Improved Descriptors for the Quantitative Structure-Activity Relationship Modeling of Peptides and Proteins.

The ability to model the activity of a protein using quantitative structure-activity relationships (QSAR) requires descriptors for the 20 naturally coded amino acids. In this work we show that by modifying some established descriptors we were able to model the activity data of 140 mutants of the enzyme epoxide hydrolase with improved accuracy. These new descriptors (referred to as physical descriptors) also gave very good results when tested against a series of four dipeptide data sets. The physical descriptors encode the amino acids using only two orthogonal scales: the first is strongly linked to hydrophilicity/hydrophobicity, and the second, to the volume of the amino acid residue. The use of these new amino acid descriptors should result in simpler and more readily interpretable models for the enzyme activity (and potentially other functions of interest, e.g., secondary and tertiary structure) of peptides and proteins.

Recommendations on the Implementation of Genetic Algorithms for the Directed Evolution of Enzymes for Industrial Purposes.

In directed evolution (DE) the assessment of candidate enzymes and their modification is essential. In this study we have investigated genetic algorithms (GAs) in this context and conducted a systematic study of the behavior of GAs on 20 fitness landscapes (FLs) of varying complexity. This has allowed the tuning of the GAs to be explored. On the basis of this study, recommendations for the best GA settings to use for a GA-directed high-throughput experimental program (in which populations and the number of generations is necessarily low) are reported. The FLs were based upon simple linear models and were characterized by the behavior of the GA on the landscape as demonstrated by stall plots and the footprints and adhesion of candidate solutions, which highlighted local optima (LOs). In order to maximize progress of the GA and to reduce the chances of becoming stuck in a LO it was best to use: 1) a large number of generations, 2) high populations, 3) removal of duplicate sequences (clones), 4) double mutation, and 5) high selection pressure (the two best individuals go to the next generation), and 6) to consider using a designed sequence as the starting point of the GA run. We believe that these recommendations might be appropriate starting points for studies employing GAs within DE experiments.

Measured Saturation Vapor Pressures of Phenolic and Nitro-aromatic Compounds.

Phenolic and nitro-aromatic compounds are extremely toxic components of atmospheric aerosol that are currently not well understood. In this Article, solid and subcooled-liquid-state saturation vapor pressures of phenolic and nitro-aromatic compounds are measured using Knudsen Effusion Mass Spectrometry (KEMS) over a range of temperatures (298-318 K). Vapor pressure estimation methods, assessed in this study, do not replicate the observed dependency on the relative positions of functional groups. With a few exceptions, the estimates are biased toward predicting saturation vapor pressures that are too high, by 5-6 orders of magnitude in some cases. Basic partitioning theory comparisons indicate that overestimation of vapor pressures in such cases would cause us to expect these compounds to be present in the gas state, whereas measurements in this study suggest these phenolic and nitro-aromatic will partition into the condensed state for a wide range of ambient conditions if absorptive partitioning plays a dominant role. While these techniques might have both structural and parametric uncertainties, the new data presented here should support studies trying to ascertain the role of nitrogen containing organics on aerosol growth and human health impacts.

Critical assessment of liquid density estimation methods for multifunctional organic compounds and their use in atmospheric science.

In order to model the properties and chemical composition of secondary organic aerosol (SOA), estimated physical property data for many thousands of organic compounds are required. Seven methods for estimating liquid density are assessed against experimental data for a test set of 56 multifunctional organic compounds. The group contribution method of Schroeder coupled with the Rackett equation using critical properties by Nannoolal was found to give the best liquid density values for this test set. During this work some problems with the representation of certain groups (aromatic amines and phenols) within the critical property estimation methods were identified, highlighting the importance (and difficulties) of deriving the parameters of group contribution methods from good quality experimental data. A selection of the estimation methods are applied to the 2742 compounds of an atmospheric chemistry mechanism, which showed that they provided consistent liquid density values for compounds with such atmospherically important (but poorly studied) functional groups as hydroperoxide, peroxide, peroxyacid, and PAN. Estimated liquid density values are also presented for a selection of compounds predicted to be important in atmospheric SOA. Hygroscopic growth factor (a property expected to depend on liquid density) has been calculated for a wide range of particle compositions. A low sensitivity of the growth factor to liquid density was found, and a single density value of 1350 kg·m(-3) could be used for all multicomponent SOA in the calculation of growth factors for comparison with experimentally measured values in the laboratory or the field without incurring significant error.

