In silico modeling and molecular docking insights of kaempferitrin for colon cancer-related molecular targets

Colorectal cancer is one of the most common cancers worldwide, and it is also one of the major causes of mortality from cancer. Chemotherapy drugs are generally limited due to various complications, as well as the development of resistance and recurrence. The in silico docking investigation involved exploration of protein or nucleotide, 3D structural modeling, molecular docking, and binding energy calculation. Protein-protein interactions are significant to many biological processes, and their disruption is a leading cause of disease. The use of small molecules to modulate them is gaining popularity, but protein interfaces usually lack specific cavities for processing small molecules. MMP-2, PARP, iNOS, Chk1, proteins were used in the molecular docking analysis of kaempferitrin and 5-flurouracil. The compound kaempferitrin had the highest binding energy scores with most of the target proteins, according to molecular docking results. The findings suggest it could be used to develop new drugs for cancer therapy.     

Mammalian plasma membrane proteins as potential biomarkers and drug targets

Defining the plasma membrane proteome is crucial to understand the role of plasma membrane in fundamental biological processes. Change in membrane proteins is one of the first events that take place under pathological conditions, making plasma membrane proteins a likely source of potential disease biomarkers with prognostic or diagnostic potential. Membrane proteins are also potential targets for monoclonal antibodies and other drugs that block receptors or inhibit enzymes essential to the disease progress. Despite several advanced methods recently developed for the analysis of hydrophobic proteins and proteins with posttranslational modifications, integral membrane proteins are still under-represented in plasma membrane proteome. Recent advances in proteomic investigation of plasma membrane proteins, defining their roles as diagnostic and prognostic disease biomarkers and as target molecules in disease treatment, are presented.     

In silico prediction of human genes as potential targets for rice miRNAs

BackgroundExogenous microRNAs (miRNAs) enter the human body through food, and their effects on metabolic processes can be considerable. It is important to determine which miRNAs from plants affect the expression of human genes and the extent of their influence.MethodThe binding sites of 738Oryza sativa miRNAs (osa-miRNAs) that interact with 17 508 mRNAs of human genes were determined using the MirTarget program.ResultThe characteristics of the binding of 46 single osa-miRNAs to 86 mRNAs of human genes with a value of free energy (ΔG) interaction equal 94%–100% from maximum ΔG were established. The findings showed that osa-miR2102-5p, osa-miR5075-3p, osa-miR2097-5p, osa-miR2919 targeted the largest number of genes at 38, 36, 23, 19 sites, respectively. mRNAs of 86 human genes were identified as targets for 93 osa-miRNAs of all family osa-miRNAs with ΔG values equal 94%–98% from maximum ΔG. Each miRNA of the osa-miR156-5p, osa-miR164-5p, osa-miR168-5p, osa-miR395-3p, osa-miR396-3p, osa-miR396-5p, osa-miR444-3p, osa-miR529-3p, osa-miR1846-3p, osa-miR2907-3p families had binding sites in mRNAs of several human target genes. The binding sites of osa-miRNAs in mRNAs of the target genes for each family of osa-miRNAs were conserved when compared to flanking nucleotide sequences.ConclusionTarget mRNA human genes of osa-miRNAs are also candidate genes of cancer, cardiovascular and neurodegenerative diseases.     

In silico prediction of drug toxicity

It is essential, in order to minimise expensive drug failures due to toxicity being found in late development or even in clinical trials, to determine potential toxicity problems as early as possible. In view of the large libraries of compounds now being handled by combinatorial chemistry and high-throughput screening, identification of putative toxicity is advisable even before synthesis. Thus the use of predictive toxicology is called for. A number of in silico approaches to toxicity prediction are discussed. Quantitative structure-activity relationships (QSARs), relating mostly to specific chemical classes, have long been used for this purpose, and exist for a wide range of toxicity endpoints. However, QSARs also exist for the prediction of toxicity of very diverse libraries, although often such QSARs are of the classification type; that is, they predict simply whether or not a compound is toxic, and do not give an indication of the level of toxicity. Examples are given of all of these. A number of expert systems are available for toxicity prediction, most of them covering a range of toxicity endpoints. Those discussed include TOPKAT, CASE, DEREK, HazardExpert, OncoLogic and COMPACT. Comparative tests of the ability of these systems to predict carcinogenicity show that improvement is still needed. The consensus approach is recommended, whereby the results from several prediction systems are pooled. It is simply amazing that we can formulate any kind of QSAR. The (desired activity) is only the starting point. The truly formidable problem is that of toxicity, especially the difficult long-term toxicities resulting from chronic usage'. (Hansch & Leo [1])     

