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Saikia B, Gogoi CR, Rahman A, Baruah A. Identification of an optimal foldability criterion to design misfolding resistant protein. J Chem Phys 2021; 155:144102. [PMID: 34654294 DOI: 10.1063/5.0057533] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022] Open
Abstract
Proteins achieve their functional, active, and operative three dimensional native structures by overcoming the possibility of being trapped in non-native energy minima present in the energy landscape. The enormous and intricate interactions that play an important role in protein folding also determine the stability of the proteins. The large number of stabilizing/destabilizing interactions makes proteins to be only marginally stable as compared to the other competing structures. Therefore, there are some possibilities that they become trapped in the non-native conformations and thus get misfolded. These misfolded proteins lead to several debilitating diseases. This work performs a comparative study of some existing foldability criteria in the computational design of misfold resistant protein sequences based on self-consistent mean field theory. The foldability criteria selected for this study are Ef, Δ, and Φ that are commonly used in protein design procedures to determine the most efficient foldability criterion for the design of misfolding resistant proteins. The results suggest that the foldability criterion Δ is significantly better in designing a funnel energy landscape stabilizing the target state. The results also suggest that inclusion of negative design features is important for designing misfolding resistant proteins, but more information about the non-native conformations in terms of Φ leads to worse results compared to even simple positive design. The sequences designed using Δ show better resistance to misfolding in the Monte Carlo simulations performed in the study.
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Affiliation(s)
- Bondeepa Saikia
- Department of Chemistry, Dibrugarh University, Dibrugarh 786004, India
| | - Chimi Rekha Gogoi
- Department of Chemistry, Dibrugarh University, Dibrugarh 786004, India
| | - Aziza Rahman
- Department of Chemistry, Dibrugarh University, Dibrugarh 786004, India
| | - Anupaul Baruah
- Department of Chemistry, Dibrugarh University, Dibrugarh 786004, India
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2
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Michael E, Polydorides S, Simonson T, Archontis G. Hybrid MC/MD for protein design. J Chem Phys 2021; 153:054113. [PMID: 32770896 DOI: 10.1063/5.0013320] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/22/2023] Open
Abstract
Computational protein design relies on simulations of a protein structure, where selected amino acids can mutate randomly, and mutations are selected to enhance a target property, such as stability. Often, the protein backbone is held fixed and its degrees of freedom are modeled implicitly to reduce the complexity of the conformational space. We present a hybrid method where short molecular dynamics (MD) segments are used to explore conformations and alternate with Monte Carlo (MC) moves that apply mutations to side chains. The backbone is fully flexible during MD. As a test, we computed side chain acid/base constants or pKa's in five proteins. This problem can be considered a special case of protein design, with protonation/deprotonation playing the role of mutations. The solvent was modeled as a dielectric continuum. Due to cost, in each protein we allowed just one side chain position to change its protonation state and the other position to change its type or mutate. The pKa's were computed with a standard method that scans a range of pH values and with a new method that uses adaptive landscape flattening (ALF) to sample all protonation states in a single simulation. The hybrid method gave notably better accuracy than standard, fixed-backbone MC. ALF decreased the computational cost a factor of 13.
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Affiliation(s)
- Eleni Michael
- Department of Physics, University of Cyprus, P.O 20537, CY678 Nicosia, Cyprus
| | - Savvas Polydorides
- Department of Physics, University of Cyprus, P.O 20537, CY678 Nicosia, Cyprus
| | - Thomas Simonson
- Laboratoire de Biochimie (CNRS UMR7654), Ecole Polytechnique, Palaiseau, France
| | - Georgios Archontis
- Department of Physics, University of Cyprus, P.O 20537, CY678 Nicosia, Cyprus
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3
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Abstract
This chapter describes two computational methods for PDZ-peptide binding: high-throughput computational protein design (CPD) and a medium-throughput approach combining molecular dynamics for conformational sampling with a Poisson-Boltzmann (PB) Linear Interaction Energy for scoring. A new CPD method is outlined, which uses adaptive Monte Carlo simulations to efficiently sample peptide variants that tightly bind a PDZ domain, and provides at the same time precise estimates of their relative binding free energies. A detailed protocol is described based on the Proteus CPD software. The medium-throughput approach can be performed with standard MD and PB software, such as NAMD and Charmm. For 40 complexes between Tiam1 and peptide ligands, it gave high a2ccuracy, with mean errors of around 0.5 kcal/mol for relative binding free energies and no large errors. It requires a moderate amount of parameter fitting before it can be applied, and its transferability to other protein families is still untested.
