Biomedical subjects
Publications and source records attributed to Jürgen Bajorath.
The judges evaluated the submissions for the McMaster University High-Throughput Data-Mining and Docking Competition based on 3 criteria: identification of active compounds, percent enrichment, and overview of the competition. Using these metrics, 4 of the participating groups found meaningful enrichment, and 3 groups made perceptive comments about the general nature of the competition.
Despite recent progress in anti-human immunodeficiency virus (HIV) therapy, drug toxicity and emergence of drug-resistant isolates during long-term treatment of HIV-infected patients necessitate the search for new targets that can be used to develop novel antiviral agents. One such target is the process of nuclear translocation of the HIV preintegration complex. Previously we described a class of arylene bis(methylketone) compounds that inhibit HIV-1 nuclear import by targeting the nuclear localization signal (NLS) in the matrix protein (MA). Here we report a different class of MA NLS-targeting compounds that was selected using computer-assisted drug design. The leading compound from this group, ITI-367, showed potent anti-HIV activity in cultures of T lymphocytes and macrophages and also inhibited HIV-1 replication in ex vivo cultured lymphoid tissue. The virus carrying inactivating mutations in MA NLS was resistant to ITI-367. Analysis by real-time PCR demonstrated that the compound specifically inhibited nuclear import of viral DNA, measured by two-long terminal repeat circle formation. Evidence of the existence of this mechanism was provided by immunofluorescent microscopy, using fluorescently labeled HIV-1, which demonstrated retention of the viral DNA in the cytoplasm of drug-treated macrophages. Compounds inhibiting HIV-1 nuclear import may be attractive candidates for further development.
Computational screening of compound databases has become increasingly popular in pharmaceutical research. Virtual screening approaches can roughly be divided into target structure-based screening (often referred to as docking) and screening using active compounds as templates (ligand-based virtual screening). Ligand-based screening techniques essentially focus on comparative molecular similarity analysis of compounds with known and unknown activity, regardless of the methods or algorithms used. In this review, we first provide an overview of widely used ligand-based virtual screening approaches including various database filters and then discuss recent trends in this field and new methodological developments.
Partitioning algorithms are described that operate in chemical reference spaces formed by combinations of binary-transformed molecular descriptors and aim at the identification of potent hits in ligand-based virtual screening. One of these approaches depends on mapping of consensus positions of compound activity sets in descriptor spaces followed by step-wise extension of the dimensionality of these spaces and re-mapping of activity-dependent consensus positions. Dimension extension is carried out to increase the discriminatory power of descriptor combinations and distinguish database compounds from potential hits. This method was originally named Dynamic Mapping of Consensus positions (DMC) and subsequently extended in order to take different potency levels of known active molecules into account and increase the probability of recognizing potent database hits. The extension was accomplished by adding potency scaling to DMC calculations, and the resulting approach was termed POT-DMC. Results of comparisons of DMC and POT-DMC calculations on different classes of active compounds with substantially varying potency levels support the validity of the POT-DMC approach.
A method for ligand-based virtual screening (LBVS), dynamic mapping of consensus positions (DMC), has been extended to take different potency levels of template compounds into account. This potency scaling technique is designed to tune search calculations toward the detection of increasingly potent hits. LBVS analysis of three different compound classes confirmed the ability of potency-scaled DMC (POT-DMC) to identify active database compounds with higher potency than conventional calculations.
Two molecules with known growth hormone secretagogue (GHS) agonist activity were used as templates to computationally screen approximately 80000 compounds. A total of 108 candidate compounds were selected, and five of them were found to be active in the low-micromolar range in both cell-based and direct binding assays. These compounds were structurally diverse and significantly differed from known GHS agonists. The most active compound was subjected to SAR evaluation, which slightly increased its potency and identified molecular regions important for specific GHS agonist activity.
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Computational approaches that 'dock' small molecules into the structures of macromolecular targets and 'score' their potential complementarity to binding sites are widely used in hit identification and lead optimization. Indeed, there are now a number of drugs whose development was heavily influenced by or based on structure-based design and screening strategies, such as HIV protease inhibitors. Nevertheless, there remain significant challenges in the application of these approaches, in particular in relation to current scoring schemes. Here, we review key concepts and specific features of small-molecule-protein docking methods, highlight selected applications and discuss recent advances that aim to address the acknowledged limitations of established approaches.
Partitioning techniques are widely used to classify compound sets or databases according to specific chemical or biological criteria. Partitioning is conceptually related to, yet algorithmically distinct from, conventional clustering methods and is particularly suitable for efficient processing of very large compound sets. Currently, some of the most popular partitioning approaches in the chemoinformatics field involve dimension reduction of initially defined chemistry spaces and creation of subsections of low-dimensional space for molecular classification. These subsections are often called cells. Original chemical reference spaces are generated through selection of various descriptors of molecular structure and properties. Principles and methodological aspects of dimension reduction of chemical spaces and compound partitioning in low-dimensional space are described herein.
