Improving the iterative Linear Interaction Energy approach using automated recognition of configurational transitions

Recently an iterative method was proposed to enhance the accuracy and efficiency of ligand-protein binding affinity prediction through linear interaction energy (LIE) theory. For ligand binding to flexible Cytochrome P450s (CYPs), this method was shown to decrease the root-mean-square error and standard deviation of error prediction by combining interaction energies of simulations starting from different conformations. Thereby, different parts of protein-ligand conformational space are sampled in parallel simulations. The iterative LIE framework relies on the assumption that separate simulations explore different local parts of phase space, and do not show transitions to other parts of configurational space that are already covered in parallel simulations. In this work, a method is proposed to (automatically) detect such transitions during the simulations that are performed to construct LIE models and to predict binding affinities. Using noise-canceling techniques and splines to fit time series of the raw data for the interaction energies, transitions during simulation between different parts of phase space are identified. Boolean selection criteria are then applied to determine which parts of the interaction energy trajectories are to be used as input for the LIE calculations. Here we show that this filtering approach benefits the predictive quality of our previous CYP 2D6-aryloxypropanolamine LIE model. In addition, an analysis is performed of the gain in computational efficiency that can be obtained from monitoring simulations using the proposed filtering method and by prematurely terminating simulations accordingly.     

Functional composition of plant communities determines the spatial and temporal stability of soil microbial properties in a long‐term plant diversity experiment

Stable provisioning of ecosystem functions and services is crucial for human well‐being in a changing world. Two essential ecological components driving vital ecosystem functions in terrestrial ecosystems are plant diversity and soil microorganisms. In this study, we tracked soil microbial basal respiration and biomass over a time period of 12 years in a grassland biodiversity experiment (the Jena Experiment) and examined the role of plant diversity and plant functional group composition for the spatial and temporal stability of soil microbial properties (basal respiration and biomass) in bulk‐soil. Spatial and temporal stability were calculated as the inverse coefficient of variation (CV^?1) of soil microbial respiration and biomass measured from soil samples taken over space and time, respectively. We found that 1) plant species richness consistently increased soil microbial properties after a time lag of four years since the establishment of the experimental plots, 2) plant species richness had minor effects on the spatial stability of soil microbial properties, whereas 3) the functional composition of plant communities significantly affected spatial stability of soil microbial properties, with legumes and tall herbs reducing both the spatial stability of microbial respiration and biomass, while grasses increased the latter, and 4) the effect of plant diversity on temporal stability of soil microbial properties turned from being negative to neutral, suggesting that the recovery of soil microbial communities from former arable land‐use takes more than a decade. Our results highlight the importance of plant functional group composition for the spatial and temporal stability of soil microbial properties, and hence for microbially‐driven ecosystem processes, such as decomposition and element cycling, in temperate semi‐natural grassland.     

Impact of mammalian cell culture conditions on monoclonal antibody charge heterogeneity: an accessory monitoring tool for process development

Recombinant monoclonal antibodies are predominantly produced in mammalian cell culture bioprocesses. Post-translational modifications affect the micro-heterogeneity of the product and thereby influence important quality attributes, such as stability, solubility, pharmacodynamics and pharmacokinetics. The analysis of the surface charge distribution of monoclonal antibodies provides aggregated information about these modifications. In this work, we established a direct injection pH gradient cation exchange chromatography method, which determines charge heterogeneity from cell culture supernatant without any purification steps. This tool was further applied to monitor processes that were performed under certain process conditions. Concretely, we were able to provide insights into charge variant formation during a fed-batch process of a Chinese hamster ovary cell culture, in turn producing a monoclonal antibody under varying temperatures and glucose feed strategies. Glucose concentration impacted the total emergence of acidic variants, whereas the variation of basic species was mainly dependent on process temperature. The formation rates of acidic species were described with a second-order reaction, where a temperature increase favored the conversion. This platform method will aid as a sophisticated optimization tool for mammalian cell culture processes. It provides a quality fingerprint for the produced mAb, which can be tested, compared to the desired target and confirmed early in the process chain.

     

Resilience of ecosystem processes: a new approach shows that functional redundancy of biological control services is reduced by landscape simplification

Functional redundancy can increase the resilience of ecosystem processes by providing insurance against species loss and the effects of abundance fluctuations. However, due to the difficulty of assessing individual species’ contributions and the lack of a metric allowing for a quantification of redundancy within communities, few attempts have been made to estimate redundancy for individual ecosystem processes. We present a new method linking interaction metrics with metabolic theory that allows for a quantification of redundancy at the level of ecosystem processes. Using this approach, redundancy in the predation on aphids and other prey by natural enemies across a landscape heterogeneity gradient was estimated. Functional redundancy of predators was high in heterogeneous landscapes, low in homogeneous landscapes and scaled with predator specialisation. Our approach allows quantifying functional redundancy within communities and can be used to assess the role of functional redundancy across a wide variety of ecosystem processes and environmental factors.