We propose a novel stochastic global optimization algorithm with applications to the refinement stage of protein docking prediction methods. Our approach can process conformations sampled from multiple clusters, each roughly corresponding to a different binding energy funnel. These clusters are obtained using a density-based clustering method. In each cluster, we identify a smooth “permissive” subspace which avoids high-energy barriers and then underestimate the binding energy function using general convex polynomials in this subspace. We use the underestimator to bias sampling towards its global minimum. Sampling and subspace underestimation are repeated several times and the conformations sampled at the last iteration form a refined ensemble. We report computational results on a comprehensive benchmark of 224 protein complexes, establishing that our refined ensemble significantly improves the quality of the conformations of the original set given to the algorithm. We also devise a method to enhance the ensemble from which near-native models are selected.

Abstract

Introduction: Systemic inflammation may play a central role in development of pulmonary hypertension (PH). We examined the association of eicosanoid metabolites (upstream regulators of pro- and anti-inflammatory activity) with PH. We hypothesized that multi-site sampling would reveal transpulmonary release or uptake of specific PH-associated eicosanoids. Methods: We studied 482 patients with preserved LVEF who underwent right heart catheterization. PH was defined as mean pulmonary artery pressure >20mmHg. Eicosanoid metabolites (n=888) were assessed using a mass spectrometry-based platform. We examined the association of eicosanoids with PH status using multivariable logistic regression. Multi-site sampling from radial and pulmonary arteries was conducted and differences assessed using paired t-tests. Analyses were deemed significant at FDR q<0.05. Results: Among 482 patients (56±16 years, 62% women), 200 (41%) had PH. Of 888 eicosanoids, 59 were associated with PH (FDR q<0.05; p<0.004 for all). Of these, 38 were known eicosanoid metabolites/classes and the rest were novel eicosanoids. Known metabolites including linoleic acid (12,13 EpOME) and eicosapentaenoic acid derivatives (11[12]-EpETE) were associated with lower odds of PH; by contrast leukotriene (LTB4) and arachidonic acid derivatives (11,12 diHETrE) were associated with higher odds of PH. Eighteen of 59 observed eicosanoids had concomitant transpulmonary gradients (Figure; FDR q<0.05; p<0.003 for all). Most metabolites associated with higher odds of PH displayed transpulmonary uptake, and those with lower odds of PH displayed transpulmonary release. Conclusions: We found specific eicosanoid metabolites including linoleic acid derivatives, thromboxanes, and leukotrienes were associated with PH. Further, transpulmonary gradients indicated potential physiologic relevance. Further studies are needed to elucidate the role of specific eicosanoids in the pathogenesis of PH.

Early phases of green material development can be accelerated by identifying driving factors that control material properties to understand potential tradeoffs. Full investigation of fabrication variables is often prohibitively expensive. We propose a pared-down design of experiments (DOE) approach to identify driving variables in limited data scenarios using tunable polydimethylsiloxane (PDMS) foams made via sacrificial templating as an example system. This new approach systematically determines the dependencies of porosity, transparency, and fluid flow by varying the template particle size and packing while using a more sustainable solvent. Factor screening identified template particle size and packing density as the driving factors for foam performance by controlling pore size and interconnectivity. The framework developed provides a robust, foundational understanding of how to green and tune a novel material's properties using an efficient and effective exploration of the design space. Recommendations for applying this method to a broad suite of experiments are provided.

Background: Systemic inflammation has been implicated in the pathobiology of HFpEF. We examined the association of upstream mediators of inflammation as ascertained by fatty-acid derived eicosanoid and eicosanoid-related metabolites with HFpEF status and exercise manifestations of HFpEF. Methods: We studied 510 participants with chronic dyspnea and preserved LVEF who underwent invasive cardiopulmonary exercise testing (CPET). We examined the association of 890 eicosanoid and eicosanoid-related metabolites ascertained using mass spectrometry with HFpEF status (defined as abnormal rest or exercise PCWP) using multivariable logistic regression (FDR q-value <0.1 deemed significant). In secondary analyses, we examined eicosanoid profiles of specific exercise traits, including cardiac vs extra-cardiac organ reserve using principal component analysis. To corroborate findings, significant metabolites were tested against incident HF in 5192 MESA participants. Results: Among 510 participants (mean age 56±16 years, 63% women), 257 had physiologic evidence of HFpEF. We found 70 eicosanoid and eicosanoid-related metabolites were associated with HFpEF status including 17 named and 53 putative eicosanoids and eicosanoid-related metabolites. Specific prostaglandin (15R-PGF2a and 11ß-dhk-PGF2a) and linoleic acid derivatives (12,13 EpOME) were associated with greater odds of HFpEF, whereas epoxide (8(9)-EpETE), docosanoid (13,14-DiHDPA), and oxylipin (12-OPDA) derivatives were associated with lower odds of HFpEF(P<0.008 for all). Eicosanoid profiles showed heterogeneous associations across cardiac vs extra-cardiac contributors to exercise intolerance. In the MESA sample, we found that 18 eicosanoids and eicosanoid-related metabolites were associated with the development of future heart failure (P<0.05 for all). Conclusions: We found 70 pro- and anti-inflammatory eicosanoid and eicosanoid-related metabolites that were associated with physiologic HFpEF, including prostaglandin, linoleic acid, and epoxide derivatives. Among these, 18 were associated with future development of heart failure in the community. Further, eicosanoid profiles highlighted contributions to exercise intolerance. Specific eicosanoid and eicosanoid-related metabolites may contribute to the pathogenesis of HFpEF and may serve as potential therapeutic targets for intervention.