Current Computer - Aided Drug Design - Volume 10, Issue 3, 2014

One of the most important public health issues is the microbial and bacterial resistance to conventional antibiotics by pathogen microorganisms. In recent years, many researches have been focused on the development of new antibiotics. Among these, antimicrobial peptides (AMPs) have raised as a promising alternative to combat antibioticresistant microorganisms. For this reason, many theoretical efforts have been done in the development of new computational tools for the rational design of both better and effective AMPs. In this review, we present an overview of the rational design of AMPs using machine learning techniques and new research fields.

Plant natural products have been intensively investigated during the past decades with a considerable amount of generated data. Databases are subsequently developed to facilitate the management and analysis of accumulated information including plant species, chemical compounds, structures and bioactivities. With the support of databases, the screening of novel bioactivities for plant natural products can benefit from advanced computational methods to accelerate the progress of drug discovery. This overview describes the contents of publicly available databases useful for computational research of plant natural products. Based on the databases, quantitative structure-activity relationship models and protein-ligand docking methods can be developed and applied to analyze and screen bioactive compounds. More public and structured databases with unique contents, search functions and links to major databases are needed for efficiently exploring the chemical space of plant natural products.

The control of beta-lactam antibiotics released through the inhibition of the New Delhi metallo-beta-lactamase 1 (NDM-1) has been identified as a potential target for the treatment of the muti-drugs resistance (MDR) bacteria disease. We have employed molecular dynamics (MD), alanine-scanning mutagenesis and molecular docking techniques to optimize the x-ray NDM-1 structure with 11 drugs (Tigecycline, BAL30072, D-captopril, Penicillin G, Ampicillin, Carbenicillin, Cephalexin, Cefaclor, Nitrocefin, Meropenem, and Imipenem). From our simulations, we found that the 5 residues Asp223, His120, His122, His162 and His189 are responsible for the selectivity of NDM-1 associated drugs.

In vitro antifungal potency of a set of potassium N-acylhydrazinecarbodithioates was tested. Some of the studied salts displayed significant antifungal activity against Candida spp. at non-toxic concentration indicating a high selectivity of their anticandidal activity. In further study, on the example of conformational analysis, we have tested several force fields and semiempirical parametrizations in order to identify those that could be effectively used for modeling of this class of compounds.

Antibody drugs are used in the treatment of many chronic diseases. Recently, however, patients and doctors have encountered problems with drug resistance, and improving the affinity of antibody drugs has therefore become a pressing issue. Ibalizumab is a humanized monoclonal antibody that binds human CD4, the primary receptor for human immunodeficiency virus type 1 (HIV-1). In this study, we sought to identify the key residues of the complementaritydetermining regions (CDRs) of ibalizumab. Virtual alanine mutations (complementarity-determining regions of ibalizumab) were also studied using solvated interaction energies derived from molecular dynamics and the explicit water model. Using 1,000 nanosecond molecular dynamic simulations, we identified six residues: Tyr50 [HCDR2], Tyr53 [HCDR3], Asp58 [HCDR2], Glu95 [HCDR2], and Arg95 [LCDR3]. The Robetta alanine-scanning mutagenesis method and crystallographic information were used to verify our simulations. Our simulated binding affinity of −17.33 kcal/mol is close to the experimentally determined value of −16.48 kcal/mol. Our findings may be useful for protein engineering the structure of the ibalizumab-human CD4 receptor complex. Moreover, the six residues that we identified may play a significant role in the development of bioactive antibody analogues.

Efflux pump - a membrane protein belonging to Major Facilitator (MF) family and associated with Multi Drug Resistance (MDR) has been a major factor in drug resistance of bacteria. In the era when no new effective antibiotic had been reported for years, the detailed study of these membrane proteins became imperative in order to improve the efficacy of existing drugs. The Bacillus subtilis membrane protein Bmr belongs to the super family of major facilitator proteins and is one of the first-discovered bacterial multidrug-efflux transporters. Development of Bmr inhibitors (B. subtilis) for least resistance, better drug sustainability and effective cellular activity requires three dimensional structure of this protein which has not yet been determined. In this communication structural characterization of this important efflux pump has been attempted using in silico approaches. The modeled structure of Bmr has been found to have 12 main helical segments interspersed by loops of variable lengths at regular intervals with both N- and C-termini on the same side of membrane. Docking of the known inhibitor reserpine on to the predicted structure of Bmr and its mutants signified the importance of the residues Phe143, Val286 and Phe306 in the interaction with the ligand. Besides this, the role of Arg313 and Phe309 in the H-bond formation and π-π interaction respectively, with reserpine was the new significant finding based on the interaction studies. The structure elucidation of Bmr and the role of these residues in binding to the ligand are expected to have a great impact on the efflux pump inhibition studies around the world and hence in the efficiency of the existing antibiotic drugs.

