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.

Auristatins, synthetic analogues of the antineoplastic natural product Dolastatin 10, are ultrapotent cytotoxic microtubule inhibitors that are clinically used as payloads in antibody-drug conjugates (ADCs). The design and synthesis of several new auristatin analogues with N-terminal modifications that include amino acids with α,α-disubstituted carbon atoms are described, including the discovery of our lead auristatin, PF-06380101. This modification of the peptide structure is unprecedented and led to analogues with excellent potencies in tumor cell proliferation assays and differential ADME properties when compared to other synthetic auristatin analogues that are used in the preparation of ADCs. In addition, auristatin cocrystal structures with tubulin are being presented that allow for the detailed examination of their binding modes. A surprising finding is that all analyzed analogues have a cis-configuration at the Val-Dil amide bond in their functionally relevant tubulin bound state, whereas in solution this bond is exclusively in the trans-configuration. This remarkable observation shines light onto the preferred binding mode of auristatins and serves as a valuable tool for structure-based drug design.

We present a workflow that leverages data from chemogenomics based target predictions with Systems Biology databases to better understand off-target related toxicities. By analyzing a set of compounds that share a common toxic phenotype and by comparing the pathways they affect with pathways modulated by nontoxic compounds we are able to establish links between pathways and particular adverse effects. We further link these predictive results with literature data in order to explain why a certain pathway is predicted. Specifically, relevant pathways are elucidated for the side effects rhabdomyolysis and hypotension. Prospectively, our approach is valuable not only to better understand toxicities of novel compounds early on but also for drug repurposing exercises to find novel uses for known drugs.

We present a novel method to better investigate adverse drug reactions in chemical space. By integrating data sources about adverse drug reactions of drugs with an established cheminformatics modeling method, we generate a data set that is then visualized with a systems biology tool. Thereby new insights into undesired drug effects are gained. In this work, we present a global analysis linking chemical features to adverse drug reactions.

As part of our efforts to develop new classes of tubulin inhibitor payloads for antibody-drug conjugate (ADC) programs, we developed a tubulysin ADC that demonstrated excellent in vitro activity but suffered from rapid metabolism of a critical acetate ester. A two-pronged strategy was employed to address this metabolism. First, the hydrolytically labile ester was replaced by a carbamate functional group resulting in a more stable ADC that retained potency in cellular assays. Second, site-specific conjugation was employed in order to design ADCs with reduced metabolic liabilities. Using the later approach, we were able to identify a conjugate at the 334C position of the heavy chain that resulted in an ADC with considerably reduced metabolism and improved efficacy. The examples discussed herein provide one of the clearest demonstrations to-date that site of conjugation can play a critical role in addressing metabolic and PK liabilities of an ADC. Moreover, a clear correlation was identified between the hydrophobicity of an ADC and its susceptibility to metabolic enzymes. Importantly, this study demonstrates that traditional medicinal chemistry strategies can be effectively applied to ADC programs.

Conformational changes of Klebsiella aerogenes urease apoprotein (UreABC) 3 induced upon binding of the UreD and UreF accessory proteins were examined by a combination of flexibility analysis, mutagenesis, and small-angle x-ray scattering (SAXS). ProFlex analysis of urease provided evidence that the major domain of UreB can move in a hinge-like motion to account for prior chemical cross-linking results. Rigidification of the UreB hinge region, accomplished through a G11P mutation, reduced the extent of urease activation, in part by decreasing the nickel content of the mutant enzyme, and by sequestering a portion of the urease apoprotein in a novel activation complex that includes all of the accessory proteins. SAXS analyses of urease, (UreABC-UreD) 3 , and (UreABC-UreDF) 3 confirm that UreD and UreF bind near UreB at the periphery of the (UreAC) 3 structure. This study supports an activation model in which a domain-shifted UreB conformation in (UreABC-UreDF) 3 allows CO 2 and nickel ions to gain access to the nascent active site. KeywordsUrease; Activation; Flexibility; Small-angle X-ray scattering Urease is a nickel-containing enzyme that hydrolyzes urea [1,2]. Crystallographic analyses of ureases from bacterial and plant sources [3][4][5][6][7] reveal a basic trimeric structure with three active sites, each composed of two nickel ions coordinated by a carboxylated Lys, four His and an Asp. Genetic and biochemical studies carried out with plants, fungi, and bacteria [reviewed in [8][9][10]] have shown that additional genes encoding accessory proteins are required for proper assembly of the urease metallocenter, with the possible exception of that from Bacillus *Corresponding author. Address: Department of Microbiology & Molecular Genetics, 2215 Biomedical Physical Sciences, Michigan State University, East Lansing, Michigan 48824-4320, . E-mail: hausinge@msu.edu. Publisher's Disclaimer: This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. [17,18]. Urease activity is generated by incubating these complexes with high concentrations of bicarbonate (to supply the CO 2 needed for Lys carboxylation) and nickel ions, but the required levels of these additives (100 mM and 100 µM, respectively) are not physiologically relevant and only a portion of the proteins are activated [19,20]. In contrast, fully active urease is generated with only 100 µM bicarbonate and 20 µM nickel ions using (UreABC-UreDFG) 3 plus UreE (M r 17,558) and GTP [21]. UreE functions as a nickel-binding protein [22,23] that delivers the metal ion to (UreABC-UreDFG) 3 as GTP is hydrolyzed [24]. Although UreE is often referred to as...

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In this work we explore the possibilities of using fragment-based screening data to prioritize compounds from a full HTS library, a method we call virtual fragment linking (VFL). The ability of VFL to identify compounds of nanomolar potency based on micromolar fragment binding data was tested on 75 target classes from the WOMBAT database and succeeded in 57 cases. Further, the method was demonstrated for seven drug targets from in-house screening programs that performed both FBS of 8800 fragments and screens of the full library. VFL captured between 28% and 67% of the hits (IC 50 < 10microM) in the top 5% of the ranked library for four of the targets (enrichment between 5-fold and 13-fold). Our findings lead us to conclude that proper coverage of chemical space by the fragment library is crucial for the VFL methodology to be successful in prioritizing HTS libraries from fragment-based screening data.