Biomedical subjects

Fabrizio Giordanetto

Publications and source records attributed to Fabrizio Giordanetto.

12 recordsLinked to original sources

PubMed2006

Apoptosis-inducing factor: vital and lethal.

Apoptosis-inducing factor (AIF) is a NADH oxidase with a local redox function that is essential for optimal oxidative phosphorylation and for an efficient anti-oxidant defense. The absence of AIF can cause neurodegeneration, skeleton muscle atrophy and dilated cardiomyopathy. In many models of apoptosis, AIF translocates to the nucleus, where it induces chromatin condensation and DNA degradation. The nuclear localization of AIF can be inhibited by blocking upstream signals of apoptosis. The contribution of AIF to cell death depends on the cell type and apoptotic insult and is only seen when caspases are inhibited or not activated. It is unknown to what extent and through which mechanisms AIF contributes to the induction of cell death. Here, we discuss recent progress in the quest to understand the contribution of AIF to life and death.

Animals↗

PubMed2005

Virtual screening to enrich a compound collection with CDK2 inhibitors using docking, scoring, and composite scoring models.

Docking programs can generate subsets of a compound collection with an increased percentage of actives against a target (enrichment) by predicting their binding mode (pose) and affinity (score), and retrieving those with the highest scores. Using the QXP and GOLD programs, we compared the ability of six single scoring functions (PLP, Ligscore, Ludi, Jain, ChemScore, PMF) and four composite scoring models (Mean Rank: MR, Rank-by-Vote: Vt, Bayesian Statistics: BS and PLS Discriminant Analysis: DA) to separate compounds that are active against CDK2 from inactives. We determined the enrichment for the entire set of actives (IC50 < 10 microM) and for three activity subsets. In all cases, the enrichment for each subset was lower than for the entire set of actives. QXP outperformed GOLD at pose prediction, but yielded only moderately better enrichments. Five to six scoring functions yielded good enrichments with GOLD poses, while typically only two worked well with QXP poses. For each program, two scoring functions generally performed better than the others (Ligscore2 and Ludi for GOLD; QXP and Jain for QXP). Composite scoring functions yielded better results than single scoring functions. The consensus approaches MR and Vt worked best when separating micromolar inhibitors from inactives. The statistical approaches BS and DA, which require training data, performed best when distinguishing between low and high nanomolar inhibitors. The key observation that all hit rate profiles for all four activity intervals for all scoring schemes for both programs are significantly better than random, is evidence that docking can be successfully applied to enrich compound collections.

Adenosine Triphosphate↗

PubMed2005

Large scale molecular dynamics simulation of native and mutant dihydropteroate synthase-sulphanilamide complexes suggests the molecular basis for dihydropteroate synthase drug resistance.

Antibiotic resistance is hampering the efficacy of drugs in the treatment of several pathological infections. Dihydropteroate synthase (DHPS) has been targeted by sulphonamide inhibitors for the past 60 years and has developed different amino acid mutations to survive sulpha drug action. We couple homology modelling techniques and massively parallel molecular dynamics simulations to study both the drug-bound and apo forms of native and mutant DHPS. Simulations of the complex between sulphanilamide and Streptomyces pneumoniae, DHPS shows how sulphanilamide is able to position itself close to 6-hydroxymethyl-7, 8-dihydropteridine-phosphate in a suitable position for the enzymatic transformation whereas in the mutant complex the sulpha drug is expelled from the catalytic site. Our simulations, therefore, provide insight into the molecular basis for drug resistance with S. pneumoniae DHPS.

Anti-Bacterial Agents↗

PubMed2004

AIF and cyclophilin A cooperate in apoptosis-associated chromatinolysis.

Cyclophilin A (CypA) was determined to interact with apoptosis-inducing factor (AIF) by mass spectroscopy, coimmunoprecipitation, pull-down assays, and molecular modeling. During the initial, caspase-independent stage of chromatin condensation that accompanies apoptosis, AIF and CypA were found to coimmunolocalize in the nucleus. Recombinant AIF and CypA proteins synergized in vitro in the degradation of plasmid DNA, as well as in the capacity to induce DNA loss in purified nuclei. The apoptogenic cooperation between AIF and CypA did not rely on the CypA peptidyl-prolyl cis-trans isomerase activity. In Cyp-expressing cells, AIF overexpression augmented apoptotic chromatinolysis. The AIF-dependent large-scale DNA fragmentation was less pronounced in CypA knockout cells as compared to controls. AIF mutants lacking the CypA-binding domain were inefficient apoptosis sensitizers in transfection experiments. Moreover, AIF failed to sensitize CypA knockout cells to apoptosis induction, and this defect in the AIF response was reversed by reintroduction of the CypA gene into CypA-deficient cells. In summary, AIF and CypA collaborate in chromatinolysis.

