iBio-GATS-A Semi-Automated Workflow for Structural Modelling of Insect Odorant Receptors.

Insects utilize seven transmembrane (7TM) odorant receptor (iOR) proteins, with an inverted topology compared to G-protein coupled receptors (GPCRs), to detect chemical cues in the environment. For pest biocontrol, chemical attractants are used to trap insect pests. However, with the influx of invasive insect pests, novel odorants are urgently needed, specifically designed to match 3D iOR structures. Experimental structural determination of these membrane receptors remains challenging and only four experimental iOR structures from two evolutionarily distant organisms have been solved. Template-based modelling (TBM) is a complementary approach, to generate model structures, selecting templates based on sequence identity. As the iOR family is highly divergent, a different template selection approach than sequence identity is needed. Bio-GATS template selection for GPCRs, based on hydrophobicity correspondence, has been morphed into iBio-GATS, for template selection from available experimental iOR structures. This easy-to-use semi-automated workflow has been extended to generate high-quality models from any iOR sequence from the selected template, using Python and shell scripting. This workflow was successfully validated on Apocrypta bakeri Orco and Machilis hrabei OR5 structures. iBio-GATS models generated for the fruit fly iOR, OR59b and Orco, yielded functional ligand binding results concordant with experimental mutagenesis findings, compared to AlphaFold2 models.

Preclinical 3D-model supports an invisibility cloak for adenoid cystic carcinoma.

The tumour-cell based initiation of immune evasion project evaluated the role of Gipie in adenoid cystic carcinoma (ACC) and mucoepidermoid carcinoma (A-253), from ninety-six 3D-ACC and A-253-immune co-culture models using natural killer cells (NK), and Jurkat cells (JK). Abnormal ACC morphology was observed in 3D-ACC immune co-culture models. Gipie-silencing conferred a "lymphoblast-like" morphology to ACC cells, a six-fold increase in apoptotic cells (compared to unaltered ACC cells, P ≤ 0.0001), a two-fold decrease in T regulatory cells (FoxP3+/IL-2Rα+/CD25+) (P ≤ 0.0001), and a three-fold increase in activated NK cells (NKp30+/IFN-γ+) (P ≤ 0.0001) with significantly higher release of granzyme (P ≤ 0.001) and perforin (P ≤ 0.0001).

Head and Neck Cancer Immunotherapy: Molecular Biological Aspects of Preclinical and Clinical Research.

Breakthrough research in the field of immune checkpoint inhibitors and the development of a human papilloma virus vaccine triggered a plethora of research in the field of cancer immunotherapy. Both had significant effects on the treatment of head and neck squamous cell carcinoma. The advent of preclinical models and multidisciplinary approaches including bioinformatics, genetic engineering, clinical oncology, and immunology helped in the development of tumour-infiltrating lymphocytes (TILs) and chimeric antigen receptor (CAR) T-cell therapy. Here, we discuss different immunotherapies such as adoptive T-cell transfer, immune checkpoint inhibitors, interleukins, and cancer vaccines for the treatment of head and neck cancer. This review showcases the intrinsic relation between the understanding and implementation of basic biology and clinical practice. We also address potential limitations of each immunotherapy approach and the advantages of personalized immunotherapy. Overall, the aim of this review is to encourage further research in the field of immunotherapy for head and neck cancer.

Metaproteomic Analysis of an Oral Squamous Cell Carcinoma Dataset Suggests Diagnostic Potential of the Mycobiome.

