Biomedical subjects

Youyi Peng

Publications and source records attributed to Youyi Peng.

4 recordsLinked to original sources

PubMed2006

Discovery of novel triazole-based opioid receptor antagonists.

We report the computer-aided design, chemical synthesis, and biological evaluation of a novel family of delta opioid receptor (DOR) antagonists containing a 1,2,4-triazole core structure that are structurally distinct from other known opioid receptor active ligands. Among those delta antagonists sharing this core structure, 8 exhibited strong binding affinity (K(i) = 50 nM) for the DOR and appreciable selectivity for delta over mu and kappa opioid receptors (delta/mu = 80; delta/kappa > 200).

Cell Line↗

PubMed2005

3D-QSAR comparative molecular field analysis on opioid receptor antagonists: pooling data from different studies.

Three-dimensional quantitative structure-activity relationship (3D-QSAR) models were constructed using comparative molecular field analysis (CoMFA) on a series of opioid receptor antagonists. To obtain statistically significant and robust CoMFA models, a sizable data set of naltrindole and naltrexone analogues was assembled by pooling biological and structural data from independent studies. A process of "leave one data set out", similar to the traditional "leave one out" cross-validation procedure employed in partial least squares (PLS) analysis, was utilized to study the feasibility of pooling data in the present case. These studies indicate that our approach yields statistically significant and highly predictive CoMFA models from the pooled data set of delta, mu, and kappa opioid receptor antagonists. All models showed excellent internal predictability and self-consistency: q(2) = 0.69/r(2) = 0.91 (delta), q(2) = 0.67/r(2) = 0.92 (mu), and q(2) = 0.60/r(2) = 0.96 (kappa). The CoMFA models were further validated using two separate test sets: one test set was selected randomly from the pooled data set, while the other test set was retrieved from other published sources. The overall excellent agreement between CoMFA-predicted and experimental binding affinities for a structurally diverse array of ligands across all three opioid receptor subtypes gives testimony to the superb predictive power of these models. CoMFA field analysis demonstrated that the variations in binding affinity of opioid antagonists are dominated by steric rather than electrostatic interactions with the three opioid receptor binding sites. The CoMFA steric-electrostatic contour maps corresponding to the delta, mu, and kappa opioid receptor subtypes reflected the characteristic similarities and differences in the familiar "message-address" concept of opioid receptor ligands. Structural modifications to increase selectivity for the delta over mu and kappa opioid receptors have been predicted on the basis of the CoMFA contour maps. The structure-activity relationships (SARs) together with the CoMFA models should find utility for the rational design of subtype-selective opioid receptor antagonists.

Least-Squares Analysis↗

PubMed2005

3D-QSAR comparative molecular field analysis on delta opioid receptor agonist SNC80 and its analogs.

Three-dimensional quantitative structure-activity relationship (3D-QSAR) models were constructed using comparative molecular field analysis (CoMFA) for a series of delta opioid receptor agonists: SNC80 analogs. Quantum chemical calculations on SNC80 show that protonation is preferred at the basic N4 atom over the alternative N1 atom, accordingly N4 protonation may contribute significantly to ligand-receptor interactions under physiologically relevant conditions. Statistically significant and predictive CoMFA models were achieved by pooling biological data from independent published sources, including compounds with both alphaR and alphaS benzylic configurations. Improved CoMFA models were obtained when the compounds were considered as N4-protonated species rather than neutral compounds. The influence of various atomic partial-charge formalisms, alignment schemes and additional molecular descriptors was evaluated in order to produce the highest quality models. In addition, separate CoMFA models were generated for compounds with only the alphaR benzylic configuration. These CoMFA models showed excellent internal predictability and consistency, and external validation using test-set compounds yielded predicted pIC50 values within 1log unit of the corresponding experimentally measured values. Key insights into the structure-activity relationship derived from the CoMFA analysis concur with experimentally observed data, thus the CoMFA models presented here find utility for predicting the binding affinity, and guiding the design, of novel SNC80 analogs and related delta opioid receptor agonists.

Animals↗

PubMed2005

Structural model of the Plasmodium CDK, Pfmrk, a novel target for malaria therapeutics.

Malaria, with 300-500 million clinical cases resulting in 1-3 million fatalities a year, is one of the most deadly tropical diseases. As current antimalarial therapeutics become increasingly ineffective due to parasitic resistance, there exists an urgent need to develop and pursue new therapeutic strategies. Recent genome sequencing and molecular cloning projects have identified several enzymes from Plasmodium (P.) falciparum that may represent novel drug targets, including a family of proteins that are homologous to the mammalian cyclin-dependent kinases (CDKs). CDKs are essential for the control of the mammalian cell cycle and, based on the conservation of the CDKs across species, the plasmodial CDKs are expected to play a crucial role in parasitic growth. Here we present a 3D structural model of Pfmrk, a putative human CDK activating kinase (CAK) homolog in P. falciparum. Notable features of the present structural model include: (1) parameterization of the Mg2+ hexacoordination system using ab initio quantum chemical calculations to accurately represent the ATP-kinase interaction; and (2) comparison between the docking scores and measured binding affinities for a series of oxindole-based Pfmrk inhibitors of known activity. Detailed analysis of inhibitor-Pfmrk binding interactions enabled us to identify specific residues (viz. Met66, Met75, Met91, Met94 and Phe143) within the Pfmrk binding pocket that may play an important role in inhibitor binding affinity and selectivity. The availability of this Pfmrk structural model, together with insights gained from analysis of ligand-receptor interactions, should promote the rational design of potent and selective Pfmrk inhibitors as antimalarial therapeutics.

Amino Acid Sequence↗