antibody

Antibody Engineering Project Documentation

Project Overview

Title: Antibody Engineering Using Immunized Rabbit Spleen RNA Sequences
Objective: Identify unique antibody sequences, predict their structures, and evaluate their binding potential through docking studies.
Background: This project aims to discover novel antibody sequences from immunized rabbits, leveraging computational tools for extraction, filtering, clustering, and analysis of immunogenic sequences.

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Materials and Methods

Data Acquisition

• Input Data: RNA sequences from immunized rabbit spleens and control samples.
• File Formats: FASTQ/FASTA.
• Source: [Specify source, e.g., sequencing platform].

Tools and Software

Tool/Software	Version	Purpose
TRUST4	[version]	Antibody sequence extraction
MMseqs2	[version]	Clustering and filtering sequences
IgFold	[version]	Antibody structure prediction
HADDOCK3	[name]	Docking studies
Python	[version]	Scripting for automation

Workflow Steps

1. Preprocessing

• Steps to clean and convert RNA sequences for analysis.
• Commands or scripts used for preprocessing.

2. Antibody Identification

• Parameters for running TRUST4 (e.g., alignment thresholds).
• Key outputs: CDR1, CDR2, CDR3 regions.

3. Pseudo-Sequence Generation

• Python script to parse and concatenate CDR regions.
• Example command: python parse_cdrs.py input.fasta output.fasta

4. Clustering and Filtering

• Clustering:

  mmseqs cluster inputDB outputDB tmpDir --min-seq-id 0.9

• Filtering:

  mmseqs createsubdb targetDB inputDB filteredDB

• Explanation of the unique sequence selection process.

5. Structure Prediction

• Parameters and methods for IgFold.
• Input and output examples.

6. Docking Studies

• Docking software setup and scoring metrics.

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Results

Data Outputs

• Initial sequences: [number].
• Filtered sequences: [number].
• Unique sequences: [number].

Clustering Statistics

• Number of clusters formed.
• Size distribution of clusters.

Structural Predictions

• Visualizations of antibody structures.

Docking Results

• Binding scores of top candidates.

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Challenges and Solutions

Challenge	Solution
Computational bottlenecks during clustering	Parallel processing or cloud computing
Ambiguities in CDR extraction	Improved regex patterns and manual validation

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Key Insights

• Total unique sequences: [number].
• Top-ranked candidates based on docking scores.

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Reproducibility

Code and Scripts

• Repository: [GitHub Link]
• Example README file:

  ## Usage
  1. Preprocess sequences: `script1.py`.
  2. Run TRUST4: `trust4 --parameters`.
  3. Cluster sequences: `mmseqs cluster ...`.

Workflow Automation

• Automated pipeline using [Nextflow].
• Include diagram of workflow.

Environment

• Dependencies:

  conda activate trust4 mmseqs2 igfold

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Future Work

• Experimental validation of top candidates.
• Extend study to other species or antibody libraries.
• Incorporate alternative docking algorithms for higher accuracy.

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References

• TRUST4: [cite].
• MMseqs2: [cite].
• IgFold: [cite].

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