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Yang YX, Wang P, Zhu BT. Relative importance of interface and surface areas in protein-protein binding affinity prediction: A machine learning analysis based on linear regression and artificial neural network. Biophys Chem 2022; 283:106762. [DOI: 10.1016/j.bpc.2022.106762] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2021] [Revised: 01/11/2022] [Accepted: 01/14/2022] [Indexed: 11/02/2022]
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Meseguer A, Bota P, Fernández-Fuentes N, Oliva B. Prediction of Protein-Protein Binding Affinities from Unbound Protein Structures. Methods Mol Biol 2022; 2385:335-351. [PMID: 34888728 DOI: 10.1007/978-1-0716-1767-0_16] [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] [Indexed: 06/13/2023]
Abstract
Proteins are the workhorses of cells to carry out sophisticated and complex cellular processes. Such processes require a coordinated and regulated interactions between proteins that are both time and location specific. The strength, or binding affinity, of protein-protein interactions ranges between the micro- and the nanomolar association constant, often dictating the molecular mechanisms underlying the interaction and the longevity of the complex, i.e., transient or permanent. In consequence, there is a need to quantify the strength of protein-protein interactions for biological, biomedical, and biotechnological applications. While experimental methods are labor intensive and costly, computational ones are useful tools to predict the affinity of protein-protein interactions. In this chapter, we review the methods developed by us to address this question. We briefly present two methods to comprehend the structure of the protein complex derived by either comparative modeling or docking. Then we introduce BADOCK, a method to predict the binding energy without requiring the structure of the protein complex, thus overcoming one of the major limitations of structure-based methods for the prediction of binding affinity. BADOCK utilizes the structure of unbound proteins and the protein docking sampling space to predict protein-protein binding affinities. We present step-by-step protocols to utilize these methods, describing the inputs and potential pitfalls as well as their respective strengths and limitations.
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Affiliation(s)
- Alberto Meseguer
- Structural Bioinformatics Lab (GRIB-IMIM), Department of Experimental and Health Science, University Pompeu Fabra, Barcelona, Catalonia, Spain
| | - Patricia Bota
- Structural Bioinformatics Lab (GRIB-IMIM), Department of Experimental and Health Science, University Pompeu Fabra, Barcelona, Catalonia, Spain
- Department of Biosciences, U Science Tech, Universitat de Vic-Universitat Central de Catalunya, Catalonia, Spain
| | - Narcis Fernández-Fuentes
- Department of Biosciences, U Science Tech, Universitat de Vic-Universitat Central de Catalunya, Catalonia, Spain
- Institute of Biological, Environmental and Rural Sciences, Aberystwyth University, Aberystwyth, UK
| | - Baldo Oliva
- Structural Bioinformatics Lab (GRIB-IMIM), Department of Experimental and Health Science, University Pompeu Fabra, Barcelona, Catalonia, Spain.
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Characterization of the AGR2 interactome uncovers new players of Protein Disulfide Isomerase network in cancer cells. Mol Cell Proteomics 2021; 21:100188. [PMID: 34929376 PMCID: PMC8816719 DOI: 10.1016/j.mcpro.2021.100188] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2021] [Revised: 12/03/2021] [Accepted: 12/14/2021] [Indexed: 12/03/2022] Open
Abstract
Anterior gradient 2 (AGR2) is an endoplasmic reticulum (ER)-resident protein disulfide isomerase (PDI) known to be overexpressed in many human epithelial cancers and is involved in cell migration, cellular transformation, angiogenesis, and metastasis. This protein inhibits the activity of the tumor suppressor p53, and its expression levels can be used to predict cancer patient outcome. However, the precise network of AGR2-interacting partners and clients remains to be fully characterized. Herein, we used label-free quantification and also stable isotope labeling with amino acids in cell culture–based LC–MS/MS analyses to identify proteins interacting with AGR2. Functional annotation confirmed that AGR2 and its interaction partners are associated with processes in the ER that maintain intracellular metabolic homeostasis and participate in the unfolded protein response, including those associated with changes in cellular metabolism, energy, and redox states in response to ER stress. As a proof of concept, the interaction between AGR2 and PDIA3, another ER-resident PDI, was studied in more detail. Pathway analysis revealed that AGR2 and PDIA3 play roles in protein folding in ER, including post-translational modification and in cellular response to stress. We confirmed the AGR2–PDIA3 complex formation in cancer cells, which was enhanced in response to ER stress. Accordingly, molecular docking characterized potential quaternary structure of this complex; however, it remains to be elucidated whether AGR2 rather contributes to PDIA3 maturation in ER, the complex directly acts in cellular signaling, or mediates AGR2 secretion. Our study provides a comprehensive insight into the protein–protein interaction network of AGR2 by identifying functionally relevant proteins and related cellular and biochemical pathways associated with the role of AGR2 in cancer cells. LC–MS/MS analysis of AGR2-interacting proteins in T47D and H1299 cells. About 15 overlapping AGR2 interactors, including PDIA3 and PDIA6, were identified in both cell lines. PDI family members represent the key part of the network. AGR2–PDIA3 interaction is even stronger under ER stress. AGR2–PDIA3 complex formation supports extracellular secretion of AGR2.
