Applications In Drug Design Research Articles

3D-Lab: A Collaborative Web-Based Platform for Molecular Modeling

The use of 3D information has shown impact in numerous applications in drug design. However, it is often under-utilized and traditionally limited to specialists. We want to change that, and present an approach making 3D information and molecular modeling accessible and easy-to-use 'for the people'. A user-friendly and collaborative web-based platform (3D-Lab) for 3D modeling, including a blazingly fast virtual screening capability, was developed. 3D-Lab provides an interface to automatic molecular modeling, like conformer generation, ligand alignments, molecular dockings and simple quantum chemistry protocols. 3D-Lab is designed to be modular, and to facilitate sharing of 3D-information to promote interactions between drug designers. Recent enhancements to our open-source virtual reality tool Molecular Rift are described. The integrated drug-design platform allows drug designers to instantaneously access 3D information and readily apply advanced and automated 3D molecular modeling tasks, with the aim to improve decision-making in drug design projects.

ChemInform Abstract: Cyclization of Disulfide‐Rich Conotoxins in Drug Design Applications

AbstractReview: [91 refs.

Further along the Road Less Traveled: AMBER ff15ipq, an Original Protein Force Field Built on a Self-Consistent Physical Model

We present the AMBER ff15ipq force field for proteins, the second-generation force field developed using the Implicitly Polarized Q (IPolQ) scheme for deriving implicitly polarized atomic charges in the presence of explicit solvent. The ff15ipq force field is a complete rederivation including more than 300 unique atomic charges, 900 unique torsion terms, 60 new angle parameters, and new atomic radii for polar hydrogens. The atomic charges were derived in the context of the SPC/Eb water model, which yields more-accurate rotational diffusion of proteins and enables direct calculation of nuclear magnetic resonance (NMR) relaxation parameters from molecular dynamics simulations. The atomic radii improve the accuracy of modeling salt bridge interactions relative to contemporary fixed-charge force fields, rectifying a limitation of ff14ipq that resulted from its use of pair-specific Lennard-Jones radii. In addition, ff15ipq reproduces penta-alanine J-coupling constants exceptionally well, gives reasonable agreement with NMR relaxation rates, and maintains the expected conformational propensities of structured proteins/peptides, as well as disordered peptides—all on the microsecond (μs) time scale, which is a critical regime for drug design applications. These encouraging results demonstrate the power and robustness of our automated methods for deriving new force fields. All parameters described here and the mdgx program used to fit them are included in the AmberTools16 distribution.

Cyclisation of Disulfide‐Rich Conotoxins in Drug Design Applications

Conotoxins are disulfide‐rich peptides found in the venoms of marine snails of the genus Conus. They have attracted great attention from the pharmaceutical industry because of their potential uses as drug leads, but like most peptides, conotoxins are susceptible to proteolysis and typically are not orally bioavailable. Here we discuss approaches that have been used to stabilise conotoxins to improve their potential pharmaceutical use. Specifically, we focus on the use of backbone cyclisation to improve their stability in biological fluids. The Microreview provides an introduction to the various classes of conotoxins, including their frameworks (cysteine patterns) and a background on the receptors that they interact with, as well as an analysis of the binding interactions between conotoxins and their receptors.

Effect of Additives on the Crystal Morphology of Amino Acids: A Theoretical and Experimental Study

The crystal morphology of amino acids can be altered in a controlled manner through inclusion of tailor-made additives in their structure, in order to widen their scope for applications in drug design and targeted delivery. In this study, the effect of multiadditive combinations of hydrophobic and hydrophilic amino acids on the growth and morphology of l-alanine was investigated. Theoretical calculations were performed using two crystal growth models in Materials Studio software: (1) build-in model; (2) surface docking model. Crystallization experiments were carried out using the metal-assisted and microwave accelerated evaporative crystallization (MA-MAEC) technique with multiple hydrophobic and hydrophilic amino acids added in stoichiometric amounts to l-alanine solution. The crystal morphology was established and compared with predicted crystal morphology. The use of hydrophilic and hydrophobic additives was predicted to have significant changes in the morphology of l-alanine crystals. Multiadditive combinations with hydrophobic amino acids resulted in elongation of l-alanine crystals through the (120) face. Experimental data corroborates with the theoretical predictions in relation to the morphological changes due to additives, indicating the accuracy of theoretical models in predicting the impact of additives in crystal growth.