Oral squamous cell carcinoma (OSCC) is the most common head and neck malignancy, with an estimated 5-year survival rate of only 40-50%, largely due to late detection and diagnosis. Emerging evidence suggests that the human microbiome may be implicated in OSCC, with oral microbiome studies putatively identifying relevant bacterial species. As the impact of other microbial organisms, such as fungi and viruses, has largely been neglected, a bioinformatic approach utilizing the Trans-Proteomic Pipeline (TPP) and the R statistical programming language was implemented here to investigate not only bacteria, but also viruses and fungi in the context of a publicly available, OSCC, mass spectrometry (MS) dataset. Overall viral, bacterial, and fungal composition was inferred in control and OSCC patient tissue from protein data, with a range of proteins observed to be differentially enriched between healthy and OSCC conditions, of which the fungal protein profile presented as the best potential discriminator of OSCC within the analysed dataset. While the current project sheds new light on the fungal and viral spheres of the oral microbiome in cancer in silico, further research will be required to validate these findings in an experimental setting.

Bioinformatic Analysis to Investigate Metaproteome Composition Using Trans-Proteomic Pipeline.

With evidence emerging that the microbiome has a role in the onset of many human diseases, including cancer, analyzing these microbial communities and their proteins (i.e., the metaproteome) has become a powerful research tool. The Trans-Proteomic Pipeline (TPP) is a free, comprehensive software suite that facilitates the analysis of mass spectrometry (MS) data. By utilizing available microbial proteomes, TPP can identify microbial proteins and species, with an acceptable peptide false-discovery rate (FDR). An application to a publicly available oral cancer dataset is presented as an example to identify the viral metaproteome on the oral cancer invasive tumor front. © 2022 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: Collection of data and resources Basic Protocol 2: Analysis of MS data using TPP Basic Protocol 3: Analysis of TPP output using R in RStudio.

Proliferation and Apoptosis Pathways and Factors in Oral Squamous Cell Carcinoma.

Oral cancer is the most common form of head and neck squamous cell carcinoma (HNSCC) and most frequently presents as oral squamous cell carcinoma (OSCC), which is associated with an alarmingly high mortality rate. Internationally, a plethora of research to further our understanding of the molecular pathways related to oral cancer is performed. This research is of value for early diagnosis, prognosis, and the investigation of new drugs that can ameliorate the harmful effects of oral cancer and provide optimal patient outcomes with minimal long-term complications. Two pathways on which the progression of OSCC depends on are those of proliferation and apoptosis, which overlap at many junctions. Herein, we aim to review these pathways and factors related to OSCC progression. Publicly available search engines, PubMed and Google Scholar, were used with the following keywords to identify relevant literature: oral cancer, proliferation, proliferation factors, genes, mutations, and tumor suppressor. We anticipate that the use of information provided through this review will further progress translational cancer research work in the field of oral cancer.

Leveraging homologies for cross-species plasma proteomics in ungulates using data-independent acquisition.

The collection of blood plasma is minimally invasive, and the fluid is a rich source of proteins for biomarker studies in both humans and animals. Plasma protein analysis by mass spectrometry (MS) can be challenging, though modern data acquisition strategies, such as sequential window acquisition of all theoretical fragment ion spectra (SWATH), enable reproducible quantitation of hundreds of proteins in non-depleted plasma from humans and laboratory model animals. Although there is strong potential to enhance veterinary and translational research, SWATH-based plasma proteomics in non-laboratory animals is virtually non-existent. One limitation to date is the lack of comprehensively annotated genomes to aid protein identification. The current study established plasma peptide spectral repositories for sheep and cattle that enabled quantification of over 200 proteins in non-depleted plasma using SWATH approach. Moreover, bioinformatics pipeline was developed to leverage inter-species homologies to enhance the depth of baseline libraries and plasma protein quantification in bovids. Finally, the practical utility of using bovid libraries for SWATH data extraction in taxonomically related non-domestic ungulate species (giraffe) has been demonstrated. SIGNIFICANCE: Ability to quickly generate comprehensive spectral libraries is limiting the applicability of data-independent acquisition, such as SWATH, to study proteomes of non-laboratory animals. We describe an approach to obtain relatively shallow foundational plasma repositories from domestic ruminants and employ homology searches to increase the depth of data, which we subsequently extend to unsequenced ungulates using SWATH method. When applied to cross-species proteomics, the number of proteins quantified by our approach far exceeds what is traditionally used in plasma protein tests.