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Aguirre-Plans J, Meseguer A, Molina-Fernandez R, Marín-López MA, Jumde G, Casanova K, Bonet J, Fornes O, Fernandez-Fuentes N, Oliva B. SPServer: split-statistical potentials for the analysis of protein structures and protein-protein interactions. BMC Bioinformatics 2021; 22:4. [PMID: 33407073 PMCID: PMC7788957 DOI: 10.1186/s12859-020-03770-5] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2020] [Accepted: 09/20/2020] [Indexed: 12/13/2022] Open
Abstract
BACKGROUND Statistical potentials, also named knowledge-based potentials, are scoring functions derived from empirical data that can be used to evaluate the quality of protein folds and protein-protein interaction (PPI) structures. In previous works we decomposed the statistical potentials in different terms, named Split-Statistical Potentials, accounting for the type of amino acid pairs, their hydrophobicity, solvent accessibility and type of secondary structure. These potentials have been successfully used to identify near-native structures in protein structure prediction, rank protein docking poses, and predict PPI binding affinities. RESULTS Here, we present the SPServer, a web server that applies the Split-Statistical Potentials to analyze protein folds and protein interfaces. SPServer provides global scores as well as residue/residue-pair profiles presented as score plots and maps. This level of detail allows users to: (1) identify potentially problematic regions on protein structures; (2) identify disrupting amino acid pairs in protein interfaces; and (3) compare and analyze the quality of tertiary and quaternary structural models. CONCLUSIONS While there are many web servers that provide scoring functions to assess the quality of either protein folds or PPI structures, SPServer integrates both aspects in a unique easy-to-use web server. Moreover, the server permits to locally assess the quality of the structures and interfaces at a residue level and provides tools to compare the local assessment between structures. SERVER ADDRESS: https://sbi.upf.edu/spserver/ .
