High Predicted Binding Affinity Research Articles

Identification of Drug Compounds for Capsular Contracture Based on Text Mining and Deep Learning.

Capsular contracture is a common and unpredictable complication after breast implant placement. Currently, the pathogenesis of capsular contracture is unclear, and the effectiveness of nonsurgical treatment is still doubtful. The authors' study aimed to investigate new drug therapies for capsular contracture by using computational methods. Genes related to capsular contracture were identified by text mining and GeneCodis. Then, the candidate key genes were selected through protein-protein interaction analysis in Search Tool for the Retrieval of Interacting Genes/Proteins and Cytoscape. Drugs targeting the candidate genes with relation to capsular contracture were screened out in Pharmaprojects. Based on the drug-target interaction analysis by DeepPurpose, candidate drugs with highest predicted binding affinity were obtained eventually. The authors' study identified 55 genes related to capsular contracture. Gene set enrichment analysis and protein-protein interaction analysis generated eight candidate genes. One hundred drugs targeting the candidate genes were selected. The seven candidate drugs with the highest predicted binding affinity were determined by DeepPurpose, including tumor necrosis factor alpha antagonist, estrogen receptor agonist, insulin-like growth factor 1 receptor, tyrosine kinase inhibitor, and matrix metallopeptidase 1 inhibitor. Text mining and DeepPurpose can be used as a promising tool for drug discovery in exploring nonsurgical treatment to capsular contracture. Therapeutic, V.

60. Panning for neoantigens to demonstrate feasibility of neoantigen vaccines in canine melanoma

Canine malignant melanoma (CMM) mimics human melanoma in invasiveness, metastatic behavior, and limited survival. Novel immunotherapies could target CMM neoantigen expression to dissect mechanisms of success and failure in a naturally occurring model of cancer. Tools to predict and validate canine MHC I presentation of neoantigens must be optimized to select candidate vaccine peptides. Tissues and a cell line from a dog responding in a trial of an autologous deglycosylated melanoma vaccine were selected. Nucleic acids were extracted for WES and RNAseq. Expressed protein-coding sequence mutations were identified by strelka, varscan, mutect, and pindel. DLA allele was inferred by pseudo-alignment of RNA against known sequences and confirmed by clinical assay. Candidate neoantigens were identified with pVACtools. Membrane-bound MHC I was purified using mAb H58A. Weak acid-eluted MHC I- bound peptides were analyzed with mass spectrometry, and peptide sequences inferred by MSGFplus. Neoantigens were predicted with strong binding affinity to DLA 88*002:01. Peptides eluted ranged from 7-16 AA with nonamers being the mode and the vast majority ranging from 9-12 AA. Candidate neoantigen sequences were compared to eluted peptides and vaccine candidates selected with highest predicted binding affinity and strongest support in the MS data. Human MHC I typing and binding affinity software tools can be modified to predict canine MHC I binding affinity. Predicted neoantigens were confirmed by presence in peptides eluted from cells derived from the patient's tumor. These tools will be used in future immunotherapy studies in companion dogs with melanoma as a model for humans.

MACAW: An Accessible Tool for Molecular Embedding and Inverse Molecular Design.

The growing capabilities of synthetic biology and organic chemistry demand tools to guide syntheses toward useful molecules. Here, we present Molecular AutoenCoding Auto-Workaround (MACAW), a tool that uses a novel approach to generate molecules predicted to meet a desired property specification (e.g., a binding affinity of 50 nM or an octane number of 90). MACAW describes molecules by embedding them into a smooth multidimensional numerical space, avoiding uninformative dimensions that previous methods often introduce. The coordinates in this embedding provide a natural choice of features for accurately predicting molecular properties, which we demonstrate with examples for cetane and octane numbers, flash points, and histamine H1 receptor binding affinity. The approach is computationally efficient and well-suited to the small- and medium-size datasets commonly used in biosciences. We showcase the utility of MACAW for virtual screening by identifying molecules with high predicted binding affinity to the histamine H1 receptor and limited affinity to the muscarinic M2 receptor, which are targets of medicinal relevance. Combining these predictive capabilities with a novel generative algorithm for molecules allows us to recommend molecules with a desired property value (i.e., inverse molecular design). We demonstrate this capability by recommending molecules with predicted octane numbers of 40, 80, and 120, which is an important characteristic of biofuels. Thus, MACAW augments classical retrosynthesis tools by providing recommendations for molecules on specification.

