Learning probabilistic models of hydrogen bond stability from molecular dynamics simulation trajectories

Hydrogen bonds (H-bonds) play a key role in both the formation and stabilization of protein structures. H-bonds involving atoms from residues that are close to each other in the main-chain sequence stabilize secondary structure elements. H-bonds between atoms from distant residues stabilize a protein’s tertiary structure. However, H-bonds greatly vary in stability. They form and break while a protein deforms. For instance, the transition of a protein from a nonfunctional to a functional state may require some H-bonds to break and others to form. The intrinsic strength of an individual H-bond has been studied from an energetic viewpoint, but energy alone may not be a very good predictor. Other local interactions may reinforce (or weaken) an H-bond. This paper describes inductive learning methods to train a protein-independent probabilistic model of H-bond stability from molecular dynamics (MD) simulation trajectories. The training data describes H-bond occurrences at successive times along these trajectories by the values of attributes called predictors. A trained model is constructed in the form of a regression tree in which each non-leaf node is a Boolean test (split) on a predictor. Each occurrence of an H-bond maps to a path in this tree from the root to a leaf node. Its predicted stability is associated with the leaf node. Experimental results demonstrate that such models can predict H-bond stability quite well. In particular, their performance is roughly 20 % better than that of models based on H-bond energy alone. In addition, they can accurately identify a large fraction of the least stable H-bonds in a give

Exploitation of Other Social Amoebae by Dictyostelium caveatum

Dictyostelium amoebae faced with starvation trigger a developmental program during which many cells aggregate and form fruiting bodies that consist of a ball of spores held aloft by a thin stalk. This developmental strategy is open to several forms of exploitation, including the remarkable case of Dictyostelium caveatum, which, even when it constitutes 1/10(3) of the cells in an aggregate, can inhibit the development of the host and eventually devour it. We show that it accomplishes this feat by inhibiting a region of cells, called the tip, which organizes the development of the aggregate into a fruiting body. We use live-cell microscopy to define the D. caveatum developmental cycle and to show that D. caveatum amoebae have the capacity to ingest amoebae of other Dictyostelid species, but do not attack each other. The block in development induced by D. caveatum does not affect the expression of specific markers of prespore cell or prestalk cell differentiation, but does stop the coordinated cell movement leading to tip formation. The inhibition mechanism involves the constitutive secretion of a small molecule by D. caveatum and is reversible. Four Dictyostelid species were inhibited in their development, while D. caveatum is not inhibited by its own compound(s). D. caveatum has evolved a predation strategy to exploit other members of its genus, including mechanisms of developmental inhibition and specific phagocytosis

Atomic Layer Deposition (ALD) to Mitigate Tin Whisker Growth and Corrosion Issues on Printed Circuit Board Assemblies

This paper presents the results of a research program set up to evaluate atomic layer deposition (ALD) conformal coatings as a method of mitigating the growth of tin whiskers from printed circuit board assemblies. The effect of ALD coating process variables on the ability of the coating to mitigate whisker growth were evaluated. Scanning electron microscopy and optical microscopy were used to evaluate both the size and distribution of tin whiskers and the coating/whisker interactions. Results show that the ALD process can achieve significant reductions in whisker growth and thus offers considerable potential as a reworkable whisker mitigation strategy. The effect of ALD layer thickness on whisker formation was also investigated. Studies indicate that thermal exposure during ALD processing may contribute significantly to the observed whisker mitigation

Evaluation of 2-deoxy-D-glucose as a chemotherapeutic agent: mechanism of cell death

Nutrient deprivation has been shown to cause cancer cell death. To exploit nutrient deprivation as anti-cancer therapy, we investigated the effects of the anti-metabolite 2-deoxy-D-glucose on breast cancer cells in vitro. This compound has been shown to inhibit glucose metabolism. Treatment of human breast cancer cell lines with 2-deoxy-D-glucose results in cessation of cell growth in a dose dependent manner. Cell viability as measured by the 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyl tetrazolium bromide conversion assay and clonogenic survival are decreased with 2-deoxy-D-glucose treatment indicating that 2-deoxy-D-glucose causes breast cancer cell death. The cell death induced by 2-deoxy-D-glucose was found to be due to apoptosis as demonstrated by induction of caspase 3 activity and cleavage of poly (ADP-ribose) polymerase. Breast cancer cells treated with 2-deoxy-D-glucose express higher levels of Glut1 transporter protein as measured by Western blot analysis and have increased glucose uptake compared to non-treated breast cancer cells. From these results we conclude that 2-deoxy-D-glucose treatment causes death in human breast cancer cell lines by the activation of the apoptotic pathway. Our data suggest that breast cancer cells treated with 2-deoxy-D-glucose accelerate their own demise by initially expressing high levels of glucose transporter protein, which allows increased uptake of 2-deoxy-D-glucose, and subsequent induction of cell death. These data support the targeting of glucose metabolism as a site for chemotherapeutic intervention by agents such as 2-deoxy-D-glucose

