Current Computer - Aided Drug Design - Volume 15, Issue 1, 2019

Background: Automated compound testing is currently the de facto standard method for drug screening, but it has not brought the great increase in the number of new drugs that was expected. Computer- aided compounds search, known as Virtual Screening, has shown the benefits to this field as a complement or even alternative to the robotic drug discovery. There are different methods and approaches to address this problem and most of them are often included in one of the main screening strategies. Machine learning, however, has established itself as a virtual screening methodology in its own right and it may grow in popularity with the new trends on artificial intelligence. Objective: This paper will attempt to provide a comprehensive and structured review that collects the most important proposals made so far in this area of research. Particular attention is given to some recent developments carried out in the machine learning field: the deep learning approach, which is pointed out as a future key player in the virtual screening landscape.

Introduction: Among the mosquito-borne human-infecting flavivirus species that include Zika, West Nile, yellow fever, Japanese encephalitis and Dengue viruses, the Zika virus is found to be closest to Dengue virus, sharing the same clade in the Flavivirus phylogenetic tree. We consider these five flaviviruses and on closer examination in our analyses, the nucleotide sequences of the Dengue viral genes (envelope and NS5) and genomes are seen to be quite widely different from the other four flaviviruses. We consider the extent of this distinction and determine the advantage and/or disadvantage such differences may confer upon the Dengue viral pathogenesis. Methods: We have primarily used a 2D graphical representation technique to show the differences in base distributions in these five flaviviruses and subsequently, obtained quantitative estimates of the differences. Similarity/dissimilarity between the viruses based on the genes were also determined which showed that the differences with the Dengue genes are more pronounced. Results: We found that the Dengue viruses compared to the other four flaviviruses spread rapidly worldwide and became endemic in various regions with small alterations in sequence composition relative to the host populations as revealed by codon usage biases and phylogenetic examination. Conclusion: We conclude that the Dengue genes are indeed more widely separated from the other aforementioned mosquito-borne human-infecting flaviviruses due to excess adenine component, a feature that is sparse in the literature. Such excesses have a bearing on drug and vaccine, especially peptide vaccine, development and should be considered appropriately.

Background: Proteins are the utmost multi-purpose macromolecules, which play a crucial function in many aspects of biological processes. For a long time, sequence arrangement of amino acid has been utilized for the prediction of protein secondary structure. Besides, in major methods for the prediction of protein secondary structure class, the impact of Gaussian noise on sequence representation of amino acids has not been considered until now; which is one of the important constraints for the functionality of a protein. Methods: In the present research, the prediction of protein secondary structure class was accomplished by integrated application of Stockwell transformation and Amino Acid Composition (AAC), on equivalent Electron-ion Interaction Potential (EIIP) representation of raw amino acid sequence. The introduced method was evaluated by using 4 benchmark datasets of low sequence homology, namely PDB25, 498, 277, and 204. Furthermore, random forest algorithm together with the out-of-bag error estimate and Support Vector Machine (SVM), using k-fold cross validation demonstrated high feature representation potential of our reported approach. Results: The overall prediction accuracy for PDB25, 498, 277, and 204 datasets with randomforest classifier was 92.5%, 94.79%, 92.45%, and 88.04% respectively, whereas with SVM, the results were 84.66%, 95.32%, 89.29%, and 84.37% respectively. Conclusion: An integrated-order-function-frequency-time (OFFT) model has been proposed for the prediction of protein secondary structure class. For the first time, we reported the effect of Gaussian noise on the prediction accuracy of protein secondary structure class and proposed a robust integrated- OFFT model, which is effectively noise resistant.

