1
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Dabrowski‐Tumanski P, Goundaroulis D, Stasiak A, Rawdon EJ, Sulkowska JI. Theta-curves in proteins. Protein Sci 2024; 33:e5133. [PMID: 39167036 PMCID: PMC11337915 DOI: 10.1002/pro.5133] [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: 03/20/2024] [Revised: 06/22/2024] [Accepted: 07/10/2024] [Indexed: 08/23/2024]
Abstract
We study and characterize the topology of connectivity circuits observed in natively folded protein structures whose coordinates are deposited in the Protein Data Bank (PDB). Polypeptide chains of some proteins naturally fold into unique knotted configurations. Another kind of nontrivial topology of polypeptide chains is observed when, in addition to covalent bonds connecting consecutive amino acids in polypeptide chains, one also considers disulfide and ionic bonds between non-consecutive amino acids. Bonds between non-consecutive amino acids introduce bifurcation points into connectivity circuits defined by bonds between consecutive and nonconsecutive amino acids in analyzed proteins. Circuits with bifurcation points can form θ-curves with various topologies. We catalog here the observed topologies of θ-curves passing through bridges between consecutive and non-consecutive amino acids in studied proteins.
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Affiliation(s)
| | - Dimos Goundaroulis
- Center for Genome Architecture, Department of Molecular and Human GeneticsBaylor College of MedicineHoustonTexasUSA
- Center for Theoretical Biological PhysicsRice UniversityHoustonTexasUSA
| | - Andrzej Stasiak
- Center for Integrative GenomicsUniversity of LausanneLausanneSwitzerland
- Swiss Institute of BioinformaticsLausanneSwitzerland
| | - Eric J. Rawdon
- Department of MathematicsUniversity of St. ThomasSt. PaulMinnesotaUSA
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2
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Ferreira SGF, Sriramoju MK, Hsu STD, Faísca PFN, Machuqueiro M. Is There a Functional Role for the Knotted Topology in Protein UCH-L1? J Chem Inf Model 2024. [PMID: 39045738 DOI: 10.1021/acs.jcim.4c00880] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/25/2024]
Abstract
Knotted proteins are present in nature, but there is still an open issue regarding the existence of a universal role for these remarkable structures. To address this question, we used classical molecular dynamics (MD) simulations combined with in vitro experiments to investigate the role of the Gordian knot in the catalytic activity of UCH-L1. To create an unknotted form of UCH-L1, we modified its amino acid sequence by truncating several residues from its N-terminus. Remarkably, we find that deleting the first two N-terminal residues leads to a partial loss of enzyme activity with conservation of secondary structural content and knotted topological state. This happens because the integrity of the N-terminus is critical to ensure the correct alignment of the catalytic triad. However, the removal of five residues from the N-terminus, which significantly disrupts the native structure and the topological state, leads to a complete loss of enzymatic activity. Overall, our findings indicate that UCH-L1's catalytic activity depends critically on the integrity of the N-terminus and the secondary structure content, with the latter being strongly coupled with the knotted topological state.
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Affiliation(s)
- Sara G F Ferreira
- BioISI - Instituto de Biossistemas e Ciências Integrativas, Departamento de Química e Bioquímica, Faculdade de Ciências, Universidade de Lisboa, 1749-016 Lisboa, Portugal
| | - Manoj K Sriramoju
- Institute of Biological Chemistry, Academia Sinica, Taipei 11529, Taiwan
| | - Shang-Te Danny Hsu
- Institute of Biological Chemistry, Academia Sinica, Taipei 11529, Taiwan
- International Institute for Sustainability with Knotted Chiral Meta Matter (WPI-SKCM2), Hiroshima University, 1-3-1 Kagamiyama, Higashi-Hiroshima, Hiroshima 739-8526, Japan
- Institute of Biochemical Sciences, National Taiwan University, Taipei 11529, Taiwan
| | - Patrícia F N Faísca
- BioISI - Instituto de Biossistemas e Ciências Integrativas, Departamento de Física, Faculdade de Ciências, Universidade de Lisboa, 1749-016 Lisboa, Portugal
| | - Miguel Machuqueiro
- BioISI - Instituto de Biossistemas e Ciências Integrativas, Departamento de Química e Bioquímica, Faculdade de Ciências, Universidade de Lisboa, 1749-016 Lisboa, Portugal
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3
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Perlinska AP, Sikora M, Sulkowska JI. Everything AlphaFold tells us about protein knots. J Mol Biol 2024; 436:168715. [PMID: 39029890 DOI: 10.1016/j.jmb.2024.168715] [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: 05/06/2024] [Revised: 06/29/2024] [Accepted: 07/14/2024] [Indexed: 07/21/2024]
Abstract
Recent advances in Machine Learning methods in structural biology opened up new perspectives for protein analysis. Utilizing these methods allows us to go beyond the limitations of empirical research, and take advantage of the vast amount of generated data. We use a complete set of potentially knotted protein models identified in all high-quality predictions from the AlphaFold Database to search for any common trends that describe them. We show that the vast majority of knotted proteins have 31 knot and that the presence of knots is preferred in neither Bacteria, Eukaryota, or Archaea domains. On the contrary, the percentage of knotted proteins in any given proteome is around 0.4%, regardless of the taxonomical group. We also verified that the organism's living conditions do not impact the number of knotted proteins in its proteome, as previously expected. We did not encounter an organism without a single knotted protein. What is more, we found four universally present families of knotted proteins in Bacteria, consisting of SAM synthase, and TrmD, TrmH, and RsmE methyltransferases.
