Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Design and synthesis of de novo cytotoxic alkaloids by mimicking the bioactive conformation of paclitaxel.

ABSTRACT: Novel paclitaxel-mimicking alkaloids were designed and synthesized based on a bioactive conformation of paclitaxel, that is, REDOR-Taxol. The alkaloid 2 bearing a 5-7-6 tricyclic scaffold mimics REDOR-Taxol best among the compounds designed and was found to be the most potent compound against several drug-sensitive and drug-resistant human cancer cell lines. MD simulation study on the paclitaxel mimics 1 and 2 as well as REDOR-Taxol bound to the 1JFF tubulin structure was quite informative to evaluate the level of mimicking. The MD simulation study clearly distinguishes the 5-6-6 and 5-7-6 tricyclic scaffolds, and also shows substantial difference in the conformational stability of the tubulin-bound structures between 2 and REDOR-Taxol. The latter may account for the large difference in potency, and provides critical information for possible improvement in the future design of paclitaxel mimics.

SUBMITTER: Sun L

PROVIDER: S-EPMC2943045 | biostudies-literature | 2010 Oct

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Publications

Design and synthesis of de novo cytotoxic alkaloids by mimicking the bioactive conformation of paclitaxel.

Sun Liang L Veith Jean M JM Pera Paula P Bernacki Ralph J RJ Ojima Iwao I

Bioorganic & medicinal chemistry 20100806 19

Novel paclitaxel-mimicking alkaloids were designed and synthesized based on a bioactive conformation of paclitaxel, that is, REDOR-Taxol. The alkaloid 2 bearing a 5-7-6 tricyclic scaffold mimics REDOR-Taxol best among the compounds designed and was found to be the most potent compound against several drug-sensitive and drug-resistant human cancer cell lines. MD simulation study on the paclitaxel mimics 1 and 2 as well as REDOR-Taxol bound to the 1JFF tubulin structure was quite informative to ev ...[more]

PMID: 20800500

Similar Datasets

De novo design of bioactive protein switches.

Project description:Allosteric regulation of protein function is widespread in biology, but is challenging for de novo protein design as it requires the explicit design of multiple states with comparable free energies. Here we explore the possibility of designing switchable protein systems de novo, through the modulation of competing inter- and intramolecular interactions. We design a static, five-helix 'cage' with a single interface that can interact either intramolecularly with a terminal 'latch' helix or intermolecularly with a peptide 'key'. Encoded on the latch are functional motifs for binding, degradation or nuclear export that function only when the key displaces the latch from the cage. We describe orthogonal cage-key systems that function in vitro, in yeast and in mammalian cells with up to 40-fold activation of function by key. The ability to design switchable protein functions that are controlled by induced conformational change is a milestone for de novo protein design, and opens up new avenues for synthetic biology and cell engineering.

| S-EPMC6733528 | biostudies-literature

DOGS: reaction-driven de novo design of bioactive compounds.

Project description:We present a computational method for the reaction-based de novo design of drug-like molecules. The software DOGS (Design of Genuine Structures) features a ligand-based strategy for automated 'in silico' assembly of potentially novel bioactive compounds. The quality of the designed compounds is assessed by a graph kernel method measuring their similarity to known bioactive reference ligands in terms of structural and pharmacophoric features. We implemented a deterministic compound construction procedure that explicitly considers compound synthesizability, based on a compilation of 25'144 readily available synthetic building blocks and 58 established reaction principles. This enables the software to suggest a synthesis route for each designed compound. Two prospective case studies are presented together with details on the algorithm and its implementation. De novo designed ligand candidates for the human histamine H₄ receptor and γ-secretase were synthesized as suggested by the software. The computational approach proved to be suitable for scaffold-hopping from known ligands to novel chemotypes, and for generating bioactive molecules with drug-like properties.

| S-EPMC3280956 | biostudies-literature

De Novo Design of Bioactive Small Molecules by Artificial Intelligence.