Pep-Whisperer: Inhibitory peptide design.

Designing peptides for protein-protein interaction inhibition is of significant interest in computer-aided drug design. Such inhibitory peptides could mimic and compete with the binding of the partner protein to the inhibition target. Experimental peptide design is a laborious, time consuming, and expensive multi-step process. Therefore, in silico peptide design can be beneficial for achieving this task. We present a novel algorithm, Pep-Whisperer, which aims to design inhibitory peptides for protein-protein interaction. The desirable peptides would have a relatively high predicted binding affinity to the target protein in a given protein-protein complex. The algorithm outputs linear peptides which are based on an initial template. The template could either be a peptide which is retrieved from the interaction site, or a patch of nonconsecutive amino acids from the protein-protein interface which is completed to a linear peptide by short polyalanine linkers. In addition, the algorithm takes into consideration the conservation of the amino acids in the ligand-protein binding site by using evolutionary information for choosing the preferred amino acids in each position of the designed peptide. Our algorithm was able to design peptides with high predicted binding affinity to the target protein. The method is fully automated and available as a web server at http://bioinfo3d.cs.tau.ac.il/PepWhisperer/.

Case Report: Pathological Complete Response in a Lung Metastasis of Phyllodes Tumor Patient Following Treatment Containing Peptide Neoantigen Nano-Vaccine

Some of the mutant peptides produced by gene mutation transcription and translation have the ability to induce specific T cells, which are called new antigens. Neoantigen-based peptide, DNA, RNA, and dendritic cell vaccines have been used in the clinic. In this paper, we describe a lung metastasis of a phyllodes tumor patient demonstrating pathological complete response following treatment containing personalized multi-epitope peptide neoantigen nano-vaccine. Based on whole-exome sequencing (WES), RNA sequencing, and new antigen prediction, several mutated peptide fragments were predicted to bind to the patient’s human leukocyte antigen (HLA) allotypes, including ten peptides with high predicted binding affinity for six genes. The pulmonary metastases remained stable after the four cycles of anti-PD1 and anlotinib. After the addition of the multi-epitope peptide neoantigen nano-vaccine, the tumor began to collapse and contracture developed, accompanied by a decrease of tumor markers to normal, and complete pathological remission was achieved. With the use of the vaccination, recombinant human granulocyte-macrophage colony-stimulating factor (rhGM-CSF) was used every time, and low-dose cyclophosphamide was injected every 3 weeks to improve efficacy. Peripheral blood immune monitoring demonstrated immune reactivity against a series of peptides, with the most robust post-vaccine T-cell response detected against the HLA-DRB1*0901-restricted SLC44A5 V54F peptide.

An Evaluation of the Potential of Essential Oils against SARS-CoV-2 from In Silico Studies through the Systematic Review Using a Chemometric Approach.

Essential oils (EOs) and their compounds have attracted particular attention for their reported beneficial properties, especially their antiviral potential. However, data regarding their anti-SARS-CoV-2 potential are scarce in the literature. Thus, this study aimed to identify the most promising EO compounds against SARS-CoV-2 based on their physicochemical, pharmacokinetic, and toxicity properties. A systematic literature search retrieved 1669 articles; 40 met the eligibility criteria, and 35 were eligible for analysis. These studies resulted in 465 EO compounds evaluated against 11 human and/or SARS-CoV-2 target proteins. Ninety-four EO compounds and seven reference drugs were clustered by the highest predicted binding affinity. Furthermore, 41 EO compounds showed suitable drug-likeness and bioactivity score indices (≥0.67). Among these EO compounds, 15 were considered the most promising against SARS-CoV-2 with the ADME/T index ranging from 0.86 to 0.81. Some plant species were identified as EO potential sources with anti-SARS-CoV-2 activity, such as Melissa officinalis Arcang, Zataria multiflora Boiss, Eugenia brasiliensis Cambess, Zingiber zerumbet Triboun & K.Larsen, Cedrus libani A.Rich, and Vetiveria zizanoides Nash. Our work can help fill the gap in the literature and guide further in vitro and in vivo studies, intending to optimize the finding of effective EOs against COVID-19.

