301
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Way JC, Collins JJ, Keasling JD, Silver PA. Integrating biological redesign: where synthetic biology came from and where it needs to go. Cell 2014; 157:151-61. [PMID: 24679533 DOI: 10.1016/j.cell.2014.02.039] [Citation(s) in RCA: 157] [Impact Index Per Article: 15.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2013] [Revised: 12/26/2013] [Accepted: 02/19/2014] [Indexed: 01/17/2023]
Abstract
Synthetic biology seeks to extend approaches from engineering and computation to redesign of biology, with goals such as generating new chemicals, improving human health, and addressing environmental issues. Early on, several guiding principles of synthetic biology were articulated, including design according to specification, separation of design from fabrication, use of standardized biological parts and organisms, and abstraction. We review the utility of these principles over the past decade in light of the field's accomplishments in building complex systems based on microbial transcription and metabolism and describe the progress in mammalian cell engineering.
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Affiliation(s)
- Jeffrey C Way
- Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA 02115, USA
| | - James J Collins
- Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA 02115, USA; Howard Hughes Medical Institute, Department of Biomedical Engineering and Center of Synthetic Biology, Boston University, Boston, MA 02115, USA
| | - Jay D Keasling
- Department of Chemical and Biomolecular Engineering, Department of Bioengineering, University of California, Berkeley, Berkeley, CA 94720, USA; Physical Biosciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA; Joint Bioenergy Institute, Emeryville, CA 94608, USA; Synthetic Biology Engineering Research Center (SynBERC), University of California, Berkeley, Berkeley, CA 94720, USA
| | - Pamela A Silver
- Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA 02115, USA; Department of Systems Biology, Harvard Medical School, Boston, MA 02115, USA; Synthetic Biology Engineering Research Center (SynBERC), University of California, Berkeley, Berkeley, CA 94720, USA.
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302
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Nick Pace C, Scholtz JM, Grimsley GR. Forces stabilizing proteins. FEBS Lett 2014; 588:2177-84. [PMID: 24846139 DOI: 10.1016/j.febslet.2014.05.006] [Citation(s) in RCA: 236] [Impact Index Per Article: 23.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2014] [Revised: 04/30/2014] [Accepted: 05/05/2014] [Indexed: 11/30/2022]
Abstract
The goal of this article is to summarize what has been learned about the major forces stabilizing proteins since the late 1980s when site-directed mutagenesis became possible. The following conclusions are derived from experimental studies of hydrophobic and hydrogen bonding variants. (1) Based on studies of 138 hydrophobic interaction variants in 11 proteins, burying a -CH2- group on folding contributes 1.1±0.5 kcal/mol to protein stability. (2) The burial of non-polar side chains contributes to protein stability in two ways: first, a term that depends on the removal of the side chains from water and, more importantly, the enhanced London dispersion forces that result from the tight packing in the protein interior. (3) Based on studies of 151 hydrogen bonding variants in 15 proteins, forming a hydrogen bond on folding contributes 1.1±0.8 kcal/mol to protein stability. (4) The contribution of hydrogen bonds to protein stability is strongly context dependent. (5) Hydrogen bonds by side chains and peptide groups make similar contributions to protein stability. (6) Polar group burial can make a favorable contribution to protein stability even if the polar group is not hydrogen bonded. (7) Hydrophobic interactions and hydrogen bonds both make large contributions to protein stability.
