GPU-accelerated high-accuracy molecular docking using guided differential evolution

Single-coordinate-driving method for molecular docking: application to modeling of guest inclusion in cyclodextrin

Molecular docking methods were applied to simulate the coupling of a set of nineteen acyl homoserine lactone analogs into the binding site of the transcriptional receptor LasR. The best pose of each ligand was explored and a qualitative analysis of the possible interactions present in the complex was performed. From the results of the protein-ligand complex analysis, it was found that residues Tyr-64 and Tyr-47 are involved in important interactions, which mainly determine the antagonistic activity of the AHL analogues considered for this study. The effect of different substituents on the aromatic ring, the common structure to all ligands, was also evaluated focusing on how the interaction with the two previously mentioned tyrosine residues was affected. Electrostatic potential map calculations based on the electron density and the van der Waals radii were performed on all ligands to graphically aid in the explanation of the variation of charge density on their structures when the substituent on the aromatic ring is changed through the elements of the halogen group series. A quantitative approach was also considered and for that purpose the ONIOM method was performed to estimate the energy change in the different ligand-receptor complex regions. Those energy values were tested for their relationship with the corresponding IC50 in order to establish if there is any correlation between energy changes in the selected regions and the biological activity. The results obtained using the two approaches may contribute to the field of quorum sensing active molecules; the docking analysis revealed the role of some binding site residues involved in the formation of a halogen bridge with ligands. These interactions have been demonstrated to be responsible for the interruption of the signal propagation needed for the quorum sensing circuit. Using the other approach, the structure-activity relationship (SAR) analysis, it was possible to establish which structural characteristics and chemical requirements are necessary to classify a compound as a possible agonist or antagonist against the LasR binding site.

Although the antiallergic properties of compounds such as CAPE, Melatonin, Curcumin, and Vitamin C have been poorly discussed by experimental studies, the antiallergic properties of these famous molecules have never been discussed with calculations. The histamine-1 receptor (H1R) belongs to the family of rhodopsin-like G-protein-coupled receptors expressed in cells that mediate allergies and other pathophysiological diseases. In this study, pharmacological activities of FDA-approved second generation H1 antihistamines (Levocetirizine, desloratadine and fexofenadine) and molecules such as CAPE, Melatonin, Curcumin, Vitamin C, ADMET (Absorption, Distribution, Metabolism, Excretion, Toxicity) profiles, density functional theory (DFT), molecular docking, biological targets and activities were compared by calculating. Since drug development is an extremely risky, costly and time-consuming process, the data obtained in this study will facilitate and guide future studies. It will also enable researchers to focus on the most promising compounds, providing an effective design strategy. Their pharmacological activity was carried out using computer-based computational techniques including DFT, molecular docking, ADMET analysis, biological targeting, and activity methods. The best binding sites of Desloratadine, Levocetirizine, Fexofenadine, CAPE, Quercetin, Melatonin, curcumin, Vitamin C ligands to Desmoglein 1, Human Histamine H1 receptor, IgE and IL13 protons were determined by molecular docking method and binding energy and interaction states were analyzed. Fexofenadine and Quercetin ligand showed the most effective binding affinity. Melatonin had the best Caco-2 permeability PPB values of Quercetin, CAPE and Curcumin were at optimal levels. On the OATP1B1 and OATP1B3 of curcumin and CAPE, Quercetin was found to have strong inhibition effects on BCRP. Melatonin and CAPE were found to have the highest inhibition values on CYP1A2, while CAPE had the highest inhibition values on CYP2C19 and CYP2C9. Vitamin C and Quercetin were found to be safer in terms of cardiac toxicity and mutagenic risks, while Desloratadine and Levocetirizine carried high risks of neurotoxicity and hematotoxicity, while CAPE was noted for its high enzyme inhibitory activities and low toxicity profiles, while the hERG blockade, DILI, and cytotoxicity values of other compounds pointed to various safety concerns. This study demonstrated the potential of machine learning methods in understanding and discovering H1 receptor blockers. The results obtained provide important clues in the development of important strategies in the clinical use of H1 receptor blockers. In the light of these data, CAPE and Quercetin are remarkable molecules.

Differential Evolution with exponential crossover can be also competitive on numerical optimization

Differential Evolution (DE) is a very simple but powerful Evolutionary Algorithm (EA) for real-world applications. There are two aspects affecting the overall optimization performance significantly, one is parameter control scheme and the other is trial vector generation strategy. As all the control parameters are employed in generating trial vectors, from this perspective of view, the trial vector generation strategy dominates the overall optimization performance. Therefore, trial vector generation strategies are often designed first, and then the corresponding parameter control schemes are well-tuned thereafter. Both of them constitute the main body of a new DE variant. Here in the paper, a novel DE variant with a new designed trial vector generation strategy as well as novel parameter control scheme is proposed to tackle freight trains scheduling problem. By incorporating both a time-stamp mechanism of the external archive and a novel parameter control scheme, the new algorithm can secure better optimization performance not only on man-made benchmarks but also on real-world applications. Both the man-made benchmarks from Congress on Evolutionary Computation (CEC) test suite and the real-world freight train scheduling problem are employed in the validation of the new DE variant. The experiment results show that it is competitive with other state-of-the-art DE techniques for these optimization problems.