Cloud Computing Based Immunopeptidomics Utilizing Community Curated Variant Libraries Simplifies and Improves Neo-Antigen Discovery in Metastatic Melanoma.

Simple SummaryPeptides expressed on the cell surface can be used to distinguish between diseased and healthy cells and for precision drug targeting. Ideal targets in cancer diagnostics and therapeutic development are the result of altered peptide sequences that make it to the surface as expressed neoantigens. Identifying these peptides requires both genomics and proteomics sequencing technologies, which makes the process both expensive and challenging. We present an alternative solution where cloud computing can be used to improve and simplify current approaches.Unique peptide neo-antigens presented on the cell surface are attractive targets for researchers in nearly all areas of personalized medicine. Cells presenting peptides with mutated or other non-canonical sequences can be utilized for both targeted therapies and diagnostics. Today’s state-of-the-art pipelines utilize complementary proteogenomic approaches where RNA or ribosomal sequencing data helps to create libraries from which tandem mass spectrometry data can be compared. In this study, we present an alternative approach whereby cloud computing is utilized to power neo-antigen searches against community curated databases containing more than 7 million human sequence variants. Using these expansive databases of high-quality sequences as a reference, we reanalyze the original data from two previously reported studies to identify neo-antigen targets in metastatic melanoma. Using our approach, we identify 79 percent of the non-canonical peptides reported by previous genomic analyses of these files. Furthermore, we report 18-fold more non-canonical peptides than previously reported. The novel neo-antigens we report herein can be corroborated by secondary analyses such as high predicted binding affinity, when analyzed by well-established tools such as NetMHC. Finally, we report 738 non-canonical peptides shared by at least five patient samples, and 3258 shared across the two studies. This illustrates the depth of data that is present, but typically missed by lower statistical power proteogenomic approaches. This large list of shared peptides across the two studies, their annotation, non-canonical origin, as well as MS/MS spectra from the two studies are made available on a web portal for community analysis.

Multistep rational molecular design and combined docking for discovery of novel classes of inhibitors of SARS-CoV-2 main protease 3CLpro

The main protease (3CLpro) of SARS-CoV and SARS-CoV-2 is a promising target for discovery of novel antiviral agents. In this paper, new possible inhibitors of 3CLpro with high predicted binding affinity were detected through multistep computer-aided molecular design and bioisosteric replacements. For discovery of prospective 3CLpro binders several virtual ligand libraries were created and combined docking was performed. Moreover, the molecular dynamics simulation was applied for evaluation of protein-ligand complexes stability. Besides, important molecular properties and ADMET pharmacokinetic profiles of possible 3CLpro inhibitors were assessed by in silico prediction.

Abstract 1899: Biochemical and functional characterization of mutant KRAS epitopes validates this oncoprotein for immunological targeting

Abstract Activating missense mutations within RAS oncogenes are among the most prevalent genomic alterations observed in human cancers and drive oncogenesis in the three most lethal tumor types. Emerging evidence suggests mutant KRAS (mKRAS) may be targeted immunologically, but mKRAS epitopes remain poorly defined. Here, we employ multi-omic approaches to characterize optimal HLA class I-restricted mKRAS epitopes. Using in silico epitope prediction, we identify candidate mKRAS G12 epitopes with high predicted binding affinity to prevalent HLA class I alleles. We utilize biochemical approaches to identify mKRAS epitopes with high measured binding affinity and stability to HLA-A*03:01, -A*11:01 and -B*07:02. Furthermore, we provide proteomic validation of epitope processing and presentation by these alleles. We report mKRAS G12V and G12R epitopes are immunogenic as evidenced by the isolation of mKRAS G12V- and G12R-specific TCRαβ from healthy donors. The transfer of mKRAS-TCRs to primary CD8+ T cells serves as sensitive probes to validate mKRAS epitope processing and presentation by tumor cells and confers cytotoxicity against mKRAS human tumor cell lines of various histologies. The kinetics of lytic activity correlate with measured TCR avidity and mKRAS/HLA class I complex abundance expressed on the tumor cell surface. Finally, the adoptive transfer of mKRAS-TCR engineered primary CD8+ T cells to NOD scid gamma mice leads to tumor eradication in an orthotopic mKRAS lung cancer model. This study validates mKRAS as a bona fide tumor neoantigen and supports strategies to identify recurrent driver alterations for immunological targeting. Based on this work, clinical studies targeting mKRAS in cancer patients using vaccines and adoptive T cell therapy are under active investigation. Citation Format: Adham S. Bear, Tatiana Blanchard, Mark H. O'Hara, John Scholler, Robert H. Vonderheide, Gerald P. Linette, Beatriz M. Carreno. Biochemical and functional characterization of mutant KRAS epitopes validates this oncoprotein for immunological targeting [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 1899.

