Emergence of an Orphan Nitrogenase Protein Following Atmospheric Oxygenation.

Molecular innovations within key metabolisms can have profound impacts on element cycling and ecological distribution. Yet, much of the molecular foundations of early evolved enzymes and metabolisms are unknown. Here, we bring one such mystery to relief by probing the birth and evolution of the G-subunit protein, an integral component of certain members of the nitrogenase family, the only enzymes capable of biological nitrogen fixation. The G-subunit is a Paleoproterozoic-age orphan protein that appears more than 1 billion years after the origin of nitrogenases. We show that the G-subunit arose with novel nitrogenase metal dependence and the ecological expansion of nitrogen-fixing microbes following the transition in environmental metal availabilities and atmospheric oxygenation that began ∼2.5 billion years ago. We identify molecular features that suggest early G-subunit proteins mediated cofactor or protein interactions required for novel metal dependency, priming ancient nitrogenases and their hosts to exploit these newly diversified geochemical environments. We further examined the degree of functional specialization in G-subunit evolution with extant and ancestral homologs using laboratory reconstruction experiments. Our results indicate that permanent recruitment of the orphan protein depended on the prior establishment of conserved molecular features and showcase how contingent evolutionary novelties might shape ecologically important microbial innovations.

An Agent-Based Model Shows How Mixed Infections Drive Multiyear Pathotype Dynamics in a Plant-Virus System.

Mathematical models are widely used to understand the evolution and epidemiology of plant pathogens under a variety of scenarios. Here, we used this approach to analyze the effects of different traits intrinsic and extrinsic to plant-virus interactions on the dynamics of virus pathotypes in genetically heterogeneous plant-virus systems. For this, we propose an agent-based epidemiological model that includes epidemiologically significant pathogen life-history traits related to virulence, transmission, and survival in the environment and allows for integrating long- and short-distance transmission, primary and secondary infections, and within-host pathogen competition in mixed infections. The study focuses on the tobamovirus-pepper pathosystem. Model simulations allowed us to integrate pleiotropic effects of resistance-breaking mutations on different virus life-history traits into the net costs of resistance breaking, allowing for predictions on multiyear pathotype dynamics. We also explored the effects of two control measures, the use of host resistance and roguing of symptomatic plants, that modify epidemiological attributes of the pathogens to understand how their populations will respond to evolutionary pressures. One major conclusion points to the importance of pathogen competition within mixed-infected hosts as a component of the overall fitness of each pathogen that, thus, drives their multiyear dynamics.

The modular biochemical reaction network structure of cellular translation.

Translation is an essential attribute of all living cells. At the heart of cellular operation, it is a chemical information decoding process that begins with an input string of nucleotides and ends with the synthesis of a specific output string of peptides. The translation process is interconnected with gene expression, physiological regulation, transcription, and responses to signaling molecules, among other cellular functions. Foundational efforts have uncovered a wealth of knowledge about the mechanistic functions of the components of translation and their many interactions between them, but the broader biochemical connections between translation, metabolism and polymer biosynthesis that enable translation to occur have not been comprehensively mapped. Here we present a multilayer graph of biochemical reactions describing the translation, polymer biosynthesis and metabolism networks of an Escherichia coli cell. Intriguingly, the compounds that compose these three layers are distinctly aggregated into three modes regardless of their layer categorization. Multimodal mass distributions are well-known in ecosystems, but this is the first such distribution reported at the biochemical level. The degree distributions of the translation and metabolic networks are each likely to be heavy-tailed, but the polymer biosynthesis network is not. A multimodal mass-degree distribution indicates that the translation and metabolism networks are each distinct, adaptive biochemical modules, and that the gaps between the modes reflect evolved responses to the functional use of metabolite, polypeptide and polynucleotide compounds. The chemical reaction network of cellular translation opens new avenues for exploring complex adaptive phenomena such as percolation and phase changes in biochemical contexts.

Informatic Capabilities of Translation and Its Implications for the Origins of Life.

The ability to encode and convert heritable information into molecular function is a defining feature of life as we know it. The conversion of information into molecular function is performed by the translation process, in which triplets of nucleotides in a nucleic acid polymer (mRNA) encode specific amino acids in a protein polymer that folds into a three-dimensional structure. The folded protein then performs one or more molecular activities, often as one part of a complex and coordinated physiological network. Prebiotic systems, lacking the ability to explicitly translate information between genotype and phenotype, would have depended upon either chemosynthetic pathways to generate its components-constraining its complexity and evolvability- or on the ambivalence of RNA as both carrier of information and of catalytic functions-a possibility which is still supported by a very limited set of catalytic RNAs. Thus, the emergence of translation during early evolutionary history may have allowed life to unmoor from the setting of its origin. The origin of translation machinery also represents an entirely novel and distinct threshold of behavior for which there is no abiotic counterpart-it could be the only known example of computing that emerged naturally at the chemical level. Here we describe translation machinery's decoding system as the basis of cellular translation's information-processing capabilities, and the four operation types that find parallels in computer systems engineering that this biological machinery exhibits.

