The Lid Domain of Caenorhabditis elegans Hsc70 Influences ATP Turnover, Cofactor Binding and Protein Folding Activity

Hsc70 is a conserved ATP-dependent molecular chaperone, which utilizes the energy of ATP hydrolysis to alter the folding state of its client proteins. In contrast to the Hsc70 systems of bacteria, yeast and humans, the Hsc70 system of C. elegans (CeHsc70) has not been studied to date

Structures of SRP54 and SRP19, the Two Proteins that Organize the Ribonucleic Core of the Signal Recognition Particle from Pyrococcus furiosus

In all organisms the Signal Recognition Particle (SRP), binds to signal sequences of proteins destined for secretion or membrane insertion as they emerge from translating ribosomes. In Archaea and Eucarya, the conserved ribonucleoproteic core is composed of two proteins, the accessory protein SRP19, the essential GTPase SRP54, and an evolutionarily conserved and essential SRP RNA. Through the GTP-dependent interaction between the SRP and its cognate receptor SR, ribosomes harboring nascent polypeptidic chains destined for secretion are dynamically transferred to the protein translocation apparatus at the membrane. We present here high-resolution X-ray structures of SRP54 and SRP19, the two RNA binding components forming the core of the signal recognition particle from the hyper-thermophilic archaeon Pyrococcus furiosus (Pfu). The 2.5 Å resolution structure of free Pfu-SRP54 is the first showing the complete domain organization of a GDP bound full-length SRP54 subunit. In its ras-like GTPase domain, GDP is found tightly associated with the protein. The flexible linker that separates the GTPase core from the hydrophobic signal sequence binding M domain, adopts a purely α-helical structure and acts as an articulated arm allowing the M domain to explore multiple regions as it scans for signal peptides as they emerge from the ribosomal tunnel. This linker is structurally coupled to the GTPase catalytic site and likely to propagate conformational changes occurring in the M domain through the SRP RNA upon signal sequence binding. Two different 1.8 Å resolution crystal structures of free Pfu-SRP19 reveal a compact, rigid and well-folded protein even in absence of its obligate SRP RNA partner. Comparison with other SRP19•SRP RNA structures suggests the rearrangement of a disordered loop upon binding with the RNA through a reciprocal induced-fit mechanism and supports the idea that SRP19 acts as a molecular scaffold and a chaperone, assisting the SRP RNA in adopting the conformation required for its optimal interaction with the essential subunit SRP54, and proper assembly of a functional SRP

The importance of the cellular stress response in the pathogenesis and treatment of type 2 diabetes

Organisms have evolved to survive rigorous environments and are not prepared to thrive in a world of caloric excess and sedentary behavior. A realization that physical exercise (or lack of it) plays a pivotal role in both the pathogenesis and therapy of type 2 diabetes mellitus (t2DM) has led to the provocative concept of therapeutic exercise mimetics. A decade ago, we attempted to simulate the beneficial effects of exercise by treating t2DM patients with 3 weeks of daily hyperthermia, induced by hot tub immersion. The short-term intervention had remarkable success, with a 1 % drop in HbA1, a trend toward weight loss, and improvement in diabetic neuropathic symptoms. An explanation for the beneficial effects of exercise and hyperthermia centers upon their ability to induce the cellular stress response (the heat shock response) and restore cellular homeostasis. Impaired stress response precedes major metabolic defects associated with t2DM and may be a near seminal event in the pathogenesis of the disease, tipping the balance from health into disease. Heat shock protein inducers share metabolic pathways associated with exercise with activation of AMPK, PGC1-a, and sirtuins. Diabetic therapies that induce the stress response, whether via heat, bioactive compounds, or genetic manipulation, improve or prevent all of the morbidities and comorbidities associated with the disease. The agents reduce insulin resistance, inflammatory cytokines, visceral adiposity, and body weight while increasing mitochondrial activity, normalizing membrane structure and lipid composition, and preserving organ function. Therapies restoring the stress response can re-tip the balance from disease into health and address the multifaceted defects associated with the disease

Leveraging multiple sources of information to search continuous spaces

Path planning algorithms can solve the problem of finding paths through continuous spaces. This problem appears in a wide range of applications, from navigating autonomous robots to automating assessments of surgical tolerances. The performance requirements on these algorithms tend to become more demanding as the problems they are applied to become more sophisticated. This simultaneous increase in performance requirements and application complexity calls for new approaches to the path planning problem and makes it an active area of research in robotics and beyond. This thesis demonstrates how different types of information can be leveraged to solve the path planning problem more effectively. Optimization-specific information can guide the search towards high-quality solutions, environment-specific information can exploit incremental information about the surroundings, and intent-specific information can directly align the search of a problem with its priorities. These three types of information are leveraged in this thesis by integrating advanced graph-search techniques in sampling-based path planning algorithms. The resulting planners, Advanced BIT* (ABIT*), Adaptively Informed Trees (AIT*), and Effort Informed Trees (EIT*), are theoretically shown to be almost-surely asymptotically optimal and experimentally demonstrated to outperform existing planners on diverse problems in abstract, robotic, and biomedical domains.</p

