Computational Drug Design Research Articles

Target Oriented Selective Synthesis of Antibacterial Active Tyrosinase Enzyme Inhibitor Coumarin Core Derivatives

Target oriented designs and selective synthesis of bioactive molecules with broad spectrum activity is a challenging job in the field of modern organic chemistry. Considering this opportunity herein, we report a highly competent selective synthesis of bioactive coumarin core derivative. Using computational drug design software only four selective antibacterial active derivatives was discovered and then synthesized. The efficiency of the synthesized compounds was scrutinized against bacterial pathogens such as P. vulgaris and B. megaterium. All the synthesized compounds showed better theoretical as well as practical results against selected pathogenic species. The minimum inhibitory concentration (MIC) values of the most active heterocycles were compared with that of ciprofloxacin. Results obtained in tyrocinase inhibition assay exactly correlate with MIC results and docking outcomes. The bioactivity of these type moieties provided a novel approach to develop new types of antibacterial drugs like entities effective against pathogens.

PH-REMD simulations indicate that the catalytic aspartates of HIV-1 protease exist primarily in a monoprotonated state.

The protonation state of the catalytic aspartates of HIV-1 protease (HIVPR) is atypical and as a result is the subject of much debate. Modeling of the correct protonation state of the aspartates is vital in computational drug design. Using pH replica-exchange molecular dynamics, we simulated the apo and bound forms of HIV-1 protease with 12 different protease inhibitors to investigate the pKa of not only the catalytic dyad but also the other titrating residues in HIVPR. The results obtained from these simulations are compared to experiment where possible. This study provides evidence that the catalytic aspartates are primarily in a monoprotonated state for both the apo and bound forms of HIVPR in the pH range where generally most experiments and computational simulations occur.

Towards understanding the molecular recognition process in prokaryotic zinc-finger domain.

Eukaryotic Cys2His2 zinc finger domain is one of the most common and important structural motifs involved in protein-DNA interaction. The recognition motif is characterized by the tetrahedral coordination of a zinc ion by conserved cysteine and histidine residues. We have characterized the prokaryotic Cys2His2 zinc finger motif, included in the DNA binding region (Ros87) of Ros protein from Agrobacterium tumefaciens, demonstrating that, although possessing a similar zinc coordination sphere, this domain presents significant differences from its eukaryotic counterpart. Furthermore, basic residues flanking the zinc binding region on either side have been demonstrated, by Electrophoretic Mobility Shift Assay (EMSA) experiments, to be essential for Ros DNA binding. In spite of this wealth of knowledge, the structural details of the mechanism through which the prokaryotic zinc fingers recognize their target genes are still unclear. Here, to gain insights into the molecular DNA recognition process of prokaryotic zinc finger domains we applied a strategy in which we performed molecular docking studies using a combination of Nuclear Magnetic Resonance (NMR) and Molecular Dynamics (MD) simulations data. The results demonstrate that the MD ensemble provides a reasonable picture of Ros87 backbone dynamics in solution. The Ros87-DNA model indicates that the interaction involves the first two residue of the first α-helix, and several residues located in the basic regions flanking the zinc finger domain. Interestingly, the prokaryotic zinc finger domain, mainly with the C-terminal tail that is wrapped around the DNA, binds a more extended recognition site than the eukaryotic counterpart. Our analysis demonstrates that the introduction of the protein flexibility in docking studies can improve, in terms of accuracy, the quality of the obtained models and could be particularly useful for protein showing high conformational heterogeneity as well as for computational drug design applications.

Improved prediction of residue flexibility by embedding optimized amino acid grouping into RSA-based linear models.

Knowledge of protein flexibility is vital for deciphering the corresponding functional mechanisms. This knowledge would help, for instance, in improving computational drug design and refinement in homology-based modeling. We propose a new predictor of the residue flexibility, which is expressed by B-factors, from protein chains that use local (in the chain) predicted (or native) relative solvent accessibility (RSA) and custom-derived amino acid (AA) alphabets. Our predictor is implemented as a two-stage linear regression model that uses RSA-based space in a local sequence window in the first stage and a reduced AA pair-based space in the second stage as the inputs. This method is easy to comprehend explicit linear form in both stages. Particle swarm optimization was used to find an optimal reduced AA alphabet to simplify the input space and improve the prediction performance. The average correlation coefficients between the native and predicted B-factors measured on a large benchmark dataset are improved from 0.65 to 0.67 when using the native RSA values and from 0.55 to 0.57 when using the predicted RSA values. Blind tests that were performed on two independent datasets show consistent improvements in the average correlation coefficients by a modest value of 0.02 for both native and predicted RSA-based predictions.