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Affiliation(s)
- Nicolas Panel
- Laboratoire de Biologie Structurale de la Cellule (CNRS UMR7654), Ecole Polytechnique, Palaiseau, France
| | - Francesco Villa
- Laboratoire de Biologie Structurale de la Cellule (CNRS UMR7654), Ecole Polytechnique, Palaiseau, France
| | - Vaitea Opuu
- Laboratoire de Biologie Structurale de la Cellule (CNRS UMR7654), Ecole Polytechnique, Palaiseau, France
| | - David Mignon
- Laboratoire de Biologie Structurale de la Cellule (CNRS UMR7654), Ecole Polytechnique, Palaiseau, France
| | - Thomas Simonson
- Laboratoire de Biologie Structurale de la Cellule (CNRS UMR7654), Ecole Polytechnique, Palaiseau, France.
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4
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Burnside D, Schoenrock A, Moteshareie H, Hooshyar M, Basra P, Hajikarimlou M, Dick K, Barnes B, Kazmirchuk T, Jessulat M, Pitre S, Samanfar B, Babu M, Green JR, Wong A, Dehne F, Biggar KK, Golshani A. In Silico Engineering of Synthetic Binding Proteins from Random Amino Acid Sequences. iScience 2018; 11:375-387. [PMID: 30660105 PMCID: PMC6348295 DOI: 10.1016/j.isci.2018.11.038] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2018] [Revised: 10/19/2018] [Accepted: 11/28/2018] [Indexed: 12/29/2022] Open
Abstract
Synthetic proteins with high affinity and selectivity for a protein target can be used as research tools, biomarkers, and pharmacological agents, but few methods exist to design such proteins de novo. To this end, the In-Silico Protein Synthesizer (InSiPS) was developed to design synthetic binding proteins (SBPs) that bind pre-determined targets while minimizing off-target interactions. InSiPS is a genetic algorithm that refines a pool of random sequences over hundreds of generations of mutation and selection to produce SBPs with pre-specified binding characteristics. As a proof of concept, we design SBPs against three yeast proteins and demonstrate binding and functional inhibition of two of three targets in vivo. Peptide SPOT arrays confirm binding sites, and a permutation array demonstrates target specificity. Our foundational approach will support the field of de novo design of small binding polypeptide motifs and has robust applicability while offering potential advantages over the limited number of techniques currently available. InSiPS engineers synthetic binding proteins (SBPs) using primary protein sequence SBPs are designed to a bind a target protein and avoid “off-target” interactions Binding and functional inhibition of two of three target proteins in yeast is demonstrated Our new approach offers advantages over alternative tools that rely on 3D models
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Affiliation(s)
- Daniel Burnside
- Department of Biology, Carleton University, Ottawa, ON K1S5B6, Canada; Ottawa Institute of Systems Biology, Carleton University, Ottawa, ON K1S5B6, Canada
| | - Andrew Schoenrock
- School of Computer Science, Carleton University, Ottawa, ON K1S5B6, Canada
| | - Houman Moteshareie
- Department of Biology, Carleton University, Ottawa, ON K1S5B6, Canada; Ottawa Institute of Systems Biology, Carleton University, Ottawa, ON K1S5B6, Canada
| | - Mohsen Hooshyar
- Department of Biology, Carleton University, Ottawa, ON K1S5B6, Canada
| | - Prabh Basra
- Department of Biology, Carleton University, Ottawa, ON K1S5B6, Canada
| | - Maryam Hajikarimlou
