Prediction of the Translocation Kinetics of a Protein from Its Mechanical Properties

AbstractProteins are actively unfolded to pass through narrow channels in macromolecular complexes that catalyze protein translocation and degradation. Catalyzed unfolding shares many features that characterize the mechanical unfolding of proteins using the atomic force microscope (AFM). However, simulations of unfolding induced by the AFM and when a protein is translocated through a pore suggest that each process occurs by distinct pathways. The link, if any, between each type of unfolding, therefore, is not known. We show that the mechanical unfolding energy landscape of a protein, obtained using an atomistic molecular model, can be used to predict both the relative mechanical strength of proteins when unfolded using the AFM and when unfolded by translocation into a pore. We thus link the two processes and show that the import rate through a pore not only depends on the location of the initiation tag but also on the mechanical properties of the protein when averaged over all the possible geometries that are relevant for a given translocation initiation site

Unfolding dynamics of proteins under applied force

Understanding the mechanisms of protein folding is a major challenge that is being addressed effectively by collaboration between researchers in the physical and life sciences. Recently, it has become possible to mechanically unfold proteins by pulling on their two termini using local force probes such as the atomic force microscope. Here, we present data from experiments in which synthetic protein polymers designed to mimic naturally occurring polyproteins have been mechanically unfolded. For many years protein folding dynamics have been studied using chemical denaturation, and we therefore firstly discuss our mechanical unfolding data in the context of such experiments and show that the two unfolding mechanisms are not the same, at least for the proteins studied here. We also report unexpected observations that indicate a history effect in the observed unfolding forces of polymeric proteins and explain this in terms of the changing number of domains remaining to unfold and the increasing compliance of the lengthening unstructured polypeptide chain produced each time a domain unfolds

Optimizing the calculation of energy landscape parameters from single-molecule protein unfolding experiments

Single-molecule force spectroscopy using an atomic force microscope (AFM) can be used to measure the average unfolding force of proteins in a constant velocity experiment. In combination with Monte Carlo simulations and through the application of the Zhurkov-Bell model, information about the parameters describing the underlying unfolding energy landscape of the protein can be obtained. Using this approach, we have completed protein unfolding experiments on the polyprotein (I27) 5 over a range of pulling velocities. In agreement with previous work, we find that the observed number of protein unfolding events observed in each approach-retract cycle varies between one and five, due to the nature of the interactions between the polyprotein, the AFM tip, and the substrate, and there is an unequal unfolding probability distribution. We have developed a Monte Carlo simulation that incorporates the impact of this unequal unfolding probability distribution on the median unfolding force and the calculation of the protein unfolding energy landscape parameters. These results show that while there is a significant, unequal unfolding probability distribution, the unfolding energy landscape parameters obtained from use of the Zhurkov-Bell model are not greatly affected. This result is important because it demonstrates that the minimum acceptance criteria typically used in force extension experiments are justified and do not skew the calculation of the unfolding energy landscape parameters. We further validate this approach by determining the error in the energy landscape parameters for two extreme cases, and we provide suggestions for methods that can be employed to increase the level of accuracy in single-molecule experiments using polyproteins

Symbiotic Effectiveness of Rhizobial Mutualists Varies in Interactions with Native Australian Legume Genera

Peer reviewe

Excavations at the site of Vasino, Lautem District, Timor-Leste

This chapter explores the archaeology and ethnohistory of one of the distinctive fortified settlements in the eastern part of Timor-Leste. In 2009, a team from The Australian National University (ANU), together with local people, partially excavated the site of Vasino, located close to the north coast of Timor-Leste, above the modern village of Moro-Parlamento (Figure 4.1). The site had been fortified with large stone walls and the aim was to provide more data on when, how and why these fortifications were used in the region. Two related questions guided the research. First, when was the main period of fort construction initiated? Secondly, what were the prevailing environmental and social conditions of those times

Cooperative folding of intrinsically disordered domains drives assembly of a strong elongated protein.