Background: HER2-positive breast cancer is a breast cancer that tests positive with human epidermal growth factor receptor 2 (HER2). Human Epidermal Growth Factor Receptor-2 (HER2) promotes the proliferation of breast cancers cells. This research aimed to find the bioactive compounds from Allium sativum for inhibiting HER2 enzyme by using molecular docking method. Materials and method: The protein tyrosin kinase HER2 structure was obtained from Protein Data Bank. Compounds were collected from previous publications of Allium sativum and these structures were retrieved from PubChem database. Molecular docking was done by Autodock vina software. Lipinski’s rule of 5 is used to compare compounds with drug-like and non-drug-like properties. Pharmacokinetic parameters of potential compounds were evaluated using the pkCSM tool. Results: Based on previous publication of Allium sativum, we have collected 55 compounds. The results showed that 2 compounds have HER2 inhibitory activity stronger than the reference compounds including biochanin A và cyanidin 3-malonylglucoside. The Lipinski’s rule of Five showed that these two compounds had propietary drug-likenesss. Moreover, predict ADMET of these compounds was also analyzed. Conclusion: Therefore, biochanin A and cyanidin 3-malonylglucoside may be potential natural product compounds for HER2-positive breast cancer treatment.
 Keywords:
 Allium sativum, tyrosin kinase HER2, breast cancer HER2 positive, in silico, molecular docking.
 References
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Differential evolution (DE) is a powerful evolutionary algorithm for global optimization problems. Generally, appropriate mutation strategies and proper equilibrium between global exploration and local exploitation are significant to the performance of DE. From this consideration, in this paper, we present a novel DE variant, abbreviated to DMIE-DE, to further enhance the optimization capacity of DE by developing a dual mutations collaboration mechanism with elites guiding and inferiors eliminating techniques. More specifically, an explorative mutation strategy DE/current-to-embest with an elite individual serving as part of the difference vector and an exploitative mutation strategy DE/ebest-to-rand with selecting an elite individual as the base vector are employed simultaneously to achieve the balance between local and global performance of the whole population instead of only one mutation strategy in classical DE algorithm. The control parameters F and CR for above mutation strategies are updated adaptively to supplement the optimization ability of DMIE-DE based on a rational probability distribution model and the successful experience from the previous iterations. Moreover, an inferior solutions eliminating technique is embedded to enhance the convergence speed and compensate cost of the fitness evaluation times during the evaluation process. To evaluate the performance of DMIE-DE, experiments are conducted by comparing with five state-of-the-art DE variants on solving 29 test functions in CEC2017 benchmark set. The experimental results indicate that the performance of DMIE-DE is significantly better than, or at least comparable to the considered DE variants.

Hydrazone derivatives possess potential antitumor activities based on modulation of the iron metabolism in cancer cell. A novel hydrazone, N'-(2,4-dimethoxybenzylidene)-2-hydroxybenzohydrazide (DBH), has been synthesized and characterized, which is an analogue of 311 possessing potent anticancer activity. The interactions between DBH and bovine serum albumin (BSA) have been investigated systematically by fluorescence, molecular docking, circular dichroism (CD), UV-vis absorption, and electrochemical impedance spectroscopy (EIS) methods under physiological conditions. The fluorescence quenching observed is attributed to the formation of a complex between BSA and DBH, and the reverse temperature effect of the fluorescence quenching has been found and discussed. The primary binding pattern is determined by hydrophobic interaction occurring in Sudlow's site I of BSA. DBH could slightly change the secondary structure and induce unfolding of the polypeptides of protein. An average binding distance of ~4.0 nm has been determined on the basis of the Förster resonance energy theory (FRET). The effects of iron on the system of DBH-BSA have also been investigated. It is found that iron could compete against BSA to bind DBH. All of these results are supported by a docking study using a BSA crystal model. It is shown that DBH can efficiently bind with BSA and be transported to the focuses needed. Subsequent antitumor test and detailed anticancer mechanism are undergoing in our lab.

Our work investigates the interaction of synthesized graphene with the SARS-CoV-2 virus using molecular docking and molecular dynamics (MD) simulation method. The layer dependent inhibitory effect of graphene nanosheets on spike receptor-binding domain of 6LZG, complexed with host receptor i.e. angiotensin-converting enzyme 2 (ACE2) of SARS-CoV-2 was investigated through computational study. Graphene sample was synthesized using mechanical exfoliation with shear stress and its mechanism of inhibition towards the SARS-CoV-2 virus was explored by molecular docking and molecular dynamics (MD) simulation method. The thermodynamics study for the free binding energy of graphene towards the SARS-CoV-2 virus was analyzed. The binding energy of graphene towards the virus increased with an increasing number of layers. It shows the highest affinity of −17.5 Kcal/mol in molecular docking while ΔGbinding is in the order of −28.01 ± 0.04 5 Kcal/mol for the seven-layers structure. The increase in carbon layers is associated with an increasing number of edge sp3 –type carbon, providing greater curvature, further increase the surface reactivity responsible for high binding efficiency. The MD simulation data reveals the high inhibition efficiency of the synthesized graphene towards SARS-CoV-2 virus which would help to design future in-vitro studies. The graphene system could find potential applications in personal protective equipment and diagnostic kits. Communicated by Ramaswamy H. Sarma

