200 research outputs found
Ivano Eberini. Spillover
No full textLa review recensisce il podcast "Spillover" condotto dal prof. Ivano Eberini dell'Università degli Studi di Milano
Ivano Eberini, Spillover
No full textIvano Eberini, Spillover (CTU - Centro per l’innovazione didattica e le tecnologie multimediali, Università degli Studi di Milano, Podcast, 2020)
di Carlotta Fiammengh
Computational biochemistry: a link between base and applied research
No full textComputational biochemistry is mainly based on molecular modelling and informatics tools, combined to try addressing structural, dynamics and functional features of biomolecules, with focus on biopolymers. Its applications include comparative modelling, molecular dynamics simulations, and several other techniques, such as ab initio calculations based on the density functional theory.
Computations and simulations are frequently used to manage biochemical problems not easily addressed by wet experimental approaches, such as deciphering the three-dimensional structure of a biopolymer, inferring its in vivo activity, characterizing at a molecular level its catalytic function or its signal transduction mechanism, or studying the impact of mutations on the structure-function relationship in proteins and eventually their effects on carriers’ phenotypes.
Besides these purposes, mainly focused on basic research, computational biochemistry is becoming one of the most relevant tools of the drug discovery pipeline. It is useful for identifying putative targets, for solving their structure via computational methods, for better understanding their pathophysiological functions, and for identifying and deploying pharmacological strategies, primarily based on the development of novel compounds with specific target-modifying activities. Not only pharmacology, but also toxicology is benefitting from computational biochemistry to clarify the mechanism of action of xenobiotics or to prioritize large datasets of compounds in risk evaluation tasks.
In my talk, I am going to report some typical applications of computational biochemistry: an investigation about the dynamic behaviour of a model protein, some application to pharmacology towards the development of novel enzymatic inhibitors for atherosclerosis and of GPCR agonists for demyelinating neurodegenerative diseases, and an example of toxicological prioritization among environmental xenobiotics involved either in teratogenic or in endocrine disrupting outcomes
Wards in the keyway: amino acids with anomalouspkas in calycins
No full textAs a follow-up to our recent analysis of the
electrostatics of bovine b-lactoglobulin (Eberini et al. in
Amino Acids 42:2019–2030, 2011), we investigated whether
the occurrence in the native structure of calycins—the
superfamily to which b-lactoglobulin belongs—of amino
acids with anomalous pKas is an infrequent or, on the
contrary, a common occurrence, and whether or not a
general pattern may be recognized. To this aim, we randomly
selected four calycins we had either purified from
natural sources or prepared with recombinant DNA technologies
during our previous and current structural and
functional studies on this family. Their pIs vary over several
pH units and their known functions are as diverse as
carriers, enzymes, immunomodulators and/or extracellular
chaperones. In our survey, we used both in silico prediction
methods and in vitro procedures, such as isoelectric
focusing, electrophoretic titration curves and spectroscopic
techniques. By comparing the results under native conditions
(no exposure of the proteins to chaotropic agents) to
those after protein unfolding (in the presence of 8 M urea),
a shift is observed in the pKa of at least one amino acid per
protein, which results in a measurable change in pI. Three
types of amino acids are involved: Cys, Glu, and His, their
position varies along the calycin sequence. Although no
common mechanism may thus be recognized, we hypothesize
that the ‘normalization’ of anomalous pKas may be
the phenomenon that accompanies, and favors, structural
rearrangements such as those involved in ligand binding by
these proteins. An interesting, if anecdotal, validation to
this view comes from the behavior of human retinol
binding protein, for which the pI of the folded and liganded
protein is intermediate between those of the folded and
unliganded and of the unfolded protein forms. Likewise,
both solid (from crystallography) and solution state (from
CD spectroscopy) data confirm that the protein undergoes
structural rearrangement upon retinol binding
Replication data for: "Caulobacter segnis Dioxygenase CsO2: A Practical Biocatalyst for Stilbenoid Ozonolysis"
No full textThis dataset contains structural files in PDB format related to the homology model and docking poses described in the accompanying research article. It contains: model.pdb (this file is a homology model of the CSO2 enzyme, generated using the crystal structure of the dioxygenase from *Pseudomonas brassicacearum* (PDB ID: 5V2D) as a template) and Compound_x.pdb (these files contain the docking poses of the compounds listed in Table 2 of the published article. Each file corresponds to a different compound, labeled with "x" as the compound identifier (e.g., Compound_1.pdb, Compound_2.pdb, etc.). All files are in PDB (Protein Data Bank) format, a plain text format widely used to represent three-dimensional structures of molecules, especially proteins and ligands. Each PDB file contains atomic coordinates and connectivity information for a given molecular structure.
