Benchmarks in antimicrobial peptide prediction are biased due to the selection of negative data

Antimicrobial peptides (AMPs) are a heterogeneous group of short polypeptides that target not only microorganisms but also viruses and cancer cells. Due to their lower selection for resistance compared with traditional antibiotics, AMPs have been attracting the ever-growing attention from researchers, including bioinformaticians. Machine learning represents the most cost-effective method for novel AMP discovery and consequently many computational tools for AMP prediction have been recently developed. In this article, we investigate the impact of negative data sampling on model performance and benchmarking. We generated 660 predictive models using 12 machine learning architectures, a single positive data set and 11 negative data sampling methods; the architectures and methods were defined on the basis of published AMP prediction software. Our results clearly indicate that similar training and benchmark data set, i.e. produced by the same or a similar negative data sampling method, positively affect model performance. Consequently, all the benchmark analyses that have been performed for AMP prediction models are significantly biased and, moreover, we do not know which model is the most accurate. To provide researchers with reliable information about the performance of AMP predictors, we also created a web server AMPBenchmark for fair model benchmarking. AMP Benchmark is available at http://BioGenies.info/AMPBenchmark

MIRRAGGE – Minimum Information Required for Reproducible AGGregation Experiments

Reports on phase separation and amyloid formation for multiple proteins and aggregation-prone peptides are recurrently used to explore the molecular mechanisms associated with several human diseases. The information conveyed by these reports can be used directly in translational investigation, e.g., for the design of better drug screening strategies, or be compiled in databases for benchmarking novel aggregation-predicting algorithms. Given that minute protocol variations determine different outcomes of protein aggregation assays, there is a strong urge for standardized descriptions of the different types of aggregates and the detailed methods used in their production. In an attempt to address this need, we assembled the Minimum Information Required for Reproducible Aggregation Experiments (MIRRAGGE) guidelines, considering first-principles and the established literature on protein self-assembly and aggregation. This consensus information aims to cover the major and subtle determinants of experimental reproducibility while avoiding excessive technical details that are of limited practical interest for non-specialized users. The MIRRAGGE table (template available in Supplementary Information) is useful as a guide for the design of new studies and as a checklist during submission of experimental reports for publication. Full disclosure of relevant information also enables other researchers to reproduce results correctly and facilitates systematic data deposition into curated databases

New data on the Geology and Geochronology of the Lower Palaeolithic Site Bizat Ruhama in the Southern Levant

The Bizat Ruhama site in the Northern Negev, Israel, has been first excavated in 1996. But geological observation of this site section began in 1995. The results obtained by geological and geophysical methods are discussed in this paper. New paleoma- gnetic data permit to date the cultural layer at Bizat Ruhama at the Matuyama epoch (0,85-0,99 My). Artifacts that belong to the Matuyama epoch differ from the others by their small sizes.Le site de Bizat Ruhama au Nord du Negev, Israel, a été fouillé pour la première fois en 1996, des observations géologiques ayant commencé en 1995. Les résultats obtenus par ces observations et par des méthodes géophysiques sont discutés ici. De nouvelles données paléomagnétiques permettent de dater la couche anthropique mise au jour à Bizat Ruhama de la période Matuyama (0,85-0,99 millions d'années). Les pièces archéologiques trouvées dans les couches répondant à la période Matuyama diffèrent par leurs très petites dimensions des artefacts trouvés dans les autres couches.Laukhin Stanislav A., Ronen A., Pospelova Genrietta Antoninovna, Sharonova Zinaida V., Ranov Vadim Aleksandrovich, Burdukiewicz Jan Michal, Volgina Valerya A., Tsatskin Alexander. New data on the Geology and Geochronology of the Lower Palaeolithic Site Bizat Ruhama in the Southern Levant. In: Paléorient, 2001, vol. 27, n°1. pp. 69-80

MIRRAGGE - Minimum Information Required for Reproducible AGGregation Experiments

Reports on phase separation and amyloid formation for multiple proteins and aggregation-prone peptides are recurrently used to explore the molecular mechanisms associated with several human diseases. The information conveyed by these reports can be used directly in translational investigation, e.g., for the design of better drug screening strategies, or be compiled in databases for benchmarking novel aggregation-predicting algorithms. Given that minute protocol variations determine different outcomes of protein aggregation assays, there is a strong urge for standardized descriptions of the different types of aggregates and the detailed methods used in their production. In an attempt to address this need, we assembled the Minimum Information Required for Reproducible Aggregation Experiments (MIRRAGGE) guidelines, considering first-principles and the established literature on protein self-assembly and aggregation. This consensus information aims to cover the major and subtle determinants of experimental reproducibility while avoiding excessive technical details that are of limited practical interest for non-specialized users. The MIRRAGGE table (template available in Supplementary Information) is useful as a guide for the design of new studies and as a checklist during submission of experimental reports for publication. Full disclosure of relevant information also enables other researchers to reproduce results correctly and facilitates systematic data deposition into curated databases