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Affiliation(s)
- Agata P Perlinska
- Centre of New Technologies, University of Warsaw, Banacha 2c, Warsaw 02-097, Poland
| | - Maciej Sikora
- Centre of New Technologies, University of Warsaw, Banacha 2c, Warsaw 02-097, Poland
| | - Joanna I Sulkowska
- Centre of New Technologies, University of Warsaw, Banacha 2c, Warsaw 02-097, Poland.
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4
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Rubach P, Sikora M, Jarmolinska A, Perlinska A, Sulkowska J. AlphaKnot 2.0: a web server for the visualization of proteins' knotting and a database of knotted AlphaFold-predicted models. Nucleic Acids Res 2024; 52:W187-W193. [PMID: 38842945 PMCID: PMC11223836 DOI: 10.1093/nar/gkae443] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2024] [Revised: 04/29/2024] [Accepted: 05/10/2024] [Indexed: 07/06/2024] Open
Abstract
The availability of 3D protein models is rapidly increasing with the development of structure prediction algorithms. With the expanding availability of data, new ways of analysis, especially topological analysis, of those predictions are becoming necessary. Here, we present the updated version of the AlphaKnot service that provides a straightforward way of analyzing structure topology. It was designed specifically to determine knot types of the predicted structure models, however, it can be used for all structures, including the ones solved experimentally. AlphaKnot 2.0 provides the user's ability to obtain the knowledge necessary to assess the topological correctness of the model. Both probabilistic and deterministic knot detection methods are available, together with various visualizations (including a trajectory of simplification steps to highlight the topological complexities). Moreover, the web server provides a list of proteins similar to the queried model within AlphaKnot's database and returns their knot types for direct comparison. We pre-calculated the topology of high-quality models from the AlphaFold Database (4th version) and there are now more than 680.000 knotted models available in the AlphaKnot database. AlphaKnot 2.0 is available at https://alphaknot.cent.uw.edu.pl/.
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Affiliation(s)
- Pawel Rubach
- Warsaw School of Economics, Al. Niepodleglosci 162, 02-554 Warsaw, Poland
| | - Maciej Sikora
- Centre of New Technologies, University of Warsaw, Banacha 2c, 02-097 Warsaw, Poland
| | | | - Agata P Perlinska
- Centre of New Technologies, University of Warsaw, Banacha 2c, 02-097 Warsaw, Poland
| | - Joanna I Sulkowska
- Centre of New Technologies, University of Warsaw, Banacha 2c, 02-097 Warsaw, Poland
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5
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Sikora M, Klimentova E, Uchal D, Sramkova D, Perlinska AP, Nguyen ML, Korpacz M, Malinowska R, Nowakowski S, Rubach P, Simecek P, Sulkowska JI. Knot or not? Identifying unknotted proteins in knotted families with sequence-based Machine Learning model. Protein Sci 2024; 33:e4998. [PMID: 38888487 PMCID: PMC11184937 DOI: 10.1002/pro.4998] [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: 12/04/2023] [Revised: 03/14/2024] [Accepted: 04/09/2024] [Indexed: 06/20/2024]
Abstract
Knotted proteins, although scarce, are crucial structural components of certain protein families, and their roles continue to be a topic of intense research. Capitalizing on the vast collection of protein structure predictions offered by AlphaFold (AF), this study computationally examines the entire UniProt database to create a robust dataset of knotted and unknotted proteins. Utilizing this dataset, we develop a machine learning (ML) model capable of accurately predicting the presence of knots in protein structures solely from their amino acid sequences. We tested the model's capabilities on 100 proteins whose structures had not yet been predicted by AF and found agreement with our local prediction in 92% cases. From the point of view of structural biology, we found that all potentially knotted proteins predicted by AF can be classified only into 17 families. This allows us to discover the presence of unknotted proteins in families with a highly conserved knot. We found only three new protein families: UCH, DUF4253, and DUF2254, that contain both knotted and unknotted proteins, and demonstrate that deletions within the knot core could potentially account for the observed unknotted (trivial) topology. Finally, we have shown that in the majority of knotted families (11 out of 15), the knotted topology is strictly conserved in functional proteins with very low sequence similarity. We have conclusively demonstrated that proteins AF predicts as unknotted are structurally accurate in their unknotted configurations. However, these proteins often represent nonfunctional fragments, lacking significant portions of the knot core (amino acid sequence).
