Charles W. O'Donnell

Design and Implementation of the AEGIS Single-Chip Secure Processor Using Physical Random Functions

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.

AEGIS: A single-chip secure processor

In this article, we introduce a single-chip secure processor called Aegis. In addition to supporting mechanisms to authenticate the platform and software, our processor incorporates mechanisms to protect the integrity and privacy of applications from physical attacks as well as software attacks. Therefore, physically secure systems can be built using this processor. Two key primitives, physical unclonable functions (PUFs) and off-chip memory protection, enable the physical security of our system. These primitives can also be easily applied to other secure computing systems to enhance their security.

MARIS: method for analyzing RNA following intracellular sorting.

Transcriptional profiling is a key technique in the study of cell biology that is limited by the availability of reagents to uniquely identify specific cell types and isolate high quality RNA from them. We report a Method for Analyzing RNA following Intracellular Sorting (MARIS) that generates high quality RNA for transcriptome profiling following cellular fixation, intracellular immunofluorescent staining and FACS. MARIS can therefore be used to isolate high quality RNA from many otherwise inaccessible cell types simply based on immunofluorescent tagging of unique intracellular proteins. As proof of principle, we isolate RNA from sorted human embryonic stem cell-derived insulin-expressing cells as well as adult human β cells. MARIS is a basic molecular biology technique that could be used across several biological disciplines.

A method for probing the mutational landscape of amyloid structure

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: lindquist_admin@wi.mit.edu; bab@csail.mit.edu Supplementary information: Supplementary data are available at Bioinformatics online.

Modeling ensembles of transmembrane β-barrel proteins

Transmembrane β‐barrel (TMB) proteins are embedded in the outer membrane of Gram‐negative bacteria, mitochondria, and chloroplasts. Despite their importance, very few nonhomologous TMB structures have been determined by X‐ray diffraction because of the experimental difficulty encountered in crystallizing transmembrane proteins. We introduce the program partiFold to investigate the folding landscape of TMBs. By computing the Boltzmann partition function, partiFold estimates inter‐β‐strand residue interaction probabilities, predicts contacts and per‐residue X‐ray crystal structure B‐values, and samples conformations from the Boltzmann low energy ensemble. This broad range of predictive capabilities is achieved using a single, parameterizable grammatical model to describe potential β‐barrel supersecondary structures, combined with a novel energy function of stacked amino acid pair statistical potentials. PartiFold outperforms existing programs for inter‐β‐strand residue contact prediction on TMB proteins, offering both higher average predictive accuracy as well as more consistent results. Moreover, the integration of these contact probabilities inside a stochastic contact map can be used to infer a more meaningful picture of the TMB folding landscape, which cannot be achieved with other methods. Partifolds predictions of B‐values are competitive with recent methods specifically designed for this problem. Finally, we show that sampling TMBs from the Boltzmann ensemble matches the X‐ray crystal structure better than single structure prediction methods. A webserver running partiFold is available at http://partiFold.csail.mit.edu/. Proteins 2008.

Information leak in the Chord lookup protocol

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.

STITCHER: Dynamic assembly of likely amyloid and prion β‐structures from secondary structure predictions

The supersecondary structure of amyloids and prions, proteins of intense clinical and biological interest, are difficult to determine by standard experimental or computational means. In addition, significant conformational heterogeneity is known or suspected to exist in many amyloid fibrils. Previous work has demonstrated that probability‐based prediction of discrete β‐strand pairs can offer insight into these structures. Here, we devise a system of energetic rules that can be used to dynamically assemble these discrete β‐strand pairs into complete amyloid β‐structures. The STITCHER algorithm progressively ‘stitches’ strand‐pairs into full β‐sheets based on a novel free‐energy model, incorporating experimentally observed amino‐acid side‐chain stacking contributions, entropic estimates, and steric restrictions for amyloidal parallel β‐sheet construction. A dynamic program computes the top 50 structures and returns both the highest scoring structure and a consensus structure taken by polling this list for common discrete elements. Putative structural heterogeneity can be inferred from sequence regions that compose poorly. Predictions show agreement with experimental models of Alzheimers amyloid beta peptide and the Podospora anserina Het‐s prion. Predictions of the HET‐s homolog HET‐S also reflect experimental observations of poor amyloid formation. We put forward predicted structures for the yeast prion Sup35, suggesting N‐terminal structural stability enabled by tyrosine ladders, and C‐terminal heterogeneity. Predictions for the Rnq1 prion and alpha‐synuclein are also given, identifying a similar mix of homogenous and heterogeneous secondary structure elements. STITCHER provides novel insight into the energetic basis of amyloid structure, provides accurate structure predictions, and can help guide future experimental studies. Proteins 2012.