Differential evolution with orthogonal array‐based initialization and a novel selection strategy

Intrinsically disordered proteins (IDPs) are a novel class of proteins that have established a significant importance and attention within a very short period of time. These proteins are essentially characterized by their inherent structural disorder, encoded mainly by their amino acid sequences. The profound abundance of IDPs and intrinsically disordered regions (IDRs) in the biological world delineates their deep-rooted functionality. IDPs and IDRs convey such extensive functionality through their unique dynamic nature, which enables them to carry out huge number of multifaceted biomolecular interactions and make them "interaction hub" of the cellular systems. Additionally, with such widespread functions, their misfunctioning is also intimately associated with multiple diseases. Thus, understanding the dynamic heterogeneity of various IDPs along with their interactions with respective binding partners is an important field with immense potentials in biomolecular research. In this context, molecular docking-based computational approaches have proven to be remarkable in case of ordered proteins. Molecular docking methods essentially model the biomolecular interactions in both structural and energetic terms and use this information to characterize the putative interactions between the two participant molecules. However, direct applications of the conventional docking methods to study IDPs are largely limited by their structural heterogeneity and demands for unique IDP-centric strategies. Thus, in this chapter, we have presented an overview of current methodologies for successful docking operations involving IDPs and IDRs. These specialized methods majorly include the ensemble-based and fragment-based approaches with their own benefits and limitations. More recently, artificial intelligence and machine learning-assisted approaches are also used to significantly reduce the complexity and computational burden associated with various docking applications. Thus, this chapter aims to provide a comprehensive summary of major challenges and recent advancements of molecular docking approaches in the IDP field for their better utilization and greater applicability.Asp (D).

The aim of this study was to systematically elucidate the crosstalk mechanisms between metastatic melanoma and vitiligo and to establish vitiligo and metastasis-based biomarkers as well as to find drug candidates. The genes associated with vitiligo and metastatic melanoma were obtained through differential expression analysis and WGCNA using publicly available data from GEO and TCGA. A prognostic model and nomogram for melanoma were subsequently constructed using hub genes based on machine learning algorithms. A comprehensive assessment was conducted of the correlation between hub genes and overall survival, functional annotations, immune cells and immune checkpoint genes. At the single-cell level, we conducted scoring using the AUCell algorithm and CellChat analysis to facilitate more profound biological exploration. The Cmap database and molecular docking methods were used to screen drug candidates. Following the screening process, a total of six hub genes (DUOX1, GJB3, NOTCH3, PKP1, PTK6 and PTPRF) were employed in the construction of prognostic model by machine learning. Patients were stratified into high-risk and low-risk groups based on the model. The expression of hub genes and the predictive ability of the model were validated in independent cohorts. The high-risk group exhibited worse prognosis, greater immunosuppression and tumor-associated macrophage infiltration. A nomogram based on the risk score had great performance in predicting survival of melanoma patients at 1-, 3-, and 5-year time points. The scRNA-seq results indicated that hub genes may exert an influence on tumor progression and metastasis by affecting fibroblasts and thus promoting epithelial-mesenchymal transition. Methyl-angolensate, byssochlamic-acid, homoharringtonine, piperacillin and cephaeline were potentially targeted therapeutic compounds for hub genes based on molecular docking. Our study firstly provides new insight into the genetic crosstalk between metastatic melanoma and vitiligo that may facilitate the development of personalized treatments.

Enhancing the search ability of differential evolution through orthogonal crossover

Differential Evolution (DE) is perhaps one of the most efficient and versatile evolutionary techniques and its different variants have been proposed in the recent past. However, all these variants often suffer from the shortcomings of slow and/or premature convergence. Further, nearest neighbour (NN) is a frequently used classifier in the domain of data mining but its primitive version is noise sensitive and shows low accuracy. Different data reduction methods such as prototype selection, prototype generation, and DE are widely used to mitigate these drawbacks of NN. However, none of the methods substantially avoids all these shortcomings. This paper proposes a new variant of DE that is based on the biological phenomenon of the centromeric location of chromosomes. The proposed ‘Chromosomes Identification based Differential Evolution (CIDE)’ extracts the knowledge from databases and constructs effective and reliable decision rules for classification. In this paper, the performance of the proposed CIDE technique is computed and compared with 1-NN classifier optimized by fourteen state-of-art variants of DE, and 1-R classifier using six-real datasets as well as KDDCup’99 dataset to illustrate its superiority over the 1-NN and 1-R classifier. Finally, the performance of CIDE is also compared with Naïve Bayes, support vector machine, Neural Network, and Random Forest for the KDDCup’99 dataset using different performance metrics.

Differential evolution improved with self-adaptive control parameters based on simulated annealing