In silico drug design and molecular docking of novel amidophosphonates and sulfamidophosphonates as inhibitors of urokinase-type plasminogen activator

Cancer is one of the most serious health problems worldwide, affecting individuals from different sexes, ages, and races. However, the most frequent cancer types in the world are lung, prostate, stomach, colorectal, and esophagus in men; and breast, lung, stomach, colorectal and cervical in women. Currently, the search for new active substances used in oral targeted therapies are legitimate and opens up the possibility of an "ambulatory shift" in cancer treatment. In order to design anti-tumor drug candidates endowed with oral bioavailability, we studied trough an in silico approach the oral bioavailability of newly synthesized biomolecules; α-sulfamidophosphonates and α-amidophosphonates as well as their mechanism of action on the new target urokinase-type plasminogen activator (uPA). The studied compounds have been found to meet the five criteria of. Lipinski's rule. The Osiris, Molinspiration and SWISS/ADME calculations related to the compounds (1d, 2a) have shown that these compounds could be good candidates for interacting with the different targets, they have convincing characteristics in relation to the standard drug used. It can be concluded that these compounds are biologically important and possessing molecular properties desirable for being a drug candidate for oral use.The molecular docking results of the studied compounds revealed a good ligand-target interactions, the compounds (1d, 2a) presented a possibility of interacting as an inhibitor of the anticancer target: urokinase-type plasminogen activator (uPA).     

In silico mutagenesis and docking study of Ralstonia solanacearum RSL lectin: performance of docking software to predict saccharide binding

In this study, in silico mutagenesis and docking in Ralstonia solanacearum lectin (RSL) were carried out, and the ability of several docking software programs to calculate binding affinity was evaluated. In silico mutation of six amino acid residues (Agr17, Glu28, Gly39, Ala40, Trp76, and Trp81) was done, and a total of 114 in silico mutants of RSL were docked with Me-α-L-fucoside. Our results show that polar residues Arg17 and Glu28, as well as nonpolar amino acids Trp76 and Trp81, are crucial for binding. Gly39 may also influence ligand binding because any mutations at this position lead to a change in the binding pocket shape. The Ala40 residue was found to be the most interesting residue for mutagenesis and can affect the selectivity and/or affinity. In general, the docking software used performs better for high affinity binders and fails to place the binding affinities in the correct order.     

Identification of potential HIV-1 integrase strand transfer inhibitors: In silico virtual screening and QM/MM docking studies

HIV-1 integrase (IN) is a retroviral enzyme that catalyses integration of the reverse-transcribed viral DNA into the host genome, which is necessary for efficient viral replication. In this study, we have performed an in silico virtual screening for the identification of potential HIV-1 IN strand transfer (ST) inhibitors. Pharmacophore modelling and atom-based 3D-QSAR studies were carried out for a series of compounds belonging to 3-Hydroxypyrimidine-2,4-diones. Based on the ligand-based pharmacophore model, we obtained a five-point pharmacophore with two hydrogen bond acceptors (A), one hydrogen bond donor (D), one hydrophobic group (H) and one aromatic ring (R) as pharmacophoric features. The pharmacophore hypothesis AADHR was used as a 3D query in a sequential virtual screening study to filter small molecule databases Maybridge, ChemBridge and Asinex. Hits matching with pharmacophore hypothesis AADHR were retrieved and passed progressively through Lipinski’s rule of five filtering, molecular docking and hierarchical clustering. The five compounds with best hits with novel and diverse chemotypes were subjected to QM/MM docking, which showed improved docking accuracy. We further performed molecular dynamics simulation and found three compounds that form stable interactions with key residues. These compounds could be used as a leads for further drug development and rational design of HIV-1 IN inhibitors.     

