The thermodynamic landscape of carbon redox biochemistry

Redox biochemistry plays a key role in the transduction of chemical energy in all living systems. Observed redox reactions in metabolic networks represent only a minuscule fraction of the space of all possible redox reactions. Here we ask what distinguishes observed, natural redox biochemistry from the space of all possible redox reactions between natural and non-natural compounds. We generate the set of all possible biochemical redox reactions involving linear chain molecules with a fixed numbers of carbon atoms. Using cheminformatics and quantum chemistry tools we analyze the physicochemical and thermodynamic properties of natural and non-natural compounds and reactions. We find that among all compounds, aldose sugars are the ones with the highest possible number of connections (reductions and oxidations) to other molecules. Natural metabolites are significantly enriched in carboxylic acid functional groups and depleted in carbonyls, and have significantly higher solubilities than non-natural compounds. Upon constructing a thermodynamic landscape for the full set of reactions as a function of pH and of steady-state redox cofactor potential, we find that, over this whole range of conditions, natural metabolites have significantly lower energies than the non-natural compounds. For the set of 4-carbon compounds, we generate a Pourbaix phase diagram to determine which metabolites are local energetic minima in the landscape as a function of pH and redox potential. Our results suggest that, across a set of conditions, succinate and butyrate are local minima and would thus tend to accumulate at equilibrium. Our work suggests that metabolic compounds could have been selected for thermodynamic stability, and yields insight into thermodynamic and design principles governing nature’s metabolic redox reactions.https://www.biorxiv.org/content/10.1101/245811v1Othe

Inverse Design of Solid-State Materials via a Continuous Representation

The non-serendipitous discovery of materials with targeted properties is the ultimate goal of materials research, but to date, materials design lacks the incorporation of all available knowledge to plan the synthesis of the next material. This work presents a framework for learning a continuous representation of materials and building a model for new discovery using latent space representation. The ability of autoencoders to generate experimental materials is demonstrated with vanadium oxides via rediscovery of experimentally known structures when the model was trained without them. Approximately 20,000 hypothetical materials are generated, leading to several completely new metastable V_xO_y materials that may be synthesizable. Comparison with genetic algorithms suggests computational efficiency of generative models that can explore chemical compositional space effectively by learning the distributions of known materials for crystal structure prediction. These results are an important step toward machine-learned inverse design of inorganic functional materials using generative models

Quantum Chemical Approach to Estimating the Thermodynamics of Metabolic Reactions

Thermodynamics plays an increasingly important role in modeling and engineering metabolism. We present the first nonempirical computational method for estimating standard Gibbs reaction energies of metabolic reactions based on quantum chemistry, which can help fill in the gaps in the existing thermodynamic data. When applied to a test set of reactions from core metabolism, the quantum chemical approach is comparable in accuracy to group contribution methods for isomerization and group transfer reactions and for reactions not including multiply charged anions. The errors in standard Gibbs reaction energy estimates are correlated with the charges of the participating molecules. The quantum chemical approach is amenable to systematic improvements and holds potential for providing thermodynamic data for all of metabolism.Chemistry and Chemical Biolog

Inverse Design of Solid-State Materials via a Continuous Representation

The non-serendipitous discovery of materials with targeted properties is the ultimate goal of materials research, but to date, materials design lacks the incorporation of all available knowledge to plan the synthesis of the next material. This work presents a framework for learning a continuous representation of materials and building a model for new discovery using latent space representation. The ability of autoencoders to generate experimental materials is demonstrated with vanadium oxides via rediscovery of experimentally known structures when the model was trained without them. Approximately 20,000 hypothetical materials are generated, leading to several completely new metastable V_xO_y materials that may be synthesizable. Comparison with genetic algorithms suggests computational efficiency of generative models that can explore chemical compositional space effectively by learning the distributions of known materials for crystal structure prediction. These results are an important step toward machine-learned inverse design of inorganic functional materials using generative models

Deletion of a Csf1r enhancer selectively impacts CSF1R expression and development of tissue macrophage populations

The proliferation, differentiation and survival of mononuclear phagocytes depend on signals from the receptor for macrophage colony-stimulating factor, CSF1R. The mammalian Csf1r locus contains a highly conserved super-enhancer, the fms-intronic regulatory element (FIRE). Here we show that genomic deletion of FIRE in mice selectively impacts CSF1R expression and tissue macrophage development in specific tissues. Deletion of FIRE ablates macrophage development from murine embryonic stem cells. Csf1rΔFIRE/ΔFIRE mice lack macrophages in the embryo, brain microglia and resident macrophages in the skin, kidney, heart and peritoneum. The homeostasis of other macrophage populations and monocytes is unaffected, but monocytes and their progenitors in bone marrow lack surface CSF1R. Finally, Csf1rΔFIRE/ΔFIRE mice are healthy and fertile without the growth, neurological or developmental abnormalities reported in Csf1r−/− rodents. Csf1rΔFIRE/ΔFIRE mice thus provide a model to explore the homeostatic, physiological and immunological functions of tissue-specific macrophage populations in adult animals