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Meir Glick

Publications and source records attributed to Meir Glick.

At least 19 recordsLinked to original sources
PubMed2006

Bridging chemical and biological space: "target fishing" using 2D and 3D molecular descriptors.

Bridging chemical and biological space is the key to drug discovery and development. Typically, cheminformatics methods operate under the assumption that similar chemicals have similar biological activity. Ideally then, one could predict a drug's biological function(s) given only its chemical structure by similarity searching in libraries of compounds with known activities. In practice, effectively choosing a similarity metric is case dependent. This work compares both 2D and 3D chemical descriptors as tools for predicting the biological targets of ligand probes, on the basis of their similarity to reference molecules in a 46,000 compound, biologically annotated chemical database. Overall, we found that the 2D methods employed here outperform the 3D (88% vs 67% success) in correct target prediction. However, the 3D descriptors proved superior in cases of probes with low structural similarity to other compounds in the database (singletons). Additionally, the 3D method (FEPOPS) shows promise for providing pharmacophoric alignment of the small molecules' chemical features consistent with those seen in experimental ligand/ receptor complexes. These results suggest that querying annotated chemical databases with a systematic combination of both 2D and 3D descriptors will prove more effective than employing single methods.

Adenosine Triphosphate↗
PubMed2006

Streamlining lead discovery by aligning in silico and high-throughput screening.

Lead discovery in the pharmaceutical environment is largely an industrial-scale process in which it is typical to screen 1-5 million compounds in a matter of weeks using High Throughput Screening (HTS). This process is a very costly endeavor. Typically a HTS campaign of 1 million compounds will cost anywhere from $500000 to $1000000. There is consequently a great deal of pressure to maximize the return on investment by finding fast and more effective ways to screen. A panacea that has emerged over the past few years to help address this issue is in silico screening. In silico screening is now incorporated in all areas of lead discovery; from target identification and library design, to hit analysis and compound profiling. However, as lead discovery has evolved over the past few years, so has the role of in silico screening.

Bayes Theorem↗
PubMed2005

Interaction between the CD8 coreceptor and major histocompatibility complex class I stabilizes T cell receptor-antigen complexes at the cell surface.

The off-rate (k(off)) of the T cell receptor (TCR)/peptide-major histocompatibility complex class I (pMHCI) interaction, and hence its half-life, is the principal kinetic feature that determines the biological outcome of TCR ligation. However, it is unclear whether the CD8 coreceptor, which binds pMHCI at a distinct site, influences this parameter. Although biophysical studies with soluble proteins show that TCR and CD8 do not bind cooperatively to pMHCI, accumulating evidence suggests that TCR associates with CD8 on the T cell surface. Here, we titrated and quantified the contribution of CD8 to TCR/pMHCI dissociation in membrane-constrained interactions using a panel of engineered pMHCI mutants that retain faithful TCR interactions but exhibit a spectrum of affinities for CD8 of >1,000-fold. Data modeling generates a "stabilization factor" that preferentially increases the predicted TCR triggering rate for low affinity pMHCI ligands, thereby suggesting an important role for CD8 in the phenomenon of T cell cross-reactivity.

Antigens↗
PubMed2005

Crystallization and preliminary X-ray structural studies of a high-affinity CD8alphaalpha co-receptor to pMHC.

The class I CD8 positive T-cell response is involved in a number of conditions in which artificial down-regulation and control would be therapeutically beneficial. Such conditions include a number of autoimmune diseases and graft rejection in transplant patients. Although the CD8 T-cell response is dominated by the TCR-pMHC interaction, activation of T cells is in most cases also dependent on a number of associated signalling molecules. Previous work has demonstrated the ability of one such molecule (CD8) to act as an antagonist to T-cell activation if added in soluble form. Therefore, a high-affinity mutant CD8 (haCD8) has been developed with the aim of developing a therapeutic immunosuppressor. In order to fully understand the nature of the haCD8 interaction, this protein was crystallized using the sitting-drop vapour-diffusion method. Single haCD8 crystals were cryocooled and used for data collection. These crystals belonged to space group P6(4)22 (assumed by similarity to the wild type), with unit-cell parameters a = 101.08, c = 56.54 A. VM calculations indicated one molecule per asymmetric unit. A 2 A data set was collected and the structure is currently being determined using molecular replacement.

