D. E. Shaw Research

About: D. E. Shaw Research is a company organization based out in New York, New York, United States. It is known for research contribution in the topics: Massively parallel & G protein-coupled receptor. The organization has 233 authors who have published 273 publications receiving 38359 citations.

Algorithmic algebraic model checking IV: characterization of metabolic networks

Abstract: A series of papers, all under the title of Algorithmic Algebraic Model Checking (AAMC), has sought to combine techniques from algorithmic algebra, model checking and dynamical systems to examine how a biochemical hybrid dynamical system can be made amenable to temporal analysis, even when the initial conditions and unknown parameters may only be treated as symbolic variables. This paper examines how to specialize this framework to metabolic control analysis (MCA) involving many reactions operating at many dissimilar time-scales. In the earlier AAMC papers, it has been shown that the dynamics of various biochemical semi-algebraic hybrid automata could be unraveled using powerful techniques from computational real algebraic geometry. More specifically, the resulting algebraic model checking techniques were found to be suitable for biochemical networks modeled using general mass action (GMA) based ODEs. This paper scrutinizes how the special properties of metabolic networks-a subclass of the biochemical networks previously handled-can be exploited to gain improvement in computational efficiency. The paper introduces a general framework for performing symbolic temporal reasoning over metabolic network hybrid automata that handles both GMA-based equilibrium estimation and flux balance analysis (FBA).While algebraic polynomial equations over Q[x1, ..., xn] can be symbolically solved using Grobner bases or Wu-Ritt characteristic sets, the FBA-based estimation can be performed symbolically by rephrasing the algebraic optimization problem as a quantifier elimination problem. Effectively, an approximate hybrid automaton that simulates the metabolic network is derived, and is thus amenable to manipulation by the algebraic model checking techniques previously described in the AAMC papers.

The Execution Migration Machine: Directoryless Shared-Memory Architecture

Abstract: For certain applications involving chip multiprocessors with more than 16 cores, a directoryless architecture with fine-grained and partial-context thread migration can outperform directory-based coherence, providing lighter on-chip traffic and reduced verification complexity.

The role of cascade, a cycle-based simulation infrastructure, in designing the anton special-purpose supercomputers

Abstract: Cascade is a cycle-based C++ simulation infrastructure used in the design and verification of two successive versions of Anton, a specialized machine designed for high-speed molecular dynamics computation. Cascade was engineered to address the size and speed challenges inherent in simulating massively parallel special-purpose machines. It provides a lightweight programming interface, rich debugging support, tight Verilog integration, fast multithreaded execution, and low memory overhead. Here, we describe the core features of Cascade that proved most valuable for our simulation efforts.

How does a small molecule bind at a cryptic binding site

Abstract: Protein-protein interactions (PPIs) are ubiquitous biomolecular processes that are central to virtually all aspects of cellular function. Identifying small molecules that modulate specific disease-related PPIs is a strategy with enormous promise for drug discovery. The design of drugs to disrupt PPIs is challenging, however, because many potential drug-binding sites at PPI interfaces are “cryptic”: When unoccupied by a ligand, cryptic sites are often flat and featureless, and thus not readily recognizable in crystal structures, with the geometric and chemical characteristics of typical small-molecule binding sites only emerging upon ligand binding. The rational design of small molecules to inhibit specific PPIs would benefit from a better understanding of how such molecules bind at PPI interfaces. To this end, we have conducted unbiased, all-atom MD simulations of the binding of four small-molecule inhibitors (SP4206 and three SP4206 analogs) to interleukin 2 (IL2)—which performs its function by forming a PPI with its receptor—without incorporating any prior structural information about the ligands’ binding. In multiple binding events, a small molecule settled into a stable binding pose at the PPI interface of IL2, resulting in a protein–small-molecule binding site and pose virtually identical to that observed in an existing crystal structure of the IL2-SP4206 complex. Binding of the small molecule stabilized the IL2 binding groove, which when the small molecule was not bound emerged only transiently and incompletely. Moreover, free energy perturbation (FEP) calculations successfully distinguished between the native and non-native IL2–small-molecule binding poses found in the simulations, suggesting that binding simulations in combination with FEP may provide an effective tool for identifying cryptic binding sites and determining the binding poses of small molecules designed to disrupt PPI interfaces by binding to such sites.