (TM6-TM7) form the core of the peptide binding pocket. These findings will contribute to the rational design of ligands that bind the receptor more effectively to produce an enhanced agonistic or antagonistic effect.
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Affiliation(s)
- Xavier Pedragosa-Badia
- From the Institute of Biochemistry, Faculty of Biosciences, Pharmacy and Psychology, Universität Leipzig, 04103 Leipzig, Germany and
| | - Gregory R. Sliwoski
- the Center for Structural Biology, Vanderbilt University Medical Center, Nashville, Tennessee 37232-8725
| | - Elizabeth Dong Nguyen
- the Center for Structural Biology, Vanderbilt University Medical Center, Nashville, Tennessee 37232-8725
| | - Diana Lindner
- From the Institute of Biochemistry, Faculty of Biosciences, Pharmacy and Psychology, Universität Leipzig, 04103 Leipzig, Germany and
| | - Jan Stichel
- From the Institute of Biochemistry, Faculty of Biosciences, Pharmacy and Psychology, Universität Leipzig, 04103 Leipzig, Germany and
| | - Kristian W. Kaufmann
- the Center for Structural Biology, Vanderbilt University Medical Center, Nashville, Tennessee 37232-8725
| | - Jens Meiler
- the Center for Structural Biology, Vanderbilt University Medical Center, Nashville, Tennessee 37232-8725
| | - Annette G. Beck-Sickinger
- From the Institute of Biochemistry, Faculty of Biosciences, Pharmacy and Psychology, Universität Leipzig, 04103 Leipzig, Germany and
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38
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Mao B, Tejero R, Baker D, Montelione GT. Protein NMR structures refined with Rosetta have higher accuracy relative to corresponding X-ray crystal structures. J Am Chem Soc 2014; 136:1893-906. [PMID: 24392845 PMCID: PMC4129517 DOI: 10.1021/ja409845w] [Citation(s) in RCA: 51] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
We have found that refinement of protein NMR structures using Rosetta with experimental NMR restraints yields more accurate protein NMR structures than those that have been deposited in the PDB using standard refinement protocols. Using 40 pairs of NMR and X-ray crystal structures determined by the Northeast Structural Genomics Consortium, for proteins ranging in size from 5-22 kDa, restrained Rosetta refined structures fit better to the raw experimental data, are in better agreement with their X-ray counterparts, and have better phasing power compared to conventionally determined NMR structures. For 37 proteins for which NMR ensembles were available and which had similar structures in solution and in the crystal, all of the restrained Rosetta refined NMR structures were sufficiently accurate to be used for solving the corresponding X-ray crystal structures by molecular replacement. The protocol for restrained refinement of protein NMR structures was also compared with restrained CS-Rosetta calculations. For proteins smaller than 10 kDa, restrained CS-Rosetta, starting from extended conformations, provides slightly more accurate structures, while for proteins in the size range of 10-25 kDa the less CPU intensive restrained Rosetta refinement protocols provided equally or more accurate structures. The restrained Rosetta protocols described here can improve the accuracy of protein NMR structures and should find broad and general for studies of protein structure and function.
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Affiliation(s)
- Binchen Mao
- Center for Advanced Biotechnology and Medicine, and Department of Molecular Biology and Biochemistry, and Department of Biochemistry and Molecular Biology of Robert Wood Johnson Medical School, and Northeast Structural Genomics Consortium, Rutgers, The State University of New Jersey , Piscataway, New Jersey 08854, United States
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39
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Abstract
Computer-aided drug discovery/design methods have played a major role in the development of therapeutically important small molecules for over three decades. These methods are broadly classified as either structure-based or ligand-based methods. Structure-based methods are in principle analogous to high-throughput screening in that both target and ligand structure information is imperative. Structure-based approaches include ligand docking, pharmacophore, and ligand design methods. The article discusses theory behind the most important methods and recent successful applications. Ligand-based methods use only ligand information for predicting activity depending on its similarity/dissimilarity to previously known active ligands. We review widely used ligand-based methods such as ligand-based pharmacophores, molecular descriptors, and quantitative structure-activity relationships. In addition, important tools such as target/ligand data bases, homology modeling, ligand fingerprint methods, etc., necessary for successful implementation of various computer-aided drug discovery/design methods in a drug discovery campaign are discussed. Finally, computational methods for toxicity prediction and optimization for favorable physiologic properties are discussed with successful examples from literature.
