Computer Science 590.01
Topics in Computational Structural Biology

Schedule and Readings

Please check this webpage, and schedule frequently, since I will post new papers and new readings and new assignments frequently, as we proceed through the term.

Please note: These dates and times might move some (see "The Queue", below), as we adapt to the time required to discuss the papers, or if I am unexpectedly called to Washington, etc.

The Queue

NOTE: To accomodate all the talks this semester, some presenters may be asked to present on the same day. Keep checking the schedule for updates.

Student presentations will proceed in a strict rotation, ordered as a queue. The queue order is:
We will not assign exact dates to presentations but only an order in which the papers will be presented. This means that if you're planning ahead, your presentation might be moved to the next class, if our discussion takes longer. It will not be possible to plan to give your presentation on a precise day for this reason. However, the order of the presentations should be relatively stable, and, in general you will not be asked to present earlier than the order dictated by the queue. Moreover, in general, the paper you are presenting will be determined well ahead of time so you can prepare.

Because of the complexities of scheduling I cannot accommodate requests to move your presentation. No exceptions will be made for (e.g.) interviews, conferences, family trips, ballet classes, sports events.

*Papers that are not available online (below) have been handed out on paper.

*RECOMB papers (Proceedings of the N^th Annual International Conference on Computational Molecular Biology (N=1,2,3,4,...)) are available online via the ACM Digital Library.

A few papers will be handed out in class. If you miss class, you can copy them from a classmate.

The Textbook for this class is: Algorithms in Structural Molecular Biology (MIT Press, 2011), abbreviated as ASMB.
Announcements will be made in class. I will try to post them here, so consult this website.

Here is a useful bibliography of papers (and PDFs) in the area of this course.

NOTE: Some PDF links may only work when accessed while on Duke's network!

1. Monday, 8/26 North 311
   Presenting: Bruce Donald.
   Introductions and Administration
   

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2. Monday, 9/2 North 311
   
   No class
   

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3. Monday, 9/9 North 311
   Presenting: Bruce Donald.
   Dead-End Elimination and Protein Design: Full Sequence Design
   
   Dear Students,
   For every class,
   Please do all the reading.
   If you don't you will be lost.
   Be sure to also do the secondary, background, and "Also Read" assignments.
   
   Primary Reading:
   Dahiyat, B. I. De Novo Protein Design: Fully Automated Sequence Selection. Science 278, 82-87 (1997). [PDF]
   
   The Textbook for this class is: Algorithms in Structural Molecular Biology (MIT Press, 2011), abbreviated as ASMB.
   
   Also read:
   ASMB Chapter 11 (Algorithms in Structural Molecular Biology (MIT Press, 2011)). [PDF]
   PDB id 1FSD, Full sequence design 1 (FSD-1) of beta beta alpha motif, NMR
   Background Reading: ASMB Chapters 10 and 9 and 12.
   

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4. Monday, 9/16 North 311
   Presenting: Jeff Martin.
   Determining Structures of Symmetric Membrane Proteins
   The NMR and Crystal Structures are Different. Why? What can we learn from this difference? What does it tell us about geometric algorithms?
   Primary Reading:
   
   • Van Horn, W. D. et al. Solution Nuclear Magnetic Resonance Structure of Membrane-Integral Diacylglycerol Kinase. Science 324, 1726-1729 (2009). [PDF]
   • Li, D. et al. Crystal structure of the integral membrane diacylglycerol kinase. Nature 497, 521-524 (2013). [PDF]
   • Martin, J. W., Yan, A. K., Bailey-Kellogg, C., Zhou, P. & Donald, B. R. A graphical method for analyzing distance restraints using residual dipolar couplings for structure determination of symmetric protein homo-oligomers. Protein Science 20, 970-985 (2011). [PDF]
   Also read:
   
   Heringa, J. & Taylor, W. R. Three-dimensional domain duplication, swapping and stealing. Current Opinion in Structural Biology 7, 416-421 (1997). [PDF]
   • ASMB Chapters 15-18.

