Protein Misfolding in Neurodegenerative Diseases: Computational Biology, Biomarkers & Drug Discovery
Explore computational approaches to decode protein misfolding mechanisms, identify disease biomarkers, and discover potential therapeutic strategies for neurodegenerative disorders.
About This Course
This 3-day mentor-based workshop provides an introduction to computational approaches used for understanding protein misfolding and its role in neurodegenerative diseases. Participants will explore protein structure analysis, molecular mechanisms of aggregation, biomarker identification from biological datasets, and computational drug discovery workflows. Through hands-on demonstrations using bioinformatics, structural biology, and molecular modeling tools, participants will understand how computational methods support modern neurodegenerative disease research.
Aim
The aim of this workshop is to provide participants with a comprehensive understanding of computational strategies used to study protein misfolding, identify molecular biomarkers, and explore therapeutic discovery approaches for neurodegenerative diseases.
Workshop Objectives
By the end of this workshop, participants will be able to:
- Understand the molecular mechanisms of protein misfolding and aggregation in neurodegenerative disorders.
- Analyze disease-associated proteins including Amyloid-β, Tau, α-Synuclein, and TDP-43 using computational approaches.
- Explore protein sequences, structures, mutations, and aggregation-related features using bioinformatics resources.
- Apply computational biology workflows for biomarker discovery and molecular signature analysis.
- Perform pathway enrichment and protein–protein interaction analysis to understand disease mechanisms.
- Understand structure-based drug discovery approaches including target analysis, molecular docking, and compound screening.
- Explore how computational tools support therapeutic research in neurodegenerative diseases.
Workshop Structure
Day 1: Protein Misfolding Mechanisms & Computational Structural Analysis
Focus: Understanding the molecular basis of protein misfolding and aggregation in neurodegenerative disorders.
Topics Covered
- Introduction to protein misfolding and its role in neurodegenerative diseases
- Understanding key disease-associated proteins:
- Amyloid-β in Alzheimer’s disease
- Tau protein in tauopathies
- α-Synuclein in Parkinson’s disease
- TDP-43 in ALS and related disorders
- Protein sequence, domain and functional region analysis
- Structural analysis of native and disease-associated protein conformations
- Identification of aggregation-prone regions and disease-associated mutations
- Understanding protein stability, folding abnormalities, and aggregation mechanisms
Hands-On Components
- Retrieve and analyze protein sequences and structures
- Explore predicted and experimental protein structures
- Visualize structural changes associated with misfolding
- Analyze mutation effects and aggregation-related regions
Tools:
AlphaFold DB | PDB | UniProt | PyMOL | ChimeraX
Day 2: Computational Biomarker Discovery & Neurodegenerative Disease Data Analysis
Focus: Applying computational biology approaches to identify molecular signatures associated with neurodegenerative diseases.
Topics Covered
- Introduction to biomarker discovery in Alzheimer’s, Parkinson’s, and related disorders
- Exploration of publicly available transcriptomics and molecular datasets
- Gene and protein expression analysis
- Identification of disease-associated molecular signatures
- Functional enrichment analysis to understand affected biological pathways
- Protein–protein interaction network analysis
- Biological interpretation of computational findings
Hands-On Components
- Explore disease-specific datasets from public repositories
- Analyze molecular expression patterns
- Identify potential biomarkers
- Construct and interpret molecular interaction networks
Tools:
GEO | STRING | Cytoscape | Enrichr | Gene Ontology | Python Workflows
Day 3: Computational Drug Discovery & Therapeutic Target Analysis
Focus: Exploring computational strategies for identifying therapeutic targets and potential drug candidates.
Topics Covered
- Understanding therapeutic targets involved in protein misfolding disorders
- Target identification and structural characterization
- Protein–ligand interaction analysis
- Introduction to structure-based drug discovery approaches
- Virtual screening and compound prioritization
- Drug repurposing strategies for neurodegenerative diseases
- Computational approaches for evaluating therapeutic candidates
Hands-On Components
- Analyze target proteins and ligand interactions
- Explore chemical databases for potential compounds
- Perform molecular docking workflow demonstration
- Interpret drug–target binding interactions
Tools:
AlphaFold DB | PubChem | ChEMBL | DrugBank | AutoDock | CB-Dock | PyMOL
Who Should Enrol?
- PhD Scholars and Researchers in Biotechnology, Bioinformatics, Computational Biology, Neuroscience, Molecular Biology, and Biomedical Sciences.
- Master’s students (M.Sc./M.Tech./MS) in Life Sciences, Biotechnology, Bioinformatics, and related disciplines.
- Faculty members and academic professionals interested in computational biomedical research.
- Research professionals working in structural biology, omics analysis, and drug discovery.
- Students and professionals interested in applying computational approaches to disease research.
Important Dates
Registration Ends
September 30, 2026
IST 4:30 PM
Workshop Dates
September 30, 2026 – October 2, 2026
IST 5:00 PM
Workshop Outcomes
- Interpret structural changes associated with protein misfolding and aggregation.
- Use computational tools for protein structure visualization and analysis.
- Analyze biological datasets to identify potential disease-associated biomarkers.
- Understand molecular networks and pathways involved in neurodegenerative disorders.
- Explore computational workflows for therapeutic target identification.
- Understand the fundamentals of virtual screening, molecular docking, and drug repurposing approaches.
- Apply basic computational strategies for research projects in neurobiology, bioinformatics, and drug discovery.
Fee Structure
Student Fee
₹2499 | $65
Ph.D. Scholar / Researcher Fee
₹3499 | $75
Academician / Faculty Fee
₹4499 | $85
Industry Professional Fee
₹5499 | $105
What You’ll Gain
- Live & recorded sessions
- e-Certificate upon completion
- Post-workshop query support
- Hands-on learning experience
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