Introduction: Caspase-3 plays a leading role in apoptosis and on activation, it cleaves many protein substrates in cells and causes cell death. Since many chemotherapeutics are known to induce apoptosis in cancer cells, promotion or activation of apoptosis via targeting apoptosis regulators has been suggested as a promising strategy for anticancer drug discovery. In this paper, we studied the interaction of 1,2,4-Oxadiazoles derivatives with anticancer drug target enzymes (PDB ID 3SRC). Methods: Molecular docking studies were performed on a series of 1,2,4-Oxadiazoles derivatives to find out molecular arrangement and spatial requirements for their binding potential for caspase-3 enzyme agonistic affinity to treat cancer. The Autodock 4.2 and GOLD 5.2 molecular modeling suites were used for the molecular docking analysis to provide information regarding important drug receptor interaction. Results and Conclusion: Both suites explained the spatial disposition of the drug with the active amino acid in the ligand binding domain of the enzyme. The amino acid asparagine 273 (ASN 273) of target has shown hydrogen bond interaction with the top ranked ligand.

Background: Generating a large number of compounds using combinatorial methods increases the possibility of finding novel bioactive compounds. Although some combinatorial structure generation algorithms are available, any method for generating structures from activity-linked substructural topological information is not yet reported. Objective: To develop a method using graph-theoretical techniques for generating structures of antitubercular compounds combinatorially from activity-linked substructural topological information, predict activity and prioritize and screen potential drug candidates. Methods: Activity related vertices are identified from datasets composed of both active and inactive or, differently active compounds and structures are generated combinatorially using the topological distance distribution associated with those vertices. Biological activities are predicted using topological distance based vertex indices and a rule based method. Generated structures are prioritized using a newly defined Molecular Priority Score (MPS). Results: Studies considering a series of Acid Alkyl Ester (AAE) compounds and three known antitubercular drugs show that active compounds can be generated from substructural information of other active compounds for all these classes of compounds. Activity predictions show high level of success rate and a number of highly active AAE compounds produced high MPS score indicating that MPS score may help prioritize and screen potential drug molecules. A possible relation of this work with scaffold hopping and inverse Quantitative Structure-Activity Relationship (iQSAR) problem has also been discussed. Conclusion: The proposed method seems to hold promise for discovering novel therapeutic candidates for combating Tuberculosis and may be useful for discovering novel drug molecules for the treatment of other diseases as well.

Background: 3C-like protease also called the main protease is an essential enzyme for the completion of the life cycle of Middle East Respiratory Syndrome Coronavirus. In our study we predicted compounds which are capable of inhibiting 3C-like protease, and thus inhibit the lifecycle of Middle East Respiratory Syndrome Coronavirus using in silico methods. Methods: Lead like compounds and drug molecules which are capable of inhibiting 3C-like protease was identified by structure-based virtual screening and ligand-based virtual screening method. Further, the compounds were validated through absorption, distribution, metabolism and excretion filtering. Results: Based on binding energy, ADME properties, and toxicology analysis, we finally selected 3 compounds from structure-based virtual screening (ZINC ID: 75121653, 41131653, and 67266079) having binding energy -7.12, -7.1 and -7.08 Kcal/mol, respectively and 5 compounds from ligandbased virtual screening (ZINC ID: 05576502, 47654332, 04829153, 86434515 and 25626324) having binding energy -49.8, -54.9, -65.6, -61.1 and -66.7 Kcal/mol respectively. All these compounds have good ADME profile and reduced toxicity. Among eight compounds, one is soluble in water and remaining 7 compounds are highly soluble in water. All compounds have bioavailability 0.55 on the scale of 0 to 1. Among the 5 compounds from structure-based virtual screening, 2 compounds showed leadlikeness. All the compounds showed no inhibition of cytochrome P450 enzymes, no blood-brain barrier permeability and no toxic structure in medicinal chemistry profile. All the compounds are not a substrate of P-glycoprotein. Conclusion: Our predicted compounds may be capable of inhibiting 3C-like protease but need some further validation in wet lab.