Machine Learning Assisted Approach for Finding Novel High Activity Agonists of Human Ectopic Olfactory Receptors.

Olfactory receptors (ORs) constitute the largest superfamily of G protein-coupled receptors (GPCRs). ORs are involved in sensing odorants as well as in other ectopic roles in non-nasal tissues. Matching of an enormous number of the olfactory stimulation repertoire to its counterpart OR through machine learning (ML) will enable understanding of olfactory system, receptor characterization, and exploitation of their therapeutic potential. In the current study, we have selected two broadly tuned ectopic human OR proteins, OR1A1 and OR2W1, for expanding their known chemical space by using molecular descriptors. We present a scheme for selecting the optimal features required to train an ML-based model, based on which we selected the random forest (RF) as the best performer. High activity agonist prediction involved screening five databases comprising ~23 M compounds, using the trained RF classifier. To evaluate the effectiveness of the machine learning based virtual screening and check receptor binding site compatibility, we used docking of the top target ligands to carefully develop receptor model structures. Finally, experimental validation of selected compounds with significant docking scores through in vitro assays revealed two high activity novel agonists for OR1A1 and one for OR2W1.

ML218 HCl Is More Efficient Than Capsaicin in Inhibiting Bacterial Antigen-Induced Cal 27 Oral Cancer Cell Proliferation.

The bacterial antigen, lipopolysaccharide (LPS) and disruptions in calcium channels are independently known to influence oral cancer progression. Previously, we found that bacterial antigens, LPS and lipoteichoic acid (LTA) act as confounders during the action of capsaicin on Cal 27 oral cancer proliferation. As calcium channel drugs may affect oral cancer cell proliferation, we investigated the effect of ML218 HCl, a T-type voltage-gated calcium channel blocker, on the proliferation of Cal 27 oral cancer cells. We hypothesized that ML218 HCl could effectively reduce LPS-induced oral cancer cell proliferation. LPS and LTA antigens were added to Cal 27 oral cancer cells either prior to and/or concurrently with ML218 HCl treatment, and the efficacy of the treatment was evaluated by measuring Cal 27 proliferation, cell death and apoptosis. ML218 HCl inhibited oral cancer cell proliferation, increased apoptosis and cell death, but their efficacy was significantly reduced in the presence of bacterial antigens. ML218 HCl proved more effective than capsaicin in reducing bacterial antigen-induced Cal 27 oral cancer cell proliferation. Our results also suggest an interplay of proliferation factors during the bacterial antigens and calcium channel drug interaction in Cal 27. Bacterial antigen reduction of drug efficacy should be considered for developing newer pharmacological agents or testing the efficacy of the existing oral cancer chemotherapeutic agents. Finally, voltage gated calcium channel drugs should be considered for future oral cancer research.

Bacterial Antigens Reduced the Inhibition Effect of Capsaicin on Cal 27 Oral Cancer Cell Proliferation.

Oral cancer is a major global health problem with high incidence and low survival rates. The oral cavity contains biofilms as dental plaques that harbour both Gram-negative and Gram-positive bacterial antigens, lipopolysaccharide (LPS) and lipoteichoic acid (LTA), respectively. LPS and LTA are known to stimulate cancer cell growth, and the bioactive phytochemical capsaicin has been reported to reverse this effect. Here, we tested the efficacy of oral cancer chemotherapy treatment with capsaicin in the presence of LPS, LTA or the combination of both antigens. LPS and LTA were administered to Cal 27 oral cancer cells prior to and/or concurrently with capsaicin, and the treatment efficacy was evaluated by measuring cell proliferation and apoptotic cell death. We found that while capsaicin inhibits oral cancer cell proliferation and metabolism (MT Glo assay) and increases cell death (Trypan blue exclusion assay and Caspase 3/7 expression), its anti-cancer effect was significantly reduced on cells that are either primed or exposed to the bacterial antigens. Capsaicin treatment significantly increased oral cancer cells' suppressor of cytokine signalling 3 gene expression. This increase was reversed in the presence of bacterial antigens during treatment. Our data establish a rationale for clinical consideration of bacterial antigens that may interfere with the treatment efficacy of oral cancer.

