1
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Viegas RG, Martins IBS, Leite VBP. Understanding the Energy Landscape of Intrinsically Disordered Protein Ensembles. J Chem Inf Model 2024; 64:4149-4157. [PMID: 38713459 DOI: 10.1021/acs.jcim.4c00080] [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: 05/08/2024]
Abstract
A substantial portion of various organisms' proteomes comprises intrinsically disordered proteins (IDPs) that lack a defined three-dimensional structure. These IDPs exhibit a diverse array of conformations, displaying remarkable spatiotemporal heterogeneity and exceptional conformational flexibility. Characterizing the structure or structural ensemble of IDPs presents significant conceptual and methodological challenges owing to the absence of a well-defined native structure. While databases such as the Protein Ensemble Database (PED) provide IDP ensembles obtained through a combination of experimental data and molecular modeling, the absence of reaction coordinates poses challenges in comprehensively understanding pertinent aspects of the system. In this study, we leverage the energy landscape visualization method (JCTC, 6482, 2019) to scrutinize four IDP ensembles sourced from PED. ELViM, a methodology that circumvents the need for a priori reaction coordinates, aids in analyzing the ensembles. The specific IDP ensembles investigated are as follows: two fragments of nucleoporin (NUL: 884-993 and NUS: 1313-1390), yeast sic 1 N-terminal (1-90), and the N-terminal SH3 domain of Drk (1-59). Utilizing ELViM enables the comprehensive validation of ensembles, facilitating the detection of potential inconsistencies in the sampling process. Additionally, it allows for identifying and characterizing the most prevalent conformations within an ensemble. Moreover, ELViM facilitates the comparative analysis of ensembles obtained under diverse conditions, thereby providing a powerful tool for investigating the functional mechanisms of IDPs.
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Affiliation(s)
- Rafael G Viegas
- Federal Institute of Education, Science and Technology of São Paulo (IFSP), Catanduva, São Paulo 15.808-305, Brazil
- Department of Physics, São Paulo State University (UNESP), Institute of Biosciences, Humanities and Exact Sciences, São José do Rio Preto, São Paulo 15054-000, Brazil
| | - Ingrid B S Martins
- Department of Physics, São Paulo State University (UNESP), Institute of Biosciences, Humanities and Exact Sciences, São José do Rio Preto, São Paulo 15054-000, Brazil
| | - Vitor B P Leite
- Department of Physics, São Paulo State University (UNESP), Institute of Biosciences, Humanities and Exact Sciences, São José do Rio Preto, São Paulo 15054-000, Brazil
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2
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Jeschke G. Protein ensemble modeling and analysis with MMMx. Protein Sci 2024; 33:e4906. [PMID: 38358120 PMCID: PMC10868441 DOI: 10.1002/pro.4906] [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: 08/04/2023] [Revised: 01/04/2024] [Accepted: 01/06/2024] [Indexed: 02/16/2024]
Abstract
Proteins, especially of eukaryotes, often have disordered domains and may contain multiple folded domains whose relative spatial arrangement is distributed. The MMMx ensemble modeling and analysis toolbox (https://github.com/gjeschke/MMMx) can support the design of experiments to characterize the distributed structure of such proteins, starting from AlphaFold2 predictions or folded domain structures. Weak order can be analyzed with reference to a random coil model or to peptide chains that match the residue-specific Ramachandran angle distribution of the loop regions and are otherwise unrestrained. The deviation of the mean square end-to-end distance of chain sections from their average over sections of the same sequence length reveals localized compaction or expansion of the chain. The shape sampled by disordered chains is visualized by superposition in the principal axes frame of their inertia tensor. Ensembles of different sizes and with weighted conformers can be compared based on a similarity parameter that abstracts from the ensemble width.
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Affiliation(s)
- Gunnar Jeschke
- Department of Chemistry and Applied BiosciencesETH ZürichZürichSwitzerland
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3
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Ghafouri H, Lazar T, Del Conte A, Tenorio Ku LG, Tompa P, Tosatto SCE, Monzon AM. PED in 2024: improving the community deposition of structural ensembles for intrinsically disordered proteins. Nucleic Acids Res 2024; 52:D536-D544. [PMID: 37904608 PMCID: PMC10767937 DOI: 10.1093/nar/gkad947] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2023] [Revised: 10/10/2023] [Accepted: 10/13/2023] [Indexed: 11/01/2023] Open
Abstract
The Protein Ensemble Database (PED) (URL: https://proteinensemble.org) is the primary resource for depositing structural ensembles of intrinsically disordered proteins. This updated version of PED reflects advancements in the field, denoting a continual expansion with a total of 461 entries and 538 ensembles, including those generated without explicit experimental data through novel machine learning (ML) techniques. With this significant increment in the number of ensembles, a few yet-unprecedented new entries entered the database, including those also determined or refined by electron paramagnetic resonance or circular dichroism data. In addition, PED was enriched with several new features, including a novel deposition service, improved user interface, new database cross-referencing options and integration with the 3D-Beacons network-all representing efforts to improve the FAIRness of the database. Foreseeably, PED will keep growing in size and expanding with new types of ensembles generated by accurate and fast ML-based generative models and coarse-grained simulations. Therefore, among future efforts, priority will be given to further develop the database to be compatible with ensembles modeled at a coarse-grained level.
