► Computational compound library design is an essential component for successful lead identification. ► Complementary virtual screening methods used in combination are superior to cascade or ...consensus virtual screening protocols. ► Computational systems-based analysis of cellular high-throughput screening data promises the possible deconvolution of black-box screens and to allow the identification of relevant compound-target pairs. ► Knowledge-based drug repositioning predicts novel drug-target interactions and is a useful lead finding approach.
Computational methods play an ever increasing role in lead finding. A vast repertoire of molecular design and virtual screening methods emerged in the past two decades and are today routinely used. There is increasing awareness that there is no single best computational protocol and correspondingly there is a shift recommending the combination of complementary methods. A promising trend for the application of computational methods in lead finding is to take advantage of the vast amounts of HTS (High Throughput Screening) data to allow lead assessment by detailed systems-based data analysis, especially for phenotypic screens where the identification of compound-target pairs is the primary goal. Herein, we review trends and provide examples of successful applications of computational methods in lead finding.
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 ...αβ 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 K D values of 1â50 μ m . The pMHCI/CD8 and pMHCII/CD4 interactions are significantly weaker than this ( K D >100 μ m ), 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 β 2 -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 ( K D > 2 m m ). 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.
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.
SHP2 is a nonreceptor protein tyrosine phosphatase encoded by the PTPN11 gene and is involved in cell growth and differentiation via the MAPK signaling pathway. SHP2 also plays an important role in ...the programed cell death pathway (PD-1/PD-L1). As an oncoprotein as well as a potential immunomodulator, controlling SHP2 activity is of high therapeutic interest. As part of our comprehensive program targeting SHP2, we identified multiple allosteric binding modes of inhibition and optimized numerous chemical scaffolds in parallel. In this drug annotation report, we detail the identification and optimization of the pyrazine class of allosteric SHP2 inhibitors. Structure and property based drug design enabled the identification of protein–ligand interactions, potent cellular inhibition, control of physicochemical, pharmaceutical and selectivity properties, and potent in vivo antitumor activity. These studies culminated in the discovery of TNO155, (3S,4S)-8-(6-amino-5-((2-amino-3-chloropyridin-4-yl)thio)pyrazin-2-yl)-3-methyl-2-oxa-8-azaspiro4.5decan-4-amine (1), a highly potent, selective, orally efficacious, and first-in-class SHP2 inhibitor currently in clinical trials for cancer.
Virtual screening using bioactivity profiles has become an integral part of currently applied hit finding methods in pharmaceutical industry. However, a significant drawback of this approach is that ...it is only applicable to compounds that have been biologically tested in the past and have sufficient activity annotations for meaningful profile comparisons. Although bioactivity data generated in pharmaceutical institutions are growing on an unprecedented scale, the number of biologically annotated compounds still covers only a minuscule fraction of chemical space. For a newly synthesized compound or an isolated natural product to be biologically characterized across multiple assays, it may take a considerable amount of time. Consequently, this chemical matter will not be included in virtual screening campaigns based on bioactivity profiles. To overcome this problem, we herein introduce bioturbo similarity searching that uses chemical similarity to map molecules without biological annotations into bioactivity space and then searches for biologically similar compounds in this reference system. In benchmark calculations on primary screening data, we demonstrate that our approach generally achieves higher hit rates and identifies structurally more diverse compounds than approaches using chemical information only. Furthermore, our method is able to discover hits with novel modes of inhibition that traditional 2D and 3D similarity approaches are unlikely to discover. Test calculations on a set of natural products reveal the practical utility of the approach for identifying novel and synthetically more accessible chemical matter.
Molecular profiling efforts aim at characterizing the biological actions of small molecules by screening them in hundreds of different biochemical and/or cell-based assays. Together, these assays ...yield a rich data landscape of target-based and phenotypic effects of the tested compounds. However, submitting an entire compound library to a molecular profiling panel can easily become cost-prohibitive. Here, we make use of historical screening assays to create comprehensive bioactivity profiles for more than 300 000 small molecules. These bioactivity profiles, termed PubChem high-throughput screening fingerprints (PubChem HTSFPs), report small molecule activities in 243 different PubChem bioassays. Although the assays originate from originally independently pursued drug or probe discovery projects, we demonstrate their value as molecular signatures when used in combination. We use these PubChem HTSFPs as molecular descriptors in hit expansion experiments for 33 different targets and phenotypes, showing that, on average, they lead to 27 times as many hits in a set of 1000 chosen molecules as a random screening subset of the same size (average ROC score: 0.82). Moreover, we demonstrate that PubChem HTSFPs retrieve hits that are structurally diverse and distinct from active compounds retrieved by chemical similarity-based hit expansion methods. PubChem HTSFPs are made freely available for the chemical biology research community.
