1
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Hansen AL, Theisen FF, Crehuet R, Marcos E, Aghajari N, Willemoës M. Carving out a Glycoside Hydrolase Active Site for Incorporation into a New Protein Scaffold Using Deep Network Hallucination. ACS Synth Biol 2024; 13:862-875. [PMID: 38357862 PMCID: PMC10949244 DOI: 10.1021/acssynbio.3c00674] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2023] [Revised: 01/16/2024] [Accepted: 01/23/2024] [Indexed: 02/16/2024]
Abstract
Enzymes are indispensable biocatalysts for numerous industrial applications, yet stability, selectivity, and restricted substrate recognition present limitations for their use. Despite the importance of enzyme engineering in overcoming these limitations, success is often challenged by the intricate architecture of enzymes derived from natural sources. Recent advances in computational methods have enabled the de novo design of simplified scaffolds with specific functional sites. Such scaffolds may be advantageous as platforms for enzyme engineering. Here, we present a strategy for the de novo design of a simplified scaffold of an endo-α-N-acetylgalactosaminidase active site, a glycoside hydrolase from the GH101 enzyme family. Using a combination of trRosetta hallucination, iterative cycles of deep-learning-based structure prediction, and ProteinMPNN sequence design, we designed proteins with 290 amino acids incorporating the active site while reducing the molecular weight by over 100 kDa compared to the initial endo-α-N-acetylgalactosaminidase. Of 11 tested designs, six were expressed as soluble monomers, displaying similar or increased thermostabilities compared to the natural enzyme. Despite lacking detectable enzymatic activity, the experimentally determined crystal structures of a representative design closely matched the design with a root-mean-square deviation of 1.0 Å, with most catalytically important side chains within 2.0 Å. The results highlight the potential of scaffold hallucination in designing proteins that may serve as a foundation for subsequent enzyme engineering.
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Affiliation(s)
- Anders Lønstrup Hansen
- The
Linderstrøm-Lang Centre for Protein Science, Section for Biomolecular
Sciences, Department of Biology, University
of Copenhagen, Ole Maaløes Vej 5, 2200 Copenhagen, Denmark
| | - Frederik Friis Theisen
- The
Linderstrøm-Lang Centre for Protein Science, Section for Biomolecular
Sciences, Department of Biology, University
of Copenhagen, Ole Maaløes Vej 5, 2200 Copenhagen, Denmark
| | - Ramon Crehuet
- Institute
for Advanced Chemistry of Catalonia (IQAC), CSIC, Carrer Jordi Girona 18-26, 08034 Barcelona, Spain
| | - Enrique Marcos
- Protein
Design and Modeling Lab, Department of Structural and Molecular Biology, Molecular Biology Institute of Barcelona (IBMB), CSIC, Baldiri Reixac 10, 08028 Barcelona, Spain
| | - Nushin Aghajari
- Molecular
Microbiology and Structural Biochemistry, CNRS, University of Lyon1, UMR5086, 7 Passage du Vercors, F-69367 Lyon CEDEX 07, France
| | - Martin Willemoës
- The
Linderstrøm-Lang Centre for Protein Science, Section for Biomolecular
Sciences, Department of Biology, University
of Copenhagen, Ole Maaløes Vej 5, 2200 Copenhagen, Denmark
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2
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Padhi AK, Kalita P, Maurya S, Poluri KM, Tripathi T. From De Novo Design to Redesign: Harnessing Computational Protein Design for Understanding SARS-CoV-2 Molecular Mechanisms and Developing Therapeutics. J Phys Chem B 2023; 127:8717-8735. [PMID: 37815479 DOI: 10.1021/acs.jpcb.3c04542] [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: 10/11/2023]
Abstract
The continuous emergence of novel SARS-CoV-2 variants and subvariants serves as compelling evidence that COVID-19 is an ongoing concern. The swift, well-coordinated response to the pandemic highlights how technological advancements can accelerate the detection, monitoring, and treatment of the disease. Robust surveillance systems have been established to understand the clinical characteristics of new variants, although the unpredictable nature of these variants presents significant challenges. Some variants have shown resistance to current treatments, but innovative technologies like computational protein design (CPD) offer promising solutions and versatile therapeutics against SARS-CoV-2. Advances in computing power, coupled with open-source platforms like AlphaFold and RFdiffusion (employing deep neural network and diffusion generative models), among many others, have accelerated the design of protein therapeutics with precise structures and intended functions. CPD has played a pivotal role in developing peptide inhibitors, mini proteins, protein mimics, decoy receptors, nanobodies, monoclonal antibodies, identifying drug-resistance mutations, and even redesigning native SARS-CoV-2 proteins. Pending regulatory approval, these designed therapies hold the potential for a lasting impact on human health and sustainability. As SARS-CoV-2 continues to evolve, use of such technologies enables the ongoing development of alternative strategies, thus equipping us for the "New Normal".