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Affiliation(s)
- Maciej Sikora
- Centre of New Technologies, University of WarsawWarsawPoland
- Faculty of Mathematics, Informatics and Mechanics, University of WarsawWarsawPoland
| | - Eva Klimentova
- Central European Institute of Technology, Masaryk UniversityBrnoCzech Republic
- National Centre for Biomolecular Research, Faculty of Science, Masaryk UniversityBrnoCzech Republic
| | - Dawid Uchal
- Centre of New Technologies, University of WarsawWarsawPoland
- Faculty of Physics, University of WarsawWarsawPoland
| | - Denisa Sramkova
- Central European Institute of Technology, Masaryk UniversityBrnoCzech Republic
- National Centre for Biomolecular Research, Faculty of Science, Masaryk UniversityBrnoCzech Republic
| | | | - Mai Lan Nguyen
- Centre of New Technologies, University of WarsawWarsawPoland
| | - Marta Korpacz
- Centre of New Technologies, University of WarsawWarsawPoland
- Faculty of Mathematics, Informatics and Mechanics, University of WarsawWarsawPoland
| | - Roksana Malinowska
- Centre of New Technologies, University of WarsawWarsawPoland
- Faculty of Mathematics, Informatics and Mechanics, University of WarsawWarsawPoland
| | - Szymon Nowakowski
- Faculty of Mathematics, Informatics and Mechanics, University of WarsawWarsawPoland
- Faculty of Physics, University of WarsawWarsawPoland
| | - Pawel Rubach
- Centre of New Technologies, University of WarsawWarsawPoland
- Warsaw School of EconomicsWarsawPoland
| | - Petr Simecek
- Central European Institute of Technology, Masaryk UniversityBrnoCzech Republic
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6
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Perlinska AP, Nguyen ML, Pilla SP, Staszor E, Lewandowska I, Bernat A, Purta E, Augustyniak R, Bujnicki JM, Sulkowska JI. Are there double knots in proteins? Prediction and in vitro verification based on TrmD-Tm1570 fusion from C. nitroreducens. Front Mol Biosci 2024; 10:1223830. [PMID: 38903539 PMCID: PMC11187310 DOI: 10.3389/fmolb.2023.1223830] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2023] [Accepted: 10/04/2023] [Indexed: 06/22/2024] Open
Abstract
We have been aware of the existence of knotted proteins for over 30 years-but it is hard to predict what is the most complicated knot that can be formed in proteins. Here, we show new and the most complex knotted topologies recorded to date-double trefoil knots (31 #31). We found five domain arrangements (architectures) that result in a doubly knotted structure in almost a thousand proteins. The double knot topology is found in knotted membrane proteins from the CaCA family, that function as ion transporters, in the group of carbonic anhydrases that catalyze the hydration of carbon dioxide, and in the proteins from the SPOUT superfamily that gathers 31 knotted methyltransferases with the active site-forming knot. For each family, we predict the presence of a double knot using AlphaFold and RoseTTaFold structure prediction. In the case of the TrmD-Tm1570 protein, which is a member of SPOUT superfamily, we show that it folds in vitro and is biologically active. Our results show that this protein forms a homodimeric structure and retains the ability to modify tRNA, which is the function of the single-domain TrmD protein. However, how the protein folds and is degraded remains unknown.