CD8-Positive T-Lymphocytes↗
PubMed2004

A 3D similarity method for scaffold hopping from known drugs or natural ligands to new chemotypes.

A primary goal of 3D similarity searching is to find compounds with similar bioactivity to a reference ligand but with different chemotypes, i.e., "scaffold hopping". However, an adequate description of chemical structures in 3D conformational space is difficult due to the high-dimensionality of the problem. We present an automated method that simplifies flexible 3D chemical descriptions in which clustering techniques traditionally used in data mining are exploited to create "fuzzy" molecular representations called FEPOPS (feature point pharmacophores). The representations can be used for flexible 3D similarity searching given one or more active compounds without a priori knowledge of bioactive conformations or pharmacophores. We demonstrate that similarity searching with FEPOPS significantly enriches for actives taken from in-house high-throughput screening datasets and from MDDR activity classes COX-2, 5-HT3A, and HIV-RT, while also scaffold or ring-system hopping to new chemical frameworks. Further, inhibitors of target proteins (dopamine 2 and retinoic acid receptor) are recalled by FEPOPS by scaffold hopping from their associated endogenous ligands (dopamine and retinoic acid). Importantly, the method excels in comparison to commonly used 2D similarity methods (DAYLIGHT, MACCS, Pipeline Pilot fingerprints) and a commercial 3D method (Pharmacophore Distance Triplets) at finding novel scaffold classes given a single query molecule.

Cyclooxygenase 2↗
PubMed2004

Combination of a naive Bayes classifier with consensus scoring improves enrichment of high-throughput docking results.

We have previously shown that a machine learning technique can improve the enrichment of high-throughput docking (HTD) results. In the previous cases studied, however, the application of a naive Bayes classifier failed to improve enrichment for instances where HTD alone was unable to generate an acceptable enrichment. We present here a protocol to rescue poor docking results a priori using a combination of rank-by-median consensus scoring and naive Bayesian categorization.

Algorithms↗
PubMed2004

Finding more needles in the haystack: A simple and efficient method for improving high-throughput docking results.

The technology underpinning high-throughput docking (HTD) has developed over the past few years to where it has become a vital tool in modern drug discovery. Although the performance of various docking algorithms is adequate, the ability to accurately and consistently rank compounds using a scoring function remains problematic. We show that by employing a simple machine learning method (naïve Bayes) it is possible to significantly overcome this deficiency. Compounds from the Available Chemical Directory (ACD), along with known active compounds, were docked into two protein targets using three software packages. In cases where HTD alone was able to show some enrichment, the application of naïve Bayes was able to improve upon the enrichment. The application of this methodology to enrich HTD results can be carried out without a priori knowledge of the activity of compounds and results in superior enrichment of known actives compared to the use of scoring methods alone.

Bayes Theorem↗
PubMed2004

Enrichment of extremely noisy high-throughput screening data using a naïve Bayes classifier.

The noise level of a high-throughput screening (HTS) experiment depends on various factors such as the quality and robustness of the assay itself and the quality of the robotic platform. Screening of compound mixtures is noisier than screening single compounds per well. A classification model based on naïve Bayes (NB) may be used to enrich such data. The authors studied the ability of the NB classifier to prioritize noisy primary HTS data of compound mixtures (5 compounds/well) in 4 campaigns in which the percentage of noise presumed to be inactive compounds ranged between 81% and 91%. The top 10% of the compounds suggested by the classifier captured between 26% and 45% of the active compounds. These results are reasonable and useful, considering the poor quality of the training set and the short computing time that is needed to build and deploy the classifier.

Bayes Theorem↗
PubMed2003

The CD8 T cell coreceptor exhibits disproportionate biological activity at extremely low binding affinities.

