Bioinformatics in drug development and assessment

Bioinformatics and In Vitro Study Reveal ERα as The Potential Target Gene of Honokiol to Enhance Trastuzumab Sensitivity in HER2+ Trastuzumab-Resistant Breast Cancer Cells

Trastuzumab resistance presents a significant challenge in the treatment of HER2+ breast cancer, necessitating the investigation of combination therapies to overcome this resistance. Honokiol, a compound with broad anticancer activity, has shown promise in this regard. This study aims to discover the effect of honokiol in increasing trastuzumab sensitivity in HER2+ trastuzumab-resistant breast cancer cells HCC1954 and the underline mechanisms behind. A bioinformatics study performed to explore the most potential target hub gene for honokiol in HER2+ breast cancer. Honokiol, trastuzumab and combined treatment cytotoxicity activity was then evaluated in both parental HCC1954 and trastuzumab resistance (TR-HCC1954) cells using MTT assay. The expression levels of these hub genes were then analyzed using qRT-PCR and those that could not be analyzed were subjected to molecular docking to determine their potential. Honokiol showed a potent cytotoxicity activity with an IC50 of 41.05 μM and 69.61 μM in parental HCC1954 and TR-HCC1954 cell line respectively. Furthermore, the combination of honokiol and trastuzumab resulted in significant differences in cytotoxicity in TR-HCC1954 cells at specific concentrations. Molecular docking and the qRT-PCR showed that the potential ERα identified from the bioinformatics analysis was affected by the treatment. Our results show that honokiol has the potential to increase the sensitivity of trastuzumab in HER2+ trastuzumab resistant breast cancer cell line HCC1954 by affecting regulating estrogen receptor signaling. Further research is necessary to validate these findings.

In silico and in vitro evaluation of designed fluconazole analogues as lanosterol 14α-demethylase inhibitors

The drugs fighting against aggressive fungal infections are in limited number, therefore, extensive research is obligatory to develop new therapeutic strategies. Fluconazole (FLZ) is a clinically approved drug, but resistant drug against most fungal pathogens, thus it is vital to identify more compounds that can better check the fungal growth. Analogue-based drug designing is a quick and economical way since it has inherent drug-like properties of marketed drugs. This study aims to generate and evaluate analogues of FLZ with better potency against fungal-borne infections. A total of 3307 analogues of FLZ were developed from six scaffold structures. Only 390 compounds passed Lipinski's rule, of which 247 analogues exhibited lower docking scores than FLZ with 5FSA. These inhibitors were further subjected to pharmacokinetics property evaluation and cytotoxicity test and it was found that only 46 analogues were suitable for further evaluation. Based on the molecular docking score of the best two analogues, 6f (-12.7 kcal/mol) and 8f (-12.8 kcal/mol) were selected for molecular dynamics and in-vitro studies. Antifungal activities of both compounds against 4 strains of Candida albicans were evaluated by disc diffusion assay and micro broth dilution assay and Minimum inhibitory concentrations (MICs) for 6f and 8f were observed as 256 µg/ml against 4719, 4918 and 5480 strains but the MIC was extended to 512 µg/ml for strain 3719. Both analogues exhibited low antifungal activities as compared to FLZ (8-16 µg/ml). The interaction of 6f with Mycostatin was also performed using a chequerboard assay that was found additive.Communicated by Ramaswamy H. Sarma.

Mechanism of XiJiaQi in the treatment of chronic heart failure: Integrated analysis by pharmacoinformatics, molecular dynamics simulation, and SPR validation

OBJECTIVE

Chronic heart failure (CHF) is a complicated clinical syndrome with a high mortality rate. XiJiaQi (XJQ) is a traditional Chinese medicine used in the clinical treatment of CHF, but its bioactive components and their modes of action remain unknown. This study was designed to unravel the molecular mechanism of XJQ in the treatment of CHF using multiple computer-assisted and experimental methods.

