1
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Qi X, Pfaendtner J. High-Throughput Computational Screening of Solid-Binding Peptides. J Chem Theory Comput 2024; 20:2959-2968. [PMID: 38499981 DOI: 10.1021/acs.jctc.3c01286] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/20/2024]
Abstract
Inspired by biomineralization, a naturally occurring, protein-facilitated process, solid-binding peptides (SBPs) have gained much attention for their potential to fabricate various shaped nanocrystals and hierarchical nanostructures. The advantage of SBPs over other traditionally used synthetic polymers or short ligands is their tunable interaction with the solid material surface via carefully programmed sequence and being solution-dependent simultaneously. However, designing a sequence with targeted binding affinity or selectivity often involves intensive processes such as phage display, and only a limited number of sequences can be identified. Other computational efforts have also been introduced, but the validation process remains prohibitively expensive once a suitable sequence has been identified. In this paper, we present a new model to rapidly estimate the binding free energy of any given sequence to a solid surface. We show how the overall binding of a polypeptide can be estimated from the free energy contribution of each residue based on the statistics of the thermodynamically stable structure ensemble. We validated our model using five silica-binding peptides of different binding affinities and lengths and showed that the model is accurate and robust across a wider range of chemistries and binding strengths. The computational cost of this method can be as low as 3% of the commonly used enhanced sampling scheme for similar studies and has a great potential to be used in high-throughput algorithms to obtain larger training data sets for machine learning SBP screening.
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Affiliation(s)
- Xin Qi
- Department of Chemistry, Dartmouth College, Hanover, New Hampshire 03784, United States
| | - Jim Pfaendtner
- Department of Chemical & Biomolecular Engineering, North Carolina State University, Raleigh, North Carolina 27695, United States
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2
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Mao CM, Sampath J, Pfaendtner J. Molecular Driving Forces in the Self-Association of Silaffin Peptide R5 from MD Simulations. Chembiochem 2024:e202300788. [PMID: 38485668 DOI: 10.1002/cbic.202300788] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2023] [Revised: 03/13/2024] [Indexed: 05/15/2024]
Abstract
The 19-residue silaffin-R5 peptide has been widely studied for its ability to precipitate uniform SiO2 particles through mild temperature and pH pathways, in the absence of any organic solvents. There is consensus that post-translational modification (PTM) of side chains has a large impact on the biomineralization process. Thus, it is imperative to understand the precise mechanisms that dictate the formation of SiO2 from R5 peptide, including the effects of PTM on peptide aggregation and peptide-surface adsorption. In this work, we use molecular dynamics (MD) simulations to study the aggregation of R5 dimer with multiple PTMs, with the presence of different ions in solution. Since this system has strong interactions with deep metastable states, we use parallel bias metadynamics with partitioned families to efficiently sample the different states of the system. We find that peptide aggregation is a prerequisite for biomineralization. We observe that the electrostatic interactions are essential in the R5 dimer aggregation; for wild type R5 that only has positively charged residues, phosphate ions HPO4 2- in the solution form a bridge between two peptides and are essential for peptide aggregation.
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Affiliation(s)
- Coco M Mao
- Department of Materials Science and Engineering, University of Washington, Seattle WA, 98195
| | - Janani Sampath
- Department of Chemical Engineering, University of Florida, Gainesville, FL, 32611
| | - Jim Pfaendtner
- Department of Chemical & Biomolecular Engineering, North Carolina State University, Raleigh, NC, 27695
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3
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Alizadeh Sahraei A, Mejia Bohorquez B, Tremblay D, Moineau S, Garnier A, Larachi F, Lagüe P. Insight into the Binding Mechanisms of Quartz-Selective Peptides: Toward Greener Flotation Processes. ACS APPLIED MATERIALS & INTERFACES 2023; 15:17922-17937. [PMID: 37010879 PMCID: PMC10103053 DOI: 10.1021/acsami.3c01275] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/27/2023] [Accepted: 03/16/2023] [Indexed: 06/19/2023]
Abstract
Mining practices, chiefly froth flotation, are being critically reassessed to replace their use of biohazardous chemical reagents in favor of biofriendly alternatives as a path toward green processes. In this regard, this study aimed at evaluating the interactions of peptides, as potential floatation collectors, with quartz using phage display and molecular dynamics (MD) simulations. Quartz-selective peptide sequences were initially identified by phage display at pH = 9 and further modeled by a robust simulation scheme combining classical MD, replica exchange MD, and steered MD calculations. Our residue-specific analyses of the peptides revealed that positively charged arginine and lysine residues were favorably attracted by the quartz surface at basic pH. The negatively charged residues at pH 9 (i.e., aspartic acid and glutamic acid) further showed affinity toward the quartz surface through electrostatic interactions with the positively charged surface-bound Na+ ions. The best-binding heptapeptide combinations, however, contained both positively and negatively charged residues in their composition. The flexibility of peptide chains was also shown to directly affect the adsorption behavior of the peptide. While attractive intrapeptide interactions were dominated by a weak peptide-quartz binding, the repulsive self-interactions in the peptides improved the binding propensity to the quartz surface. Our results showed that MD simulations are fully capable of revealing mechanistic details of peptide adsorption to inorganic surfaces and are an invaluable tool to accelerate the rational design of peptide sequences for mineral processing applications.
