A new approach to estimate soil organic carbon content targets in European croplands topsoils.

Adopting land management practices that increase the stock of soil organic carbon (SOC) in croplands is widely promoted as a win-win strategy to enhance soil health and mitigate climate change. In this context, the definition of reference SOC content and stock values is needed to provide reliable targets to farmers, policymakers, and stakeholders. In this study, we used the LUCAS dataset to compare different methods for evaluating reference SOC content and stock values in European croplands topsoils (0-20 cm depth). Methods gave generally similar estimates although being built on very different assumptions. In the absence of an objective criterion to establish which approach is the most suitable to determine SOC reference values, we propose an ensemble modelling approach that consists in extracting the estimates using different relevant methods and retaining the median value among them. Interestingly, this approach led us to select values from the three different approaches with similar frequencies. Using estimated bulk density values, we obtained a first rough estimate of 3.5 Gt C of SOC storage potential in the cropland topsoils that we interpret as a long-term aspirational target that would be reachable only under extreme changes in agricultural practices. The use of additional methods in the ensemble modelling approach and more valid statistical spatial estimates may further refine our approach designed for the estimation of SOC reference values for croplands.

GCAT: A network model of mutational influences between amino acid positions in PSD95pdz3.

Proteins exist for more than 3 billion years: proof of a sustainable design. They have mechanisms coping with internal perturbations (e.g., amino acid mutations), which tie genetic backgrounds to diseases or drug therapy failure. One difficulty to grasp these mechanisms is the asymmetry of amino acid mutational impact: a mutation at position i in the sequence, which impact a position j does not imply that the mutation at position j impacts the position i. Thus, to distinguish the influence of the mutation of i on j from the influence of the mutation of j on i, position mutational influences must be represented with directions. Using the X ray structure of the third PDZ domain of PDS-95 (Protein Data Bank 1BE9) and in silico mutations, we build a directed network called GCAT that models position mutational influences. In the GCAT, a position is a node with edges that leave the node (out-edges) for the influences of the mutation of the position on other positions and edges that enter the position (in-edges) for the influences of the mutation of other positions on the position. 1BE9 positions split into four influence categories called G, C, A and T going from positions influencing on average less other positions and influenced on average by less other positions (category C) to positions influencing on average more others positions and influenced on average by more other positions (category T). The four categories depict position neighborhoods in the protein structure with different tolerance to mutations.

A protocol to measure slow protein dynamics of the cholera toxin B pentamers using broadband dielectric spectroscopy.

The present protocol describes how to measure experimentally the slow protein dynamics that take place upon the thermal unfolding of the B subunit cholera toxin pentamers using broadband dielectric spectroscopy (BDS) in weakly hydrated and nanoconfined conditions. Transient unfolding intermediates, rarely identified otherwise, are revealed thanks to the B subunit's remarkable heat resistance up to 180°C and distinct molecular dynamics. The frequencies detected experimentally are consistent with the spatiotemporal scales of motions of molecular dynamics simulation. For complete details on the use and execution of this protocol, please refer to Bourgeat et al. (2021, 2019).

A computational methodology to diagnose sequence-variant dynamic perturbations by comparing atomic protein structures.

MOTIVATION: The objective is to diagnose dynamics perturbations caused by amino-acid mutations as prerequisite to assess protein functional health or drug failure, simply using network models of protein X-ray structures. RESULTS: We find that the differences in the allocation of the atomic interactions of each amino acid to 1D, 2D, 3D, 4D structural levels between variants structurally robust, recover experimental dynamic perturbations. The allocation measure validated on two B-pentamers variants of AB5 toxins having 17 mutations, also distinguishes dynamic perturbations of pathogenic and non-pathogenic Transthyretin single-mutants. Finally, the main proteases of the coronaviruses SARS-CoV and SARS-CoV-2 exhibit changes in the allocation measure, raising the possibility of drug failure despite the main proteases structural similarity. AVAILABILITY AND IMPLEMENTATION: The Python code used for the production of the results is available at github.com/lorpac/protein_partitioning_atomic_contacts. The authors will run the analysis on any PDB structures of protein variants upon request. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Mapping Function from Dynamics: Future Challenges for Network-Based Models of Protein Structures.