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Affiliation(s)
- Gregory Sliwoski
- Jr., Center for Structural Biology, 465 21st Ave South, BIOSCI/MRBIII, Room 5144A, Nashville, TN 37232-8725.
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40
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Merenbakh-Lamin K, Ben-Baruch N, Yeheskel A, Dvir A, Soussan-Gutman L, Jeselsohn R, Yelensky R, Brown M, Miller VA, Sarid D, Rizel S, Klein B, Rubinek T, Wolf I. D538G mutation in estrogen receptor-α: A novel mechanism for acquired endocrine resistance in breast cancer. Cancer Res 2013; 73:6856-64. [PMID: 24217577 DOI: 10.1158/0008-5472.can-13-1197] [Citation(s) in RCA: 300] [Impact Index Per Article: 27.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Resistance to endocrine therapy occurs in virtually all patients with estrogen receptor α (ERα)-positive metastatic breast cancer, and is attributed to various mechanisms including loss of ERα expression, altered activity of coregulators, and cross-talk between the ERα and growth factor signaling pathways. To our knowledge, acquired mutations of the ERα have not been described as mediating endocrine resistance. Samples of 13 patients with metastatic breast cancer were analyzed for mutations in cancer-related genes. In five patients who developed resistance to hormonal therapy, a mutation of A to G at position 1,613 of ERα, resulting in a substitution of aspartic acid at position 538 to glycine (D538G), was identified in liver metastases. Importantly, the mutation was not detected in the primary tumors obtained prior to endocrine treatment. Structural modeling indicated that D538G substitution leads to a conformational change in the ligand-binding domain, which mimics the conformation of activated ligand-bound receptor and alters binding of tamoxifen. Indeed, experiments in breast cancer cells indicated constitutive, ligand-independent transcriptional activity of the D538G receptor, and overexpression of it enhanced proliferation and conferred resistance to tamoxifen. These data indicate a novel mechanism of acquired endocrine resistance in breast cancer. Further studies are needed to assess the frequency of D538G-ERα among patients with breast cancer and explore ways to inhibit its activity and restore endocrine sensitivity.
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Affiliation(s)
- Keren Merenbakh-Lamin
- Authors' Affiliations: Department of Oncology, Tel Aviv Sourasky Medical Center; Sackler Faculty of Medicine; The Bioinformatics Unit, Goerge S. Wise Faculty of Life Sciences, Tel Aviv University; Assuta Medical Center, Tel Aviv; Kaplan Medical Center, Rehovot; Oncotest-Teva Pharmaceutical Industries; Institute of Oncology, Davidoff Center, Rabin Medical Center, Petach Tikva, Israel; Department of Medical Oncology, Dana-Farber Cancer Institute and Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston; and Foundation Medicine, Cambridge, Massachusetts
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41
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Kilambi KP, Pacella MS, Xu J, Labonte JW, Porter JR, Muthu P, Drew K, Kuroda D, Schueler-Furman O, Bonneau R, Gray JJ. Extending RosettaDock with water, sugar, and pH for prediction of complex structures and affinities for CAPRI rounds 20-27. Proteins 2013; 81:2201-9. [PMID: 24123494 PMCID: PMC4037910 DOI: 10.1002/prot.24425] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2013] [Revised: 09/12/2013] [Accepted: 09/13/2013] [Indexed: 11/09/2022]
Abstract