Lynx: a programmatic SAT solver for the RNA-folding problem

This paper introduces Lynx, an incremental programmatic SAT solver that allows non-expert users to introduce domain-specific code into modern conflict-driven clause-learning (CDCL) SAT solvers, thus enabling users to guide the behavior of the solver. The key idea of Lynx is a callback interface that enables non-expert users to specialize the SAT solver to a class of Boolean instances. The user writes specialized code for a class of Boolean formulas, which is periodically called by Lynxs search routine in its inner loop through the callback interface. The user-provided code is allowed to examine partial solutions generated by the solver during its search, and to respond by adding CNF clauses back to the solver dynamically and incrementally. Thus, the user-provided code can specialize and influence the solvers search in a highly targeted fashion. While the power of incremental SAT solvers has been amply demonstrated in the SAT literature and in the context of DPLL(T), it has not been previously made available as a programmatic API that is easy to use for non-expert users. Lynxs callback interface is a simple yet very effective strategy that addresses this need. We demonstrate the benefits of Lynx through a case-study from computational biology, namely, the RNA secondary structure prediction problem. The constraints that make up this problem fall into two categories: structural constraints, which describe properties of the biological structure of the solution, and energetic constraints, which encode quantitative requirements that the solution must satisfy. We show that by introducing structural constraints on-demand through user provided code we can achieve, in comparison with standard SAT approaches, upto 30x reduction in memory usage and upto 100x reduction in time.

On the existence of hazard-free multi-level logic

This paper introduces a new method which, given an arbitrary Boolean function and specified set of (function hazard-free) input transitions, determines if any hazard free multilevel logic implementation exists. The algorithm is based on iterative decomposition, using disjunction and inversion. Earlier approaches by Nowick and Dill (1995) and Theobald and Nowick (1998) have been proposed to determine if a hazard free two-level logic implementation exists. However, it is well-known that the effects of multi-level transformations are quite complex: since they can both decrease and increase logic hazards in a given circuit. In this paper, a method is proposed to solve the hazard free multi-level existence problem. The method is proven to be both sound and complete for a large class of multi-level implementations. A novel contribution is to show that, if any hazard free multi-level solution exists, then a hazard free solution always exists using only 3 logic levels, in a 3-level NAND or OR-AND-OR structure. Moreover, in this case, it is shown there always exists a unique canonical hazard free 3-level implementation.

Efficient traversal of beta-sheet protein folding pathways using ensemble models

Molecular dynamics (MD) simulations can now predict ms-timescale folding processes of small proteins; however, this presently requires hundreds of thousands of CPU hours and is primarily applicable to short peptides with few long-range interactions. Larger and slower-folding proteins, such as many with extended β-sheet structure, would require orders of magnitude more time and computing resources. Furthermore, when the objective is to determine only which folding events are necessary and limiting, atomistic detail MD simulations can prove unnecessary. Here, we introduce the program tFolder as an efficient method for modelling the folding process of large β-sheet proteins using sequence data alone. To do so, we extend existing ensemble β-sheet prediction techniques, which permitted only a fixed anti-parallel β-barrel shape, with a method that predicts arbitrary β-strand/β-strand orientations and strand-order permutations. By accounting for all partial and final structural states, we can then model the transition from random coil to native state as a Markov process, using a master equation to simulate population dynamics of folding over time. Thus, all putative folding pathways can be energetically scored, including which transitions present the greatest barriers. Since correct folding pathway prediction is likely determined by the accuracy of contact prediction, we demonstrate the accuracy of tFolder to be comparable with state-of-the-art methods designed specifically for the contact prediction problem alone. We validate our method for dynamics prediction by applying it to the folding pathway of the well-studied Protein G. With relatively very little computation time, tFolder is able to reveal critical features of the folding pathways which were only previously observed through time-consuming MD simulations and experimental studies. Such a result greatly expands the number of proteins whose folding pathways can be studied, while the algorithmic integration of ensemble prediction with Markovian dynamics can be applied to many other problems.