Biomedical subjects

A Yasri

Publications and source records attributed to A Yasri.

4 recordsLinked to original sources

PubMed1998

Computer-assisted rational design of immunosuppressive compounds.

We describe the rational design of immunosuppressive peptides without relying on information regarding their receptors or mechanisms of action. The design strategy uses a variety of topological and shape descriptors in combination with an analysis of molecular dynamics trajectories for the identification of potential drug candidates. This strategy was applied to the development of immunosuppressive peptides with enhanced potency. The lead compounds were peptides, derived from the heavy chain of HLA class I, that modulate immune responses in vitro and in vivo. In particular, a peptide derived from HLA-B2702, amino acids 75-84 (2702.75-84) prolonged skin and heart allograft survival in mice. The biological activity of the rationally designed peptides was tested in a heterotopic mouse heart allograft model. The molecule predicted to be most potent displayed an immunosuppressive activity approximately 100 times higher than the lead compound.

Animals↗

PubMed1997

17 alpha (haloacetamidoalkyl) estradiols alkylate the human estrogen receptor at cysteine residues 417 and 530.

Results obtained in a previous study suggested that cysteine residues in the estrogen receptor were covalent attachment sites for four 17 alpha-(haloacetamidoalkyl) estradiols (halo, bromo or iodo; alkyl, methyl, ethyl, or propyl). To identify the putative concerned cysteines, we expressed wild-type and various cysteine --> alanine mutants of the human estrogen receptor in COS cells and determined their ability to be alkylated by the four electrophiles. The quadruple mutant, in which all the cysteines (residues 381, 417, 447, and 530) of the hormone-binding site were changed to alanines, showed very little electrophile labeling, whereas the four single mutants (C381A, C417A, C447A, and C530A) were alkylated as efficiently as the wild-type receptor. These results (i) demonstrate that cysteine residues were covalent attachment sites of electrophiles and (ii) indicate that more than one cysteine residue could be alkylated. Analysis of three double mutants (C381A/C530A, C417A/C530A, and C447A/C530A) provided strong evidence that only C417 and C530 were sites for electrophile covalent attachment. Since C530 was also alkylated by tamoxifen aziridine, a nonsteroidal affinity-labeling agent, we propose a selective mode of superimposition of tamoxifen-class antiestrogens with estradiol, which could account for the relative positioning of the two types of ligands in the receptor hormone-binding pocket. According to the structure of the hormone-binding pocket of nuclear receptors, as inferred from crystallographic studies and general sequence alignment of hormone-binding domains, C417 and C530 appear to be (1) located at the extreme border or in structural elements involved in delineation of the hormone-binding pocket, (2) spatially in close proximity to each other, and (3) in positions highly homologous to those of glucocorticoid receptor sites alkylated by affinity- and photoaffinity-labeling agents, respectively.

Affinity Labels↗

PubMed1996

Rational choice of molecular dynamics simulation parameters through the use of the three-dimensional autocorrelation method: application to calmodulin flexibility study.

We examined the effects of several adjustable parameters for use in molecular dynamics simulations of proteins using both standard criteria (radius of gyration, root mean square deviation from starting coordinates, molecular mechanics energy) and a new description of protein conformations by 3-D autocorrelation vectors (3-D ACV). We chose calmodulin (CaM) as a protein model and analysed 23 simulations using different combinations of the four molecular dynamics parameters studied, such as the dielectric constant (epsilon), the heating phase time (H), the thermal bath coupling time (zeta T) and the time step size (delta t). The correctness of the various trajectories generated with different parameter sets was evaluated through geometric analysis and use of a knowledge-based profile method. It is shown that 3-D ACV combined with multivariate statistical analysis provides a convenient way to describe and compare molecular dynamics simulations and constitutes a valuable complementary tool to standard methods. Using these methods, comparison of the various simulations performed on CaM indicated that the best in vacuo parameter set was epsilon = 1 x r, H = 15 ps, zeta T = 0.1 ps and delta t = 1 fs in fairly good agreement with previous less extensive comparisons of molecular dynamics trajectories.

Binding Sites↗

PubMedDate not supplied

Toward an optimal procedure for variable selection and QSAR model building.

In this work, we report the development of a novel QSAR technique combining genetic algorithms and neural networks for selecting a subset of relevant descriptors and building the optimal neural network architecture for QSAR studies. This technique uses a neural network to map the dependent property of interest with the descriptors preselected by the genetic algorithm. This technique differs from other variable selection techniques combining genetic algorithms to neural networks by two main features: (1) The variable selection search performed by the genetic algorithm is not constrained to a defined number of descriptors. (2) The optimal neural network architecture is explored in parallel with the variable selection by dynamically modifying the size of the hidden layer. By using both artificial data and real biological data, we show that this technique can be used to build both classification and regression models and outperforms simpler variable selection techniques mainly for nonlinear data sets. The results obtained on real data are compared to previous work using other modeling techniques. We also discuss some important issues in building QSAR models and good practices for QSAR studies.

Algorithms↗