METHODS

Pharmacoinformatics-based methods were used to explore the active components and targets of XJQ in the treatment of CHF. ADMETlab was then utilized to evaluate the pharmacokinetic and toxicological properties of core components. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were to explore the underlying mechanism of XJQ treatment. Molecular docking, surface plasmon resonance (SPR), and molecular dynamics (MD) were employed to evaluate the binding of active components to putative targets.

RESULTS

Astragaloside IV, formononetin, kirenol, darutoside, periplocin and periplocymarin were identified as core XJQ-related components, and IL6 and STAT3 were identified as core XJQ targets. ADME/T results indicated that periplocin and periplocymarin may have potential toxicity. GO and KEGG pathway analyses revealed that XJQ mainly intervenes in inflammation, apoptosis, diabetes, and atherosclerosis-related biological pathways. Molecular docking and SPR revealed that formononetin had a high affinity with IL6 and STAT3. Furthermore, MD simulation confirmed that formononetin could firmly bind to the site 2 region of IL6 and the DNA binding domain of STAT3.

CONCLUSION

This study provides a mechanistic rationale for the clinical application of XJQ. Modulation of STAT3 and IL-6 by XJQ can impact CHF, further guiding research efforts into the molecular underpinnings of CHF.

Identification of key genes of anti-programmed death ligand 1 for meningioma immunotherapy by bioinformatic analysis

Meningioma is one of the most common primary tumors in the central nervous system (CNS). A deeper understanding of its molecular characterization could provide potential therapeutic targets to reduce recurrence. In this study, we attempted to identify specific gene mutations in meningioma for immunotherapy. One GSE43290 dataset was obtained from the Gene Expression Omnibus (GEO) database to find differentially expressed genes (DEGs) between meningioma tissues and normal meninges. In total, 420 DEGs were identified, including 15 up-regulated and 405 down-regulated genes. Functional enrichment analysis showed that these DEGs were mainly enriched in PI3K-Akt signaling pathway, Focal adhesion, and MAPK signaling pathway. We identified 20 hub genes by protein-protein interaction (PPI) analysis. Among the hub genes, the expression of FLT1, CXCL8, JUN, THBS1, FECAM1, CD34, and FGF13 were negatively correlated with Programmed Death Ligand-1 (PD-L1). Additionally, the expression of those genes was co-regulated by miR-155-5p. The findings suggest that miR-155-5p play an important role in the pathogenesis of meningioma and may represent potential therapeutic targets for its anti-PD-L1 immunotherapy.

Biological Therapies in the Treatment of Cancer-Update and New Directions

Biological therapies have changed the face of oncology by targeting cancerous cells while reducing the effect on normal tissue. This publication focuses mainly on new therapies that have contributed to the advances in treatment of certain malignancies. Immunotherapy, which has repeatedly proven to be a breakthrough therapy in melanoma, as well as B-ALL therapy with CAR T cells, are of great merit in this progress. These therapies are currently being developed by modifying bispecific antibodies and CAR T cells to improve their efficiency and bioavailability. Work on improving the therapy with oncolytic viruses is also progressing, and efforts are being made to improve the immunogenicity and stability of cancer vaccines. Combining various biological therapies, immunotherapy with oncolytic viruses or cancer vaccines is gaining importance in cancer therapy. New therapeutic targets are intensively sought among neoantigens, which are not immunocompromised, or antigens associated with tumor stroma cells. An example is fibroblast activation protein α (FAPα), the overexpression of which is observed in the case of tumor progression. Universal therapeutic targets are also sought, such as the neurotrophic receptor tyrosine kinase (NTRK) gene fusion, a key genetic driver present in many types of cancer. This review also raises the problem of the tumor microenvironment. Stromal cells can protect tumor cells from chemotherapy and contribute to relapse and progression. This publication also addresses the problem of cancer stem cells resistance to treatment and presents attempts to avoid this phenomenon. This review focuses on the most important strategies used to improve the selectivity of biological therapies.

