11 research outputs found
Molecular Recognition in a Diverse Set of ProteināLigand Interactions Studied with Molecular Dynamics Simulations and End-Point Free Energy Calculations
End-point
free energy calculations using MM-GBSA and MM-PBSA provide
a detailed understanding of molecular recognition in proteināligand
interactions. The binding free energy can be used to rank-order proteināligand
structures in virtual screening for compound or target identification.
Here, we carry out free energy calculations for a diverse set of 11
proteins bound to 14 small molecules using extensive explicit-solvent
MD simulations. The structure of these complexes was previously solved
by crystallography and their binding studied with isothermal titration
calorimetry (ITC) data enabling direct comparison to the MM-GBSA and
MM-PBSA calculations. Four MM-GBSA and three MM-PBSA calculations
reproduced the ITC free energy within 1 kcalĀ·mol<sup>ā1</sup> highlighting the challenges in reproducing the absolute free energy
from end-point free energy calculations. MM-GBSA exhibited better
rank-ordering with a Spearman <i>Ļ</i> of 0.68 compared
to 0.40 for MM-PBSA with dielectric constant (Īµ = 1). An increase
in Īµ resulted in significantly better rank-ordering for MM-PBSA
(<i>Ļ</i> = 0.91 for Īµ = 10), but larger Īµ
significantly reduced the contributions of electrostatics, suggesting
that the improvement is due to the nonpolar and entropy components,
rather than a better representation of the electrostatics. The SVRKB
scoring function applied to MD snapshots resulted in excellent rank-ordering
(<i>Ļ</i> = 0.81). Calculations of the configurational
entropy using normal-mode analysis led to free energies that correlated
significantly better to the ITC free energy than the MD-based quasi-harmonic
approach, but the computed entropies showed no correlation with the
ITC entropy. When the adaptation energy is taken into consideration
by running separate simulations for complex, apo, and ligand (MM-PBSA<sub>ADAPT</sub>), there is less agreement with the ITC data for the individual
free energies, but remarkably good rank-ordering is observed (<i>Ļ</i> = 0.89). Interestingly, filtering MD snapshots by
prescoring proteināligand complexes with a machine learning-based
approach (SVMSP) resulted in a significant improvement in the MM-PBSA
results (Īµ = 1) from <i>Ļ</i> = 0.40 to <i>Ļ</i> = 0.81. Finally, the nonpolar components of MM-GBSA
and MM-PBSA, but not the electrostatic components, showed strong correlation
to the ITC free energy; the computed entropies did not correlate with
the ITC entropy
Development of Selective Inhibitors for Aldehyde Dehydrogenases Based on Substituted Indole-2,3-diones
Aldehyde
dehydrogenases (ALDH) participate in multiple metabolic
pathways and have been indicated to play a role in several cancerous
disease states. Our laboratory is interested in developing novel and
selective ALDH inhibitors. We looked to further work recently published
by developing a class of isoenzyme-selective inhibitors using similar
indole-2,3-diones that exhibit differential inhibition of ALDH1A1,
ALDH2, and ALDH3A1. Kinetic and X-ray crystallography data suggest
that these inhibitors are competitive against aldehyde binding, forming
direct interactions with active-site cysteine residues. The selectivity
is precise in that these compounds appear to interact directly with
the catalytic nucleophile, Cys243, in ALDH3A1 but not in ALDH2. In
ALDH2, the 3-keto group is surrounded by the adjacent Cys301/303.
Surprisingly, the orientation of the interaction changes depending
on the nature of the substitutions on the basic indole ring structure
and correlates well with the observed structureāactivity relationships
for each ALDH isoenzyme
Average of pairwise distances for ADH1 intronic regions (shown in Table 1) among paralogs and between orthologs.
<p>Average of pairwise distances for ADH1 intronic regions (shown in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0041175#pone-0041175-t001" target="_blank">Table 1</a>) among paralogs and between orthologs.</p
Phylogeny of primate <i>ADH1</i> paralogs.