The Multifaceted Roles of Molecular Dynamics Simulations in Drug Discovery.

Discovery of new therapeutics is a very challenging, expensive and time-consuming process. With the number of approved drugs declining steadily, combined with increasing costs, a rational approach is needed to facilitate, expedite and streamline the drug discovery process. In silico methods are playing key roles in the discovery of a growing number of marketed drugs. The use of computational approaches, particularly molecular dynamics, in drug design is rapidly gaining momentum and acceptance as an essential part of the toolkit for modern drug discovery. From analysing atomistic details for explaining experimentally observed phenomena, to designing drugs with increased efficacy and specificity, the insight that such simulations can provide is generating new ideas and applications that have previously been unexplored. Here we discuss physics-based simulation methodologies and applications in drug design: from locating pockets to designing novel lead compounds, from small molecules to peptides. With developments in hardware, software and theory, the improved predictive abilities of in silico efforts are becoming an essential part of efficient, economic and accurate drug development strategies.

Discovery, structure, function, and applications of cyclotides: circular proteins from plants.

Cyclotides are plant-derived cyclic peptides that have a head-to-tail cyclic backbone and three conserved disulphide bonds that form a cyclic cystine knot motif. They occur in plants from the Violaceae, Rubiaceae, Cucurbitaceae, Fabaceae, and Solanaceae families, typically with 10-100 cyclotides in a given plant species, in a wide range of tissues, including flowers, leaves, stems, and roots. Some cyclotides are expressed in large amounts (up to 1g kg(-1) wet plant weight) and their natural function appears to be to protect plants from pests or pathogens. This article provides a brief overview of their discovery, distribution in plants, and applications. In particular, their exceptional stability has led to their use as peptide-based scaffolds in drug design applications. They also have potential as natural 'ecofriendly' insecticides, and as protein engineering frameworks.

Structural class prediction of protein using novel feature extraction method from chaos game representation of predicted secondary structure

Protein structural class prediction plays an important role in protein structure and function analysis, drug design and many other biological applications. Extracting good representation from protein sequence is fundamental for this prediction task. In recent years, although several secondary structure based feature extraction strategies have been specially proposed for low-similarity protein sequences, the prediction accuracy still remains limited. To explore the potential of secondary structure information, this study proposed a novel feature extraction method from the chaos game representation of predicted secondary structure to mainly capture sequence order information and secondary structure segments distribution information in a given protein sequence. Several kinds of prediction accuracies obtained by the jackknife test are reported on three widely used low-similarity benchmark datasets (25PDB, 1189 and 640). Compared with the state-of-the-art prediction methods, the proposed method achieves the highest overall accuracies on all the three datasets. The experimental results confirm that the proposed feature extraction method is effective for accurate prediction of protein structural class. Moreover, it is anticipated that the proposed method could be extended to other graphical representations of protein sequence and be helpful in future research.

Using DFT methodology for more reliable predictive models: Design of inhibitors of Golgi α-Mannosidase II

Human Golgi α-mannosidase II (GMII), a zinc ion co-factor dependent glycoside hydrolase (E.C.3.2.1.114), is a pharmaceutical target for the design of inhibitors with anti-cancer activity. The discovery of an effective inhibitor is complicated by the fact that all known potent inhibitors of GMII are involved in unwanted co-inhibition with lysosomal α-mannosidase (LMan, E.C.3.2.1.24), a relative to GMII. Routine empirical QSAR models for both GMII and LMan did not work with a required accuracy. Therefore, we have developed a fast computational protocol to build predictive models combining interaction energy descriptors from an empirical docking scoring function (Glide-Schrödinger), Linear Interaction Energy (LIE) method, and quantum mechanical density functional theory (QM-DFT) calculations. The QSAR models were built and validated with a library of structurally diverse GMII and LMan inhibitors and non-active compounds. A critical role of QM-DFT descriptors for the more accurate prediction abilities of the models is demonstrated. The predictive ability of the models was significantly improved when going from the empirical docking scoring function to mixed empirical-QM-DFT QSAR models (Q2=0.78–0.86 when cross-validation procedures were carried out; and R2=0.81–0.83 for a testing set). The average error for the predicted ΔGbind decreased to 0.8–1.1kcalmol−1. Also, 76–80% of non-active compounds were successfully filtered out from GMII and LMan inhibitors. The QSAR models with the fragmented QM-DFT descriptors may find a useful application in structure-based drug design where pure empirical and force field methods reached their limits and where quantum mechanics effects are critical for ligand-receptor interactions. The optimized models will apply in lead optimization processes for GMII drug developments.

