Alpeshkumar K. Malde

An Automated Force Field Topology Builder (ATB) and Repository: Version 1.0

The Automated force field Topology Builder (ATB, http://compbio.biosci.uq.edu.au/atb ) is a Web-accessible server that can provide topologies and parameters for a wide range of molecules appropriate for use in molecular simulations, computational drug design, and X-ray refinement. The ATB has three primary functions: (1) to act as a repository for molecules that have been parametrized as part of the GROMOS family of force fields, (2) to act as a repository for pre-equilibrated systems for use as starting configurations in molecular dynamics simulations (solvent mixtures, lipid systems pre-equilibrated to adopt a specific phase, etc.), and (3) to generate force field descriptions of novel molecules compatible with the GROMOS family of force fields in a variety of formats (GROMOS, GROMACS, and CNS). Force field descriptions of novel molecules are derived using a multistep process in which results from quantum mechanical (QM) calculations are combined with a knowledge-based approach to ensure compatibility (as far as possible) with a specific parameter set of the GROMOS force field. The ATB has several unique features: (1) It requires that the user stipulate the protonation and tautomeric states of the molecule. (2) The symmetry of the molecule is analyzed to ensure that equivalent atoms are assigned identical parameters. (3) Charge groups are assigned automatically. (4) Where the assignment of a given parameter is ambiguous, a range of possible alternatives is provided. The ATB also provides several validation tools to assist the user to assess the degree to which the topology generated may be appropriate for a given task. In addition to detailing the steps involved in generating a force field topology compatible with a specific GROMOS parameter set (GROMOS 53A6), the challenges involved in the automatic generation of force field parameters for atomic simulations in general are discussed.

A Dynamic Pharmacophore Drives the Interaction between Psalmotoxin-1 and the Putative Drug Target Acid-Sensing Ion Channel 1a

Acid-sensing ion channel 1a (ASIC1a) is a primary acid sensor in the peripheral and central nervous system. It has been implicated as a novel therapeutic target for a broad range of pathophysiological conditions including pain, ischemic stroke, depression, and autoimmune diseases such as multiple sclerosis. The only known selective blocker of ASIC1a is π-TRTX-Pc1a (PcTx1), a disulfide-rich 40-residue peptide isolated from spider venom. π-TRTX-Pc1a is an effective analgesic in rodent models of acute pain and it provides neuroprotection in a mouse model of ischemic stroke. Thus, understanding the molecular basis of the π-TRTX-Pc1a–ASIC1a interaction should facilitate development of therapeutically useful ASIC1a blockers. We therefore developed an efficient bacterial expression system to produce a panel of π-TRTX-Pc1a mutants for probing structure-activity relationships as well as isotopically labeled toxin for determination of its solution structure and dynamics. We demonstrate that the toxin pharmacophore resides in a β-hairpin loop that was revealed to be mobile over a wide range of time scales using molecular dynamics simulations in combination with NMR spin relaxation and relaxation dispersion measurements. The toxin-receptor interaction was modeled by in silico docking of the toxin structure onto a homology model of rat ASIC1a in a restraints-driven approach that was designed to take account of the dynamics of the toxin pharmacophore and the consequent remodeling of side-chain conformations upon receptor binding. The resulting model reveals new insights into the mechanism of action of π-TRTX-Pc1a and provides an experimentally validated template for the rational design of therapeutically useful π-TRTX-Pc1a mimetics.

Pharmacophore Modeling in Drug Discovery and Development: An Overview

Pharmacophore mapping is one of the major elements of drug design in the absence of structural data of the target receptor. The tool initially applied to discovery of lead molecules now extends to lead optimization. Pharmacophores can be used as queries for retrieving potential leads from structural databases (lead discovery), for designing molecules with specific desired attributes (lead optimization), and for assessing similarity and diversity of molecules using pharmacophore fingerprints. It can also be used to align molecules based on the 3D arrangement of chemical features or to develop predictive 3D QSAR models. This review begins with a brief historical overview of the pharmacophore evolution followed by a coverage of the developments in methodologies for pharmacophore identification over the period from inception of the pharmacophore concept to recent developments of the more sophisticated tools such as Catalyst, GASP, and DISCO. In addition, we present some very recent successes of the widely used pharmacophore generation methods in drug discovery.

Synthesis, anti-tubercular activity and 3D-QSAR study of coumarin-4-acetic acid benzylidene hydrazides

A set of 25 coumarin-4-acetic acid benzylidene hydrazides were synthesized and characterized by NMR, IR and mass spectroscopic techniques. The compounds were evaluated for their anti-tubercular activity against Mycobacterium tuberculosis H(37)Rv strain using the BACTEC 460 system to determine percentage inhibition. To understand the relationship between structure and activity, a 3D-QSAR analysis has been carried out by Comparative Molecular Field Analysis (CoMFA). Several statistically significant CoMFA models were generated. The CoMFA model generated with database alignment was the best in terms of overall statistics. The CoMFA contours provide a good insight into the structure activity relationships of the compounds reported herein.

