Nanobodies raised against monomeric α-synuclein distinguish between fibrils at different maturation stages

Nanobodies are single-domain fragments of camelid antibodies that are emerging as versatile tools in biotechnology. We describe here the interactions of a specific nanobody, NbSyn87, with the monomeric and fibrillar forms of α-synuclein (αSyn), a 140-residue protein whose aggregation is associated with Parkinson's disease. We have characterized these interactions using a range of biophysical techniques, including nuclear magnetic resonance and circular dichroism spectroscopy, isothermal titration calorimetry and quartz crystal microbalance measurements. In addition, we have compared the results with those that we have reported previously for a different nanobody, NbSyn2, also raised against monomeric αSyn. This comparison indicates that NbSyn87 and NbSyn2 bind with nanomolar affinity to distinctive epitopes within the C-terminal domain of soluble αSyn, comprising approximately amino acids 118-131 and 137-140, respectively. The calorimetric and quartz crystal microbalance data indicate that the epitopes of both nanobodies are still accessible when αSyn converts into its fibrillar structure. The apparent affinities and other thermodynamic parameters defining the binding between the nanobody and the fibrils, however, vary significantly with the length of time that the process of fibril formation has been allowed to progress and with the conditions under which formation occurs, indicating that the environment of the C-terminal domain of αSyn changes as fibril assembly takes place. These results demonstrate that nanobodies are able to target forms of potentially pathogenic aggregates that differ from each other in relatively minor details of their structure, such as those associated with fibril maturation. © 2013 Elsevier Ltd.

Predictive entropy search for efficient global optimization of black-box functions

We propose a novel information-theoretic approach for Bayesian optimization called Predictive Entropy Search (PES). At each iteration, PES selects the next evaluation point that maximizes the expected information gained with respect to the global maximum. PES codifies this intractable acquisition function in terms of the expected reduction in the differential entropy of the predictive distribution. This reformulation allows PES to obtain approximations that are both more accurate and efficient than other alternatives such as Entropy Search (ES). Furthermore, PES can easily perform a fully Bayesian treatment of the model hyperparameters while ES cannot. We evaluate PES in both synthetic and real-world applications, including optimization problems in machine learning, finance, biotechnology, and robotics. We show that the increased accuracy of PES leads to significant gains in optimization performance.