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Affiliation(s)
- C Nick Pace
- Department of Biochemistry and Biophysics, Texas A&M University, College Station, TX 77843, United States; Department of Molecular and Cellular Medicine, Texas A&M University Health Science Center, College Station, TX 77843, United States.
| | - J Martin Scholtz
- Department of Biochemistry and Biophysics, Texas A&M University, College Station, TX 77843, United States; Department of Molecular and Cellular Medicine, Texas A&M University Health Science Center, College Station, TX 77843, United States
| | - Gerald R Grimsley
- Department of Molecular and Cellular Medicine, Texas A&M University Health Science Center, College Station, TX 77843, United States
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303
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Anand P, Nagarajan D, Mukherjee S, Chandra N. PLIC: protein-ligand interaction clusters. DATABASE-THE JOURNAL OF BIOLOGICAL DATABASES AND CURATION 2014; 2014:bau029. [PMID: 24763918 PMCID: PMC3998096 DOI: 10.1093/database/bau029] [Citation(s) in RCA: 29] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Abstract
Most of the biological processes are governed through specific protein–ligand interactions. Discerning different components that contribute toward a favorable protein– ligand interaction could contribute significantly toward better understanding protein function, rationalizing drug design and obtaining design principles for protein engineering. The Protein Data Bank (PDB) currently hosts the structure of ∼68 000 protein–ligand complexes. Although several databases exist that classify proteins according to sequence and structure, a mere handful of them annotate and classify protein–ligand interactions and provide information on different attributes of molecular recognition. In this study, an exhaustive comparison of all the biologically relevant ligand-binding sites (84 846 sites) has been conducted using PocketMatch: a rapid, parallel, in-house algorithm. PocketMatch quantifies the similarity between binding sites based on structural descriptors and residue attributes. A similarity network was constructed using binding sites whose PocketMatch scores exceeded a high similarity threshold (0.80). The binding site similarity network was clustered into discrete sets of similar sites using the Markov clustering (MCL) algorithm. Furthermore, various computational tools have been used to study different attributes of interactions within the individual clusters. The attributes can be roughly divided into (i) binding site characteristics including pocket shape, nature of residues and interaction profiles with different kinds of atomic probes, (ii) atomic contacts consisting of various types of polar, hydrophobic and aromatic contacts along with binding site water molecules that could play crucial roles in protein–ligand interactions and (iii) binding energetics involved in interactions derived from scoring functions developed for docking. For each ligand-binding site in each protein in the PDB, site similarity information, clusters they belong to and description of site attributes are provided as a relational database—protein–ligand interaction clusters (PLIC). Database URL: http://proline.biochem.iisc.ernet.in/PLIC
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Affiliation(s)
- Praveen Anand
- Department of Biochemistry, Indian Institute of Science, Bangalore 560012, Karnataka, India and IISc Mathematics Initiative, Indian Institute of Science, Banglaore 560012, Karnataka, India
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304
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Wang W, Li M, Wei Z, Wang Z, Bu X, Lai W, Yang S, Gong H, Zheng H, Wang Y, Liu Y, Li Q, Fang Q, Hu Z. Bimodal Imprint Chips for Peptide Screening: Integration of High-Throughput Sequencing by MS and Affinity Analyses by Surface Plasmon Resonance Imaging. Anal Chem 2014; 86:3703-7. [DOI: 10.1021/ac500465e] [Citation(s) in RCA: 25] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2023]
Affiliation(s)
- Weizhi Wang
- CAS Key Laboratory for Biomedical Effects of Nanomaterials & Nanosafety, National Center for Nanoscience and Technology of China, Beijing 100190, China
| | - Menglin Li
- Department
of Biomedical Engineering, Beijing Institute of Technology, Beijing 100081, China
| | - Zewen Wei
