Biomedical subjects

Gert Vriend

Publications and source records attributed to Gert Vriend.

9 recordsLinked to original sources

PubMed2003

NRSAS: Nuclear Receptor Structure Analysis Servers.

We present a coherent series of servers that can perform a large number of structure analyses on nuclear hormone receptors. These servers are part of the NucleaRDB project, which provides a powerful information system for nuclear hormone receptors. The computations performed by the servers include homology modelling, structure validation, calculating contacts, accessibility values, hydrogen bonding patterns, predicting mutations and a host of two- and three-dimensional visualisations. The Nuclear Receptor Structure Analysis Servers (NRSAS) are freely accessible at http://www.cmbi.kun.nl/NR/servers/html/ and in-house copies can be obtained upon request.

Hormones↗

PubMed2003

NRMD: Nuclear Receptor Mutation Database.

The NRMD is a database for nuclear receptor mutation information. It includes mutation information from SWISS-PROT/TrEMBL, several web-based mutation data resources, and data extracted from the literature in a fully automatic manner. Because it is also possible to add mutations manually, a hundred mutations were added for completeness. At present, the NRMD contains information about 893 mutations in 54 nuclear receptors. A common numbering scheme for all nuclear receptors eases the use of the information for many kinds of studies. The NRMD is freely available to academia and industry as a stand-alone version at: www.receptors.org/NR/.

Animals↗

PubMed2003

Homology modeling.

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Amino Acid Sequence↗

PubMed2003

The precision of NMR structure ensembles revisited.

Biomolecular structures provide the basis for many studies in research areas such as structure-based drug design and homology modeling. In order to use molecular coordinates it is important that they are reliable in terms of accurate description of the experimental data and in terms of the overall and local geometry. Besides these primary quality criteria an indication is needed for the uncertainty in the atomic coordinates that may arise from the dynamic behavior of the considered molecules as well as from experimental- and computational procedures. In contrast to the crystallographic B-factor, a good measure for the uncertainty in NMR-derived atomic coordinates is still not available. It has become clear in recent years that the widely used atomic Root Mean Square Deviation (RMSD), which is a measure for the precision of the data, overestimates the accuracy of NMR structure ensembles and therefore is a problematic measure for the uncertainty in the atomic coordinates. In this study we report a method that yields a more realistic estimate of the uncertainty in the atomic coordinates by maximizing the RMSD of an ensemble of structures, while maintaining the accordance with the experimentally derived data. The results indicate that the RMSD of most NMR structure ensembles can be significantly increased compromising neither geometric quality nor NMR data. This maximized RMSD therefore seems a better estimate of the true uncertainty in the atomic coordinates.

Crystallography, X-Ray↗

PubMed2002

Increasing the precision of comparative models with YASARA NOVA--a self-parameterizing force field.

One of the conclusions drawn at the CASP4 meeting in Asilomar was that applying various force fields during refinement of template-based models tends to move predictions in the wrong direction, away from the experimentally determined coordinates. We have derived an all-atom force field aimed at protein and nucleotide optimization in vacuo (NOVA), which has been specifically designed to avoid this problem. NOVA resembles common molecular dynamics force fields but has been automatically parameterized with two major goals: (i) not to make high resolution X-ray structures worse and (ii) to improve homology models built by WHAT IF. Force-field parameters were not required to be physically correct; instead, they were optimized with random Monte Carlo moves in force-field parameter space, each one evaluated by simulated annealing runs of a 50-protein optimization set. Errors inherent to the approximate force-field equation could thus be canceled by errors in force-field parameters. Compared with the optimization set, the force field did equally well on an independent validation set and is shown to move in silico models closer to reality. It can be applied to modeling applications as well as X-ray and NMR structure refinement. A new method to assign force-field parameters based on molecular trees is also presented. A NOVA server is freely accessible at http://www.yasara.com/servers

Algorithms↗

PubMed2002

The effects of modifying the surface charge on the catalytic activity of a thermolysin-like protease.