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Affiliation(s)
- Aditya K Padhi
- Laboratory for Computational Biology & Biomolecular Design, School of Biochemical Engineering, Indian Institute of Technology (BHU), Varanasi 221005, Uttar Pradesh, India
| | - Parismita Kalita
- Molecular and Structural Biophysics Laboratory, Department of Biochemistry, North-Eastern Hill University, Shillong 793022, India
| | - Shweata Maurya
- Laboratory for Computational Biology & Biomolecular Design, School of Biochemical Engineering, Indian Institute of Technology (BHU), Varanasi 221005, Uttar Pradesh, India
| | - Krishna Mohan Poluri
- Department of Biosciences and Bioengineering, Indian Institute of Technology Roorkee, Roorkee 247667, Uttarakhand, India
- Centre for Nanotechnology, Indian Institute of Technology Roorkee, Roorkee 247667, Uttarakhand, India
| | - Timir Tripathi
- Molecular and Structural Biophysics Laboratory, Department of Biochemistry, North-Eastern Hill University, Shillong 793022, India
- Department of Zoology, School of Life Sciences, North-Eastern Hill University, Shillong 793022, India
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3
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Legault S, Fraser-Halberg DP, McAnelly RL, Eason MG, Thompson MC, Chica RA. Generation of bright monomeric red fluorescent proteins via computational design of enhanced chromophore packing. Chem Sci 2022; 13:1408-1418. [PMID: 35222925 PMCID: PMC8809391 DOI: 10.1039/d1sc05088e] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2021] [Accepted: 01/07/2022] [Indexed: 12/11/2022] Open
Abstract
Red fluorescent proteins (RFPs) have found widespread application in chemical and biological research due to their longer emission wavelengths. Here, we use computational protein design to increase the quantum yield and thereby brightness of a dim monomeric RFP (mRojoA, quantum yield = 0.02) by optimizing chromophore packing with aliphatic residues, which we hypothesized would reduce torsional motions causing non-radiative decay. Experimental characterization of the top 10 designed sequences yielded mSandy1 (λ em = 609 nm, quantum yield = 0.26), a variant with equivalent brightness to mCherry, a widely used RFP. We next used directed evolution to further increase brightness, resulting in mSandy2 (λ em = 606 nm, quantum yield = 0.35), the brightest Discosoma sp. derived monomeric RFP with an emission maximum above 600 nm reported to date. Crystallographic analysis of mSandy2 showed that the chromophore p-hydroxybenzylidene moiety is sandwiched between the side chains of Leu63 and Ile197, a structural motif that has not previously been observed in RFPs, and confirms that aliphatic packing leads to chromophore rigidification. Our results demonstrate that computational protein design can be used to generate bright monomeric RFPs, which can serve as templates for the evolution of novel far-red fluorescent proteins.
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Affiliation(s)
- Sandrine Legault
- Department of Chemistry and Biomolecular Sciences, University of Ottawa 10 Marie-Curie Ottawa Ontario K1N 6N5 Canada
| | - Derek P Fraser-Halberg
- Department of Chemistry and Biomolecular Sciences, University of Ottawa 10 Marie-Curie Ottawa Ontario K1N 6N5 Canada
| | - Ralph L McAnelly
- Department of Chemistry and Biochemistry, University of California, Merced Merced California 95343 USA
| | - Matthew G Eason
- Department of Chemistry and Biomolecular Sciences, University of Ottawa 10 Marie-Curie Ottawa Ontario K1N 6N5 Canada
| | - Michael C Thompson
- Department of Chemistry and Biochemistry, University of California, Merced Merced California 95343 USA
| | - Roberto A Chica
- Department of Chemistry and Biomolecular Sciences, University of Ottawa 10 Marie-Curie Ottawa Ontario K1N 6N5 Canada
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4
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Defresne M, Barbe S, Schiex T. Protein Design with Deep Learning. Int J Mol Sci 2021; 22:11741. [PMID: 34769173 PMCID: PMC8584038 DOI: 10.3390/ijms222111741] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2021] [Revised: 10/23/2021] [Accepted: 10/26/2021] [Indexed: 12/21/2022] Open
Abstract
Computational Protein Design (CPD) has produced impressive results for engineering new proteins, resulting in a wide variety of applications. In the past few years, various efforts have aimed at replacing or improving existing design methods using Deep Learning technology to leverage the amount of publicly available protein data. Deep Learning (DL) is a very powerful tool to extract patterns from raw data, provided that data are formatted as mathematical objects and the architecture processing them is well suited to the targeted problem. In the case of protein data, specific representations are needed for both the amino acid sequence and the protein structure in order to capture respectively 1D and 3D information. As no consensus has been reached about the most suitable representations, this review describes the representations used so far, discusses their strengths and weaknesses, and details their associated DL architecture for design and related tasks.
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Affiliation(s)
- Marianne Defresne
- Toulouse Biotechnology Institute, Université de Toulouse, CNRS, INRAE, INSA, ANITI, 31077 Toulouse, France; (M.D.); (S.B.)