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Affiliation(s)
| | - Mai Lan Nguyen
- Centre of New Technologies, University of Warsaw, Warsaw, Poland
- Polish-Japanese Academy of Information Technology, Warsaw, Poland
| | - Smita P. Pilla
- Centre of New Technologies, University of Warsaw, Warsaw, Poland
| | - Emilia Staszor
- Centre of New Technologies, University of Warsaw, Warsaw, Poland
- Faculty of Chemistry, University of Warsaw, Warsaw, Poland
| | | | - Agata Bernat
- Laboratory of Bioinformatics and Protein Engineering, International Institute of Molecular and Cell Biology in Warsaw, Warsaw, Poland
| | - Elżbieta Purta
- Laboratory of Bioinformatics and Protein Engineering, International Institute of Molecular and Cell Biology in Warsaw, Warsaw, Poland
| | | | - Janusz M. Bujnicki
- Laboratory of Bioinformatics and Protein Engineering, International Institute of Molecular and Cell Biology in Warsaw, Warsaw, Poland
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7
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Zhang W, Guo J, Lv X, Zhang F. Combined Machine Learning and High-Throughput Calculations Predict Heyd-Scuseria-Ernzerhof Band Gap of 2D Materials and Potential MoSi 2N 4 Heterostructures. J Phys Chem Lett 2024; 15:5413-5419. [PMID: 38743311 DOI: 10.1021/acs.jpclett.4c01013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/16/2024]
Abstract
We present a novel target-driven methodology devised to predict the Heyd-Scuseria-Ernzerhof (HSE) band gap of two-dimensional (2D) materials leveraging the comprehensive C2DB database. This innovative approach integrates machine learning and density functional theory (DFT) calculations to predict the HSE band gap, conduction band minimum (CBM), and valence band maximum (VBM) of 2176 types of 2D materials. Subsequently, we collected a comprehensive data set comprising 3539 types of 2D materials, each characterized by its HSE band gaps, CBM, and VBM. Considering the lattice disparities between MoSi2N4 (MSN) and 2D materials, our analysis predicted 766 potential MSN/2D heterostructures. These heterostructures are further categorized into four distinct types based on the relative positions of their CBM and VBM: Type I encompasses 230 variants, Type II comprises 244 configurations, Type III consists of 284 permutations, and 0 band gap comprises 8 types.
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Affiliation(s)
- Weibin Zhang
- College of Physics and Electronics Information, Yunnan Key Laboratory of Optoelectronic Information Technology, Key Laboratory of Advanced Technique & Preparation for Renewable Energy Materials-Ministry of Education, Yunnan Normal University, Kunming 650500, P.R. China
| | - Jie Guo
- College of Physics and Electronics Information, Yunnan Key Laboratory of Optoelectronic Information Technology, Key Laboratory of Advanced Technique & Preparation for Renewable Energy Materials-Ministry of Education, Yunnan Normal University, Kunming 650500, P.R. China
| | - Xiankui Lv
- College of Physics and Electronics Information, Yunnan Key Laboratory of Optoelectronic Information Technology, Key Laboratory of Advanced Technique & Preparation for Renewable Energy Materials-Ministry of Education, Yunnan Normal University, Kunming 650500, P.R. China
| | - Fuchun Zhang
- College of Physics and Electronic Information, Yan'an University, Yan'an 716000, P. R. China
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8
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Lu B, Xia Y, Ren Y, Xie M, Zhou L, Vinai G, Morton SA, Wee ATS, van der Wiel WG, Zhang W, Wong PKJ. When Machine Learning Meets 2D Materials: A Review. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2024; 11:e2305277. [PMID: 38279508 PMCID: PMC10987159 DOI: 10.1002/advs.202305277] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Revised: 10/21/2023] [Indexed: 01/28/2024]
Abstract
The availability of an ever-expanding portfolio of 2D materials with rich internal degrees of freedom (spin, excitonic, valley, sublattice, and layer pseudospin) together with the unique ability to tailor heterostructures made layer by layer in a precisely chosen stacking sequence and relative crystallographic alignments, offers an unprecedented platform for realizing materials by design. However, the breadth of multi-dimensional parameter space and massive data sets involved is emblematic of complex, resource-intensive experimentation, which not only challenges the current state of the art but also renders exhaustive sampling untenable. To this end, machine learning, a very powerful data-driven approach and subset of artificial intelligence, is a potential game-changer, enabling a cheaper - yet more efficient - alternative to traditional computational strategies. It is also a new paradigm for autonomous experimentation for accelerated discovery and machine-assisted design of functional 2D materials and heterostructures. Here, the study reviews the recent progress and challenges of such endeavors, and highlight various emerging opportunities in this frontier research area.
