Biomedical subjects

J Willem M Nissink

Publications and source records attributed to J Willem M Nissink.

5 recordsLinked to original sources

PubMed2005

Modeling water molecules in protein-ligand docking using GOLD.

We implemented a novel approach to score water mediation and displacement in the protein-ligand docking program GOLD. The method allows water molecules to switch on and off and to rotate around their three principal axes. A constant penalty, sigma(p), representing the loss of rigid-body entropy, is added for water molecules that are switched on, hence rewarding water displacement. We tested the methodology in an extensive validation study. First, sigma(p) is optimized against a training set of 58 protein-ligand complexes. For this training set, our algorithm correctly predicts water mediation/displacement in approximately 92% of the cases. We observed small improvements in the quality of the predicted binding modes for water-mediated complexes. In the second part of this work, an entirely independent set of 225 complexes is used. For this test set, our algorithm correctly predicts water mediation/displacement in approximately 93% of the cases. Improvements in binding mode quality were observed for individual water-mediated complexes.

Algorithms↗

PubMed2005

Comparing protein-ligand docking programs is difficult.

There is currently great interest in comparing protein-ligand docking programs. A review of recent comparisons shows that it is difficult to draw conclusions of general applicability. Statistical hypothesis testing is required to ensure that differences in pose-prediction success rates and enrichment rates are significant. Numerical measures such as root-mean-square deviation need careful interpretation and may profitably be supplemented by interaction-based measures and visual inspection of dockings. Test sets must be of appropriate diversity and of good experimental reliability. The effects of crystal-packing interactions may be important. The method used for generating starting ligand geometries and positions may have an appreciable effect on docking results. For fair comparison, programs must be given search problems of equal complexity (e.g. binding-site regions of the same size) and approximately equal time in which to solve them. Comparisons based on rescoring require local optimization of the ligand in the space of the new objective function. Re-implementations of published scoring functions may give significantly different results from the originals. Ostensibly minor details in methodology may have a profound influence on headline success rates.

Algorithms↗

PubMed2004

Mimicry by asx- and ST-turns of the four main types of beta-turn in proteins.

Hydrogen-bonded beta-turns in proteins occur in four categories: type I (the most common), type II, type II', and type I'. Asx-turns resemble beta-turns, in that both have an NH. . .OC hydrogen bond forming a ring of 10 atoms. Serine and threonine side chains also commonly form hydrogen-bonded turns, here called ST-turns. Asx-turns and ST-turns can be categorized into four classes, based on side chain rotamers and the conformation of the central turn residue, which are geometrically equivalent to the four types of beta-turns. We propose asx- and ST-turns be named using the type I, II, I', and II' beta-turn nomenclature. Using this, the frequency of occurrence of both asx- and ST-turns is: type II' > type I > type II > type I', whereas for beta-turns it is type I > type II > type I' > type II'. Almost all type II asx-turns occur as a recently described three residue feature named an asx-nest.

Hydrogen Bonding↗

PubMed2004

Combined use of physicochemical data and small-molecule crystallographic contact propensities to predict interactions in protein binding sites.

Knowledge-based methods are a good alternative to force-field-based methods for the analysis of sites of interaction in protein binding cavities. Both the Protein Data Bank (PDB) and the Cambridge Structural Database (CSD) offer a good amount of data on non-covalent interactions. Although different from protein-derived data, small-molecule crystal data from the CSD are worth looking at as they provide a much more abundant and diverse set of intermolecular contacts. CSD data, when properly corrected by use of octanol-water pi values, can be used to predict the type of ligand chemical group most likely to occupy a given position within a protein binding site. Comparison with observed positions of ligand groups shows that the success rates of these predictions vary from 23% to 84%. Often, the group predicted to be most preferred at a given position is similar but not identical to the observed ligand group; if these are considered successes, prediction success rates range from 71% to 94%. Using PDB data, the corresponding rates are 16% to 79%, and 61% to 96%. Specificity of prediction of NH groups is somewhat better when using PDB interaction data, but results of prediction of hydrophobic groups seem worse than those obtained with CSD data. We have analysed the importance of data selection by applying different filters to eliminate unwanted interactions from our knowledge-base. The presence of certain types of interactions can be undesirable if they are unrepresentative of biological situations (contact to solvent molecules in small-molecule crystal structures, secondary crystallographic contacts) or if they are likely to add noise to the data without conveying much new information (long-distance contacts, sparsely-populated data sets). The elimination of solvent contacts was found to have no effect on the prediction of ligand groups in our test set. Both secondary-contact filtering and noise filtering were found to have a clear beneficial effect on predictive ability.

Binding Sites↗

PubMed2002

A new test set for validating predictions of protein-ligand interaction.

We present a large test set of protein-ligand complexes for the purpose of validating algorithms that rely on the prediction of protein-ligand interactions. The set consists of 305 complexes with protonation states assigned by manual inspection. The following checks have been carried out to identify unsuitable entries in this set: (1) assessing the involvement of crystallographically related protein units in ligand binding; (2) identification of bad clashes between protein side chains and ligand; and (3) assessment of structural errors, and/or inconsistency of ligand placement with crystal structure electron density. In addition, the set has been pruned to assure diversity in terms of protein-ligand structures, and subsets are supplied for different protein-structure resolution ranges. A classification of the set by protein type is available. As an illustration, validation results are shown for GOLD and SuperStar. GOLD is a program that performs flexible protein-ligand docking, and SuperStar is used for the prediction of favorable interaction sites in proteins. The new CCDC/Astex test set is freely available to the scientific community (http://www.ccdc.cam.ac.uk).

Algorithms↗