- Université Fédérale de Toulouse, ANITI, INRAE, UR 875, 31326 Toulouse, France
| | - Sophie Barbe
- Toulouse Biotechnology Institute, Université de Toulouse, CNRS, INRAE, INSA, ANITI, 31077 Toulouse, France; (M.D.); (S.B.)
| | - Thomas Schiex
- Université Fédérale de Toulouse, ANITI, INRAE, UR 875, 31326 Toulouse, France
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5
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Tanaka M, Kato T, Oda M. Conformational changes of α-helical peptides with different hydrophobic residues induced by metal-ion binding. Biophys Chem 2021; 277:106661. [PMID: 34388679 DOI: 10.1016/j.bpc.2021.106661] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2021] [Revised: 07/08/2021] [Accepted: 07/28/2021] [Indexed: 11/22/2022]
Abstract
We designed peptides that formed helix bundle structures upon binding of the metal-ions to His residues to form a stable hydrophobic core, in order to analyze the effects of Ala, Val, Ile, and Leu residues, located in the hydrophobic core, together with His, on the conformational changes in respective peptides designated as HA, HV, HI, and HL, respectively. Circular dichroism measurements showed that HV and HI changed from random coil to helix bundle structures upon Zn2+ binding, similar to that observed for HA, while HL existed in the helix bundle structure even in the absence of Zn2+. Electron spin resonance measurements showed that Cu2+ coordination of HI and HL was quite different from that of HA and HV, indicating that HA and HV fluctuated to a greater extent in the solution, despite that their apparent α-helical contents being similar to those of HI and HL. This was also supported by the results obtained from the analyses of thermal stabilities. The change in the structural fluctuation for each peptide upon Zn2+ binding was evaluated based on binding thermodynamics using isothermal titration calorimetry. The structural flexibility in the metal-ion-bound state was found to be in the order HA > HV > HI, and that in the metal-ion-unbound state was found to be greater for HI than that for HL.
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Affiliation(s)
- Masahiro Tanaka
- Faculty of Life and Environmental Sciences, Kyoto Prefectural University, 1-5 Hangi-cho, Shimogamo, Sakyo-ku, Kyoto, Kyoto 606-8522, Japan
| | - Tatsuhisa Kato
- Fukui Institute for Fundamental Chemistry, Kyoto University, 34-4 Takano-Nishihiraki-cho, Sakyo-ku, Kyoto, Kyoto 606-8103, Japan
| | - Masayuki Oda
- Faculty of Life and Environmental Sciences, Kyoto Prefectural University, 1-5 Hangi-cho, Shimogamo, Sakyo-ku, Kyoto, Kyoto 606-8522, Japan; Graduate School of Life and Environmental Sciences, Kyoto Prefectural University, 1-5 Hangi-cho, Shimogamo, Sakyo-ku, Kyoto, Kyoto 606-8522, Japan.
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6
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Michael E, Simonson T. How much can physics do for protein design? Curr Opin Struct Biol 2021; 72:46-54. [PMID: 34461593 DOI: 10.1016/j.sbi.2021.07.011] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2021] [Revised: 07/22/2021] [Accepted: 07/25/2021] [Indexed: 01/03/2023]
Abstract
Physics and physical chemistry are an important thread in computational protein design, complementary to knowledge-based tools. They provide molecular mechanics scoring functions that need little or no ad hoc parameter readjustment, methods to thoroughly sample equilibrium ensembles, and different levels of approximation for conformational flexibility. They led recently to the successful redesign of a small protein using a physics-based folded state energy. Adaptive Monte Carlo or molecular dynamics schemes were discovered where protein variants are populated as per their ligand-binding free energy or catalytic efficiency. Molecular dynamics have been used for backbone flexibility. Implicit solvent models have been refined, polarizable force fields applied, and many physical insights obtained.
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Affiliation(s)
- Eleni Michael
- Laboratoire de Biologie Structurale de la Cellule (CNRS UMR7654), Ecole Polytechnique, 91128, Palaiseau, France
| | - Thomas Simonson
- Laboratoire de Biologie Structurale de la Cellule (CNRS UMR7654), Ecole Polytechnique, 91128, Palaiseau, France.
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7
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Guaranteed Diversity and Optimality in Cost Function Network Based Computational Protein Design Methods. ALGORITHMS 2021. [DOI: 10.3390/a14060168] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/02/2023]
Abstract
Proteins are the main active molecules of life. Although natural proteins play many roles, as enzymes or antibodies for example, there is a need to go beyond the repertoire of natural proteins to produce engineered proteins that precisely meet application requirements, in terms of function, stability, activity or other protein capacities. Computational Protein Design aims at designing new proteins from first principles, using full-atom molecular models. However, the size and complexity of proteins require approximations to make them amenable to energetic optimization queries. These approximations make the design process less reliable, and a provable optimal solution may fail. In practice, expensive libraries of solutions are therefore generated and tested. In this paper, we explore the idea of generating libraries of provably diverse low-energy solutions by extending cost function network algorithms with dedicated automaton-based diversity constraints on a large set of realistic full protein redesign problems. We observe that it is possible to generate provably diverse libraries in reasonable time and that the produced libraries do enhance the Native Sequence Recovery, a traditional measure of design methods reliability.
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