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Affiliation(s)
- Bin Lu
- ARTIST Lab for Artificial Electronic Materials and Technologies, School of MicroelectronicsNorthwestern Polytechnical UniversityXi'an710072P. R. China
- Yangtze River Delta Research Institute of Northwestern Polytechnical UniversityTaicang215400P. R. China
| | - Yuze Xia
- ARTIST Lab for Artificial Electronic Materials and Technologies, School of MicroelectronicsNorthwestern Polytechnical UniversityXi'an710072P. R. China
- Yangtze River Delta Research Institute of Northwestern Polytechnical UniversityTaicang215400P. R. China
| | - Yuqian Ren
- ARTIST Lab for Artificial Electronic Materials and Technologies, School of MicroelectronicsNorthwestern Polytechnical UniversityXi'an710072P. R. China
- Yangtze River Delta Research Institute of Northwestern Polytechnical UniversityTaicang215400P. R. China
| | - Miaomiao Xie
- ARTIST Lab for Artificial Electronic Materials and Technologies, School of MicroelectronicsNorthwestern Polytechnical UniversityXi'an710072P. R. China
- Yangtze River Delta Research Institute of Northwestern Polytechnical UniversityTaicang215400P. R. China
| | - Liguo Zhou
- ARTIST Lab for Artificial Electronic Materials and Technologies, School of MicroelectronicsNorthwestern Polytechnical UniversityXi'an710072P. R. China
- Yangtze River Delta Research Institute of Northwestern Polytechnical UniversityTaicang215400P. R. China
| | - Giovanni Vinai
- Instituto Officina dei Materiali (IOM)‐CNRLaboratorio TASCTriesteI‐34149Italy
| | - Simon A. Morton
- Advanced Light Source (ALS)Lawrence Berkeley National LaboratoryBerkeleyCA94720USA
| | - Andrew T. S. Wee
- Department of Physics and Centre for Advanced 2D Materials (CA2DM) and Graphene Research Centre (GRC)National University of SingaporeSingapore117542Singapore
| | - Wilfred G. van der Wiel
- NanoElectronics Group, MESA+ Institute for Nanotechnology and BRAINS Center for Brain‐Inspired Nano SystemsUniversity of TwenteEnschede7500AEThe Netherlands
- Institute of PhysicsUniversity of Münster48149MünsterGermany
| | - Wen Zhang
- ARTIST Lab for Artificial Electronic Materials and Technologies, School of MicroelectronicsNorthwestern Polytechnical UniversityXi'an710072P. R. China
- Yangtze River Delta Research Institute of Northwestern Polytechnical UniversityTaicang215400P. R. China
- NanoElectronics Group, MESA+ Institute for Nanotechnology and BRAINS Center for Brain‐Inspired Nano SystemsUniversity of TwenteEnschede7500AEThe Netherlands
| | - Ping Kwan Johnny Wong
- ARTIST Lab for Artificial Electronic Materials and Technologies, School of MicroelectronicsNorthwestern Polytechnical UniversityXi'an710072P. R. China
- Yangtze River Delta Research Institute of Northwestern Polytechnical UniversityTaicang215400P. R. China
- NPU Chongqing Technology Innovation CenterChongqing400000P. R. China
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9
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Martin J. AlphaFold2 Predicts Whether Proteins Interact Amidst Confounding Structural Compatibility. J Chem Inf Model 2024; 64:1473-1480. [PMID: 38373070 DOI: 10.1021/acs.jcim.3c01805] [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: 02/21/2024]
Abstract
Predicting whether two proteins physically interact is one of the holy grails of computational biology, galvanized by rapid advancements in deep learning. AlphaFold2, although not developed with this goal, is promising in this respect. Here, I test the prediction capability of AlphaFold2 on a very challenging data set, where proteins are structurally compatible, even when they do not interact. AlphaFold2 achieves high discrimination between interacting and non-interacting proteins, and the cases of misclassifications can either be rescued by revisiting the input sequences or can suggest false positives and negatives in the data set. AlphaFold2 is thus not impaired by the compatibility between protein structures and has the potential to be applied on a large scale.
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Affiliation(s)
- Juliette Martin
- Univ Lyon, CNRS, UMR 5086 MMSB, 7 passage du Vercors F-69367, Lyon, France
- Laboratory of Biology and Modeling of the Cell, Ecole Normale Supérieure de Lyon, CNRS UMR 5239, Inserm U1293, University Claude Bernard Lyon 1, 69364, Lyon, France
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10
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Hsu MF, Sriramoju MK, Lai CH, Chen YR, Huang JS, Ko TP, Huang KF, Hsu STD. Structure, dynamics, and stability of the smallest and most complex 7 1 protein knot. J Biol Chem 2024; 300:105553. [PMID: 38072060 PMCID: PMC10840475 DOI: 10.1016/j.jbc.2023.105553] [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: 06/29/2023] [Revised: 11/21/2023] [Accepted: 12/04/2023] [Indexed: 12/29/2023] Open
Abstract
Proteins can spontaneously tie a variety of intricate topological knots through twisting and threading of the polypeptide chains. Recently developed artificial intelligence algorithms have predicted several new classes of topological knotted proteins, but the predictions remain to be authenticated experimentally. Here, we showed by X-ray crystallography and solution-state NMR spectroscopy that Q9PR55, an 89-residue protein from Ureaplasma urealyticum, possesses a novel 71 knotted topology that is accurately predicted by AlphaFold 2, except for the flexible N terminus. Q9PR55 is monomeric in solution, making it the smallest and most complex knotted protein known to date. In addition to its exceptional chemical stability against urea-induced unfolding, Q9PR55 is remarkably robust to resist the mechanical unfolding-coupled proteolysis by a bacterial proteasome, ClpXP. Our results suggest that the mechanical resistance against pulling-induced unfolding is determined by the complexity of the knotted topology rather than the size of the molecule.