Identification of drug compounds for keloids and hypertrophic scars: drug discovery based on text mining and DeepPurpose.

BackgroundKeloids (KL) and hypertrophic scars (HS) are forms of abnormal cutaneous scarring characterized by excessive deposition of extracellular matrix and fibroblast proliferation. Currently, the efficacy of drug therapies for KL and HS is limited. The present study aimed to investigate new drug therapies for KL and HS by using computational methods.MethodsText mining and GeneCodis were used to mine genes closely related to KL and HS. Protein-protein interaction analysis was performed using Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) and Cytoscape. The selection of drugs targeting the genes closely related to KL and HS was carried out using Pharmaprojects. Drug-target interaction prediction was performed using DeepPurpose, through which candidate drugs with the highest predicted binding affinity were finally obtained.ResultsOur analysis using text mining identified 69 KL- and HS-related genes. Gene enrichment analysis generated 25 genes, representing 7 pathways and 130 targeting drugs. DeepPurpose recommended 14 drugs as the final drug list, including 2 phosphatidylinositol-4,5-bisphosphate 3-kinase (PI3K) inhibitors, 10 prostaglandin-endoperoxide synthase 2 (PTGS2) inhibitors and 2 vascular endothelial growth factor A (VEGFA) antagonists.ConclusionsDrug discovery using in silico text mining and DeepPurpose may be a powerful and effective way to identify drugs targeting the genes related to KL and HS.

Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) Exhibits High Predicted Binding Affinity to ACE2 from Lagomorphs (Rabbits and Pikas).

Simple SummarySevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is responsible for the pandemic COVID-19. The virus infects human cells by binding of the virus spike to the cell receptor ACE2. Some studies suggest that dogs, cats and other animal species could be infected by SARS-CoV-2, while very limited data are available on lagomorphs. There are several occasions where rabbits and other lagomorphs are in close contact with humans. To investigate the interaction between SARS-CoV-2 spikes and ACE2 of lagomorphs, predictive computer-based models were used in this study. The structure of ACE2 of lagomorphs was obtained on the basis of the amino acid sequences computationally. The interaction with the model of SARS-CoV-2 spikes was studied and described in depth on the basis of the complex human ACE2-SARS-CoV-2 published before. The interaction among SARS-CoV-2 spikes and ACE2 of other companion or laboratory animals is also described for comparative purposes. The results predict that ACE2 of lagomorphs are likely to bind SARS-CoV-2 spikes and suggest that further studies would be justified to confirm these results and to evaluate the risks to humans being in close contact with lagomorphs, such as veterinarians, farmers, slaughterhouse workers, butchers or pet owners.Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is responsible for the pandemic COVID-19. The virus infects human cells by binding of the virus spike to the cell receptor ACE2. The crystal structure of SARS-CoV-2 spikes in complex with human ACE2 has recently been solved, and the main amino acid residues involved in the virus–receptor complex have been detected. To investigate the affinity of ACE2 of lagomorphs to the SARS-CoV-2 spike, ACE2 sequences from rabbits and American pikas were compared with human ACE2 and with ACE2 from mammals with different susceptibility to the virus. Models of the complex formed by SARS-CoV-2 spike and ACE2 from lagomorphs and from other mammals were created for comparative studies. ACE2 of lagomorphs showed fewer substitutions than human ACE2 in residues involved in the ACE2-SARS-CoV-2 spike complex, similar to cats. Analysis of the binding interface of the simulated complexes ACE2-SARS-CoV-2 spike showed high affinity of the ACE2 of lagomorphs to the viral spike protein. These findings suggest that the spike of SARS-CoV-2 could bind the ACE2 receptor of lagomorphs, and future studies should investigate the role of lagomorphs in SARS-CoV-2 epidemiology. Furthermore, the risks to humans coming into close contacts with these animals should be evaluated.

Computational evaluation of natural compounds as potential inhibitors of human PEPCK-M: an alternative for lung cancer therapy.