Methods for Protein Structure Prediction and Its Application in Drug Design Using Hidden Markov Model

Methods for Protein Structure Prediction and Its Application in Drug Design Using Hidden Markov Model

Accurate Atom-by-Atom Predictions of Solvation Electrostatics Using a Hydration-Shell Poisson-Boltzmann Model

Traditional Poisson-Boltzmann models of molecular electrostatics fail to model the behavior of the hydration-shell solvent, which deviates significantly from bulk. Here we present a hydration-shell Poisson-Boltzmann (HSPB) model that accounts for these deviations by perturbing the displacement-field boundary condition. The HSPB model correctly predicts charging free energies for Born ions, amino acids in multiple protonation states, and challenging test cases for charge-hydration asymmetry. Remarkably, the model achieves this accuracy using standard Lennard-Jones radii from molecular-dynamics force fields, and thus it has an order of magnitude fewer fitting parameters than traditional PB theories. Free-energy calculations in explicit solvent establish that HSPB also accurately predicts the charging free energies of individual atoms in biological molecules.In contrast, calculations using the traditional Poisson--Boltzmann model indicate substantial errors in intramolecular screening. The HSPB model's accuracy in predicting free-energy changes recommend it for drug design and protein engineering applications, where electrostatic contributions to binding are often assessed relative to hydrophobic isosteres.

Genome-wide metabolic model to improve understanding of CD4(+) T cell metabolism, immunometabolism and application in drug design.

CD4(+) T cells play a critical role in adaptive immunity and have been well studied in past decades. However, the systematic metabolism features are less clear. Here, we reconstructed the genome-wide metabolic network of naïve CD4(+) T cells, CD4T1670, by integrating transcriptome and metabolism data. We performed simulations for three critical metabolic subsystems (carbohydrate metabolism, fatty acid metabolism and glutaminolysis). The results were consistent with most experimental observations. Furthermore, we found that depletion of either glucose or glutamine did not significantly affect ATP production and biomass, but dramatically unbalanced the metabolic network and increased the release of some inflammation or anti-inflammation related factors, such as lysophosphatidylcholine, leukotriene and hyaluronan. Genome-wide single gene knockout analysis showed that acetyl-CoA carboxylase 1 (ACC1) was essential for T cell activation. We further investigated the role of immunometabolic genes in metabolic network stability, and found that over 25% of them were essential. The results also showed that although PTEN is a well-studied proliferation inhibitor, it was essential for maintaining the stability of CD4 metabolic networks. Finally, we applied CD4T1670 to evaluate the side-effects of certain drugs in preclinical experiments. These results suggested that CD4T1670 would be useful in understanding CD4(+) T cells and drug design systematically.

A multiple information fusion method for predicting subcellular locations of two different types of bacterial protein simultaneously

Subcellular localization prediction of bacterial protein is an important component of bioinformatics, which has great importance for drug design and other applications. For the prediction of protein subcellular localization, as we all know, lots of computational tools have been developed in the recent decades. In this study, we firstly introduce three kinds of protein sequences encoding schemes: physicochemical-based, evolutionary-based, and GO-based. The original and consensus sequences were combined with physicochemical properties. And elements information of different rows and columns in position-specific scoring matrix were taken into consideration simultaneously for more core and essence information. Computational methods based on gene ontology (GO) have been demonstrated to be superior to methods based on other features. Then principal component analysis (PCA) is applied for feature selection and reduced vectors are input to a support vector machine (SVM) to predict protein subcellular localization. The proposed method can achieve a prediction accuracy of 98.28% and 97.87% on a stringent Gram-positive (Gpos) and Gram-negative (Gneg) dataset with Jackknife test, respectively. At last, we calculate “absolute true overall accuracy (ATOA)”, which is stricter than overall accuracy. The ATOA obtained from the proposed method is also up to 97.32% and 93.06% for Gpos and Gneg. From both the rationality of testing procedure and the success rates of test results, the current method can improve the prediction quality of protein subcellular localization.

Target the More Druggable Protein States in a Highly Dynamic Protein--Protein Interaction System.