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5. Monday, 9/23 North 311
   Presenting: Pablo Gainza.
   Evolving Drug Resistance
   Primary Reading:
   
   • Weinreich, D. M. et al. Darwinian Evolution Can Follow Only Very Few Mutational Paths to Fitter Proteins. Science 312, 111-114 (2006). [PDF]
   • Lozovsky, E. R. et al. Stepwise acquisition of pyrimethamine resistance in the malaria parasite. Proceedings of the National Academy of Sciences 106, 12025-12030 (2009). [PDF]
   • Costanzo, M. S., Brown, K. M. & Hartl, D. L. Fitness Trade-Offs in the Evolution of Dihydrofolate Reductase and Drug Resistance in Plasmodium falciparum. PLoS ONE 6, e19636 (2011). [PDF]
   Also read:
   
   • Predicting resistance mutations using protein design algorithms. Proc. Nat. Acad. Sci. U.S.A. (PNAS) 2010; 107(31):13707-12. PubMed PMID: 20643959.
     • [Abstract, Full text, PDB Structures: 3GL4, 3F0Q]
     • Conitzer, V. The Exact Computational Complexity of Evolutionarily Stable Strategies. [PDF]

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6. Tuesday, 9/24 2237 French Family Science Center at 11:40 AM
   Presenting: Professor Charles L. Brooks.
   Integrating pH into Modeling of Biological Processes
   More Information

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7. Monday, 9/30 North 311
   Presenting: Kyle Roberts.
   Computational Antibody Redesign
   Primary Reading:
   
   • Gainza, P., Roberts, K. E. & Donald, B. R. Protein Design Using Continuous Rotamers. PLoS Computational Biology 8, e1002335 (2012). [PDF]
   • Zhou, T. et al. Structural Basis for Broad and Potent Neutralization of HIV-1 by Antibody VRC01. Science 329, 811-817 (2010). [PDF]
   Also read:
   
   • Goldstein, R. F. Efficient rotamer elimination applied to protein side-chains and related spin glasses. Biophysical Journal 66, 1335-1340 (1994). [PDF]
   • Leach, A. R. & Lemon, A. P. Exploring the conformational space of protein side chains using dead-end elimination and the A* algorithm. Proteins 33, 227-239 (1998). [PDF]
   • Kingsford, C. L., Chazelle, B. & Singh, M. Solving and analyzing side-chain positioning problems using linear and integer programming. Bioinformatics 21, 1028-1039 (2004). [PDF]

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8. Friday, 10/4 2237 French Family Science Center at 2:45 PM
   Presenting: Professor David N. Beratan
   Does Evolution Care About Quantum Mechanics? Electrons, Bioenergetics, and Life
   More Information

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9. Monday, 10/7 North 311
   Presenting: Swati Jain.
   Dynamic Programming and Branch-Width Minimization Algorithms for Protein Design
   Primary Reading:
   
   • Georgiev, I. Novel Algorithms for Computational Protein Design, with Applications to Enzyme Redesign and Small-Molecule Inhibitor Design (Chapter 8 only) [PDF]
   • Xu, J. & Berger, B. Fast and accurate algorithms for protein side-chain packing. Journal of the ACM 53, 533-557 (2006). [PDF]
   • Gainza, P., Roberts, K. E. & Donald, B. R. Protein Design Using Continuous Rotamers. PLoS Computational Biology 8, e1002335 (2012). [PDF]
   Also read:
   
   • Leaver-Fay, A., Kuhlman, B. & Snoeyink, J. An adaptive dynamic programming algorithm for the side chain placement problem. Pac Symp Biocomput 16-27 (2005). [PDF]
   • Krivov, G. G., Shapovalov, M. V. & Dunbrack, R. L., Jr. Improved prediction of protein side-chain conformations with SCWRL4. Proteins 77, 778-795 (2009). [PDF]
   • Reading on Heaps (also known as Priority Queues) [Text]
   • Streams [Text]
   • Infinite Streams [Text]
   • Structure and Interpretation of Computer Programs [PDF]

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10. Monday, 10/14 North 311
    
    Fall Break
    Primary Reading:
    
    Also read:
    

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11. Friday, 10/18 LSRC D240 at 1 PM
    Presenting: Bruce Donald.
    Introduction to Research In Protein Design:
    Computational Protein Interface Design, Cystic Fibrosis, and HIV
    Primary Reading:
    
    • Zhou, T. et al. Structural Basis for Broad and Potent Neutralization of HIV-1 by Antibody VRC01. Science 329, 811-817 (2010). [PDF]
      