Apoptosis↗

PubMed2003

Chemosensitization by a non-apoptogenic heat shock protein 70-binding apoptosis-inducing factor mutant.

Heat shock protein 70 (HSP70) inhibits apoptosis and thereby increases the survival of cells exposed to a wide range of lethal stimuli. HSP70 has also been shown to increase the tumorigenicity of cancer cells in rodent models. The protective function of this chaperone involves interaction and neutralization of the caspase activator apoptotic protease activation factor-1 and the mitochondrial flavoprotein apoptosis-inducing factor (AIF). In this work, we determined by deletional mutagenesis that a domain of AIF comprised between amino acids 150 and 228 is engaged in a molecular interaction with the substrate-binding domain of HSP70. Computer calculations favored this conclusion. On the basis of this information, we constructed an AIF-derived protein, which is cytosolic, noncytotoxic, yet maintains its capacity to interact with HSP70. This protein, designated ADD70, sensitized different human cancer cells to apoptosis induced by a variety of death stimuli by its capacity to interact with HSP70 and therefore to sequester HSP70. Thus, its chemosensitizing effect was lost in cells in which inducible HSP70 genes had been deleted. These data delineate a novel strategy for the selective neutralization of HSP70.

Apoptosis↗

PubMed2003

Exploring the molecular basis of selectivity in A1 adenosine receptors agonists: a case study.

Adenosine is a naturally occurring purine nucleoside that has a wide variety of well-documented regulatory functions and physiological roles. Selective activation of the adenosine A1 receptor has drawn attention in drug discovery for the therapeutic effects on neural and cardiovascular disorders. We have developed a model of the human A1 adenosine receptor using bovine rhodopsin as a template. A flexible docking approach has been subsequently carried out for evaluating the molecular interactions of twenty-one selective A1 agonists with the receptor model. The results of these studies are consistent with mutational and biochemical data. In particular, they highlight a wide hydrogen-bonding network between the nucleoside portion of the ligands and the A1 receptor as well as key amino acids for hydrophobic interactions with the different N6-groups of the agonists. The models presented here provide a detailed molecular map for the selective stimulation of the adenosine A1 receptor subtype and a steady basis for the rational design of new A1 selective ligands.

Adenosine↗

PubMed2003

In silico rationalization of the structural and physicochemical requirements for photobiological activity in angelicine derivatives and their heteroanalogues.

In PUVA (Psoralen plus UVA) chemotherapy 8-methoxypsoralen is the most widely used compound, although its efficacy is endowed with undesired side effects. In order to have an evident anti-proliferative activity with a reduced phototoxicity, many linear and angular derivatives have been synthesised. In this paper we describe a QSAR study in which, by means of the neural networks methodology, a useful model for predicting biological activity, expressed as ID50 (the UVA dose that reduces to 50% the DNA synthesis in Ehrlich cells), has been derived. A decision tree that is able to discriminate between active and inactive compounds has been built based on recursive partitioning. The study shows the key structural features responsible for the activity and could be a helpful tool in the rational design of new, less toxic, photochemotherapeuthic agents.

Algorithms↗

PubMed2003

A three-dimensional model of Suppressor Of Cytokine Signalling 1 (SOCS-1).

Suppressor Of Cytokine Signalling 1 (SOCS-1) is one of the proteins responsible for the negative regulation of the JAK-STAT pathway triggered by many cytokines. This important inhibition involves complex formation between SOCS-1 and JAK2, which requires particular structural domains (KIR, ESS and SH2) on SOCS-1. A three-dimensional theoretical model of SOCS-1 is presented here. The model was generated by the application of different modelling techniques, including threading, structure-based modelling, surface analysis and protein docking. The structure accounts for the interactions between SOCS-1 and two other key proteins in the JAK-STAT pathway, namely JAK2 and Elongin BC. The proposed model for the interaction between SOCS-1 and JAK2 suggests that the SOCS-1 suppress the kinase activity of JAK2 by obstructing the catalytic groove of the tyrosine kinase. Subsequent interaction of the JAK-SOCS complex with Elongin BC was also modelled. A sequence and structural comparison between the SH2 domain of SOCS-1 and the SH2 domains of other proteins highlights key residues that could be responsible for SOCS-1 specificity. Currently available mutational data are evaluated. The results are consistent with the experimental data and they provide deeper insights into the inhibitory function of SOCS-1 at a molecular level.

