Syllabus
Computational structural biology aims to model and predict the structure of biomolecules (e.g., proteins, DNA, RNA). The topics covered will range from the classic techniques in the field (e.g. molecular dynamics, Monte Carlo, dynamic programming) to more recent advances in analyzing and predicting RNA and protein structure, ranging from Hidden Markov Models and 3D lattice models to attribute Grammars and tree Grammars.
The class is intended for grad students, senior and junior undergrads interested in the application of mathematical and computational methods to structural biology and also for biologists and biochemists interested in the algorithmic foundations of the approaches used in this field. Prerequisites: Basic understanding of algorithms. No biological background required. The class will provide background for any advanced methods discussed. The evaluation will consist of a mid-term exam and a final project.
Topics
RNAs:
- Secondary structure prediction,
- Multiple alignment,
- Equilibrium statistical mechanics of RNAs,
- Saturated structures and RNA shapes,
- RNA mutants and RNA design,
- RNA-RNA interaction,
- RNA 3D structure and alignment,
- RNA folding kynetics.
- Secondary structure prediction,
- Threading, side-chain packing,
- Molecular mechanics, motion planning,
- Protein folding on 3D lattice,
- Structure template methods,
- NMR related algorithms,
- Folding pathways.
Image produced with RNAmovies
Course informations
Lectures: Tuesday-Thursday 9:30am - 11:00pm in 2-135.
Office hours: By appointment.
On Tuesday, April 8th, the class will start at 9:45am.
Material
Lecture 1:
Introduction to Computational Structural Biology, Databases & Viewer.
[Slides]
Lecture 2:
Introduction to RNAs. Classical secondary structure prediction algorithms.
[Slides]
Lecture 3:
RNA sequence/structure alignment.
[Slides]
Lecture 4:
RNA partition function and base pairing probabilities.
[Slides]
Lecture 5:
Kinetical traps and RNA saturated structures.
[Slides]
Lecture 6:
Prediction of RNA secondary structure with pseudo-knots.
[Slides]
Lecture 7:
RNA-RNA interaction prediction.
[Slides]
Lecture 8:
Formal Languages: A unified framework for RNA secondary structure prediction methods.
[Slides]
Lecture 9:
Modeling RNA tertiary structure.
[Slides]
Lecture 10:
Evolution of RNA sequences in the sequence/structure network.
[Slides]
Lecture 11:
Algorithms for exloring the mutation landscape of RNA molecules.
Lecture 12:
Integration of experimental observations for RNA structure intermediates prediction.
Lecture 13:
Introduction to protein structure.
[Slides]
Lecture 14:
Asymptotics of RNA secondary structure.
[Slides]
Lecture 15:
Conformational search and molecular dynamics.
[Slides]
Lecture 16:
Protein secondary structure prediction.
[Slides]
Lecture 17:
Transmembrane protein structure prediction.
[Slides]
Lecture 18:
Transmembrane protein structure prediction Part II.
[Slides]
Lecture 19:
Complements on language theory.
[Slides]
Lecture 20:
Protein folding on HP lattice model
[Slides]
[Supplements]
Lecture 21:
Folding routes.
[Slides]
Lecture 22:
Residue contact predictions and NMR assignment problem.
[Slides]
Lecture 23:
Threading, fragment assembly, comparative modeling and side-chain packing.
[Slides]
References
Computational Molecular Biology: An Introduction
Peter Clote, Rolf Backofen
Wiley Series in Mathematical and Computational Biology
Credits
Notes have been released using material kindly provided by Peter Clote, Ivo Hofacker, David Mathews, Thomas Simonson and Jinbo Xu.