Biomedical subjects

Marta Murcia

Publications and source records attributed to Marta Murcia.

8 recordsLinked to original sources

PubMed2007

Mapping early conformational changes in alphaIIb and beta3 during biogenesis reveals a potential mechanism for alphaIIbbeta3 adopting its bent conformation.

Current evidence supports a model in which the low-affinity state of the platelet integrin alphaIIbbeta3 results from alphaIIbbeta3 adopting a bent conformation. To assess alphaIIbbeta3 biogenesis and how alphaIIbbeta3 initially adopts the bent conformation, we mapped the conformational states occupied by alphaIIb and beta3 during biogenesis using conformation-specific monoclonal antibodies (mAbs). We found that alphaIIbbeta3 complex formation was not limited by the availability of either free pro-alphaIIb or free beta3, suggesting that other molecules, perhaps chaperones, control complex formation. Five beta3-specific, ligand-induced binding site (LIBS) mAbs reacted with much or all free beta3 but not with beta3 when in complex with mature alphaIIb, suggesting that beta3 adopts its mature conformation only after complex formation. Conversely, 2 alphaIIb-specific LIBS mAbs directed against the alphaIIb Calf-2 region adjacent to the membrane reacted with only minor fractions of free pro-alphaIIb, raising the possibility that pro-alphaIIb adopts a bent conformation early in biogenesis. Our data suggest a working model in which pro-alphaIIb adopts a bent conformation soon after synthesis, and then beta3 assumes its bent conformation by virtue of its interaction with the bent pro-alphaIIb.

Cell Line↗

PubMed2006

Comparative binding energy analysis considering multiple receptors: a step toward 3D-QSAR models for multiple targets.

Comparative binding energy analysis, a technique to derive receptor-based three-dimensional quantitative structure-activity relationships (3D-QSAR), is herein extended to consider both affinity and selectivity in the derivation of the QSAR model. The extension is based on allowing multiple structurally related receptors to enter the X-matrix employed in the derivation of the structure-activity model. As a result, a single model common to all of them is obtained that considers both intra- and inter-receptor affinity differences for a given congeneric series. We applied the technique to a series of 88 3-amidinophenylalanines, binding to thrombin, trypsin, and factor Xa (fXa). A single predictive regression model for the three receptors involving 202 complexes, with a leave-one out (LOO) cross-validated Q(2) of 0.689, was obtained, and selectivity requirements were investigated. We find that total or partial occupancy of any of the three main pockets in the binding site (D-site, P-site, and the rim of the S1-site) leads to higher affinity across the family. However, the fact that thrombin can make stronger interactions in the P-site, as a result of its exclusive 60-loop, makes of this site a specificity pocket for this thrombin. Occupancy of the D-site leads to more active inhibitors toward fXa for the same reason, but the model does not highlight strongly the D-box because inhibitors are too short to fully occupy it. Negative charge density in the neighborhood of position 88 (a Lys insertion in thrombin) is found to be a determinant for thrombin recognition. These results were consistent with previous studies on selectivity in the thrombin/trypsin/fXa system.

Amino Acid Sequence↗

PubMed2006

Modeling the structure of the StART domains of MLN64 and StAR proteins in complex with cholesterol.

Steroidogenic acute regulatory protein-related lipid transfer (StART) domains are ubiquitously involved in intracellular lipid transport and metabolism and other cell-signaling events. In this work, we use a flexible docking algorithm, comparative modeling, and molecular dynamics (MD) simulations to generate plausible three-dimensional atomic models of the StART domains of human metastatic lymph node 64 (MLN64) and steroidogenic acute regulatory protein (StAR) proteins in complex with cholesterol. Our results show that cholesterol can adopt a similar conformation in the binding cavity in both cases and that the main contribution to the protein-ligand interaction energy derives from hydrophobic contacts. However, hydrogen-bonding and water-mediated interactions appear to be important in the fine-tuning of the binding affinity and the position of the ligand. To gain insights into the mechanism of binding, we carried out steered MD simulations in which cholesterol was gradually extracted from within the StAR model. These simulations indicate that a transient opening of loop Omega1 may be sufficient for uptake and release, and they also reveal a pathway of intermediate states involving residues known to be crucial for StAR activity. Based on these observations, we suggest specific mutagenesis targets for binding studies of cholesterol and its derivatives that could improve our understanding of the structural determinants for ligand binding by sterol carrier proteins.

Algorithms↗

PubMed2006

Computational approaches to model ligand selectivity in drug design.

To be effective, a designed drug must discriminate successfully the macromolecular target from alternative structures present in the organism. The last few years have witnessed the emergence of different computational tools aimed to the understanding and modeling of this process at molecular level. Although still rudimentary, these methods are shaping a coherent approach to help in the design of molecules with high affinity and specificity, both in lead discovery and in lead optimization. It is the purpose of this review to illustrate the array of computational tools available to consider selectivity in the design process, to summarize the most relevant applications, and to sketch the challenges ahead.

Amino Acid Sequence↗

PubMed2004

Virtual screening with flexible docking and COMBINE-based models. Application to a series of factor Xa inhibitors.