Finding Appropriate Signal Peptides for Secretory Production of Recombinant Glucarpidase: An In SilicoMethod

BACKGROUND

Methotrexate (MTX) is a general chemotherapeutic agent utilized to treat a variety of malignancies, woefully, its high doses can cause nephrotoxicity and subsequent defect in the process of MTX excretion. The recombinant form of glucarpidase is produced by engineered E. coli and is a confirmed choice to overcoming this problem.

OBJECTIVE

In the present study, in silico analyses were performed to select suitable SPs for the secretion of recombinant glucarpidase in E. coli.

METHODS

The signal peptide website and UniProt database were employed to collect the SPs and protein sequences. In the next step, SignalP-5.0 helped us to predict the SPs and the position of cleavage sites. Moreover, physicochemical properties and solubility were evaluated using Prot- Param and Protein-sol online software, and finally, ProtCompB was used to predict the final subcellular localization.

RESULTS

Luckily, all SPs could form soluble fusion proteins. At last, it was found that PPB and TIBA could translocate the glucarpidase into the extracellular compartment.

CONCLUSION

This study showed that there are only 2 applicable SPs for the extracellular translocation of glucarpidase. Although the findings were remarkable with high degrees of accuracy and precision based on the utilization of bioinformatics analyses, additional experimental assessments are required to confirm and validate it. Recent patents revealed several inventions related to the clinical aspects of vaccine peptides against human disorders.

Finding inhibitors for PCSK9 using computational methods

Proprotein convertase subtilisin/kexin type 9 (PCSK9) is one of the key targets for atherosclerosis drug development as its binding with low-density lipoprotein receptor leads to atherosclerosis. The protein-ligand interaction helps to understand the actual mechanism for the pharmacological action. This research aims to discover the best inhibitory candidates targeting PCSK9. To start with, reported ACE inhibitors were incorporated into pharmacophore designing using PharmaGist to produce pharmacophore models. Selected models were later screened against the ZINC database using ZINCPHARMER to define potential drug candidates that were docked with the target protein to understand their interactions. Molecular docking revealed the top 10 drug candidates against PCSK9, with binding energies ranging from -9.8 kcal·mol^-1 to -8.2 kcal·mol^-1, which were analyzed for their pharmacokinetic properties and oral bioavailability. Some compounds were identified as plant-derived compounds like (S)-canadine, hesperetin or labetalol (an antihypertensive drug). Molecular dynamics results showed that these substances formed stable protein-ligand complexes. (S)-canadine-PCSK9 complex was the most stable with the lowest RMSD. It was concluded that (S)-canadine may act as a potential inhibitor against atherosclerosis for the development of new PCSK9 inhibitory drugs in future in vitro research.

Mass Spectrometry- and Computational Structural Biology-Based Investigation of Proteins and Peptides

Recent developments of mass spectrometry (MS) allow us to identify, estimate, and characterize proteins and protein complexes. At the same time, structural biology helps to determine the protein structure and its structure-function relationship. Together, they aid to understand the protein structure, property, function, protein-complex assembly, protein-protein interaction, and dynamics. The present chapter is organized with illustrative results to demonstrate how experimental mass spectrometry can be combined with computational structural biology for detailed studies of protein's structures. We have used tumor differentiation factor protein/peptide as ligand and Hsp70/Hsp90 as receptor protein as examples to study ligand-protein interaction. To investigate possible protein conformation, we will describe two proteins-lysozyme and myoglobin. As an application of MS-based assignment of disulfide bridges, the case of the spider venom polypeptide Phα1β will also be discussed.

Epigenetics, Nutrition, Disease and Drug Development

Epigenetic mechanisms comprising of DNA methylation, histone modifications and gene silencing by RNA interference have been strongly linked to the development and progression of various diseases. These findings have triggered research on epigenetic functions and signal pathways as targets for novel drug discovery. Dietary intake has also presented significant influence on human health and disease development and nutritional modifications have proven important in prevention, but also the treatment of disease. Moreover, a strong link between nutrition and epigenetic changes has been established. Therefore, in attempts to develop novel safer and more efficacious drugs, both nutritional requirements and epigenetic mechanisms need to be addressed.