T lymphocytes recognize peptides presented in the context of major histocompatibility complex (MHC) molecules on the surface of antigen presenting cells. Recognition specificity is determined by the alphabeta T cell receptor (TCR). The T lymphocyte surface glycoproteins CD8 and CD4 enhance T cell antigen recognition by binding to MHC class I and class II molecules, respectively. Biophysical measurements have determined that equilibrium binding of the TCR with natural agonist peptide-MHC (pMHC) complexes occurs with KD values of 1-50 microm. The pMHCI/CD8 and pMHCII/CD4 interactions are significantly weaker than this (KD >100 microm), and the relative roles of TCR/pMHC and pMHC/coreceptor affinity in T cell activation remain controversial. Here, we engineer mutations in the MHCI heavy chain and beta2-microglobulin that further reduce or abolish the pMHCI/CD8 interaction to probe the significance of pMHC/coreceptor affinity in T cell activation. We demonstrate that the pMHCI/CD8 coreceptor interaction retains the vast majority of its biological activity at affinities that are reduced by over 15-fold (KD > 2 mm). In contrast to previous reports, we observe that the weak interaction between HLA A68 and CD8, which falls within this spectrum of reduced affinities, retains substantial functional activity. These findings are discussed in the context of current concepts of coreceptor dependence and the mechanism by which TCR coreceptors facilitate T cell activation.

Antigen Presentation↗
PubMed2003

Refined structure of bovine carboxypeptidase A at 1.25 A resolution.

The crystal structure of the bovine zinc metalloproteinase carboxypeptidase A (CPA) has been refined to 1.25 A resolution based on room-temperature X-ray synchrotron data. The significantly improved structure of CPA at this resolution (anisotropic temperature factors, R factor = 10.4%, R(free) = 14.5%) allowed the modelling of conformational disorders of side chains, improved the description of the protein solvent network (375 water molecules) and provided a more accurate picture of the interactions between the active-site zinc and its ligands. The calculation of standard uncertainties in individual atom positions of the refined model of CPA allowed the deduction of the protonation state of some key residues in the active site and confirmed that Glu72 and Glu270 are negatively charged in the resting state of the enzyme at pH 7.5. These results were further validated by theoretical calculations that showed significant reduction of the pK(a) of these side chains relative to solution values. The distance between the zinc-bound solvent molecule and the metal ion is strongly suggestive of a neutral water molecule and not a hydroxide ion in the resting state of the enzyme. These findings could support both the general acid/general base mechanism, as well as the anhydride mechanism suggested for CPA.

Amino Acids↗
PubMed2003

Stable, soluble T-cell receptor molecules for crystallization and therapeutics.

Antibody and T-cell receptors (TCRs) are the primary recognition molecules of the adaptive immune system. Antibodies have been extensively characterized and are being developed for a large number of therapeutic applications. This has been possible because of the ability to manufacture stable, soluble, monoclonal antibodies which retain the antigen specificity of B cells. Unlike antibodies, TCRs are not expressed in a soluble form, but are anchored to the T-cell surface by an insoluble trans-membrane domain. Characterization and development of TCRs has been hampered by the lack of suitable methods for producing them as soluble and stable proteins. Here we report the engineering of soluble human TCRs suitable for crystallization studies and potentially for in vivo therapeutic use.

Crystallization↗
PubMed2002

Docking of flexible molecules using multiscale ligand representations.

Structural genomics will yield an immense number of protein three-dimensional structures in the near future. Automated theoretical methodologies are needed to exploit this information and are likely to play a pivotal role in drug discovery. Here, we present a fully automated, efficient docking methodology that does not require any a priori knowledge about the location of the binding site or function of the protein. The method relies on a multiscale concept where we deal with a hierarchy of models generated for the potential ligand. The models are created using the k-means clustering algorithm. The method was tested on seven protein-ligand complexes. In the largest complex, human immunodeficiency virus reverse transcriptase/nevirapin, the root mean square deviation value when comparing our results to the crystal structure was 0.29 A. We demonstrate on an additional 25 protein-ligand complexes that the methodology may be applicable to high throughput docking. This work reveals three striking results. First, a ligand can be docked using a very small number of feature points. Second, when using a multiscale concept, the number of conformers that require to be generated can be significantly reduced. Third, fully flexible ligands can be treated as a small set of rigid k-means clusters.

Algorithms↗
PubMed2002

Novel CD8+ T cell antagonists based on beta 2-microglobulin.