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Affiliation(s)
| | - Tamas Lazar
- VIB-VUB Center for Structural Biology, Vlaams Instituut voor Biotechnologie (VIB), Brussels, Belgium
- Structural Biology Brussels, Department of Bioengineering, Vrije Universiteit Brussel (VUB), Brussels, Belgium
| | - Alessio Del Conte
- Department of Biomedical Sciences, University of Padova, Padova, Italy
| | | | - Peter Tompa
- VIB-VUB Center for Structural Biology, Vlaams Instituut voor Biotechnologie (VIB), Brussels, Belgium
- Structural Biology Brussels, Department of Bioengineering, Vrije Universiteit Brussel (VUB), Brussels, Belgium
- Institute of Enzymology, Research Centre for Natural Sciences (RCNS), Budapest, Hungary
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4
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Maiti P, Nand M, Mathpal S, Wahab S, Kuniyal JC, Sharma P, Joshi T, Ramakrishnan MA, Chandra S. Potent multi-target natural inhibitors against SARS-CoV-2 from medicinal plants of the Himalaya: a discovery from hybrid machine learning, chemoinformatics, and simulation assisted screening. J Biomol Struct Dyn 2023:1-14. [PMID: 37732349 DOI: 10.1080/07391102.2023.2257333] [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: 02/03/2023] [Accepted: 09/05/2023] [Indexed: 09/22/2023]
Abstract
The emergence and immune evasion ability of SARS-CoV-2 Omicron strains, mainly BA.5.2 and BF.7 and other variants of concern have raised global apprehensions. With this context, the discovery of multitarget inhibitors may be proven more comprehensive paradigm than its one-drug-to-one target counterpart. In the current study, a library of 271 phytochemicals from 25 medicinal plants from the Indian Himalayan Region has been virtually screened against SARS-CoV-2 by targeting nine virus proteins, viz., papain-like protease, main protease, nsp12, helicase, nsp14, nsp15, nsp16, envelope, and nucleocapsid for screening of a multi-target inhibitor against the viral replication. Initially, 94 phytochemicals were screened by a hybrid machine learning model constructed by combining 6 confirmatory bioassays against SARS-CoV-2 replication using an instance-based learner lazy k-nearest neighbour classifier. Further, 25 screened compounds with excellent drug-like properties were subjected to molecular docking. The phytochemical Cepharadione A from the plant Piper longum showed binding potential against four proteins with the highest binding energy of -10.90 kcal/mol. The compound has acceptable absorption, distribution, metabolism, excretion, and toxicity properties and exhibits stable binding behaviour in terms of root mean square deviation (0.068 ± 0.05 nm), root-mean-square fluctuation, hydrogen bonds, solvent accessible surface area (83.88-161.89 nm2), and molecular mechanics Poisson-Boltzmann surface area during molecular dynamics simulation of 200 ns with selected target proteins. Concerning the utility of natural compounds in the therapeutics formulation, Cepharadione A could be further investigated as a remarkable lead candidate for the development of therapeutic drugs against SARS-CoV-2.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Priyanka Maiti
- G.B. Pant National Institute of Himalayan Environment (NIHE), Almora, India
| | - Mahesha Nand
- G.B. Pant National Institute of Himalayan Environment (NIHE), Almora, India
| | - Shalini Mathpal
- Department of Biotechnology, Kumaun University, Nainital, India
| | - Shadma Wahab
- Department of Pharmacognosy, College of Pharmacy, King Khalid University, Abha, Saudi Arabia
| | | | - Priyanka Sharma
- Department of Botany, D.S.B. Campus, Kumaun University, Nainital, India
| | - Tushar Joshi
- Department of Biotechnology, Kumaun University, Nainital, India
| | | | - Subhash Chandra
- Department of Botany, Soban Singh Jeena University, Almora, India
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5
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Tsangaris TE, Smyth S, Gomes GNW, Liu ZH, Milchberg M, Bah A, Wasney GA, Forman-Kay JD, Gradinaru CC. Delineating Structural Propensities of the 4E-BP2 Protein via Integrative Modeling and Clustering. J Phys Chem B 2023; 127:7472-7486. [PMID: 37595014 PMCID: PMC10858721 DOI: 10.1021/acs.jpcb.3c04052] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/20/2023]
Abstract