In silico allergenicity prediction of several lipid transfer proteins

Non-specific lipid transfer proteins (nsLTPs) are common allergens and they are particularly widespread within the plant kingdom. They have a highly conserved three-dimensional structure that generate a strong cross-reactivity among the members of this family. In the last years several web tools for the prediction of allergenicity of new molecules based on their homology with known allergens have been released, and guidelines to assess potential allergenicity of proteins through bioinformatics have been established. Even if such tools are only partially reliable yet, they can provide important indications when other kinds of molecular characterization are lacking. The potential allergenicity of 28 amino acid sequences of LTPs homologs, either retrieved from the UniProt database or in silico deduced from the corresponding EST coding sequence, was predicted using 7 publicly available web tools. Moreover, their similarity degree to their closest known LTP allergens was calculated, in order to evaluate their potential cross-reactivity. Finally, all sequences were studied for their identity degree with the peach allergen Pru p 3, considering the regions involved in the formation of its known conformational IgE-binding epitope. Most of the analyzed sequences displayed a high probability to be allergenic according to all the software employed. The analyzed LTPs from bell pepper, cassava, mango, mungbean and soybean showed high homology (>70%) with some known allergenic LTPs, suggesting a potential risk of cross-reactivity for sensitized individuals. Other LTPs, like for example those from canola, cassava, mango, mungbean, papaya or persimmon, displayed a high degree of identity with Pru p 3 within the consensus sequence responsible for the formation, at three-dimensional level, of its major conformational epitope. Since recent studies highlighted how in patients mono-sensitized to peach LTP the levels of IgE seem directly proportional to the chance of developing cross-reactivity to LTPs from non-Rosaceae foods, and these chances increase the more similar the protein is to Pru p 3, these proteins should be taken into special account for future studies aimed at evaluating the risk of cross-allergenicity in highly sensitized individuals.     

In silico fragment-based drug design using a PASS approach

Fragment-based drug design integrates different methods to create novel ligands using fragment libraries focused on particular biological activities. Experimental approaches to the preparation of fragment libraries have some drawbacks caused by the need for target crystallization (X-ray and nuclear magnetic resonance) and careful immobilization (surface plasmon resonance). Molecular modelling (docking) requires accurate data on protein-ligand interactions, which are difficult to obtain for some proteins. The main drawbacks of QSAR application are associated with the need to collect large homogeneous datasets of chemical structures with experimentally determined self-consistent quantitative values (potency). We propose a ligand-based approach to the selection of fragments with positive contribution to biological activity, developed on the basis of the PASS algorithm. The robustness of the PASS algorithm for heterogeneous datasets has been shown earlier. PASS estimates qualitative (yes/no) prediction of biological activity spectra for over 4000 biological activities and, therefore, provides the basis for the preparation of a fragment library corresponding to multiple criteria. The algorithm for fragment selection has been validated using the fractions of intermolecular interactions calculated for known inhibitors of nine enzymes extracted from the Protein Data Bank database. The statistical significance of differences between fractions of intermolecular interactions corresponds, for several enzymes, to the estimated positive and negative contribution of fragments in enzyme inhibition.     

In silico study of novel niclosamide derivatives,SARS-CoV-2 nonstructural proteins catalytic residue-targeting small molecules drug candidates

The severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2)-mediated coronavirus disease 2019 (COVID-19) infection remains a global pandemic and health emergency with overwhelming social and economic impacts throughout the world. Therapeutics for COVID-19 are limited to only remdesivir; therefore, there is a need for combined, multidisciplinary efforts to develop new therapeutic molecules and explore the effectiveness of existing drugs against SARS-CoV-2. In the present study, we reported eight (SCOV-L-02, SCOV-L-09, SCOV-L-10, SCOV-L-11, SCOV-L-15, SCOV-L-18, SCOV-L-22, and SCOV-L-23) novel structurally related small-molecule derivatives of niclosamide (SCOV-L series) for their targeting potential against angiotensin-converting enzyme-2 (ACE2), type II transmembrane serine protease (TMPRSS2), and SARS-COV-2 nonstructural proteins (NSPs) including NSP5 (3CLpro), NSP3 (PLpro), and RdRp. Our correlation analysis suggested that ACE2 and TMPRSS2 modulate host immune response via regulation of immune-infiltrating cells at the site of tissue/organs entries. In addition, we identified some TMPRSS2 and ACE2 microRNAs target regulatory networks in SARS-CoV-2 infection and thus open up a new window for microRNAs-based therapy for the treatment of SARS-CoV-2 infection. Our in vitro study revealed that with the exception of SCOV-L-11 and SCOV-L-23 which were non-active, the SCOV-L series exhibited strict antiproliferative activities and non-cytotoxic effects against ACE2- and TMPRSS2-expressing cells. Our molecular docking for the analysis of receptor-ligand interactions revealed that SCOV-L series demonstrated high ligand binding efficacies (at higher levels than clinical drugs) against the ACE2, TMPRSS2, and SARS-COV-2 NSPs. SCOV-L-18, SCOV-L-15, and SCOV-L-09 were particularly found to exhibit strong binding affinities with three key SARS-CoV-2’s proteins: 3CLpro, PLpro, and RdRp. These compounds bind to the several catalytic residues of the proteins, and satisfied the criteria of drug-like candidates, having good adsorption, distribution, metabolism, excretion, and toxicity (ADMET) pharmacokinetic profile. Altogether, the present study suggests the therapeutic potential of SCOV-L series for preventing and managing SARs-COV-2 infection and are currently under detailed investigation in our lab.     