    • Diskin, R. et al. Increasing the Potency and Breadth of an HIV Antibody by Using Structure-Based Rational Design. Science 334, 1289-1293 (2011). [PDF] [Supplemental Materials]
    • Roberts, K. E., Cushing, P. R., Boisguerin, P., Madden, D. R. & Donald, B. R. Computational Design of a PDZ Domain Peptide Inhibitor that Rescues CFTR Activity. PLoS Computational Biology 8, e1002477 (2012). [PDF]
    Also read:
    
    • ASMB Chapter 12
      

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12. Monday, 10/21 North 311
    Presenting: Goke.
    Protein Conformation Analysis
    Primary Reading:
    
    • Bryant, D. H., Moll, M., Finn, P. W. & Kavraki, L. E. Combinatorial Clustering of Residue Position Subsets Predicts Inhibitor Affinity across the Human Kinome. PLoS Computational Biology 9, e1003087 (2013). [PDF]
    • Gipson, B., Moll, M. & Kavraki, L. E. SIMS: A Hybrid Method for Rapid Conformational Analysis. PLoS ONE 8, e68826 (2013). [PDF]
    Also read:
    
    • Donald, B., Xavier, P., Canny, J. & Reif, J. Kinodynamic motion planning. Journal of the ACM 40, 1048-1066 (1993). [PDF]
    • ASMB chapters 12, 20-26, 41, 42

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13. Monday, 10/28 LSRC D106 at 11:45 AM - 12:45 PM
    
    Colloquium: Quantum Communication in Rindler Spacetime
    We will be attending the talk by Prakash Panangaden in lieu of classes this day. Those with schedule conflicts can watch the video here:
    http://compsci.capture.duke.edu/
    Primary Reading:
    
    • Event Description
    Quantum information theory offers an opportunity for a dialogue between quantum field theory and computer science. In this talk I will describe a fascinating situation at the boundary of quantum communication, relativity and quantum field theory. There is a well known effect, called the Unruh effect, whereby an accelerating observer observes a thermal bath of radiation while an inertial observer would claim to be in a vacuum. This is an explicitly quantum field theoretic effect. What happens to quantum communication in such a scenario? Specifically we consider two inertial observers who are trying to communicate while an accelerating observer attempts to eavesdrop. We show that one can exploit the Unruh noise to mask the communication scenario.. Of course, this is not meant to be a realistic scenario; rather, we are exploring ideas. There is a whole lot of technical material that one needs in order to grasp the details, so in order to not completely mask my communication with the audience, I will describe everything at a conceptual level. No background in physics or computer science is necessary. This is joint work with Kamil Bradler and Patrick Hayden.

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14. Monday, 11/4 North 311
    Presenting: Meredith.
    Protein-Protein Interface Binding
    [Slides]
    Primary Reading:
    
    • Acharya, P. et al. Heavy Chain-Only IgG2b Llama Antibody Effects Near-Pan HIV-1 Neutralization by Recognizing a CD4-Induced Epitope That Includes Elements of Coreceptor- and CD4-Binding Sites. Journal of Virology 87, 10173-10181 (2013). [PDF]
    • DeLano, W. L., Ultsch, M. H., de Vos, A. M. & Wells, J. A. Convergent solutions to binding at a protein-protein interface. Science 287, 1279-1283 (2000). [PDF]
    Also read:
    
    • Moretti, R. et al. Community-wide evaluation of methods for predicting the effect of mutations on protein-protein interactions. Proteins (2013). doi:10.1002/prot.24356 [PDF]
    • Lewis, S. M. & Kuhlman, B. A. Anchored design of protein-protein interfaces. PLoS ONE 6, e20872 (2011). [PDF][Supplementary Information]
    • Fleishman, S. J. et al. Community-wide assessment of protein-interface modeling suggests improvements to design methodology. J. Mol. Biol. 414, 289-302 (2011). [PDF][Supplementary Information]

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15. Friday, 11/8 Nanaline Duke 147 at 12:00 PM
    Dorothee Kern
    The evolution of the dynamic personalities of kinases over 1 Billion years reveal Gleevec's selectivity
    

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16. Friday, 11/8 LSRC D240 at 1:00 PM
    Presenting: Yang.
    Protein Structure Prediction
    [Slides]
    Primary Reading:
    
    • Boomsma, W. et al. A generative, probabilistic model of local protein structure. Proc. Natl. Acad. Sci. U.S.A. 105, 8932-8937 (2008). [PDF]
    • Boomsma, W. et al. PHAISTOS: a framework for Markov chain Monte Carlo simulation and inference of protein structure. J Comput Chem 34, 1697-1705 (2013). [PDF]
    Also read:
    