The CD8 coreceptor of cytotoxic T lymphocytes binds to a conserved region of major histocompatibility complex class I molecules during recognition of peptide-major histocompatibility complex (MHC) class I antigens on the surface of target cells. This event is central to the activation of cytotoxic T lymphocyte (CTL) effector functions. The contribution of the MHC complex class I light chain, beta(2)-microglobulin, to CD8alphaalpha binding is relatively small and is mediated mainly through the lysine residue at position 58. Despite this, using molecular modeling, we predict that its mutation should have a dramatic effect on CD8alphaalpha binding. The predictions are confirmed using surface plasmon resonance binding studies and human CTL activation assays. Surprisingly, the charge-reversing mutation, Lys(58) --> Glu, enhances beta(2)m-MHC class I heavy chain interactions. This mutation also significantly reduces CD8alphaalpha binding and is a potent antagonist of CTL activation. These results suggest a novel approach to CTL-specific therapeutic immunosuppression.

Amino Acid Sequence↗
PubMed2002

Identification of ligand binding sites on proteins using a multi-scale approach.

Identification of a ligand binding site on a protein is pivotal to drug discovery. To date, no reliable and computationally feasible general approach to this problem has been published. Here we present an automated efficient method for determining binding sites on proteins for potential ligands without any a priori knowledge. Our method is based upon the multiscale concept where we deal with a hierarchy of models generated using a k-means clustering algorithm for the potential ligand. This is done in a simple approach whereby a potential ligand is represented by a growing number of feature points. At each increasing level of detail, a pruning of potential binding site is performed. A nonbonding energy function is used to score the interactions between molecules at each step. The technique was successfully employed to seven protein-ligand complexes. In the current paper we show that the algorithm considerably reduces the computational effort required to solve this problem. This approach offers real opportunities for exploiting the large number of structures that will evolve from structural genomics.

Algorithms↗
PubMed2002

A stochastic algorithm for global optimization and for best populations: a test case of side chains in proteins.

The problem of global optimization is pivotal in a variety of scientific fields. Here, we present a robust stochastic search method that is able to find the global minimum for a given cost function, as well as, in most cases, any number of best solutions for very large combinatorial "explosive" systems. The algorithm iteratively eliminates variable values that contribute consistently to the highest end of a cost function's spectrum of values for the full system. Values that have not been eliminated are retained for a full, exhaustive search, allowing the creation of an ordered population of best solutions, which includes the global minimum. We demonstrate the ability of the algorithm to explore the conformational space of side chains in eight proteins, with 54 to 263 residues, to reproduce a population of their low energy conformations. The 1,000 lowest energy solutions are identical in the stochastic (with two different seed numbers) and full, exhaustive searches for six of eight proteins. The others retain the lowest 141 and 213 (of 1,000) conformations, depending on the seed number, and the maximal difference between stochastic and exhaustive is only about 0.15 Kcal/mol. The energy gap between the lowest and highest of the 1,000 low-energy conformers in eight proteins is between 0.55 and 3.64 Kcal/mol. This algorithm offers real opportunities for solving problems of high complexity in structural biology and in other fields of science and technology.

Algorithms↗
PubMed2002

Pattern recognition and massively distributed computing.

A feature of Peter Kollman's research was his exploitation of the latest computational techniques to devise novel applications of the free energy perturbation method. He would certainly have seized upon the opportunities offered by massively distributed computing. Here we describe the use of over a million personal computers to perform virtual screening of 3.5 billion druglike molecules against protein targets by pharmacophore pattern matching, together with other applications of pattern recognition such as docking ligands without any a priori knowledge about the binding site location.

Algorithms↗
PubMedDate not supplied

Application of machine learning to improve the results of high-throughput docking against the HIV-1 protease.

We have previously reported that the application of a Laplacian-modified naive Bayesian (NB) classifier may be used to improve the ranking of known inhibitors from a random database of compounds after High-Throughput Docking (HTD). The method relies upon the frequency of substructural features among the active and inactive compounds from 2D fingerprint information of the compounds. Here we present an investigation of the role of extended connectivity fingerprints in training the NB classifier against HTD studies on the HIV-1 protease using three docking programs: Glide, FlexX, and GOLD. The results show that the performance of the NB classifier is due to the presence of a large number of features common to the set of known active compounds rather than a single structural or substructural scaffold. We demonstrate that the Laplacian-modified naive Bayesian classifier trained with data from high-throughput docking is superior at identifying active compounds from a target database in comparison to conventional two-dimensional substructure search methods alone.

Algorithms↗