Dihydrodipicolinate synthase (DHDPS) catalyzes the first committed step in the diaminopimelate pathway of bacteria, yielding amino acids required for cell wall and protein biosyntheses. The essentiality of the enzyme to bacteria, coupled with its absence in humans, validates DHDPS as an antibacterial drug target. Conventional drug design efforts have thus far been unsuccessful in identifying potent DHDPS inhibitors. Here, we make use of contemporary molecular dynamics simulation and Markov state models to explore the interactions between DHDPS from the human pathogen Staphylococcus aureus and its cognate substrate, pyruvate. Our simulations recover the crystallographic DHDPS-pyruvate complex without a priori knowledge of the final bound structure. The highly conserved residue Arg140 was found to have a pivotal role in coordinating the entry of pyruvate into the active site from bulk solvent, consistent with previous kinetic reports, indicating an indirect role for the residue in DHDPS catalysis. A metastable binding intermediate characterized by multiple points of intermolecular interaction between pyruvate and key DHDPS residue Arg140 was found to be a highly conserved feature of the binding trajectory when comparing alternative binding pathways. By means of umbrella sampling we show that these binding intermediates are thermodynamically metastable, consistent with both the available experimental data and the substrate binding model presented in this study. Our results provide insight into an important enzyme-substrate interaction in atomistic detail that offers the potential to be exploited for the discovery of more effective DHDPS inhibitors and, in a broader sense, dynamic protein-drug interactions.

JavaScript is used everywhere from the browser to the server, including desktops and mobile devices. However, the current state of the art in JavaScript static analysis lags far behind that of other languages such as C and Java. Our goal is to help remedy this lack. We describe JSAI, a formally specified, robust abstract interpreter for JavaScript. JSAI uses novel abstract domains to compute a reduced product of type inference, pointer analysis, control-flow analysis, string analysis, and integer and boolean constant propagation. Part of JSAI's novelty is user-configurable analysis sensitivity, i.e., context-, path-, and heap-sensitivity. JSAI is designed to be provably sound with respect to a specific concrete semantics for JavaScript, which has been extensively tested against a commercial JavaScript implementation. We provide a comprehensive evaluation of JSAI's performance and precision using an extensive benchmark suite, including real-world JavaScript applications, machine generated JavaScript code via Emscripten, and browser addons. We use JSAI's configurability to evaluate a large number of analysis sensitivities (some well-known, some novel) and observe some surprising results that go against common wisdom. These results highlight the usefulness of a configurable analysis platform such as JSAI.

Static analysis of JavaScript has proven useful for a variety of purposes, including optimization, error checking, security auditing, program refactoring, and more. We propose a technique called type refinement that can improve the precision of such static analyses for JavaScript without any discernible performance impact. Refinement is a known technique that uses the conditions in branch guards to refine the analysis information propagated along each branch path. The key insight of this paper is to recognize that JavaScript semantics include many implicit conditional checks on types, and that performing type refinement on these implicit checks provides significant benefit for analysis precision.

Negative feedback loops are a common cellular motif underlying many important gene regulation pathways, and therefore a clear understanding of their dynamics is important for the study of gene regulation. Here we analyze the linear stability of negative feedback loops with and without explicit time delays. When the degradation rates of each loop component are identical, we derive the analytical solution of the condition of marginal stability. In the most general case, when the degradation rates of each component differ, we derive a novel expansion of the condition of marginal stability in terms of the geometric mean and variance of the degradation rates. We use these results to demonstrate how the mathematical representation of a negative feedback loop can be simplified such that the stability characteristics are maintained. Finally we apply these results to study the stability structure of the p53-Mdm2 feedback loop. By simplifying a model involving nuclear and cytoplasmatic molecular species of mdm2 mRNA and Mdm2 protein to a model involving only nuclear components and a time delay (which summarizes transcriptional, nuclear import and export, and translational time scales), we demonstrate how our methods allow for an elucidation of the dynamic instabilities recently observed experimentally in the p53-Mdm2 system as the transcription of p53 changes from low to middle to high rates.