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Grants
- BIO2017-85329-R (FEDER,UE) Ministerio de Economía, Industria y Competitividad, Gobierno de España
- BIO2017-83591-R(FEDER,UE Ministerio de Economía, Industria y Competitividad, Gobierno de España
- RYC-2015-17519 Ministerio de Economía, Industria y Competitividad, Gobierno de España
- MDM-2014-0370 Ministerio de Economía, Industria y Competitividad, Gobierno de España
- FI Agència de Gestió d'Ajuts Universitaris i de Recerca
- 2017 SGR 01020 Agència de Gestió d'Ajuts Universitaris i de Recerca
- PT13/0001/0023 Instituto de Salud Carlos III
- Agència de Gestió d’Ajuts Universitaris i de Recerca
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Affiliation(s)
- Joaquim Aguirre-Plans
- Structural Bioinformatics Lab, Department of Experimental and Health Science, Universitat Pompeu Fabra, 08003, Barcelona, Catalonia, Spain
| | - Alberto Meseguer
- Structural Bioinformatics Lab, Department of Experimental and Health Science, Universitat Pompeu Fabra, 08003, Barcelona, Catalonia, Spain
| | - Ruben Molina-Fernandez
- Structural Bioinformatics Lab, Department of Experimental and Health Science, Universitat Pompeu Fabra, 08003, Barcelona, Catalonia, Spain
| | - Manuel Alejandro Marín-López
- Structural Bioinformatics Lab, Department of Experimental and Health Science, Universitat Pompeu Fabra, 08003, Barcelona, Catalonia, Spain
| | - Gaurav Jumde
- Structural Bioinformatics Lab, Department of Experimental and Health Science, Universitat Pompeu Fabra, 08003, Barcelona, Catalonia, Spain
| | - Kevin Casanova
- Structural Bioinformatics Lab, Department of Experimental and Health Science, Universitat Pompeu Fabra, 08003, Barcelona, Catalonia, Spain
| | - Jaume Bonet
- Laboratory of Protein Design and Immuno-Enginneering, School of Engineering, Ecole Polytechnique Federale de Lausanne, 1015, Lausanne, Vaud, Switzerland
| | - Oriol Fornes
- Centre for Molecular Medicine and Therapeutics, Department of Medical Genetics, BC Children's Hospital Research Institute, University of British Columbia, Vancouver, BC, V5Z 4H4, Canada
| | - Narcis Fernandez-Fuentes
- Department of Biosciences, U Science Tech, Universitat de Vic-Universitat Central de Catalunya, Vic 08500, Barcelona, Catalonia, Spain
- Institute of Biological, Environ-Mental and Rural Sciences, Aberystwyth University, Aberystwyth, SY23 3EB, UK
| | - Baldo Oliva
- Structural Bioinformatics Lab, Department of Experimental and Health Science, Universitat Pompeu Fabra, 08003, Barcelona, Catalonia, Spain.
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Meseguer A, Dominguez L, Bota PM, Aguirre‐Plans J, Bonet J, Fernandez‐Fuentes N, Oliva B. Using collections of structural models to predict changes of binding affinity caused by mutations in protein-protein interactions. Protein Sci 2020; 29:2112-2130. [PMID: 32797645 PMCID: PMC7513729 DOI: 10.1002/pro.3930] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2020] [Revised: 08/04/2020] [Accepted: 08/05/2020] [Indexed: 12/24/2022]
Abstract
Protein-protein interactions (PPIs) in all the molecular aspects that take place both inside and outside cells. However, determining experimentally the structure and affinity of PPIs is expensive and time consuming. Therefore, the development of computational tools, as a complement to experimental methods, is fundamental. Here, we present a computational suite: MODPIN, to model and predict the changes of binding affinity of PPIs. In this approach we use homology modeling to derive the structures of PPIs and score them using state-of-the-art scoring functions. We explore the conformational space of PPIs by generating not a single structural model but a collection of structural models with different conformations based on several templates. We apply the approach to predict the changes in free energy upon mutations and splicing variants of large datasets of PPIs to statistically quantify the quality and accuracy of the predictions. As an example, we use MODPIN to study the effect of mutations in the interaction between colicin endonuclease 9 and colicin endonuclease 2 immune protein from Escherichia coli. Finally, we have compared our results with other state-of-art methods.
