Browse
Submit Data
Databases
API
Help

Dataset Information

79 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Design of Selective Benzoxazepin PI3K? Inhibitors Through Control of Dihedral Angles.

ABSTRACT: A novel selective benzoxazepin inhibitor of PI3K? has been discovered. Beginning from compound 3, an ?PI3K inhibitor, we utilized structure-based drug design and computational analysis of dihedral torsion angles to optimize for PI3K? isoform potency and isoform selectivity. Further medicinal chemistry optimization of the series led to the identification of 24, a highly potent and selective inhibitor of PI3K?.

SUBMITTER: Safina BS

PROVIDER: S-EPMC5601369 | biostudies-literature | 2017 Sep

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Publications

Design of Selective Benzoxazepin PI3Kδ Inhibitors Through Control of Dihedral Angles.

Safina Brian S BS Elliott Richard L RL Forrest Andrew K AK Heald Robert A RA Murray Jeremy M JM Nonomiya Jim J Pang Jodie J Salphati Laurent L Seward Eileen M EM Staben Steven T ST Ultsch Mark M Wei Binqing B Yang Wenqian W Sutherlin Daniel P DP

ACS medicinal chemistry letters 20170825 9

A novel selective benzoxazepin inhibitor of PI3Kδ has been discovered. Beginning from compound <b>3</b>, an αPI3K inhibitor, we utilized structure-based drug design and computational analysis of dihedral torsion angles to optimize for PI3Kδ isoform potency and isoform selectivity. Further medicinal chemistry optimization of the series led to the identification of <b>24</b>, a highly potent and selective inhibitor of PI3Kδ. ...[more]

PMID: 28947940

Similar Datasets

Prediction of disulfide dihedral angles using chemical shifts.

Project description:Cystine residues result from the formation of disulfide bonds between pairs of cysteine residues. This cross linking of the backbone is essential for the structure and activity of peptides and proteins. The conformation of a cystine side chain can be described using five dihedral angles, χ1, χ2, χ3, χ2', and χ1', with cystines favouring certain combinations of these angles. 2D NMR spectroscopy is ideally suited for structure determination of disulfide-rich peptides, because of their small size and constrained nature. However, only limited information of the cystine side chain conformation can be determined by NMR spectroscopy, leading to ambiguity in the deduced 3D structures. Resolving accurate structures is important as disulfide-rich peptides have proven to be promising drug candidates in a number of fields, either as bioactive leads or scaffolds. Using a database of NMR chemical shifts combined with crystallographic structures, we have developed a method called DISH that uses support vector machines to predict the dihedral angles of cysteine side chains. It is able to successfully predict χ2 angles with 91% accuracy, and has improved performance over existing prediction methods for χ1 angles, with 87% accuracy. For 81% of cysteine residues, DISH successfully predicted both the χ1 and χ2 angles. By revisiting published solution structures of peptides determined using NMR spectroscopy, we assessed the impact of additional cystine dihedral restraints on the quality of 3D models. DISH improved the resolution and accuracy, highlighting the potential for improving the understanding of structure-activity relationships and rational development of peptide drugs.

| S-EPMC6115640 | biostudies-literature

Optimization of Protein Backbone Dihedral Angles by Means of Hamiltonian Reweighting.

Project description:Molecular dynamics simulations depend critically on the accuracy of the underlying force fields in properly representing biomolecules. Hence, it is crucial to validate the force-field parameter sets in this respect. In the context of the GROMOS force field, this is usually achieved by comparing simulation data to experimental observables for small molecules. In this study, we develop new amino acid backbone dihedral angle potential energy parameters based on the widely used 54A7 parameter set by matching to experimental J values and secondary structure propensity scales. In order to find the most appropriate backbone parameters, close to 100?000 different combinations of parameters have been screened. However, since the sheer number of combinations considered prohibits actual molecular dynamics simulations for each of them, we instead predicted the values for every combination using Hamiltonian reweighting. While the original 54A7 parameter set fails to reproduce the experimental data, we are able to provide parameters that match significantly better. However, to ensure applicability in the context of larger peptides and full proteins, further studies have to be undertaken.

| S-EPMC5039763 | biostudies-literature

Design, Synthesis and Biological Evaluation of Novel Benzothiazole Derivatives as Selective PI3K? Inhibitors.

