Water-Mediated Peptide Bond Formation in the Gas Phase: A Model Prebiotic Reaction

The emergence of life on the prebiotic Earth must have involved the formation of polypeptides, yet the polymerization of amino acids is thermodynamically unfavorable under biologically relevant aqueous conditions because amino acids are zwitterions in solution and because of the production of a water molecule through a condensation reaction. Many mechanisms for overcoming this thermodynamic unfavorability have been proposed, but the role of gas phase water clusters has not been investigated. We present the thermodynamics of the water-mediated gas phase dimerization reaction of glycine as a model for the atmospheric polymerization of amino acids prior to the emergence of biological machinery. We hypothesize that atmospheric aerosols may have played a major role in the prebiotic formation of peptide bonds by providing the thermodynamic driving force to facilitate increasingly stable linear oligopeptides. In addition, we hypothesize that small aerosols orient amino acids on their surfaces, thus providing the correct molecular orientations to funnel the reaction pathways of peptides through transition states that lead eventually to polypeptide products. Using density functional theory and a thorough configurational sampling technique, we show that the thermodynamic spontaneity of the linear dimerization of glycine in the gas phase can be driven by the addition of individual water molecules

Computation of Atmospheric Concentrations of Molecular Clusters from \u3cem\u3eab initio\u3c/em\u3e Thermochemistry

The computational study of the formation and growth of atmospheric aerosols requires an accurate Gibbs free energy surface, which can be obtained from gas phase electronic structure and vibrational frequency calculations. These quantities are valid for those atmospheric clusters whose geometries correspond to a minimum on their potential energy surfaces. The Gibbs free energy of the minimum energy structure can be used to predict atmospheric concentrations of the cluster under a variety of conditions such as temperature and pressure. We present a computationally inexpensive procedure built on a genetic algorithm-based configurational sampling followed by a series of increasingly accurate screening calculations. The procedure starts by generating and evolving the geometries of a large set of configurations using semi-empirical models then refines the resulting unique structures at a series of high-level ab initio levels of theory. Finally, thermodynamic corrections are computed for the resulting set of minimum-energy structures and used to compute the Gibbs free energies of formation, equilibrium constants, and atmospheric concentrations. We present the application of this procedure to the study of hydrated glycine clusters under ambient conditions

Computation of Atmospheric Concentrations of Molecular Clusters from ab initio Thermochemistry

The computational study of the formation and growth of atmospheric aerosols requires an accurate Gibbs free energy surface, which can be obtained from gas phase electronic structure and vibrational frequency calculations. These quantities are valid for those atmospheric clusters whose geometries correspond to a minimum on their potential energy surfaces. The Gibbs free energy of the minimum energy structure can be used to predict atmospheric concentrations of the cluster under a variety of conditions such as temperature and pressure. We present a computationally inexpensive procedure built on a genetic algorithm-based configurational sampling followed by a series of increasingly accurate screening calculations. The procedure starts by generating and evolving the geometries of a large set of configurations using semi-empirical models then refines the resulting unique structures at a series of high-level ab initio levels of theory. Finally, thermodynamic corrections are computed for the resulting set of minimum-energy structures and used to compute the Gibbs free energies of formation, equilibrium constants, and atmospheric concentrations. We present the application of this procedure to the study of hydrated glycine clusters under ambient conditions

Particle formation and surface processes on atmospheric aerosols: a review of applied quantum chemical calculations

Aerosols significantly influence atmospheric processes such as cloud nucleation, het- erogeneous chemistry, and heavy-metal transport in the troposphere. The chemical and physical complexity of atmospheric aerosols results in large uncertainties in their climate and health effects. In this article, we review recent advances in scientific understanding of aerosol processes achieved by the application of quantum chemical calculations. In particular, we emphasize recent work in two areas: new particle for- mation and heterogeneous processes. Details in quantum chemical methods are pro- vided, elaborating on computational models for prenucleation, secondary organic aerosol formation, and aerosol interface phenomena. Modeling of relative humidity effects, aerosol surfaces, and chemical kinetics of reaction pathways is discussed. Because of their relevance, quantum chemical calculations and field and laboratory experiments are compared. In addition to describing the atmospheric relevance of the computational models, this article also presents future challenges in quantum chemical calculations applied to aerosols

Duplication of a well-conserved homeodomain-leucine zipper transcription factor gene in barley generates a copy with more specific functions

Three spikelets are formed at each rachis node of the cultivated barley (Hordeum vulgare ssp. vulgare) spike. In two-rowed barley, the central one is fertile and the two lateral ones are sterile, whereas in the six-rowed type, all three are fertile. This characteristic is determined by the allelic constitution at the six-rowed spike 1 (vrs1) locus on the long arm of chromosome 2H, with the recessive allele (vrs1) being responsible for the six-rowed phenotype. The Vrs1 (HvHox1) gene encodes a homeodomain-leucine zipper (HD-Zip) transcription factor. Here, we show that the Vrs1 gene evolved in the Poaceae via a duplication, with a second copy of the gene, HvHox2, present on the short arm of chromosome 2H. Micro-collinearity and polypeptide sequences were both well conserved between HvHox2 and its Poaceae orthologs, but Vrs1 is unique to the barley tribe. The Vrs1 gene product lacks a motif which is conserved among the HvHox2 orthologs. A phylogenetic analysis demonstrated that Vrs1 and HvHox2 must have diverged after the separation of Brachypodium distachyon from the Pooideae and suggests that Vrs1 arose following the duplication of HvHox2, and acquired its new function during the evolution of the barley tribe. HvHox2 was expressed in all organs examined but Vrs1 was predominantly expressed in immature inflorescence

