Jung-Hsin Lin

HIV-1 protease molecular dynamics of a wild-type and of the V82F/I84V mutant: Possible contributions to drug resistance and a potential new target site for drugs

The protease from type 1 human immunodeficiency virus (HIV‐1) is a critical drug target against which many therapeutically useful inhibitors have been developed; however, the set of viral strains in the population has been shifting to become more drug‐resistant. Because indirect effects are contributing to drug resistance, an examination of the dynamic structures of a wild‐type and a mutant could be insightful. Consequently, this study examined structural properties sampled during 22 nsec, all atom molecular dynamics (MD) simulations (in explicit water) of both a wild‐type and the drug‐resistant V82F/I84V mutant of HIV‐1 protease. The V82F/I84V mutation significantly decreases the binding affinity of all HIV‐1 protease inhibitors currently used clinically. Simulations have shown that the curling of the tips of the active site flaps immediately results in flap opening. In the 22‐nsec MD simulations presented here, more frequent and more rapid curling of the mutants active site flap tips was observed. The mutant proteases flaps also opened farther than the wild‐types flaps did and displayed more flexibility. This suggests that the effect of the mutations on the equilibrium between the semiopen and closed conformations could be one aspect of the mechanism of drug resistance for this mutant. In addition, correlated fluctuations in the active site and periphery were noted that point to a possible binding site for allosteric inhibitors.

Statins Increase p21 through Inhibition of Histone Deacetylase Activity and Release of Promoter-Associated HDAC1/2

Statins are 3-hydroxy-3-methylglutaryl-CoA reductase inhibitors broadly used for the control of hypercholesterolemia. Recently, they are reported to have beneficial effects on certain cancers. In this study, we show that statins inhibited the histone deacetylase (HDAC) activity and increased the accumulation of acetylated histone-H3 and the expression of p21(WAF/CIP) in human cancer cells. Computational modeling showed the direct interaction of the carboxylic acid moiety of statins with the catalytic site of HDAC2. In the subsequent enzymatic assay, it was shown that lovastatin inhibited HDAC2 activity competitively with a K(i) value of 31.6 micromol/L. Sp1 but not p53 sites were found to be the statins-responsive element shown by p21 luciferase-promoter assays. DNA affinity protein binding assay and chromatin immunoprecipitation assay showed the dissociation of HDAC1/2 and association of CBP, leading to the histone-H3 acetylation on the Sp1 sites of p21 promoter. In vitro cell proliferation and in vivo tumor growth were both inhibited by statins. These results suggest a novel mechanism for statins through abrogation of the HDAC activity and promoter histone-H3 acetylation to regulate p21 expression. Therefore, statins might serve as novel HDAC inhibitors for cancer therapy and chemoprevention.

Increased Membrane Affinity of the C1 Domain of Protein Kinase Cδ Compensates for the Lack of Involvement of Its C2 Domain in Membrane Recruitment

Protein kinase C (PKC) family members are allosterically activated following membrane recruitment by specific membrane-targeting modules. Conventional PKC isozymes are recruited to membranes by two such modules: a C1 domain, which binds diacylglycerol (DAG), and a C2 domain, which is a Ca2+-triggered phospholipid-binding module. In contrast, novel PKC isozymes respond only to DAG, despite the presence of a C2 domain. Here, we address the molecular mechanism of membrane recruitment of the novel isozyme PKCδ. We show that PKCδ and a conventional isozyme, PKCβII, bind membranes with comparable affinities. However, dissection of the contribution of individual domains to this binding revealed that, although the C2 domain is a major determinant in driving the interaction of PKCβII with membranes, the C2 domain of PKCδ does not bind membranes. Instead, the C1B domain is the determinant that drives the interaction of PKCδ with membranes. The C2 domain also does not play any detectable role in the activity or subcellular location of PKCδ in cells; in vivo imaging studies revealed that deletion of the C2 domain does not affect the stimulus-dependent translocation or activity of PKCδ. Thus, the increased affinity of the C1 domain of PKCδ allows this isozyme to respond to DAG alone, whereas conventional PKC isozymes require the coordinated action of Ca2+ binding to the C2 domain and DAG binding to the C1 domain for activation.

