Academia

Teaching

Graduate-level courses in computational structural biology and bioinformatics at the University of South Carolina.

Department of Computer Science and Engineering, University of South Carolina, Columbia. Courses offered as part of the graduate and advanced undergraduate curriculum.

CSCE 769

Computational Structural Biology

Section 001 · CRN 27574 3 Credit Hours Last Updated 8/21/2024
Face-to-Face Instruction
USC Columbia Campus
Columbia Full Term (Part of Term: 30)

This course is intended to familiarize investigators with theoretical concepts and algorithmic tools in the field of protein folding, including AlphaFold, ROSETTA, and I-TASSER. Widely-used software packages such as VMD and NAMD are also introduced. Upon completion, participants are expected to engage in competitive research in protein folding.

The course provides a brief introduction to main concepts of molecular biology, experimental methods of protein structure determination (principally NMR), and general classes of protein folding approaches (ab initio versus threading). Visualization, evaluation, and computation tools are introduced throughout.

Topics Covered
  • Topics in structural biology
  • Protein structure characterization and classification
  • Experimental data — NOE and RDC data from NMR
  • Computational protein folding by force field minimization
  • Constrained optimization in protein folding
  • Protein folding based on threading algorithms
  • Molecular modeling and molecular mechanics
  • Introduction to quantum mechanics
  • Visualization tools: PyMOL, VMD
  • AlphaFold, ROSETTA, I-TASSER — practical use

CSCE 555

Algorithms in Bioinformatics

Section 001 · CRN 59207 3 Credit Hours Last Updated 8/21/2024
Face-to-Face Instruction
USC Columbia Campus
Columbia Full Term (Part of Term: 30)

This course introduces the central concepts, algorithms, and tools that define bioinformatics, covering important problems including nucleotide and amino acid sequence alignment, DNA fragment assembly, phylogenetic reconstruction, and protein structure visualization and assessment.

The curriculum begins with a brief introduction to biological concepts and programming in Python, then delves into core bioinformatics algorithms and their biological applications.

Topics Covered
  • Biology, genetics, and molecular biology foundations
  • Introductory programming in Python
  • Pairwise sequence alignment and dynamic programming
  • Multiple sequence alignment
  • DNA fragment assembly
  • Phylogenetic analysis and visualization
  • Biopython — practical applications
  • Introduction to biological databases
  • Probabilistic motifs and stochastic algorithms
  • Hidden Markov Models (HMM)
  • Protein structure visualization and assessment