- Department of Biology, Carleton University, Ottawa, ON K1S5B6, Canada; Ottawa Institute of Systems Biology, Carleton University, Ottawa, ON K1S5B6, Canada
| | - Kevin Dick
- Department of Systems and Computer Engineering, Carleton University, Ottawa, ON K1S5B6, Canada
| | - Brad Barnes
- School of Computer Science, Carleton University, Ottawa, ON K1S5B6, Canada
| | - Tom Kazmirchuk
- Department of Biology, Carleton University, Ottawa, ON K1S5B6, Canada
| | - Matthew Jessulat
- Department of Biochemistry, Research and Innovation Centre, University of Regina, Regina, SK S4S 0A2, Canada
| | - Sylvain Pitre
- School of Computer Science, Carleton University, Ottawa, ON K1S5B6, Canada
| | - Bahram Samanfar
- Department of Biology, Carleton University, Ottawa, ON K1S5B6, Canada; Ottawa Research and Development Centre (ORDC), Agriculture and Agri-Food Canada, Ottawa, ON K1A 0C5, Canada
| | - Mohan Babu
- Department of Biochemistry, Research and Innovation Centre, University of Regina, Regina, SK S4S 0A2, Canada
| | - James R Green
- Department of Systems and Computer Engineering, Carleton University, Ottawa, ON K1S5B6, Canada
| | - Alex Wong
- Department of Biology, Carleton University, Ottawa, ON K1S5B6, Canada
| | - Frank Dehne
- School of Computer Science, Carleton University, Ottawa, ON K1S5B6, Canada
| | - Kyle K Biggar
- Department of Biology, Carleton University, Ottawa, ON K1S5B6, Canada; Institute of Biochemistry, Carleton University, Ottawa, ON K1S5B6, Canada
| | - Ashkan Golshani
- Department of Biology, Carleton University, Ottawa, ON K1S5B6, Canada; Ottawa Institute of Systems Biology, Carleton University, Ottawa, ON K1S5B6, Canada; Institute of Biochemistry, Carleton University, Ottawa, ON K1S5B6, Canada.
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5
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Ganji MD, Mirzaei S, Dalirandeh Z. Molecular origin of drug release by water boiling inside carbon nanotubes from reactive molecular dynamics simulation and DFT perspectives. Sci Rep 2017; 7:4669. [PMID: 28680131 PMCID: PMC5498575 DOI: 10.1038/s41598-017-04981-2] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2016] [Accepted: 05/22/2017] [Indexed: 11/25/2022] Open
Abstract
Owing to their nanosized hollow cylindrical structure, CNTs hold the promise to be utilized as desired materials for encapsulating molecules which demonstrate wide inferences in drug delivery. Here we evaluate the possibility of drug release from the CNTs with various types and edge chemistry by reactive MD simulation to explain the scientifically reliable relations for proposed process. It was shown that heating of CNTs (up to 750 K) cannot be used for release of incorporated drug (phenylalanine) into water and even carbonated water solvent with very low boiling temperature. This is due to the strong physisorption (π-stacking interaction) between the aromatic of encapsulated drug and CNT sidewall which causes the drug to bind the nanotube sidewall. We have further investigated the interaction nature and release mechanism of water and drug confined/released within/from the CNTs by DFT calculations and the results confirmed our MD simulation findings. The accuracy of DFT method was also validated against the experimental and theoretical values at MP2/CCSD level. Therefore, we find that boiling of water/carbonated water confined within the CNTs could not be a suitable technique for efficient drug release. Our atomistic simulations provide a well-grounded understanding for the release of drug molecules confined within CNTs.
