Aim
Protein Structure Prediction and Validation in Structural Biology focuses on techniques to predict and validate protein structures from sequence data. Learn computational methods for structure prediction, model validation, and their applications in drug design, enzyme engineering, and protein engineering.
Program Objectives
- Protein Structure Basics: Primary, secondary, tertiary, and quaternary structure concepts.
- Prediction Methods: Homology modeling, ab initio prediction, and threading techniques.
- Validation Techniques: RMSD, Ramachandran plot, and other quality assessment metrics.
- Applications: Drug design, enzyme engineering, and understanding protein function.
- Advanced Topics: AlphaFold, deep learning in structure prediction, and computational challenges.
- Capstone: Predict and validate a protein structure for a biological target.
Program Structure
Module 1: Introduction to Protein Structure
- Levels of protein structure: primary to quaternary.
- Protein folding: thermodynamics and kinetics.
- Conformational flexibility and dynamics.
- Importance of structure in biological function.
Module 2: Computational Protein Structure Prediction
- Homology modeling: sequence alignment and template search.
- Ab initio methods: energy-based modeling and fragment assembly.
- Threading techniques: template-free prediction.
- Tools and software: MODELLER, I-TASSER, Rosetta (overview).
Module 3: Model Validation and Quality Assessment
- RMSD (Root Mean Square Deviation) and model comparison.
- Ramachandran plot: assessing backbone conformation.
- Validation software: ProSa, VERIFY3D, MolProbity (overview).
- Assessing model reliability and accuracy.
Module 4: Advanced Structure Prediction Methods
- AlphaFold: revolutionizing protein structure prediction.
- Deep learning techniques in structural biology.
- Challenges in predicting multi-chain complexes and membrane proteins.
- Hybrid methods combining experimental and computational data.
Module 5: Protein-Drug Interaction Prediction
- Understanding protein-ligand binding sites.
- Docking simulations: virtual screening for drug discovery.
- Binding affinity prediction: molecular dynamics and free energy calculations.
- Applications in drug design: targeted therapies and enzyme inhibitors.
Module 6: Structural Bioinformatics Tools
- Protein structure visualization: PyMOL, Chimera, VMD (overview).
- Sequence alignment and database searching: BLAST, UniProt.
- Protein structure databases: PDB, ModBase.
- Software for prediction and validation: usage tips and best practices.
Module 7: Protein Engineering Applications
- Engineering enzymes with enhanced stability and activity.
- Designing novel proteins and functionalized nanomaterials.
- Applications in biotechnology and synthetic biology.
- Therapeutic protein design: optimizing binding properties and efficacy.
Module 8: Future Directions in Protein Structure Prediction
- Integrating AI and machine learning into structural prediction.
- Challenges in predicting protein-protein interactions and complex structures.
- Next-gen methods for structure-guided drug design and biomarker identification.
- Impact of structure prediction on personalized medicine.
Final Project
- Predict the structure of a protein of interest.
- Validate the model using appropriate tools and metrics.
- Provide biological context, possible drug design applications, and structural insights.
- Submit a project report with visualizations and analysis.
Participant Eligibility
- Students and professionals in Molecular Biology, Bioinformatics, Biochemistry, Biotechnology
- Researchers interested in computational drug discovery and protein engineering
- Basic knowledge of molecular biology and structural biology required
Program Outcomes
- Understand protein structure prediction methods and tools.
- Perform protein modeling and validate predicted structures.
- Apply computational techniques to drug discovery and enzyme design.
- Generate reports with structure predictions, validations, and biological insights.
Program Deliverables
- e-LMS Access: lessons, case studies, templates, and tools.
- Toolkit: structure prediction guide, validation templates, analysis tools.
- Capstone Support: feedback on project design and results.
- Assessment: certification after final project submission.
- e-Certification and e-Marksheet: digital credentials upon completion.
Future Career Prospects
- Computational Biologist
- Structural Bioinformatician
- Drug Discovery Scientist
- Protein Engineer
Job Opportunities
- Biotech/Pharma: computational biology and drug discovery teams.
- Academic/Research Institutes: structural biology research groups.
- Contract Research Organizations (CROs): structure-based drug design services.
- Healthcare Industry: biomarker discovery, diagnostic tools development.
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