3D-QSAR Studies on thieno[3,2-d]pyrimidines as Phosphodiesterase IV Inhibitors

Cyclic nucleotide phosphodiesterase IV (PDE IV) inhibitors find utility in asthma and Chronic Obstructive Pulmonary Disease (COPD) therapy. A series of 29 thieno[3,2-d]pyrimidines with affinity for PDE IV was subjected to three dimensional quantitative structure activity relationship (3D-QSAR) studies using comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA). Both CoMFA and CoMSIA provided statistically valid models with good correlative and predictive power. The incorporation of hydrophobic, hydrogen bond donor and hydrogen bond acceptor fields showed insignificant improvement in CoMSIA model. The 3D-QSAR models provide information for predicting the affinity of related compounds and designing more potent inhibitors.

Comparative molecular field analysis (CoMFA) of phthalazine derivatives as phosphodiesterase IV inhibitors

A comparative molecular field analysis (CoMFA) of phthalazine class of phosphodiesterase IV (PDE IV) inhibitors has been performed to correlate their chemical structures with their observed biological activity. A statistically valid model with good correlative and predictive power is reported. The leave one out cross-validation study gave cross-validation r(2)(cv) of value 0.507 at six optimum components and conventional r(2) of value 0.98. The predictive ability of the model was tested by predicting the seven molecules belonging to the test set giving predictive correlation coefficient of 0.59. This model is potentially helpful in the design of novel and more potent PDE IV inhibitors.

Challenges in the determination of the binding modes of non-standard ligands in X-ray crystal complexes

Despite its central role in structure based drug design the determination of the binding mode (position, orientation and conformation in addition to protonation and tautomeric states) of small heteromolecular ligands in protein:ligand complexes based on medium resolution X-ray diffraction data is highly challenging. In this perspective we demonstrate how a combination of molecular dynamics simulations and free energy (FE) calculations can be used to correct and identify thermodynamically stable binding modes of ligands in X-ray crystal complexes. The consequences of inappropriate ligand structure, force field and the absence of electrostatics during X-ray refinement are highlighted. The implications of such uncertainties and errors for the validation of virtual screening and fragment-based drug design based on high throughput X-ray crystallography are discussed with possible solutions and guidelines.

Missing Fragments: Detecting Cooperative Binding in Fragment-Based Drug Design

The aim of fragment-based drug design (FBDD) is to identify molecular fragments that bind to alternate subsites within a given binding pocket leading to cooperative binding when linked. In this study, the binding of fragments to human phenylethanolamine N-methyltransferase is used to illustrate how (a) current protocols may fail to detect fragments that bind cooperatively, (b) theoretical approaches can be used to validate potential hits, and (c) apparent false positives obtained when screening against cocktails of fragments may in fact indicate promising leads.

The Characterization of Modified Starch Branching Enzymes: Toward the Control of Starch Chain-Length Distributions

Starch is a complex branched glucose polymer whose branch molecular weight distribution (the chain-length distribution, CLD) influences nutritionally important properties such as digestion rate. Chain-stopping in starch biosynthesis is by starch branching enzyme (SBE). Site-directed mutagenesis was used to modify SBEIIa from Zea mays (mSBEIIa) to produce mutants, each differing in a single conserved amino-acid residue. Products at different times from in vitro branching were debranched and the time evolution of the CLD measured by size-exclusion chromatography. The results confirm that Tyr352, Glu513, and Ser349 are important for mSBEIIa activity while Arg456 is important for determining the position at which the linear glucan is cut. The mutant mSBEIIa enzymes have different activities and suggest the length of the transferred chain can be varied by mutation. The work shows analysis of the molecular weight distribution can yield information regarding the enzyme branching sites useful for development of plants yielding starch with improved functionality.

A comprehensive analysis of the thermodynamic events involved in ligand–receptor binding using CoRIA and its variants

Quantitative Structure-Activity Relationships (QSAR) are being used since decades for prediction of biological activity, lead optimization, classification, identification and explanation of the mechanisms of drug action, and prediction of novel structural leads in drug discovery. Though the technique has lived up to its expectations in many aspects, much work still needs to be done in relation to problems related to the rational design of peptides. Peptides are the drugs of choice in many situations, however, designing them rationally is a complicated task and the complexity increases with the length of their sequence. In order to deal with the problem of peptide optimization, one of our recently developed QSAR formalisms CoRIA (Comparative Residue Interaction Analysis) is being expanded and modified as: reverse-CoRIA (rCoRIA) and mixed-CoRIA (mCoRIA) approaches. In these methodologies, the peptide is fragmented into individual units and the interaction energies (van der Waals, Coulombic and hydrophobic) of each amino acid in the peptide with the receptor as a whole (rCoRIA) and with individual active site residues in the receptor (mCoRIA) are calculated, which along with other thermodynamic descriptors, are used as independent variables that are correlated to the biological activity by chemometric methods. As a test case, the three CoRIA methodologies have been validated on a dataset of diverse nonamer peptides that bind to the Class I major histocompatibility complex molecule HLA-A*0201, and for which some structure activity relationships have already been reported. The different models developed, and validated both internally as well as externally, were found to be robust with statistically significant values of r2 (correlation coefficient) and r2pred (predictive r2). These models were able to identify all the structure activity relationships known for this class of peptides, as well uncover some new relationships. This means that these methodologies will perform well for other peptide datasets too. The major advantage of these approaches is that they explicitly utilize the 3D structures of small molecules or peptides as well as their macromolecular targets, to extract position-specific information about important interactions between the ligand and receptor, which can assist the medicinal and computational chemists in designing new molecules, and biologists in studying the influence of mutations in the target receptor on ligand binding.