Rounds 20-27 of the Critical Assessment of PRotein Interactions (CAPRI) provided a testing platform for computational methods designed to address a wide range of challenges. The diverse targets drove the creation of and new combinations of computational tools. In this study, RosettaDock and other novel Rosetta protocols were used to successfully predict four of the 10 blind targets. For example, for DNase domain of Colicin E2-Im2 immunity protein, RosettaDock and RosettaLigand were used to predict the positions of water molecules at the interface, recovering 46% of the native water-mediated contacts. For α-repeat Rep4-Rep2 and g-type lysozyme-PliG inhibitor complexes, homology models were built and standard and pH-sensitive docking algorithms were used to generate structures with interface RMSD values of 3.3 Å and 2.0 Å, respectively. A novel flexible sugar-protein docking protocol was also developed and used for structure prediction of the BT4661-heparin-like saccharide complex, recovering 71% of the native contacts. Challenges remain in the generation of accurate homology models for protein mutants and sampling during global docking. On proteins designed to bind influenza hemagglutinin, only about half of the mutations were identified that affect binding (T55: 54%; T56: 48%). The prediction of the structure of the xylanase complex involving homology modeling and multidomain docking pushed the limits of global conformational sampling and did not result in any successful prediction. The diversity of problems at hand requires computational algorithms to be versatile; the recent additions to the Rosetta suite expand the capabilities to encompass more biologically realistic docking problems.
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Affiliation(s)
- Krishna Praneeth Kilambi
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, Maryland
| | - Michael S. Pacella
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland
| | - Jianqing Xu
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, Maryland
| | - Jason W. Labonte
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, Maryland
| | - Justin R. Porter
- Thomas C. Jenkins Department of Biophysics, Johns Hopkins University, Baltimore, Maryland
| | - Pravin Muthu
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, Maryland
| | - Kevin Drew
- Department of Biology, Center for Genomics and Systems Biology, New York University, New York, New York
| | - Daisuke Kuroda
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, Maryland
| | - Ora Schueler-Furman
- Department of Microbiology and Molecular Genetics, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Richard Bonneau
- Department of Biology, Center for Genomics and Systems Biology, New York University, New York, New York
| | - Jeffrey J. Gray
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, Maryland
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42
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Khoury GA, Tamamis P, Pinnaduwage N, Smadbeck J, Kieslich CA, Floudas CA. Princeton_TIGRESS: protein geometry refinement using simulations and support vector machines. Proteins 2013; 82:794-814. [PMID: 24174311 DOI: 10.1002/prot.24459] [Citation(s) in RCA: 21] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2013] [Revised: 10/18/2013] [Accepted: 10/22/2013] [Indexed: 12/30/2022]
Abstract
Protein structure refinement aims to perform a set of operations given a predicted structure to improve model quality and accuracy with respect to the native in a blind fashion. Despite the numerous computational approaches to the protein refinement problem reported in the previous three CASPs, an overwhelming majority of methods degrade models rather than improve them. We initially developed a method tested using blind predictions during CASP10 which was officially ranked in 5th place among all methods in the refinement category. Here, we present Princeton_TIGRESS, which when benchmarked on all CASP 7,8,9, and 10 refinement targets, simultaneously increased GDT_TS 76% of the time with an average improvement of 0.83 GDT_TS points per structure. The method was additionally benchmarked on models produced by top performing three-dimensional structure prediction servers during CASP10. The robustness of the Princeton_TIGRESS protocol was also tested for different random seeds. We make the Princeton_TIGRESS refinement protocol freely available as a web server at http://atlas.princeton.edu/refinement. Using this protocol, one can consistently refine a prediction to help bridge the gap between a predicted structure and the actual native structure.