MEDock: a web server for efficient prediction of ligand binding sites based on a novel optimization algorithm

The prediction of ligand binding sites is an essential part of the drug discovery process. Knowing the location of binding sites greatly facilitates the search for hits, the lead optimization process, the design of site-directed mutagenesis experiments and the hunt for structural features that influence the selectivity of binding in order to minimize the drugs adverse effects. However, docking is still the rate-limiting step for such predictions; consequently, much more efficient algorithms are required. In this article, the design of the MEDock web server is described. The goal of this sever is to provide an efficient utility for predicting ligand binding sites. The MEDock web server incorporates a global search strategy that exploits the maximum entropy property of the Gaussian probability distribution in the context of information theory. As a result of the global search strategy, the optimization algorithm incorporated in MEDock is significantly superior when dealing with very rugged energy landscapes, which usually have insurmountable barriers. This article describes four different benchmark cases that span a diverse set of different types of ligand binding interactions. These benchmarks were compared with the use of the Lamarckian genetic algorithm (LGA), which is the major workhorse of the well-known AutoDock program. These results demonstrate that MEDock consistently converged to the correct binding modes with significantly smaller numbers of energy evaluations than the LGA required. When judged by a threshold of the number of energy evaluations consumed in the docking simulation, MEDock also greatly elevates the rate of accurate predictions for all benchmark cases. MEDock is available at and .

idTarget: a web server for identifying protein targets of small chemical molecules with robust scoring functions and a divide-and-conquer docking approach

Identification of possible protein targets of small chemical molecules is an important step for unravelling their underlying causes of actions at the molecular level. To this end, we construct a web server, idTarget, which can predict possible binding targets of a small chemical molecule via a divide-and-conquer docking approach, in combination with our recently developed scoring functions based on robust regression analysis and quantum chemical charge models. Affinity profiles of the protein targets are used to provide the confidence levels of prediction. The divide-and-conquer docking approach uses adaptively constructed small overlapping grids to constrain the searching space, thereby achieving better docking efficiency. Unlike previous approaches that screen against a specific class of targets or a limited number of targets, idTarget screen against nearly all protein structures deposited in the Protein Data Bank (PDB). We show that idTarget is able to reproduce known off-targets of drugs or drug-like compounds, and the suggested new targets could be prioritized for further investigation. idTarget is freely available as a web-based server at http://idtarget.rcas.sinica.edu.tw.

Bridging implicit and explicit solvent approaches for membrane electrostatics.

Conformations of a zwitterionic bilayer were sampled from a molecular dynamics simulation and their electrostatic properties analyzed by solution of the Poisson equation. These traditionally implicit electrostatic calculations were performed in the presence of varying amounts of explicit solvent to assess the magnitude of error introduced by a uniform dielectric description of water surrounding the bilayer. It was observed that membrane dipole potential calculations in the presence of explicit water were significantly different than wholly implicit solvent calculations with the calculated dipole potential converging to a reasonable value when four or more hydration layers were included explicitly.

A new drug design targeting the adenosinergic system for Huntington's disease.

