Name: AI for Protein Structure Validation and Interpretation
Start: 2026-10-05
End: 2026-10-07

Contact

AI for Protein Structure Validation and Interpretation | Nanoschool

About Workshop

Artificial Intelligence has transformed protein structure prediction through models such as AlphaFold, enabling rapid advances in structural biology and drug discovery. However, ensuring the reliability, confidence, and biological validity of AI-generated protein structures remains a critical challenge. This 3-day international workshop introduces participants to practical methods for validating and interpreting AI-predicted protein structures using structural bioinformatics tools, public protein databases, Explainable AI (XAI), and Google Colab. Through hands-on sessions, participants will learn to assess prediction confidence, evaluate structural quality, and develop trustworthy AI workflows for protein research.

Aim

To provide participants with comprehensive knowledge of trustworthy AI in protein structure prediction by integrating structural bioinformatics, machine learning, confidence assessment, validation techniques, and Explainable AI to improve the reliability and interpretability of computational protein models for biological research and drug discovery.

What Participants Will Learn

Understand the principles of AI-driven protein structure prediction.
Explore AlphaFold confidence metrics and structural quality assessment.
Retrieve and analyze protein structures from public databases.
Perform structural validation using widely used bioinformatics tools.
Compare experimentally determined and AI-predicted protein structures.
Apply Explainable AI (XAI) methods to interpret AI-based biological predictions.
Understand uncertainty estimation and failure modes in protein AI.
Design reproducible validation workflows for structural bioinformatics research.
Explore industrial applications of trustworthy AI in biotechnology and pharmaceutical research.

Structure

📅 Day 1: Foundations of AI-Driven Protein Structure Prediction and Structural Validation

Introduction to protein structure prediction and structural bioinformatics
Evolution of AI in protein structure prediction
AlphaFold, RoseTTAFold, and modern protein foundation models
Protein Data Bank (PDB) and AlphaFold Protein Structure Database
Protein structure representation and confidence metrics (pLDDT, PAE)
Structural quality assessment and validation concepts
Biological interpretation of predicted protein structures
Challenges and limitations of AI-generated protein models

🛠️ Hands-on

Hands-on 1: Exploring AI-Predicted Protein Structures

Participants will retrieve experimentally determined and AI-predicted protein structures from the Protein Data Bank (PDB) and AlphaFold Protein Structure Database, visualize protein structures using Mol* or PyMOL, compare confidence scores (pLDDT and PAE), and identify high-confidence and low-confidence structural regions.

Hands-on 2: Basic Protein Structure Validation

Participants will use publicly available structural validation tools to examine stereochemical quality, structural consistency, residue confidence, and basic geometric validation of protein models and generate a preliminary validation report.

📅 Day 2: Structural Validation, Explainable AI, and Confidence Assessment

Principles of trustworthy AI in structural biology
Confidence estimation and uncertainty quantification
Common failure modes of AI protein prediction
Structural comparison using RMSD and TM-score
Explainable AI (XAI) concepts for biological prediction
Interpreting feature importance in AI-based structural models
Benchmarking AI predictions against experimental structures
Best practices for reproducible protein AI validation

🛠️ Hands-on

Hands-on 1: Comparing Experimental and AI-Predicted Protein Structures

Participants will use Google Colab to compare experimentally solved protein structures with AI-predicted models by calculating structural similarity metrics such as RMSD and TM-score, identifying structural deviations, and interpreting model confidence.

Hands-on 2: Explainable AI for Protein Prediction Assessment

Participants will explore simplified Explainable AI (XAI) techniques to interpret machine learning predictions using protein structural features. They will visualize feature importance, analyze confidence estimates, and investigate potential sources of prediction uncertainty.

📅 Day 3: Trustworthy Protein AI for Drug Discovery and Future Perspectives

AI-assisted functional annotation of proteins
Integrating protein structures with biological pathways and interaction networks
AI in protein engineering and therapeutic target discovery
Trustworthy AI in pharmaceutical research and regulatory science
Foundation Models and Large Language Models (LLMs) in structural biology
FAIR principles, reproducibility, and responsible AI
Emerging trends: Protein language models, multimodal biological AI, autonomous protein design, digital biology, and AI-guided drug discovery

🛠️ Hands-on

Hands-on 1: Protein Function Prediction and Biological Interpretation

Participants will analyze an AI-predicted protein structure alongside protein annotation resources such as UniProt, InterPro, and Pfam to infer protein function, conserved domains, and biological significance.

Hands-on 2: Building a Trustworthy Protein AI Validation Workflow

Participants will develop a complete validation workflow by integrating protein structure retrieval, confidence assessment, structural comparison, functional annotation, and AI-assisted interpretation into a reproducible analysis pipeline using Google Colab and publicly available bioinformatics resources.

Important Dates

Registration Ends

4:30 pm

Workshop Dates

2026-10-05

5:00 pm

What You Will Gain

Outcomes

Understand AI-based protein structure prediction technologies and their limitations.
Retrieve, visualize, and analyze protein structures from public repositories.
Assess prediction confidence using established structural validation metrics.
Compare AI-generated and experimentally determined protein structures.
Apply Explainable AI methods to improve biological interpretation and model transparency.
Design reproducible validation workflows for protein AI research.
Evaluate the reliability of AI-generated protein models before downstream biological experiments.
Explore emerging applications of trustworthy AI in structural biology, biotechnology, and pharmaceutical research.

Who Should Attend

This workshop is designed for undergraduate and postgraduate students, Ph.D. scholars and research fellows, faculty members and academicians, structural biologists, bioinformatics and computational biology researchers, biotechnology and pharmaceutical professionals, AI and machine learning researchers, protein engineering and drug discovery scientists, as well as industry professionals working in structural biology, computational biology, artificial intelligence, and computational drug discovery.