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Affiliation(s)
- Abolfazl Alizadeh Sahraei
- Department
of Chemical Engineering, Université
Laval, 1065 Avenue de la Médecine, Québec, Québec G1V 0A6, Canada
| | - Barbara Mejia Bohorquez
- Department
of Chemical Engineering, Université
Laval, 1065 Avenue de la Médecine, Québec, Québec G1V 0A6, Canada
- PROTEO,
The Quebec Network for Research on Protein Function, Engineering,
and Applications, 1045
Avenue de la Médecine, Québec, Québec G1V 0A6, Canada
| | - Denise Tremblay
- PROTEO,
The Quebec Network for Research on Protein Function, Engineering,
and Applications, 1045
Avenue de la Médecine, Québec, Québec G1V 0A6, Canada
- IBIS,
Institut de biologie intégrative et des systèmes, 1030 Avenue de la Médecine, Québec, Québec G1V 0A6, Canada
- Department
of Biochemistry, Microbiology and Bioinformatics, Université Laval, 1045 Avenue de la Médecine, Québec, Québec G1V 0A6, Canada
| | - Sylvain Moineau
- PROTEO,
The Quebec Network for Research on Protein Function, Engineering,
and Applications, 1045
Avenue de la Médecine, Québec, Québec G1V 0A6, Canada
- IBIS,
Institut de biologie intégrative et des systèmes, 1030 Avenue de la Médecine, Québec, Québec G1V 0A6, Canada
- Department
of Biochemistry, Microbiology and Bioinformatics, Université Laval, 1045 Avenue de la Médecine, Québec, Québec G1V 0A6, Canada
| | - Alain Garnier
- Department
of Chemical Engineering, Université
Laval, 1065 Avenue de la Médecine, Québec, Québec G1V 0A6, Canada
- PROTEO,
The Quebec Network for Research on Protein Function, Engineering,
and Applications, 1045
Avenue de la Médecine, Québec, Québec G1V 0A6, Canada
| | - Faïçal Larachi
- Department
of Chemical Engineering, Université
Laval, 1065 Avenue de la Médecine, Québec, Québec G1V 0A6, Canada
| | - Patrick Lagüe
- PROTEO,
The Quebec Network for Research on Protein Function, Engineering,
and Applications, 1045
Avenue de la Médecine, Québec, Québec G1V 0A6, Canada
- IBIS,
Institut de biologie intégrative et des systèmes, 1030 Avenue de la Médecine, Québec, Québec G1V 0A6, Canada
- Department
of Biochemistry, Microbiology and Bioinformatics, Université Laval, 1045 Avenue de la Médecine, Québec, Québec G1V 0A6, Canada
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4
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Shao L, Ma J, Prelesnik JL, Zhou Y, Nguyen M, Zhao M, Jenekhe SA, Kalinin SV, Ferguson AL, Pfaendtner J, Mundy CJ, De Yoreo JJ, Baneyx F, Chen CL. Hierarchical Materials from High Information Content Macromolecular Building Blocks: Construction, Dynamic Interventions, and Prediction. Chem Rev 2022; 122:17397-17478. [PMID: 36260695 DOI: 10.1021/acs.chemrev.2c00220] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/25/2023]
Abstract
Hierarchical materials that exhibit order over multiple length scales are ubiquitous in nature. Because hierarchy gives rise to unique properties and functions, many have sought inspiration from nature when designing and fabricating hierarchical matter. More and more, however, nature's own high-information content building blocks, proteins, peptides, and peptidomimetics, are being coopted to build hierarchy because the information that determines structure, function, and interfacial interactions can be readily encoded in these versatile macromolecules. Here, we take stock of recent progress in the rational design and characterization of hierarchical materials produced from high-information content blocks with a focus on stimuli-responsive and "smart" architectures. We also review advances in the use of computational simulations and data-driven predictions to shed light on how the side chain chemistry and conformational flexibility of macromolecular blocks drive the emergence of order and the acquisition of hierarchy and also on how ionic, solvent, and surface effects influence the outcomes of assembly. Continued progress in the above areas will ultimately usher in an era where an understanding of designed interactions, surface effects, and solution conditions can be harnessed to achieve predictive materials synthesis across scale and drive emergent phenomena in the self-assembly and reconfiguration of high-information content building blocks.
