From Protein Sequence Analysis to 3D Structure Prediction, Confidence Assessment & Research-Ready Structural Interpretation
This three-day, mentor-led hands-on workshop explores AI-powered protein structure prediction and biomolecular interaction analysis using AlphaFold 3 and Google Colab. Participants will gain practical experience in protein sequence identification, 3D structure visualization, confidence assessment, and protein interaction analysis using Python, Biopython, UniProt, and RCSB PDB. Designed for faculty, researchers, postdoctoral scholars, and industry professionals, the workshop develops research-oriented computational skills applicable to structural biology, protein engineering, and drug discovery.
By the end of this workshop, participants will be able to:
Understand the principles of protein structure prediction and the applications of AlphaFold 3 in structural biology.
Identify and retrieve protein sequences and structural information using UniProt and RCSB PDB.
Perform protein sequence analysis using Python and Biopython in Google Colab.
Visualize and interpret three-dimensional protein structures.
Evaluate predicted protein structures using confidence metrics such as pLDDT, PAE, and pTM.
Perform basic structural comparison and protein interaction analysis.
Apply computational structural analysis techniques to research in biotechnology, protein engineering, and drug discovery.
Topics Covered
Protein sequence–structure–function relationships.
AI-based structural biology and the development of AlphaFold models.
Protein identification and functional annotation using UniProt.
Protein sequence retrieval and FASTA preparation.
Experimental structure identification through RCSB PDB.
Accessing available predicted structures from AlphaFold DB.
Protein domain organization and conserved sequence regions.
Introduction to Python-based sequence analysis in Google Colab.
Hands-On: Protein Sequence Analysis Using Google Colab
Participants will build a guided Google Colab notebook to retrieve protein sequences, calculate basic physicochemical properties, inspect available structural information, and visualize a selected protein.
Tools: Google Colab | Python | Biopython | UniProt | RCSB PDB | AlphaFold DB
Expected Output: An annotated protein sequence, physicochemical property summary, available structure information, and an executable research notebook.
Topics Covered
AlphaFold 3 architecture and protein structure prediction principles.
Overview of protein folding and model input requirements.
Preparing sequences for AI-based protein structure prediction.
Understanding pLDDT, PAE, and pTM confidence metrics.
Interpreting high- and low-confidence structural regions.
Downloading and processing predicted molecular structures.
Structural alignment against reference structures.
Introduction to RMSD and TM-score for evaluating structural similarity.
Limitations of AI-predicted protein models.
Hands-On: Computational Protein Structure Evaluation in Google Colab
Participants will use a guided Colab notebook to analyze suitable precomputed or appropriately licensed structural predictions.
Practical Activities
Load a protein structure file in PDB/mmCIF format.
Extract residue-level confidence scores where available.
Visualize protein regions by structural confidence.
Generate confidence distribution plots.
Compare a predicted structure with a reference structure.
Interpret alignment quality and structural deviations.
Tools: Google Colab | Python | Biopython | Matplotlib | Mol* | Licensed Structure Datasets
Expected Output: Three-dimensional protein visualization, confidence plots, structural comparison results, and an evaluation notebook.
Topics Covered
Fundamentals of protein–protein complexes.
Protein interaction interfaces and residue contacts.
AlphaFold 3 capabilities for biomolecular interaction prediction.
Understanding inter-chain PAE and ipTM.
Identifying candidate protein interaction regions.
Structural contact analysis using Python.
Interpreting interface quality and structural uncertainty.
Applications in drug target research and molecular biology.
Reproducibility and scientific reporting of AI-predicted structures.
Hands-On: Protein Interface and Interaction Analysis in Google Colab
Participants will investigate a selected protein complex using appropriately licensed structural data and computational analysis.
Practical Activities
Import a protein complex structure.
Identify protein chains and interface residues.
Calculate candidate inter-chain contacts using Python.
Visualize the predicted or experimentally determined protein interface.
Interpret available model confidence metrics.
Prepare figures and summarize findings in a mini research report.
Tools: Google Colab | Python | Biopython | NumPy | Matplotlib | Mol*
Expected Output: Protein interface analysis, residue-contact summary, annotated structural figures, and a research-ready report.
Live, mentor-led interactive sessions
Lifetime access to recorded sessions
Live mentor support during hands-on activities
e-Certificate upon successful completion
Post-workshop query and technical support
Practical, hands-on learning using real research datasets
Access to workshop study materials and Google Colab notebooks
By the end of this workshop, participants will be able to:
Identify and retrieve protein sequences using UniProt and RCSB PDB.
Analyze protein sequences using Python and Biopython in Google Colab.
Visualize and interpret 3D protein structures using computational tools.
Understand AlphaFold 3-based protein structure prediction and its research applications.
Evaluate protein structure confidence using pLDDT, PAE, and pTM metrics.
Analyze protein–protein interactions and identify potential interaction regions.
Generate structural visualizations, confidence plots, and analysis reports using Google Colab.
Apply AI-assisted structural analysis workflows to research in structural biology, protein engineering, and drug discovery.
Faculty Members & Academicians
PhD Scholars & Postdoctoral Researchers
Biotechnology & Bioinformatics Researchers
Structural & Computational Biologists
Pharmaceutical & Drug Discovery Professionals
Protein Engineering & Industry R&D Scientists