An electronic environment and contact direction sensitive scoring function for predicting affinities of protein–ligand complexes in Contour®

Contour® is a computational structure-based drug design technology that grows drug-like molecules by assembling context sensitive fragments in well-defined binding pockets. The grown molecules are scored by a novel empirical scoring function developed using high-resolution crystal structures of diverse classes of protein–ligand complexes and associated experimental binding affinities. An atomic model bearing features of the valence bond and VSEPR theories embodying their molecular electronic environment has been developed for non-covalent intermolecular interactions. On the basis of atomic hybridization and polarization states, each atom is modeled by features representing electron lone pairs, p-orbitals, and polar and non-polar hydrogens. A simple formal charge model was used to differentiate between polar and non-polar atoms. The interaction energy and the desolvation contribution of the protein–ligand association energy is computed as a linear sum of pair-wise interactions and desolvation terms. The pair-wise interaction energy captures short-range positive electrostatic interactions via hydrogen bonds, electrostatic repulsion of like charges, and non-bond contacts. The desolvation energy is estimated by calculating the energy required to desolvate interaction surfaces of the protein and the ligand in the complex. The scoring function predicts binding energies of a diverse set of protein–ligand complexes used for training with a correlation coefficient of 0.61. It also performs equally well in predicting association energies of a diverse validation set of protein–ligand complexes with a correlation coefficient of 0.57, which is equivalent to or better than 12 other scoring functions tested against this set including X-Score, GOLD, and DrugScore.

Encompassing receptor flexibility in virtual screening using ensemble docking-based hybrid QSAR: discovery of novel phytochemicals for BACE1 inhibition.

Mimicking receptor flexibility during receptor-ligand binding is a challenging task in computational drug design since it is associated with a large increase in the conformational search space. In the present study, we have devised an in silico design strategy incorporating receptor flexibility in virtual screening to identify potential lead compounds as inhibitors for flexible proteins. We have considered BACE1 (β-secretase), a key target protease from a therapeutic perspective for Alzheimer's disease, as the highly flexible receptor. The protein undergoes significant conformational transitions from open to closed form upon ligand binding, which makes it a difficult target for inhibitor design. We have designed a hybrid structure-activity model containing both ligand based descriptors and energetic descriptors obtained from molecular docking based on a dataset of structurally diverse BACE1 inhibitors. An ensemble of receptor conformations have been used in the docking study, further improving the prediction ability of the model. The designed model that shows significant prediction ability judged by several statistical parameters has been used to screen an in house developed 3-D structural library of 731 phytochemicals. 24 highly potent, novel BACE1 inhibitors with predicted activity (Ki) ≤ 50 nM have been identified. Detailed analysis reveals pharmacophoric features of these novel inhibitors required to inhibit BACE1.

Editorial (Thematic Issue: Current Status and Perspective on Drug Targets in Tubercle Bacilli and Drug Design of Antituberculous Agents Based on Structure-Activity Relationship)

Editorial (Thematic Issue: Current Status and Perspective on Drug Targets in Tubercle Bacilli and Drug Design of Antituberculous Agents Based on Structure-Activity Relationship)

MORT: a powerful foundational library for computational biology and CADD

BackgroundA foundational library called MORT (Molecular Objects and Relevant Templates) for the development of new software packages and tools employed in computational biology and computer-aided drug design (CADD) is described here.ResultsMORT contains several advantages compared with the other libraries. Firstly, MORT written in C++ natively supports the paradigm of object-oriented design, and thus it can be understood and extended easily. Secondly, MORT employs the relational model to represent a molecule, and it is more convenient and flexible than the traditional hierarchical model employed by many other libraries. Thirdly, a lot of functions have been included in this library, and a molecule can be manipulated easily at different levels. For example, it can parse a variety of popular molecular formats (MOL/SDF, MOL2, PDB/ENT, SMILES/SMARTS, etc.), create the topology and coordinate files for the simulations supported by AMBER, calculate the energy of a specific molecule based on the AMBER force fields, etc.ConclusionsWe believe that MORT can be used as a foundational library for programmers to develop new programs and applications for computational biology and CADD. Source code of MORT is available at http://cadd.suda.edu.cn/MORT/index.htm.