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Affiliation(s)
- M Darvish Ganji
- Department of Nanochemistry, Faculty of Pharmaceutical Chemistry, Pharmaceutical Sciences Branch, Islamic Azad University (IAUPS), Tehran, Iran.
| | - Sh Mirzaei
- Young Researchers & Elite Club, Pharmaceutical Sciences Branch, Islamic Azad University, Tehran, Iran
| | - Z Dalirandeh
- Young Researchers and Elite Club, Central Tehran Branch, Islamic Azad University, Tehran, Iran
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6
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Kowalski AE, Huber TR, Ni TW, Hartje LF, Appel KL, Yost JW, Ackerson CJ, Snow CD. Gold nanoparticle capture within protein crystal scaffolds. NANOSCALE 2016; 8:12693-12696. [PMID: 27264210 DOI: 10.1039/c6nr03096c] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/05/2023]
Abstract
DNA assemblies have been used to organize inorganic nanoparticles into 3D arrays, with emergent properties arising as a result of nanoparticle spacing and geometry. We report here the use of engineered protein crystals as an alternative approach to biologically mediated assembly of inorganic nanoparticles. The protein crystal's 13 nm diameter pores result in an 80% solvent content and display hexahistidine sequences on their interior. The hexahistidine sequence captures Au25(glutathione)∼17 (nitrilotriacetic acid)∼1 nanoclusters throughout a chemically crosslinked crystal via the coordination of Ni(ii) to both the cluster and the protein. Nanoparticle loading was validated by confocal microscopy and elemental analysis. The nanoparticles may be released from the crystal by exposure to EDTA, which chelates the Ni(ii) and breaks the specific protein/nanoparticle interaction. The integrity of the protein crystals after crosslinking and nanoparticle capture was confirmed by single crystal X-ray crystallography.
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Affiliation(s)
- Ann E Kowalski
- Department of Chemical and Biological Engineering, Colorado State University, Fort Collins, CO 80521, USA.
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7
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Polydorides S, Michael E, Mignon D, Druart K, Archontis G, Simonson T. Proteus and the Design of Ligand Binding Sites. Methods Mol Biol 2016; 1414:77-97. [PMID: 27094287 DOI: 10.1007/978-1-4939-3569-7_6] [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] [Indexed: 12/14/2022]
Abstract
This chapter describes the organization and use of Proteus, a multitool computational suite for the optimization of protein and ligand conformations and sequences, and the calculation of pK α shifts and relative binding affinities. The software offers the use of several molecular mechanics force fields and solvent models, including two generalized Born variants, and a large range of scoring functions, which can combine protein stability, ligand affinity, and ligand specificity terms, for positive and negative design. We present in detail the steps for structure preparation, system setup, construction of the interaction energy matrix, protein sequence and structure optimizations, pK α calculations, and ligand titration calculations. We discuss illustrative examples, including the chemical/structural optimization of a complex between the MHC class II protein HLA-DQ8 and the vinculin epitope, and the chemical optimization of the compstatin analog Ac-Val4Trp/His9Ala, which regulates the function of protein C3 of the complement system.
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Affiliation(s)
- Savvas Polydorides
- Theoretical and Computational Biophysics Group, Department of Physics, University of Cyprus, 1678, Nicosia, Cyprus
| | - Eleni Michael
- Theoretical and Computational Biophysics Group, Department of Physics, University of Cyprus, 1678, Nicosia, Cyprus
| | - David Mignon
- Department of Biology, Laboratoire de Biochimie (CNRS UMR7654), Ecole Polytechnique, 91128, Palaiseau, France
| | - Karen Druart
- Department of Biology, Laboratoire de Biochimie (CNRS UMR7654), Ecole Polytechnique, 91128, Palaiseau, France
| | - Georgios Archontis
- Theoretical and Computational Biophysics Group, Department of Physics, University of Cyprus, 1678, Nicosia, Cyprus.
| | - Thomas Simonson
- Department of Biology, Laboratoire de Biochimie (CNRS UMR7654), Ecole Polytechnique, 91128, Palaiseau, France.