Training Genetic Counsellors to Deliver an Innovative Therapeutic Intervention: their views and experience of facilitating multi-family discussion groups

Innovations in clinical genetics have increased diagnosis, treatment and prognosis of inherited genetic conditions (IGCs). This has led to an increased number of families seeking genetic testing and / or genetic counselling and increased the clinical load for genetic counsellors (GCs). Keeping pace with biomedical discoveries, interventions are required to support families to understand, communicate and cope with their Inherited Genetic Condition. The Socio-Psychological Research in Genomics (SPRinG) collaborative have developed a new intervention, based on multi-family discussion groups (MFDGs), to support families affected by IGCs and train GCs in its delivery. A potential challenge to implementing the intervention was whether GCs were willing and able to undergo the training to deliver the MFDG. In analysing three multi-perspective interviews with GCs, this paper evaluates the training received. Findings suggests that MFDGs are a potential valuable resource in supporting families to communicate genetic risk information and can enhance family function and emotional well-being. Furthermore, we demonstrate that it is feasible to train GCs in the delivery of the intervention and that it has the potential to be integrated into clinical practice. Its longer term implementation into routine clinical practice however relies on changes in both organisation of clinical genetics services and genetic counsellors' professional development

Formation and Growth of Oligomers: A Monte Carlo Study of an Amyloid Tau Fragment

Small oligomers formed early in the process of amyloid fibril formation may be the major toxic species in Alzheimer's disease. We investigate the early stages of amyloid aggregation for the tau fragment AcPHF6 (Ac-VQIVYK-NH2) using an implicit solvent all-atom model and extensive Monte Carlo simulations of 12, 24, and 36 chains. A variety of small metastable aggregates form and dissolve until an aggregate of a critical size and conformation arises. However, the stable oligomers, which are β-sheet-rich and feature many hydrophobic contacts, are not always growth-ready. The simulations indicate instead that these supercritical oligomers spend a lengthy period in equilibrium in which considerable reorganization takes place accompanied by exchange of chains with the solution. Growth competence of the stable oligomers correlates with the alignment of the strands in the β-sheets. The larger aggregates seen in our simulations are all composed of two twisted β-sheets, packed against each other with hydrophobic side chains at the sheet–sheet interface. These β-sandwiches show similarities with the proposed steric zipper structure for PHF6 fibrils but have a mixed parallel/antiparallel β-strand organization as opposed to the parallel organization found in experiments on fibrils. Interestingly, we find that the fraction of parallel β-sheet structure increases with aggregate size. We speculate that the reorganization of the β-sheets into parallel ones is an important rate-limiting step in the formation of PHF6 fibrils

Guidelines for Genome-Scale Analysis of Biological Rhythms

Genome biology approaches have made enormous contributions to our understanding of biological rhythms, particularly in identifying outputs of the clock, including RNAs, proteins, and metabolites, whose abundance oscillates throughout the day. These methods hold significant promise for future discovery, particularly when combined with computational modeling. However, genome-scale experiments are costly and laborious, yielding “big data” that are conceptually and statistically difficult to analyze. There is no obvious consensus regarding design or analysis. Here we discuss the relevant technical considerations to generate reproducible, statistically sound, and broadly useful genome-scale data. Rather than suggest a set of rigid rules, we aim to codify principles by which investigators, reviewers, and readers of the primary literature can evaluate the suitability of different experimental designs for measuring different aspects of biological rhythms. We introduce CircaInSilico, a web-based application for generating synthetic genome biology data to benchmark statistical methods for studying biological rhythms. Finally, we discuss several unmet analytical needs, including applications to clinical medicine, and suggest productive avenues to address them