Canonical bases in tensor products revisited

We construct canonical bases in tensor products of several lowest and highest weight integrable modules, generalizing Lusztig's work.Comment: 7 pages, v2, improved exposition, one reference added, to appear in Amer. J. Mat

Structural studies on substrate and inhibitor selectivity of phosphodiesterases

Cyclic nucleotide phosphodiesterases (PDEs) control the cellular concentration of "second messengers" adenosine or guanosine 3´, 5´-cyclic monophosphate (cAMP or cGMP). All PDEs contain a conserved catalytic domain, but each family possesses different substrate specificity and selective inhibitors. Selective inhibitors of PDEs have been studied as therapeutic agents for various diseases. However, many essential questions about the structure and function of PDEs remain mysteries. This dissertation will focus on inhibitor selectivity and substrate specificity studies of PDEs by X-ray crystallography, mutageneisis and enzymology. The crystal structure of PDE7 and kinetic analysis revealed multiple elements that jointly determinate inhibitor selectivity of PDEs. Crystal structures of PDE5 in complex with inhibitors showed multiple conformations of PDE5, providing insights into the enzyme function and for drug development. Crystal structures of PDE10 and structures of PDE4 subfamilies in complex with a PDE4D selective inhibitor will provide insights into the substrate specificity and subfamily inhibitor selectivity

The Molecular Basis for Different Recognition of Substrates by Phosphodiesterase Families 4 and 10

Phosphodiesterases (PDEs) are key enzymes that control the cellular concentrations of the second messengers cAMP and cGMP. The mechanism for selective recognition of substrates cAMP and cGMP by individual PDE families remains a puzzle. To understand the mechanism for substrate recognition by PDE enzymes, the crystal structure of the catalytic domain of an inactive D201N mutant of PDE4D2 in complex with substrate cAMP has been determined at 1.56 Å resolution. The structure shows that Gln369 forms only one hydrogen bond with the adenine of cAMP. This finding provides experimental evidence against the hypothesis of two hydrogen bonds between the invariant glutamine and the substrate cAMP in PDE4, and thus suggests that the widely circulated “glutamine switch” model is unlikely the mechanism for substrate recognition by PDEs. A structure comparison between PDE4D2-cAMP and PDE10A2-cAMP reveals an anti configuration of cAMP in PDE4D2 but syn in PDE10A2, in addition to different contact patterns of cAMP in these two structures. These observations imply that individual PDE families have their characteristic mechanisms for substrate recognition

Conformation Changes, N-terminal Involvement, and cGMP Signal Relay in the Phosphodiesterase-5 GAF Domain

The activity of phosphodiesterase-5 (PDE5) is specific for cGMP and is regulated by cGMP binding to GAF-A in its regulatory domain. To better understand the regulatory mechanism, x-ray crystallographic and biochemical studies were performed on constructs of human PDE5A1 containing the N-terminal phosphorylation segment, GAF-A, and GAF-B. Superposition of this unliganded GAF-A with the previously reported NMR structure of cGMP-bound PDE5 revealed dramatic conformational differences and suggested that helix H4 and strand B3 probably serve as two lids to gate the cGMP-binding pocket in GAF-A. The structure also identified an interfacial region among GAF-A, GAF-B, and the N-terminal loop, which may serve as a relay of the cGMP signal from GAF-A to GAF-B. N-terminal loop 98–147 was physically associated with GAF-B domains of the dimer. Biochemical analyses showed an inhibitory effect of this loop on cGMP binding and its involvement in the cGMP-induced conformation changes

SRL: Scaling Distributed Reinforcement Learning to Over Ten Thousand Cores

The ever-growing complexity of reinforcement learning (RL) tasks demands a distributed RL system to efficiently generate and process a massive amount of data to train intelligent agents. However, existing open-source libraries suffer from various limitations, which impede their practical use in challenging scenarios where large-scale training is necessary. While industrial systems from OpenAI and DeepMind have achieved successful large-scale RL training, their system architecture and implementation details remain undisclosed to the community. In this paper, we present a novel abstraction on the dataflows of RL training, which unifies practical RL training across diverse applications into a general framework and enables fine-grained optimizations. Following this abstraction, we develop a scalable, efficient, and extensible distributed RL system called ReaLly Scalable RL (SRL). The system architecture of SRL separates major RL computation components and allows massively parallelized training. Moreover, SRL offers user-friendly and extensible interfaces for customized algorithms. Our evaluation shows that SRL outperforms existing academic libraries in both a single machine and a medium-sized cluster. In a large-scale cluster, the novel architecture of SRL leads to up to 3.7x speedup compared to the design choices adopted by the existing libraries. We also conduct a direct benchmark comparison to OpenAI's industrial system, Rapid, in the challenging hide-and-seek environment. SRL reproduces the same solution as reported by OpenAI with up to 5x speedup in wall-clock time. Furthermore, we also examine the performance of SRL in a much harder variant of the hide-and-seek environment and achieve substantial learning speedup by scaling SRL to over 15k CPU cores and 32 A100 GPUs. Notably, SRL is the first in the academic community to perform RL experiments at such a large scale.Comment: 15 pages, 12 figures, 6 table

Refolding and kinetic characterization of the phosphodiesterase-8A catalytic domain

Cyclic nucleotide phosphodiesterase-8 (PDE8) hydrolyzes the second messenger cAMP and is involved in many biological processes such as testosterone production. Although the bacterial and mammalian expression systems have been extensively tried, production of large quantity of soluble and active PDE8 remains to be a major hurdle for pharmacological and structural studies. Reported here is a detailed protocol of refolding and purification of large quantity of the PDE8A1 catalytic domain (residues 480–820) and kinetic characterization of the refolded protein. This protocol yielded about 8 mg of the PDE8A catalytic domain from 2 liter E. coli culture, which has at least 40-fold higher activity than those reported in literature. The PDE8A1 catalytic domain has kcat of 4.0 s−1 for Mn2+ and 2.9 s−1 for Mg2+, and the KM values of 1–1.8 μM. In addition, the PDE8A1 (205–820) fragment that contains both PAS and catalytic domains was expressed in E. coli and refolded. This PDE8A1 (205–820) fragment has kcat of 1.1 s−1 and KM of 0.28 μM, but aggregated at high concentration. The KM of PDE8A1 (205–820) is 2- to 7-fold higher than the KM values of 40–150 nM for the full-length PDE8s in literature, but about 6-fold lower than that of the catalytic domain. Thus, the KM difference likely implies an allosteric regulation of the PDE8A activity by its PAS domain