📚 Syllabus & Course Curriculum
Data Science & Analytics
Module-by-module breakdown of Predicting 3D Structures of Proteins and Nucleic Acids, from foundations to a certified capstone project.
Basis
• Sequence determines structure, and the limits of that statement
• Experimental methods and the coverage gaps prediction is filling
• CASP and CASP-RNA as the only unbiased assessments of accuracy
Proteins
• Homology modelling with SWISS-MODEL and MODELLER, and template selection
• AlphaFold and ESMFold, and the dependence on MSA depth
• Model quality assessment: pLDDT, PAE, Ramachandran and MolProbity
Nucleic Acids
• RNA secondary structure with RNAfold and the accuracy actually achieved
• Tertiary prediction with SimRNA or RoseTTAFoldNA and its much lower reliability
• SHAPE and chemical probing data as restraints on prediction
Dynamics
• Molecular dynamics with GROMACS to test model stability
• Force field choice, solvation and the timescale a simulation can reach
• Conformational ensembles and why one predicted structure misleads
Use
• Docking and virtual screening into predicted models, with caveats
• Interpreting mutations and disease variants structurally
• Visualisation and honest figure preparation in PyMOL or ChimeraX
Earn government-registered certification in Predicting 3D Structures of Proteins and Nucleic Acids
e-Certificate and e-Marksheet issued on successful completion.