The impact of long range electrostatic interactions on catalysis in the thermolysin-like protease from Bacillus stearothermophilus was studied by analyzing the effects of inserting or removing charges on the protein surface. Various mutations were introduced at six different positions, and double-mutant cycle analysis was used to study the extent to which mutational effects were interdependent. The effects of single point mutations on the k(cat)/K(m) were non-additive, even in cases where the point mutations were located 10 A or more from the active site Zn(2+) and separated from each other by up to 25 A. This shows that catalysis is affected by large electrostatic networks that involve major parts of the enzyme. The interdependence of mutations at positions as much as 25 A apart in space also indicates that other effects, such as active site dynamics, play an important role in determining active site electrostatics. Several mutations yielded a significant increase in the activity, the most active (quadruple) mutant being almost four times as active as the wild type. In some cases the shape of the pH-activity profile was changed significantly. Remarkably, large changes in the pH-optimum were not observed.

Bacterial Proteins↗

PubMed2002

Identification of different binding sites in the dendritic cell-specific receptor DC-SIGN for intercellular adhesion molecule 3 and HIV-1.

The novel dendritic cell (DC)-specific human immunodeficiency virus type 1 (HIV-1) receptor DC-SIGN plays a key role in the dissemination of HIV-1 by DC. DC-SIGN is thought to capture HIV-1 at mucosal sites of entry, facilitating transport to lymphoid tissues, where DC-SIGN efficiently transmits HIV-1 to T cells. DC-SIGN is also important in the initiation of immune responses by regulating DC-T cell interactions through intercellular adhesion molecule 3 (ICAM-3). We have characterized the mechanism of ligand binding by DC-SIGN and identified the crucial amino acids involved in this process. Strikingly, the HIV-1 gp120 binding site in DC-SIGN is different from that of ICAM-3, consistent with the observation that glycosylation of gp120, in contrast to ICAM-3, is not crucial to the interaction with DC-SIGN. A specific mutation in DC-SIGN abrogated ICAM-3 binding, whereas the HIV-1 gp120 interaction was unaffected. This DC-SIGN mutant captured HIV-1 and infected T cells in trans as efficiently as wild-type DC-SIGN, demonstrating that ICAM-3 binding is not necessary for HIV-1 transmission. This study provides a basis for the design of drugs that inhibit or alter interactions of DC-SIGN with gp120 but not with ICAM-3 or vice versa and that have a therapeutic value in immunological diseases and/or HIV-1 infections.

Amino Acid Sequence↗

PubMed2002

Models@Home: distributed computing in bioinformatics using a screensaver based approach.

MOTIVATION: Due to the steadily growing computational demands in bioinformatics and related scientific disciplines, one is forced to make optimal use of the available resources. A straightforward solution is to build a network of idle computers and let each of them work on a small piece of a scientific challenge, as done by Seti@Home (http://setiathome.berkeley.edu), the world's largest distributed computing project. RESULTS: We developed a generally applicable distributed computing solution that uses a screensaver system similar to Seti@Home. The software exploits the coarse-grained nature of typical bioinformatics projects. Three major considerations for the design were: (1) often, many different programs are needed, while the time is lacking to parallelize them. Models@Home can run any program in parallel without modifications to the source code; (2) in contrast to the Seti project, bioinformatics applications are normally more sensitive to lost jobs. Models@Home therefore includes stringent control over job scheduling; (3) to allow use in heterogeneous environments, Linux and Windows based workstations can be combined with dedicated PCs to build a homogeneous cluster. We present three practical applications of Models@Home, running the modeling programs WHAT IF and YASARA on 30 PCs: force field parameterization, molecular dynamics docking, and database maintenance.

Computational Biology↗

PubMed2002

Prediction of protein residue contacts with a PDB-derived likelihood matrix.

Proteins with similar folds often display common patterns of residue variability. A widely discussed question is how these patterns can be identified and deconvoluted to predict protein structure. In this respect, correlated mutation analysis (CMA) has shown considerable promise. CMA compares multiple members of a protein family and detects residues that remain constant or mutate in tandem. Often this behavior points to structural or functional interdependence between residues. CMA has been used to predict pairs of amino acids that are distant in the primary sequence but likely to form close contacts in the native three-dimensional structure. Until now these methods have used evolutionary or biophysical models to score the fit between residues. We wished to test whether empirical methods, derived from known protein structures, would provide useful predictive power for CMA. We analyzed 672 known protein structures, derived contact likelihood scores for all possible amino acid pairs, and used these scores to predict contacts. We then tested the method on 118 different protein families for which structures have been solved to atomic resolution. The mean performance was almost seven times better than random prediction. Used in concert with secondary structure prediction, the new CMA method could supply restraints for predicting still undetermined structures.

Amino Acid Sequence↗