PacBio assembly of a Plasmodium knowlesi genome sequence with Hi-C correction and manual annotation of the SICAvar gene family.

Plasmodium knowlesi has risen in importance as a zoonotic parasite that has been causing regular episodes of malaria throughout South East Asia. The P. knowlesi genome sequence generated in 2008 highlighted and confirmed many similarities and differences in Plasmodium species, including a global view of several multigene families, such as the large SICAvar multigene family encoding the variant antigens known as the schizont-infected cell agglutination proteins. However, repetitive DNA sequences are the bane of any genome project, and this and other Plasmodium genome projects have not been immune to the gaps, rearrangements and other pitfalls created by these genomic features. Today, long-read PacBio and chromatin conformation technologies are overcoming such obstacles. Here, based on the use of these technologies, we present a highly refined de novo P. knowlesi genome sequence of the Pk1(A+) clone. This sequence and annotation, referred to as the 'MaHPIC Pk genome sequence', includes manual annotation of the SICAvar gene family with 136 full-length members categorized as type I or II. This sequence provides a framework that will permit a better understanding of the SICAvar repertoire, selective pressures acting on this gene family and mechanisms of antigenic variation in this species and other pathogens

Computational modelling of cancerous mutations in the EGFR/ERK signalling pathway

This article has been made available through the Brunel Open Access Publishing Fund - Copyright @ 2009 Orton et al.BACKGROUND: The Epidermal Growth Factor Receptor (EGFR) activated Extracellular-signal Regulated Kinase (ERK) pathway is a critical cell signalling pathway that relays the signal for a cell to proliferate from the plasma membrane to the nucleus. Deregulation of the EGFR/ERK pathway due to alterations affecting the expression or function of a number of pathway components has long been associated with numerous forms of cancer. Under normal conditions, Epidermal Growth Factor (EGF) stimulates a rapid but transient activation of ERK as the signal is rapidly shutdown. Whereas, under cancerous mutation conditions the ERK signal cannot be shutdown and is sustained resulting in the constitutive activation of ERK and continual cell proliferation. In this study, we have used computational modelling techniques to investigate what effects various cancerous alterations have on the signalling flow through the ERK pathway. RESULTS: We have generated a new model of the EGFR activated ERK pathway, which was verified by our own experimental data. We then altered our model to represent various cancerous situations such as Ras, B-Raf and EGFR mutations, as well as EGFR overexpression. Analysis of the models showed that different cancerous situations resulted in different signalling patterns through the ERK pathway, especially when compared to the normal EGF signal pattern. Our model predicts that cancerous EGFR mutation and overexpression signals almost exclusively via the Rap1 pathway, predicting that this pathway is the best target for drugs. Furthermore, our model also highlights the importance of receptor degradation in normal and cancerous EGFR signalling, and suggests that receptor degradation is a key difference between the signalling from the EGF and Nerve Growth Factor (NGF) receptors. CONCLUSION: Our results suggest that different routes to ERK activation are being utilised in different cancerous situations which therefore has interesting implications for drug selection strategies. We also conducted a comparison of the critical differences between signalling from different growth factor receptors (namely EGFR, mutated EGFR, NGF, and Insulin) with our results suggesting the difference between the systems are large scale and can be attributed to the presence/absence of entire pathways rather than subtle difference in individual rate constants between the systems.This work was funded by the Department of Trade and Industry (DTI), under their Bioscience Beacon project programme. AG was funded by an industrial PhD studentship from Scottish Enterprise and Cyclacel

ParaHaplo: A program package for haplotype-based whole-genome association study using parallel computing

<p>Abstract</p> <p>Background</p> <p>Since more than a million single-nucleotide polymorphisms (SNPs) are analyzed in any given genome-wide association study (GWAS), performing multiple comparisons can be problematic. To cope with multiple-comparison problems in GWAS, haplotype-based algorithms were developed to correct for multiple comparisons at multiple SNP loci in linkage disequilibrium. A permutation test can also control problems inherent in multiple testing; however, both the calculation of exact probability and the execution of permutation tests are time-consuming. Faster methods for calculating exact probabilities and executing permutation tests are required.</p> <p>Methods</p> <p>We developed a set of computer programs for the parallel computation of accurate P-values in haplotype-based GWAS. Our program, ParaHaplo, is intended for workstation clusters using the Intel Message Passing Interface (MPI). We compared the performance of our algorithm to that of the regular permutation test on JPT and CHB of HapMap.</p> <p>Results</p> <p>ParaHaplo can detect smaller differences between 2 populations than SNP-based GWAS. We also found that parallel-computing techniques made ParaHaplo 100-fold faster than a non-parallel version of the program.</p> <p>Conclusion</p> <p>ParaHaplo is a useful tool in conducting haplotype-based GWAS. Since the data sizes of such projects continue to increase, the use of fast computations with parallel computing--such as that used in ParaHaplo--will become increasingly important. The executable binaries and program sources of ParaHaplo are available at the following address: <url>http://sourceforge.jp/projects/parallelgwas/?_sl=1</url></p