BackgroundLung cancer is the leading cause of cancer-related death worldwide. Among its subtypes, non-small cell lung cancer (NSCLC) is the most common. Recently, the mitochondrial isoform of the enzyme phosphoenolpyruvate carboxykinase (HsPEPCK-M) was identified as responsible for the metabolic adaptation in the NSCLC allowing tumor growth even under conditions of glucose deficiency. This adaptation is possible due to the role of HsPEPCK-M in gluconeogenesis, converting the oxaloacetate to phosphoenolpyruvate in the presence of GTP, which plays an important role in the energetic support of these tumors. In this context, it was shown that the inhibition or knockdown of this enzyme was able to induce apoptosis in NSCLC under low glucose conditions.PurposeIn this study, novel putative inhibitors were proposed for the human PEPCK-M (HsPEPCK-M) based on a computer-aided approach.MethodsComparative modeling was used to generate 3D models for HsPEPCK-M. Subsequently, the set of natural compounds of the ZINC database was screened against HsPEPCK-M models using structure-based pharmacophore modeling and molecular docking approaches. The selected compounds were evaluated according to its chemical diversity and clustered based on chemical similarity.ResultsThe pharmacophore hypotheses, generated based on known PEPCK inhibitors, were able to select 7,124 candidate compounds. These compounds were submitted to molecular docking studies using three conformations of HsPEPCK-M generated by comparative modeling. The aim was to select compounds with high predicted binding affinity for at least one of the conformations of HsPEPCK-M. After molecular docking, 612 molecules were selected as potential inhibitors of HsPEPCK-M. These compounds were clustered according to their structural similarity. Chemical profiling and binding mode analyses of these compounds allowed the proposal of four promising compounds: ZINC01656421, ZINC895296, ZINC00895535 and ZINC02571340.ConclusionThese compounds may be considered as potential candidates for HsPEPCK-M inhibitors and may also be used as lead compounds for the development of novel HsPEPCK-M inhibitors.

Evidence for Highly Variable, Region-Specific Patterns of T-Cell Epitope Mutations Accumulating in Mycobacterium tuberculosis Strains.

Vaccines that confer protection through induction of adaptive T-cell immunity rely on understanding T-cell epitope (TCE) evolution induced by immune escape. This is poorly understood in tuberculosis (TB), an ancient, chronic disease, where CD4 T-cell immunity is of recognized importance. We probed 905 functionally validated, curated human CD4 T cell epitopes in 79 Mycobacterium tuberculosis (Mtb) whole genomes from India. This screen resulted in identifying 64 mutated epitopes in these strains initially using a computational pipeline and subsequently verified by single nucleotide polymorphism (SNP) analysis. SNP based phylogeny revealed the 79 Mtb strains to cluster to East African Indian (EAI), Central Asian Strain (CAS), and Beijing (BEI) lineages. Eighty-nine percent of the mutated T-cell epitopes (mTCEs) identified in the 79 Mtb strains from India has not previously been reported. These mTCEs were encoded by genes with high nucleotide diversity scores including seven mTCEs encoded by six antigens in the top 10% of rapidly divergent Mtb genes encoded by these strains. Using a T cell functional assay readout, we demonstrate 62% of mTCEs tested to significantly alter CD4 T-cell IFNγ and/or IL2 secretion with associated changes in predicted HLA-DR binding affinity: the gain of function mutations displayed higher predicted HLA-DR binding affinity and conversely mutations resulting in loss of function displayed lower predicted HLA-DR binding affinity. Most mutated antigens belonged to the cell wall/cell processes, and, intermediary metabolism and respiration families though all known Mtb proteins encoded mutations. Analysis of the mTCEs in an SNP database of 5,310 global Mtb strains identified 82% mTCEs to be significantly more prevalent in Mtb strains isolated from India, including 36 mTCEs identified exclusively in strains from India. These epitopes had a significantly higher predicted binding affinity to HLA-DR alleles that were highly prevalent in India compared to HLA-DR alleles rare in India, highlighting HLA-DR maybe an important driver of these mutations. This first evidence of region-specific TCE mutations potentially employed by Mtb to escape host immunity has important implications for TB vaccine design.