Project description:A novel series of PI3K? (Phosphatidylinositol-3-kinases beta subunit) inhibitors with the structure of benzothiazole scaffold have been designed and synthesized. All the compounds have been evaluated for inhibitory activities against PI3K?, ?, ?, ? and mTOR (Mammalian target of rapamycin). Two superior compounds have been further evaluated for the IC50 values against PI3Ks/mTOR. The most promising compound 11 displays excellent anti-proliferative activity and selectivity in multiple cancer cell lines, especially in the prostate cancer cell line. Docking studies indicate the morpholine group in 2-position of benzothiazole is necessary for the potent antitumor activity, which confirms our design is reasonable.

| S-EPMC6274018 | biostudies-literature

RaptorX-Angle: real-value prediction of protein backbone dihedral angles through a hybrid method of clustering and deep learning.

Project description:BACKGROUND:Protein dihedral angles provide a detailed description of protein local conformation. Predicted dihedral angles can be used to narrow down the conformational space of the whole polypeptide chain significantly, thus aiding protein tertiary structure prediction. However, direct angle prediction from sequence alone is challenging. RESULTS:In this article, we present a novel method (named RaptorX-Angle) to predict real-valued angles by combining clustering and deep learning. Tested on a subset of PDB25 and the targets in the latest two Critical Assessment of protein Structure Prediction (CASP), our method outperforms the existing state-of-art method SPIDER2 in terms of Pearson Correlation Coefficient (PCC) and Mean Absolute Error (MAE). Our result also shows approximately linear relationship between the real prediction errors and our estimated bounds. That is, the real prediction error can be well approximated by our estimated bounds. CONCLUSIONS:Our study provides an alternative and more accurate prediction of dihedral angles, which may facilitate protein structure prediction and functional study.

| S-EPMC5998898 | biostudies-literature

Rational Design, Synthesis, and SAR of a Novel Thiazolopyrimidinone Series of Selective PI3K-beta Inhibitors.

Project description:A novel thiazolopyrimidinone series of PI3K-beta selective inhibitors has been identified. This chemotype has provided an excellent tool compound, 18, that showed potent growth inhibition in the PTEN-deficient breast cancer cell line MDA-MB-468 under anchorage-independent conditions, and it also demonstrated pharmacodynamic effects and efficacy in a PTEN-deficient prostate cancer PC-3 xenograft mouse model.

| S-EPMC4025772 | biostudies-other

Prediction of backbone dihedral angles and protein secondary structure using support vector machines.

Project description:BackgroundThe prediction of the secondary structure of a protein is a critical step in the prediction of its tertiary structure and, potentially, its function. Moreover, the backbone dihedral angles, highly correlated with secondary structures, provide crucial information about the local three-dimensional structure.ResultsWe predict independently both the secondary structure and the backbone dihedral angles and combine the results in a loop to enhance each prediction reciprocally. Support vector machines, a state-of-the-art supervised classification technique, achieve secondary structure predictive accuracy of 80% on a non-redundant set of 513 proteins, significantly higher than other methods on the same dataset. The dihedral angle space is divided into a number of regions using two unsupervised clustering techniques in order to predict the region in which a new residue belongs. The performance of our method is comparable to, and in some cases more accurate than, other multi-class dihedral prediction methods.ConclusionsWe have created an accurate predictor of backbone dihedral angles and secondary structure. Our method, called DISSPred, is available online at http://comp.chem.nottingham.ac.uk/disspred/.

| S-EPMC2811710 | biostudies-literature

Predicting beta-turns and their types using predicted backbone dihedral angles and secondary structures.

