Evolutionary History and Phylodynamics of Influenza A and B Neuraminidase (NA) Genes Inferred from Large-Scale Sequence Analyses

Background: Influenza neuraminidase (NA) is an important surface glycoprotein and plays a vital role in viral replication and drug development. The NA is found in influenza A and B viruses, with nine subtypes classified in influenza A. The complete knowledge of influenza NA evolutionary history and phylodynamics, although critical for the prevention and control of influenza epidemics and pandemics, remains lacking. Methodology/Principal findings: Evolutionary and phylogenetic analyses of influenza NA sequences using Maximum Likelihood and Bayesian MCMC methods demonstrated that the divergence of influenza viruses into types A and B occurred earlier than the divergence of influenza A NA subtypes. Twenty-three lineages were identified within influenza A, two lineages were classified within influenza B, and most lineages were specific to host, subtype or geographical location. Interestingly, evolutionary rates vary not only among lineages but also among branches within lineages. The estimated tMRCAs of influenza lineages suggest that the viruses of different lineages emerge several months or even years before their initial detection. The dN/dS ratios ranged from 0.062 to 0.313 for influenza A lineages, and 0.257 to 0.259 for influenza B lineages. Structural analyses revealed that all positively selected sites are at the surface of the NA protein, with a number of sites found to be important for host antibody and drug binding. Conclusions/Significance: The divergence into influenza type A and B from a putative ancestral NA was followed by the divergence of type A into nine NA subtypes, of which 23 lineages subsequently diverged. This study provides a better understanding of influenza NA lineages and their evolutionary dynamics, which may facilitate early detection of newly emerging influenza viruses and thus improve influenza surveillance

Molecular Modeling Studies on the Binding Mode of the PD-1/PD-L1 Complex Inhibitors

The programmed cell death protein 1 (PD-1)/programmed cell death ligand 1 (PD-L1) is an immune checkpoint (ICP) overexpressed in various types of tumors; thus, it has been considered as an important target for cancer therapy. To determine important residues for ligand binding, we applied molecular docking studies to PD-1/PD-L1 complex inhibitors against the PD-L1 protein. Our data revealed that the residues Tyr56, Asp122, and Lys124 play critical roles in ligand binding to the PD-L1 protein and they could be used to design ligands that are active against the PD-1/PD-L1 complex. The formation of H-bonds with Arg125 of the PD-L1 protein may enhance the potency of the PD-1/PD-L1 binding

Computational studies and peptidomimetic design for the human p53–MDM2 complex

The interaction between human p53 and MDM2 is a key event in controlling cell growth. Many studies have suggested that a p53 mimic would be sufficient to inhibit MDM2 to reduce cell growth in cancerous tissue. In order to design a potent p53 mimic, molecular dynamics (MD) simulations were used to examine the binding interface and the effect of mutating key residues in the human p53–MDM2 complex. The Generalized Born surface area (GBSA) method was used to estimate free energies of binding, and a computational alanine-scanning approach was used to calculate the relative effects in the free energy of binding for key mutations. Our calculations determine the free energy of binding for a model p53–MDM2 complex to be −7.4 kcal/mol, which is in very good agreement with the experimentally determined values (−6.6–−8.8 kcal/mol). The alanine-scanning results are in good agreement with experimental data and calculations by other groups. We have used the information from our studies of human p53–MDM2 to design a Β-peptide mimic of p53. MD simulations of the mimic bound to MDM2 estimate a free energy of binding of −8.8 kcal/mol. We have also applied alanine scanning to the mimic–MDM2 complex and reveal which mutations are most likely to alter the binding affinity, possibly giving rise to escape mutants. The mimic was compared to nutlins, a new class of inhibitors that block the formation of the p53–MDM2 complex. There are interesting similarities between the nutlins and our mimic, and the differences point to ways that both inhibitors may be improved. Finally, an additional hydrophobic pocket is noted in the interior of MDM2. It may be possible to design new inhibitors to take advantage of that pocket. Proteins 2005. © 2004 Wiley-Liss, Inc.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/34979/1/20275_ftp.pd