Background: Breast cancer is one of the most common cancers diagnosed among women. It is now recognized that two receptors mediate estrogen action and the presence of estrogen receptor alpha (ERα) correlates with better prognosis and the likelihood of response to hormonal therapy. ERα is an attractive target for the treatment of breast cancer. Most of the drugs currently used for the breast cancer treatment have numerous side effects and they are often unsuccessful in removing the tumour completely. Hence, we focused on natural compounds like flavonoids, polyphenols, etc. which do not exhibit any high toxic effects against normal cells. Objectives: To identify the potential natural inhibitors for BCa through an optimised in silico approach. Methods: Structural modification and molecular docking-based screening approaches were imposed to identify the novel natural compounds by using Schrödinger (Maestro 9.5). The Qikprop v3.5 was used for the evaluation of important ADME parameters and its permissible ranges. Cytotoxicity of the compounds was evaluated by MTT assay against MCF-7 Cell lines. Results: From the docking studies, we found that the compounds, Myricetin, Quercetin, Apigenin, Luteolin and Baicalein showed the highest Glide Scores -10.78, -9.48, -8.92, -8.87 and -8.82 kcal mol-1 respectively. Of these, Luteolin and Baicalein showed the significant IC50 values (25 ± 4.0 and 58.3 ± 4.4 μM, respectively) against MCF-7 cell line. The ADME profiling of the test compounds was evaluated to find the drug-likeness and pharmacokinetic parameters. Conclusion: We mainly focused on in silico study to dock the compounds into the human estrogen receptor ligand binding domain (hERLBD) and compare their predicted binding affinity with known antiestrogens. Myricetin, Quercetin, Apigenin, Luteolin and Baicalein were identified as the most promising among all. Of these, Luteolin and Baicalein showed significant anticancer activities against MCF-7 cell line. These findings may provide basic information for the development of anti-breast cancer agents.

Introduction: Apolipoprotein E4 (ApoE) is a major genetic factor for developing Alzheimer’s disease (AD). It plays a vital role in brain to maintain a constant supply of neuronal lipids for rapid and dynamic membrane synthesis. Aggregation of beta amyloid plaques (Aβ) and neurofibrillary tangles in brain are responsible for onset of AD. The current study is designed to predict a drug against over activity of apoE4. 22 natural compounds (marine, microorganism and plant derivative) were used in current study. Methods: These compounds were used as inhibitors to target apoE4 protein activity. Moreover, six synthetic compounds were docked with target protein to compare and analyze the docking results with natural compounds. S-Allyl-L-Cysteine, Epicatechin Gallate and Fulvic acid showed highest binding affinity (-7.1, - 7 and -7 kcal /mol respectively). Analysis of the docked complex showed that Epicatechin Gallate bonded with Gln156 and Asp35. Furthermore, Fulvic Acid showed hydrogen bonding with Glu27. Among synthetic compound, Tideglusib had highest binding affinity with target protein but did not show hydrogen bonding with any amino acid residue. Moreover, a natural compound S-Allyl-LCysteine also showed highest binding affinity but did not show hydrogen bonding with any amino acid residue. Results and Conclusion: Our study highlighted Epicatechin Gallate as a potential lead compound on the basis of binding affinity and hydrogen bonding to inhibit the progression of AD.

Introduction: Irreversible inhibition of the acetylcholinesterase upon intoxication with organophosphorus compounds leads to an accumulation of acetylcholine in the synaptic cleft and a subsequent desensitization of nicotinic acetylcholine receptors which may ultimately result in respiratory failure. A direct intervention at the nicotinic acetylcholine receptor (nAChR) was proposed as an alternative therapeutic approach to the treatment with atropine and oximes. Methods: The bispyridinium compound MB327 has been found to recover functional activity of nAChR thus representing a promising starting point for the development of new drugs for the treatment of organophosphate poisoning. Recent solid-supported membrane-based electrophysiological experiments have identified symmetrically substituted bispyridinium compounds e.g. MB327, MB583, and PTM0001 that are able to resensitize nAChR of Torpedo californica. In addition, six compounds have been found not to show any resensitizing potential and were thus classified as inactive. This set of active and inactive bispyridinium compounds was taken to develop a pharmacophore model and in silico screening of a virtual database of bispyridinium compounds to identify new compounds that are able to restore the functional activity of desensitized nAChR. Results: Screening of a virtual compound database of symmetrically substituted bispyridinium compounds with the derived pharmacophore yielded several promising compounds which satisfy the pharmacophore and ought to have the same or even better resensitizing effect on nAChR as the parent compound MB327.