Biomedical subjects

Andrew Smellie

Publications and source records attributed to Andrew Smellie.

6 recordsLinked to original sources

PubMed2005

Surrogate docking: structure-based virtual screening at high throughput speed.

Structure-based screening using fully flexible docking is still too slow for large molecular libraries. High quality docking of a million molecule library can take days even on a cluster with hundreds of CPUs. This performance issue prohibits the use of fully flexible docking in the design of large combinatorial libraries. We have developed a fast structure-based screening method, which utilizes docking of a limited number of compounds to build a 2D QSAR model used to rapidly score the rest of the database. We compare here a model based on radial basis functions and a Bayesian categorization model. The number of compounds that need to be actually docked depends on the number of docking hits found. In our case studies reasonable quality models are built after docking of the number of molecules containing approximately 50 docking hits. The rest of the library is screened by the QSAR model. Optionally a fraction of the QSAR-prioritized library can be docked in order to find the true docking hits. The quality of the model only depends on the training set size - not on the size of the library to be screened. Therefore, for larger libraries the method yields higher gain in speed no change in performance. Prioritizing a large library with these models provides a significant enrichment with docking hits: it attains the values of approximately 13 and approximately 35 at the beginning of the score-sorted libraries in our two case studies: screening of the NCI collection and a combinatorial libraries on CDK2 kinase structure. With such enrichments, only a fraction of the database must actually be docked to find many of the true hits. The throughput of the method allows its use in screening of large compound collections and in the design of large combinatorial libraries. The strategy proposed has an important effect on efficiency but does not affect retrieval of actives, the latter being determined by the quality of the docking method itself.

Bayes Theorem↗

PubMed2005

Identification of a small molecule that induces mitotic arrest using a simplified high-content screening assay and data analysis method.

High-content screening has emerged as a new and powerful technique for identifying small-molecule modulators of mammalian cell biology. The authors describe the development and execution of a high-content screen to identify small molecules that induce mitotic arrest in mammalian cancer cells. Many widely used chemotherapeutics, such as Taxol and vinblastine, induce mitotic arrest, and the creation of new drugs that also induce mitotic arrest may have tremendous therapeutic value. In their screen, the authors employed a simple DNA stain (DAPI) and a sensitive nonparametric statistical test to identify compounds from an internal collection of approximately 13,000 high-quality lead-like small molecules. Subsequent analysis of 1 active compound indicated that it induces mitotic arrest, assessed using a high-content phosphohistone H3 detection assay, and caused cell proliferation defects in multiple cancer cell lines. The active compound, a quinazolinone originating from a natural product-like subset of the screened compounds, is active in cells at approximately 500 nM and appears to act by inhibiting the polymerization of tubulin.

Biological Assay↗

PubMed2005

Computational identification of proteins for selectivity assays.

At the stage of optimization of a chemical series the compounds are normally assayed for binding or inhibition on the target protein as well as on several proteins from a selectivity panel. These proteins are normally identified on the basis of sequence homology to the target protein. Experimental selectivity data are also taken into account if available. Cases when a nonhomologous protein has a significant affinity to the compound series are going to be missed if the selectivity panel is identified by homology. Experimental data is usually either unavailable or limited to a small fraction of proteins that should be considered. We have developed a computational method of identification of selectivity panel proteins. It is based on the evaluation of binding site similarity to the target protein using docking scores of target-selected molecular probes. These probes are obtained by docking a large library of drug-like compounds to the target protein followed by selecting a diverse subset from the best virtual binders. Docking scores of these probes to other proteins measure binding site similarity to the target. Because the method does not require prior knowledge of either affinities or structures of inhibitors for the target, it can be applied to any protein with known 3D structure. Validation of the method includes rediscovery of nonhomologous proteins that bind common ligands: estradiol, tamoxifen, and riboflavin. Given 3D structures, the method can effectively discriminate proteins with similar binding sites from random proteins independent of sequence homology.

Algorithms↗

PubMed2003

Conformational analysis by intersection: CONAN.

As high throughput techniques in chemical synthesis and screening improve, more demands are placed on computer assisted design and virtual screening. Many of these computational methods require one or more three-dimensional conformations for molecules, creating a demand for a conformational analysis tool that can rapidly and robustly cover the low-energy conformational spaces of small molecules. A new algorithm of intersection is presented here, which quickly generates (on average <0.5 seconds/stereoisomer) a complete description of the low energy conformational space of a small molecule. The molecule is first decomposed into nonoverlapping nodes N (usually rings) and overlapping paths P with conformations (N and P) generated in an offline process. In a second step the node and path data are combined to form distinct conformers of the molecule. Finally, heuristics are applied after intersection to generate a small representative collection of conformations that span the conformational space. In a study of approximately 97,000 randomly selected molecules from the MDDR, results are presented that explore these conformations and their ability to cover low-energy conformational space.

Journal Article↗

PubMedDate not supplied

Accelerated K-means clustering in metric spaces.

The K-means method is a popular technique for clustering data into k-partitions. In the adaptive form of the algorithm, Lloyds method, an iterative procedure alternately assigns cluster membership based on a set of centroids and then redefines the centroids based on the computed cluster membership. The most time-consuming part of this algorithm is the determination of which points being clustered belong to which cluster center. This paper discusses the use of the vantage-point tree as a method of more quickly assigning cluster membership when the points being clustered belong to intrinsically low- and medium-dimensional metric spaces. Results will be discussed from simulated data sets and real-world data in the clustering of molecular databases based upon physicochemical properties. Comparisons will be made to a highly optimized brute-force implementation of Lloyd's method and to other pruning strategies.

Journal Article↗

PubMedDate not supplied

Visualization and interpretation of high content screening data.

High content screening is a method for identifying small molecule modulators of mammalian cell biology. The nature of the experiment generates an enormous amount of data in the form of photographic images of cells after treatment with compounds of interest. The interpretation of data from these experiments is challenging both in terms of automatically perceiving the images, extracting, and understanding differences between screened compounds and visualizing the results. This paper discusses the application of statistical and visual methods that have been used to interpret data from a simplified DNA stain (DAPI) screen to quickly identify compounds of interest. An understanding of the mechanism of action of the screened compounds can be obtained by comparing them to control compounds of known mechanism of action. Statistical and visual methods will be shown that facilitate easy comparison of screened compounds against these control compounds. As an example, a subset of the internal repository at ArQule was screened, together with control compounds that were known to induce characteristic mitotic arrest. Subsequent data processing described in this paper permitted the easy identification of compounds that were similar to (and very different from) the control compounds.

Antineoplastic Agents↗