The intrinsically disordered 4E-BP2 protein regulates mRNA cap-dependent translation through interaction with the predominantly folded eukaryotic initiation factor 4E (eIF4E). Phosphorylation of 4E-BP2 dramatically reduces the level of eIF4E binding, in part by stabilizing a binding-incompatible folded domain. Here, we used a Rosetta-based sampling algorithm optimized for IDRs to generate initial ensembles for two phospho forms of 4E-BP2, non- and 5-fold phosphorylated (NP and 5P, respectively), with the 5P folded domain flanked by N- and C-terminal IDRs (N-IDR and C-IDR, respectively). We then applied an integrative Bayesian approach to obtain NP and 5P conformational ensembles that agree with experimental data from nuclear magnetic resonance, small-angle X-ray scattering, and single-molecule Förster resonance energy transfer (smFRET). For the NP state, inter-residue distance scaling and 2D maps revealed the role of charge segregation and pi interactions in driving contacts between distal regions of the chain (∼70 residues apart). The 5P ensemble shows prominent contacts of the N-IDR region with the two phosphosites in the folded domain, pT37 and pT46, and, to a lesser extent, delocalized interactions with the C-IDR region. Agglomerative hierarchical clustering led to partitioning of each of the two ensembles into four clusters with different global dimensions and contact maps. This helped delineate an NP cluster that, based on our smFRET data, is compatible with the eIF4E-bound state. 5P clusters were differentiated by interactions of C-IDR with the folded domain and of the N-IDR with the two phosphosites in the folded domain. Our study provides both a better visualization of fundamental structural poses of 4E-BP2 and a set of falsifiable insights on intrachain interactions that bias folding and binding of this protein.
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Affiliation(s)
- Thomas E Tsangaris
- Department of Physics, University of Toronto, Toronto, Ontario M5S 1A7, Canada
- Department of Chemical & Physical Sciences, University of Toronto Mississauga, Mississauga, Ontario L5L 1C6, Canada
| | - Spencer Smyth
- Department of Physics, University of Toronto, Toronto, Ontario M5S 1A7, Canada
- Department of Chemical & Physical Sciences, University of Toronto Mississauga, Mississauga, Ontario L5L 1C6, Canada
| | - Gregory-Neal W Gomes
- Department of Physics, University of Toronto, Toronto, Ontario M5S 1A7, Canada
- Department of Chemical & Physical Sciences, University of Toronto Mississauga, Mississauga, Ontario L5L 1C6, Canada
| | - Zi Hao Liu
- Program in Molecular Medicine, Hospital for Sick Children, Toronto, Ontario M5G 0A4, Canada
- Department of Biochemistry, University of Toronto, Toronto, Ontario M5S 1A8, Canada
| | - Moses Milchberg
- Program in Molecular Medicine, Hospital for Sick Children, Toronto, Ontario M5G 0A4, Canada
- Department of Biochemistry, University of Toronto, Toronto, Ontario M5S 1A8, Canada
| | - Alaji Bah
- Program in Molecular Medicine, Hospital for Sick Children, Toronto, Ontario M5G 0A4, Canada
- Department of Biochemistry, University of Toronto, Toronto, Ontario M5S 1A8, Canada
| | - Gregory A Wasney
- Peter Gilgan Centre for Research and Learning, Hospital for Sick Children, Toronto, Ontario M5G 0A4, Canada
| | - Julie D Forman-Kay
- Program in Molecular Medicine, Hospital for Sick Children, Toronto, Ontario M5G 0A4, Canada
- Department of Biochemistry, University of Toronto, Toronto, Ontario M5S 1A8, Canada
| | - Claudiu C Gradinaru
- Department of Physics, University of Toronto, Toronto, Ontario M5S 1A7, Canada
- Department of Chemical & Physical Sciences, University of Toronto Mississauga, Mississauga, Ontario L5L 1C6, Canada
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6
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Aina A, Hsueh SCC, Plotkin SS. PROTHON: A Local Order Parameter-Based Method for Efficient Comparison of Protein Ensembles. J Chem Inf Model 2023. [PMID: 37178169 DOI: 10.1021/acs.jcim.3c00145] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/15/2023]
Abstract
The comparison of protein conformational ensembles is of central importance in structural biology. However, there are few computational methods for ensemble comparison, and those that are readily available, such as ENCORE, utilize methods that are sufficiently computationally expensive to be prohibitive for large ensembles. Here, a new method is presented for efficient representation and comparison of protein conformational ensembles. The method is based on the representation of a protein ensemble as a vector of probability distribution functions (pdfs), with each pdf representing the distribution of a local structural property such as the number of contacts between Cβ atoms. Dissimilarity between two conformational ensembles is quantified by the Jensen-Shannon distance between the corresponding set of probability distribution functions. The method is validated for conformational ensembles generated by molecular dynamics simulations of ubiquitin, as well as experimentally derived conformational ensembles of a 130 amino acid truncated form of human tau protein. In the ubiquitin ensemble data set, the method was up to 88 times faster than the existing ENCORE software, while simultaneously utilizing 48 times fewer computing cores. We make the method available as a Python package, called PROTHON, and provide a GitHub page with the Python source code at https://github.com/PlotkinLab/Prothon.