High-throughput screening (HTS) is an integral part of early drug discovery. Herein, we focused on those small molecules in a screening collection that have never shown biological activity despite ...having been exhaustively tested in HTS assays. These compounds are referred to as 'dark chemical matter' (DCM). We quantified DCM, validated it in quality control experiments, described its physicochemical properties and mapped it into chemical space. Through analysis of prospective reporter-gene assay, gene expression and yeast chemogenomics experiments, we evaluated the potential of DCM to show biological activity in future screens. We demonstrated that, despite the apparent lack of activity, occasionally these compounds can result in potent hits with unique activity and clean safety profiles, which makes them valuable starting points for lead optimization efforts. Among the identified DCM hits was a new antifungal chemotype with strong activity against the pathogen Cryptococcus neoformans but little activity at targets relevant to human safety.
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A fragment library consisting of 3D-shaped, natural product-like fragments was assembled. Library construction was mainly performed by natural product degradation and natural product ...diversification reactions and was complemented by the identification of 3D-shaped, natural product like fragments available from commercial sources. In addition, during the course of these studies, novel rearrangements were discovered for Massarigenin C and Cytochalasin E. The obtained fragment library has an excellent 3D-shape and natural product likeness, covering a novel, unexplored and underrepresented chemical space in fragment based drug discovery (FBDD).
Different molecular descriptors capture different aspects of molecular structures, but this effect has not yet been quantified systematically on a large scale. In this work, we calculate the ...similarity of 37 descriptors by repeatedly selecting query compounds and ranking the rest of the database. Euclidean distances between the rank-ordering of different descriptors are calculated to determine descriptor (as opposed to compound) similarity, followed by PCA for visualization. Four broad descriptor classes are identified, which are circular fingerprints; circular fingerprints considering counts; path-based and keyed fingerprints; and pharmacophoric descriptors. Descriptor behavior is much more defined by those four classes than the particular parametrization. Using counts instead of the presence/absence of fingerprints significantly changes descriptor behavior, which is crucial for performance of topological autocorrelation vectors, but not circular fingerprints. Four-point pharmacophores (piDAPH4) surprisingly lead to much higher retrieval rates than three-point pharmacophores (28.21% vs 19.15%) but still similar rank-ordering of compounds (retrieval of similar actives). Looking into individual rankings, circular fingerprints seem more appropriate than path-based fingerprints if complex ring systems or branching patterns are present; count-based fingerprints could be more suitable in databases with a large number of repeated subunits (amide bonds, sugar rings, terpenes). Information-based selection of diverse fingerprints for consensus scoring (ECFP4/TGD fingerprints) led only to marginal improvement over single fingerprint results. While it seems to be nontrivial to exploit orthogonal descriptor behavior to improve retrieval rates in consensus virtual screening, those descriptors still each retrieve different actives which corroborates the strategy of employing diverse descriptors individually in prospective virtual screening settings.
SHP2 is a cytoplasmic protein tyrosine phosphatase encoded by the PTPN11 gene and is involved in cell proliferation, differentiation, and survival. Recently, we reported an allosteric mechanism of ...inhibition that stabilizes the auto-inhibited conformation of SHP2. SHP099 (1) was identified and characterized as a moderately potent, orally bioavailable, allosteric small molecule inhibitor, which binds to a tunnel-like pocket formed by the confluence of three domains of SHP2. In this report, we describe further screening strategies that enabled the identification of a second, distinct small molecule allosteric site. SHP244 (2) was identified as a weak inhibitor of SHP2 with modest thermal stabilization of the enzyme. X-ray crystallography revealed that 2 binds and stabilizes the inactive, closed conformation of SHP2, at a distinct, previously unexplored binding sitea cleft formed at the interface of the N-terminal SH2 and PTP domains. Derivatization of 2 using structure-based design resulted in an increase in SHP2 thermal stabilization, biochemical inhibition, and subsequent MAPK pathway modulation. Downregulation of DUSP6 mRNA, a downstream MAPK pathway marker, was observed in KYSE-520 cancer cells. Remarkably, simultaneous occupation of both allosteric sites by 1 and 2 was possible, as characterized by cooperative biochemical inhibition experiments and X-ray crystallography. Combining an allosteric site 1 inhibitor with an allosteric site 2 inhibitor led to enhanced pharmacological pathway inhibition in cells. This work illustrates a rare example of dual allosteric targeted protein inhibition, demonstrates screening methodology and tactics to identify allosteric inhibitors, and enables further interrogation of SHP2 in cancer and related pathologies.
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