- CAS Key Laboratory for Biomedical Effects of Nanomaterials & Nanosafety, National Center for Nanoscience and Technology of China, Beijing 100190, China
| | - Zihua Wang
- CAS Key Laboratory for Biomedical Effects of Nanomaterials & Nanosafety, National Center for Nanoscience and Technology of China, Beijing 100190, China
| | - Xiangli Bu
- CAS Key Laboratory for Biomedical Effects of Nanomaterials & Nanosafety, National Center for Nanoscience and Technology of China, Beijing 100190, China
| | - Wenjia Lai
- CAS Key Laboratory for Biomedical Effects of Nanomaterials & Nanosafety, National Center for Nanoscience and Technology of China, Beijing 100190, China
| | - Shu Yang
- CAS Key Laboratory for Biomedical Effects of Nanomaterials & Nanosafety, National Center for Nanoscience and Technology of China, Beijing 100190, China
| | - He Gong
- CAS Key Laboratory for Biomedical Effects of Nanomaterials & Nanosafety, National Center for Nanoscience and Technology of China, Beijing 100190, China
| | - Hui Zheng
- CAS Key Laboratory for Biomedical Effects of Nanomaterials & Nanosafety, National Center for Nanoscience and Technology of China, Beijing 100190, China
| | - Yuqiao Wang
- CAS Key Laboratory for Biomedical Effects of Nanomaterials & Nanosafety, National Center for Nanoscience and Technology of China, Beijing 100190, China
| | - Ying Liu
- Beijing
Proteome Research Center, Beijing Institute of Radiation Medicine, Beijing 102206, China
| | - Qin Li
- Department
of Biomedical Engineering, Beijing Institute of Technology, Beijing 100081, China
| | - Qiaojun Fang
- CAS Key Laboratory for Biomedical Effects of Nanomaterials & Nanosafety, National Center for Nanoscience and Technology of China, Beijing 100190, China
| | - Zhiyuan Hu
- CAS Key Laboratory for Biomedical Effects of Nanomaterials & Nanosafety, National Center for Nanoscience and Technology of China, Beijing 100190, China
- Beijing
Proteome Research Center, Beijing Institute of Radiation Medicine, Beijing 102206, China
- Institute for Systems Biology, 401 Terry Avenue N, Seattle, Washington 98109, United States
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305
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Residue specific contributions to stability and activity inferred from saturation mutagenesis and deep sequencing. Curr Opin Struct Biol 2014; 24:63-71. [DOI: 10.1016/j.sbi.2013.12.001] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2013] [Revised: 11/25/2013] [Accepted: 12/03/2013] [Indexed: 12/23/2022]
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306
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Sun H, Li Y, Shen M, Tian S, Xu L, Pan P, Guan Y, Hou T. Assessing the performance of MM/PBSA and MM/GBSA methods. 5. Improved docking performance using high solute dielectric constant MM/GBSA and MM/PBSA rescoring. Phys Chem Chem Phys 2014; 16:22035-45. [DOI: 10.1039/c4cp03179b] [Citation(s) in RCA: 345] [Impact Index Per Article: 34.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
Abstract
We have thoroughly investigated the performance of MM/GBSA and MM/PBSA methodologies on virtual screening based on various protocols for kinase targets.
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Affiliation(s)
- Huiyong Sun
- Institute of Functional Nano and Soft Materials (FUNSOM)
- Jiangsu Key Laboratory for Carbon-Based Functional Materials and Devices and Collaborative Innovation Center of Suzhou Nano Science and Technology
- Soochow University
- Suzhou, P. R. China
- College of Pharmaceutical Sciences
| | - Youyong Li
- Institute of Functional Nano and Soft Materials (FUNSOM)
- Jiangsu Key Laboratory for Carbon-Based Functional Materials and Devices and Collaborative Innovation Center of Suzhou Nano Science and Technology
- Soochow University
- Suzhou, P. R. China
| | - Mingyun Shen
- Institute of Functional Nano and Soft Materials (FUNSOM)
- Jiangsu Key Laboratory for Carbon-Based Functional Materials and Devices and Collaborative Innovation Center of Suzhou Nano Science and Technology
- Soochow University
- Suzhou, P. R. China
| | - Sheng Tian
- Institute of Functional Nano and Soft Materials (FUNSOM)
- Jiangsu Key Laboratory for Carbon-Based Functional Materials and Devices and Collaborative Innovation Center of Suzhou Nano Science and Technology
- Soochow University
- Suzhou, P. R. China
| | - Lei Xu
- College of Pharmaceutical Sciences
- Zhejiang University
- Hangzhou, P. R. China
| | - Peichen Pan
