Cabral, P. | bioRxiv Science

bioRxiv2025

PDBCleanV2: A Python Library for Generating Consistent Structure Datasets

The number of structures in the Protein Data Bank has grown rapidly in recent years. Nonetheless, comparing structures of a specific system often proves challenging due to variant nomenclature, origins from different species, presence of various ligands, and missing atoms or chains. To address these issues, we have developed PDBCleanV2, a Python package that enables users to create consistent datasets of structures, simplifying comparison among structures. This library generates individual files for each molecule in a structure file, corrects labeling errors, and standardizes chain names and numbering. Our aim is to provide researchers with consistent datasets that streamline their analysis. The source code, installation instructions, and tutorials can be found in PDBCleanV2s GitHub repository and Zenodo accessible at https://github.com/fatipardo/PDBClean-0.0.2/ and https://doi.org/10.5281/zenodo.14014241, respectively.

bioinformatics↗

bioRxiv2021

Water yield modelling, sensitivity analysis and validation: a study for Portugal

The spatially explicit assessment of freshwater is key to introduce the ecosystem service (ES) concept into decision-making processes. Many tools are being developed to model water balance and to analyse the effects of meteorological conditions on water ES behaviours at multiple spatial scales. Nevertheless, there is a lack of studies that analyse the sensitivity of these models and estimate the models accuracy. The current research uses the InVEST Water Yield Model (WYM) to assess freshwater ES between 1990 and 2018 at River Basin District (RBD) level in mainland Portugal. The methodology included sensitivity analysis simulations to test different parameters of the model over two different sources of meteorological data. We validated the model with the European Environment Agency (EEA) database on the quantity of Europes freshwater resources. To evaluate the models sensitivity, Pearsons correlation coefficients and statistical methods were calculated for each simulation. The model which obtained the best performance in the sensitivity tests was retained for further analysis and calibration and its accuracy was assessed by comparing the mean estimated water yield (WY) with mean observed values for 2018. Results at the national level show a correlation coefficient of 0.803 with statistical significance for 0.01 one-tail. The WY in the RBDs of the North of the country was underestimated by 56.5 mm/ha/year and for the RBDs in the South of Portugal, the WY was overestimated by 58.1 mm/ha/year. This difference was explained through the spatial-temporal assessment of the main climatic variables used as input. This study contributes to a methodology to assess the level of confidence in WYM outputs and can be used to support the trustworthiness of water availability studies at sub-watershed, watershed, or RBD, using open access data and software.

ecology↗