In silico prediction of cytochrome P450-mediated drug metabolism

The application of combinatorial chemistry and high-throughput screening technique enables the large number of chemicals to be generated and tested simultaneously, which will facilitate the drug development and discovery. At the same time, it brings about a challenge of how to efficiently identify the potential drug candidates from thousands of compounds. A way used to deal with the challenge is to consider the drug pharmacokinetic properties, such as absorption, distribution, metabolism and excretion (ADME), in the early stage of drug development. Among ADME properties, metabolism is of importance due to the strong association with efficacy and safety of drug. The review will focus on in silico approaches for prediction of Cytochrome P450-mediated drug metabolism. We will describe these predictive methods from two aspects, structure-based and data-based. Moreover, the applications and limitations of various methods will be discussed. Finally, we provide further direction toward improving the predictive accuracy of these in silico methods.     

Trypanosomal dUTPases as potential targets for drug design

Parasites of the Trypanosomatidae family are responsible for diseases that afflict several million people worldwide. Currently there is an urgent need for new drugs against these diseases and an approach to drug discovery is the study of biochemical and structural properties of a potential target and the subsequent design of specific compounds. Trypanosomatid genes coding for enzymes which distinctively hydrolyze dUTP have been isolated by genetic complementation in Escherichia coli mutants defective in dUTPase activity. An analysis of these sequences from Leishmania major and Trypanosoma cruzi showed that no significant similarity could be established with the family of known dUTPases and that the five consensus motifs were absent. However, limited similarity was identified for three motifs present in an enzyme related in function the dCTPase-dUTPase from T phages and 35 percent identity with a putative dUTPase identified in the eubacteria Campylobacter jejuni. T. cruzi and L. major dUTPases were highly similar and catalyzed in a specific fashion the hydrolysis of dUTP. A detailed kinetic study of both enzymes revealed that dUDP is also an efficient substrate of the enzyme while other nucleotides are poorly hydrolyzed. The enzyme is essential for viability in Leishmania and is up-regulated by inhibitors of dTMP synthesis. Thus, a new family of dUTPases might exist in certain organisms that bear no sequence or structure similarity with eukaryotic enzymes accomplishing the same function and that may constitute potential drug targets for the development of specific inhibitors.     

In silico analysis of proteins and microRNAs related to human African trypanosomiasis in tsetse fly

Human African trypanosomiasis (HAT), also known as sleeping sickness, causes millions of deaths worldwide. HAT is primarily transmitted by the vector tsetse fly (Glossina morsitans). Early diagnosis remains a key objective for treating this disease. MicroRNAs (miRNAs) are evolutionarily conserved small non-coding RNAs that play key roles in vector-borne diseases. To date, the roles of proteins and miRNAs in HAT disease have not been thoroughly elucidated. In this study, we have re-annotated the function of protein-coding genes and identified several miRNAs based on a series of bioinformatics tools. A batch of 81.1 % of tsetse fly proteins could be determined homology in mosquito genome, suggesting their probable similar mechanisms in vector-borne diseases. A set of 11 novel salivary proteins and 14 midgut proteins were observed in the tsetse fly, which could be applied to the development of vaccine candidates for the control of HAT disease. In addition, 35 novel miRNAs were identified, among which 10 miRNAs were found to be unique in tsetse fly. Pathway analysis of these 10 miRNAs indicated that targets of miR-15a-5p were significantly enriched in the HAT-related neurotrophin signaling pathway. Besides, topological analysis of the miRNA-gene network indicated that miR-619-5p and miR-2490-3p targeted several genes that respond to trypanosome infection, including thioester-containing protein Tep1 and heat shock protein Hsp60a. In conclusion, our work helps to elucidate the function of miRNAs in tsetse fly and establishes a foundation for further investigations into the molecular regulatory mechanisms of HAT disease.     