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Affiliation(s)
- Min-Feng Hsu
- Institute of Biological Chemistry, Academia Sinica, Taipei, Taiwan
| | | | - Chih-Hsuan Lai
- Institute of Biological Chemistry, Academia Sinica, Taipei, Taiwan
| | - Yun-Ru Chen
- Academia Sinica Protein Clinic, Academia Sinica, Taipei, Taiwan
| | - Jing-Siou Huang
- Academia Sinica Protein Clinic, Academia Sinica, Taipei, Taiwan
| | - Tzu-Ping Ko
- Institute of Biological Chemistry, Academia Sinica, Taipei, Taiwan
| | - Kai-Fa Huang
- Institute of Biological Chemistry, Academia Sinica, Taipei, Taiwan; Academia Sinica Protein Clinic, Academia Sinica, Taipei, Taiwan
| | - Shang-Te Danny Hsu
- Institute of Biological Chemistry, Academia Sinica, Taipei, Taiwan; Academia Sinica Protein Clinic, Academia Sinica, Taipei, Taiwan; Institute of Biochemical Sciences, National Taiwan University, Taipei, Taiwan; International Institute for Sustainability with Knotted Chiral Meta Matter (WPI-SKCM(2)), Hiroshima University, Higashihiroshima, Japan.
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11
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Liu N, Dong W, Yang H, Li JH, Chiu TY. Application of artificial scaffold systems in microbial metabolic engineering. Front Bioeng Biotechnol 2023; 11:1328141. [PMID: 38188488 PMCID: PMC10771841 DOI: 10.3389/fbioe.2023.1328141] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2023] [Accepted: 12/12/2023] [Indexed: 01/09/2024] Open
Abstract
In nature, metabolic pathways are often organized into complex structures such as multienzyme complexes, enzyme molecular scaffolds, or reaction microcompartments. These structures help facilitate multi-step metabolic reactions. However, engineered metabolic pathways in microbial cell factories do not possess inherent metabolic regulatory mechanisms, which can result in metabolic imbalance. Taking inspiration from nature, scientists have successfully developed synthetic scaffolds to enhance the performance of engineered metabolic pathways in microbial cell factories. By recruiting enzymes, synthetic scaffolds facilitate the formation of multi-enzyme complexes, leading to the modulation of enzyme spatial distribution, increased enzyme activity, and a reduction in the loss of intermediate products and the toxicity associated with harmful intermediates within cells. In recent years, scaffolds based on proteins, nucleic acids, and various organelles have been developed and employed to facilitate multiple metabolic pathways. Despite varying degrees of success, synthetic scaffolds still encounter numerous challenges. The objective of this review is to provide a comprehensive introduction to these synthetic scaffolds and discuss their latest research advancements and challenges.
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Affiliation(s)
- Nana Liu
- College of Pharmaceutical Science, Zhejiang University of Technology, Hangzhou, China
- HIM-BGI Omics Center, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences (CAS), Hangzhou, China
| | - Wei Dong
- HIM-BGI Omics Center, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences (CAS), Hangzhou, China
| | - Huanming Yang
- HIM-BGI Omics Center, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences (CAS), Hangzhou, China
| | - Jing-Hua Li
- College of Pharmaceutical Science, Zhejiang University of Technology, Hangzhou, China
| | - Tsan-Yu Chiu
- College of Pharmaceutical Science, Zhejiang University of Technology, Hangzhou, China
- HIM-BGI Omics Center, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences (CAS), Hangzhou, China
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12
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Hsu STD. Folding and functions of knotted proteins. Curr Opin Struct Biol 2023; 83:102709. [PMID: 37778185 DOI: 10.1016/j.sbi.2023.102709] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2023] [Revised: 09/02/2023] [Accepted: 09/05/2023] [Indexed: 10/03/2023]
Abstract
Topologically knotted proteins have entangled structural elements within their native structures that cannot be disentangled simply by pulling from the N- and C-termini. Systematic surveys have identified different types of knotted protein structures, constituting as much as 1% of the total entries within the Protein Data Bank. Many knotted proteins rely on their knotted structural elements to carry out evolutionarily conserved biological functions. Being knotted may also provide mechanical stability to withstand unfolding-coupled proteolysis. Reconfiguring a knotted protein topology by circular permutation or cyclization provides insights into the importance of being knotted in the context of folding and functions. With the explosion of predicted protein structures by artificial intelligence, we are now entering a new era of exploring the entangled protein universe.
