12 research outputs found
Visually Interpretable Models of Kinase Selectivity Related Features Derived from Field-Based Proteochemometrics
Achieving selectivity for small organic
molecules toward biological
targets is a main focus of drug discovery but has been proven difficult,
for example, for kinases because of the high similarity of their ATP
binding pockets. To support the design of more selective inhibitors
with fewer side effects or with altered target profiles for improved
efficacy, we developed a method combining ligand- and receptor-based
information. Conventional QSAR models enable one to study the interactions
of multiple ligands toward a single protein target, but in order to
understand the interactions between multiple ligands and multiple
proteins, we have used proteochemometrics, a multivariate statistics
method that aims to combine and correlate both ligand and protein
descriptions with affinity to receptors. The superimposed binding
sites of 50 unique kinases were described by molecular interaction
fields derived from knowledge-based potentials and SchroÌdingerâs
WaterMap software. Eighty ligands were described by Mold<sup>2</sup>, Open Babel, and Volsurf descriptors. Partial least-squares regression
including cross-terms, which describe the selectivity, was used for
model building. This combination of methods allows interpretation
and easy visualization of the models within the context of ligand
binding pockets, which can be translated readily into the design of
novel inhibitors
Visually Interpretable Models of Kinase Selectivity Related Features Derived from Field-Based Proteochemometrics
Achieving selectivity for small organic
molecules toward biological
targets is a main focus of drug discovery but has been proven difficult,
for example, for kinases because of the high similarity of their ATP
binding pockets. To support the design of more selective inhibitors
with fewer side effects or with altered target profiles for improved
efficacy, we developed a method combining ligand- and receptor-based
information. Conventional QSAR models enable one to study the interactions
of multiple ligands toward a single protein target, but in order to
understand the interactions between multiple ligands and multiple
proteins, we have used proteochemometrics, a multivariate statistics
method that aims to combine and correlate both ligand and protein
descriptions with affinity to receptors. The superimposed binding
sites of 50 unique kinases were described by molecular interaction
fields derived from knowledge-based potentials and SchroÌdingerâs
WaterMap software. Eighty ligands were described by Mold<sup>2</sup>, Open Babel, and Volsurf descriptors. Partial least-squares regression
including cross-terms, which describe the selectivity, was used for
model building. This combination of methods allows interpretation
and easy visualization of the models within the context of ligand
binding pockets, which can be translated readily into the design of
novel inhibitors
Representative Western blots following co-immunoprecipitation of GATA4 mutant proteins and NKX2-5-FLAG produced in COS-1 cells.
<p>For each GATA4 mutation immunoprecipitation was carried-out in two independent experiments with two technical replicates on western blot. The level of GATA4 protein in the input is shown below each co-ip blot. IP, immunoprecipitation; IB, immunoblotting</p
Effect of GATA4 mutations on transcriptional activity in COS-1 cells.
<p>(A) Results using the rat BNP minimal promoter and (B) the rat BNP promoter with the -90 tandem GATA sites (2XGATA). Results (mean±SEM) are expressed as fold-changes compared to pMT2 control group and are averages from three independent experiments with parallel samples (n = 3). (C) The mutant GATA4 proteins were expressed at the same level as the wtGATA4 protein. A representative western blot shows that 1.5-fold higher amount of the M298Y, R319C and R319S plasmids, and 2.5-fold higher of the K299A plasmid compared to the wtGATA4 were transfected to achieve equal protein levels. (D) A reporter construct containing three high affinity binding sites for NKX2-5 (NKE) was created to study transcriptional interaction between GATA4 and NKX2-5 proteins. The experiment was repeated two times with parallel samples (n = 2). The raw data of the luciferase assay is available in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0144145#pone.0144145.s002" target="_blank">S2 File</a>. Results (mean±SEM) are expressed as fold-changes compared to wtGATA4-NKX2-5 control group. * <i>P</i><0.05, ** <i>P</i><0.01 and *** <i>P</i><0.001, vs. wtGATA4 in A and B and vs. wtGATA4-NKX2-5 in D (Dunnettâs t-test).</p
GATA4 binding to the tandem GATA site of the BNP promoter.
<p>(A) Schematic representation of the probes used in EMSA studies. (B) GATA4 binding to rBNP -90 tandem GATA site created two different bands. Supershift (SS) with 4 ÎŒg of GATA4 antibody C-20 confirms GATA4 presence in both bands. (C) The rBNP -90 tandem GATA site was used to study the effect of mutations on DNA binding. (D) R264A and K299A mutant proteins caused distinct binding pattern. (E) WtGATA4 binding to tandem GATA sites was studied with different mutated probes. 2xG = binding of two GATA4 molecules, 1xG = binding of one GATA4 molecule. Arrowheads denote free probes. (F) Effect of the GATA4 mutations (black bar) on DNA binding activity is expressed as fold-changes compared to wtGATA4 control group (white bar). Results (mean±SEM) are averages from two to nine independent reproducible EMSA samples using the -90 tandem GATA probe (n = 1â3). * <i>P</i><0.05, ** <i>P</i><0.01 and *** <i>P</i><0.001 vs. control group (Student's t-test).</p
Effect of GATA4 mutations on ANP promoter activation in NIH3T3 cells.
<p>The -699bp ANP promoter was utilized to study the transcriptional activity of the GATA4 mutants on a validated GATA4 target gene. (A) Promoter activation by the indicated GATA4 proteins; activation by wtGATA4 is set at 1. (B) Effect of mutated GATA4 on synergy with NKX2-5 and (C) on synergy with KLF13. The data shown are from two independent experiments conducted in duplicate using five different DNA concentrations. Results (mean±SEM) are expressed as fold-changes compared to the control groups wtGATA4 in (A), wtGATA4-NKX2-5 in (B), and wtGATA4-KLF13 in (C). * <i>P</i><0.05, ** <i>P</i><0.01 and *** <i>P</i><0.001 vs. control group with Student's t-test.</p
Primers used to create the GATA4 mutations.
<p>Primers used to create the GATA4 mutations.</p
Nuclear receptor like structure of GATA4-NKX2-5 interaction.
<p>(A) DNA-binding domain of the estrogen receptor (Protein Data Bank: 1HCQ) showing R63, K66 and C67 mediating the interaction between the zinc fingers. (B) Homology model of the C-terminal zinc finger of GATA4 (green) and the homeodomain of NKX2-5 (blue) showing amino acids R190, K193 and C194 (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0144145#pone.0144145.s003" target="_blank">S3 File</a>). Structure similarity between GATA model and Estrogen receptor (1HCQ) is 1.81 RMSD (alpha-carbon). Due the sequence and structural fold differences between NKX2-5 and Estrogen receptor, RMSD values are not reported. Zinc atoms are represented as light blue spheres.</p
Homology models of transcription factor GATA4 bound to DNA.
<p>(A) Double GATA4 bound to a tandem GATA binding site. (B) C-terminal zinc finger of GATA4 bound to the major groove of DNA along with the N-terminal zinc finger bound to the minor groove. Amino acids N272 and R283 are highlighted as ball spheres and the location of amino acids H234, R264, M298, K299 and R319 are indicated. (C) Amino acid sequence of the mouse GATA4 zinc fingers. Darker shades indicate residues mutated in this study.</p