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Affiliation(s)
- Alberto Meseguer
- Structural Bioinformatics Group, Research Programme on Biomedical Informatics, Department of Experimental and Health ScienceUniversitat Pompeu FabraBarcelonaCataloniaSpain
| | - Lluis Dominguez
- Integrative Biomedical Informatics Group (GRIB‐IMIM). Department of Experimental and Life SciencesUniversitat Pompeu FabraBarcelonaCataloniaSpain
| | - Patricia M. Bota
- Structural Bioinformatics Group, Research Programme on Biomedical Informatics, Department of Experimental and Health ScienceUniversitat Pompeu FabraBarcelonaCataloniaSpain
- Department of BiosciencesUniversitat de Vic‐Universitat Central de CatalunyaVicCataloniaSpain
| | - Joaquim Aguirre‐Plans
- Structural Bioinformatics Group, Research Programme on Biomedical Informatics, Department of Experimental and Health ScienceUniversitat Pompeu FabraBarcelonaCataloniaSpain
| | - Jaume Bonet
- Structural Bioinformatics Group, Research Programme on Biomedical Informatics, Department of Experimental and Health ScienceUniversitat Pompeu FabraBarcelonaCataloniaSpain
| | - Narcis Fernandez‐Fuentes
- Department of BiosciencesUniversitat de Vic‐Universitat Central de CatalunyaVicCataloniaSpain
- Institute of Biological, Environmental and Rural SciencesAberystwyth UniversityAberystwythUK
| | - Baldo Oliva
- Structural Bioinformatics Group, Research Programme on Biomedical Informatics, Department of Experimental and Health ScienceUniversitat Pompeu FabraBarcelonaCataloniaSpain
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Galaxy InteractoMIX: An Integrated Computational Platform for the Study of Protein-Protein Interaction Data. J Mol Biol 2020; 433:166656. [PMID: 32976910 DOI: 10.1016/j.jmb.2020.09.015] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2020] [Revised: 08/30/2020] [Accepted: 09/16/2020] [Indexed: 12/19/2022]
Abstract
Protein interactions play a crucial role among the different functions of a cell and are central to our understanding of cellular processes both in health and disease. Here we present Galaxy InteractoMIX (http://galaxy.interactomix.com), a platform composed of 13 different computational tools each addressing specific aspects of the study of protein-protein interactions, ranging from large-scale cross-species protein-wide interactomes to atomic resolution level of protein complexes. Galaxy InteractoMIX provides an intuitive interface where users can retrieve consolidated interactomics data distributed across several databases or uncover links between diseases and genes by analyzing the interactomes underlying these diseases. The platform makes possible large-scale prediction and curation protein interactions using the conservation of motifs, interology, or presence or absence of key sequence signatures. The range of structure-based tools includes modeling and analysis of protein complexes, delineation of interfaces and the modeling of peptides acting as inhibitors of protein-protein interactions. Galaxy InteractoMIX includes a range of ready-to-use workflows to run complex analyses requiring minimal intervention by users. The potential range of applications of the platform covers different aspects of life science, biomedicine, biotechnology and drug discovery where protein associations are studied.
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Vakser IA. Challenges in protein docking. Curr Opin Struct Biol 2020; 64:160-165. [PMID: 32836051 DOI: 10.1016/j.sbi.2020.07.001] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2020] [Revised: 06/19/2020] [Accepted: 07/11/2020] [Indexed: 11/30/2022]
Abstract
Current developments in protein docking aim at improvement of applicability, accuracy and utility of modeling macromolecular complexes. The challenges include the need for greater emphasis on protein docking to molecules of different types, proper accounting for conformational flexibility upon binding, new promising methodologies based on residue co-evolution and deep learning, affinity prediction, and further development of fully automated docking servers. Importantly, new developments increasingly focus on realistic modeling of protein interactions in vivo, including crowded environment inside a cell, which involves multiple transient encounters, and propagating the system in time. This opinion paper offers the author's perspective on these challenges in structural modeling of protein interactions and the future of protein docking.
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Affiliation(s)
- Ilya A Vakser
- Computational Biology Program and Department of Molecular Biosciences, The University of Kansas, Lawrence, KS 66045, USA.