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Affiliation(s)
- George A Khoury
- Department of Chemical and Biological Engineering, Princeton University, Princeton, New Jersey, 08540
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43
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Alexander NS, Stein RA, Koteiche HA, Kaufmann KW, Mchaourab HS, Meiler J. RosettaEPR: rotamer library for spin label structure and dynamics. PLoS One 2013; 8:e72851. [PMID: 24039810 PMCID: PMC3764097 DOI: 10.1371/journal.pone.0072851] [Citation(s) in RCA: 35] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2013] [Accepted: 07/15/2013] [Indexed: 11/18/2022] Open
Abstract
An increasingly used parameter in structural biology is the measurement of distances between spin labels bound to a protein. One limitation to these measurements is the unknown position of the spin label relative to the protein backbone. To overcome this drawback, we introduce a rotamer library of the methanethiosulfonate spin label (MTSSL) into the protein modeling program Rosetta. Spin label rotamers were derived from conformations observed in crystal structures of spin labeled T4 lysozyme and previously published molecular dynamics simulations. Rosetta’s ability to accurately recover spin label conformations and EPR measured distance distributions was evaluated against 19 experimentally determined MTSSL labeled structures of T4 lysozyme and the membrane protein LeuT and 73 distance distributions from T4 lysozyme and the membrane protein MsbA. For a site in the core of T4 lysozyme, the correct spin label conformation (Χ1 and Χ2) is recovered in 99.8% of trials. In surface positions 53% of the trajectories agree with crystallized conformations in Χ1 and Χ2. This level of recovery is on par with Rosetta performance for the 20 natural amino acids. In addition, Rosetta predicts the distance between two spin labels with a mean error of 4.4 Å. The width of the experimental distance distribution, which reflects the flexibility of the two spin labels, is predicted with a mean error of 1.3 Å. RosettaEPR makes full-atom spin label modeling available to a wide scientific community in conjunction with the powerful suite of modeling methods within Rosetta.
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Affiliation(s)
- Nathan S. Alexander
- Center for Structural Biology, Vanderbilt University, Nashville, Tennessee, United States of America
- Department of Chemistry, Vanderbilt University, Nashville, Tennessee, United States of America
| | - Richard A. Stein
- Department of Molecular Physiology and Biophysics, Vanderbilt University, Nashville, Tennessee, United States of America
| | - Hanane A. Koteiche
- Department of Molecular Physiology and Biophysics, Vanderbilt University, Nashville, Tennessee, United States of America
| | - Kristian W. Kaufmann
- Center for Structural Biology, Vanderbilt University, Nashville, Tennessee, United States of America
- Department of Chemistry, Vanderbilt University, Nashville, Tennessee, United States of America
| | - Hassane S. Mchaourab
- Center for Structural Biology, Vanderbilt University, Nashville, Tennessee, United States of America
- Department of Molecular Physiology and Biophysics, Vanderbilt University, Nashville, Tennessee, United States of America
| | - Jens Meiler
- Center for Structural Biology, Vanderbilt University, Nashville, Tennessee, United States of America
- Department of Chemistry, Vanderbilt University, Nashville, Tennessee, United States of America
- * E-mail:
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44
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Han L, Ruotolo BT. Hofmeister Salts Recover a Misfolded Multiprotein Complex for Subsequent Structural Measurements in the Gas Phase. Angew Chem Int Ed Engl 2013. [DOI: 10.1002/ange.201301893] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
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45
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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/.