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Affiliation(s)
- Adekunle Aina
- Department of Physics and Astronomy, The University of British Columbia, Vancouver, BC V6T 1Z1, Canada
| | - Shawn C C Hsueh
- Department of Physics and Astronomy, The University of British Columbia, Vancouver, BC V6T 1Z1, Canada
| | - Steven S Plotkin
- Department of Physics and Astronomy, The University of British Columbia, Vancouver, BC V6T 1Z1, Canada
- Genome Science and Technology Program, The University of British Columbia, Vancouver, BC V6T 1Z1, Canada
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7
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González-Delgado J, Sagar A, Zanon C, Lindorff-Larsen K, Bernadó P, Neuvial P, Cortés J. WASCO: A Wasserstein-based statistical tool to compare conformational ensembles of intrinsically disordered proteins. J Mol Biol 2023:168053. [PMID: 36934808 DOI: 10.1016/j.jmb.2023.168053] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2022] [Revised: 02/10/2023] [Accepted: 03/14/2023] [Indexed: 03/19/2023]
Abstract
The structural investigation of intrinsically disordered proteins (IDPs) requires ensemble models describing the diversity of the conformational states of the molecule. Due to their probabilistic nature, there is a need for new paradigms that understand and treat IDPs from a purely statistical point of view, considering their conformational ensembles as well-defined probability distributions. In this work, we define a conformational ensemble as an ordered set of probability distributions and provide a suitable metric to detect differences between two given ensembles at the residue level, both locally and globally. The underlying geometry of the conformational space is properly integrated, one ensemble being characterized by a set of probability distributions supported on the three-dimensional Euclidean space (for global-scale comparisons) and on the two-dimensional flat torus (for local-scale comparisons). The inherent uncertainty of the data is also taken into account to provide finer estimations of the differences between ensembles. Additionally, an overall distance between ensembles is defined from the differences at the residue level. We illustrate the interest of the approach with several examples of applications for the comparison of conformational ensembles: (i) produced from molecular dynamics (MD) simulations using different force fields, and (ii) before and after refinement with experimental data. We also show the usefulness of the method to assess the convergence of MD simulations, and discuss other potential applications such as in machine-learning-based approaches. The numerical tool has been implemented in Python through easy-to-use Jupyter Notebooks available at https://gitlab.laas.fr/moma/WASCO.