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Chen LY. Thermodynamic Integration in 3n Dimensions without Biases or Alchemy for Protein Interactions. FRONTIERS IN PHYSICS 2020; 8:202. [PMID: 32542181 PMCID: PMC7295167 DOI: 10.3389/fphy.2020.00202] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
Thermodynamic integration (TI), a powerful formalism for computing Gibbs free energy, has been implemented for many biophysical processes with alchemical schemes that require delicate human efforts to choose/design biasing potentials for sampling the desired biophysical events and to remove their artifactitious consequences afterwards. Theoretically, an alchemical scheme is exact but practically, an unsophisticated implementation of this exact formula can cause error amplifications. Small relative errors in the input parameters can be amplified many times in their propagation into the computed free energy [due to subtraction of similar numbers such as (105 ± 5)‒(100 ± 5) = 5 ± 7]. In this paper, we present an unsophisticated implementation of TI in 3n dimensions (3nD) (n=1,2,3…) for the potential of mean force along a 3nD path connecting one state in the bound state ensemble to one state in the unbound state ensemble. Fluctuations in these 3nD are integrated in the bound and unbound state ensembles but not along the 3nD path. Using TI3nD, we computed the standard binding free energies of three protein complexes: trometamol in Salmonella effector SpvD (n=1), biotin-avidin (n=2), and Colicin E9 endonuclease with cognate immunity protein Im9 (n=3). We employed three different protocols in three independent computations of E9-Im9 to show TI3nD's robustness. We also computed the hydration energies of ten biologically relevant compounds (n=1 for water, acetamide, urea, glycerol, trometamol, ammonium and n=2 for erythritol, 1,3-propanediol, xylitol, biotin). Each of the 15 computations is accomplishable within one (for hydration) to ten (for E9-Im9) days on an inexpensive GPU workstation. The computed results all agree with the available experimental data.
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Affiliation(s)
- Liao Y Chen
- Department of Physics, University of Texas at San Antonio, San Antonio, Texas 78249 U.S.A
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Schweke H, Mucchielli MH, Sacquin-Mora S, Bei W, Lopes A. Protein Interaction Energy Landscapes are Shaped by Functional and also Non-functional Partners. J Mol Biol 2020; 432:1183-1198. [DOI: 10.1016/j.jmb.2019.12.047] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2019] [Revised: 12/19/2019] [Accepted: 12/30/2019] [Indexed: 10/25/2022]
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Picón-Pagès P, Bonet J, García-García J, Garcia-Buendia J, Gutierrez D, Valle J, Gómez-Casuso CE, Sidelkivska V, Alvarez A, Perálvarez-Marín A, Suades A, Fernàndez-Busquets X, Andreu D, Vicente R, Oliva B, Muñoz FJ. Human Albumin Impairs Amyloid β-peptide Fibrillation Through its C-terminus: From docking Modeling to Protection Against Neurotoxicity in Alzheimer's disease. Comput Struct Biotechnol J 2019; 17:963-971. [PMID: 31360335 PMCID: PMC6639691 DOI: 10.1016/j.csbj.2019.06.017] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2019] [Revised: 06/03/2019] [Accepted: 06/13/2019] [Indexed: 12/01/2022] Open
Abstract
Alzheimer's disease (AD) is a neurodegenerative process characterized by the accumulation of extracellular deposits of amyloid β-peptide (Aβ), which induces neuronal death. Monomeric Aβ is not toxic but tends to aggregate into β-sheets that are neurotoxic. Therefore to prevent or delay AD onset and progression one of the main therapeutic approaches would be to impair Aβ assembly into oligomers and fibrils and to promote disaggregation of the preformed aggregate. Albumin is the most abundant protein in the cerebrospinal fluid and it was reported to bind Aβ impeding its aggregation. In a previous work we identified a 35-residue sequence of clusterin, a well-known protein that binds Aβ, that is highly similar to the C-terminus (CTerm) of albumin. In this work, the docking experiments show that the average binding free energy of the CTerm-Aβ1-42 simulations was significantly lower than that of the clusterin-Aβ1-42 binding, highlighting the possibility that the CTerm retains albumin's binding properties. To validate this observation, we performed in vitro structural analysis of soluble and aggregated 1 μM Aβ1-42 incubated with 5 μM CTerm, equimolar to the albumin concentration in the CSF. Reversed-phase chromatography and electron microscopy analysis demonstrated a reduction of Aβ1-42 aggregates when the CTerm was present. Furthermore, we treated a human neuroblastoma cell line with soluble and aggregated Aβ1-42 incubated with CTerm obtaining a significant protection against Aβ-induced neurotoxicity. These in silico and in vitro data suggest that the albumin CTerm is able to impair Aβ aggregation and to promote disassemble of Aβ aggregates protecting neurons.