Structural Dissection of Crotalicidin, a Rattlesnake Venom Cathelicidin, Retrieves a Fragment with Antimicrobial and Antitumor Activity

In silico dissection of crotalicidin (Ctn), a cathelicidin from a South American pit viper, yielded fragments Ctn[1-14] and Ctn[15-34], which were tested to ascertain to what extent they reproduced the structure and activity of the parent peptide. NMR data showing Ctn to be α-helical at the N-terminus and unstructured at the C-terminus were matched by similar data from the fragments. The peptides were tested against Gram-positive and -negative bacteria and for toxicity against both tumor and healthy cells. Despite its amphipathic α-helical structure, Ctn[1-14] was totally inert toward bacteria or eukaryotic cells. In contrast, unstructured Ctn[15-34] replicated the activity of parent Ctn against Gram-negative bacteria and tumor cells while being significantly less toxic toward eukaryotic cells. This selectivity for bacteria and tumor cells, plus a stability to serum well above that of Ctn, portrays Ctn[15-34] as an appealing candidate for further development as an anti-infective or antitumor lead.

Discovery of a new antiviral protein isolated Lonomia obliqua analysed by bioinformatics and real-time approaches

This study presents a new recombinant protein that acts as a powerful antiviral (rAVLO—recombinant Antiviral protein of Lonomia obliqua). It was able to reduce the replication by 10⁶ fold for herpes virus and by 10⁴ fold for rubella virus. RT-PCR of viral RNA rAVLO treated infected cells also showed similar rate of inhibition in replication. The analysis of this protein by bioinformatics suggests that this protein is globular, secreted with a signal peptide and has the ability to bind to MHC class I. It was found that there are several protein binding sites with various HLA and a prevalence of α-helices in the N-terminal region (overall classified as a α/β protein type). BLAST similarity sequence search for corresponding cDNA did not reveal a similar sequence in Genbank, suggesting that it is from a novel protein family. In this study we have observed that this recombinant protein and hemolymph has a potent antiviral action. This protein was produced in a baculovirus/Sf-9 system. Therefore, these analyses suggest that this novel polypeptide is a candidate as a broad spectrum antiviral.

phiBIOTICS: catalogue of therapeutic enzybiotics, relevant research studies and practical applications

Background

The incidence of bacterial infections in humans along with the growing problem of antibiotic resistance is a major public health concern worldwide. Therefore it is necessary to develop novel therapeutic agents to control microbial pathogens. In this regard, enzybiotics, lytic enzymes endowed with the capacity to degrade bacterial cell wall, are a very promising group of alternative antimicrobials.

Description

Numerous experimental studies have confirmed unique therapeutic capabilities of enzybiotics and hence they are worth of wider attention of the medical community. In order to summarize the state of current knowledge of enzybiotics, we have developed phiBIOTICS, an information portal about known and studied therapeutic enzybiotics. phiBIOTICS contains information on chemical and biological properties of enzybiotics together with compendium of facts retrieved from research studies, where enzybiotics were applied. Our auxiliary phiBiScan program utility is dedicated for prediction of novel potential enzybiotics.

Conclusions

phiBIOTICS presents a solid body of knowledge about all studied therapeutic enzybiotics to date. The database brings high-value information on outcomes of applied research and pre-clinical trials of these prospective antimicrobial agents. This information which was scattered in research papers with heterogeneous quality and relevance is now available in the form of manually curated database. phiBIOTICS and phiBiScan are freely accessible at http://www.phibiotics.org/.

