MOTIVATION: Atomic Force Microscopy-based Infrared spectroscopy (AFM-IR) is a novel and innovative method for label-free high-resolution structural biology. However, the nature of the data files generated by AFM-IR instruments precludes investigation by conventional open-source scientific image analysis software suites. As a result, reporting of AFM-IR datasets is not standardized and the data itself is difficult to audit. RESULTS: We have developed a web application that allows anyone to open, review, and audit raw AFM-IR data files easily and without deep knowledge of the method. It also exposes all metadata recorded by the microscope at the time of measurement. The web application is based on a Python package that supports custom data analyses within the scientific Python ecosystem. This tool provides an accessible, transparent solution for AFM-IR data review, with the potential to support reproducibility and standardization in AFM-IR research and encourage wider adoption of this innovative spectroscopy method. AVAILABILITY AND IMPLEMENTATION: The web app is hosted at https://anasys-python-tools-gui.streamlit.app. Its source code is listed at https://github.com/wduverger/anasys-python-tools-gui. The underlying Python package is available at https://github.com/GeorgRamer/anasys-python-tools and can be installed using pip.

MOTIVATION: Proteins, the molecular workhorses of biological systems, execute a multitude of critical functions dictated by their precise three-dimensional structures. In a complex and dynamic cellular environment, proteins can undergo misfolding, leading to the formation of aggregates that take up various forms, including amorphous and ordered aggregation in the shape of amyloid fibrils. This phenomenon is closely linked to a spectrum of widespread debilitating pathologies, such as Alzheimer's disease, Parkinson's disease, type-II diabetes, and several other proteinopathies, but also hampers the engineering of soluble agents, as in the case of antibody development. As such, the accurate prediction of aggregation propensity within protein sequences has become pivotal due to profound implications in understanding disease mechanisms, as well as in improving biotechnological and therapeutic applications. RESULTS: We previously developed Cordax, a structure-based predictor that utilizes logistic regression to detect aggregation motifs in protein sequences based on their structural complementarity to the amyloid cross-beta architecture. Here, we present a dedicated web server interface for Cordax. This online platform combines several features including detailed scoring of sequence aggregation propensity, as well as 3D visualization with several customization options for topology models of the structural cores formed by predicted aggregation motifs. In addition, information is provided on experimentally determined aggregation-prone regions that exhibit sequence similarity to predicted motifs, scores, and links to other predictor outputs, as well as simultaneous predictions of relevant sequence propensities, such as solubility, hydrophobicity, and secondary structure propensity. AVAILABILITY AND IMPLEMENTATION: The Cordax webserver is freely accessible at https://cordax.switchlab.org/.

SUMMARY: Amyloid polymorphism is emerging as a key property that is differentially linked to various conformational diseases, including major neurodegenerative disorders, but also as a feature that potentially relates to complex structural mechanisms mediating transmissibility barriers and selective vulnerability of amyloids. In response to the rapidly expanding number of amyloid fibril structures formed by full-length proteins, we here have developed StAmP-DB, a public database that supports the curation and cross-comparison of experimentally determined three-dimensional amyloid polymorph structures. AVAILABILITY AND IMPLEMENTATION: StAmP-DB is freely accessible for queries and downloads at https://stamp.switchlab.org.