The proteins of the Bcl-2 family play key roles in the regulation of programmed cell death by controlling the integrity of the outer mitochondrial membrane and the initiation of the apoptosis process. We performed extensive molecular dynamics simulations to investigate the conformational flexibility of the Bcl-xL protein in both the apo and holo (with Bad peptide and ABT-737) states. The accelerated molecular dynamics method implemented in Amber 14 was used to produce broader conformational sampling of 200 ns simulations. The pocket mining method based on the variational implicit-solvent model tracks the dynamic evolution of the ligand binding site with a druggability score characterizing the maximal affinity achievable by a drug-like molecule. Major movements were observed around the α3-helical domain and the loop region connecting the α1 and α2 helices, reshaping the ligand interaction in the BH3 binding groove. Starting with the apo crystal structure, which is recognized as "closed" and undruggable, the BH3 groove transitioned between the "open" and "closed" states during equilibrium simulation. Further analysis revealed a small percentage of the trajectory frames (∼10%) with a moderate degree of druggability that mimic the ligand-bound states. The ability to attain and detect by computer simulation the most suitable conformational states for ligand binding in advance of compound synthesis and crystal structure solution is of immense value to the application and success of structure-based drug design.

Structural and functional characterization of the recombinant thioredoxin reductase from Candida albicans as a potential target for vaccine and drug design.

The thioredoxin system plays a critical role in maintaining the cytoplasm redox state, participating in functions that are important to the cellular viability of fungi. Although functional and structural information on targets in human pathogenic fungi has been scarcely described in the literature, such studies are essential for in silico drug design and biotechnological applications. Therefore, the aims of the present study were to produce recombinant proteins of the thioredoxin system from Candida albicans and evaluate their possible use as prophylactic or alternative therapies against the most important pathogenic fungus associated with nosocomial infections. We focused on biochemical and structural analyses of recombinant thioredoxin reductase from C. albicans with His-tag (CaTrxR-His) for further biotechnology applications. Heterologous CaTrxR-His was efficiently expressed in the soluble fraction of the Escherichia coli lysate. CaTrxR-His was obtained with a high level of purity and presented specific enzymatic activity. Conformational changes of the protein were observed at different pHs and temperatures, with higher thermal stability at pH 8.0. The CaTrxR-His vaccine was shown to effectively induce high levels of CaTrxR-specific immunoglobulin G antibodies in Balb/c mice and reduce the renal fungal burden of experimental disseminated candidiasis in mice. These data may greatly impact future development strategies for vaccine and drug designs against C. albicans infection.

MOWGLI: prediction of protein–MannOse interacting residues With ensemble classifiers usinG evoLutionary Information

Proteins interact with carbohydrates to perform various cellular interactions. Of the many carbohydrate ligands that proteins bind with, mannose constitute an important class, playing important roles in host defense mechanisms. Accurate identification of mannose-interacting residues (MIR) may provide important clues to decipher the underlying mechanisms of protein–mannose interactions during infections. This study proposes an approach using an ensemble of base classifiers for prediction of MIR using their evolutionary information in the form of position-specific scoring matrix. The base classifiers are random forests trained by different subsets of training data set Dset128 using 10-fold cross-validation. The optimized ensemble of base classifiers, MOWGLI, is then used to predict MIR on protein chains of the test data set Dtestset29 which showed a promising performance with 92.0% accurate prediction. An overall improvement of 26.6% in precision was observed upon comparison with the state-of-art. It is hoped that this approach, yielding enhanced predictions, could be eventually used for applications in drug design and vaccine development.

Transforming conotoxins into cyclotides: Backbone cyclization of P-superfamily conotoxins.

Peptide backbone cyclization is a widely used approach to improve the activity and stability of small peptides but until recently it had not been applied to peptides with multiple disulfide bonds. Conotoxins are disulfide-rich conopeptides derived from the venoms of cone snails that have applications in drug design and development. However, because of their peptidic nature, they can suffer from poor bioavailability and poor stability in vivo. In this study two P-superfamily conotoxins, gm9a and bru9a, were backbone cyclized by joining the N- and C-termini with short peptide linkers using intramolecular native chemical ligation chemistry. The cyclized derivatives had conformations similar to the native peptides showing that backbone cyclization can be applied to three disulfide-bonded peptides with cystine knot motifs. Cyclic gm9a was more potent at high voltage-activated (HVA) calcium channels than its acyclic counterpart, highlighting the value of this approach in developing active and stable conotoxins containing cyclic cystine knot motifs.

Evaluation of Hybrid Theoretical Approaches for Structural Determination of a Glycine-Linked Cisplatin Derivative via Infrared Multiple Photon Dissociation (IRMPD) Action Spectroscopy.