- College of Pharmaceutical Sciences
- Zhejiang University
- Hangzhou, P. R. China
| | - Yan Guan
- Institute of Functional Nano and Soft Materials (FUNSOM)
- Jiangsu Key Laboratory for Carbon-Based Functional Materials and Devices and Collaborative Innovation Center of Suzhou Nano Science and Technology
- Soochow University
- Suzhou, P. R. China
| | - Tingjun Hou
- Institute of Functional Nano and Soft Materials (FUNSOM)
- Jiangsu Key Laboratory for Carbon-Based Functional Materials and Devices and Collaborative Innovation Center of Suzhou Nano Science and Technology
- Soochow University
- Suzhou, P. R. China
- College of Pharmaceutical Sciences
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307
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Abstract
Over the past three decades, a powerful array of techniques has been developed for expressing heterologous proteins and saccharides on the surface of bacteria. Surface-engineered bacteria, in turn, have proven useful in a variety of settings, including high-throughput screening, biofuel production, and vaccinology. In this chapter, we provide a comprehensive review of methods for displaying polypeptides and sugars on the bacterial cell surface, and discuss the many innovative applications these methods have found to date. While already an important biotechnological tool, we believe bacterial surface display may be further improved through integration with emerging methodology in other fields, such as protein engineering and synthetic chemistry. Ultimately, we envision bacterial display becoming a multidisciplinary platform with the potential to transform basic and applied research in bacteriology, biotechnology, and biomedicine.
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308
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Computational tools for designing and engineering enzymes. Curr Opin Chem Biol 2013; 19:8-16. [PMID: 24780274 DOI: 10.1016/j.cbpa.2013.12.003] [Citation(s) in RCA: 132] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2013] [Revised: 12/04/2013] [Accepted: 12/04/2013] [Indexed: 01/23/2023]
Abstract
Protein engineering strategies aimed at constructing enzymes with novel or improved activities, specificities, and stabilities greatly benefit from in silico methods. Computational methods can be principally grouped into three main categories: bioinformatics; molecular modelling; and de novo design. Particularly de novo protein design is experiencing rapid development, resulting in more robust and reliable predictions. A recent trend in the field is to combine several computational approaches in an interactive manner and to complement them with structural analysis and directed evolution. A detailed investigation of designed catalysts provides valuable information on the structural basis of molecular recognition, biochemical catalysis, and natural protein evolution.
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309
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Feldmeier K, Höcker B. Computational protein design of ligand binding and catalysis. Curr Opin Chem Biol 2013; 17:929-33. [DOI: 10.1016/j.cbpa.2013.10.002] [Citation(s) in RCA: 40] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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310
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Nivón LG, Bjelic S, King C, Baker D. Automating human intuition for protein design. Proteins 2013; 82:858-66. [DOI: 10.1002/prot.24463] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2013] [Revised: 09/25/2013] [Accepted: 10/21/2013] [Indexed: 11/11/2022]
Affiliation(s)
- Lucas G. Nivón
- Department of BiochemistryUniversity of WashingtonSeattle Washington98195
| | - Sinisa Bjelic
- Department of BiochemistryUniversity of WashingtonSeattle Washington98195
| | - Chris King
- Department of BiochemistryUniversity of WashingtonSeattle Washington98195
| | - David Baker
- Department of BiochemistryUniversity of WashingtonSeattle Washington98195
- Howard Hughes Medical Institute (HHMI)University of WashingtonSeattle Washington98195
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311
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Doerr A. Designer binders. Nat Methods 2013; 10:1057. [DOI: 10.1038/nmeth.2719] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
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312