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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:
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46
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Han L, Ruotolo BT. Hofmeister salts recover a misfolded multiprotein complex for subsequent structural measurements in the gas phase. Angew Chem Int Ed Engl 2013; 52:8329-32. [PMID: 23818426 DOI: 10.1002/anie.201301893] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2013] [Revised: 04/26/2013] [Indexed: 01/05/2023]
Affiliation(s)
- Linjie Han
- Department of Chemistry, University of Michigan, 930 North University Avenue, Ann Arbor, MI 48109-1055, USA
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47
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Stein A, Kortemme T. Improvements to robotics-inspired conformational sampling in rosetta. PLoS One 2013; 8:e63090. [PMID: 23704889 PMCID: PMC3660577 DOI: 10.1371/journal.pone.0063090] [Citation(s) in RCA: 140] [Impact Index Per Article: 12.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2013] [Accepted: 03/28/2013] [Indexed: 02/04/2023] Open
Abstract
To accurately predict protein conformations in atomic detail, a computational method must be capable of sampling models sufficiently close to the native structure. All-atom sampling is difficult because of the vast number of possible conformations and extremely rugged energy landscapes. Here, we test three sampling strategies to address these difficulties: conformational diversification, intensification of torsion and omega-angle sampling and parameter annealing. We evaluate these strategies in the context of the robotics-based kinematic closure (KIC) method for local conformational sampling in Rosetta on an established benchmark set of 45 12-residue protein segments without regular secondary structure. We quantify performance as the fraction of sub-Angstrom models generated. While improvements with individual strategies are only modest, the combination of intensification and annealing strategies into a new “next-generation KIC” method yields a four-fold increase over standard KIC in the median percentage of sub-Angstrom models across the dataset. Such improvements enable progress on more difficult problems, as demonstrated on longer segments, several of which could not be accurately remodeled with previous methods. Given its improved sampling capability, next-generation KIC should allow advances in other applications such as local conformational remodeling of multiple segments simultaneously, flexible backbone sequence design, and development of more accurate energy functions.
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Affiliation(s)
- Amelie Stein
- California Institute for Quantitative Biomedical Research and Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, California, United States of America
- * E-mail: (AS); (TK)
| | - Tanja Kortemme
- California Institute for Quantitative Biomedical Research and Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, California, United States of America
- * E-mail: (AS); (TK)
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48
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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.
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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
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49
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Kilambi KP, Gray JJ. Rapid calculation of protein pKa values using Rosetta. Biophys J 2013; 103:587-595. [PMID: 22947875 DOI: 10.1016/j.bpj.2012.06.044] [Citation(s) in RCA: 42] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2012] [Revised: 06/08/2012] [Accepted: 06/11/2012] [Indexed: 12/21/2022] Open
Abstract
We developed a Rosetta-based Monte Carlo method to calculate the pK(a) values of protein residues that commonly exhibit variable protonation states (Asp, Glu, Lys, His, and Tyr). We tested the technique by calculating pK(a) values for 264 residues from 34 proteins. The standard Rosetta score function, which is independent of any environmental conditions, failed to capture pK(a) shifts. After incorporating a Coulomb electrostatic potential and optimizing the solvation reference energies for pK(a) calculations, we employed a method that allowed side-chain flexibility and achieved a root mean-square deviation (RMSD) of 0.83 from experimental values (0.68 after discounting 11 predictions with an error over 2 pH units). Additional degrees of side-chain conformational freedom for the proximal residues facilitated the capture of charge-charge interactions in a few cases, resulting in an overall RMSD of 0.85 pH units. The addition of backbone flexibility increased the overall RMSD to 0.93 pH units but improved relative pK(a) predictions for proximal catalytic residues. The method also captures large pK(a) shifts of lysine and some glutamate point mutations in staphylococcal nuclease. Thus, a simple and fast method based on the Rosetta score function and limited conformational sampling produces pK(a) values that will be useful when rapid estimation is essential, such as in docking, design, and folding.
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Affiliation(s)
- Krishna Praneeth Kilambi
- Department of Chemical and Biomolecular Engineering, The Johns Hopkins University, Baltimore, Maryland
| | - Jeffrey J Gray
- Department of Chemical and Biomolecular Engineering, The Johns Hopkins University, Baltimore, Maryland; Program in Molecular Biophysics, The Johns Hopkins University, Baltimore, Maryland.
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50
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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.
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Affiliation(s)
- V K Vyas
- Department of Pharmaceutical Chemistry, Institute of Pharmacy, Nirma University, Ahmedabad-382 481, India
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