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Magi Meconi G, Sasselli IR, Bianco V, Onuchic JN, Coluzza I. Key aspects of the past 30 years of protein design. REPORTS ON PROGRESS IN PHYSICS. PHYSICAL SOCIETY (GREAT BRITAIN) 2022; 85:086601. [PMID: 35704983 DOI: 10.1088/1361-6633/ac78ef] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/03/2021] [Accepted: 06/15/2022] [Indexed: 06/15/2023]
Abstract
Proteins are the workhorse of life. They are the building infrastructure of living systems; they are the most efficient molecular machines known, and their enzymatic activity is still unmatched in versatility by any artificial system. Perhaps proteins' most remarkable feature is their modularity. The large amount of information required to specify each protein's function is analogically encoded with an alphabet of just ∼20 letters. The protein folding problem is how to encode all such information in a sequence of 20 letters. In this review, we go through the last 30 years of research to summarize the state of the art and highlight some applications related to fundamental problems of protein evolution.
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Affiliation(s)
- Giulia Magi Meconi
- Computational Biophysics Lab, Center for Cooperative Research in Biomaterials (CIC biomaGUNE), Basque Research and Technology Alliance (BRTA), Paseo de Miramon 182, 20014, Donostia-San Sebastián, Spain
| | - Ivan R Sasselli
- Computational Biophysics Lab, Center for Cooperative Research in Biomaterials (CIC biomaGUNE), Basque Research and Technology Alliance (BRTA), Paseo de Miramon 182, 20014, Donostia-San Sebastián, Spain
| | | | - Jose N Onuchic
- Center for Theoretical Biological Physics, Department of Physics & Astronomy, Department of Chemistry, Department of Biosciences, Rice University, Houston, TX 77251, United States of America
| | - Ivan Coluzza
- BCMaterials, Basque Center for Materials, Applications and Nanostructures, Bld. Martina Casiano, UPV/EHU Science Park, Barrio Sarriena s/n, 48940 Leioa, Spain
- Basque Foundation for Science, Ikerbasque, 48009, Bilbao, Spain
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2
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A backbone-centred energy function of neural networks for protein design. Nature 2022; 602:523-528. [PMID: 35140398 DOI: 10.1038/s41586-021-04383-5] [Citation(s) in RCA: 42] [Impact Index Per Article: 21.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2021] [Accepted: 12/23/2021] [Indexed: 12/29/2022]
Abstract
A protein backbone structure is designable if a substantial number of amino acid sequences exist that autonomously fold into it1,2. It has been suggested that the designability of backbones is governed mainly by side chain-independent or side chain type-insensitive molecular interactions3-5, indicating an approach for designing new backbones (ready for amino acid selection) based on continuous sampling and optimization of the backbone-centred energy surface. However, a sufficiently comprehensive and precise energy function has yet to be established for this purpose. Here we show that this goal is met by a statistical model named SCUBA (for Side Chain-Unknown Backbone Arrangement) that uses neural network-form energy terms. These terms are learned with a two-step approach that comprises kernel density estimation followed by neural network training and can analytically represent multidimensional, high-order correlations in known protein structures. We report the crystal structures of nine de novo proteins whose backbones were designed to high precision using SCUBA, four of which have novel, non-natural overall architectures. By eschewing use of fragments from existing protein structures, SCUBA-driven structure design facilitates far-reaching exploration of the designable backbone space, thus extending the novelty and diversity of the proteins amenable to de novo design.
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3
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Takahashi T, Chikenji G, Tokita K. Lattice protein design using Bayesian learning. Phys Rev E 2021; 104:014404. [PMID: 34412286 DOI: 10.1103/physreve.104.014404] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2020] [Accepted: 06/11/2021] [Indexed: 01/01/2023]
Abstract
Protein design is the inverse approach of the three-dimensional (3D) structure prediction for elucidating the relationship between the 3D structures and amino acid sequences. In general, the computation of the protein design involves a double loop: A loop for amino acid sequence changes and a loop for an exhaustive conformational search for each amino acid sequence. Herein, we propose a novel statistical mechanical design method using Bayesian learning, which can design lattice proteins without the exhaustive conformational search. We consider a thermodynamic hypothesis of the evolution of proteins and apply it to the prior distribution of amino acid sequences. Furthermore, we take the water effect into account in view of the grand canonical picture. As a result, on applying the 2D lattice hydrophobic-polar (HP) model, our design method successfully finds an amino acid sequence for which the target conformation has a unique ground state. However, the performance was not as good for the 3D lattice HP models compared to the 2D models. The performance of the 3D model improves on using a 20-letter lattice proteins. Furthermore, we find a strong linearity between the chemical potential of water and the number of surface residues, thereby revealing the relationship between protein structure and the effect of water molecules. The advantage of our method is that it greatly reduces computation time, because it does not require long calculations for the partition function corresponding to an exhaustive conformational search. As our method uses a general form of Bayesian learning and statistical mechanics and is not limited to lattice proteins, the results presented here elucidate some heuristics used successfully in previous protein design methods.