A two-step, fully automatic virtual screening procedure consisting of flexible docking followed by activity prediction by COMparative BINding Energy (COMBINE) analysis is presented. This novel approach has been successfully applied, as an example with medicinal chemistry interest, to a recently reported series of 133 factor Xa (fXa)(1) inhibitors whose activities encompass 4 orders of magnitude. The docking algorithm is linked to the COMBINE analysis program and used to derive independent regression models of the 133 inhibitors docked within three different fXa structures (PDB entries 1fjs, 1f0r, and 1xka), so as to explore the effect of receptor conformation on the overall results. Reliable docking conformations and predictive regression models requiring eight latent variables could be derived for two of the fXa structures, with the best model achieving a Q(2) of 0.63 and a standard deviation of errors of prediction (SDEP) of 0.51 (leave-one-out). The two-step procedure was then employed to screen a designed virtual library of 112 ligands, containing both active and inactive compounds. While docking energies alone could show a good performance for selecting hits, including structurally diverse ones, inclusion of COMBINE analysis regression models provided improved rankings for the identification of structurally related molecules in external sets. In our best case, a recognition rate of approximately 80% of known binders at approximately 15% false positives rate was achieved, corresponding to an enrichment factor of approximately 450% over random.

Algorithms↗

PubMed2004

Gaussian mapping of chemical fragments in ligand binding sites.

We present a new approach to automatically define a quasi-optimal minimal set of pharmacophoric points mapping the interaction properties of a user-defined ligand binding site. The method is based on a fitting algorithm where a grid of sampled interaction energies of the target protein with small chemical fragments in the binding site is approximated by a linear expansion of Gaussian functions. A heuristic approximation selects from this expansion the smallest possible set of Gaussians required to describe the interaction properties of the binding site within a prespecified accuracy. We have evaluated the performance of the approach by comparing the computed Gaussians with the positions of aromatic sites found in experimental protein-ligand complexes. For a set of 53 complexes, good correspondence is found in general. At a 95% significance level, approximately 65% of the predicted interaction points have an aromatic binding site within 1.5 A. We then studied the utility of these points in docking using the program DOCK. Short docking times, with an average of approximately 0.18 s per conformer, are obtained, while retaining, both for rigid and flexible docking, the ability to sample native-like binding modes for the ligand. An average 4-5-fold speed-up in docking times and a similar success rate is estimated with respect to the standard DOCK protocol.

Algorithms↗

PubMed2003

Benzimidazole derivatives. 4. The recognition of the voluminous substituent attached to the basic amino group of 5-HT4 receptor antagonists.

We report a structure-affinity analysis of an important element in the pharmacophore model for the recognition of 5-HT4 receptor antagonists: the voluminous substituent attached to the basic nitrogen of the ligand. We have designed, synthesized and pharmacologically evaluated a series of benzimidazole derivatives 1 containing a common molecular skeleton formed by N-[(4-piperidyl)methyl]-6-chlorobenzimidazole-4-carboxamide and four different substituents (R = butyl, 2-[(methylsulfonyl)amino]ethyl, 5-[(phenylacetyl)amino]pentyl, and 5-[(benzylsulfonyl)amino]pentyl). These compounds possess binding affinities in the nM range (Ki = 0.11-1.50 nM). Moreover, a ligand that contains a hydrogen atom attached to the basic nitrogen (R = H; Ki = 150 nM) is used as a control for structure-affinity relationships.

Amino Acid Sequence↗

PubMed2002

Benzimidazole derivatives. 3. 3D-QSAR/CoMFA model and computational simulation for the recognition of 5-HT(4) receptor antagonists.

A three-dimensional quantitative structure-affinity relationship study (3D-QSAR), using the comparative molecular field analysis (CoMFA) method, and subsequent computational simulation of ligand recognition have been successfully applied to explain the binding affinities for the 5-HT(4) receptor (5-HT(4)R) of a series of benzimidazole-4-carboxamides and carboxylates derivatives 1-24. The K(i) values of these compounds are in the range from 0.11 to 10 000 nM. The derived 3D-QSAR model shows high predictive ability (q(2) = 0.789 and r(2) = 0.997). Steric (contribution of 43.5%) and electrostatic (50.3%) fields and solvation energy (6.1%) of this novel class of 5-HT(4)R antagonists are relevant descriptors for structure-activity relationships. Computational simulation of the complexes between the benzimidazole-4-carboxamide UCM-21195 (5) and the carboxylate UCM-26995 (21) and a 3D model of the transmembrane domain of the 5-HT(4)R, constructed using the reported crystal structure of rhodopsin, have allowed us to define the molecular details of the ligand-receptor interaction that includes (i) the ionic interaction between the NH group of the protonated piperidine of the ligand and the carboxylate group of Asp(3.32), (ii) the hydrogen bond between the carbonyl oxygen of the ligand and the hydroxyl group of Ser(5.43), (iii) the hydrogen bond between the NH group of Asn(6.55) and the aromatic ring of carboxamides or the ether oxygen of carboxylates, (iv) the interaction of the electron-rich clouds of the aromatic ring of Phe(6.51) and the electron-poor hydrogens of the carbon atoms adjacent to the protonated piperidine nitrogen of the ligand, and (v) the pi-sigma stacking interaction between the benzimidazole system of the ligand and the benzene ring of Tyr(5.38). Moreover, the noticeable increase in potency at the 5-HT(4)R sites, by the introduction of a chloro or bromo atom at the 6-position of the aromatic ring, is attributed to the additional electrostatic and van der Waals interaction of the halogen atom in a small cavity located between transmembrane domains 5 and 6.

Animals↗