Molecular Dynamics and Docking Studies on Acetylcholinesterase (AChE) Inhibitors

Recently we have performed molecular dynamics and docking studies on several inhibitors on acetylcholinesterase (AChE), an important target in Alzheimer disease. We have assessed by docking and molecular dynamics the binding mechanisms of three FDA-approved Alzheimer drugs: donepezil, galantamine and rivastigmine. Dockings by the softwares Autodock-Vina, PatchDock and Plant reproduced the docked conformations of the inhibitor-enzyme complexes within 2Å of RMSD of the X-ray structure. Free-energy scores show strong affinity of the inhibitors for the enzyme binding pocket. Three independent Molecular Dynamics simulation runs indicated general stability of donepezil, galantamine and rivastigmine in their respective enzyme binding pocket as well as the tendency to form hydrogen bonds with the water molecules. The binding of rivastigmine in the Torpedo California AChE binding pocket is interesting as it eventually undergoes carbamylation and breaks apart according to the X-ray structure of the complex. Similarity search in the ZINC database and targeted docking on the gorge region of the AChE enzyme have identified new inhibitor molecules with high predicted binding affinity. We will present the findings as how computational and experimental biophysical techniques can lead to identify potent molecules in a fatal disease.

Assessing the binding of cholinesterase inhibitors by docking and molecular dynamics studies

In this report we assessed by docking and molecular dynamics the binding mechanisms of three FDA-approved Alzheimer drugs, inhibitors of the enzyme acetylcholinesterase (AChE): donepezil, galantamine and rivastigmine. Dockings by the softwares Autodock-Vina, PatchDock and Plant reproduced the docked conformations of the inhibitor-enzyme complexes within 2Å of RMSD of the X-ray structure. Free-energy scores show strong affinity of the inhibitors for the enzyme binding pocket. Three independent Molecular Dynamics simulation runs indicated general stability of donepezil, galantamine and rivastigmine in their respective enzyme binding pocket (also referred to as gorge) as well as the tendency to form hydrogen bonds with the water molecules. The binding of rivastigmine in the Torpedo California AChE binding pocket is interesting as it eventually undergoes carbamylation and breaks apart according to the X-ray structure of the complex. Similarity search in the ZINC database and targeted docking on the gorge region of the AChE enzyme gave new putative inhibitor molecules with high predicted binding affinity, suitable for potential biophysical and biological assessments.

Genetic Algorithm Managed Peptide Mutant Screening: Optimizing Peptide Ligands for Targeted Receptor Binding.

This study demonstrates the utility of genetic algorithms to search exceptionally large and otherwise intractable mutant libraries for sequences with optimal binding affinities for target receptors. The Genetic Algorithm Managed Peptide Mutant Screening (GAMPMS) program was used to search an α-conotoxin (α-CTx) MII mutant library of approximately 41 billion possible peptide sequences for those exhibiting the greatest binding affinity for the α3β2-nicotinic acetylcholine receptor (nAChR) isoform. A series of top resulting peptide ligands with high sequence homology was obtained, with each mutant having an estimated ΔGbind approximately double that of the potent native α-CTx MII ligand. A consensus sequence from the top GAMPMS results was subjected to more rigorous binding free energy calculations by molecular dynamics and compared to α-CTx MII and other related variants for binding with α3β2-nAChR. In this study, the efficiency of GAMPMS to substantially reduce the sample population size through evolutionary selection criteria to produce ligands with higher predicted binding affinity is demonstrated.

Small Molecule Inhibitors of Constitutively Active Gsa

Constitutive activation of Gsα is seen in the genetic disorder McCune‐Albright Syndrome, as well as some endocrine adenomas and in tissues affected by cholera toxin. Random mutagenesis of constitutively active Gα subunits in yeast identified three sites that, when mutated, suppressed the constitutive activity caused by R201H: F142, R231, and L266. Because R231 is on the surface of the protein, near the GTP‐binding site it was selected as a candidate site for drug targeting, with the goal of developing inhibitors of the constitutively active Gs alleles. Docking molecules in silico to the R231 binding pocket on the surface of Gs identified commercially available molecules with high potential to bind to the suppressor site. Eighteen molecules with high predicted binding affinity were tested for the ability to decrease the elevation in basal cAMP observed in HEK cells overexpressing the constitutively active Gs‐R201H allele. Two molecules, 6‐oxo‐N‐{[2‐(pyrrolidin‐1‐yl)pyridin‐4‐yl]methyl}‐1,6‐dihydropyridazine‐3‐carboxamide and N‐{[3‐(3‐methylphenyl)phenyl]methyl}‐6‐oxo‐1,6‐dihydropyridazine‐3‐carboxamide were able to significantly reduce cAMP levels by at least 10% from baseline. Neither of these molecules significantly reduced basal cAMP levels in cells transfected with wild‐type Gs constructs. These molecules will form the basis for further studies to develop antagonists of mutant Gs proteins associated with human disease.This work was Supported by NIH grant 1R15ED020190‐01

