A mechanistic understanding of the intermolecular interactions and structural changes during fibrillation is crucial for the design of safe and efficacious glucagon formulations. Amide hydrogen/deuterium exchange with mass spectrometric analysis was used to identify the interactions and amino acids involved in the initial stages of glucagon fibril formation at acidic pH. Kinetic measurements from intrinsic and thioflavin T fluorescence showed sigmoidal behavior. Secondary structural measurement of fibrillating glucagon using far-UV circular dichroism spectroscopy showed changes in structure from random coil → α-helix → β-sheet, with increase in α-helix content during the lag phase followed by increase in β-sheet content during the growth phase. Hydrogen/deuterium exchange with mass spectrometric analysis of fibrillating glucagon suggested that C-terminal residues 22-29 are involved in interactions during the lag phase, during which N-terminal residues 1-6 showed no changes. Molecular dynamics simulations of glucagon fragments showed C-terminal to C-terminal interactions with greater α-helix content for the 20-29 fragment, with hydrophobic and aromatic residues (Phe-22, Trp-25, Val-23, and Met-27) predominantly involved. Overall, the study shows that glucagon interactions during the early phase of fibrillation are mediated through C-terminal residues, which facilitate the formation of α-helix-rich oligomers, which further undergo structural rearrangement and elongation to form β-sheet-rich mature fibrils.

Amyloid fibrils are important in diseases such as Alzheimer's disease and Parkinson's disease, and are also a common instability in peptide and protein drug products. Despite their importance, experimental structures of amyloid fibrils in atomistic detail are rare. To address this limitation, we have developed a novel, rapid computational method to predict amyloid fibril structures (Fibpredictor). The method combines β-sheet model building, β-sheet replication, and symmetry operations with side-chain prediction and statistical scoring functions. When applied to nine amyloid fibrils with experimentally determined structures, the method predicted the correct structures of amyloid fibrils and enriched those among the top-ranked structures. These models can be used as the initial heuristic structures for more complicated computational studies. Fibpredictor is available at http://nanohub.org/resources/fibpredictor .

This paper offers a practical argument for metaphysical emergence. The main message is that the growing reliance on so-called irrational scientific methods provides evidence that objects of science are indecomposable and as such, are better described by metaphysical emergence as opposed to the prevalent reductionistic metaphysics. I show that a potential counterargument that science will eventually reduce everything to physics has little weight given where science is heading with its current methodological trend. I substantiate my arguments by detailed examples from biological engineering, but the conclusions are extendable beyond that discipline.

Distance-based statistical potentials have long been used to model condensed matter systems, e.g. as scoring functions in differentiating native-like protein structures from decoys. These scoring functions are based on the assumption that the total free energy of the protein can be calculated as the sum of pairwise free energy contributions derived from a statistical analysis of pair-distribution functions. However, this fundamental assumption has been challenged theoretically. In fact the free energy of a system with N particles is only exactly related to the N-body distribution function. Based on this argument coarse-grained multi-body statistical potentials have been developed to capture higher-order interactions. Having a coarse representation of the protein and using geometric contacts instead of pairwise interaction distances renders these models insufficient in modeling details of multi-body effects. In this study, we investigated if extending distance-dependent pairwise atomistic statistical potentials to corresponding interaction functions that are conditional on a third interacting body, defined as quasi-three-body statistical potentials, could model details of three-body interactions. We also tested if this approach could improve the predictive capabilities of statistical scoring functions for protein structure prediction. We analyzed the statistical dependency between two simultaneous pairwise interactions and showed that there is surprisingly little if any dependency of a third interacting site on pairwise atomistic statistical potentials. Also the protein structure prediction performance of these quasi-three-body potentials is comparable with their corresponding two-body counterparts. The scoring functions developed in this study showed better or comparable performances compared to some widely used scoring functions for protein structure prediction.

A phenomenon resulting from a computationally irreducible (or computationally incompressible) process is supposedly unpredictable except via simulation. This notion of unpredictability has been deployed to formulate some recent accounts of computational emergence. Via a technical analysis of computational irreducibility, I show that computationally irreducibility can establish the impossibility of prediction only with respect to maximum standards of precision. By articulating the graded nature of prediction, I show that unpredictability to maximum standards is not equivalent to being unpredictable in general. I conclude that computational irreducibility fails to fulfill its assigned philosophical roles in theories of computational emergence.

Knowledge-based methods for analyzing protein structures, such as statistical potentials, primarily consider the distances between pairs of bodies (atoms or groups of atoms). Considerations of several bodies simultaneously are generally used to characterize bonded structural elements or those in close contact with each other, but historically do not consider atoms that are not in direct contact with each other. In this report, we introduce an information-theoretic method for detecting and quantifying distance-dependent through-space multibody relationships between the sidechains of three residues. The technique introduced is capable of producing convergent and consistent results when applied to a sufficiently large database of randomly chosen, experimentally solved protein structures. The results of our study can be shown to reproduce established physico-chemical properties of residues as well as more recently discovered properties and interactions. These results offer insight into the numerous roles that residues play in protein structure, as well as relationships between residue function, protein structure, and evolution. The techniques and insights presented in this work should be useful in the future development of novel knowledge-based tools for the evaluation of protein structure.

It is a plausible speculation that conventional choices in outcome measures might influence the results of meta-analyses. We test that speculation by simulating data from trials on antidepressants. We vary real drug effectiveness while modulating conventional values for outcome measures. We had previously shown that one conventional choice used in meta-analyses of antidepressants falls in a narrow range of values that maximise estimates of effectiveness. Our present analysis investigates why this phenomenon occurs. Moreover, our results suggest the superiority of absolute outcome measures over relative measures. This research program can be extended to test numerous other aspects of clinical research.

Telomerase is a reverse transcriptase enzyme that activates in more than 85% of cancer cells and it associated with the acquisition of a malignant phenotype. Some experimental strategies have been suggested to avoid the enzyme effect on unstopped telomere elongation. One of them, the stabilization of the G-quartet structure has been widely studied. Nevertheless, no QSAR studies to predict the activity and identify the required pharmacophore have been developed. In this project, multiple linear regression (MLR) and artificial neural network (ANN) analyses were used to determine the required pharmacophore for telomerase inhibition activity and predicting potency (IC 50 ) of newly designed compounds. A dataset containing 96 compounds were analyzed, and two models were developed from MLR and three models from ANN analyses. The best MLR model has R = 0.90. Errors were calculated using mean percentage error (MPE) criterion, and the best MLR model has MPE of 34% and the best ANN model possesses MPE of 28%. The selected parameters showed that fused phenyl rings or a planer aromatic core, the number of nitrogen and oxygen atoms, having a cationic centre and partial positive charge are essential for describing telomerase inhibitory.