Conformational Studies of Glucose Transporter 1 (GLUT1) as an Anticancer Drug Target

Glucose transporter 1 (GLUT1) is a facilitative glucose transporter overexpressed in various types of tumors; thus, it has been considered as an important target for cancer therapy. GLUT1 works through conformational switching from an outward-open (OOP) to an inward-open (IOP) conformation passing through an occluded conformation. It is critical to determine which conformation is preferred by bound ligands because the success of structure-based drug design depends on the appropriate starting conformation of the target protein. To find out the most favorable GLUT 1 conformation for ligand binding, we ran systemic molecular docking studies for different conformations of GLUT1 using known GLUT1 inhibitors. Our data revealed that the IOP is the preferred conformation and that residues Phe291, Phe379, Glu380, Trp388, and Trp412 may play critical roles in ligand binding to GLUT1. Our data suggests that conformational differences in these five amino acids in the different conformers of GLUT1 may be used to design ligands that inhibit GLUT1

Evolutionary History and Phylodynamics of Influenza A and B Neuraminidase (NA) Genes Inferred from Large- Scale Sequence Analyses

Background: Influenza neuraminidase (NA) is an important surface glycoprotein and plays a vital role in viral replication and drug development. The NA is found in influenza A and B viruses, with nine subtypes classified in influenza A. The complete knowledge of influenza NA evolutionary history and phylodynamics, although critical for the prevention and control of influenza epidemics and pandemics, remains lacking. Methodology/Principal findings: Evolutionary and phylogenetic analyses of influenza NA sequences using Maximum Likelihood and Bayesian MCMC methods demonstrated that the divergence of influenza viruses into types A and B occurred earlier than the divergence of influenza A NA subtypes. Twenty-three lineages were identified within influenza A, two lineages were classified within influenza B, and most lineages were specific to host, subtype or geographical location. Interestingly, evolutionary rates vary not only among lineages but also among branches within lineages. The estimated tMRCAs of influenza lineages suggest that the viruses of different lineages emerge several months or even years before their initial detection. The dN/dS ratios ranged from 0.062 to 0.313 for influenza A lineages, and 0.257 to 0.259 for influenza B lineages. Structural analyses revealed that all positively selected sites are at the surface of the NA protein, with a number of sites found to be important for host antibody and drug binding. Conclusions/Significance: The divergence into influenza type A and B from a putative ancestral NA was followed by the divergence of type A into nine NA subtypes, of which 23 lineages subsequently diverged. This study provides a better understanding of influenza NA lineages and their evolutionary dynamics, which may facilitate early detection of newly emerging influenza viruses and thus improve influenza surveillance

Design, Synthesis and Biological Evaluation of novel Hedgehog Inhibitors for treating Pancreatic Cancer

Hedgehog (Hh) pathway is involved in epithelial-mesenchymal transition (EMT) and cancer stem cell (CSC) maintenance resulting in tumor progression. GDC-0449, an inhibitor of Hh pathway component smoothened (Smo) has shown promise in the treatment of various cancers including pancreatic cancer. However, the emergence of resistance during GDC-0449 treatment with numerous side effects limits its use. Therefore, here we report the design, synthesis and evaluation of novel GDC-0449 analogs using N-[3-(2-pyridinyl) phenyl] benzamide scaffold. Cell-based screening followed by molecular simulation revealed 2-chloro-N1-[4-chloro-3-(2-pyridinyl)phenyl]-N4,N4-bis(2-pyridinylmethyl)-1,4- benzenedicarboxamide (MDB5) as most potent analog, binding with an extra interactions in seventransmembrane (7-TM) domain of Smo due to an additional 2-pyridylmethyl group than GDC-0449. Moreover, MDB5 was more efficient in inhibiting Hh pathway components as measured by Gli-1 and Shh at transcriptional and translational levels. Additionally, a significant reduction of ALDH1, CD44 and Oct-3/4, key markers of pancreatic CSC was observed when MIA PaCa-2 cells were treated with MDB5 compared to GDC-0449. In a pancreatic tumor mouse model, MDB5 containing nanoparticles treated group showed significant inhibition of tumor growth without loss in body weight. These evidence highlight the enhanced Hh pathway inhibition and anticancer properties of MDB5 leaving a platform for mono and/or combination therapy