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8
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Chino M, Maglio O, Nastri F, Pavone V, DeGrado WF, Lombardi A. Artificial Diiron Enzymes with a De Novo Designed Four-Helix Bundle Structure. Eur J Inorg Chem 2015; 2015:3371-3390. [PMID: 27630532 PMCID: PMC5019575 DOI: 10.1002/ejic.201500470] [Citation(s) in RCA: 58] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2015] [Indexed: 12/26/2022]
Abstract
A single polypeptide chain may provide an astronomical number of conformers. Nature selected only a trivial number of them through evolution, composing an alphabet of scaffolds, that can afford the complete set of chemical reactions needed to support life. These structural templates are so stable that they allow several mutations without disruption of the global folding, even having the ability to bind several exogenous cofactors. With this perspective, metal cofactors play a crucial role in the regulation and catalysis of several processes. Nature is able to modulate the chemistry of metals, adopting only a few ligands and slightly different geometries. Several scaffolds and metal-binding motifs are representing the focus of intense interest in the literature. This review discusses the widespread four-helix bundle fold, adopted as a scaffold for metal binding sites in the context of de novo protein design to obtain basic biochemical components for biosensing or catalysis. In particular, we describe the rational refinement of structure/function in diiron-oxo protein models from the due ferri (DF) family. The DF proteins were developed by us through an iterative process of design and rigorous characterization, which has allowed a shift from structural to functional models. The examples reported herein demonstrate the importance of the synergic application of de novo design methods as well as spectroscopic and structural characterization to optimize the catalytic performance of artificial enzymes.
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Affiliation(s)
- Marco Chino
- Department of Chemical Sciences, University of Naples “Federico II”, Via Cintia, 80126 Naples, Italy
| | - Ornella Maglio
- Department of Chemical Sciences, University of Naples “Federico II”, Via Cintia, 80126 Naples, Italy
- IBB, CNR, Via Mezzocannone 16, 80134 Naples, Italy
| | - Flavia Nastri
- Department of Chemical Sciences, University of Naples “Federico II”, Via Cintia, 80126 Naples, Italy
| | - Vincenzo Pavone
- Department of Structural and Functional Biology, University of Naples “Federico II”, Via Cintia, 80126 Naples, Italy
| | - William F. DeGrado
- Department of Pharmaceutical Chemistry, School of Pharmacy, University of California, San Francisco San Francisco, CA 94158, USA
| | - Angela Lombardi
- Department of Chemical Sciences, University of Naples “Federico II”, Via Cintia, 80126 Naples, Italy
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9
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Fusco D, Barnum TJ, Bruno AE, Luft JR, Snell EH, Mukherjee S, Charbonneau P. Statistical analysis of crystallization database links protein physico-chemical features with crystallization mechanisms. PLoS One 2014; 9:e101123. [PMID: 24988076 PMCID: PMC4079662 DOI: 10.1371/journal.pone.0101123] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2013] [Accepted: 06/03/2014] [Indexed: 11/19/2022] Open
Abstract
X-ray crystallography is the predominant method for obtaining atomic-scale information about biological macromolecules. Despite the success of the technique, obtaining well diffracting crystals still critically limits going from protein to structure. In practice, the crystallization process proceeds through knowledge-informed empiricism. Better physico-chemical understanding remains elusive because of the large number of variables involved, hence little guidance is available to systematically identify solution conditions that promote crystallization. To help determine relationships between macromolecular properties and their crystallization propensity, we have trained statistical models on samples for 182 proteins supplied by the Northeast Structural Genomics consortium. Gaussian processes, which capture trends beyond the reach of linear statistical models, distinguish between two main physico-chemical mechanisms driving crystallization. One is characterized by low levels of side chain entropy and has been extensively reported in the literature. The other identifies specific electrostatic interactions not previously described in the crystallization context. Because evidence for two distinct mechanisms can be gleaned both from crystal contacts and from solution conditions leading to successful crystallization, the model offers future avenues for optimizing crystallization screens based on partial structural information. The availability of crystallization data coupled with structural outcomes analyzed through state-of-the-art statistical models may thus guide macromolecular crystallization toward a more rational basis.