See the attached readme file and the related article for more information
DISC867317_SupplementalMaterial – Supplemental material for SLC6A14, a Pivotal Actor on Cancer Stage: When Function Meets Structure
No full textSupplemental material, DISC867317_SupplementalMaterial for SLC6A14, a Pivotal Actor on Cancer Stage: When Function Meets Structure by Luca Palazzolo, Chiara Paravicini, Tommaso Laurenzi, Sara Adobati, Simona Saporiti, Uliano Guerrini, Elisabetta Gianazza, Cesare Indiveri, Catriona M. H. Anderson, David T. Thwaites and Ivano Eberini in SLAS Discovery</p
Limonene monooxygenases Molecular Modeling
No full textLimonene monooxygenase is a heme iron-dependent monooxygenase belonging to the Cyp153A family.lt catalvzes the allvlic hydroxlationwith high regioselectivity, We propose to use computational approaches to study the enzyme structure and function
Stereoselective monoreduction of bulky 1,2-dicarbonyls catalyzed by a benzyl reductase from Pichia glucozyma (KRED1-Pglu)
No full textEnantiomerically enriched hydroxyketones are well-established intermediates for the synthesis of several bioactive compounds [1] and can be chemically obtained by stereoselective reduction of one of the carbonyl moieties of the corresponding diketones. However, enzymatic strategies are characterized by higher catalytic efficiency, milder reaction conditions, higher stereo- and regioselectivity, and fewer numbers of synthetic steps. Therefore, they can be chosen as convenient and environmentally friendly alternatives.[2]
A NADPH-dependent benzyl reductase from the non-conventional yeast Pichia glucozyma (KRED1-Pglu [3]) was over-expressed in E. coli, purified and exploited to catalyze the asymmetric monoreduction of bulky aromatic 1,2-dicarbonyl compounds. The cofactor was recycled by an enzyme-coupled system (glucose-glucose dehydrogenase (GDH) from Bacillus megaterium). The recombinant KRED1-Pglu showed a wide range of activity (24-97% conversion) and excellent stereoselectivity (ee ≥ 96% in all but one case). On the contrary, it proved either inactive or very poorly active towards most 1,3- and 1,4-dicarbonyls tested as potential substrates. In order to understand this peculiar behavior, the enzyme was crystallized (1.77 Å resolution) and its active site was investigated to identify the recognition residues involved in the desymmetrization reaction. QM and classical calculations also allowed for a proposal of the catalytic mechanism, along with an in silico reactivity prediction.[4]
[1] G. Aullón; P. Romea; F. Urpí Synthesis, 2017, 49, 484-503.
[2] P. Hoyos; J.-V. Sinisterra; F. Molinari; A.R. Alántara; P. Domínguez de María Acc. Chem. Res., 2010, 43, 288-299.
[3] M.L. Contente; I. Serra; M. Brambilla; I. Eberini; E. Giannazza; V. De Vitis; F. Molinari; P. Zambelli; D. Romano Appl. Microbiol. Biotechnol., 2016, 100, 193-201.
[4] M. Rabuffetti; P. Cannazza; M.L. Contente; A. Pinto; D. Romano; P. Hoyos; A.R. Alcántara; I. Eberini; T. Laurenzi; L. Gourlay; F. Di Pisa; F. Molinari Bioorg. Chem., 2021, 108, 104644
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