Gomesin peptides prevent proliferation and lead to the cell death of devil facial tumour disease cells.

The Tasmanian devil faces extinction due to devil facial tumour disease (DFTD), a highly transmittable clonal form of cancer without available treatment. In this study, we report the cell-autonomous antiproliferative and cytotoxic activities exhibited by the spider peptide gomesin (AgGom) and gomesin-like homologue (HiGom) in DFTD cells. Mechanistically, both peptides caused a significant reduction at G0/G1 phase, in correlation with an augmented expression of the cell cycle inhibitory proteins p53, p27, p21, necrosis, exacerbated generation of reactive oxygen species and diminished mitochondrial membrane potential, all hallmarks of cellular stress. The screening of a novel panel of AgGom-analogues revealed that, unlike changes in the hydrophobicity and electrostatic surface, the cytotoxic potential of the gomesin analogues in DFTD cells lies on specific arginine substitutions in the eight and nine positions and alanine replacement in three, five and 12 positions. In conclusion, the evidence supports gomesin as a potential antiproliferative compound against DFTD disease

MUC-1 gene is associated with prostate cancer death: a 20-year follow-up of a population-based study in Sweden

Anti-adhesion mucins have proven to play an important part in the biology of several types of cancer. Therefore, we test the hypothesis that altered expression of MUC-1 is associated with prostate cancer progression. We retrieved archival tumour tissue from a population-based cohort of 195 men with localised prostate cancer (T1a-b, Nx, M0) that has been followed for up to 20 years with watchful waiting. Semi-automated, quantitative immunohistochemistry was undertaken to evaluate MUC-1 expression. We modelled prostate cancer-specific death as a function of MUC-1 levels accounting for age, Gleason grade and tumour extent, and calculated age-adjusted and multivariate adjusted hazard ratios (HR). Men that had tumours with an MUC-intensity lower or higher than normal tissue had a higher risk of dying in prostate cancer, independent of tumour extent and Gleason score (HR 5.1 and 4.5, respectively). Adjustment for Gleason grade and tumour stage did not alter the results. Men with a Gleason score ⩾7 and MUC-1 deviating from the normal had a 17 (RR=17.1 95% confidence interval=2.3–128) times higher risk to die in prostate cancer compared with men with Gleason score <7 and normal MUC-1 intensity. In summary, our data show that MUC-1 is an independent prognostic marker for prostate cancer death

Complex folding and misfolding effects of deer-specific amino acid substitutions in the β2-α2 loop of murine prion protein

The β2–α2 loop of PrPC is a key modulator of disease-associated prion protein misfolding. Amino acids that differentiate mouse (Ser169, Asn173) and deer (Asn169, Thr173) PrPC appear to confer dramatically different structural properties in this region and it has been suggested that amino acid sequences associated with structural rigidity of the loop also confer susceptibility to prion disease. Using mouse recombinant PrP, we show that mutating residue 173 from Asn to Thr alters protein stability and misfolding only subtly, whilst changing Ser to Asn at codon 169 causes instability in the protein, promotes oligomer formation and dramatically potentiates fibril formation. The doubly mutated protein exhibits more complex folding and misfolding behaviour than either single mutant, suggestive of differential effects of the β2–α2 loop sequence on both protein stability and on specific misfolding pathways. Molecular dynamics simulation of protein structure suggests a key role for the solvent accessibility of Tyr168 in promoting molecular interactions that may lead to prion protein misfolding. Thus, we conclude that ‘rigidity’ in the β2–α2 loop region of the normal conformer of PrP has less effect on misfolding than other sequence-related effects in this region

Conformational Changes and Slow Dynamics through Microsecond Polarized Atomistic Molecular Simulation of an Integral Kv1.2 Ion Channel