In silico mutagenesis and docking studies of Pseudomonas aeruginosa PA-IIL lectin predicting binding modes and energies

This article is focused on the application of two types of docking software, AutoDock and DOCK. It is aimed at studying the interactions of a calcium-dependent bacterial lectin PA-IIL (from Pseudomonas aeruginosa) and its in silico mutants with saccharide ligands. The effect of different partial charges assigned to the calcium ions was tested and evaluated in terms of the best agreement with the crystal structure. The results of DOCK were further optimized by molecular dynamics and rescored using AMBER. For both software, the agreement of the docked structures and the provided binding energies were evaluated in terms of prediction accuracy. This was carried out by comparing the computed results to the crystal structures and experimentally determined binding energies, respectively. The performance of both docking software applied on a studied problem was evaluated as well. The molecular docking methods proved efficient in identifying the correct binding modes in terms of geometry and partially also in predicting the preference changes caused by mutation. Obtaining a reasonable in silico method for the prediction of lectin-saccharide interactions may be possible in the future.     

In silico drug repositioning of FDA-approved drugs to predict new inhibitors for alpha-synuclein aggregation

One of the hallmarks of Parkinson’s disease (PD), a long-term neurodegenerative syndrome, is the accumulation of alpha-synuclein (α-syn) fibrils. Despite numerous studies and efforts, inhibition of α-syn protein aggregation is still a challenge. To overcome this issue, we propose an in silico pharmacophore-based repositioning strategy, to find a pharmaceutical drug that, in addition to their defined role, can be used to prevent aggregation of the α-syn protein. Ligand-based pharmacophore modeling was developed and the best model was selected with validation parameters including 72 % sensitivity, 98 % specificity and goodness score about 0.7. The optimal model has three groups of hydrogen bond donor (HBD), three groups of hydrogen bond acceptor (HBA), and two aromatic rings (AR). The FDA-Approved reports in the ZINC15 database were screened with the pharmacophore model taken from inhibitor compounds. The model identified 22 hits, as promising candidate drugs for Parkinson's therapy. It is noteworthy that among these, 10 drugs have been reported to inhibition of α-syn aggregation or treat/reduce Parkinson's pathogenesis. This model was used to virtual screen ZINC, NCI databases, and natural products from the pomegranate. The results of this screen were filtered for their inability to cross the blood-brain barrier, poor oral bioavailability, etc. Finally, the selected compounds of two ZINC and NCI databases were combined and structurally clustered. Remained compounds were clustered in 28 different clusters, and the 17 compounds were introduced as final candidates.     

X-ray structure and in silico molecular docking of a natural phaeosphaeride A derivative for targets associated with kinase cascades

The structure of the lead compound, a natural phaeospha- eride A derivative AV-6, (2S,3R,4R)-3-hydroxy-6-methoxy-3-methyl-7-methylene-2-pentyl-4-pyrrolidin-1-yl-3,4,6,7-tetrahydropyrano[2,3-c]pyrrol-5(2H)-one, was unambiguously determined by X-ray crystallography. When modeling in silico the interaction of AV-6 with targets in kinase cascades, high values of the binding energy (below –9 kcal mol^-1) for some protein targets were shown. Our results identified that MAPK11, MAPK12 and AKT1 could be targets of AV-6.     

In silico designing breast cancer peptide vaccine for binding to MHC class I and II: A molecular docking study

Antigenic peptides or cancer peptide vaccines can be directly delivered to cancer patients to produce immunologic responses against cancer cells. Specifically, designed peptides can associate with Major Histocompatibility Complex (MHC) class I or II molecules on the cell surface of antigen presenting cells activating anti-tumor effector mechanisms by triggering helper T cell (Th) or cytotoxic T cells (CTL). In general, high binding to MHCs approximately correlates with in vivo immunogenicity. Consequently, a molecular docking technique was run on a library of novel discontinuous peptides predicted by PEPOP from Human epidermal growth factor receptor 2 (HER2 ECD) subdomain III. This technique is expected to improve the prediction accuracy in order to identify the best MHC class I and II binder peptides. Molecular docking analysis through GOLD identified the peptide 1412 as the best MHC binder peptide to both MHC class I and II molecules used in the study. The GOLD results predicted HLA-DR4, HLA-DP2 and TCR as the most often targeted receptors by the peptide 1412. These findings, based on bioinformatics analyses, can be exploited in further experimental analyses in vaccine design and cancer therapy to find possible proper approaches providing beneficial effects.