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Affiliation(s)
- Javier González-Delgado
- LAAS-CNRS, Université de Toulouse, CNRS, Toulouse, France; Institut de Mathématiques de Toulouse, Université de Toulouse, CNRS, Toulouse, France
| | - Amin Sagar
- Centre de Biologie Structurale, Université de Montpellier, INSERM, CNRS, Montpellier, France
| | | | - Kresten Lindorff-Larsen
- The Linderstrøm-Lang Centre for Protein Science, Department of Biology, University of Copenhagen, Denmark
| | - Pau Bernadó
- Centre de Biologie Structurale, Université de Montpellier, INSERM, CNRS, Montpellier, France
| | - Pierre Neuvial
- Institut de Mathématiques de Toulouse, Université de Toulouse, CNRS, Toulouse, France
| | - Juan Cortés
- LAAS-CNRS, Université de Toulouse, CNRS, Toulouse, France
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8
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Exploring the Potential of Black Soldier Fly Larval Proteins as Bioactive Peptide Sources through in Silico Gastrointestinal Proteolysis: A Cheminformatic Investigation. Catalysts 2023. [DOI: 10.3390/catal13030605] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/19/2023] Open
Abstract
Despite their potential as a protein source for human consumption, the health benefits of black soldier fly larvae (BSFL) proteins following human gastrointestinal (GI) digestion are poorly understood. This computational study explored the potential of BSFL proteins to release health-promoting peptides after human GI digestion. Twenty-six proteins were virtually proteolyzed with GI proteases. The resultant peptides were screened for high GI absorption and non-toxicity. Shortlisted peptides were searched against the BIOPEP-UWM and Scopus databases to identify their bioactivities. The potential of the peptides as inhibitors of myeloperoxidase (MPO), NADPH oxidase (NOX), and xanthine oxidase (XO), as well as a disruptor of Keap1–Nrf2 protein–protein interaction, were predicted using molecular docking and dynamics simulation. Our results revealed that about 95% of the 5218 fragments generated from the proteolysis of BSFL proteins came from muscle proteins. Dipeptides comprised the largest group (about 25%) of fragments arising from each muscular protein. Screening of 1994 di- and tripeptides using SwissADME and STopTox tools revealed 65 unique sequences with high GI absorption and non-toxicity. A search of the databases identified 16 antioxidant peptides, 14 anti-angiotensin-converting enzyme peptides, and 17 anti-dipeptidyl peptidase IV peptides among these sequences. Results from molecular docking and dynamic simulation suggest that the dipeptide DF has the potential to inhibit Keap1–Nrf2 interaction and interact with MPO within a short time frame, whereas the dipeptide TF shows promise as an XO inhibitor. BSFL peptides were likely weak NOX inhibitors. Our in silico results suggest that upon GI digestion, BSFL proteins may yield high-GI-absorbed and non-toxic peptides with potential health benefits. This study is the first to investigate the bioactivity of peptides liberated from BSFL proteins following human GI digestion. Our findings provide a basis for further investigations into the potential use of BSFL proteins as a functional food ingredient with significant health benefits.
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9
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Wittung-Stafshede P. Another pearl in the "copper-transport" necklace. Biophys J 2021; 120:4305-4306. [PMID: 34499850 DOI: 10.1016/j.bpj.2021.08.030] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2021] [Revised: 08/24/2021] [Accepted: 08/25/2021] [Indexed: 10/20/2022] Open
Affiliation(s)
- Pernilla Wittung-Stafshede
- Department of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg, Sweden.
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10
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Sacquin-Mora S, Prévost C. When Order Meets Disorder: Modeling and Function of the Protein Interface in Fuzzy Complexes. Biomolecules 2021; 11:1529. [PMID: 34680162 PMCID: PMC8533853 DOI: 10.3390/biom11101529] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2021] [Revised: 10/11/2021] [Accepted: 10/12/2021] [Indexed: 11/30/2022] Open
Abstract
The degree of proteins structural organization ranges from highly structured, compact folding to intrinsic disorder, where each degree of self-organization corresponds to specific functions: well-organized structural motifs in enzymes offer a proper environment for precisely positioned functional groups to participate in catalytic reactions; at the other end of the self-organization spectrum, intrinsically disordered proteins act as binding hubs via the formation of multiple, transient and often non-specific interactions. This review focusses on cases where structurally organized proteins or domains associate with highly disordered protein chains, leading to the formation of interfaces with varying degrees of fuzziness. We present a review of the computational methods developed to provide us with information on such fuzzy interfaces, and how they integrate experimental information. The discussion focusses on two specific cases, microtubules and homologous recombination nucleoprotein filaments, where a network of intrinsically disordered tails exerts regulatory function in recruiting partner macromolecules, proteins or DNA and tuning the atomic level association. Notably, we show how computational approaches such as molecular dynamics simulations can bring new knowledge to help bridging the gap between experimental analysis, that mostly concerns ensemble properties, and the behavior of individual disordered protein chains that contribute to regulation functions.