In Silico Augmentation of the Drug Development Pipeline: Examples from the study of Acute Inflammation

The clinical translation of promising basic biomedical findings, whether derived from reductionist studies in academic laboratories or as the product of extensive high-throughput and -content screens in the biotechnology and pharmaceutical industries, has reached a period of stagnation in which ever higher research and development costs are yielding ever fewer new drugs. Systems biology and computational modeling have been touted as potential avenues by which to break through this logjam. However, few mechanistic computational approaches are utilized in a manner that is fully cognizant of the inherent clinical realities in which the drugs developed through this ostensibly rational process will be ultimately used. In this article, we present a Translational Systems Biology approach to inflammation. This approach is based on the use of mechanistic computational modeling centered on inherent clinical applicability, namely that a unified suite of models can be applied to generate in silico clinical trials, individualized computational models as tools for personalized medicine, and rational drug and device design based on disease mechanism.

Identifying putative drug targets and potential drug leads: starting points for virtual screening and docking

The availability of three-dimensional (3D) models of both drug leads (small molecule ligands) and drug targets (proteins) is essential to molecular docking and computational drug discovery. This chapter describes an emerging methodology that can be used to identify both drug leads and drug targets using three newly developed web-accessible databases: 1) DrugBank; 2) The Human Metabolome Database; and 3) PubChem. Specifically, it illustrates how putative drug targets and drug leads for exogenous diseases (i.e., infectious diseases) can be readily identified and their 3D structures selected using only the genomic sequences from pathogenic bacteria or viruses as input. It also illustrates how putative drug targets and drug leads for endogenous diseases (i.e., non-infectious diseases or chronic conditions) can be identified using similar databases and similar sequence input. This chapter is intended to illustrate how bioinformatics and cheminformatics can work synergistically to help provide the necessary inputs for computer-aided drug design.

Identification of new drug classification terms in textual resources

Knowledge about biological effects of small molecules helps in the understanding of biological processes and supports the development of new therapeutic agents. DrugBank is a high quality database providing such information about drugs that contains annotation of drug effects and classification of therapeutic effects. However, to broaden the scope of such a database in classifying and annotating drugs, systems for automatic extraction of classification terms and the corresponding annotation of drugs are needed. We have developed an approach for the identification of new terms used in unstructured text that provide information about drug properties. It is based on the identification and extraction of phrases corresponding to lexico-syntactic patterns--so-called Hearst patterns that contain drug names and directly related drug annotation terms. Such phrases could be identified with a high performance in DrugBank text (0.89 F-score) and in Medline abstracts (0.83 F-score). In comparison to DrugBank annotation terminology, a huge amount of new drug annotation terms could be found. The evaluation of terms extracted from Medline showed that 29-53% of them are new valid drug property terms. They could be assigned to existing and new drug property classes not provided by the DrugBank drug annotation. We come to the conclusion that our system can support database content update by providing additionally drug descriptions of pharmacological effects not yet found in databases like DrugBank. Moreover, we propose that automatic normalization of terms improves the annotation and the retrieval of relevant database entries.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Testing the potential for computational chemistry to quantify biophysical properties of the non-proteinaceous amino acids

Although most proteins of most living organisms are constructed from the same set of 20 amino acids, all indications are that this standard alphabet represents a mere subset of what was available to life during early evolution. However, we currently lack an appropriate quantitative framework with which to test the qualitative hypotheses that have been offered to date as explanations for nature's "choices." Specifically, although many indices have been developed to describe the 20 standard amino acids, few or no comparable data extend to prebiotically plausible alternatives because of the costly and time-consuming bench experiments that would be required. Computational chemistry (specifically quantitative structure property relationship methods) offers a potentially fast, cost-effective remedy for this knowledge gap by predicting such molecular properties in silico. Thus, we investigated the use of various freely accessible programs to predict three key amino acid properties (hydrophobicity, charge, and size). We assessed the accuracy of these predictions by comparisons with experimentally determined counterparts for appropriate test data sets. In light of these results, and factors of software accessibility and transparency, we suggest a method for further computational assessments of prebiotically plausible amino acids. The results serve as a starting point for future quantitative analysis of amino acid alphabet evolution.