Automated genome mining for natural products

Abstract Background Discovery of new medicinal agents from natural sources has largely been an adventitious process based on screening of plant and microbial extracts combined with bioassay-guided identification and natural product structure elucidation. Increasingly rapid and more cost-effective genome sequencing technologies coupled with advanced computational power have converged to transform this trend toward a more rational and predictive pursuit. Results We have developed a rapid method of scanning genome sequences for multiple polyketide, nonribosomal peptide, and mixed combination natural products with output in a text format that can be readily converted to two and three dimensional structures using conventional software. Our open-source and web-based program can assemble various small molecules composed of twenty standard amino acids and twenty two other chain-elongation intermediates used in nonribosomal peptide systems, and four acyl-CoA extender units incorporated into polyketides by reading a hidden Markov model of DNA. This process evaluates and selects the substrate specificities along the assembly line of nonribosomal synthetases and modular polyketide synthases. Conclusion Using this approach we have predicted the structures of natural products from a diverse range of bacteria based on a limited number of signature sequences. In accelerating direct DNA to metabolomic analysis, this method bridges the interface between chemists and biologists and enables rapid scanning for compounds with potential therapeutic value.http://deepblue.lib.umich.edu/bitstream/2027.42/112362/1/12859_2008_Article_2915.pd

A poke in the eye: Inhibiting HIV-1 protease through its flap-recognition pocket

A novel mechanism of inhibiting HIV-1 protease (HIVp) is presented. Using computational solvent mapping to identify complementary interactions and the Multiple Protein Structure method to incorporate protein flexibility, we generated a receptor-based pharmacophore model of the flexible flap region of the semiopen, apo state of HIVp. Complementary interactions were consistently observed at the base of the flap, only within a cleft with a specific structural role. In the closed, bound state of HIVp, each flap tip docks against the opposite monomer, occupying this cleft. This flap-recognition site is filled by the protein and cannot be identified using traditional approaches based on bound, closed structures. Virtual screening and dynamics simulations show how small molecules can be identified to complement this cleft. Subsequent experimental testing confirms inhibitory activity of this new class of inhibitor. This may be the first new inhibitor class for HIVp since dimerization inhibitors were introduced 17 years ago. © 2008 Wiley Periodicals, Inc. Biopolymers 89: 643–652, 2008. This article was originally published online as an accepted preprint. The “Published Online” date corresponds to the preprint version. You can request a copy of the preprint by emailing the Biopolymers editorial office at [email protected] Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/58588/1/20993_ftp.pd

Integrated computational and Drosophila cancer model platform captures previously unappreciated chemicals perturbing a kinase network

Drosophila provides an inexpensive and quantitative platform for measuring whole animal drug response. A complementary approach is virtual screening, where chemical libraries can be efficiently screened against protein target(s). Here, we present a unique discovery platform integrating structure-based modeling with Drosophila biology and organic synthesis. We demonstrate this platform by developing chemicals targeting a Drosophila model of Medullary Thyroid Cancer (MTC) characterized by a transformation network activated by oncogenic dRetM955T. Structural models for kinases relevant to MTC were generated for virtual screening to identify unique preliminary hits that suppressed dRetM955T-induced transformation. We then combined features from our hits with those of known inhibitors to create a ‘hybrid’ molecule with improved suppression of dRetM955T transformation. Our platform provides a framework to efficiently explore novel kinase inhibitors outside of explored inhibitor chemical space that are effective in inhibiting cancer networks while minimizing whole body toxicity

Cell-targeted vaccines: implications for adaptive immunity

Recent advancements in immunology and chemistry have facilitated advancements in targeted vaccine technology. Targeting specific cell types, tissue locations, or receptors can allow for modulation of the adaptive immune response to vaccines. This review provides an overview of cellular targets of vaccines, suggests methods of targeting and downstream effects on immune responses, and summarizes general trends in the literature. Understanding the relationships between vaccine targets and subsequent adaptive immune responses is critical for effective vaccine design. This knowledge could facilitate design of more effective, disease-specialized vaccines

CSAR Benchmark Exercise of 2010: Selection of the Protein–Ligand Complexes

ABSTRACT: A major goal in drug design is the improvement of computational methods for docking and scoring. The Community Structure Activity Resource (CSAR) aims to collect available data from industry and academia which may be used for this purpose (www.csardock.org). Also, CSAR is charged with organizing community-wide exercises based on the collected data. The first of these exercises was aimed to gauge the overall state of docking and scoring, using a large and diverse data set of protein ligand complexes. Participants were asked to calculate the affinity of the complexes as provided and then recalculate with changes which may improve their specific method. This first data set was selected from existing PDB entries which had binding data (Kd or Ki) in Binding MOAD, augmented with entries from PDBbind. The final data set contains 343 diverse protein ligand complexes and spans 14 pKd. Sixteen proteins have three or more complexes in the data set, from which a user could start an inspection of congeneric series. Inherent experimental error limits the possible correlation between scores and measured affinity; R 2 is limited to ∼0.9 when fitting to the data set without over parametrizing. R 2 is limited to ∼0.8 when scoring the data set with a method trained on outside data. The details of how the data set was initially selected, and the process by which it matured t

Molecular dynamics simulations and in silico peptide ligand screening of the Elk-1 ETS domain

Background: The Elk-1 transcription factor is a member of a group of proteins called ternary complex factors, which serve as a paradigm for gene regulation in response to extracellular signals. Its deregulation has been linked to multiple human diseases including the development of tumours. The work herein aims to inform the design of potential peptidomimetic compounds that can inhibit the formation of the Elk-1 dimer, which is key to Elk-1 stability. We have conducted molecular dynamics simulations of the Elk-1 ETS domain followed by virtual screening. Results: We show the ETS dimerisation site undergoes conformational reorganisation at the a1b1 loop. Through exhaustive screening of di- and tri-peptide libraries against a collection of ETS domain conformations representing the dynamics of the loop, we identified a series of potential binders for the Elk-1 dimer interface. The di-peptides showed no particular preference toward the binding site; however, the tri-peptides made specific interactions with residues: Glu17, Gln18 and Arg49 that are pivotal to the dimer interface. Conclusions: We have shown molecular dynamics simulations can be combined with virtual peptide screening to obtain an exhaustive docking protocol that incorporates dynamic fluctuations in a receptor. Based on our findings, we suggest experimental binding studies to be performed on the 12 SILE ranked tri-peptides as possible compounds for the design of inhibitors of Elk-1 dimerisation. It would also be reasonable to consider the score ranked tri-peptides as a comparative test to establish whether peptide size is a determinant factor of binding to the ETS domain