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Affiliation(s)
- Sophie Sacquin-Mora
- CNRS, Laboratoire de Biochimie Théorique, UPR9080, Université de Paris, 13 Rue Pierre et Marie Curie, 75005 Paris, France;
- Institut de Biologie Physico-Chimique, Fondation Edmond de Rothschild, PSL Research University, 75006 Paris, France
| | - Chantal Prévost
- CNRS, Laboratoire de Biochimie Théorique, UPR9080, Université de Paris, 13 Rue Pierre et Marie Curie, 75005 Paris, France;
- Institut de Biologie Physico-Chimique, Fondation Edmond de Rothschild, PSL Research University, 75006 Paris, France
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11
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Quaglia F, Lazar T, Hatos A, Tompa P, Piovesan D, Tosatto SCE. Exploring Curated Conformational Ensembles of Intrinsically Disordered Proteins in the Protein Ensemble Database. Curr Protoc 2021; 1:e192. [PMID: 34252246 DOI: 10.1002/cpz1.192] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
The Protein Ensemble Database (PED; https://proteinensemble.org/) is the major repository of conformational ensembles of intrinsically disordered proteins (IDPs). Conformational ensembles of IDPs are primarily provided by their authors or occasionally collected from literature, and are subsequently deposited in PED along with the corresponding structured, manually curated metadata. The modeling of conformational ensembles usually relies on experimental data from small-angle X-ray scattering (SAXS), fluorescence resonance energy transfer (FRET), NMR spectroscopy, and molecular dynamics (MD) simulations, or a combination of these techniques. The growing number of scientific studies based on these data, along with the astounding and swift progress in the field of protein intrinsic disorder, has required a significant update and upgrade of PED, first published in 2014. To this end, the database was entirely renewed in 2020 and now has a dedicated team of biocurators providing manually curated descriptions of the methods and conditions applied to generate the conformational ensembles and for checking consistency of the data. Here, we present a detailed description on how to explore PED with its protein pages and experimental pages, and how to interpret entries of conformational ensembles. We describe how to efficiently search conformational ensembles deposited in PED by means of its web interface and API. We demonstrate how to make sense of the PED protein page and its associated experimental entry pages with reference to the yeast Sic1 use case. © 2021 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: Performing a search in PED Support Protocol 1: Programmatic access with the PED API Basic Protocol 2: Interpreting the protein page and the experimental entry page-the Sic1 use case Support Protocol 2: Downloading options Support Protocol 3: Understanding the validation report-the Sic1 use case Basic Protocol 3: Submitting new conformational ensembles to PED Basic Protocol 4: Providing feedback in PED.
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Affiliation(s)
- Federica Quaglia
- Department of Biomedical Sciences, University of Padova, Padova, Italy.,Institute of Biomembranes, Bioenergetics and Molecular Biotechnologies, National Research Council (CNR-IBIOM), Bari, Italy
| | - Tamas Lazar
- Structural Biology Brussels, Vrije Universiteit Brussel, Brussels, Belgium.,VIB-VUB Center for Structural Biology, Brussels, Belgium
| | - András Hatos
- Department of Biomedical Sciences, University of Padova, Padova, Italy
| | - Peter Tompa
- Structural Biology Brussels, Vrije Universiteit Brussel, Brussels, Belgium.,VIB-VUB Center for Structural Biology, Brussels, Belgium.,Institute of Enzymology, Research Centre for Natural Sciences, Budapest, Hungary
| | - Damiano Piovesan
- Department of Biomedical Sciences, University of Padova, Padova, Italy
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12
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Argudo PG, Giner-Casares JJ. Folding and self-assembly of short intrinsically disordered peptides and protein regions. NANOSCALE ADVANCES 2021; 3:1789-1812. [PMID: 36133101 PMCID: PMC9417027 DOI: 10.1039/d0na00941e] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/10/2020] [Accepted: 01/17/2021] [Indexed: 05/15/2023]
Abstract
Proteins and peptide fragments are highly relevant building blocks in self-assembly for nanostructures with plenty of applications. Intrinsically disordered proteins (IDPs) and protein regions (IDRs) are defined by the absence of a well-defined secondary structure, yet IDPs/IDRs show a significant biological activity. Experimental techniques and computational modelling procedures for the characterization of IDPs/IDRs are discussed. Directed self-assembly of IDPs/IDRs allows reaching a large variety of nanostructures. Hybrid materials based on the derivatives of IDPs/IDRs show a promising performance as alternative biocides and nanodrugs. Cell mimicking, in vivo compartmentalization, and bone regeneration are demonstrated for IDPs/IDRs in biotechnological applications. The exciting possibilities of IDPs/IDRs in nanotechnology with relevant biological applications are shown.