    Hamelryck, T., Kent, J. T. & Krogh, A. Sampling Realistic Protein Conformations Using Local Structural Bias. PLoS Computational Biology 2, e131 (2006). [PDF]
    • ASMB chapters 5,15-18,42,33

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17. Monday, 11/11 North 311
    Presenting: Anna.
    HIV Antibody Design
    [Slides]
    Primary Reading:
    
    • Jardine, J. et al. Rational HIV immunogen design to target specific germline B cell receptors. Science 340, 711-716 (2013). [PDF][Supplementary Information]
    • Scharf, L. et al. Structural basis for HIV-1 gp120 recognition by a germ-line version of a broadly neutralizing antibody. Proc. Natl. Acad. Sci. U.S.A. 110, 6049-6054 (2013). [PDF][Supplementary Information]
    Also read:
    
    • Bellows, M. L. et al. Discovery of Entry Inhibitors for HIV-1 via a New De Novo Protein Design Framework. Biophysical Journal 99, 3445-3453 (2010). [PDF][Supplementary Information]

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18. Monday, 11/18 North 311
    Presenting: Hunter.
    Modeling Protein Evolution
    [Slides]
    Primary Reading:
    
    • Coluzza, I., MacDonald, J. T., Sadowski, M. I., Taylor, W. R. & Goldstein, R. A. Analytic markovian rates for generalized protein structure evolution. PLoS ONE 7, e34228 (2012). [PDF][Supplementary Information]
    Also read:
    
    ASMB Chapter 25 (Markov Random Fields)
    ASMB Chapter 50 (Geometry and Topology)
    ASMB Chapter 45 (Monte Carlo, Simulated Annealing )
    

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19. Monday, 11/25 North 311
    Presenting: JJ.
    Branch Decomposition in Search
    Primary Reading:
    
    • De Givry, S., Schiex, T. & Verfaillie, G. Exploiting tree decomposition and soft local consistency in weighted CSP. in AAAI 6, 1-6 (2006). [PDF]
    • Greco, G. & Scarcello, F. Structural tractability of enumerating CSP solutions. Constraints 18, 38-74 (2012). [PDF]
    Also read:
    
    • Dechter, R. & Pearl, J. Tree clustering for constraint networks. Artificial Intelligence 38, 353-366 (1989). [PDF]
    • Sanchez, M., Allouche, D., de Givry, S. & Schiex, T. Russian Doll Search with Tree Decomposition. in IJCAI 603-608 (2009). at [PDF]
    Monday, 11/25 North 311
    Presenting: Mark Hallen.
    Integer Linear Programming-based Protein Design
    Primary Reading:
    
    • Traoré, S. et al. A new framework for computational protein design through cost function network optimization. Bioinformatics 29, 2129-2136 (2013). [PDF]
    Also read:
    
    • Hallen, M. A., Keedy, D. A. & Donald, B. R. Dead-end elimination with perturbations (DEEPer): A provable protein design algorithm with continuous sidechain and backbone flexibility. Proteins: Structure, Function, and Bioinformatics 81, 18-39 (2013). [PDF]
    • Gainza, P. et al. OSPREY: protein design with ensembles, flexibility, and provable algorithms. Meth. Enzymol. 523, 87-107 (2013). [PDF]
    • Cooper, M. & Schiex, T. Arc consistency for soft constraints. Artificial Intelligence 154, 199-227 (2004). [PDF]
    • Larrosa, J. & Schiex, T. Solving weighted CSP by maintaining arc consistency. Artificial Intelligence 159, 1-26 (2004). [PDF]
    Course Evaluations

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20. Monday, 12/2 North 311
    Presenting: Zack.
    Computational Enzyme Design
    Primary Reading:
    
    • Giger, L. et al. Evolution of a designed retro-aldolase leads to complete active site remodeling. Nat. Chem. Biol. 9, 494-498 (2013). [PDF][Supplementary Information]
    • Privett, H. K. et al. Iterative approach to computational enzyme design. Proc. Natl. Acad. Sci. U.S.A. 109, 3790-3795 (2012). [PDF][Supplementary Information]
    Also read:
    
    • Tinberg, C. E. et al. Computational design of ligand-binding proteins with high affinity and selectivity. Nature 501, 212-216 (2013). [PDF][Supplementary Information]

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