Biomedical subjects

Peter Willett

Publications and source records attributed to Peter Willett.

47 records · Page 3Linked to original sources

PubMedDate not supplied

A fourier fingerprint-based method for protein surface representation.

A crucial enabling technology for structural genomics is the development of algorithms that can predict the putative function of novel protein structures: the proposed functions can subsequently be experimentally tested by functional studies. Testable assignments of function can be made if it is possible to attribute a putative, or indeed probable, function on the basis of the shapes of the binding sites on the surface of a protein structure. However the comparison of the surfaces of 3D protein structures is a computationally demanding task. Here we present four surface representations that can be used locally to describe the global shape of specifically bounded local region models. The most successful of these representations is obtained by a Fourier analysis of the distribution of surface curvature on concentric spheres around a surface point and summarizes a 24 A diameter spherically clipped region of protein surface by a fingerprint of 18 Fourier amplitude values. Searching experiments using these fingerprints on a set of 366 proteins demonstrate that this provides an effective and an efficient technique for the matching of protein surfaces.

Fourier Analysis↗

PubMedDate not supplied

Unsupervised 3D ring template searching as an ideas generator for scaffold hopping: use of the LAMDA, RigFit, and field-based similarity search (FBSS) methods.

Crystal structures taken from the Cambridge Structural Database were used to build a ring scaffold database containing 19 050 3D structures, with each such scaffold then being used to generate a centroid connecting path (CCP) representation. The CCP is a novel object that connects ring centroids, ring linker atoms, and other important points on the connection path between ring centroids. Unsupervised searching in the scaffold and CCP data sets was carried out using the atom-based LAMDA and RigFit search methods and the field-based similarity search method. The performance of these methods was tested with three different ring scaffold queries. These searches demonstrated that unsupervised 3D scaffold searching methods can find not only the types of ring systems that might be retrieved in carefully defined pharmacophore searches (supervised approach) but also additional, structurally diverse ring systems that could form the starting point for lead discovery programs or other scaffold-hopping applications. Not only are the methods effective but some are sufficiently rapid to permit scaffold searching in large chemical databases on a routine basis.

Database Management Systems↗

PubMedDate not supplied

Enhancing the effectiveness of virtual screening by fusing nearest neighbor lists: a comparison of similarity coefficients.

This paper evaluates the effectiveness of various similarity coefficients for 2D similarity searching when multiple bioactive target structures are available. Similarity searches using several different activity classes within the MDL Drug Data Report and the Dictionary of Natural Products databases are performed using BCI 2D fingerprints. Using data fusion techniques to combine the resulting nearest neighbor lists we obtain group recall results which, in many cases, are a considerable improvement on standard average recall values obtained for individual structures. It is shown that the degree of improvement can be related to the structural diversity of the activity class that is searched for, the best results being found for the most diverse groups. The group recall of active compounds using subsets of the class is also investigated: for highly self-similar activity classes, the group recall improvement saturates well before the full activity class size is reached. A rough correlation is found between the relative improvement using the group recall and the square of the number of unique compounds available in all of the merged lists. The Tanimoto coefficient is found unambiguously to be the best coefficient to use for the recovery of active compounds using multiple targets. Furthermore, when using the Tanimoto coefficient, the "MAX" fusion rule is found to be more effective than the "SUM" rule for the combination of similarity searches from multiple targets. The use of group recall can lead to improved enrichment in database searches and virtual screening.

Pharmaceutical Preparations↗

PubMedDate not supplied

Scaffold hopping using clique detection applied to reduced graphs.

Similarity-based methods for virtual screening are widely used. However, conventional searching using 2D chemical fingerprints or 2D graphs may retrieve only compounds which are structurally very similar to the original target molecule. Of particular current interest then is scaffold hopping, that is, the ability to identify molecules that belong to different chemical series but which could form the same interactions with a receptor. Reduced graphs provide summary representations of chemical structures and, therefore, offer the potential to retrieve compounds that are similar in terms of their gross features rather than at the atom-bond level. Using only a fingerprint representation of such graphs, we have previously shown that actives retrieved were more diverse than those found using Daylight fingerprints. Maximum common substructures give an intuitively reasonable view of the similarity between two molecules. However, their calculation using graph-matching techniques is too time-consuming for use in practical similarity searching in larger data sets. In this work, we exploit the low cardinality of the reduced graph in graph-based similarity searching. We reinterpret the reduced graph as a fully connected graph using the bond-distance information of the original graph. We describe searches, using both the maximum common induced subgraph and maximum common edge subgraph formulations, on the fully connected reduced graphs and compare the results with those obtained using both conventional chemical and reduced graph fingerprints. We show that graph matching using fully connected reduced graphs is an effective retrieval method and that the actives retrieved are likely to be topologically different from those retrieved using conventional 2D methods.

Algorithms↗

PubMedDate not supplied

New methods for ligand-based virtual screening: use of data fusion and machine learning to enhance the effectiveness of similarity searching.