Adaptively Informed Trees (AIT*): fast asymptotically optimal path planning through adaptive heuristics

Informed sampling-based planning algorithms exploit problem knowledge for better search performance. This knowledge is often expressed as heuristic estimates of solution cost and used to order the search. The practical improvement of this informed search depends on the accuracy of the heuristic.Selecting an appropriate heuristic is difficult. Heuristics applicable to an entire problem domain are often simple to define and inexpensive to evaluate but may not be beneficial for a specific problem instance. Heuristics specific to a problem instance are often difficult to define or expensive to evaluate but can make the search itself trivial.This paper presents Adaptively Informed Trees (AIT*), an almost-surely asymptotically optimal sampling-based planner based on BIT*. AIT* adapts its search to each problem instance by using an asymmetric bidirectional search to simultaneously estimate and exploit a problem-specific heuristic. This allows it to quickly find initial solutions and converge towards the optimum. AIT* solves the tested problems as fast as RRT-Connect while also converging towards the optimum

A survey of asymptotically optimal sampling-based motion planning methods

Motion planning is a fundamental problem in autonomous robotics. It requires finding a path to a specified goal that avoids obstacles and obeys a robot’s limitations and constraints. It is often desirable for this path to also optimize a cost function, such as path length. Formal path-quality guarantees for continuously valued search spaces are an active area of research interest. Recent results have proven that some sampling-based planning methods probabilistically converge towards the optimal solution as computational effort approaches infinity. This survey summarizes the assumptions behind these popular asymptotically optimal techniques and provides an introduction to the significant ongoing research on this topic

Advanced BIT* (ABIT*): sampling-based planning with advanced graph-search techniques

Path planning is an active area of research essential for many applications in robotics. Popular techniques include graph-based searches and sampling-based planners. These approaches are powerful but have limitations.This paper continues work to combine their strengths and mitigate their limitations using a unified planning paradigm. It does this by viewing the path planning problem as the two subproblems of search and approximation and using advanced graph-search techniques on a sampling-based approximation.This perspective leads to Advanced BIT*. ABIT* combines truncated anytime graph-based searches, such as ATD*, with anytime almost-surely asymptotically optimal sampling-based planners, such as RRT*. This allows it to quickly find initial solutions and then converge towards the optimum in an anytime manner. ABIT* outperforms existing single-query, sampling- based planners on the tested problems in ℝ4 and ℝ8, and was demonstrated on real-world problems with NASA/JPL-Caltech

Task and motion informed trees (TMIT*): Almost-surely asymptotically optimal integrated task and motion planning

High-level autonomy requires discrete and continuous reasoning to decide both what actions to take and how to execute them. Integrated Task and Motion Planning (TMP) algorithms solve these hybrid problems jointly to consider constraints between the discrete symbolic actions (i.e., the task plan ) and their continuous geometric realization (i.e., motion plans ). This joint approach solves more difficult problems than approaches that address the task and motion subproblems independently. TMP algorithms combine and extend results from both task and motion planning. TMP has mainly focused on computational performance and completeness and less on solution optimality. Optimal TMP is difficult because the independent optima of the subproblems may not be the optimal integrated solution, which can only be found by jointly optimizing both plans. This letter presents Task and Motion Informed Trees (TMIT*), an optimal TMP algorithm that combines results from makespan-optimal task planning and almost-surely asymptotically optimal motion planning. TMIT* interleaves asymmetric forward and reverse searches to delay computationally expensive operations until necessary and perform an efficient informed search directly in the problem's hybrid state space. This allows it to solve problems quickly and then converge towards the optimal solution with additional computational time, as demonstrated on the evaluated robotic-manipulation benchmark problems

Effort Informed Roadmaps (EIRM*): efficient asymptotically optimal multiquery planning by actively reusing validation effort

Multiquery planning algorithms find paths between various different starts and goals in a single search space. They are designed to do so efficiently by reusing information across planning queries. This information may be computed before or during the search and often includes knowledge of valid paths. Using known valid paths to solve an individual planning query takes less computational effort than finding a completely new solution. This allows multiquery algorithms, such as PRM*, to outperform single-query algorithms, such as RRT*, on many problems but their relative performance depends on how much information is reused. Despite this, few multiquery planners explicitly seek to maximize path reuse and, as a result, many do not consistently outperform single-query alternatives. This paper presents Effort Informed Roadmaps (EIRM*), an almost-surely asymptotically optimal multiquery planning algorithm that explicitly prioritizes reusing computational effort. EIRM* uses an asymmetric bidirectional search to identify existing paths that may help solve an individual planning query and then uses this information to order its search and reduce computational effort. This allows it to find initial solutions up to an order-of-magnitude faster than state-of-the-art planning algorithms on the tested abstract and robotic multiquery planning problems.Comment: 16 pages, 7 figures, 1 table. Video and code available at https://robotic-esp.com/code/eirmstar