Proteins fulfill complex and diverse biological functions through the controlled atomic motions of their structures (functional dynamics). The protein composition is given by its amino-acid sequence, which was assumed to encode the function. However, the discovery of functional sequence variants proved that the functional encoding does not come down to the sequence, otherwise a change in the sequence would mean a change of function. Likewise, the discovery that function is fulfilled by a set of structures and not by a unique structure showed that the functional encoding does not come down to the structure either. That leaves us with the possibility that a set of atomic motions, achievable by different sequences and different structures, encodes a specific function. Thanks to the exponential growth in annual depositions in the Protein Data Bank of protein tridimensional structures at atomic resolutions, network models using the Cartesian coordinates of atoms of a protein structure as input have been used over 20 years to investigate protein features. Combining networks with experimental measures or with Molecular Dynamics (MD) simulations and using typical or ad-hoc network measures is well suited to decipher the link between protein dynamics and function. One perspective is to consider static structures alone as alternatives to address the question and find network measures relevant to dynamics that can be subsequently used for mining and classification of dynamic sequence changes functionally robust, adaptable or faulty. This way the set of dynamics that fulfill a function over a diversity of sequences and structures will be determined.

Experimental diagnostic of sequence-variant dynamic perturbations revealed by broadband dielectric spectroscopy.

Genetic diversity leads to protein robustness, adaptability, and failure. Some sequence variants are structurally robust but functionally disturbed because mutations bring the protein onto unfolding/refolding routes resulting in misfolding diseases (e.g., Parkinson). We assume dynamic perturbations introduced by mutations foster the alternative unfolding routes and test this possibility by comparing the unfolding dynamics of the heat-labile enterotoxin B pentamers and the cholera toxin B pentamers, two pentamers structurally and functionally related and robust to 17 sequence variations. The B-subunit thermal unfolding dynamics are monitored by broadband dielectric spectroscopy in nanoconfined and weakly hydrated conditions. Distinct dielectric signals reveal the different B-subunits unfolding dynamics. Combined with network analyses, the experiments pinpoint the role of three mutations A1T, E7D, and E102A, in diverting LTB5 to alternative unfolding routes that protect LTB5 from dissociation. Altogether, the methodology diagnoses dynamics faults that may underlie functional disorder, drug resistance, or higher virulence of sequence variants.

Exploring Allosteric Pathways of a V-Type Enzyme with Dynamical Perturbation Networks.

Elucidation of the allosteric pathways in proteins is a computational challenge that strongly benefits from combination of atomistic molecular dynamics (MD) simulations and coarse-grained analysis of the complex dynamical network of chemical interactions based on graph theory. Here, we introduce and assess the performances of the dynamical perturbation network analysis of allosteric pathways in a prototypical V-type allosteric enzyme. Dynamical atomic contacts obtained from MD simulations are used to weight the allosteric protein graph, which involves an extended network of contacts perturbed by the effector binding in the allosteric site. The outcome showed good agreement with previously reported theoretical and experimental extended studies and it provided recognition of new potential allosteric spots that can be exploited in future mutagenesis experiments. Overall, the dynamical perturbation network analysis proved to be a powerful computational tool, complementary to other network-based approaches that can assist the full exploitation of allosteric phenomena for advances in protein engineering and rational drug design.

In proteins, the structural responses of a position to mutation rely on the Goldilocks principle: not too many links, not too few.

A disease has distinct genetic and molecular hallmarks such as sequence variants that are likely to produce the alternative protein structures accountable for individual responses to drugs and disease development. Thus, to set up customized therapies, the structural influences of amino acids on one another need to be tracked down. Using network-based models and classical analysis of amino acid and atomic packing in protein structures, the influence of first shell neighbors on the structural fate of a position upon mutation, is revisited. Regardless of the type and position in a structure, amino acids satisfy on average over their neighbors, a low and similar number of atomic interactions, the average called the neighborhood watch (Nw). The structural tolerance of a position to mutation depends on the modulation of the composition and/or proximity of neighbors to maintain the same Nw, before and after mutation, at every position. Changes, upon mutation of the number of atomic interactions at the level of individual pairs (wij) are structurally tolerated but influence structural dynamics. Robust, fragile and rescue interactions can be identified with Nw and wij, offering a framework to classify sequence variants according to position-dependent structural changes.