Amino Acid Sequence↗

PubMed2002

Prediction of the structure of human Janus kinase 2 (JAK2) comprising JAK homology domains 1 through 7.

A theoretical model of human Janus kinase 2 (JAK2) comprising all seven Janus homology domains is presented. The model was generated by application of homology modelling approaches. The three-dimensional structure contains, starting from the N-terminus, FERM (4.1, ezrin, radixin, moesin), SH2 (Src homology region 2), tyrosine kinase-like, and tyrosine kinase domains. The predicted inter-domain orientation in JAK2 is discussed and the currently existing mutational data for Janus kinases are evaluated. Structural details of the SH2 and the FERM domains are presented. The predictions indicate that the SH2 domain is not fully functional. A number of hydrophobic amino acids of the FERM domain that are predicted to be involved in the constitutive association with the cytokine receptors are highlighted. The model gives new insights into the structure-function relationship of this important protein, and areas that could be investigated by mutation studies are highlighted.

Amino Acid Sequence↗

PubMedDate not supplied

Novel scoring functions comprising QXP, SASA, and protein side-chain entropy terms.

Novel scoring functions that predict the affinity of a ligand for its receptor have been developed. They were built with several statistical tools (partial least squares, genetic algorithms, neural networks) and trained on a data set of 100 crystal structures of receptor-ligand complexes, with affinities spanning 10 log units. The new scoring functions contain both descriptors generated by the QXP docking program and new descriptors that were developed in-house. These new descriptors are based on solvent accessible surface areas and account for conformational entropy changes and desolvation effects of both ligand and receptor upon binding. The predictive r(2) values for a test set of 24 complexes are in the 0.712-0.741 range and RMS prediction errors in the 1.09-1.16 log K(d) range. Inclusion of the new descriptors led to significant improvements in affinity prediction, compared to scoring functions based on QXP descriptors alone. However, the QXP descriptors by themselves perform better in binding mode prediction. The performance of the linear models is comparable to that of the neural networks. The new functions perform very well, but they still need to be validated as universal tools for the prediction of binding affinity.

Algorithms↗

PubMedDate not supplied

Assessment of docking poses: interactions-based accuracy classification (IBAC) versus crystal structure deviations.

Six docking programs (FlexX, GOLD, ICM, LigandFit, the Northwestern University version of DOCK, and QXP) were evaluated in terms of their ability to reproduce experimentally observed binding modes (poses) of small-molecule ligands to macromolecular targets. The accuracy of a pose was assessed in two ways: First, the RMS deviation of the predicted pose from the crystal structure was calculated. Second, the predicted pose was compared to the experimentally observed one regarding the presence of key interactions with the protein. The latter assessment is referred to as interactions-based accuracy classification (IBAC). In a number of cases significant discrepancies were found between IBAC and RMSD-based classifications. Despite being more subjective, the IBAC proved to be a more meaningful measure of docking accuracy in all these cases.

Crystallography, X-Ray↗

PubMedDate not supplied

On evaluating molecular-docking methods for pose prediction and enrichment factors.

Four of the most well-known, commercially available docking programs, FlexX, GOLD, GLIDE, and ICM, have been examined for their ligand-docking and virtual-screening capabilities. The relative performance of the programs in reproducing the native ligand conformation from starting SMILES strings for 164 high-resolution protein-ligand complexes is presented and compared. Applying only the native scoring functions, the latest versions of these four docking programs were also used to conduct virtual screening for 12 protein targets of therapeutic interest, involving both publicly available structures and AstraZeneca in-house structures. The capability of the four programs to correctly rank-order target-specific active compounds over alternative binders and nonbinders (decoys plus randomly selected compounds) and thereby enrich a small subset of a screening library is compared. Enrichments from the virtual-screening experiments are contrasted with those obtained with alternative 3D shape-matching and 2D similarity database-search methods.

Journal Article↗