Averting biodiversity collapse in tropical forest protected areas

The rapid disruption of tropical forests probably imperils global biodiversity more than any other contemporary phenomenon¹⁻³. With deforestation advancing quickly, protected areas are increasingly becoming final refuges for threatened species and natural ecosystem processes. However, many protected areas in the tropics are themselves vulnerable to human encroachment and other environmental stresses⁴⁻⁹. As pressures mount, it is vital to know whether existing reserves can sustain their biodiversity. A critical constraint in addressing this question has been that data describing a broad array of biodiversity groups have been unavailable for a sufficiently large and representative sample of reserves. Here we present a uniquely comprehensive data set on changes over the past 20 to 30 years in 31 functional groups of species and 21 potential drivers of environmental change, for 60 protected areas stratified across the world’s major tropical regions. Our analysis reveals great variation in reserve ‘health’: about half of all reserves have been effective or performed passably, but the rest are experiencing an erosion of biodiversity that is often alarmingly widespread taxonomically and functionally. Habitat disruption, hunting and forest-product exploitation were the strongest predictors of declining reserve health. Crucially, environmental changes immediately outside reserves seemed nearly as important as those inside in determining their ecological fate, with changes inside reserves strongly mirroring those occurring around them. These findings suggest that tropical protected areas are often intimately linked ecologically to their surrounding habitats, and that a failure to stem broad-scale loss and degradation of such habitats could sharply increase the likelihood of serious biodiversity declines.Keywords: Ecology, Environmental scienc

The Catalytic Activity of Water Clusters Towards Peptide Bond Formation as a Model for the Prebiotic Origins of Oligopeptides: A Spontaneous First Step Towards Life

The emergence of life in the prebiotic Earth must have involved the formation of polypeptides, yet the polymerization of amino acids is thermodynamically unfavorable under biologically relevant conditions due to the production of a water molecule via condensation. We hypothesize that atmospheric aerosols catalyzed the prebiotic formation of peptide bonds to form oligopeptides by providing the correct molecular orientations to start the condensation reaction. We have tested this hypothesis using density-functional theory combined with an extensive sampling scheme to sample configurational space. The dimerization and trimerization of glycine through condensation are spontaneous in the gas phase and increase in spontaneity as one to three catalytic water molecules are added. This increase is driven by the stability of the product clusters which can bend to maximize the intra- and intermolecular binding interactions, specifically hydrogen bonding

Bridging the experiment-calculation divide: machine learning corrections to redox potential calculations in implicit and explicit solvent models

Prediction of redox potentials is essential for catalysis and energy storage. Although density functional theory (DFT) calculations have enabled rapid redox potential predictions for numerous compounds, prominent errors persist compared to experimental measurements. In this work, we develop machine learning (ML) models to reduce the errors of redox potential calculations in both implicit and explicit solvent models. Training and testing of the ML correction models are based on the diverse ROP313 dataset with experimental redox potentials measured for organic and organometallic compounds in a variety of solvents. For the implicit solvent approach, our ML models can reduce both the systematic bias and the number of outliers. ML corrected redox potentials also demonstrate less sensitivity to DFT functional choice. For the explicit solvent approach, we significantly reduce the computational costs by embedding the microsolvated cluster in implicit bulk solvent, obtaining converged redox potential results with a smaller solvation shell. This combined implicit-explicit solvent model, together with GPU-accelerated quantum chemistry methods, enabled rapid generation of a large dataset of explicit-solvent-calculated redox potentials for 165 organic compounds, allowing detailed investigation of the error sources in explicit solvent redox potential calculations

Quantum Chemistry for Molecules at Extreme Pressure on Graphical Processing Units: Implementation of Extreme Pressure Polarizable Continuum Model

Pressure plays essential roles in chemistry by altering structures and controlling chemical reactions. The extreme-pressure polarizable continuum model (XP-PCM) is an emerging method with an efficient quantum mechanical description of small and medium-size molecules at high pressure (on the order of GPa). However, its application to large molecular systems was previously hampered by CPU computation bottleneck: the Pauli repulsion potential unique to XP-PCM requires the evaluation of a large number of electric field integrals, resulting in significant computational overhead compared to the gas-phase or standard-pressure polarizable continuum model calculations. Here, we exploit advances in Graphical Processing Units (GPUs) to accelerate the XP-PCM integral evaluations. This enables high-pressure quantum chemistry simulation of proteins that used to be computationally intractable. We benchmarked the performance using 18 small proteins in aqueous solutions. Using a single GPU, our method evaluates the XP-PCM free energy of a protein with over 500 atoms and 4000 basis functions within half an hour. The time taken by the XP-PCM-integral evaluation is typically 1\% of the time taken for a gas-phase density functional theory (DFT) on the same system. The overall XP-PCM calculations require less computational effort than that for their gas-phase counterpart due to the improved convergence of self-consistent field iterations. Therefore, the description of the high-pressure effects with our GPU accelerated XP-PCM is feasible for any molecule tractable for gas-phase DFT calculation. We have also validated the accuracy of our method on small molecules whose properties under high pressure are known from experiments or previous theoretical studies