Molecular Modeling of Allosteric Site of Isoform-Specific Inhibition of the Peroxisome Proliferator-Activated Receptor PPARγ

The peroxisome proliferator-activated receptor gamma (PPARγ) is a nuclear receptor and controls a number of gene expressions. The ligand binding domain (LBD) of PPARγ is large and involves two binding sites: orthosteric and allosteric binding sites. Increased evidence has shown that PPARγ is an oncogene and thus the PPARγ antagonists have potential as anticancer agents. In this paper, we use Glide Dock approach to determine which binding site, orthosteric or allosteric, would be a preferred pocket for PPARγ antagonist binding, though antidiabetic drugs such as thiazolidinediones (TZDs) bind to the orthosteric site. The Glide Dock results show that the binding of PPARγ antagonists at the allosteric site yielded results that were much closer to the experimental data than at the orthosteric site. The PPARγ antagonists seem to selectively bind to residues Lys265, Ser342 and Arg288 at the allosteric binding site, whereas PPARγ agonists would selectively bind to residues Leu228, Phe363, and His449, though Phe282 and Lys367 may also play a role for agonist binding at the orthosteric binding pocket. This finding will provide new perspectives in the design and optimization of selective and potent PPARγ antagonists or agonists

Encyclopedia of Pharmaceutical Science and Technology, Six Volume Set

The following contributions were made to the encyclopedia by HZ Zhong (UNO faculty) and JP Bowen: Zhong HZ, and Bowen JP. Computer-assisted drug design, pp. 620-633. Bowen JP and Zhong HZ. Computational Chemistry, pp. 600-614. Pharmaceutical science deals with the whole spectrum of drug development from start to finish. There are many different facets to the pharmaceutical industry, from initial research to the finished product, including the equipment used, trials performed, and regulations that must be followed. Presenting an overview of all of these different aspects, the Encyclopedia of Pharmaceutical Science and Technology, Fourth Edition is a must-have reference guide for all laboratories and libraries in the pharmaceutical field. Bringing together leaders from every specialty related to pharmaceutical science and technology, this is the single-source reference at the forefront of pharmaceutical R&D.The strength of this work is not only its breadth but also the caliber of contributing writers, all experts in their field, writing on all aspects of pharmaceutical science and technology. The fourth edition offers 29 new chapters ranging from biomarkers, computational chemistry, and contamination control to high-throughput screening, orally disintegrating tablets, and quality by design. The encyclopedia details best practices of equipment used, methods for manufacturing, options for packaging, and routes for drug delivery. The volumes also provide a thorough understanding of the choices behind each method. In addition, the regulations, safety aspects, patent guidance, and methods of analysis are presented.Key Areas Covered: Analytics Biomarkers Dosage forms Drug delivery Formulation Informatics Manufacturing Packaging Processing Regulatory affairs Systems validation This is an authoritative reference source for those practicing in any area of pharmaceutical science and technology, enabling the pharmaceutical specialist and novice alike to keep abreast of developments in this constantly evolving and highly competitive field.https://digitalcommons.unomaha.edu/chemfacbooks/1001/thumbnail.jp

Molecular Modeling Studies on the Binding Mode of the PD-1/PD-L1 Complex Inhibitors

The programmed cell death protein 1 (PD-1)/programmed cell death ligand 1 (PD-L1) is an immune checkpoint (ICP) overexpressed in various types of tumors; thus, it has been considered as an important target for cancer therapy. To determine important residues for ligand binding, we applied molecular docking studies to PD-1/PD-L1 complex inhibitors against the PD-L1 protein. Our data revealed that the residues Tyr56, Asp122, and Lys124 play critical roles in ligand binding to the PD-L1 protein and they could be used to design ligands that are active against the PD-1/PD-L1 complex. The formation of H-bonds with Arg125 of the PD-L1 protein may enhance the potency of the PD-1/PD-L1 binding