A significant role for nitrate and peroxide groups on indoor secondary organic aerosol.

This paper reports indoor secondary organic aerosol, SOA, composition based on the results from an improved model for indoor air chemistry. The model uses a detailed chemical mechanism that is near-explicit to describe the gas-phase degradation of relevant indoor VOC species. In addition, gas-to-particle partitioning is included for oxygenated products formed from the degradation of limonene, the most ubiquitous terpenoid species in the indoor environment. The detail inherent in the chemical mechanism permits the indoor SOA composition to be reported in greater detail than currently possible using experimental techniques. For typical indoor conditions in the suburban UK, SOA concentrations are ~1 µg m(-3) and dominated by nitrated material (~85%), with smaller contributions from peroxide (12%), carbonyl (3%), and acidic (1%) material. During cleaning activities, SOA concentrations can reach 20 µg m(-3) with the composition dominated by peroxide material (73%), with a smaller contribution from nitrated material (21%). The relative importance of these different moieties depends crucially (in order) on the outdoor concentration of O(3), the deposition rates employed and the scaling factor value applied to the partitioning coefficient. There are currently few studies that report observation of aerosol composition indoors, and most of these have been carried out under conditions that are not directly relevant. This study highlights the need to investigate SOA composition in real indoor environments. Further, there is a need to measure deposition rates for key indoor air species on relevant indoor surfaces and to reduce the uncertainties that still exist in gas-to-particle phase parametrization for both indoor and outdoor air chemistry models.

Characterization of Mixtures. Part 2: QSPR Models for Prediction of Excess Molar Volume and Liquid Density Using Neural Networks.

In our earlier work, we have demonstrated that it is possible to characterize binary mixtures using single component descriptors by applying various mixing rules. We also showed that these methods were successful in building predictive QSPR models to study various mixture properties of interest. Here in, we developed a QSPR model of an excess thermodynamic property of binary mixtures i.e. excess molar volume (V(E) ). In the present study, we use a set of mixture descriptors which we earlier designed to specifically account for intermolecular interactions between the components of a mixture and applied successfully to the prediction of infinite-dilution activity coefficients using neural networks (part 1 of this series). We obtain a significant QSPR model for the prediction of excess molar volume (V(E) ) using consensus neural networks and five mixture descriptors. We find that hydrogen bond and thermodynamic descriptors are the most important in determining excess molar volume (V(E) ), which is in line with the theory of intermolecular forces governing excess mixture properties. The results also suggest that the mixture descriptors utilized herein may be sufficient to model a wide variety of properties of binary and possibly even more complex mixtures.

Application of QSPR to mixtures.

In this paper we report an attempt to apply the QSPR approach for the analysis of data for mixtures. This is an extension of the conventional QSPR approach to the analysis of data for single molecules. The QSPR methodology was applied to a data set of experimental measured density of binary liquid mixtures compiled from the literature. The present study is aimed to develop models to predict the "delta" value of a mixture i.e., deviation of the experimental mixture density (MED) from the ideal, mole-weighted calculated mixture density (MCD). The QSPR was investigated in two perspectives (QMD-I and QMD-II) with respect to the creation of training and test sets. The study resulted in significant ensemble neural network and k-nearest neighbor models having statistical parameters r2, q2(10cv) greater than 0.9, and pred_r2 greater than 0.75. The developed models can be used to predict the delta and hence the density of a new mixture. The QSPR analysis shows the importance of hydrogen bond, polar, shape, and thermodynamic descriptors in determining mixture density, thus aiding in the understanding of molecular interactions important in molecular packing in the mixtures.