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Affiliation(s)
- Li Shao
- Physical Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99354, United States
| | - Jinrong Ma
- Molecular Engineering and Sciences Institute, University of Washington, Seattle, Washington 98195, United States
| | - Jesse L Prelesnik
- Department of Chemistry, University of Washington, Seattle, Washington 98195, United States
| | - Yicheng Zhou
- Physical Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99354, United States
| | - Mary Nguyen
- Department of Chemical Engineering, University of Washington, Seattle, Washington 98195, United States.,Department of Chemistry, University of Washington, Seattle, Washington 98195, United States
| | - Mingfei Zhao
- Pritzker School of Molecular Engineering, University of Chicago, Chicago, Illinois 60637, United States
| | - Samson A Jenekhe
- Department of Chemical Engineering, University of Washington, Seattle, Washington 98195, United States.,Department of Chemistry, University of Washington, Seattle, Washington 98195, United States
| | - Sergei V Kalinin
- Department of Materials Science and Engineering, University of Tennessee, Knoxville, Tennessee 37996, United States
| | - Andrew L Ferguson
- Pritzker School of Molecular Engineering, University of Chicago, Chicago, Illinois 60637, United States
| | - Jim Pfaendtner
- Physical Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99354, United States.,Materials Science and Engineering, University of Washington, Seattle, Washington 98195, United States
| | - Christopher J Mundy
- Physical Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99354, United States.,Department of Chemical Engineering, University of Washington, Seattle, Washington 98195, United States
| | - James J De Yoreo
- Physical Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99354, United States.,Materials Science and Engineering, University of Washington, Seattle, Washington 98195, United States
| | - François Baneyx
- Molecular Engineering and Sciences Institute, University of Washington, Seattle, Washington 98195, United States.,Department of Chemical Engineering, University of Washington, Seattle, Washington 98195, United States
| | - Chun-Long Chen
- Physical Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99354, United States.,Department of Chemical Engineering, University of Washington, Seattle, Washington 98195, United States
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5
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Suyetin M, Rauwolf S, Schwaminger SP, Turrina C, Wittmann L, Bag S, Berensmeier S, Wenzel W. Peptide adsorption on silica surfaces: Simulation and experimental insights. Colloids Surf B Biointerfaces 2022; 218:112759. [PMID: 36027680 DOI: 10.1016/j.colsurfb.2022.112759] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2022] [Revised: 08/02/2022] [Accepted: 08/04/2022] [Indexed: 11/16/2022]
Abstract
The understanding of interactions between proteins with silica surface is crucial for a wide range of different applications: from medical devices, drug delivery and bioelectronics to biotechnology and downstream processing. We show the application of EISM (Effective Implicit Surface Model) for discovering the set of peptide interactions with silica surface. The EISM is employed for a high-speed computational screening of peptides to model the binding affinity of small peptides to silica surfaces. The simulations are complemented with experimental data of peptides with silica nanoparticles from microscale thermophoresis and from infrared spectroscopy. The experimental work shows excellent agreement with computational results and verifies the EISM model for the prediction of peptide-surface interactions. 57 peptides, with amino acids favorable for adsorption on Silica surface, are screened by EISM model for obtaining results, which are worth to be considered as a guidance for future experimental and theoretical works. This model can be used as a broad platform for multiple challenges at surfaces which can be applied for multiple surfaces and biomolecules beyond silica and peptides.
