Scripts for MAP
Explore the computational tools and algorithms designed to unlock the power of the MAP format.
This page outlines our algorithmic pipeline for effectively handling and analyzing protein data in the enriched MAP format. From seamless format conversions to advanced machine learning for property prediction, discover the tools that empower your research.
Jump to: Format Conversion Preprocessing Feature Extraction (mFeatures) Model Training
Format Conversion
Seamlessly transition between the MAP format and other essential bioinformatics formats using our conversion tools. We provide both CLI (Commmand Line Interface) Script and GUI (Graphical User Interface) versions of our conversion tools. The Code/Scripts are designed for high-performance, large-scale conversions, such as those encountered in genome-scale projects. The GUI tools offer a user-friendly interface for smaller datasets and individual conversions.
| Conversion | Description | CLI Script | GUI Script |
|---|---|---|---|
| MAP to FASTA | Convert MAP to basic FASTA, discarding annotations. | 📁 | 📁 |
| PDB to FASTA | Convert PDB file to FASTA format. | 📁 | 📁 |
| PDB to MAP | Convert PDB file to MAP format. (Coming Soon) | 📁 | 📁 |
| MAP to HELM | Convert MAP to HELM notation for structural representation. | 📁 | 📁 |
| HELM to MAP | Convert HELM notation back to MAP format. | 📁 | 📁 |
| SMILES to MAP | Convert SMILES strings to MAP format (where applicable). (Coming Soon) | 📁 | 📁 |
| MAP to SMILES | Convert MAP to SMILES strings (where applicable). | 📁 | 📁 |
MAP YOUR SEQUENCE
The provided Python script is designed to help users annotate protein sequences with various modifications and features, adhering to a specific format. It begins by prompting the user to input a protein sequence. The script then guides the user through a series of interactive steps. First, it gathers protein-level annotations such as organism and function. Next, it allows the user to add residue-level annotations, including a wide range of modifications like phosphorylation, glycosylation, mutations, and insertions. The script displays the sequence, prompts for modification categories, positions, and any specific details related to the chosen modification. Finally, it generates the annotated sequence in the specified format, which can then be saved to a file.
| Conversion | Description | CLI Script | GUI Script |
|---|---|---|---|
| Sequence To MAP | This convet the sequences by adding desired annotations. | 📁 | 📁 |
More details and tools for preprocessing will be available soon.
Feature Extraction (mFeatures)
The MAP format contains comprehensive protein information. Converting it into a rich set of numerical MAP-features (mFeatures) enables effective downstream analysis.
- Extraction of amino acid composition and physicochemical properties, considering modifications.
- Encoding of annotation tags (e.g., PTMs, binding sites) into numerical vectors.
- Context-aware features incorporating information from neighboring residues and annotations.
- Advanced techniques for handling variable-length annotations.
Details on specific feature extraction methods and tools will be released in a future update.
Model Training
Leverage the power of machine learning to predict key protein properties using mFeatures derived from MAP data.
- Supervised learning models for predicting protein function, modification sites, and binding affinities.
- Integration of various machine learning algorithms (e.g., Random Forests, Support Vector Machines, Deep Learning).
- Strategies for handling imbalanced datasets and ensuring model robustness.
- Evaluation metrics and validation procedures for assessing model performance.
This will be released in a future update.