Here we describe a statistically based partitioning method called median partitioning (MP), which involves the transformation of value distributions of molecular property descriptors into a binary classification scheme. The MP approach fundamentally differs from other partitioning approaches that involve dimension reduction of chemical spaces such as cell-based partitioning, since MP directly operates in original, albeit simplified, chemical space. Modified versions of the MP algorithm have been implemented and successfully applied in diversity selection, compound classification, and virtual screening. These findings have demonstrated that dimension reduction techniques, although elegant in their design, are not necessarily required for effective partitioning of molecular datasets. An attractive feature of statistical partitioning approaches such as decision tree methods or MP is their computational efficiency, which is becoming an important criterion for the analysis of compound databases containing millions of molecules.
In this review, we discuss a number of computational methods that have been developed or adapted for molecule classification and virtual screening (VS) of compound databases. In particular, we focus on approaches that are complementary to high-throughput screening (HTS). The discussion is limited to VS methods that operate at the small molecular level, which is often called ligand-based VS (LBVS), and does not take into account docking algorithms or other structure-based screening tools. We describe areas that greatly benefit from combining virtual and biological screening and discuss computational methods that are most suitable to contribute to the integration of screening technologies. Relevant approaches range from established methods such as clustering or similarity searching to techniques that have only recently been introduced for LBVS applications such as statistical methods or support vector machines. Finally, we discuss a number of representative applications at the interface between VS and HTS.
B7-H1 and B7-DC are ligands for PD-1, a receptor implicated in negative regulation of T and B cell functions. These ligands, however, also costimulate T cell responses. It remains elusive whether or not costimulation is mediated through PD-1. By comparative molecular modeling and site-directed mutagenesis, we found that nonconserved residues between these ligands on the A'GFCC'C" face mediate interaction with PD-1. This indicates significant structural heterogeneity of the interactions between PD-1 and its ligands. Importantly, ligand mutants with abolished PD-1 binding capacity could still costimulate proliferation and cytokine production of T cells from normal and PD-1-deficient mice. Our results reveal unique binding characteristics of B7-H1 and B7-DC and provide direct evidence for an independent costimulatory receptor other than PD-1.
We identify a B7 family molecule, B7-H4, by protein sequence analysis and comparative molecular modeling. While B7-H4 mRNA is widely distributed in mouse and human peripheral tissues, cell surface expression of B7-H4 protein is limited and shows an inducible pattern on hematopoietic cells. Putative receptor of B7-H4 can be upregulated on activated T cells. By arresting cell cycle, B7-H4 ligation of T cells has a profound inhibitory effect on the growth, cytokine secretion, and development of cytotoxicity. Administration of B7-H4Ig into mice impairs antigen-specific T cell responses whereas blockade of endogenous B7-H4 by specific monoclonal antibody promotes T cell responses. B7-H4 thus may participate in negative regulation of cell-mediated immunity in peripheral tissues.
The dramatically increasing number of compounds that become available for biological evaluation presents a significant challenge for database design, management, and mining. Computational approaches for screening, profiling, or filtering of large compound collections are by now widely used in pharmaceutical research. Among popular compound classification and database mining techniques, partitioning methods are computationally very efficient and particularly suitable for the analysis of increasingly large molecular databases, as they do not depend on pair-wise comparisons of compounds to assess molecular similarity or diversity. Promising applications of partitioning algorithms include diversity selection, searching for compounds with desired biological activity, or the derivation of predictive models from screening datasets. Compound partitioning is introduced here in the context of virtual screening and different partitioning methods are discussed that operate in low-dimensional or other chemical descriptor spaces, including a number of practical drug-discovery-related applications.
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Bio- and chemo-informatics are now thought to be crucial to the success and integration of biotechnology and drug discovery. Research in this area has expanded to go beyond data- and information-management. Here, we review exemplary areas, such as target identification and validation, virtual screening, and prediction of downstream characteristics of leads, where further research will play a key role in progressing the field.
Interaction between inducible costimulator (ICOS) and its ligand is implicated in the induction of cell-mediated and humoral immune responses. However, the molecular details of this interaction are unknown. We report here a mutagenesis analysis of residues in ICOS that are critical for ligand binding. A three-dimensional model of the extracellular immunoglobulin-like domain of ICOS was used to map the residues conserved within the CD28 family. This analysis identified a surface patch containing the characteristic "PPP" sequence and is conserved in human and mouse ICOS. Mutations in this region of human ICOS reduce or abolish ligand binding. Our results suggest that the ligand binding site in ICOS maps to a region overlapping yet distinct from the CD80/CD86 binding sites in CD28 and cytotoxic T lymphocyte antigen (CTLA)-4. Thus, the analysis suggests that differences in ligand binding specificity between these related costimulatory molecules have evolved by utilization of overlapping regions with different patterns of conserved and nonconserved residues. Two site-specific mutants generated in the course of our studies bound ICOS ligand with higher avidity than wild-type ICOS. An S76E mutant protein of ICOS blocked T cell costimulatory function of ICOS ligand and inhibited T cell response to allogeneic antigens superior to wild-type ICOS. Our studies thus identified critical residues involving in ICOS receptor-ligand interaction and provide new modulators for immune responses.