Electrostatic interactions are essential for controlling the protein structure and function. Whereas so far experimental and theoretical efforts focused on the effect of local electrostatics, this work aims at elucidating the long-range modulation of electric fields in proteins upon binding to charged surfaces. The study is based on cytochrome c (Cytc) variants carrying nitrile reporters for the vibrational Stark effect that are incorporated into the protein via genetic engineering and chemical modification. The Cytc variants were thoroughly characterized with respect to possible structural perturbations due to labeling. For the proteins in solution, the relative hydrogen bond occupancy and the calculated electric fields, both obtained from molecular dynamics (MD) simulations, and the experimental nitrile stretching frequencies were used to develop a relationship for separating hydrogen-bonding and non-hydrogen-bonding electric field effects. This relationship provides an excellent description for the stable Cytc variants in solution. For the proteins bound to Au electrodes coated with charged self-assembled monolayers (SAMs), the underlying MD simulations can only account for the electric field changes Δ E due to the formation of the electrostatic SAM-Cytc complexes but not for the additional contribution, Δ E, representing the consequences of the potential drops over the electrode/SAM/protein interfaces. Both Δ E and Δ E, determined at distances between 20 and 30 Å with respect to the SAM surface, are comparable in magnitude to the non-hydrogen-bonding electric field in the unbound protein. This long-range modulation of the internal electric field may be of functional relevance for proteins in complexes with partner proteins (Δ E) and attached to membranes (Δ E + Δ E).

With the ongoing cost decrease of genotyping and sequencing technologies, accurate and fast phenotyping remains the bottleneck in the utilizing of plant genetic resources for breeding and breeding research. Although cost-efficient high-throughput phenotyping platforms are emerging for specific traits and/or species, manual phenotyping is still widely used and is a time- and money-consuming step. Approaches that improve data recording, processing or handling are pivotal steps towards the efficient use of genetic resources and are demanded by the research community. Therefore, we developed PhenoApp, an open-source Android app for tablets and smartphones to facilitate the digital recording of phenotypical data in the field and in greenhouses. It is a versatile tool that offers the possibility to fully customize the descriptors/scales for any possible scenario, also in accordance with international information standards such as MIAPPE (Minimum Information About a Plant Phenotyping Experiment) and FAIR (Findable, Accessible, Interoperable, and Reusable) data principles. Furthermore, PhenoApp enables the use of pre-integrated ready-to-use BBCH (Biologische Bundesanstalt für Land- und Forstwirtschaft, Bundessortenamt und CHemische Industrie) scales for apple, cereals, grapevine, maize, potato, rapeseed and rice. Additional BBCH scales can easily be added. The simple and adaptable structure of input and output files enables an easy data handling by either spreadsheet software or even the integration in the workflow of laboratory information management systems (LIMS). PhenoApp is therefore a decisive contribution to increase efficiency of digital data acquisition in genebank management but also contributes to breeding and breeding research by accelerating the labour intensive and time-consuming acquisition of phenotyping data.

No abstract

With the ongoing cost decrease of genotyping and sequencing technologies, accurate and fast phenotyping remains the bottleneck in the utilizing of plant genetic resources for breeding and breeding research. Although cost-efficient high-throughput phenotyping platforms are emerging for specific traits and/or species, manual phenotyping is still widely used and is a time- and money-consuming step. Approaches that improve data recording, processing or handling are pivotal steps towards the efficient use of genetic resources and are demanded by the research community. Therefore, we developed PhenoApp, an open-source Android app for tablets and smartphones to facilitate the digital recording of phenotypical data in the field and in greenhouses. It is a versatile tool that offers the possibility to fully customize the descriptors/scales for any possible scenario, also in accordance with international information standards such as MIAPPE (Minimum Information About a Plant Phenotyping Experiment) and FAIR (Findable, Accessible, Interoperable, and Reusable) data principles. Furthermore, PhenoApp enables the use of pre-integrated ready-to-use BBCH (Biologische Bundesanstalt für Land- und Forstwirtschaft, Bundessortenamt und CHemische Industrie) scales for apple, cereals, grapevine, maize, potato, rapeseed and rice. Additional BBCH scales can easily be added. The simple and adaptable structure of input and output files enables an easy data handling by either spreadsheet software or even the integration in the workflow of laboratory information management systems (LIMS). PhenoApp is therefore a decisive contribution to increase efficiency of digital data acquisition in genebank management but also contributes to breeding and breeding research by accelerating the labour intensive and time-consuming acquisition of phenotyping data.

No abstract