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Affiliation(s)
- Tomoei Takahashi
- Graduate School of Informatics, Nagoya University, Nagoya 464-8601, Japan
| | - George Chikenji
- Graduate School of Engineering, Nagoya University, Nagoya 464-8603, Japan
| | - Kei Tokita
- Graduate School of Informatics, Nagoya University, Nagoya 464-8601, Japan
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4
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Soranno A. Physical basis of the disorder-order transition. Arch Biochem Biophys 2020; 685:108305. [DOI: 10.1016/j.abb.2020.108305] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2019] [Revised: 02/10/2020] [Accepted: 02/14/2020] [Indexed: 12/29/2022]
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Nerattini F, Figliuzzi M, Cardelli C, Tubiana L, Bianco V, Dellago C, Coluzza I. Identification of Protein Functional Regions. Chemphyschem 2020; 21:335-347. [PMID: 31944517 DOI: 10.1002/cphc.201900898] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2019] [Revised: 11/01/2019] [Indexed: 11/12/2022]
Abstract
Protein sequence stores the information relative to both functionality and stability, thus making it difficult to disentangle the two contributions. However, the identification of critical residues for function and stability has important implications for the mapping of the proteome interactions, as well as for many pharmaceutical applications, e. g. the identification of ligand binding regions for targeted pharmaceutical protein design. In this work, we propose a computational method to identify critical residues for protein functionality and stability and to further categorise them in strictly functional, structural and intermediate. We evaluate single site conservation and use Direct Coupling Analysis (DCA) to identify co-evolved residues both in natural and artificial evolution processes. We reproduce artificial evolution using protein design and base our approach on the hypothesis that artificial evolution in the absence of any functional constraint would exclusively lead to site conservation and co-evolution events of the structural type. Conversely, natural evolution intrinsically embeds both functional and structural information. By comparing the lists of conserved and co-evolved residues, outcomes of the analysis on natural and artificial evolution, we identify the functional residues without the need of any a priori knowledge of the biological role of the analysed protein.
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Affiliation(s)
- Francesca Nerattini
- Faculty of Physics, University of Vienna, Boltzmanngasse 5, 1090, Vienna, Austria
| | - Matteo Figliuzzi
- Sorbonne Universites, UPMC, Institut de Biologie Paris-Seine, CNRS, Laboratoire de Biologie Computationnelle et Quantitative UMR, 7238, Paris, France
| | - Chiara Cardelli
- Faculty of Physics, University of Vienna, Boltzmanngasse 5, 1090, Vienna, Austria
| | - Luca Tubiana
- Physics Department, Universitá degli studi di Trento, via Sommarive 14, 38123, Trento, IT
| | - Valentino Bianco
- Faculty of Physics, University of Vienna, Boltzmanngasse 5, 1090, Vienna, Austria.,Faculty of Chemistry, Chemical Physics Department, Universidad Complutense de Madrid, Plaza de las Ciencias, Ciudad Universitaria, Madrid, 28040, Spain
| | - Christoph Dellago
- Faculty of Physics, University of Vienna, Boltzmanngasse 5, 1090, Vienna, Austria
| | - Ivan Coluzza
- CIC biomaGUNE, Paseo Miramon 182, 20014 San Sebastian, Spain, and IKERBASQUE, Basque Foundation for Science, 48013, Bilbao, Spain
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Sanyal T, Mittal J, Shell MS. A hybrid, bottom-up, structurally accurate, Go¯-like coarse-grained protein model. J Chem Phys 2019; 151:044111. [PMID: 31370551 PMCID: PMC6663515 DOI: 10.1063/1.5108761] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2019] [Accepted: 06/24/2019] [Indexed: 12/21/2022] Open
Abstract
Coarse-grained (CG) protein models in the structural biology literature have improved over the years from being simple tools to understand general folding and aggregation driving forces to capturing detailed structures achieved by actual folding sequences. Here, we ask whether such models can be developed systematically from recent advances in bottom-up coarse-graining methods without relying on bioinformatic data (e.g., protein data bank statistics). We use relative entropy coarse-graining to develop a hybrid CG but Go¯-like CG peptide model, hypothesizing that the landscape of proteinlike folds is encoded by the backbone interactions, while the sidechain interactions define which of these structures globally minimizes the free energy in a unique native fold. To construct a model capable of capturing varied secondary structures, we use a new extended ensemble relative entropy method to coarse-grain based on multiple reference atomistic simulations of short polypeptides with varied α and β character. Subsequently, we assess the CG model as a putative protein backbone forcefield by combining it with sidechain interactions based on native contacts but not incorporating native distances explicitly, unlike standard Go¯ models. We test the model's ability to fold a range of proteins and find that it achieves high accuracy (∼2 Å root mean square deviation resolution for both short sequences and large globular proteins), suggesting the strong role that backbone conformational preferences play in defining the fold landscape. This model can be systematically extended to non-natural amino acids and nonprotein polymers and sets the stage for extensions to non-Go¯ models with sequence-specific sidechain interactions.