Characterization of binding specificities of bovine leucocyte class I molecules: impacts for rational epitope discovery.

The binding of peptides to classical major histocompatibility complex (MHC) class I proteins is the single most selective step in antigen presentation. However, the peptide-binding specificity of cattle MHC (bovine leucocyte antigen, BoLA) class I (BoLA-I) molecules remains poorly characterized. Here, we demonstrate how a combination of high-throughput assays using positional scanning combinatorial peptide libraries, peptide dissociation, and peptide-binding affinity binding measurements can be combined with bioinformatics to effectively characterize the functionality of BoLA-I molecules. Using this strategy, we characterized eight BoLA-I molecules, and found the peptide specificity to resemble that of human MHC-I molecules with primary anchors most often at P2 and P9, and occasional auxiliary P1/P3/P5/P6 anchors. We analyzed nine reported CTL epitopes from Theileria parva, and in eight cases, stable and high affinity binding was confirmed. A set of peptides were tested for binding affinity to the eight BoLA proteins and used to refine the predictors of peptide-MHC binding NetMHC and NetMHCpan. The inclusion of BoLA-specific peptide-binding data led to a significant improvement in prediction accuracy for reported T. parva CTL epitopes. For reported CTL epitopes with weak or no predicted binding, these refined prediction methods suggested presence of nested minimal epitopes with high-predicted binding affinity. The enhanced affinity of the alternative peptides was in all cases confirmed experimentally. This study demonstrates how biochemical high-throughput assays combined with immunoinformatics can be used to characterize the peptide-binding motifs of BoLA-I molecules, boosting performance of MHC peptide-binding prediction methods, and empowering rational epitope discovery in cattle.

Evidence for the contribution of non-covalent steroid interactions between DNA and topoisomerase in the genotoxicity of steroids

Fifty two steroids and 9 Vitamin D analogs were docked into ten crystallographically-defined DNA dinucleotide sites and two human topoisomerase II ATP binding sites using two computational programs, Autodock and Surflex. It is shown that both steroids and Vitamin D analogs exhibit a propensity for non-covalent intercalative binding to DNA. A higher predicted binding affinity was found, however, for steroids and the ATP binding site of topoisomerase; in fact these drugs exhibited among the highest topo II binding observed in over 1370 docked drugs. These findings along with genotoxicity data from 26 additional steroids not subjected to docking analysis, support a mechanism wherein the long known, but poorly understood, clastogenicity of steroids may be attributable to inhibition of topoisomerase. A “proof of principle” experiment with dexamethasone demonstrated this to be the likely mechanism of clastogenicity of, at least, this steroid. The generality of this proposed mechanism of genotoxicity across the steroids and vitamin-D analogs is discussed.

Screening from the world's largest TCM database for inhibiting DNA repair protein XRCC4

Human XRCC4 protein is a key component of DNA double-strand break (DSB) repair pathway related to the diseases of stroke and cancer. Cancer cells being treated with the drugs that interfere with DSBs repair mechanism have shown increased radiosensitivity to ionising radiation. Therefore, the development of novel radiosensitiser for radiation therapy becomes important for cancer treatment. We screened from the world's largest traditional Chinese medicine (TCM) database and found potential TCM molecules that can dock at XRCC4 functional site. Among the selected potential TCM compounds, we specifically investigated the top-ranked molecules: glycyrrhizic acid and macedonoside C. The molecular docking and molecular dynamics simulations on these compounds show similar location with high predicted binding affinity. Both compounds form continuous interaction with Lys188 and Arg192 of chain C and Lys187 and Lys190 of chain D. All these protein residues are required to form key hydrophobic interactions with other components participating in DNA repair. We suggested both glycyrrhizic acid and macedonoside C as potential lead compounds for inhibiting XRCC4.