To gain a better understanding of the binding mechanism and assist in the optimization of chemical probing and drug design applications, experimental and theoretical studies of a series of amino acid-linked cisplatin derivatives are being pursued. Glyplatin (glycine-linked cisplatin) was chosen for its structural simplicity and to enable backbone effects to be separated from side-chain effects on the structure and reactivity of ornithine- and lysine-linked cisplatin (Ornplatin and Lysplatin, respectively). Infrared multiple photon dissociation (IRMPD) action spectroscopy experiments were performed on Glyplatin to characterize its structure and guide the selection of the most effective hybrid theoretical approach for determining its structure and IR spectrum. The simplicity of the Glyplatin system allows a wide variety of density functionals, treatments of the Pt center including the use of all-electron basis sets vs valence basis sets combined with an effective core potential (ECP), and basis sets for all other atoms to be evaluated at a reasonable computational cost. The results for Glyplatin provide the foundation for calculations of more complex amino acid-linked cisplatin derivatives such as Ornplatin and Lysplatin. Present results suggest that the B3LYP/mDZP/def2-TZVP hybrid method can be effectively employed for structural and IR characterization of more complex amino acid-linked cisplatin complexes and their nucleic acid derivatives.

Exploring Strong Interactions in Proteins with Quantum Chemistry and Examples of Their Applications in Drug Design.

ObjectivesThree strong interactions between amino acid side chains (salt bridge, cation-π, and amide bridge) are studied that are stronger than (or comparable to) the common hydrogen bond interactions, and play important roles in protein-protein interactions.MethodsQuantum chemical methods MP2 and CCSD(T) are used in calculations of interaction energies and structural optimizations.ResultsThe energies of three types of amino acid side chain interactions in gaseous phase and in aqueous solutions are calculated using high level quantum chemical methods and basis sets. Typical examples of amino acid salt bridge, cation-π, and amide bridge interactions are analyzed, including the inhibitor design targeting neuraminidase (NA) enzyme of influenza A virus, and the ligand binding interactions in the HCV p7 ion channel. The inhibition mechanism of the M2 proton channel in the influenza A virus is analyzed based on strong amino acid interactions.Conclusion(1) The salt bridge interactions between acidic amino acids (Glu- and Asp-) and alkaline amino acids (Arg+, Lys+ and His+) are the strongest residue-residue interactions. However, this type of interaction may be weakened by solvation effects and broken by lower pH conditions. (2) The cation- interactions between protonated amino acids (Arg+, Lys+ and His+) and aromatic amino acids (Phe, Tyr, Trp and His) are 2.5 to 5-fold stronger than common hydrogen bond interactions and are less affected by the solvation environment. (3) The amide bridge interactions between the two amide-containing amino acids (Asn and Gln) are three times stronger than hydrogen bond interactions, which are less influenced by the pH of the solution. (4) Ten of the twenty natural amino acids are involved in salt bridge, or cation-, or amide bridge interactions that often play important roles in protein-protein, protein-peptide, protein-ligand, and protein-DNA interactions.

Application of Multivariate Linear and Nonlinear Calibration and Classification Methods in Drug Design.

Data manipulation and maximum efficient extraction of useful information need a range of searching, modeling, mathematical, and statistical approaches. Hence, an adequate multivariate characterization is the first necessary step in investigation and the results are interpreted after multivariate analysis. Multivariate data analysis is capable of not only large dataset management but also interpret them surely and rapidly. Application of chemometrics and cheminformatics methods may be useful for design and discovery of new drug compounds. In this review, we present a variety of information sources on chemometrics, which we consider useful in different fields of drug design. This review describes exploratory analysis (PCA), classification and multivariate calibration (PCR, PLS) methods to data analysis. It summarizes the main facts of linear and nonlinear multivariate data analysis in drug discovery and provides an introduction to manipulation of data in this field. It handles the fundamental aspects of basic concepts of multivariate methods, principles of projections (PCA and PLS) and introduces the popular modeling and classification techniques. Enough theory behind these methods, more particularly concerning the chemometrics tools is included for those with little experience in multivariate data analysis techniques such as PCA, PLS, SIMCA, etc. We describe each method by avoiding unnecessary equations, and details of calculation algorithms. It provides a synopsis of the method followed by cases of applications in drug design (i.e., QSAR) and some of the features for each method.