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.