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Affiliation(s)
- Tanmoy Sanyal
- Department of Chemical Engineering, University of California Santa Barbara, Santa Barbara, California 93106, USA
| | - Jeetain Mittal
- Department of Chemical and Biomolecular Engineering, Lehigh University, Bethlehem, Pennsylvania 18015, USA
| | - M. Scott Shell
- Department of Chemical Engineering, University of California Santa Barbara, Santa Barbara, California 93106, USA
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Tubiana L, Jurásek M, Coluzza I. Implementing efficient concerted rotations using Mathematica and C code ⋆. THE EUROPEAN PHYSICAL JOURNAL. E, SOFT MATTER 2018; 41:87. [PMID: 30022359 DOI: 10.1140/epje/i2018-11694-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/22/2018] [Accepted: 06/28/2018] [Indexed: 06/08/2023]
Abstract
In this article we demonstrate a general and efficient metaprogramming implementation of concerted rotations using Mathematica. Concerted rotations allow the movement of a fixed portion of a polymer backbone with fixed bending angles, like a protein, while maintaining the correct geometry of the backbone and the initial and final points of the portion fixed. Our implementation uses Mathematica to generate a C code which is then wrapped in a library by a Python script. The user can modify the Mathematica notebook to generate a set of concerted rotations suited for a particular backbone geometry, without having to write the C code himself. The resulting code is highly optimized, performing on the order of thousands of operations per second.
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Affiliation(s)
- Luca Tubiana
- Computational Physics Department, University of Vienna, Sensengasse 8/10, 1090, Vienna, Austria.
| | - Miroslav Jurásek
- Faculty of Science, Masaryk University, Kotlářská 2, 602 00, Brno, Czech Republic
- CEITEC - Central European Institute of Technology, Kamenice 5, 625 00, Brno, Czech Republic
| | - Ivan Coluzza
- CIC biomaGUNE Parque Cientfico y Tecnolgico de Gipuzkoa, Paseo Miramn 182, 20014, Donostia / San Sebastin, Gipuzkoa, Spain
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8
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Dijkstra M, Fokkink W, Heringa J, van Dijk E, Abeln S. The characteristics of molten globule states and folding pathways strongly depend on the sequence of a protein. Mol Phys 2018. [DOI: 10.1080/00268976.2018.1496290] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/28/2022]
Affiliation(s)
- M.J.J. Dijkstra
- Department of Computer Science, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - W.J. Fokkink
- Department of Computer Science, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - J. Heringa
- Department of Computer Science, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - E. van Dijk
- Department of Computer Science, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - S. Abeln
- Department of Computer Science, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
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9
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Chu H, Liu H. TetraBASE: A Side Chain-Independent Statistical Energy for Designing Realistically Packed Protein Backbones. J Chem Inf Model 2018; 58:430-442. [PMID: 29314837 DOI: 10.1021/acs.jcim.7b00677] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
To construct backbone structures of high designability is a primary aspect of computational protein design. We report here a side chain-independent statistical energy that aims at realistic modeling of through-space packing of polypeptide backbones. To mitigate the lack of explicit amino acid side chains, the model treats the interbackbone site packing as being dependent on peptide local conformation. In addition, new variables suitable for statistical analysis, one for relative orientation and another for distance, have been introduced to represent the intersite geometry based on the asymmetrical tetrahedron organization of distinct chemical groups surrounding the Cα-carbon atoms. The resulting tetrahedron-based backbone statistical energy (tetraBASE) model has been used to optimize the tertiary organizations of secondary structure elements (SSEs) of designated types with Monte Caro simulated annealing, starting from artificial initial configurations. The tetraBASE minimum energy structures can reproduce SSE packing frequently observed in native proteins with atomic root-mean-square deviations of 1-2 Å. The model has also been tested by examining the stability of native SSE arrangements under tetraBASE. The results suggest that tetraBASE model can be used to effectively represent interbackbone packing when designing backbone structures without explicitly knowing side chain types.