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Affiliation(s)
- Pablo G Argudo
- Université de Bordeaux, CNRS, Bordeaux INP, LCPO 16 Avenue Pey-Berland 33600 Pessac France
| | - Juan J Giner-Casares
- Departamento de Química Física y T. Aplicada, Instituto Universitario de Nanoquímica IUNAN, Facultad de Ciencias, Universidad de Córdoba (UCO) Campus de Rabanales, Ed. Marie Curie E-14071 Córdoba Spain
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13
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Lazar T, Martínez-Pérez E, Quaglia F, Hatos A, Chemes L, Iserte JA, Méndez NA, Garrone NA, Saldaño T, Marchetti J, Rueda A, Bernadó P, Blackledge M, Cordeiro TN, Fagerberg E, Forman-Kay JD, Fornasari M, Gibson TJ, Gomes GNW, Gradinaru C, Head-Gordon T, Jensen MR, Lemke E, Longhi S, Marino-Buslje C, Minervini G, Mittag T, Monzon A, Pappu RV, Parisi G, Ricard-Blum S, Ruff KM, Salladini E, Skepö M, Svergun D, Vallet S, Varadi M, Tompa P, Tosatto SCE, Piovesan D. PED in 2021: a major update of the protein ensemble database for intrinsically disordered proteins. Nucleic Acids Res 2021; 49:D404-D411. [PMID: 33305318 PMCID: PMC7778965 DOI: 10.1093/nar/gkaa1021] [Citation(s) in RCA: 71] [Impact Index Per Article: 23.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2020] [Revised: 10/13/2020] [Accepted: 12/08/2020] [Indexed: 12/21/2022] Open
Abstract
The Protein Ensemble Database (PED) (https://proteinensemble.org), which holds structural ensembles of intrinsically disordered proteins (IDPs), has been significantly updated and upgraded since its last release in 2016. The new version, PED 4.0, has been completely redesigned and reimplemented with cutting-edge technology and now holds about six times more data (162 versus 24 entries and 242 versus 60 structural ensembles) and a broader representation of state of the art ensemble generation methods than the previous version. The database has a completely renewed graphical interface with an interactive feature viewer for region-based annotations, and provides a series of descriptors of the qualitative and quantitative properties of the ensembles. High quality of the data is guaranteed by a new submission process, which combines both automatic and manual evaluation steps. A team of biocurators integrate structured metadata describing the ensemble generation methodology, experimental constraints and conditions. A new search engine allows the user to build advanced queries and search all entry fields including cross-references to IDP-related resources such as DisProt, MobiDB, BMRB and SASBDB. We expect that the renewed PED will be useful for researchers interested in the atomic-level understanding of IDP function, and promote the rational, structure-based design of IDP-targeting drugs.
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Affiliation(s)
- Tamas Lazar
- VIB-VUB Center for Structural Biology, Flanders Institute for Biotechnology, Brussels 1050, Belgium
- Structural Biology Brussels, Bioengineering Sciences Department, Vrije Universiteit Brussel, Brussels 1050, Belgium
| | - Elizabeth Martínez-Pérez
- Bioinformatics Unit, Fundación Instituto Leloir, Buenos Aires, C1405BWE, Argentina
- Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg 69117, Germany
| | - Federica Quaglia
- Dept. of Biomedical Sciences, University of Padua, Padova 35131, Italy
| | - András Hatos
- Dept. of Biomedical Sciences, University of Padua, Padova 35131, Italy
| | - Lucía B Chemes
- Instituto de Investigaciones Biotecnológicas “Dr. Rodolfo A. Ugalde’’, IIB-UNSAM, IIBIO-CONICET, Universidad Nacional de SanMartín, CP1650 San Martín, Buenos Aires, Argentina
| | - Javier A Iserte
- Bioinformatics Unit, Fundación Instituto Leloir, Buenos Aires, C1405BWE, Argentina
| | - Nicolás A Méndez
- Instituto de Investigaciones Biotecnológicas “Dr. Rodolfo A. Ugalde’’, IIB-UNSAM, IIBIO-CONICET, Universidad Nacional de SanMartín, CP1650 San Martín, Buenos Aires, Argentina
| | - Nicolás A Garrone
- Instituto de Investigaciones Biotecnológicas “Dr. Rodolfo A. Ugalde’’, IIB-UNSAM, IIBIO-CONICET, Universidad Nacional de SanMartín, CP1650 San Martín, Buenos Aires, Argentina
| | - Tadeo E Saldaño
- Laboratorio de Química y Biología Computacional, Departamento de Ciencia y Tecnología, Universidad Nacional de Quilmes, Bernal B1876BXD, Buenos Aires, Argentina