G_mmpbsa--a GROMACS tool for high-throughput MM-PBSA calculations.

Molecular mechanics Poisson-Boltzmann surface area (MM-PBSA), a method to estimate interaction free energies, has been increasingly used in the study of biomolecular interactions. Recently, this method has also been applied as a scoring function in computational drug design. Here a new tool g_mmpbsa, which implements the MM-PBSA approach using subroutines written in-house or sourced from the GROMACS and APBS packages is described. g_mmpbsa was developed as part of the Open Source Drug Discovery (OSDD) consortium. Its aim is to integrate high-throughput molecular dynamics (MD) simulations with binding energy calculations. The tool provides options to select alternative atomic radii and different nonpolar solvation models including models based on the solvent accessible surface area (SASA), solvent accessible volume (SAV), and a model which contains both repulsive (SASA-SAV) and attractive components (described using a Weeks-Chandler-Andersen like integral method). We showcase the effectiveness of the tool by comparing the calculated interaction energy of 37 structurally diverse HIV-1 protease inhibitor complexes with their experimental binding free energies. The effect of varying several combinations of input parameters such as atomic radii, dielectric constant, grid resolution, solute-solvent dielectric boundary definition, and nonpolar models was investigated. g_mmpbsa can also be used to estimate the energy contribution per residue to the binding energy. It has been used to identify those residues in HIV-1 protease that are most critical for binding a range of inhibitors.

Insights and Ideas Garnered from Marine Metabolites for Development of Dual-Function Acetylcholinesterase and Amyloid-β Aggregation Inhibitors

Due to the diversity of biological activities that can be found in aquatic ecosystems, marine metabolites have been an active area of drug discovery for the last 30 years. Marine metabolites have been found to inhibit a number of enzymes important in the treatment of human disease. Here, we focus on marine metabolites that inhibit the enzyme acetylcholinesterase, which is the cellular target for treatment of early-stage Alzheimer’s disease. Currently, development of anticholinesterase drugs with improved potency, and drugs that act as dual acetylcholinesterase and amyloid-β aggregation inhibitors, are being sought to treat Alzheimer’s disease. Seven classes of marine metabolites are reported to possess anti-cholinesterase activity. We compared these metabolites to clinically-used acetylcholinesterase inhibitors having known mechanisms of inhibition. We performed a docking simulation and compared them to published experimental data for each metabolite to determine the most likely mechanism of inhibition for each class of marine inhibitor. Our results indicate that several marine metabolites bind to regions of the acetylcholinesterase active site that are not bound by the clinically-used drugs rivastigmine, galanthamine, donepezil, or tacrine. We use the novel poses adopted for computational drug design of tighter binding anticholinesterase drugs likely to act as inhibitors of both acetylcholinesterase activity and amyloid-β aggregation inhibition.

Molpher: a software framework for systematic chemical space exploration

BackgroundChemical space is virtual space occupied by all chemically meaningful organic compounds. It is an important concept in contemporary chemoinformatics research, and its systematic exploration is vital to the discovery of either novel drugs or new tools for chemical biology.ResultsIn this paper, we describe Molpher, an open-source framework for the systematic exploration of chemical space. Through a process we term ‘molecular morphing’, Molpher produces a path of structurally-related compounds. This path is generated by the iterative application of so-called ‘morphing operators’ that represent simple structural changes, such as the addition or removal of an atom or a bond. Molpher incorporates an optimized parallel exploration algorithm, compound logging and a two-dimensional visualization of the exploration process. Its feature set can be easily extended by implementing additional morphing operators, chemical fingerprints, similarity measures and visualization methods. Molpher not only offers an intuitive graphical user interface, but also can be run in batch mode. This enables users to easily incorporate molecular morphing into their existing drug discovery pipelines.ConclusionsMolpher is an open-source software framework for the design of virtual chemical libraries focused on a particular mechanistic class of compounds. These libraries, represented by a morphing path and its surroundings, provide valuable starting data for future in silico and in vitro experiments. Molpher is highly extensible and can be easily incorporated into any existing computational drug design pipeline.