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Affiliation(s)
- Shang-Te Danny Hsu
- Institute of Biological Chemistry, Academia Sinica, Taipei 11529, Taiwan; Institute of Biochemical Sciences, National Taiwan University, Taipei 10617, Taiwan; International Institute for Sustainability with Knotted Chiral Meta Matter (WPI-SKCM(2)), Hiroshima University, Higashi-Hiroshima, Hiroshima 739-8526, Japan.
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Dabrowski-Tumanski P, Stasiak A. AlphaFold Blindness to Topological Barriers Affects Its Ability to Correctly Predict Proteins' Topology. Molecules 2023; 28:7462. [PMID: 38005184 PMCID: PMC10672856 DOI: 10.3390/molecules28227462] [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: 08/22/2023] [Revised: 11/03/2023] [Accepted: 11/04/2023] [Indexed: 11/26/2023] Open
Abstract
AlphaFold is a groundbreaking deep learning tool for protein structure prediction. It achieved remarkable accuracy in modeling many 3D structures while taking as the user input only the known amino acid sequence of proteins in question. Intriguingly though, in the early steps of each individual structure prediction procedure, AlphaFold does not respect topological barriers that, in real proteins, result from the reciprocal impermeability of polypeptide chains. This study aims to investigate how this failure to respect topological barriers affects AlphaFold predictions with respect to the topology of protein chains. We focus on such classes of proteins that, during their natural folding, reproducibly form the same knot type on their linear polypeptide chain, as revealed by their crystallographic analysis. We use partially artificial test constructs in which the mutual non-permeability of polypeptide chains should not permit the formation of complex composite knots during natural protein folding. We find that despite the formal impossibility that the protein folding process could produce such knots, AlphaFold predicts these proteins to form complex composite knots. Our study underscores the necessity for cautious interpretation and further validation of topological features in protein structures predicted by AlphaFold.
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Affiliation(s)
- Pawel Dabrowski-Tumanski
- Faculty of Mathematics and Natural Sciences, School of Exact Sciences, Cardinal Wyszynski University in Warsaw, Wóycickiego 1/3, 01-938 Warsaw, Poland
| | - Andrzej Stasiak
- Center for Integrative Genomics, University of Lausanne, 1015 Lausanne, Switzerland
- SIB Swiss Institute of Bioinformatics, 1015 Lausanne, Switzerland
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14
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Hou Y, Xie T, He L, Tao L, Huang J. Topological links in predicted protein complex structures reveal limitations of AlphaFold. Commun Biol 2023; 6:1098. [PMID: 37898666 PMCID: PMC10613300 DOI: 10.1038/s42003-023-05489-4] [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: 02/21/2023] [Accepted: 10/19/2023] [Indexed: 10/30/2023] Open
Abstract
AlphaFold is making great progress in protein structure prediction, not only for single-chain proteins but also for multi-chain protein complexes. When using AlphaFold-Multimer to predict protein‒protein complexes, we observed some unusual structures in which chains are looped around each other to form topologically intertwining links at the interface. Based on physical principles, such topological links should generally not exist in native protein complex structures unless covalent modifications of residues are involved. Although it is well known and has been well studied that protein structures may have topologically complex shapes such as knots and links, existing methods are hampered by the chain closure problem and show poor performance in identifying topologically linked structures in protein‒protein complexes. Therefore, we address the chain closure problem by using sliding windows from a local perspective and propose an algorithm to measure the topological-geometric features that can be used to identify topologically linked structures. An application of the method to AlphaFold-Multimer-predicted protein complex structures finds that approximately 1.72% of the predicted structures contain topological links. The method presented in this work will facilitate the computational study of protein‒protein interactions and help further improve the structural prediction of multi-chain protein complexes.
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Affiliation(s)
- Yingnan Hou
- Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, 18 Shilongshan Road, Hangzhou, 310024, Zhejiang, China
- Westlake AI Therapeutics Lab, Westlake Laboratory of Life Sciences and Biomedicine, 18 Shilongshan Road, Hangzhou, 310024, Zhejiang, China
| | - Tengyu Xie
- Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, 18 Shilongshan Road, Hangzhou, 310024, Zhejiang, China
- Westlake AI Therapeutics Lab, Westlake Laboratory of Life Sciences and Biomedicine, 18 Shilongshan Road, Hangzhou, 310024, Zhejiang, China
| | - Liuqing He
- Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, 18 Shilongshan Road, Hangzhou, 310024, Zhejiang, China
- Center for Infectious Disease Research, Westlake Laboratory of Life Sciences and Biomedicine, 18 Shilongshan Road, Hangzhou, 310024, Zhejiang, China
| | - Liang Tao
- Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, 18 Shilongshan Road, Hangzhou, 310024, Zhejiang, China
- Center for Infectious Disease Research, Westlake Laboratory of Life Sciences and Biomedicine, 18 Shilongshan Road, Hangzhou, 310024, Zhejiang, China
| | - Jing Huang
- Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, 18 Shilongshan Road, Hangzhou, 310024, Zhejiang, China.
