MOTIVATION: Proteins of all kinds can self-assemble into highly ordered β-sheet aggregates known as amyloid fibrils, important both biologically and clinically. However, the specific molecular structure of a fibril can vary dramatically depending on sequence and environmental conditions, and mutations can drastically alter amyloid function and pathogenicity. Experimental structure determination has proven extremely difficult with only a handful of NMR-based models proposed, suggesting a need for computational methods. RESULTS: We present AmyloidMutants, a statistical mechanics approach for de novo prediction and analysis of wild-type and mutant amyloid structures. Based on the premise of protein mutational landscapes, AmyloidMutants energetically quantifies the effects of sequence mutation on fibril conformation and stability. Tested on non-mutant, full-length amyloid structures with known chemical shift data, AmyloidMutants offers roughly 2-fold improvement in prediction accuracy over existing tools. Moreover, AmyloidMutants is the only method to predict complete super-secondary structures, enabling accurate discrimination of topologically dissimilar amyloid conformations that correspond to the same sequence locations. Applied to mutant prediction, AmyloidMutants identifies a global conformational switch between Aβ and its highly-toxic 'Iowa' mutant in agreement with a recent experimental model based on partial chemical shift data. Predictions on mutant, yeast-toxic strains of HET-s suggest similar alternate folds. When applied to HET-s and a HET-s mutant with core asparagines replaced by glutamines (both highly amyloidogenic chemically similar residues abundant in many amyloids), AmyloidMutants surprisingly predicts a greatly reduced capacity of the glutamine mutant to form amyloid. We confirm this finding by conducting mutagenesis experiments. AVAILABILITY: Our tool is publically available on the web at http://amyloid.csail.mit.edu/. CONTACT: [email protected]; [email protected].

BACKGROUND: Our goal is to develop a state-of-the-art protein secondary structure predictor, with an intuitive and biophysically-motivated energy model. We treat structure prediction as an optimization problem, using parameterizable cost functions representing biological "pseudo-energies". Machine learning methods are applied to estimate the values of the parameters to correctly predict known protein structures. RESULTS: Focusing on the prediction of alpha helices in proteins, we show that a model with 302 parameters can achieve a Qalpha value of 77.6% and an SOValpha value of 73.4%. Such performance numbers are among the best for techniques that do not rely on external databases (such as multiple sequence alignments). Further, it is easier to extract biological significance from a model with so few parameters. CONCLUSION: The method presented shows promise for the prediction of protein secondary structure. Biophysically-motivated elementary free-energies can be learned using SVM techniques to construct an energy cost function whose predictive performance rivals state-of-the-art. This method is general and can be extended beyond the all-alpha case described here.

Secure processors enable new applications by ensuring private and authentic program execution even in the face of physical attack. In this paper, we present the AEGIS secure processor architecture, and evaluate its RTL implementation on FPGAs. By using physical random functions, we propose a new way of reliably protecting and sharing secrets that is more secure than existing solutions based on non-volatile memory. Our architecture gives applications the flexibility of trusting and protecting only a portion of a given process, unlike prior proposals which require a process to be protected in entirety. We also put forward a specific model of how secure applications can be programmed in a high-level language and compiled to run on our system. Finally, we evaluate a fully functional FPGA implementation of our processor, assess the implementation tradeoffs, compare performance, and demonstrate the benefits of partially protecting a program.

In peer-to-peer (P2P) systems, it is often essential that connected systems (nodes) relay messages which did not originate locally, on to the greater network. As a result, an intermediate node might be able to determine a large amount of information about the system, such as the querying tendencies of other nodes. This represents an inherent security issue in P2P networks. Therefore, we ask the following question: through the observation of the network traffic in a P2P network, what kind of information can an adversarial node learn about another node in the same network? We study this question in the case of a specific P2P system - Chord. We also study the effects of the parameters of Chord (such as finger-table size) and the various enhancements to Chord (such as location caching and data caching) on the amount of information leaked.