Computational drug design of potential α-amylase inhibitors using some commercially available flavonoids

The primary objective of this study was to investigate the α-amylase inhibitory activity of flavonoids using in silico docking studies. In this perspective, flavonoids like biochanin, chrysin, hesperitin, morin, tricin and vitexycarpin were selected. Acarbose, a known α-amylase inhibitor was used as the stan-dard. In silico docking studies were carried out using AutoDock 4.2, based on the Lamarckian genetic algorithm principle. The results showed that all the selected flavonoids showed binding energy ranging between -7.20 kcal/mol to -6.21 kcal/mol when compared with that of the standard (-2.94 kcal/mol). Inhibition constant (5.31 µM to 27.89 µM) and intermolecular energy (-8.99 kcal/mol to -7.41 kcal/mol) of the flavonoids also coincide with the binding energy. The α-amylase inhibitory activity of the selected flavonoids was in order of tricin > hesperitin > vitexycarpin > chrysin > morin > biochanin. These molecular docking analyses could lead to the further development of potent α-amylase inhibitors for the treatment of diabetes. DOI: http://dx.doi.org/10.3329/bjp.v9i1.17502 Bangladesh J Pharmacol 2014; 9: 72-76

Towards breaking the curse of dimensionality in computational methods for the conformational analysis of molecules

Background In computational molecular biology, the conformational analysis of a molecule refers to the identification of conformations - the clusters of molecular configurations sharing a large-scale geometric structure - and the description of conformation dynamics. These data help to describe the structures and functions of molecules which are relevant in biology. In applications such as computational drug design, they help to identify suitable compounds for disrupting processes involved in disease mechanisms. Today, many computational methods for conformational analysis are mesh-based: they involve discretizing configuration space using regular lattices, and simulating trajectories on energy landscapes. These methods suffer from the ‘curse of dimensionality’ in that the cost increases exponentially with the number of atoms in the molecule. Given that many important molecules in biology - such as proteins have thousands of atoms, the curse of dimensionality severely limits the applicability of these methods. Finding an alternative approach to conformational analysis that does not suffer from the curse of dimensionality would enlarge the set of molecules and biological processes which may be studied using computational methods. Results We construct a mesh-free method [1] using random sampling of energy landscapes, Monte Carlo quadrature, and coarse-graining [2]. We provide proof of concept by testing the method on a toy model of a molecule from the literature and find that the results of our method are comparable to those obtained by an exact, mesh-based method. Using results from linear programming theory and discrete geometry, we establish that the computational cost of our method increases only polynomially with the size of the molecule – an improvement over the exponential increase in the case of mesh-based methods. Conclusions Our findings demonstrate that one does not need meshes for conformational analysis. For the specific problem we study in our paper – that of finding conformations and computing conformation transition rates – our findings suggest that mesh-free methods may be just as accurate as mesh-based methods, while costing less computationally. Our findings emphasize the utility of using ideas from different mathematical fields when devising new methods to tackle computational challenges.

Combining in silico and in cerebro approaches for virtual screening and pose prediction in SAMPL4

The SAMPL challenges provide an ideal opportunity for unbiased evaluation and comparison of different approaches used in computational drug design. During the fourth round of this SAMPL challenge, we participated in the virtual screening and binding pose prediction on inhibitors targeting the HIV-1 integrase enzyme. For virtual screening, we used well known and widely used in silico methods combined with personal in cerebro insights and experience. Regular docking only performed slightly better than random selection, but the performance was significantly improved upon incorporation of additional filters based on pharmacophore queries and electrostatic similarities. The best performance was achieved when logical selection was added. For the pose prediction, we utilized a similar consensus approach that amalgamated the results of the Glide-XP docking with structural knowledge and rescoring. The pose prediction results revealed that docking displayed reasonable performance in predicting the binding poses. However, prediction performance can be improved utilizing scientific experience and rescoring approaches. In both the virtual screening and pose prediction challenges, the top performance was achieved by our approaches. Here we describe the methods and strategies used in our approaches and discuss the rationale of their performances.

Molecular dynamics simulation-based evaluation of the binding free energies of computationally designed drug candidates: importance of the dynamical effects.

The computational structure-based drug design (SBDD) mainly aims at generating or discovering new chemical compounds with sufficiently large binding free energy. In any de novo drug design methods and virtual screening methods, drug candidates are selected by approximately evaluating the binding free energy (or the binding affinity). This approximate binding free energy, usually called "empirical score," is critical to the success of the SBDD. The purpose of this work is to yield physical insight into the approximate evaluation method in comparison with an exact molecular dynamics (MD) simulation-based method (named MP-CAFEE), which can predict binding free energies accurately. We calculate the binding free energies for 58 selected drug candidates with MP-CAFEE. Here, the compounds are generated by OPMF, a novel fragment-based de novo drug design method, and the ligand-protein interaction energy is used as an empirical score. The results show that the correlation between the binding free energy and the interaction energy is not strong enough to clearly distinguish compounds with nM-affinity from those with µM-affinity. This implies that it is necessary to take into account the natural protein motion with explicitly surrounded by water molecules to improve the efficiency of the drug candidate selection procedure.