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Key Words
- AD, Alzheimer's disease
- APP, amyloid precursor protein
- Albumin
- Alzheimer's disease
- Amyloid
- Aß, Amyloid-ß peptide
- CD, Circular dichroism
- CSF, cerebrospinal fluid
- CTerm, albumin C-terminus
- Docking
- HPLC, high performance liquid chromatography
- LC-MS, Liquid chromatography-mass spectrometry
- MTT, 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide
- NMR, nuclear magnetic resonance
- PBS, phosphate-buffered saline
- PDB, Protein Data Bank
- PPI, protein-protein interactions
- SDS, sodium dodecyl sulfate
- TEM, transmission electron microscopy
- TFA, trifluoroacetic acid
- UV, ultraviolet
- fAβ1–42, HiLyte Fluor488 labelled human Aβ1–42
- β-Sheet
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Affiliation(s)
- Pol Picón-Pagès
- Laboratory of Molecular Physiology, Faculty of Health and Life Sciences, Universitat Pompeu Fabra, Barcelona, Spain
| | - Jaume Bonet
- Laboratory of Structural Bioinformatics (GRIB), Faculty of Health and Life Sciences, Universitat Pompeu Fabra, Barcelona, Spain
| | - Javier García-García
- Laboratory of Structural Bioinformatics (GRIB), Faculty of Health and Life Sciences, Universitat Pompeu Fabra, Barcelona, Spain
| | - Joan Garcia-Buendia
- Laboratory of Molecular Physiology, Faculty of Health and Life Sciences, Universitat Pompeu Fabra, Barcelona, Spain
| | - Daniela Gutierrez
- Cell Signaling Laboratory, Centro UC de Envejecimiento y Regeneración (CARE), Department of Cellular and Molecular Biology, Biological Sciences Faculty, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - Javier Valle
- Laboratory of Proteomics and Protein Chemistry, Faculty of Health and Life Sciences, Universitat Pompeu Fabra, Barcelona, Spain
| | - Carmen E.S. Gómez-Casuso
- Laboratory of Molecular Physiology, Faculty of Health and Life Sciences, Universitat Pompeu Fabra, Barcelona, Spain
| | - Valeriya Sidelkivska
- Laboratory of Molecular Physiology, Faculty of Health and Life Sciences, Universitat Pompeu Fabra, Barcelona, Spain
| | - Alejandra Alvarez
- Cell Signaling Laboratory, Centro UC de Envejecimiento y Regeneración (CARE), Department of Cellular and Molecular Biology, Biological Sciences Faculty, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - Alex Perálvarez-Marín
- Unitat de Biofísica, Departament de Bioquímica i de Biologia Molecular, Facultat de Medicina, Centre d'Estudis en Biofísica, Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Albert Suades
- Unitat de Biofísica, Departament de Bioquímica i de Biologia Molecular, Facultat de Medicina, Centre d'Estudis en Biofísica, Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Xavier Fernàndez-Busquets
- Nanomalaria Group, Institute for Bioengineering of Catalonia (IBEC), The Barcelona Institute of Science and Technology, Baldiri Reixac 10-12, ES-08028 Barcelona, Spain
- Barcelona Institute for Global Health (ISGlobal, Hospital Clínic-Universitat de Barcelona), Rosselló 149-153, ES-08036 Barcelona, Spain
| | - David Andreu
- Laboratory of Proteomics and Protein Chemistry, Faculty of Health and Life Sciences, Universitat Pompeu Fabra, Barcelona, Spain
| | - Rubén Vicente
- Laboratory of Molecular Physiology, Faculty of Health and Life Sciences, Universitat Pompeu Fabra, Barcelona, Spain
| | - Baldomero Oliva
- Laboratory of Structural Bioinformatics (GRIB), Faculty of Health and Life Sciences, Universitat Pompeu Fabra, Barcelona, Spain
| | - Francisco J. Muñoz
- Laboratory of Molecular Physiology, Faculty of Health and Life Sciences, Universitat Pompeu Fabra, Barcelona, Spain
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