BIO-GATS: A Tool for Automated GPCR Template Selection Through a Biophysical Approach for Homology Modeling.

G protein-coupled receptors (GPCRs) are the largest family of membrane proteins with more than 800 members. GPCRs are involved in numerous physiological functions within the human body and are the target of more than 30% of the United States Food and Drug Administration (FDA) approved drugs. At present, over 400 experimental GPCR structures are available in the Protein Data Bank (PDB) representing 76 unique receptors. The absence of an experimental structure for the majority of GPCRs demand homology models for structure-based drug discovery workflows. The generation of good homology models requires appropriate templates. The commonly used methods for template selection are based on sequence identity. However, there exists low sequence identity among the GPCRs. Sequences with similar patterns of hydrophobic residues are often structural homologs, even with low sequence identity. Extending this, we propose a biophysical approach for template selection based principally on hydrophobicity correspondence between the target and the template. Our approach takes into consideration other relevant parameters, including resolution, similarity within the orthosteric binding pocket of GPCRs, and structure completeness, for template selection. The proposed method was implemented in the form of a free tool called Bio-GATS, to provide the user with easy selection of the appropriate template for a query GPCR sequence. Bio-GATS was successfully validated with recent published benchmarking datasets. An application to an olfactory receptor to select an appropriate template has also been provided as a case study.

Mass spectrometry-based protein identification in proteomics-a review.

Statistically, accurate protein identification is a fundamental cornerstone of proteomics and underpins the understanding and application of this technology across all elements of medicine and biology. Proteomics, as a branch of biochemistry, has in recent years played a pivotal role in extending and developing the science of accurately identifying the biology and interactions of groups of proteins or proteomes. Proteomics has primarily used mass spectrometry (MS)-based techniques for identifying proteins, although other techniques including affinity-based identifications still play significant roles. Here, we outline the basics of MS to understand how data are generated and parameters used to inform computational tools used in protein identification. We then outline a comprehensive analysis of the bioinformatics and computational methodologies used in protein identification in proteomics including discussing the most current communally acceptable metrics to validate any identification.

Enhancing protein backbone angle prediction by using simpler models of deep neural networks.

Protein structure prediction is a grand challenge. Prediction of protein structures via the representations using backbone dihedral angles has recently achieved significant progress along with the on-going surge of deep neural network (DNN) research in general. However, we observe that in the protein backbone angle prediction research, there is an overall trend to employ more and more complex neural networks and then to throw more and more features to the neural networks. While more features might add more predictive power to the neural network, we argue that redundant features could rather clutter the scenario and more complex neural networks then just could counterbalance the noise. From artificial intelligence and machine learning perspectives, problem representations and solution approaches do mutually interact and thus affect performance. We also argue that comparatively simpler predictors can more easily be reconstructed than the more complex ones. With these arguments in mind, we present a deep learning method named Simpler Angle Predictor (SAP) to train simpler DNN models that enhance protein backbone angle prediction. We then empirically show that SAP can significantly outperform existing state-of-the-art methods on well-known benchmark datasets: for some types of angles, the differences are 6-8 in terms of mean absolute error (MAE). The SAP program along with its data is available from the website https://gitlab.com/mahnewton/sap .

iSwathX 2.0 for Processing DDA Spectral Libraries for DIA Data Analysis.