Similarity searching using a single bioactive reference structure is a well-established technique for accessing chemical structure databases. This paper describes two extensions of the basic approach. First, we discuss the use of group fusion to combine the results of similarity searches when multiple reference structures are available. We demonstrate that this technique is notably more effective than conventional similarity searching in scaffold-hopping searches for structurally diverse sets of active molecules; conversely, the technique will do little to improve the search performance if the actives are structurally homogeneous. Second, we make the assumption that the nearest neighbors resulting from a similarity search, using a single bioactive reference structure, are also active and use this assumption to implement approximate forms of group fusion, substructural analysis, and binary kernel discrimination. This approach, called turbo similarity searching, is notably more effective than conventional similarity searching.

Artificial Intelligence↗

PubMedDate not supplied

Genomic data analysis using DNA structure: an analysis of conserved nongenic sequences and ultraconserved elements.

Recent comparative studies of the human and mouse genomes have revealed sets of conserved nongenic sequences (CNGs) and sets of ultraconserved elements (UCEs). Both sets of sequences, which exhibit extremely high levels of conservation, extend over hundreds of bases and have no known function. Since there is no detectable sequence homology between paralogous CNGs or UCEs in either of the species, an alignment-free technique is needed for their analysis. We have previously compiled a database of the structural properties of all 32,896 unique DNA octamers, including information on stability, the minimum energy conformation, and flexibility. We have used Fourier techniques to analyze the UCEs and CNGs in terms of their octamer structural properties, to reveal structural correlations which may indicate possible functions for some of these sequences.

Animals↗

PubMedDate not supplied

Structural DNA profiles: single sequence queries.

Structural DNA profiles use the structural properties of the constituent octamers either to observe any characteristics of a single sequence that are unusual (a single sequence query) or to visualize a pattern common to a set of sequences (a multiple sequence query). They are an aid in understanding structural reasons for functional DNA activity. Profiles that answer single sequence queries are introduced and Profile Manager (a software application developed to automate profile generation) is presented. Two sequences that are similar by their nucleotide composition but are known to be very different by structure are analyzed, resulting in useful illustrations that agree with the experimental nuclear magnetic resonance structures.

Animals↗

PubMedDate not supplied

Similarity searching in databases of flexible 3D structures using autocorrelation vectors derived from smoothed bounded distance matrices.

This paper presents an exploratory study of a novel method for flexible 3-D similarity searching based on autocorrelation vectors and smoothed bounded distance matrices. Although the new approach is unable to outperform an existing 2-D similarity searching in terms of enrichment factors, it is able to retrieve different compounds at a given percentage of the hit-list and so may be a useful adjunct to other similarity searching methods.

Computing Methodologies↗

PubMedDate not supplied

Virtual screening using binary kernel discrimination: analysis of pesticide data.

This paper discusses the use of binary kernel discrimination (BKD) for identifying potential active compounds in lead-discovery programs. BKD was compared with established virtual screening methods in a series of experiments using pesticide data from the Syngenta corporate database. It was found to be superior to methods based on similarity searching and substructural analysis but inferior to a support vector machine. Similar conclusions resulted from application of the methods to a pesticide data set for which categorical activity data were available.

Algorithms↗

PubMedDate not supplied

Knowledge-based interaction fingerprint scoring: a simple method for improving the effectiveness of fast scoring functions.

A new method for the postprocessing of docking outputs has been developed, based on encoding putative 3D binding modes (docking solutions) as ligand-protein interactions into simple bit strings, a method analogous to the structural interaction fingerprint. Instead of employing traditional scoring functions, the method uses a series of new, knowledge-based scores derived from the similarity of the bit strings for each docking solution to that of a known reference binding mode. A GOLD docking study was carried out using the Bissantz estrogen receptor antagonist set along with the new scoring method. Superior recovery rates, with up to 2-fold enrichments, were observed when the new knowledge-based scoring was compared to the GOLD fitness score. In addition, top ranking sets of molecules (actives and potential actives or decoys) were structurally diverse with low molecular weights and structural complexities. Principal component analysis and clustering of the fingerprints permits the easy separation of active from inactive binding modes and the visualization of diverse binding modes.

Cluster Analysis↗

PubMedDate not supplied

Virtual screening using binary kernel discrimination: effect of noisy training data and the optimization of performance.

Binary kernel discrimination (BKD) uses a training set of compounds, for which structural and qualitative activity data are available, to produce a model that can then be applied to the structures of other compounds in order to predict their likely activity. Experiments with the MDL Drug Data Report database show that the optimal value of the smoothing parameter, and hence the predictive power of BKD, is crucially dependent on the number of false positives in the training set. It is also shown that the best results for BKD are achieved using one particular optimization method for the determination of the smoothing parameter that lies at the heart of the method and using the Jaccard/Tanimoto coefficient in the kernel function that is used to compute the similarity between a test set molecule and the members of the training set.

Algorithms↗