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Affiliation(s)
- Diana Fusco
- Program in Computational Biology and Bioinformatics, Duke University, Durham, North Carolina, United States of America
- Department of Chemistry, Duke University, Durham, North Carolina, United States of America
| | - Timothy J. Barnum
- Department of Chemistry, Duke University, Durham, North Carolina, United States of America
- Department of Chemistry, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America
| | - Andrew E. Bruno
- Center for Computational Research, State University of New York, Buffalo, New York, United States of America
| | - Joseph R. Luft
- Hauptman-Woodward Medical Research Institute, Buffalo, New York, United States of America
| | - Edward H. Snell
- Hauptman-Woodward Medical Research Institute, Buffalo, New York, United States of America
- Department of Structural Biology, State University of New York, Buffalo, New York, United States of America
| | - Sayan Mukherjee
- Department of Statistical Science, Department of Computer Science and Department of Mathematics, Duke University, Durham, North Carolina, United States of America
| | - Patrick Charbonneau
- Department of Chemistry, Duke University, Durham, North Carolina, United States of America
- Department of Physics, Duke University, Durham, North Carolina, United States of America
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Smadbeck J, Chan KH, Khoury GA, Xue B, Robinson RC, Hauser CAE, Floudas CA. De novo design and experimental characterization of ultrashort self-associating peptides. PLoS Comput Biol 2014; 10:e1003718. [PMID: 25010703 PMCID: PMC4091692 DOI: 10.1371/journal.pcbi.1003718] [Citation(s) in RCA: 32] [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: 01/07/2014] [Accepted: 05/31/2014] [Indexed: 12/19/2022] Open
Abstract
Self-association is a common phenomenon in biology and one that can have positive and negative impacts, from the construction of the architectural cytoskeleton of cells to the formation of fibrils in amyloid diseases. Understanding the nature and mechanisms of self-association is important for modulating these systems and in creating biologically-inspired materials. Here, we present a two-stage de novo peptide design framework that can generate novel self-associating peptide systems. The first stage uses a simulated multimeric template structure as input into the optimization-based Sequence Selection to generate low potential energy sequences. The second stage is a computational validation procedure that calculates Fold Specificity and/or Approximate Association Affinity (K*association) based on metrics that we have devised for multimeric systems. This framework was applied to the design of self-associating tripeptides using the known self-associating tripeptide, Ac-IVD, as a structural template. Six computationally predicted tripeptides (Ac-LVE, Ac-YYD, Ac-LLE, Ac-YLD, Ac-MYD, Ac-VIE) were chosen for experimental validation in order to illustrate the self-association outcomes predicted by the three metrics. Self-association and electron microscopy studies revealed that Ac-LLE formed bead-like microstructures, Ac-LVE and Ac-YYD formed fibrillar aggregates, Ac-VIE and Ac-MYD formed hydrogels, and Ac-YLD crystallized under ambient conditions. An X-ray crystallographic study was carried out on a single crystal of Ac-YLD, which revealed that each molecule adopts a β-strand conformation that stack together to form parallel β-sheets. As an additional validation of the approach, the hydrogel-forming sequences of Ac-MYD and Ac-VIE were shuffled. The shuffled sequences were computationally predicted to have lower K*association values and were experimentally verified to not form hydrogels. This illustrates the robustness of the framework in predicting self-associating tripeptides. We expect that this enhanced multimeric de novo peptide design framework will find future application in creating novel self-associating peptides based on unnatural amino acids, and inhibitor peptides of detrimental self-aggregating biological proteins.
