Protein docking refinement by convex underestimation in the low-dimensional subspace of encounter complexes

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.Published versio

Budget-constrained experimental optimization

Many problems of design and operation in science and engineering can be formulated as optimization of a properly defined performance/objective function over a design space. This thesis considers optimization problems where information about the performance function can be obtained only through experimentation/function evaluation, in other words, optimization of black box functions. Furthermore, it is assumed that the optimization is performed with limited budget, namely, where only a limited number of function evaluations are feasible. Two classes of optimization approaches are considered. The first, consisting of Design of Experiment (DOE) and Response Surface Methodology (RSM), explores the design space locally by identifying directions of improvement and incrementally moving towards the optimum. The second, referred to as Bayesian Optimization (BO), corresponds to a global search of the design space based on a stochastic model of the function over the design space that is updated after each experimentation/function evaluation. Two independent projects related to the above optimization approaches are reported in the thesis. The first, the result of a collaborative effort with experimental and computational material scientists, involves adaptations of the above approaches in order to solve two specific new materials development projects. The goal of the first project was to develop an integrated computational-statistical-experimental methodology for calibration of an activated carbon adsorption bed. The second project consisted of the application and modification of existing DOE approaches to a highly data limited environment. The second part consists of a new contribution to the methodology of Bayesian Optimization (BO) by significantly generalizing a non-myopic approach to BO. Different BO algorithms vary based on their choice of stochastic model of the unknown objective function, referred to as the surrogate model, and that of the so-called acquisition function, which often represents an expected utility of sampling at various points of the design space. Various myopic BO approaches which evaluate the benefit of taking only a single sample from the objective function have been considered in the literature. More recently, a number of non-myopic approaches have been proposed that go beyond evaluating the benefit of a single sample. In this thesis, a non-myopic approach/algorithm, referred to as z* policy, is considered that takes a different approach to evaluating the benefits of sampling. The resulting search approach is motivated by a non-myopic index policy in a sequential sampling problem that is shown to be optimal in a non-adaptive setting. An analysis of the z* policy is presented and it is placed within the broader context of non-myopic policies. Finally, using empirical evaluations, it is shown that in some instances the z* policy outperforms a number of other commonly used myopic and non-myopic policies.2022-11-30T00:00:00

Identifying high risk clinical phenogroups of pulmonary hypertension through a clustering analysis.

IntroductionThe classification and management of pulmonary hypertension (PH) is challenging due to clinical heterogeneity of patients. We sought to identify distinct multimorbid phenogroups of patients with PH that are at particularly high-risk for adverse events.MethodsA hospital-based cohort of patients referred for right heart catheterization between 2005-2016 with PH were included. Key exclusion criteria were shock, cardiac arrest, cardiac transplant, or valvular surgery. K-prototypes was used to cluster patients into phenogroups based on 12 clinical covariates.ResultsAmong 5208 patients with mean age 64±12 years, 39% women, we identified 5 distinct multimorbid PH phenogroups with similar hemodynamic measures yet differing clinical outcomes: (1) "young men with obesity", (2) "women with hypertension", (3) "men with overweight", (4) "men with cardiometabolic and cardiovascular disease", and (5) "men with structural heart disease and atrial fibrillation." Over a median follow-up of 6.3 years, we observed 2182 deaths and 2002 major cardiovascular events (MACE). In age- and sex-adjusted analyses, phenogroups 4 and 5 had higher risk of MACE (HR 1.68, 95% CI 1.41-2.00 and HR 1.52, 95% CI 1.24-1.87, respectively, compared to the lowest risk phenogroup 1). Phenogroup 4 had the highest risk of mortality (HR 1.26, 95% CI 1.04-1.52, relative to phenogroup 1).ConclusionsCluster-based analyses identify patients with PH and specific comorbid cardiometabolic and cardiovascular disease burden that are at highest risk for adverse clinical outcomes. Interestingly, cardiopulmonary hemodynamics were similar across phenogroups, highlighting the importance of multimorbidity on clinical trajectory. Further studies are needed to better understand comorbid heterogeneity among patients with PH

Eicosanoid and eicosanoid-related inflammatory mediators and exercise intolerance in heart failure with preserved ejection fraction

Abstract Systemic inflammation has been implicated in the pathobiology of heart failure with preserved ejection fraction (HFpEF). Here, we examine 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. Among 510 participants with chronic dyspnea and preserved LVEF who underwent invasive cardiopulmonary exercise testing, we find that 70 of 890 eicosanoid and related metabolites are associated with HFpEF status, including 17 named and 53 putative eicosanoids (FDR q-value < 0.1). Prostaglandin (15R-PGF2α, 11ß-dhk-PGF2α) and linoleic acid derivatives (12,13 EpOME) are associated with greater odds of HFpEF, while epoxides (8(9)-EpETE), docosanoids (13,14-DiHDPA), and oxylipins (12-OPDA) are associated with lower odds of HFpEF. Among 70 metabolites, 18 are associated with future development of heart failure in the community. Pro- and anti-inflammatory eicosanoid and related metabolites may contribute to the pathogenesis of HFpEF and serve as potential targets for intervention