Environment and coordination of FeMo-co in the nitrogenase metallochaperone NafY.

In nitrogenase biosynthesis, the iron-molybdenum cofactor (FeMo-co) is externally assembled at scaffold proteins and delivered to the NifDK nitrogenase component by the NafY metallochaperone. Here we have used nuclear magnetic resonance, molecular dynamics, and functional analysis to elucidate the environment and coordination of FeMo-co in NafY. H121 stands as the key FeMo-co ligand. Regions near FeMo-co diverge from H121 and include the η1, α1, α2 helical lobe and a narrow path between H121 and C196.

Machine Learning of Analytical Electron Density in Large Molecules Through Message-Passing.

Machine learning milestones in computational chemistry are overshadowed by their unaccountability and the overwhelming zoo of tools for each specific task. A promising path to tackle these problems is using machine learning to reproduce physical magnitudes as a basis to derive many other properties. By using a model of the electron density consisting of an analytical expansion on a linear set of isotropic and anisotropic functions, we implemented in this work a message-passing neural network able to reproduce electron density in molecules with just a 2.5% absolute error in complex cases. We also adapted our methodology to describe electron density in large biomolecules (proteins) and to obtain atomic charges, interaction energies, and DFT energies. We show that electron density learning is a new promising avenue with a variety of forthcoming applications.

The key to the allergenicity of lipid transfer protein (LTP) ligands: A structural characterization.

Plant lipid transfer proteins are a large family that can be found in all land plants. They have a hydrophobic cavity that allows them to harbor lipids and facilitates their traffic between membranes. However, in humans, this plant protein family is responsible for the main food allergies in the Mediterranean area. Nevertheless, not only the protein itself but also its ligand is relevant for allergic sensitization. The main aim of the present work is to analyse the natural ligands carried by four allergenic LTPs (Tri a 14, Art v 3, Par j 2, and Ole e 7), compared with the previously identified ligand of Pru p 3 (CPT-PHS ligand), and clarify their role within the immunological reactions. Results showed that the ligands of the LTPs studied shared a chemical identity, in which the presence of a polar head was essential to the protein-ligand binding. This ligand was transported through a skin cellular model, and phosphorylated phytosphingosine could be detected as result of cell metabolism. Since sphingosine kinase 1 was overexpressed in keratinocytes incubated with the LTP-ligand complex, this enzyme might be responsible for the phosphorylation of the phytosphingosine fraction of the CPT-PHS ligand. This way, phytosphingosine-1-phosphate could be mimicking the role of the human inflammatory mediator sphingosine-1-phosphate, explaining why LTPs are associated with more severe allergic responses. In conclusion, this work contributes to the understanding of the chemical nature and behavior of lipid ligands carried by allergens, which would help to gain insight into their role during allergic sensitization.

Analytical Model of Electron Density and Its Machine Learning Inference.

We present an analytical model representation of the electron density ρ(r) in molecules in the form of expansions of a few functions (exponentials and a Gaussian) per atom. Based on a former analytical model of ρ(r) in atoms, we devised its molecular implementation by introducing the anisotropy inherent in the electron distribution of atoms in molecules by means of proper anisotropic functions. The resulting model named A2MD (anisotropic analytical model of density) takes an analytical form highly suitable for obtaining the electron density in large biomolecules as its computational cost scales linearly with the number of atoms. To obtain the parameters of the model, we first devised a fitting procedure to reference electron densities obtained in ab initio correlated quantum calculations. Second, in order to skip costly ab initio calculations, we also developed a machine learning (ML)-based predictor that used neural networks trained on broad molecular datasets to determine the parameters of the model. The resulting ML methodology that we named A2MDnet (A2MD network-trained) was able to provide reliable electron densities as a basis to predict molecular features without requiring quantum calculations. The results presented together with the low computational scaling associated to the A2MD representation of ρ(r) suggest potential applications to obtain reliable electron densities and ρ(r)-based molecular properties in biomacromolecules.

A computational structural study on the DNA-protecting role of the tardigrade-unique Dsup protein.

The remarkable ability of tardigrades to withstand a wide range of physical and chemical extremes has attracted a considerable interest in these small invertebrates, with a particular focus on the protective roles of proteins expressed during such conditions. The discovery that a tardigrade-unique protein named Dsup (damage suppressor) protects DNA from damage produced by radiation and radicals, has raised expectations concerning its potential applications in biotechnology and medicine. We present in this paper what might be dubbed a "computational experiment" on the Dsup-DNA system. By means of molecular modelling, calculations of electrostatic potentials and electric fields, and all-atom molecular dynamics simulations, we obtained a dynamic picture of the Dsup-DNA interaction. Our results suggest that the protein is intrinsically disordered, which enables Dsup to adjust its structure to fit DNA shape. Strong electrostatic attractions and high protein flexibility drive the formation of a molecular aggregate in which Dsup shields DNA. While the precise mechanism of DNA protection conferred by Dsup remains to be elucidated, our study provides some molecular clues of their association that could be of interest for further investigation in this line.