Background Huntingtons disease (HD) is a neurodegenerative disease caused by a CAG trinucleotide expansion in the Huntingtin (Htt) gene. The expanded CAG repeats are translated into polyglutamine (polyQ), causing aberrant functions as well as aggregate formation of mutant Htt. Effective treatments for HD are yet to be developed. Methodology/Principal Findings Here, we report a novel dual-function compound, N 6-(4-hydroxybenzyl)adenine riboside (designated T1-11) which activates the A2AR and a major adenosine transporter (ENT1). T1-11 was originally isolated from a Chinese medicinal herb. Molecular modeling analyses showed that T1-11 binds to the adenosine pockets of the A2AR and ENT1. Introduction of T1-11 into the striatum significantly enhanced the level of striatal adenosine as determined by a microdialysis technique, demonstrating that T1-11 inhibited adenosine uptake in vivo. A single intraperitoneal injection of T1-11 in wildtype mice, but not in A2AR knockout mice, increased cAMP level in the brain. Thus, T1-11 enters the brain and elevates cAMP via activation of the A2AR in vivo. Most importantly, addition of T1-11 (0.05 mg/ml) to the drinking water of a transgenic mouse model of HD (R6/2) ameliorated the progressive deterioration in motor coordination, reduced the formation of striatal Htt aggregates, elevated proteasome activity, and increased the level of an important neurotrophic factor (brain derived neurotrophic factor) in the brain. These results demonstrate the therapeutic potential of T1-11 for treating HD. Conclusions/Significance The dual functions of T1-11 enable T1-11 to effectively activate the adenosinergic system and subsequently delay the progression of HD. This is a novel therapeutic strategy for HD. Similar dual-function drugs aimed at a particular neurotransmitter system as proposed herein may be applicable to other neurotransmitter systems (e.g., the dopamine receptor/dopamine transporter and the serotonin receptor/serotonin transporter) and may facilitate the development of new drugs for other neurodegenerative diseases.

Design and synthesis of dual-action inhibitors targeting histone deacetylases and 3-hydroxy-3-methylglutaryl coenzyme A reductase for cancer treatment.

A series of dual-action compounds were designed to target histone deacetylase (HDAC) and 3-hydroxy-3-methylglutaryl coenzyme A reductase (HMGR) by having a hydroxamate group essential for chelation with the zinc ion in the active site of HDAC and the key structural elements of statin for binding with both proteins. In our study, the statin hydroxamic acids prepared by a fused strategy are most promising in cancer treatments. These compounds showed potent inhibitory activities against HDACs and HMGR with IC50 values in the nanomolar range. These compounds also effectively reduced the HMGR activity as well as promoted the acetylations of histone and tubulin in cancer cells, but were not toxic to normal cells.

Accommodating Protein Flexibility for Structure-Based Drug Design

Proper incorporation of protein flexibility for prediction of binding poses and affinities of small compounds has attracted increasing attention recently in computational drug design. Various approaches have been proposed to accommodate protein flexibility in the prediction of binding modes and the binding free energy of ligands in an efficient manner. In this review, the significance of incorporating protein flexibility is discussed from the structural biophysical point of view, and then various approaches of generating protein conformation ensembles, as well as their successes and limitations, are introduced and compared. Special emphasis is on how to generate a proper ensemble of conformation for a specific purpose, as well as the computational efficiency of various approaches. Different searching algorithms for the prediction of optimal binding poses of ligands, which are the core engines of docking programs, are accounted for. Scoring functions for evaluation of protein-ligand complexes are compared. Two end-point methods of free energy calculation, Molecular Mechanics/Poisson-Boltzmann Surface Area (MM/PBSA) and the Linear Interaction Energy (LIE) method, are briefly reviewed. Finally, we also provide an example for the extension of the conventional protein-ligand docking algorithm for prediction of multiple binding sites and ligand translocation pathways.

Scoring Functions for Prediction of Protein-Ligand Interactions

The scoring functions for protein-ligand interactions plays central roles in computational drug design, virtual screening of chemical libraries for new lead identification, and prediction of possible binding targets of small chemical molecules. An ideal scoring function for protein-ligand interactions is expected to be able to recognize the native binding pose of a ligand on the protein surface among decoy poses, and to accurately predict the binding affinity (or binding free energy) so that the active molecules can be discriminated from the non-active ones. Due to the empirical nature of most, if not all, scoring functions for protein-ligand interactions, the general applicability of empirical scoring functions, especially to domains far outside training sets, is a major concern. In this review article, we will explore the foundations of different classes of scoring functions, their possible limitations, and their suitable application domains. We also provide assessments of several scoring functions on weakly-interacting protein-ligand complexes, which will be useful information in computational fragment-based drug design or virtual screening.