School of Bioinformatics · Structural biology & drug discovery
Protein Structure Prediction & Modelling
Predicted structures are now available for almost every known protein. This course teaches how prediction works, how to run it, and, most importantly, how to read a model’s confidence before using it for biology or drug design.
Course overview
Protein Structure Prediction & Modelling starts with protein structure itself: secondary structure, domains, the PDB and experimental methods. It then explains template-based modelling and deep-learning prediction, and what multiple sequence alignments contribute.
You run AlphaFold through ColabFold, interpret pLDDT and PAE, model complexes, validate models with stereochemical and agreement metrics, and compare structures by superposition. The capstone asks you to decide whether a model is good enough for a specific use.
Learning outcomes
By the end of the course you will be able to:
- Describe protein structure hierarchy and how structures are determined
- Build homology models and run AlphaFold or ColabFold predictions
- Interpret pLDDT and PAE and identify unreliable regions
- Validate models with MolProbity-style checks
- Compare structures with RMSD and TM-score
Skills you will practise
Eligibility and prerequisites
Who can apply
- Students and graduates of B.Sc., M.Sc., B.Tech., M.Tech., B.Pharm., M.Pharm., MBBS, BDS, B.V.Sc. or a PhD programme in the life sciences, biotechnology, bioinformatics, pharmacy, medicine, agriculture, chemistry, statistics or computer science
- Researchers, faculty and industry professionals moving into data-driven biology
- Final-year students may apply before their results are declared
Prerequisites
- Bioinformatics Foundations or equivalent
- Undergraduate biochemistry
Best suited to
- Biochemists, biotechnologists and pharmacy students
- Researchers planning mutagenesis or docking
- Students preparing for structural bioinformatics roles
Module-wise syllabus
6 modules. Each pairs instructor-led sessions with a hands-on lab on public data.
Topics
- Secondary, tertiary and quaternary structure
- X-ray, cryo-EM and NMR
- The PDB and AlphaFold DB
Topics
- Template search and alignment
- Modelling with SWISS-MODEL and MODELLER
- Loop and side-chain modelling
Topics
- MSAs and coevolution
- How AlphaFold reasons about structure
- Running ColabFold
Topics
- pLDDT and PAE
- Ramachandran and clash analysis
- Disordered regions
Topics
- Multimer prediction and ipTM
- Superposition, RMSD and TM-score
- Structure-based function inference
Topics
- A use case
- Modelling
- Recommendation
Practical sessions and projects
Capstone project
Is this model good enough?
Predict and validate the structure of a target protein and argue, with confidence metrics, whether it can support a specific experiment or docking campaign.
Assessed on the correctness of the analysis, the quality of the code and the reasoning in the write-up. A pass is required for the certificate.
- Protein structure essentials
Compare an experimental structure and its AlphaFold DB model and list where they differ.
- Template-based modelling
Build homology models from two templates of different identity and compare their quality.
- Deep-learning prediction
Predict a protein with ColabFold and examine how MSA depth affects the result.
- Confidence and validation
Decide which regions of a predicted model are reliable enough for mutagenesis planning.
- Complexes and comparison
Predict a protein–protein complex and judge the interface with confidence metrics and superposition.
- Project: fit for purpose
Model a target, validate it and write a recommendation on whether it is suitable for the intended experiment or docking study.
Software, tools and datasets
Software and tools
All software is free and open source or free for academic use. Recommended computer: 8 GB RAM, 30 GB free disk, Linux, macOS or Windows with WSL2.
Datasets
- AlphaFold Protein Structure Database entries: Model interpretation.EMBL-EBI AlphaFold DB (CC-BY 4.0)
- Matched experimental structures: Validation against experiment.RCSB PDB
Core reading
- Jumper J et al. (2021). Highly accurate protein structure prediction with AlphaFold. Nature 596:583–589.
- Mirdita M et al. (2022). ColabFold: making protein folding accessible to all. Nature Methods 19:679–682.
Duration, format and fees
- Next cohort
- Dates are sent with your admission offer
- Timetable
- Three evening or weekend sessions a week (IST), plus a lab clinic; intensive and weekend-only schedules on request
- Payment
- Secure online payment link with your admission offer (UPI, cards, net banking). Instalments on request for fees above ₹15,000.
- Refunds
- See the refund and cancellation policy.
Instructor
The instructor for each cohort is named in your admission offer and on this page when the cohort is announced. Vedicgen instructors are selected for hands-on research or industry experience in the subject they teach, and every course is reviewed by a second subject expert.
Questions about who teaches a cohort? [email protected]
Certificate
Learners who complete the course and pass its capstone receive a Vedicgen certificate of completion for Protein Structure Prediction & Modelling, issued by Vivofold, stating the guided hours and skills assessed. Each certificate has a unique ID and QR code that anyone can check.
Apply for Protein Structure Prediction & Modelling
Apply
Send the form below. It takes about five minutes.
Review
An adviser confirms your fit and format within two working days.
Offer
You receive dates, timetable and a secure payment link.
Onboard
Set-up guide, pre-reading and joining links.
Frequently asked questions
In the live cohort, modules are taught in instructor-led sessions with a weekly lab clinic where you work on real public data with help at hand; every session is recorded. The self-paced format uses the same lessons, labs and datasets with mentor review of your capstone. Both formats end with an assessed project reviewed for the code and the science.
A laptop with at least 8 GB of RAM and about 30 GB of free disk space, running Linux, macOS, or Windows with WSL2. Predictions run on free cloud notebooks; no GPU is needed on your laptop. Installation instructions are sent before the first session, and lab clinics help with any set-up problems.
Yes. Learners who complete the course and pass its assessment receive a Vedicgen certificate of completion, issued by Vivofold. Every certificate carries a unique ID and a QR code that anyone, including an employer or admissions office, can check on the certificate verification page.
Apply with the form on this page. Live and in-person cohorts run through the year; the dates, timetable and joining details of the next cohort are sent with your admission offer and listed on this page once announced. Self-paced enrolment opens as soon as your application is accepted.
Yes. No course runs longer than four weeks, and the length is customisable: choose the standard schedule, an intensive one- to two-week schedule, weekends only, or a custom timetable. Pick your preference on the application form; colleges and teams can book their own dates.
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