<p>Phylogeny of primate <i>ADH1</i> paralogs inferred from (A) exonic sequence data (āexonic treeā) and (B) intronic data (āintronic treeā). Parallel black lines indicate bifurcations associated with gene duplications without speciation. <i>ADH1</i> genes from New World monkeys (represented by marmoset) form a separate clade from the hominid/OWM genes in the exonic tree (A), while they interleave with hominid/OWM genes in the intronic tree (B). The lower panels, (C) and (D), redraw the gene tree from (A) and (B) in a species tree format, highlighting where each gene duplication occurs relative to the divergence of each primate lineage. The exonic tree is rooted using multiple non-primate <i>ADH1</i> genes (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0041175#pone.0041175.s002" target="_blank">Figure S2</a>). The intronic tree is unrooted (due to ambiguity, see text). The names of <i>ADH1</i> paralogs have been shortened (e.g. the marmoset (<i>Callthrix jacchus</i>) ADH1 paralog āCal_<i>ADH1.1ā</i> is simply referred to as āmarmoset ADH1.1ā). Numbers at nodes refer to the Bayesian posterior probability values.</p
Pairwise distance estimates for orthologous intronic regions in a dataset composed of non-<i>ADH1</i> genes.
<p>Pairwise distance estimates for orthologous intronic regions in a dataset composed of non-<i>ADH1</i> genes.</p
Overview of primate phylogeny.
<p>An overview of primate phylogeny is shown, with the number of <i>ADH1</i> paralogs identified within select taxon indicated by the circled numbers at the leaves of the tree. Black numbers are derived from analysis of public databases, while red numbers were determined from cDNA sequencing reported here. The ā4+1ā designation for the macaque taxon indicates the presence of four <i>ADH1</i> paralogous genes plus one <i>ADH1</i> pseudogene. The genome sequencing projects are not completed for any lemur, so additional <i>ADH1</i> paralogs may be present (see text).</p
Estimate of the ADH1 paralog duplications relative to the time of the major primate speciation events.
<p>The average pairwise distances separating the introns of the <i>ADH1</i> paralogs were compared with the average pairwise distances separating a set of introns in paired taxa. (A) This schematic illustrates the various ortholog comparisons used to estimate the relative age among the ADH1 paralogs. (B) This plot summaries the data in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0041175#pone-0041175-t002" target="_blank">Table 2</a> and <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0041175#pone-0041175-t003" target="_blank">3</a>. The distances among the <i>ADH1</i> paralogs in marmoset, macaque and human (black diamonds) are somewhat larger than those separating catarrhine and platyrrhine orthologs (green circles), implying that these <i>ADH1</i> paralogs diverged (duplicated) before the catarrhine-platyrrhine split. Conversely, distances separating the <i>ADH1</i> paralogs in marmoset, macaque and human are somewhat smaller than those separating orthologous introns among strepsirhine and haplorhine (red squares), implying that these <i>ADH1</i> paralogs diverged after the split between strepsirhine and haplorhine.</p
Gene duplication can generate āwhole geneā and āchimeric geneā paralogs.
<p>(a) When unequal crossing-over (denoted with an āXā) occurs within the intergenic region between two paralogs, one chromosome gains an extra copy of a paralog, while the other chromosome loses one of the paralogs. This is followed by divergence of each paralog (only shown for the chromosome that gained a paralog and denoted as shift in color). A similar process can lead to the creation of the original paralog duplication, if, for example, transposons generate regions of sequence similarity on either side of a gene, thus enabling unequal crossing-over (not shown). (b) The same process can also lead to a chimeric gene duplicate if the crossing over occurs within the intragenic region (most likely within an intronic region).</p
Pairwise distance estimates of <i>ADH1</i> intronic regions.
<p>Pairwise distance among the <i>ADH1</i> paralogs for the concatenated intronic dataset were calculated using the Maximum Composite Likelihood method implemented by MEGAv4.0. Pairwise distances are shown in the lower left of the table, with variance estimates in the upper right of table.</p
Model of <i>ADH1</i> paralog duplication and subsequent evolution in haplorhines.
<p>Thin vertical black arrows indicate the direction of the chromosome while thick vertical arrows identify ADH genes in the direction of transcription, with <i>ADH1</i> paralogs in primates colored according to the intronic phylogeny in Fig. 2B. Dashed lines connect orthologs. Diagonal lines indicate the proposed phylogeny of haplorrhine <i>ADH1</i> paralogs. The root of the haplorhine <i>ADH1</i> tree is not specified because the duplication order of haplorhine <i>ADH1</i> paralogs is ambiguous (see text). Putative gene conversions are indicated with open circles connected by vertical lines (from <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0041175#pone-0041175-t004" target="_blank">Table 4</a>).</p