skip to main content

Title: Bayesian regression and model selection for isothermal titration calorimetry with enantiomeric mixtures

Bayesian regression is performed to infer parameters of thermodynamic binding models from isothermal titration calorimetry measurements in which the titrant is an enantiomeric mixture. For some measurements the posterior density is multimodal, indicating that additional data with a different protocol are required to uniquely determine the parameters. Models of increasing complexity—two-component binding, racemic mixture, and enantiomeric mixture—are compared using model selection criteria. To precisely estimate one of these criteria, the Bayes factor, a variation of bridge sampling is developed.

more » « less
Award ID(s):
Author(s) / Creator(s):
; ; ;
Vashistha, Vinod Kumar
Publisher / Repository:
Date Published:
Journal Name:
Page Range / eLocation ID:
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. Abstract

    A sterically encumbered aminoborane sensor is introduced and used for quantitative stereochemical analysis of monoalcohols, diols and amino alcohols. The small‐molecule probe exhibits a rigid ortho‐substituted arene scaffold with a proximate boron binding site and a triarylamine circular dichroism (CD) reporter unit which proved to be crucial for the observed chiroptical signal induction. Coordination of the chiral target molecule produces strong Cotton effects and UV changes that are readily correlated to its absolute configuration, enantiomeric composition and concentration to achieve comprehensive stereochemical analysis within a 5 % absolute error margin. The sensing method was successfully applied in the chromatography‐free analysis of less than one milligram of a crude asymmetric reaction mixture and the advantages of this chiroptical sensing approach, which is amenable to high‐throughput experimentation equipment and automation, over traditional methods is discussed.

    more » « less
  2. A boron-rich boron–carbide material (B4+δC) was synthesized by spark plasma sintering of a ball-milled mixture of high-purity boron powder and graphitic carbon at a pressure of 7 MPa and a temperature of 1930 °C. This high-pressure, high-temperature synthesized material was recovered and characterized by X-ray diffraction, X-ray photoelectron spectroscopy, Raman spectroscopy, Vickers hardness measurements, and thermal oxidation studies. The X-ray diffraction studies revealed a single-phase rhombohedral structure (space group R-3m) with lattice parameters in hexagonal representation as a = 5.609 ± 0.007 Å and c = 12.082 ± 0.02 Å. The experimental lattice parameters result in a value of δ = 0.55, or the composition of the synthesized compound as B4.55C. The high-resolution scans of boron binding energy reveal the existence of a B-C bond at 188.5 eV. Raman spectroscopy reveals the existence of a 386 cm−1 vibrational mode representative of C-B-B linear chain formation due to excess boron in the lattice. The measured Vickers microhardness at a load of 200 gf shows a high hardness value of 33.8 ± 2.3 GPa. Thermal gravimetric studies on B4.55C were conducted at a temperature of 1300 °C in a compressed dry air environment, and its behavior is compared to other high-temperature ceramic materials such as high-entropy transition metal boride. The high neutron absorption cross section, high melting point, high mechanical strength, and thermal oxidation resistance make this material ideal for applications in extreme environments.

    more » « less
  3. Abstract

    Accurate helium White Dwarf (DB) masses are critical for understanding the star’s evolution. DB masses derived from the spectroscopic and photometric methods are inconsistent. Photometric masses agree better with currently accepted DB evolutionary theories and are mostly consistent across a large range of surface temperatures. Spectroscopic masses rely on untested HeiStark line-shape and Van der Waals broadening predictions, show unexpected surface temperature trends, and are thus viewed as less reliable. To test this conclusion, we present in this paper detailed HeiStark line-shape measurements at conditions relevant to DB atmospheres (Telectron≈12,000–17,000 K,nelectron≈ 1017cm−3). We use X-rays from Sandia National Laboratories’Z-machine to create a uniform ≈120 mm long hydrogen–helium mixture plasma. Van der Waals broadening is negligible at our experimental conditions, allowing us to measure HeiStark profiles only. Hβ, which has been well-studied in our platform and elsewhere, serves as thenediagnostic. We find that HeiStark broadening models used in DB analyses are accurate within errors at tested conditions. It therefore seems unlikely that line-shape models are solely responsible for the observed spectroscopic mass trends. Our results should motivate the WD community to further scrutinize the validity of other spectroscopic and photometric input parameters, like atmospheric structure assumptions and convection corrections. These parameters can significantly change the derived DB mass. Identifying potential weaknesses in any input parameters could further our understanding of DBs, help elucidate their evolutionary origins, and strengthen confidence in both spectroscopic and photometric masses.

    more » « less
  4. To reduce experimental effort associated with directed protein evolution and to explore the sequence space encoded by mutating multiple positions simultaneously, we incorporate machine learning into the directed evolution workflow. Combinatorial sequence space can be quite expensive to sample experimentally, but machine-learning models trained on tested variants provide a fast method for testing sequence space computationally. We validated this approach on a large published empirical fitness landscape for human GB1 binding protein, demonstrating that machine learning-guided directed evolution finds variants with higher fitness than those found by other directed evolution approaches. We then provide an example application in evolving an enzyme to produce each of the two possible product enantiomers (i.e., stereodivergence) of a new-to-nature carbene Si–H insertion reaction. The approach predicted libraries enriched in functional enzymes and fixed seven mutations in two rounds of evolution to identify variants for selective catalysis with 93% and 79%ee(enantiomeric excess). By greatly increasing throughput with in silico modeling, machine learning enhances the quality and diversity of sequence solutions for a protein engineering problem.

    more » « less
  5. Abstract

    Valid surrogate endpoints S can be used as a substitute for a true outcome of interest T to measure treatment efficacy in a clinical trial. We propose a causal inference approach to validate a surrogate by incorporating longitudinal measurements of the true outcomes using a mixed modeling approach, and we define models and quantities for validation that may vary across the study period using principal surrogacy criteria. We consider a surrogate-dependent treatment efficacy curve that allows us to validate the surrogate at different time points. We extend these methods to accommodate a delayed-start treatment design where all patients eventually receive the treatment. Not all parameters are identified in the general setting. We apply a Bayesian approach for estimation and inference, utilizing more informative prior distributions for selected parameters. We consider the sensitivity of these prior assumptions as well as assumptions of independence among certain counterfactual quantities conditional on pretreatment covariates to improve identifiability. We examine the frequentist properties (bias of point and variance estimates, credible interval coverage) of a Bayesian imputation method. Our work is motivated by a clinical trial of a gene therapy where the functional outcomes are measured repeatedly throughout the trial.

    more » « less