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Bjelic S, Kipnis Y, Wang L, Pianowski Z, Vorobiev S, Su M, Seetharaman J, Xiao R, Kornhaber G, Hunt JF, Tong L, Hilvert D, Baker D. Exploration of alternate catalytic mechanisms and optimization strategies for retroaldolase design. J Mol Biol 2013; 426:256-71. [PMID: 24161950 DOI: 10.1016/j.jmb.2013.10.012] [Citation(s) in RCA: 31] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2013] [Revised: 10/08/2013] [Accepted: 10/09/2013] [Indexed: 12/13/2022]
Abstract
Designed retroaldolases have utilized a nucleophilic lysine to promote carbon-carbon bond cleavage of β-hydroxy-ketones via a covalent Schiff base intermediate. Previous computational designs have incorporated a water molecule to facilitate formation and breakdown of the carbinolamine intermediate to give the Schiff base and to function as a general acid/base. Here we investigate an alternative active-site design in which the catalytic water molecule was replaced by the side chain of a glutamic acid. Five out of seven designs expressed solubly and exhibited catalytic efficiencies similar to previously designed retroaldolases for the conversion of 4-hydroxy-4-(6-methoxy-2-naphthyl)-2-butanone to 6-methoxy-2-naphthaldehyde and acetone. After one round of site-directed saturation mutagenesis, improved variants of the two best designs, RA114 and RA117, exhibited among the highest kcat (>10(-3)s(-1)) and kcat/KM (11-25M(-1)s(-1)) values observed for retroaldolase designs prior to comprehensive directed evolution. In both cases, the >10(5)-fold rate accelerations that were achieved are within 1-3 orders of magnitude of the rate enhancements reported for the best catalysts for related reactions, including catalytic antibodies (kcat/kuncat=10(6) to 10(8)) and an extensively evolved computational design (kcat/kuncat>10(7)). The catalytic sites, revealed by X-ray structures of optimized versions of the two active designs, are in close agreement with the design models except for the catalytic lysine in RA114. We further improved the variants by computational remodeling of the loops and yeast display selection for reactivity of the catalytic lysine with a diketone probe, obtaining an additional order of magnitude enhancement in activity with both approaches.
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Affiliation(s)
- Sinisa Bjelic
- Department of Biochemistry, University of Washington, Seattle, WA 98195, USA
| | - Yakov Kipnis
- Department of Biochemistry, University of Washington, Seattle, WA 98195, USA
| | - Ling Wang
- Department of Biochemistry, University of Washington, Seattle, WA 98195, USA
| | | | - Sergey Vorobiev
- Department of Biological Sciences, Northeast Structural Genomics Consortium, Columbia University, New York, NY 10027, USA
| | - Min Su
- Department of Biological Sciences, Northeast Structural Genomics Consortium, Columbia University, New York, NY 10027, USA
| | - Jayaraman Seetharaman
- Department of Biological Sciences, Northeast Structural Genomics Consortium, Columbia University, New York, NY 10027, USA
| | - Rong Xiao
- Center for Advanced Biotechnology and Medicine, Rutgers, The State University of New Jersey, NJ 08854, USA
| | - Gregory Kornhaber
- Center for Advanced Biotechnology and Medicine, Rutgers, The State University of New Jersey, NJ 08854, USA; Robert Wood Johnson Medical School, University of Medicine and Dentistry of New Jersey, NJ 08854, USA; Northeast Structural Genomics Consortium, 679 Hoes Lane, Piscataway, NJ 08854, USA
| | - John F Hunt
- Department of Biological Sciences, Northeast Structural Genomics Consortium, Columbia University, New York, NY 10027, USA
| | - Liang Tong
- Department of Biological Sciences, Northeast Structural Genomics Consortium, Columbia University, New York, NY 10027, USA
| | - Donald Hilvert
- Laboratory of Organic Chemistry, ETH Zurich, 8093 Zurich, Switzerland
| | - David Baker
- Department of Biochemistry, University of Washington, Seattle, WA 98195, USA; Howard Hughes Medical Institute, University of Washington, Seattle, WA 98195, USA.
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313
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Harrison C. Designing optimal ligand-binding proteins. Nat Rev Drug Discov 2013. [DOI: 10.1038/nrd4141] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
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314
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