Project description:BackgroundBeta-turns are secondary structure elements usually classified as coil. Their prediction is important, because of their role in protein folding and their frequent occurrence in protein chains.ResultsWe have developed a novel method that predicts beta-turns and their types using information from multiple sequence alignments, predicted secondary structures and, for the first time, predicted dihedral angles. Our method uses support vector machines, a supervised classification technique, and is trained and tested on three established datasets of 426, 547 and 823 protein chains. We achieve a Matthews correlation coefficient of up to 0.49, when predicting the location of beta-turns, the highest reported value to date. Moreover, the additional dihedral information improves the prediction of beta-turn types I, II, IV, VIII and "non-specific", achieving correlation coefficients up to 0.39, 0.33, 0.27, 0.14 and 0.38, respectively. Our results are more accurate than other methods.ConclusionsWe have created an accurate predictor of beta-turns and their types. Our method, called DEBT, is available online at http://comp.chem.nottingham.ac.uk/debt/.

| S-EPMC2920885 | biostudies-literature

Dataset of normalized probability distributions of virtual bond lengths, bond angles, and dihedral angles for the coarse-grained single-stranded DNA structures.

Project description:The utility of the coarse-grained (CG) single-stranded DNA (ssDNA) model can drastically reduce the compute time for simulating the ssDNA dynamics. The model-matched CG potentials and the inherent potential constants can be derived by coarse-graining the experimentally measured ssDNA structures. A useful and widespread treatment of the CG model is to use three different pseudo-atoms P, S, and B to represent the atomic groups of phosphate, sugar, and base, respectively, in each nucleotide of the ssDNA structures. The three pseudo-atoms generate nine types of the structural parameters to characterize the unstructured ssDNA conformations, including three (virtual) bond lengths (P-S, S-B, and S-P) between two neighbouring beads, four bond angles (P-S-P, S-P-S, P-S-B, and B-S-P) between three adjacent bonds, and two dihedral angles (P-S-P-S and S-P-S-P) between three successive bonds. This paper mainly presents the data of normalized probability distributions of the bond lengths, bond angles, and dihedral angles for the CG ssDNAs.

| S-EPMC9142626 | biostudies-literature

GeoPCA: a new tool for multivariate analysis of dihedral angles based on principal component geodesics.

Project description:The GeoPCA package is the first tool developed for multivariate analysis of dihedral angles based on principal component geodesics. Principal component geodesic analysis provides a natural generalization of principal component analysis for data distributed in non-Euclidean space, as in the case of angular data. GeoPCA presents projection of angular data on a sphere composed of the first two principal component geodesics, allowing clustering based on dihedral angles as opposed to Cartesian coordinates. It also provides a measure of the similarity between input structures based on only dihedral angles, in analogy to the root-mean-square deviation of atoms based on Cartesian coordinates. The principal component geodesic approach is shown herein to reproduce clusters of nucleotides observed in an ?-? plot. GeoPCA can be accessed via http://pca.limlab.ibms.sinica.edu.tw.

| S-EPMC3273787 | biostudies-literature

Protein Structure Idealization: How accurately is it possible to model protein structures with dihedral angles?

Project description:: Previous studies show that the same type of bond lengths and angles fit Gaussian distributions well with small standard deviations on high resolution protein structure data. The mean values of these Gaussian distributions have been widely used as ideal bond lengths and angles in bioinformatics. However, we are not aware of any research done to evaluate how accurately we can model protein structures with dihedral angles and ideal bond lengths and angles.Here, we introduce the protein structure idealization problem. We focus on the protein backbone structure idealization. We describe a fast O(nm/ε) dynamic programming algorithm to find an idealized protein backbone structure that is approximately optimal according to our scoring function. The scoring function evaluates not only the free energy, but also the similarity with the target structure. Thus, the idealized protein structures found by our algorithm are guaranteed to be protein-like and close to the target protein structure.We have implemented our protein structure idealization algorithm and idealized the high resolution protein structures with low sequence identities of the CULLPDB_PC30_RES1.6_R0.25 data set. We demonstrate that idealized backbone structures always exist with small changes and significantly better free energy. We also applied our algorithm to refine protein pseudo-structures determined in NMR experiments.

| S-EPMC3655034 | 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