Design, synthesis and characterization of novel inhibitors against mycobacterial β-ketoacyl CoA reductase FabG4

We report the design and synthesis of triazole-polyphenol hybrid compounds 1 and 2 as inhibitors of the FabG4 (Rv0242c) enzyme of Mycobacterium tuberculosis for the first time. A major advance in this field occurred only a couple of years ago with the X-ray crystal structure of FabG4, which has helped us to design these inhibitors by the computational fragment-based drug design (FBDD) approach. Compound 1 has shown competitive inhibition with an inhibition constant (Ki) value of 3.97 ± 0.02 μM. On the other hand, compound 2 has been found to be a mixed type inhibitor with a Ki value of 0.88 ± 0.01 μM. Thermodynamic analysis using isothermal titration calorimetry (ITC) reveals that both inhibitors bind at the NADH co-factor binding domain. Their MIC values, as determined by resazurin assay against M. smegmatis, indicated their good anti-mycobacterial properties. A preliminary structure-activity relationship (SAR) study supports the design of these inhibitors. These compounds may be possible candidates as lead compounds for alternate anti-tubercular drugs. All of the reductase enzymes of the Mycobacterium family have a similar ketoacyl reductase (KAR) domain. Hence, this work may be extrapolated to find structure-based inhibitors of other reductase enzymes.

2P021 高精度自由エネルギー計算によるドラッグデザイン(01B. 蛋白質:構造機能相関,ポスター,第52回日本生物物理学会年会(2014年度))

2P021 高精度自由エネルギー計算によるドラッグデザイン(01B. 蛋白質:構造機能相関,ポスター,第52回日本生物物理学会年会(2014年度))

Computational Medicinal Chemistry for Rational Drug Design: Identification of Novel Chemical Structures with Potential Anti-Tuberculosis Activity

Tuberculosis (TB) is caused by the bacterium Mycobacterium tuberculosis and is a common infectious disease with high mortality and morbidity. The increasing prevalence of drug-resistant strains of TB presents a major public health problem. Due to the lack of effective drugs to treat these drug-resistant strains, the discovery or development of novel anti-TB drugs is important. Computer-aided drug design has become an established strategy for the identification of novel active chemicals through a combination of several drug design tools. In this review, we summarise the current chemotherapy for TB, describe attractive target proteins for the development of antibiotics against TB, and detail several computational drug design strategies that may contribute to the further identification of active chemicals for the treatment of not only TB but also other diseases.

Bandwidth Requirements of Large Scale Computing Systems A Case Study

Complex scientific problems like weather forecasting, computational fluid and combustion dynamics, computational drug design etc. essentially require large scale computational resources in order to obtain solution to the equations governing them. These solutions can be obtained by developing large legacy codes and then executing them using parallel processing. The parallel processing computers generally demand huge bandwidth as they consist of large number of networked processing elements. One such legacy code VARSHA is a meteorological code used for weather forecasting developed at Flosolver, CSIR-NAL under the joint project from NMITLI (New Millennium Indian Technological Leadership Initiative) and MoES (Ministry of Earth Science). The parallel efficiency of VARSHA code using ethernet connectivity has been anything but satisfactory. This paper discusses the bandwidth utilisation of VARSHA code in its existing and modified forms in order to draw some important conclusions on the bandwidth requirements of the future stateof-art parallel computers used to execute such legacy codes. General Terms Bandwidth Scaling, Timing Analysis, VARSHA Code.

Medizin aus dem Computer

Small molecules can have a significant effect on human metabolic processes. Computational drug design aims at constructing specialized small molecules that selectively and efficiently address specific proteins. The basic ideas of computational molecular design are presented and it will be shown how a virtual protein can be computer designed. This virtual protein can be used to predict the binding affinity of given small molecules without having to synthesize them in a laboratory. Modern computational drug design goes far beyond the lock and key principle. Possible future developments are discussed and a current successful example of computational drug design in the field of painkiller medication is demonstrated.