Xanthine-based molecules such as serine protease dipeptidyl peptidase 4 (DPP4) inhibitors are compounds often used in improving glycemic control in type 2 diabetic patients and also used for their effects as mild stimulants and as bronchodilators, notably in treating asthma symptoms. Here, we aim to better understand the molecular features affecting activity of xanthine-based DPP4 inhibitors such as sitagliptin and related compounds and use these features to de novo predict improved sitagliptin derivatives. To this end, we performed a clinical study to examine the efficacy and safety of once-daily 100 mg oral sitagliptin as monotherapy in Romanian patients with type 2 diabetes. This study indicates that sitagliptin effectively decreases the glycemic level and provides very good glycemic equilibrium. To predict putative new drugs with identical pharmacological effects at lower dosages, we generate QSAR models based on compound series containing 35 DPP4 inhibitors. We establish that the physicochemical parameters critical for DPP4 inhibitory activity are: hydrophobicity described by the logarithm of the octanol/water partition coefficient, counts of rotatable bonds, hydrogen bond donor and acceptor atoms, and topological polar surface area. The predictive power of our QSAR models is indicated by significant values of statistical coefficients: cross-validated correlation q2 (0.77), fitted correlation coefficient r2 (0.85) and standard error of prediction (0.34). Based on the established QSAR equations, we propose and analyse 19 new sitagliptin derivatives with possibly improved pharmacological effect as DPP4 inhibitors.

The most common mechanism of the so-called multidrug resistance (MDR), is mainly associated with an over expression of P-glycoprotein (Pgp). It is an ATP-dependent transport protein that limits the intracellular accumulation of a variety of structurally unrelated compounds within various organs and normal tissues such as kidney, small intestine and the blood brain barrier. Thus, the expression of Pgp has a major impact on the pharmacokinetic profile of many therapeutic agents and therefore, overcoming Pgp-mediated efflux constitutes an attractive means of potentially enhancing their therapeutic efficacy. The flavonoids comprise a large group of polyphenolic compounds that occur in plants and vegetables, and they have been shown to display a wide variety of biological activities. For example, anti-inflammatory, antioxidant, antiallergic, hepatoprotective, antithrombotic, antiviral, and anticarcinogenic activities. The interactions between flavonoids and Pgp have also been extensively studied and some quantitative structure-activity relationships (QSAR) have been reported. In the present work, we have employed 2D-QSAR analysis to evaluate the interactions between Pgp and several flavonoid compounds with the aim of identifying the molecular factors responsible for the Pgp-binding affinity evidenced by these compounds. Thus, the reported data for dissociation constants (KD) between Pgp and 62 flavonoid compounds were modeled by means of multiple regression analysis (MLR), and structures of the compounds under study were characterized by means of calculated physicochemical properties and several topological and constitutional descriptors, as well as geometrical and quantum chemical indexes. The obtained results suggest that the hydrophobic and especially geometric factors are of prime importance for binding, whereas in the case of flavonoid derivatives with flavone (flavonols), flavanone and isoflavone nuclei, the electronic factors are also involved in electron donor/acceptor interactions. In addition, in the case of chalcones, the results suggest that the affinity toward P-gp of such compounds is mainly governed by intermolecular dispersive interactions at the binding site.

Quantitative structure-activity relationship studies on a series of selective inhibitors of thrombin and factor Xa were performed by using Associative Neural Network. To overcome the problem of overfitting due to descriptor selection, 5-fold cross-validation with variable selection in each step of the analysis was performed. The predictive ability of the models was tested through leave-one-out cross-validation, giving a Q2=0.74 - 0.87 for regression models. Predictions for the external evaluation sets obtained accuracies in the range of 0.71 - 0.82 for regressions. The proposed models can be potential tools for finding new drug candidates.

Monte Carlo method has been used as a computational tool for building QSAR models for the reactivation of sarin inhibited acetylcholinesterase (AChE) by quaternary pyridinium oximes. Simplified molecular input line entry system (SMILES) together with hydrogen-suppressed graph (HSG) was used to represent molecular structure. Total number of considered oximes was 46 and activity was defined as logarithm of the AChE reactivation percentage by oximes with concentration of 0.001 M. One-variable models have been calculated with CORAL software for one data split into training, calibration and test set. Computational experiments indicated that this approach can satisfactorily predict the desired endpoint. Best QSAR model had the following statistical parameters: for training set r2 = 0.7096, s = 0.177, MAE = 0.148; calibration set: r2 = 0.6759, s = 0.330, MAE = 0.271 and test set: r2 = 0.8620, s = 0.182, MAE = 0.150. Structural indicators (SMILES based molecular fragments) for the increase and the decrease of the stated activity are defined. Using defined structural alerts computer aided design of new oxime derivatives with desired activity is presented.

Thiazolidine-4-carboxylic acid derivatives were recognized recently for their potent antitubercular activity. A total of sixty four thiazolidine derivatives published in the recent times were collected and 3D QSAR models were developed, using CoMFA and COMSIA with high predictability. Later, we selected three new molecules, recently synthesized in our lab and evaluated them using the developed QSAR models. The in vitro antitubercular activity (MIC) obtained for these new molecules is in agreement with the predicted values (pMIC).