Project description:Generative artificial intelligence offers a fresh view on molecular design. We present the first-time prospective application of a deep learning model for designing new druglike compounds with desired activities. For this purpose, we trained a recurrent neural network to capture the constitution of a large set of known bioactive compounds represented as SMILES strings. By transfer learning, this general model was fine-tuned on recognizing retinoid X and peroxisome proliferator-activated receptor agonists. We synthesized five top-ranking compounds designed by the generative model. Four of the compounds revealed nanomolar to low-micromolar receptor modulatory activity in cell-based assays. Apparently, the computational model intrinsically captured relevant chemical and biological knowledge without the need for explicit rules. The results of this study advocate generative artificial intelligence for prospective de novo molecular design, and demonstrate the potential of these methods for future medicinal chemistry.

| S-EPMC5838524 | biostudies-literature

De novo design of bioactive protein-resembling nanospheres via dendrimer-templated peptide amphiphile assembly.

Project description:Self-assembling peptide amphiphiles (PAs) have been extensively used in the development of novel biomaterials. Because of their propensity to form cylindrical micelles, their use is limited in applications where small spherical micelles are desired. Here we present a platform method for controlling the self-assembly of biofunctional PAs into spherical 50 nm particles using dendrimers as shape-directing scaffolds. This templating approach results in biocompatible, stable protein-like assemblies displaying peptides with native secondary structure and biofunctionality.

| S-EPMC3223106 | biostudies-literature

Generative and reinforcement learning approaches for the automated de novo design of bioactive compounds.

Project description:Deep generative neural networks have been used increasingly in computational chemistry for de novo design of molecules with desired properties. Many deep learning approaches employ reinforcement learning for optimizing the target properties of the generated molecules. However, the success of this approach is often hampered by the problem of sparse rewards as the majority of the generated molecules are expectedly predicted as inactives. We propose several technical innovations to address this problem and improve the balance between exploration and exploitation modes in reinforcement learning. In a proof-of-concept study, we demonstrate the application of the deep generative recurrent neural network architecture enhanced by several proposed technical tricks to design inhibitors of the epidermal growth factor (EGFR) and further experimentally validate their potency. The proposed technical solutions are expected to substantially improve the success rate of finding novel bioactive compounds for specific biological targets using generative and reinforcement learning approaches.

| S-EPMC9814657 | biostudies-literature

Unified Total Synthesis of the Limonoid Alkaloids: Strategies for the De Novo Synthesis of Highly Substituted Pyridine Scaffolds.

Project description:Highly substituted pyridine scaffolds are found in many biologically active natural products and therapeutics. Accordingly, numerous complementary de novo approaches to obtain differentially substituted pyridines have been disclosed. This article delineates the evolution of the synthetic strategies designed to assemble the demanding tetrasubstituted pyridine core present in the limonoid alkaloids isolated from Xylocarpus granatum, including xylogranatopyridine B, granatumine A and related congeners. In addition, NMR calculations suggested structural misassignment of several limonoid alkaloids, and predicted their C3-epimers as the correct structures, which was further validated unequivocally through chemical synthesis. The materials produced in this study were evaluated for cytotoxicity, anti-oxidant effects, anti-inflammatory action, PTP1B and Nlrp3 inflammasome inhibition, which led to compelling anti-inflammatory activity and anti-oxidant effects being discovered.

| S-EPMC10311986 | biostudies-literature

De novo metalloprotein design.