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Affiliation(s)
- James Smadbeck
- Department of Chemical and Biological Engineering, Princeton University, Princeton, New Jersey, United States of America
| | - Kiat Hwa Chan
- Institute of Bioengineering and Nanotechnology, Singapore, Singapore
| | - George A. Khoury
- Department of Chemical and Biological Engineering, Princeton University, Princeton, New Jersey, United States of America
| | - Bo Xue
- Institute of Molecular and Cell Biology, A*STAR (Agency of Science, Technology and Research), Biopolis, Singapore, Singapore
| | - Robert C. Robinson
- Institute of Molecular and Cell Biology, A*STAR (Agency of Science, Technology and Research), Biopolis, Singapore, Singapore
| | | | - Christodoulos A. Floudas
- Department of Chemical and Biological Engineering, Princeton University, Princeton, New Jersey, United States of America
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11
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Gaillard T, Simonson T. Pairwise decomposition of an MMGBSA energy function for computational protein design. J Comput Chem 2014; 35:1371-87. [PMID: 24854675 DOI: 10.1002/jcc.23637] [Citation(s) in RCA: 33] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2014] [Revised: 04/14/2014] [Accepted: 05/01/2014] [Indexed: 02/02/2023]
Abstract
Computational protein design (CPD) aims at predicting new proteins or modifying existing ones. The computational challenge is huge as it requires exploring an enormous sequence and conformation space. The difficulty can be reduced by considering a fixed backbone and a discrete set of sidechain conformations. Another common strategy consists in precalculating a pairwise energy matrix, from which the energy of any sequence/conformation can be quickly obtained. In this work, we examine the pairwise decomposition of protein MMGBSA energy functions from a general theoretical perspective, and an implementation proposed earlier for CPD. It includes a Generalized Born term, whose many-body character is overcome using an effective dielectric environment, and a Surface Area term, for which we present an improved pairwise decomposition. A detailed evaluation of the error introduced by the decomposition on the different energy components is performed. We show that the error remains reasonable, compared to other uncertainties.
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Affiliation(s)
- Thomas Gaillard
- Department of Biology, Laboratoire de Biochimie (CNRS UMR7654), Ecole Polytechnique, 91128, Palaiseau, France
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12
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Costanzo JA, O'Brien CJ, Tiller K, Tamargo E, Robinson AS, Roberts CJ, Fernandez EJ. Conformational stability as a design target to control protein aggregation. Protein Eng Des Sel 2014; 27:157-67. [DOI: 10.1093/protein/gzu008] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
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13
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Simonson T, Gaillard T, Mignon D, Schmidt am Busch M, Lopes A, Amara N, Polydorides S, Sedano A, Druart K, Archontis G. Computational protein design: the Proteus software and selected applications. J Comput Chem 2013; 34:2472-84. [PMID: 24037756 DOI: 10.1002/jcc.23418] [Citation(s) in RCA: 37] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2013] [Revised: 07/08/2013] [Accepted: 07/28/2013] [Indexed: 12/13/2022]
Abstract
We describe an automated procedure for protein design, implemented in a flexible software package, called Proteus. System setup and calculation of an energy matrix are done with the XPLOR modeling program and its sophisticated command language, supporting several force fields and solvent models. A second program provides algorithms to search sequence space. It allows a decomposition of the system into groups, which can be combined in different ways in the energy function, for both positive and negative design. The whole procedure can be controlled by editing 2-4 scripts. Two applications consider the tyrosyl-tRNA synthetase enzyme and its successful redesign to bind both O-methyl-tyrosine and D-tyrosine. For the latter, we present Monte Carlo simulations where the D-tyrosine concentration is gradually increased, displacing L-tyrosine from the binding pocket and yielding the binding free energy difference, in good agreement with experiment. Complete redesign of the Crk SH3 domain is presented. The top 10000 sequences are all assigned to the correct fold by the SUPERFAMILY library of Hidden Markov Models. Finally, we report the acid/base behavior of the SNase protein. Sidechain protonation is treated as a form of mutation; it is then straightforward to perform constant-pH Monte Carlo simulations, which yield good agreement with experiment. Overall, the software can be used for a wide range of application, producing not only native-like sequences but also thermodynamic properties with errors that appear comparable to other current software packages.
