Mnova Gears – Affinity Screen (Starting Guide)

Significance of the Topic

Affinity Selection Mass Spectrometry (AS-MS) has become a cornerstone technique in proteomics for detecting and characterizing protein–ligand interactions. Automating AS-MS data processing is crucial to accelerate drug discovery and streamline high‐throughput screening. Manual data handling can be laborious and error-prone; an integrated software solution enhances reproducibility, consistency, and speed of analysis.

Objectives and Study Overview

This guide introduces Affinity Screen, a plugin for Mnova Gears designed to automate the AS-MS workflow. It details the installation of the plugin, configuration of input data and processing parameters, and generation of comprehensive reports and visualizations. The aim is to enable users to identify ligand binders, quantify binding scores, apply quality controls, and review results interactively.

Methodology and Instrumentation

• Software Environment: Mnova MSChrom (version ≥ 15.0) and Mgears (version ≥ 2.5) with valid licenses.
• Plugin Installation: Installed via Mnova Advanced Plug-ins menu or drag-and-drop installer, followed by a Mnova restart.
• Input Configuration: LC–MS data sourced from disk folders; sample grouping via regular expressions or custom scripts to define Bound, Unbound, and Reference classes.
• Compound File: CSV table with columns for sample name, compound name/ID, SMILES, molecular formula, or monoisotopic mass; validated via built-in CSV inspector.
• Data Processing: Optional custom script upload for tailored preprocessing.
• Analysis Settings: Peak detection parameters (area threshold, smoothing, RT tolerance), EIC generation mass tolerance, molecule-match settings, and peak-assignment criteria.
• Scoring Function: Default hits scored as Bound/Unbound ratio; customizable mathematical expressions with optional qualifiers.
• Quality Controls: Automated tests for MS overlap (identical formulas/masses, multiple peaks), missing peaks, and peak-assignment failures, with user-defined thresholds and flag colors.
• Output Options: Generation of Mnova documents, PDF pages per ligand, CSV summary file, and an interactive HTML overview.

Main Results and Discussion

Upon execution, Affinity Screen produces:

• An HTML summary report aggregating total ligands, matches, and hits with graphics and control charts.
• A CSV dataset listing each ligand’s m/z, retention time, peak areas/heights, calculated score, status (Hit/Matched/Not Matched), and any failure flags.
• Mnova and PDF files featuring extracted ion chromatograms for Reference, Bound, and Unbound samples, embedded molecular structures (when SMILES provided), and a concise ligand report.
• Log and settings backup files for traceability.

Results can be inspected through two viewers:

• Mgears Viewer: Visual well-plate layout with colored indicators for hits/matches/controls, tabular ligand lists, and control outcome summaries.
• Affinity Screen Viewer: Detailed ligand panel showing structure, score, status, comments, and peak assignment tables; supports manual peak reassignment and on-the-fly recalculation.

Benefits and Practical Applications

• High‐throughput screening: Automates large datasets, reducing manual intervention.
• Reproducibility: Standardized workflows and saved settings ensure consistent analysis.
• Interactive review: Integrated viewers and editable comments facilitate exception‐based review and decision making.
• Customizable scoring and controls: Tailor analysis criteria to project needs, improving hit confidence.

Future Trends and Possibilities

Advances may include machine‐learning models for peak assignment and hit prediction, tighter integration with cloud databases for real-time data sharing, and expansion to multi‐omics data fusion. Semi-supervised algorithms could refine quality controls and adapt scoring functions dynamically based on historical assay performance.

Conclusion

Affinity Screen offers a robust, automated platform for AS-MS data analysis. By integrating input configuration, processing, scoring, quality controls, and reporting into a single plugin, it accelerates the identification of protein–ligand interactions and supports decision‐making in drug discovery and chemical biology.