Modeling of protein structure and protein complexes using mass spectrometry data

• Grant number: 16/13195-2
• Support Opportunities: Scholarships in Brazil - Doctorate
• Start date until: May 01, 2017
• End date until: August 31, 2019
• Field of knowledge: Biological Sciences - Biochemistry - Chemistry of Macromolecules
• Agreement: Coordination of Improvement of Higher Education Personnel (CAPES)
• Principal Investigator: Fabio Cesar Gozzo
• Grantee: Allan Jhonathan Ramos Ferrari
• Host Institution: Instituto de Química (IQ). Universidade Estadual de Campinas (UNICAMP). Campinas , SP, Brazil
• Associated research grant: 14/17264-3 - New frontiers in structural proteomics: characterizing protein and protein complex structures by mass spectrometry, AP.TEM
• Associated scholarship(s): 17/17544-4 - Protein design applying computational biology with Rosetta, BE.EP.DR

Abstract

Proteins constitute the structural factory by which cells perform all the metabolic and regulatory processes. Understand these biological events at the molecular level necessarily requires the description of the structure of these biomolecules at the atomic level. Protein Crystallography (DRX) and Nuclear Magnetic Resonance (NMR) are the well stablished methods for the study of the protein and protein complex structure and are the standard techniques for this purpose. Despite the very high structural detail provided by these techniques, both have limited applicability to proteins, in general due to some experimental feature, among which is the need for a large amount of sample (in the order of several milligrams) and with high purity. In the case of NMR, there is still a need for the sample to be stable in any of the buffers that do not interfere in the analysis over a long period of time (days to weeks) at room temperature. Besides that, current techniques limit the maximum size of the component studied to approximately 30 kDa, which often is the mass of a single component of a protein complex. In turn, DRX requires that the sample to be in the form of single crystal. This limitation is even more restrictive when one intends to characterize protein complexes due to the increased difficulty in obtaining pure complex aplenty, the largest system size and, in turn, to obtain single crystals of such species. Furthermore, there is a great disparity between the rate at which one can determine structures at high resolution with respect to that in which one gets information about protein at the gene level, since the technology involved in sequencing and annotation of genomes currently. The development of bioinformatics tools for prediction of protein and complex protein structure is a quite attractive field in order to fill this gap in structural biology area. These tools are primarily based on two approaches: 1) comparative modeling in which one uses a homologue of known structure, and 2) abinitio modeling, the only option when there are no homologous structures resolved. In particular, the success of the abinitio prediction are limited to sequences up to 100 amino acid residues, once the conformation space grows exponentially with the size of the sequence. In this sense, there is interest in the development and application of predictive hybrid methods that use an integrative approach. The use of mass spectrometry (MS) for characterization of proteins is extremely interesting since it combines the inherent advantages of the technique, such as sensitivity, rapidity and versatility. The cross-linking phenomena (XL) comprises bonding two species through a covalent bond, usually employing a cross-linking agent. The information arising from cross-linking experiments coupled to MS (XL-MS) can be used to obtain structural information of proteins. This project aims to contribute by creating strategies for structural prediction of proteins and protein complexes using the experimental data of distance restraints that comes from cross-linking experiments. To do so, it will be used two proteins as models: SalBIII, 17.2 kDa, and Agg1, which is composed of two domains of 15.7 and 21.8 kDa, respectively. Both proteins have low identity with any other protein that has already been solved and deposited on PDB and to which data were obtained from X-ray diffraction with experimental phasing. (AU)

Scientific publications

(6)

(References retrieved automatically from Web of Science and SciELO through information on FAPESP grants and their corresponding numbers as mentioned in the publications by the authors)

BOTTINO, GUILHERME F.; FERRARI, ALLAN J. R.; GOZZO, FABIO C.; MARTINEZ, LEANDRO. Structural discrimination analysis for constraint selection in protein modeling. Bioinformatics, v. 37, n. 21, p. 3766-3773, NOV 1 2021. (13/08293-7, 16/13195-2, 19/17007-4, 10/16947-9, 18/14274-9)

DOLCE, LUCIANO G.; OHBAYASHI, NORIHIKO; DA SILVA, DANIEL F. C.; FERRARI, ALLAN J. R.; PIROLLA, RENAN A. S.; SCHWARZER, ANA C. DE A. P.; ZANPHORLIN, LETICIA M.; CABRAL, LUCELIA; FIORAMONTE, MARIANA; RAMOS, CARLOS H. I.; et al. Unveiling the interaction between the molecular motor Myosin Vc and the small GTPase Rab3A. JOURNAL OF PROTEOMICS, v. 212, FEB 10 2020. (14/09720-9, 16/13195-2, 14/00584-5)

DOS SANTOS, RICARDO N.; FERRARI, ALLAN J. R.; DE JESUS, HUGO C. R.; GOZZO, FABIO C.; MORCOS, FARUCK; MARTINEZ, LEANDRO. Enhancing protein fold determination by exploring the complementary information of chemical cross-linking and coevolutionary signals. Bioinformatics, v. 34, n. 13, p. 2201-2208, JUL 1 2018. (13/08293-7, 13/05475-7, 16/13195-2, 14/17264-3, 15/13667-9, 10/16947-9)

FIORAMONTE, MARIANA; RAMOS DE JESUS, HUGO CESAR; RAMOS FERRARI, ALLAN JHONATHAN; LIMA, DIOGO BORGES; DREKENER, ROBERTA LOPES; DUARTE CORREIA, CARLOS ROQUE; OLIVEIRA, LUCIANA GONZAGA; DA COSTA NEVES-FERREIRA, ANA GISELE; CARVALHO, PAULO COSTA; GOZZO, FABIO CESAR. XPlex: An Effective, Multiplex Cross-Linking Chemistry for Acidic Residues. Analytical Chemistry, v. 90, n. 10, p. 6043-6050, MAY 15 2018. (14/50249-8, 16/13195-2, 14/17264-3, 12/10862-7, 14/12727-5)

FERRARI, ALLAN J. R.; GOZZO, FABIO C.; MARTINEZ, LEANDRO. Statistical force-field for structural modeling using chemical cross-linking/mass spectrometry distance constraints. Bioinformatics, v. 35, n. 17, p. 3005-3012, SEP 1 2019. (13/08293-7, 13/05475-7, 18/14274-9, 16/13195-2, 14/17264-3, 13/23814-3, 10/16947-9)

FERRARI, ALLAN J. R.; CLASEN, MILAN A.; KURT, LOUISE; CARVALHO, PAULO C.; GOZZO, FABIO C.; MARTINEZ, LEANDRO. TopoLink: evaluation of structural models using chemical crosslinking distance constraints. Bioinformatics, v. 35, n. 17, p. 3169-3170, SEP 1 2019. (13/08293-7, 13/05475-7, 18/14274-9, 16/13195-2, 10/16947-9)

Academic Publications

(References retrieved automatically from State of São Paulo Research Institutions)

FERRARI, Allan Jhonathan Ramos. Modeling protein structure based on constraints obtained from chemical cross-linking and mass spectrometry. 2019. Doctoral Thesis - Universidade Estadual de Campinas (UNICAMP). Instituto de Química Campinas, SP.