Project description:Natural metalloproteins perform many functions - ranging from sensing to electron transfer and catalysis - in which the position and property of each ligand and metal, is dictated by protein structure. De novo protein design aims to define an amino acid sequence that encodes a specific structure and function, providing a critical test of the hypothetical inner workings of (metallo)proteins. To date, de novo metalloproteins have used simple, symmetric tertiary structures - uncomplicated by the large size and evolutionary marks of natural proteins - to interrogate structure-function hypotheses. In this Review, we discuss de novo design applications, such as proteins that induce complex, increasingly asymmetric ligand geometries to achieve function, as well as the use of more canonical ligand geometries to achieve stability. De novo design has been used to explore how proteins fine-tune redox potentials and catalyse both oxidative and hydrolytic reactions. With an increased understanding of structure-function relationships, functional proteins including O2-dependent oxidases, fast hydrolases, and multi-proton/multi-electron reductases, have been created. In addition, proteins can now be designed using xeno-biological metals or cofactors and principles from inorganic chemistry to derive new-to-nature functions. These results and the advances in computational protein design suggest a bright future for the de novo design of diverse, functional metalloproteins.

| S-EPMC9264687 | biostudies-literature

Design and synthesis of simplified taxol analogs based on the T-Taxol bioactive conformation.

Project description:A series of compounds designed to adopt a conformation similar to the tubulin-binding T-Taxol conformation of the anticancer drug paclitaxel has been synthesized. Both the internally bridged analogs 37-39, 41 and the open-chain analogs 27-29 and 43 were prepared. The bridged analogs 37-39 and 41 were synthesized by Grubbs' metatheses of compounds 30-32 and 33, which, in turn, were prepared by coupling ?-lactams 24-26 with alcohols 22 and 23. Both the bridged and the open-chain analogs showed moderate to good cytotoxicity.

| S-EPMC3225578 | biostudies-literature

Building a eukaryotic chromosome arm by de novo design and synthesis.

Project description:The genome of an organism is inherited from its ancestor and continues to evolve over time, however, the extent to which the current version could be altered remains unknown. To probe the genome plasticity of Saccharomyces cerevisiae, here we replace the native left arm of chromosome XII (chrXIIL) with a linear artificial chromosome harboring small sets of reconstructed genes. We find that as few as 12 genes are sufficient for cell viability, whereas 25 genes are required to recover the partial fitness defects observed in the 12-gene strain. Next, we demonstrate that these genes can be reconstructed individually using synthetic regulatory sequences and recoded open-reading frames with a "one-amino-acid-one-codon" strategy to remain functional. Finally, a synthetic neochromsome with the reconstructed genes is assembled which could substitute chrXIIL for viability. Together, our work not only highlights the high plasticity of yeast genome, but also illustrates the possibility of making functional eukaryotic chromosomes from entirely artificial sequences.

| S-EPMC10689750 | biostudies-literature

Deep Learning-Driven Library Design for the <i>De Novo</i> Discovery of Bioactive Thiopeptides.

Project description:Broad substrate tolerance of ribosomally synthesized and post-translationally modified peptide (RiPP) biosynthetic enzymes has allowed numerous strategies for RiPP engineering. However, despite relaxed specificities, exact substrate preferences of RiPP enzymes are often difficult to pinpoint. Thus, when designing combinatorial libraries of RiPP precursors, balancing the compound diversity with the substrate fitness can be challenging. Here, we employed a deep learning model to streamline the design of mRNA display libraries. Using an in vitro reconstituted thiopeptide biosynthesis platform, we performed mRNA display-based profiling of substrate fitness for the biosynthetic pathway involving five enzymes to train an accurate deep learning model. We then utilized the model to design optimal mRNA libraries and demonstrated their utility in affinity selections against IRAK4 kinase and the TLR10 cell surface receptor. The selections led to the discovery of potent thiopeptide ligands against both target proteins (KD up to 1.3 nM for the best compound against IRAK4 and 300 nM for TLR10). The IRAK4-targeting compounds also inhibited the kinase at single-digit μM concentrations in vitro, exhibited efficient internalization into HEK293H cells, and suppressed NF-kB-mediated signaling in cells. Altogether, the developed approach streamlines the discovery of pseudonatural RiPPs with de novo designed biological activities and favorable pharmacological properties.

| S-EPMC10683472 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data