Structural Dynamics of the Lipid Antigen-Binding Site of CD1d Protein.

CD1 molecules present lipid antigens to T-cells in early stages of immune responses. Whereas CD1‒lipid‒T-cell receptors interactions are reasonably understood, molecular details on initial trafficking and loading of lipids onto CD1 proteins are less complete. We present a molecular dynamics (MD) study of human CD1d, the isotype that activates iNKT cells. MD simulations and calculations of properties and Poisson-Boltzmann electrostatic potentials were used to explore the dynamics of the antigen-binding domain of the apo-form, CD1d complexes with three lipid-antigens that activate iNKT cells and CD1d complex with GM2AP, a protein that assists lipid loading onto CD1 molecules in endosomes/lysosomes. The study was done at pH 7 and 4.5, values representative of strongly acidic environments in endosomal compartments. Our findings revealed dynamic features of the entrance to the hydrophobic channels of CD1d modulated by two α helices with sensitivity to the type of lipid. We also found lipid- and pH-dependent dynamic changes in three exposed tryptophans unique to CD1d among the five human CD1 isotypes. On the basis of modelled structures, our data also revealed external effects produced by the helper protein GM2AP only when it interacts in its open form, thus suggesting that the own assistant protein also adapts conformation to association with CD1d.

Dynamic plasticity of the lipid antigen-binding site of CD1d is crucially favoured by acidic pH and helper proteins.

CD1 molecules present lipid antigens for recognition by T-cell receptors (TCRs). Although a reasonably detailed picture of the CD1-lipid-TCR interaction exists, the initial steps regarding lipid loading onto and exchange between CD1 proteins remain elusive. The hydrophobic nature of lipids and the fact that CD1 molecules are unable to extract lipids from membranes raise the need for the assistance of helper proteins in lipid trafficking. However, the experimental study of this traffic in the endosomal compartments at which it occurs is so challenging that computational studies can help provide mechanistic insight into the associated processes. Here we present a multifaceted computational approach to obtain dynamic structural data on the human CD1d isotype. Conformational dynamics analysis shows an intrinsic flexibility associated with the protein architecture. Electrostatic properties together with molecular dynamics results for CD1d complexes with several lipids and helper proteins unravel the high dynamic plasticity of the antigen-binding site that is crucially favoured by acidic pH and the presence of helper proteins.

Energy Landscapes of Ligand Motion Inside the Tunnel-Like Cavity of Lipid Transfer Proteins: The Case of the Pru p 3 Allergen.

Allergies are a widespread problem in western countries, affecting a large part of the population, with levels of prevalence increasingly rising due to reasons still not understood. Evidence accumulated in recent years points to an essential role played by ligands of allergen proteins in the sensitization phase of allergies. In this regard, we recently identified the natural ligand of Pru p 3, a lipid transfer protein, a major allergen from peach fruit and a model of food allergy. The ligand of Pru p 3 has been shown to play a key role in the sensitization to peach and to other plant food sources that provoke cross-reactivity in a large proportion of patients allergic to peach. However, the question of which is the binding pose of this ligand in its carrier protein, and how it can be transferred to receptors of the immune system where it develops its function as a coadjuvant was not elucidated. In this work, different molecular dynamics simulations have been considered as starting points to study the properties of the ligand⁻protein system in solution. Besides, an energy landscape based on collective variables that describe the process of ligand motion within the cavity of Pru p 3 was obtained by using well-tempered metadynamics. The simulations revealed the differences between distinct binding modes, and also revealed important aspects of the motion of the ligand throughout its carrier protein, relevant to its binding⁻unbinding process. Our findings are potentially interesting for studying protein⁻ligand systems beyond the specific case of the allergen protein dealt with here.

A Comparative Study of Human Saposins.

Saposins are small proteins implicated in trafficking and loading of lipids onto Cluster of Differentiation 1 (CD1) receptor proteins that in turn present lipid antigens to T cells and a variety of T-cell receptors, thus playing a crucial role in innate and adaptive immune responses in humans. Despite their low sequence identity, the four types of human saposins share a similar folding pattern consisting of four helices linked by three conserved disulfide bridges. However, their lipid-binding abilities as well as their activities in extracting, transporting and loading onto CD1 molecules a variety of sphingo- and phospholipids in biological membranes display two striking characteristics: a strong pH-dependence and a structural change between a compact, closed conformation and an open conformation. In this work, we present a comparative computational study of structural, electrostatic, and dynamic features of human saposins based upon their available experimental structures. By means of structural alignments, surface analyses, calculation of pH-dependent protonation states, Poisson-Boltzmann electrostatic potentials, and molecular dynamics simulations at three pH values representative of biological media where saposins fulfill their function, our results shed light into their intrinsic features. The similarities and differences in this class of proteins depend on tiny variations of local structural details that allow saposins to be key players in triggering responses in the human immune system.