| | - Julia Marchetti
- Laboratorio de Química y Biología Computacional, Departamento de Ciencia y Tecnología, Universidad Nacional de Quilmes, Bernal B1876BXD, Buenos Aires, Argentina
| | - Ana Julia Velez Rueda
- Laboratorio de Química y Biología Computacional, Departamento de Ciencia y Tecnología, Universidad Nacional de Quilmes, Bernal B1876BXD, Buenos Aires, Argentina
| | - Pau Bernadó
- Centre de Biochimie Structurale (CBS), CNRS, INSERM, University of Montpellier, Montpellier 34090, France
| | | | - Tiago N Cordeiro
- Centre de Biochimie Structurale (CBS), CNRS, INSERM, University of Montpellier, Montpellier 34090, France
- Instituto de Tecnologia Química e Biológica António Xavier, Universidade Nova de Lisboa, Av. da República, Oeiras 2780-157, Portugal
| | - Eric Fagerberg
- Theoretical Chemistry, Lund University, Lund, POB 124, SE-221 00, Sweden
| | - Julie D Forman-Kay
- Molecular Medicine Program, Hospital for Sick Children, Toronto, M5G 1X8, Ontario, Canada
- Department of Biochemistry, University of Toronto, Toronto, M5S 1A8, Ontario, Canada
| | - Maria S Fornasari
- Laboratorio de Química y Biología Computacional, Departamento de Ciencia y Tecnología, Universidad Nacional de Quilmes, Bernal B1876BXD, Buenos Aires, Argentina
| | - Toby J Gibson
- Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg 69117, Germany
| | - Gregory-Neal W Gomes
- Department of Physics, University of Toronto, Toronto, M5S 1A7, Ontario, Canada
- Department of Chemical and Physical Sciences, University of Toronto Mississauga, Mississauga, L5L 1C6, Ontario, Canada
| | - Claudiu C Gradinaru
- Department of Physics, University of Toronto, Toronto, M5S 1A7, Ontario, Canada
- Department of Chemical and Physical Sciences, University of Toronto Mississauga, Mississauga, L5L 1C6, Ontario, Canada
| | - Teresa Head-Gordon
- Departments of Chemistry, Bioengineering, Chemical and Biomolecular Engineering University of California, Berkeley, CA 94720, USA
| | | | - Edward A Lemke
- Biocentre, Johannes Gutenberg-University Mainz, Mainz 55128, Germany
- Institute of Molecular Biology, Mainz 55128, Germany
| | - Sonia Longhi
- Aix-Marseille University, CNRS, Architecture et Fonction des Macromolécules Biologiques (AFMB), Marseille 13288, France
| | | | | | - Tanja Mittag
- Department of Structural Biology, St. Jude Children's Research Hospital, Memphis, TN 38105, USA
| | | | - Rohit V Pappu
- Department of Biomedical Engineering, Center for Science & Engineering of Living Systems (CSELS), Washington University in St. Louis, MO 63130, USA
| | - Gustavo Parisi
- Laboratorio de Química y Biología Computacional, Departamento de Ciencia y Tecnología, Universidad Nacional de Quilmes, Bernal B1876BXD, Buenos Aires, Argentina
| | - Sylvie Ricard-Blum
- Univ Lyon, University Claude Bernard Lyon 1, CNRS, INSA Lyon, CPE, Institute of Molecular and Supramolecular Chemistry and Biochemistry (ICBMS), UMR 5246, Villeurbanne, 69629 Lyon Cedex 07, France
| | - Kiersten M Ruff
- Department of Biomedical Engineering, Center for Science & Engineering of Living Systems (CSELS), Washington University in St. Louis, MO 63130, USA
| | - Edoardo Salladini
- Aix-Marseille University, CNRS, Architecture et Fonction des Macromolécules Biologiques (AFMB), Marseille 13288, France
| | - Marie Skepö
- Theoretical Chemistry, Lund University, Lund, POB 124, SE-221 00, Sweden
- LINXS - Lund Institute of Advanced Neutron and X-ray Science, Lund 223 70, Sweden
| | - Dmitri Svergun
- European Molecular Biology Laboratory, Hamburg Unit, Hamburg 22607, Germany
| | - Sylvain D Vallet
- Univ Lyon, University Claude Bernard Lyon 1, CNRS, INSA Lyon, CPE, Institute of Molecular and Supramolecular Chemistry and Biochemistry (ICBMS), UMR 5246, Villeurbanne, 69629 Lyon Cedex 07, France
| | - Mihaly Varadi
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, CB10 1SD, UK
| | - Peter Tompa
- To whom correspondence should be addressed. Tel +32 473 785386;
| | - Silvio C E Tosatto
- Correspondence may also be addressed to Silvio C. E. Tosatto. Tel: +39 049 827 6269;
| | - Damiano Piovesan
- Dept. of Biomedical Sciences, University of Padua, Padova 35131, Italy
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