The Impact of Small Molecule Binding on the Energy Landscape of the Intrinsically Disordered Protein C-Myc

Intrinsically disordered proteins are attractive therapeutic targets owing to their prevalence in several diseases. Yet their lack of well-defined structure renders ligand discovery a challenging task. An intriguing example is provided by the oncoprotein c-Myc, a transcription factor that is over expressed in a broad range of cancers. Transcriptional activity of c-Myc is dependent on heterodimerization with partner protein Max. This protein-protein interaction is disrupted by the small molecule 10058-F4 (1), that binds to monomeric and disordered c-Myc. To rationalize the mechanism of inhibition, structural ensembles for the segment of the c-Myc domain that binds to 1 were computed in the absence and presence of the ligand using classical force fields and explicit solvent metadynamics molecular simulations. The accuracy of the computed structural ensembles was assessed by comparison of predicted and measured NMR chemical shifts. The small molecule 1 was found to perturb the composition of the apo equilibrium ensemble and to bind weakly to multiple distinct c-Myc conformations. Comparison of the apo and holo equilibrium ensembles reveals that the c-Myc conformations binding 1 are already partially formed in the apo ensemble, suggesting that 1 binds to c-Myc through an extended conformational selection mechanism. The present results have important implications for rational ligand design efforts targeting intrinsically disordered proteins

Finishing the euchromatic sequence of the human genome

The sequence of the human genome encodes the genetic instructions for human physiology, as well as rich information about human evolution. In 2001, the International Human Genome Sequencing Consortium reported a draft sequence of the euchromatic portion of the human genome. Since then, the international collaboration has worked to convert this draft into a genome sequence with high accuracy and nearly complete coverage. Here, we report the result of this finishing process. The current genome sequence (Build 35) contains 2.85 billion nucleotides interrupted by only 341 gaps. It covers ∼99% of the euchromatic genome and is accurate to an error rate of ∼1 event per 100,000 bases. Many of the remaining euchromatic gaps are associated with segmental duplications and will require focused work with new methods. The near-complete sequence, the first for a vertebrate, greatly improves the precision of biological analyses of the human genome including studies of gene number, birth and death. Notably, the human enome seems to encode only 20,000-25,000 protein-coding genes. The genome sequence reported here should serve as a firm foundation for biomedical research in the decades ahead

Developing a Low-Cost Robot Colony

Taking inspiration from nature, we have developed a colony of small, low-cost robots. We have created a robotic base which is inexpensive and utilizes simple sensors, yet has the capabilities required to form a colony. To overcome computational limitations, we have developed custom sensors and algorithms that enable the robots to communicate, localize relative to one another, and sense the environment around them. Using these noisy sensors and simple local rules, the Colony as a whole is able to exhibit more complex global behaviors. We present our work developing an autonomous robot colony and algorithms for efficient communication, localization, and robot behaviors. We also highlight recent developments that enable our Colony to recharge autonomously

System-Call Based Problem Diagnosis for PVFS

We present a syscall-based approach to automatically diagnose performance problems, server-to-client propagated errors, and server crash/hang problems in PVFS. Our approach compares the statistical and semantic attributes of syscalls across PVFS servers in order to diagnose the culprit server, under these problems, for different file-system benchmarks—dd, PostMark and IOzone—in a PVFS cluster

ASDF: Automated, Online Fingerpointing for Hadoop (CMU-PDL-08-104)

Localizing performance problems (or fingerpointing) is essential for distributed systems such as Hadoop that support long-running, parallelized, data-intensive computations over a large cluster of nodes. Manual fingerpointing does not scale in such environments because of the number of nodes and the number of performance metrics to be analyzed on each node. ASDF is an automated, online fingerpointing framework that transparently extracts and parses different time-varying data sources (e.g., sysstat, Hadoop logs) on each node, and implements multiple techniques (e.g., log analysis, correlation, clustering) to analyze these data sources jointly or in isolation. We demonstrate ASDF’s online fingerpointing for documented performance problems in Hadoop, under different workloads; our results indicate that ASDF incurs an average monitoring overhead of 0.38% of CPU time, and exhibits average online fingerpointing latencies of less than 1 minute with false-positive rates of less than 1%

Ultralight Modular Robotic Building Blocks for the Rapid Deployment of Planetary Outposts

We examine how modular robots can be used to enable remote robotic construction of planetary and orbital outposts. Each modular robot, called a catom, contains sufficient actuation, adhesion, control, and power to allow it to function as part of an ensemble of similar units. We describe the catom design and construction as well as initial experiments carried out to verify the system