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Affiliation(s)
- Mikhail Suyetin
- Institute of Nanotechnology, Karlsruhe Institute of Technology, Hermann-von-Helmholtz-Platz 1, 76344, Eggenstein-Leopoldshafen, Germany
| | - Stefan Rauwolf
- Bioseparation Engineering Group, School of Engineering and Design, Technical University of Munich, 85748, Garching, Germany
| | - Sebastian Patrick Schwaminger
- Bioseparation Engineering Group, School of Engineering and Design, Technical University of Munich, 85748, Garching, Germany; Division of Medicinal Chemistry, Otto Loewi Research Center, Medical University of Graz, 8010, Graz, Austria.
| | - Chiara Turrina
- Bioseparation Engineering Group, School of Engineering and Design, Technical University of Munich, 85748, Garching, Germany
| | - Leonie Wittmann
- Bioseparation Engineering Group, School of Engineering and Design, Technical University of Munich, 85748, Garching, Germany
| | - Saientan Bag
- Institute of Nanotechnology, Karlsruhe Institute of Technology, Hermann-von-Helmholtz-Platz 1, 76344, Eggenstein-Leopoldshafen, Germany
| | - Sonja Berensmeier
- Bioseparation Engineering Group, School of Engineering and Design, Technical University of Munich, 85748, Garching, Germany.
| | - Wolfgang Wenzel
- Institute of Nanotechnology, Karlsruhe Institute of Technology, Hermann-von-Helmholtz-Platz 1, 76344, Eggenstein-Leopoldshafen, Germany.
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6
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Qi X, Zhao Y, Lachowski K, Boese J, Cai Y, Dollar O, Hellner B, Pozzo L, Pfaendtner J, Chun J, Baneyx F, Mundy CJ. Predictive Theoretical Framework for Dynamic Control of Bioinspired Hybrid Nanoparticle Self-Assembly. ACS NANO 2022; 16:1919-1928. [PMID: 35073061 DOI: 10.1021/acsnano.1c04923] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
At-will tailoring of the formation and reconfiguration of hierarchical structures is a key goal of modern nanomaterial design. Bioinspired systems comprising biomacromolecules and inorganic nanoparticles have potential for new functional material structures. Yet, consequential challenges remain because we lack a detailed understanding of the temporal and spatial interplay between participants when it is mediated by fundamental physicochemical interactions over a wide range of scales. Motivated by a system in which silica nanoparticles are reversibly and repeatedly assembled using a homobifunctional solid-binding protein and single-unit pH changes under near-neutral solution conditions, we develop a theoretical framework where interactions at the molecular and macroscopic scales are rigorously coupled based on colloidal theory and atomistic molecular dynamics simulations. We integrate these interactions into a predictive coarse-grained model that captures the pH-dependent reversibility and accurately matches small-angle X-ray scattering experiments at collective scales. The framework lays a foundation to connect microscopic details with the macroscopic behavior of complex bioinspired material systems and to control their behavior through an understanding of both equilibrium and nonequilibrium characteristics.
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Affiliation(s)
- Xin Qi
- Department of Chemical Engineering, University of Washington, Seattle, Washington 98195, United States
| | - Yundi Zhao
- Department of Chemical Engineering, University of Washington, Seattle, Washington 98195, United States
| | - Kacper Lachowski
- Department of Chemical Engineering, University of Washington, Seattle, Washington 98195, United States
- Molecular Engineering and Sciences Institute, University of Washington, Seattle, Washington 98195, United States
| | - Julia Boese
- Department of Chemical Engineering, University of Washington, Seattle, Washington 98195, United States
| | - Yifeng Cai
- Department of Chemical Engineering, University of Washington, Seattle, Washington 98195, United States
| | - Orion Dollar
- Department of Chemical Engineering, University of Washington, Seattle, Washington 98195, United States
| | - Brittney Hellner
- Department of Chemical Engineering, University of Washington, Seattle, Washington 98195, United States
| | - Lilo Pozzo
- Department of Chemical Engineering, University of Washington, Seattle, Washington 98195, United States
| | - Jim Pfaendtner
- Department of Chemical Engineering, University of Washington, Seattle, Washington 98195, United States
- Physical and Computational Sciences Directorate, Pacific Northwest National Laboratory, Richland, Washington 99354, United States
| | - Jaehun Chun
- Physical and Computational Sciences Directorate, Pacific Northwest National Laboratory, Richland, Washington 99354, United States
- Levich Institute and Department of Chemical Engineering, CUNY City College of New York, New York, New York 10031, United States
| | - François Baneyx
- Department of Chemical Engineering, University of Washington, Seattle, Washington 98195, United States
| | - Christopher J Mundy
- Department of Chemical Engineering, University of Washington, Seattle, Washington 98195, United States
- Physical and Computational Sciences Directorate, Pacific Northwest National Laboratory, Richland, Washington 99354, United States
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