Bacteria exploit surface proteins to adhere to other bacteria, surfaces and host cells. Such proteins need to project away from the bacterial surface and resist significant mechanical forces. SasG is a protein that forms extended fibrils on the surface of Staphylococcus aureus and promotes host adherence and biofilm formation. Here we show that although monomeric and lacking covalent cross-links, SasG maintains a highly extended conformation in solution. This extension is mediated through obligate folding cooperativity of the intrinsically disordered E domains that couple non-adjacent G5 domains thermodynamically, forming interfaces that are more stable than the domains themselves. Thus, counterintuitively, the elongation of the protein appears to be dependent on the inherent instability of its domains. The remarkable mechanical strength of SasG arises from tandemly arrayed 'clamp' motifs within the folded domains. Our findings reveal an elegant minimal solution for the assembly of monomeric mechano-resistant tethers of variable length.This research was supported by Biotechnology and Biological Research Council Grants BB/J006459/1 (D.T.G. and J.C.), BB/J005029/1 (F.W. and J.R.P), BB/G019452/1 (O.E.F and D.J.B) and BB/G020671/1 (C.G.B. and J.R.P.). H.K.H.F. is supported by a studentship from a Wellcome Trust 4-year PhD programme (WT095024MA). C.M.J. is supported by the German Federal Ministry of Education and Research (BMBF), grant BIOSCAT (contract N° 05K12YE1). J.C. is a Wellcome Trust Senior Research Fellow (WT/095195). J.R.P holds a British Heart Foundation Senior Basic Science Fellowship (FS/12/36/29588). The authors acknowledge the use of EMBL SAXS beamline P12 at Petra-3 (DESY, Hamburg, Germany) and Maxim Petoukhov (EMBL) for providing a modified version of SASR EF. The research leading to these results has received funding from the European Community's Seventh Framework Programme (FP7/2007-2013) under BioStruct-X (grant agreement N°283570). The authors would like to thank Diamond Light Source for beamtime (proposal mx-7864) and Johan Turkenburg and Sam Hart for assistance with crystal testing and data collection.This is the final version of the article. It first appeared from NPG via http://dx.doi.org/10.1038/ncomms827

Bayesian inference of biochemical kinetic parameters using the linear noise approximation

Background Fluorescent and luminescent gene reporters allow us to dynamically quantify changes in molecular species concentration over time on the single cell level. The mathematical modeling of their interaction through multivariate dynamical models requires the deveopment of effective statistical methods to calibrate such models against available data. Given the prevalence of stochasticity and noise in biochemical systems inference for stochastic models is of special interest. In this paper we present a simple and computationally efficient algorithm for the estimation of biochemical kinetic parameters from gene reporter data. Results We use the linear noise approximation to model biochemical reactions through a stochastic dynamic model which essentially approximates a diffusion model by an ordinary differential equation model with an appropriately defined noise process. An explicit formula for the likelihood function can be derived allowing for computationally efficient parameter estimation. The proposed algorithm is embedded in a Bayesian framework and inference is performed using Markov chain Monte Carlo. Conclusion The major advantage of the method is that in contrast to the more established diffusion approximation based methods the computationally costly methods of data augmentation are not necessary. Our approach also allows for unobserved variables and measurement error. The application of the method to both simulated and experimental data shows that the proposed methodology provides a useful alternative to diffusion approximation based methods

Force-induced remodelling of proteins and their complexes

Force can drive conformational changes in proteins, as well as modulate their stability and the affinity of their complexes, allowing a mechanical input to be converted into a biochemical output. These properties have been utilised by nature and force is now recognised to be widely used at the cellular level. The effects of force on the biophysical properties of biological systems can be large and varied. As these effects are only apparent in the presence of force, studies on the same proteins using traditional ensemble biophysical methods can yield apparently conflicting results. Where appropriate, therefore, force measurements should be integrated with other experimental approaches to understand the physiological context of the system under study

Single residue modulators of amyloid formation in the N-terminal P1-region of α-synuclein

Abstract: Alpha-synuclein (αSyn) is a protein involved in neurodegenerative disorders including Parkinson’s disease. Amyloid formation of αSyn can be modulated by the ‘P1 region’ (residues 36-42). Here, mutational studies of P1 reveal that Y39A and S42A extend the lag-phase of αSyn amyloid formation in vitro and rescue amyloid-associated cytotoxicity in C. elegans. Additionally, L38I αSyn forms amyloid fibrils more rapidly than WT, L38A has no effect, but L38M does not form amyloid fibrils in vitro and protects from proteotoxicity. Swapping the sequence of the two residues that differ in the P1 region of the paralogue γSyn to those of αSyn did not enhance fibril formation for γSyn. Peptide binding experiments using NMR showed that P1 synergises with residues in the NAC and C-terminal regions to initiate aggregation. The remarkable specificity of the interactions that control αSyn amyloid formation, identifies this region as a potential target for therapeutics, despite their weak and transient nature