Structure and dynamics of voltage-gated ion channels, in particular the motion of the S4 helix, is a highly interesting and hotly debated topic in current membrane protein research. It has critical implications for insertion and stabilization of membrane proteins as well as for finding how transitions occur in membrane proteins—not to mention numerous applications in drug design. Here, we present a full 1 µs atomic-detail molecular dynamics simulation of an integral Kv1.2 ion channel, comprising 120,000 atoms. By applying 0.052 V/nm of hyperpolarization, we observe structural rearrangements, including up to 120° rotation of the S4 segment, changes in hydrogen-bonding patterns, but only low amounts of translation. A smaller rotation (∼35°) of the extracellular end of all S4 segments is present also in a reference 0.5 µs simulation without applied field, which indicates that the crystal structure might be slightly different from the natural state of the voltage sensor. The conformation change upon hyperpolarization is closely coupled to an increase in 310 helix contents in S4, starting from the intracellular side. This could support a model for transition from the crystal structure where the hyperpolarization destabilizes S4–lipid hydrogen bonds, which leads to the helix rotating to keep the arginine side chains away from the hydrophobic phase, and the driving force for final relaxation by downward translation is partly entropic, which would explain the slow process. The coordinates of the transmembrane part of the simulated channel actually stay closer to the recently determined higher-resolution Kv1.2 chimera channel than the starting structure for the entire second half of the simulation (0.5–1 µs). Together with lipids binding in matching positions and significant thinning of the membrane also observed in experiments, this provides additional support for the predictive power of microsecond-scale membrane protein simulations

Binding mode analyses and pharmacophore model development for stilbene derivatives as a novel and competitive class of α-glucosidase inhibitors

Stilbene urea derivatives as a novel and competitive class of non-glycosidic α-glucosidase inhibitors are effective for the treatment of type II diabetes and obesity. The main purposes of our molecular modeling study are to explore the most suitable binding poses of stilbene derivatives with analyzing the binding affinity differences and finally to develop a pharmacophore model which would represents critical features responsible for α-glucosidase inhibitory activity. Three-dimensional structure of S. cerevisiae α-glucosidase was built by homology modeling method and the structure was used for the molecular docking study to find out the initial binding mode of compound 12, which is the most highly active one. The initial structure was subjected to molecular dynamics (MD) simulations for protein structure adjustment at compound 12-bound state. Based on the adjusted conformation, the more reasonable binding modes of the stilbene urea derivatives were obtained from molecular docking and MD simulations. The binding mode of the derivatives was validated by correlation analysis between experimental Ki value and interaction energy. Our results revealed that the binding modes of the potent inhibitors were engaged with important hydrogen bond, hydrophobic, and π-interactions. With the validated compound 12-bound structure obtained from combining approach of docking and MD simulation, a proper four featured pharmacophore model was generated. It was also validated by comparison of fit values with the Ki values. Thus, these results will be helpful for understanding the relationship between binding mode and bioactivity and for designing better inhibitors from stilbene derivatives

Computational Fragment-Based Binding Site Identification by Ligand Competitive Saturation

Fragment-based drug discovery using NMR and x-ray crystallographic methods has proven utility but also non-trivial time, materials, and labor costs. Current computational fragment-based approaches circumvent these issues but suffer from limited representations of protein flexibility and solvation effects, leading to difficulties with rigorous ranking of fragment affinities. To overcome these limitations we describe an explicit solvent all-atom molecular dynamics methodology (SILCS: Site Identification by Ligand Competitive Saturation) that uses small aliphatic and aromatic molecules plus water molecules to map the affinity pattern of a protein for hydrophobic groups, aromatic groups, hydrogen bond donors, and hydrogen bond acceptors. By simultaneously incorporating ligands representative of all these functionalities, the method is an in silico free energy-based competition assay that generates three-dimensional probability maps of fragment binding (FragMaps) indicating favorable fragment∶protein interactions. Applied to the two-fold symmetric oncoprotein BCL-6, the SILCS method yields two-fold symmetric FragMaps that recapitulate the crystallographic binding modes of the SMRT and BCOR peptides. These FragMaps account both for important sequence and structure differences in the C-terminal halves of the two peptides and also the high mobility of the BCL-6 His116 sidechain in the peptide-binding groove. Such SILCS FragMaps can be used to qualitatively inform the design of small-molecule inhibitors or as scoring grids for high-throughput in silico docking that incorporate both an atomic-level description of solvation and protein flexibility