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Affiliation(s)
- Thomas Simonson
- Laboratoire de Biochimie (CNRS UMR7654), Department of Biology, Ecole Polytechnique, Palaiseau, 91128, France
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14
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Az'hari S, Ghayeb Y. Effect of chirality, length and diameter of carbon nanotubes on the adsorption of 20 amino acids: a molecular dynamics simulation study. MOLECULAR SIMULATION 2013. [DOI: 10.1080/08927022.2013.812210] [Citation(s) in RCA: 24] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
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Fujieda N, Hasegawa A, Ishihama KI, Itoh S. Artificial Dicopper Oxidase: Rational Reprogramming of Bacterial Metallo-β-lactamase into a Catechol Oxidase. Chem Asian J 2012; 7:1203-7. [DOI: 10.1002/asia.201101014] [Citation(s) in RCA: 20] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2011] [Indexed: 11/09/2022]
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Polydorides S, Amara N, Aubard C, Plateau P, Simonson T, Archontis G. Computational protein design with a generalized Born solvent model: application to Asparaginyl-tRNA synthetase. Proteins 2011; 79:3448-68. [PMID: 21563215 DOI: 10.1002/prot.23042] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2010] [Revised: 02/25/2011] [Accepted: 03/03/2011] [Indexed: 12/13/2022]
Abstract
Computational Protein Design (CPD) is a promising method for high throughput protein and ligand mutagenesis. Recently, we developed a CPD method that used a polar-hydrogen energy function for protein interactions and a Coulomb/Accessible Surface Area (CASA) model for solvent effects. We applied this method to engineer aspartyl-adenylate (AspAMP) specificity into Asparaginyl-tRNA synthetase (AsnRS), whose substrate is asparaginyl-adenylate (AsnAMP). Here, we implement a more accurate function, with an all-atom energy for protein interactions and a residue-pairwise generalized Born model for solvent effects. As a first test, we compute aminoacid affinities for several point mutants of Aspartyl-tRNA synthetase (AspRS) and Tyrosyl-tRNA synthetase and stability changes for three helical peptides and compare with experiment. As a second test, we readdress the problem of AsnRS aminoacid engineering. We compare three design criteria, which optimize the folding free-energy, the absolute AspAMP affinity, and the relative (AspAMP-AsnAMP) affinity. The sequences and conformations are improved with respect to our previous, polar-hydrogen/CASA study: For several designed complexes, the AspAMP carboxylate forms three interactions with a conserved arginine and a designed lysine, as in the active site of the AspRS:AspAMP complex. The conformations and interactions are well maintained in molecular dynamics simulations and the sequences have an inverted specificity, favoring AspAMP over AsnAMP. The method is not fully successful, since experimental measurements with the seven most promising sequences show that they do not catalyze at a detectable level the adenylation of Asp (or Asn) with ATP. This may be due to weak AspAMP binding and/or disruption of transition-state stabilization.
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Zuo G, Huang Q, Wei G, Zhou R, Fang H. Plugging into proteins: poisoning protein function by a hydrophobic nanoparticle. ACS NANO 2010; 4:7508-7514. [PMID: 21080666 DOI: 10.1021/nn101762b] [Citation(s) in RCA: 100] [Impact Index Per Article: 7.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/30/2023]
Abstract
Nanoscale particles have become promising materials in many fields, such as cancer therapeutics, diagnosis, imaging, drug delivery, catalysis, as well as biosensors. In order to stimulate and facilitate these applications, there is an urgent need for the understanding of the nanoparticle toxicity and other risks involved with these nanoparticles to human health. In this study, we use large-scale molecular dynamics simulations to study the interaction between several proteins (WW domains) and carbon nanotubes (one form of hydrophobic nanoparticles). We have found that the carbon nanotube can plug into the hydrophobic core of proteins to form stable complexes. This plugging of nanotubes disrupts and blocks the active sites of WW domains from binding to the corresponding ligands, thus leading to the loss of the original function of the proteins. The key to this observation is the hydrophobic interaction between the nanoparticle and the hydrophobic residues, particularly tryptophans, in the core of the domain. We believe that these findings might provide a novel route to the nanoparticle toxicity on the molecular level for the hydrophobic nanoparticles.
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Affiliation(s)
- Guanghong Zuo
- T-Life Research Center, Department of Physics, Fudan University, Shanghai 200433, China
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Roy S, Shinde S, Hamilton GA, Hartnett HE, Jones AK. Artificial [FeFe]-Hydrogenase: On Resin Modification of an Amino Acid to Anchor a Hexacarbonyldiiron Cluster in a Peptide Framework. Eur J Inorg Chem 2010. [DOI: 10.1002/ejic.201000979] [Citation(s) in RCA: 38] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
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