The iSwathX web application processes and normalizes mass spectrometry-based proteomics spectral libraries generated in the data-dependent acquisition (DDA) approach. These libraries are stored in various proteomics repositories such as PeptideAtlas and NIST, or are user-generated and provide reference data for data-independent acquisition (DIA) targeted data extraction and analysis. iSwathX 2.0 can efficiently normalize DDA data from different instruments, gathered at different instances, and make it compatible with specific DIA experiments. Novel functions for parallel processing of DDA libraries and DIA report files, along with various data visualizations, are available in iSwathX 2.0. The step-by-step protocols provided here describe how the libraries are uploaded, processed, visualized, and downloaded using various modules of the application. They also provide detailed guidelines on the use of DIA report files for data analysis and visualization. © 2020 Wiley Periodicals LLC. Basic Protocol 1: Processing, combining, and visualizing two DDA libraries Basic Protocol 2: Parallel processing and combination of multiple DDA libraries Basic Protocol 3: Downstream processing, comparison, and visualization of DIA report files.

A two-stage computational approach to predict novel ligands for a chemosensory receptor.

Olfactory receptor (OR) 1A2 is the member of largest superfamily of G protein-coupled receptors (GPCRs). OR1A2 is an ectopically expressed receptor with only 13 known ligands, implicated in reducing hepatocellular carcinoma progression, with enormous therapeutic potential. We have developed a two-stage screening approach to identify novel putative ligands of OR1A2. We first used a pharmacophore model based on atomic property field (APF) to virtually screen a library of 5942 human metabolites. We then carried out structure-based virtual screening (SBVS) for predicting the potential agonists, based on a 3D homology model of OR1A2. This model was developed using a biophysical approach for template selection, based on multiple parameters including hydrophobicity correspondence, applied to the complete set of available GPCR structures to pick the most appropriate template. Finally, the membrane-embedded 3D model was refined by molecular dynamics (MD) simulations in both the apo and holo forms. The refined model in the apo form was selected for SBVS. Four novel small molecules were identified as strong binders to this olfactory receptor on the basis of computed binding energies.

Prediction of novel mouse TLR9 agonists using a random forest approach.

BACKGROUND: Toll-like receptor 9 is a key innate immune receptor involved in detecting infectious diseases and cancer. TLR9 activates the innate immune system following the recognition of single-stranded DNA oligonucleotides (ODN) containing unmethylated cytosine-guanine (CpG) motifs. Due to the considerable number of rotatable bonds in ODNs, high-throughput in silico screening for potential TLR9 activity via traditional structure-based virtual screening approaches of CpG ODNs is challenging. In the current study, we present a machine learning based method for predicting novel mouse TLR9 (mTLR9) agonists based on features including count and position of motifs, the distance between the motifs and graphically derived features such as the radius of gyration and moment of Inertia. We employed an in-house experimentally validated dataset of 396 single-stranded synthetic ODNs, to compare the results of five machine learning algorithms. Since the dataset was highly imbalanced, we used an ensemble learning approach based on repeated random down-sampling. RESULTS: Using in-house experimental TLR9 activity data we found that random forest algorithm outperformed other algorithms for our dataset for TLR9 activity prediction. Therefore, we developed a cross-validated ensemble classifier of 20 random forest models. The average Matthews correlation coefficient and balanced accuracy of our ensemble classifier in test samples was 0.61 and 80.0%, respectively, with the maximum balanced accuracy and Matthews correlation coefficient of 87.0% and 0.75, respectively. We confirmed common sequence motifs including 'CC', 'GG','AG', 'CCCG' and 'CGGC' were overrepresented in mTLR9 agonists. Predictions on 6000 randomly generated ODNs were ranked and the top 100 ODNs were synthesized and experimentally tested for activity in a mTLR9 reporter cell assay, with 91 of the 100 selected ODNs showing high activity, confirming the accuracy of the model in predicting mTLR9 activity. CONCLUSION: We combined repeated random down-sampling with random forest to overcome the class imbalance problem and achieved promising results. Overall, we showed that the random forest algorithm outperformed other machine learning algorithms including support vector machines, shrinkage discriminant analysis, gradient boosting machine and neural networks. Due to its predictive performance and simplicity, the random forest technique is a useful method for prediction of mTLR9 ODN agonists.