- Westlake AI Therapeutics Lab, Westlake Laboratory of Life Sciences and Biomedicine, 18 Shilongshan Road, Hangzhou, 310024, Zhejiang, China.
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Doyle LA, Takushi B, Kibler RD, Milles LF, Orozco CT, Jones JD, Jackson SE, Stoddard BL, Bradley P. De novo design of knotted tandem repeat proteins. Nat Commun 2023; 14:6746. [PMID: 37875492 PMCID: PMC10598012 DOI: 10.1038/s41467-023-42388-y] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2023] [Accepted: 10/09/2023] [Indexed: 10/26/2023] Open
Abstract
De novo protein design methods can create proteins with folds not yet seen in nature. These methods largely focus on optimizing the compatibility between the designed sequence and the intended conformation, without explicit consideration of protein folding pathways. Deeply knotted proteins, whose topologies may introduce substantial barriers to folding, thus represent an interesting test case for protein design. Here we report our attempts to design proteins with trefoil (31) and pentafoil (51) knotted topologies. We extended previously described algorithms for tandem repeat protein design in order to construct deeply knotted backbones and matching designed repeat sequences (N = 3 repeats for the trefoil and N = 5 for the pentafoil). We confirmed the intended conformation for the trefoil design by X ray crystallography, and we report here on this protein's structure, stability, and folding behaviour. The pentafoil design misfolded into an asymmetric structure (despite a 5-fold symmetric sequence); two of the four repeat-repeat units matched the designed backbone while the other two diverged to form local contacts, leading to a trefoil rather than pentafoil knotted topology. Our results also provide insights into the folding of knotted proteins.
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Affiliation(s)
- Lindsey A Doyle
- Division of Basic Sciences, Fred Hutchinson Cancer Center, 1100 Fairview Ave. North, Seattle, WA, 98109, USA
| | - Brittany Takushi
- Division of Basic Sciences, Fred Hutchinson Cancer Center, 1100 Fairview Ave. North, Seattle, WA, 98109, USA
| | - Ryan D Kibler
- Department of Biochemistry, University of Washington, Seattle, WA, 98195, USA
| | - Lukas F Milles
- Department of Biochemistry, University of Washington, Seattle, WA, 98195, USA
| | - Carolina T Orozco
- Yusuf Hamied Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge, CB2 1EW, UK
| | - Jonathan D Jones
- Yusuf Hamied Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge, CB2 1EW, UK
| | - Sophie E Jackson
- Yusuf Hamied Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge, CB2 1EW, UK
| | - Barry L Stoddard
- Division of Basic Sciences, Fred Hutchinson Cancer Center, 1100 Fairview Ave. North, Seattle, WA, 98109, USA.
| | - Philip Bradley
- Division of Basic Sciences, Fred Hutchinson Cancer Center, 1100 Fairview Ave. North, Seattle, WA, 98109, USA.
- Division of Public Health Sciences and Program in Computational Biology, Fred Hutchinson Cancer Center, 1100 Fairview Ave. N, Seattle, WA, 98009, USA.
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A Note on the Effects of Linear Topology Preservation in Monte Carlo Simulations of Knotted Proteins. Int J Mol Sci 2022; 23:ijms232213871. [PMID: 36430350 PMCID: PMC9695063 DOI: 10.3390/ijms232213871] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2022] [Revised: 11/07/2022] [Accepted: 11/08/2022] [Indexed: 11/12/2022] Open
Abstract
Monte Carlo simulations are a powerful technique and are widely used in different fields. When applied to complex molecular systems with long chains, such as those in synthetic polymers and proteins, they have the advantage of providing a fast and computationally efficient way to sample equilibrium ensembles and calculate thermodynamic and structural properties under desired conditions. Conformational Monte Carlo techniques employ a move set to perform the transitions in the simulation Markov chain. While accepted conformations must preserve the sequential bonding of the protein chain model and excluded volume among its units, the moves themselves may take the chain across itself. We call this a break in linear topology preservation. In this manuscript, we show, using simple protein models, that there is no difference in equilibrium properties calculated with a move set that preserves linear topology and one that does not. However, for complex structures, such as those of deeply knotted proteins, the preservation of linear topology provides correct equilibrium results but only after long relaxation. In any case, to analyze folding pathways, knotting mechanisms and folding kinetics, the preservation of linear topology may be an unavoidable requirement.
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