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Affiliation(s)
- Huanyu Chu
- School of Life Sciences, University of Science and Technology of China , 230027 Hefei, Anhui China.,Hefei National Laboratory for Physical Sciences at the Microscales , 230027 Hefei, Anhui China
| | - Haiyan Liu
- School of Life Sciences, University of Science and Technology of China , 230027 Hefei, Anhui China.,Hefei National Laboratory for Physical Sciences at the Microscales , 230027 Hefei, Anhui China.,Collaborative Innovation Center of Chemistry for Life Sciences , 230027 Hefei, Anhui China
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10
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Coluzza I. Computational protein design: a review. JOURNAL OF PHYSICS. CONDENSED MATTER : AN INSTITUTE OF PHYSICS JOURNAL 2017; 29:143001. [PMID: 28140371 DOI: 10.1088/1361-648x/aa5c76] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/06/2023]
Abstract
Proteins are one of the most versatile modular assembling systems in nature. Experimentally, more than 110 000 protein structures have been identified and more are deposited every day in the Protein Data Bank. Such an enormous structural variety is to a first approximation controlled by the sequence of amino acids along the peptide chain of each protein. Understanding how the structural and functional properties of the target can be encoded in this sequence is the main objective of protein design. Unfortunately, rational protein design remains one of the major challenges across the disciplines of biology, physics and chemistry. The implications of solving this problem are enormous and branch into materials science, drug design, evolution and even cryptography. For instance, in the field of drug design an effective computational method to design protein-based ligands for biological targets such as viruses, bacteria or tumour cells, could give a significant boost to the development of new therapies with reduced side effects. In materials science, self-assembly is a highly desired property and soon artificial proteins could represent a new class of designable self-assembling materials. The scope of this review is to describe the state of the art in computational protein design methods and give the reader an outline of what developments could be expected in the near future.
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Affiliation(s)
- Ivan Coluzza
- Computational Physics, Faculty of Physics, University of Vienna, Vienna, Austria
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11
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Bonomi M, Heller GT, Camilloni C, Vendruscolo M. Principles of protein structural ensemble determination. Curr Opin Struct Biol 2017; 42:106-116. [PMID: 28063280 DOI: 10.1016/j.sbi.2016.12.004] [Citation(s) in RCA: 222] [Impact Index Per Article: 31.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2016] [Revised: 11/18/2016] [Accepted: 12/06/2016] [Indexed: 01/19/2023]
Abstract
The biological functions of protein molecules are intimately dependent on their conformational dynamics. This aspect is particularly evident for disordered proteins, which constitute perhaps one-third of the human proteome. Therefore, structural ensembles often offer more useful representations of proteins than individual conformations. Here, we describe how the well-established principles of protein structure determination should be extended to the case of protein structural ensembles determination. These principles concern primarily how to deal with conformationally heterogeneous states, and with experimental measurements that are averaged over such states and affected by a variety of errors. We first review the growing literature of recent methods that combine experimental and computational information to model structural ensembles, highlighting their similarities and differences. We then address some conceptual problems in the determination of structural ensembles and define future goals towards the establishment of objective criteria for the comparison, validation, visualization and dissemination of such ensembles.
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Affiliation(s)
| | | | - Carlo Camilloni
- Department of Chemistry and Institute for Advanced Study, Technische Universität München, D-85747 Garching, Germany
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12
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van Dijk E, Varilly P, Knowles TPJ, Frenkel D, Abeln S. Consistent Treatment of Hydrophobicity in Protein Lattice Models Accounts for Cold Denaturation. PHYSICAL REVIEW LETTERS 2016; 116:078101. [PMID: 26943560 DOI: 10.1103/physrevlett.116.078101] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/24/2015] [Indexed: 05/04/2023]
Abstract
The hydrophobic effect stabilizes the native structure of proteins by minimizing the unfavorable interactions between hydrophobic residues and water through the formation of a hydrophobic core. Here, we include the entropic and enthalpic contributions of the hydrophobic effect explicitly in an implicit solvent model. This allows us to capture two important effects: a length-scale dependence and a temperature dependence for the solvation of a hydrophobic particle. This consistent treatment of the hydrophobic effect explains cold denaturation and heat capacity measurements of solvated proteins.
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Affiliation(s)
- Erik van Dijk
- Department of Chemistry, University of Cambridge, Cambridge CB2 1EW, United Kingdom
- Centre for Integrative Bioinformatics (IBIVU), Vrije Universiteit, De Boelelaan 1081A, 1081 HV Amsterdam, Netherlands
| | - Patrick Varilly
- Department of Chemistry, University of Cambridge, Cambridge CB2 1EW, United Kingdom
| | - Tuomas P J Knowles
- Department of Chemistry, University of Cambridge, Cambridge CB2 1EW, United Kingdom
| | - Daan Frenkel
- Department of Chemistry, University of Cambridge, Cambridge CB2 1EW, United Kingdom
| | - Sanne Abeln
- Centre for Integrative Bioinformatics (IBIVU), Vrije Universiteit, De Boelelaan 1081A, 1081 HV Amsterdam, Netherlands
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