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Physical Biomodeling and
Foldable, Coarse-Grained
Physical Model of Polypeptide
Chain
Promita Chakraborty
Feb 15, 2015
AAA...
2
A vision: Dynamic physical models of
macromolecules that fold and convey
information
BioTable
Computer interface and physical models
• BioTable is a computer monitor
• An interactive unit for translation
• I...
Dynamic backbone, amino
acids and proteins
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A vision: Dynamic physical models of
proteins and DNA that fold
Foundations of Nanoscience, 2010
AAAS Annu...
Lessons learned for these studies
• Foldable macromolecules: Can they be built at all and folded
with accuracy?
• How can ...
Folding with accuracy (alpha-
helix and beta-sheet)
7Peppytides
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AAAS feb15 2015 Physical Biomodeling and Foldable, Coarse-Grained Physical Model of Polypeptide Chain

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https://aaas.confex.com/aaas/2015/webprogram/Paper14853.html
Session: Emerging Trends in Visualizing Physical Models and Rapid Prototyping for Biological Systems

TITLE: Physical Biomodeling and Foldable, Coarse-Grained Physical Model of Polypeptide Chain
Sunday, 15 February 2015: 8:30 AM-11:30 AM
Room 230C (San Jose Convention Center)
AUTHOR: Promita Chakraborty

ABSTRACT: Physical Biomodeling is a new area of exploration at the interface of computer science and the biological systems. While tremendous advances have been made in computational biology, the cutting-edge 3D printing provides unprecedented opportunities for a third angle into the landscape, thus uncovering this new computational space for modeling that has remained unexplored so far. We tie together these concepts of form-specific physical-digital interfaces. With these principles, a new computational paradigm emerges for physical-digital interfaces for studying of biological phenomenon (e.g. protein folding timesteps) that focuses on shape and dynamics. We define 3 categories, and 6 processes connecting these 3 categories. These categories and processes together form the basis of the philosophy behind the field of Physical Biomodeling. Through exploring Processes 1-6, we see that a relationship-triangle exists between the experimental data from natural systems (N), the computational models for biosystems (C), and scaled, accurate physical models of biosystems (P). Processes 1 and 2 have already existed in the literature for a long time. Physical Biomodeling brings forth Processes 3-6 that will provide a new way to look at the old problems in biology. We arrive at a computational space at the intersection of N, C and P that has so far remained unexplored because of the difficulty in designing and fabricating accurate, scaled physical models of biosystems.

As a first step towards building computer-augmented physical polypeptide chain model that may have applications in structural biology and drug design, we have designed, fabricated and validated a dimensionally accurate, physical model of the polypeptide chain (called Peppytide), that has a flexible backbone where the dihedral bonds are rotationally constrained to match their molecular counterparts. Biological systems involve complex phenomena, and encapsulating these characteristics within a physical body is thought to be difficult. For example, representing the polypeptide chain, a generalized protein chain, along with its complex degrees-of-freedom, by a physical scaled model that will fold dimensionally-accurately, was thought to be quite difficult before the Peppytide project proved otherwise. It opens up new possibilities and challenges with guiding principles that can be extended to build other form-specific physical bio-models. Now with 3D-printing technologies, and possibilities for CAD-cum-biocomputation platforms, we are poised to explore this new domain of study.

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  • This presentation shows the journey of Peppytide, starting from TableTop project and Paper Models in 2009 at Virginia Tech.
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AAAS feb15 2015 Physical Biomodeling and Foldable, Coarse-Grained Physical Model of Polypeptide Chain

  1. 1. Physical Biomodeling and Foldable, Coarse-Grained Physical Model of Polypeptide Chain Promita Chakraborty Feb 15, 2015 AAAS Annual Meeting 2015, San Jose Symposium: Emerging Trends inVisualizing Physical Models and Rapid Prototyping for Biological Systems 1
  2. 2. 2 A vision: Dynamic physical models of macromolecules that fold and convey information
  3. 3. BioTable Computer interface and physical models • BioTable is a computer monitor • An interactive unit for translation • Idea was to detect foldable models and molecule-molecule interaction with computers + head-mount cameras 3
  4. 4. Dynamic backbone, amino acids and proteins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lanine Planning / brainstorming bond rotations (atomistic)Simplifying the problem: from atomistic to coarse-graining? Chakraborty, Tatar, Harrison, Quek. Foundations of Nanoscience, 2010
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  30. 30. Lessons learned for these studies • Foldable macromolecules: Can they be built at all and folded with accuracy? • How can models interact with computer without cumbersome designs, but retaining accuracy? • There exists no computational/CAD platform for physical models to biocomputation platforms 6 Color-coded nucleotides Coarse-graining
  31. 31. Folding with accuracy (alpha- helix and beta-sheet) 7Peppytides
  32. 32. Folding into secondary and tertiary str pid: 1FSD 28 amino ββα motifβ turns
  33. 33. Beta turns 9 Type I Type I' Type II Type II' ααα - Fish Osteocalcin pid: 1VZM, chain A 45 amino acids N-term C-term
  34. 34. The future of physical biomodeling for protein structures 10
  35. 35. -62° ψ ϕ -42° -118° ψ ϕ138° PyMol Configuration change of ψ and ϕ: α-helix→β-sheet α-helix β-sheet ψ ϕ A flexible biased backbone Model/ CAD AA1 AA2 AA3 AA4 ϕ ψ
  36. 36. 12 Protein data to model
  37. 37. Ramachandran plot: a comparison PDB data Chakraborty and Zuckermann, PNAS,Vol. 110, No. 33, 2013. Peppytides at 0.7 RVDW with rotational barrier constraints ϕ ψ
  38. 38. Atom-radii = 0.6 RVDW Atom-radii = 0.7 RVDW Atom-radii = 0.8 RVDW Measured at 5˚ intervals Ramachandran plot generated using approx. 80,000 structure files from Protein Data bank 14
  39. 39. O H N 15 A scaled model: Peppytide At Lawrence Hall of Science, UC Berkeley 93,000,000 times magnified
  40. 40. 18.4Å Measuring folding accuracy 6.75(~18.362Å) alphaC1 to alphaC13 pid: 2ZTA chain B {AA-16} KNYHLENEVARLKKL 16 Hydrogen bond Measuring N1 to N5 13.4 Å 12.9 Å 4.853” ± 0.044” (~13.202 ± 0.121Å) 4.853” ± 0.103” (~13.202 ± 0.281Å) pid: 202J Representing with magnets • acceptor/donor as N/S pole O–N distance is typically 3.00 ± 0.12 Å, from an α-helix crystal structure In the Peppytide model, the O–N distance is 1.17 ̋ ± 0.04 ̋ (equivalent to 3.18 ± 0.11 Å) Alpha helix Parallel beta sheet
  41. 41. Peppytides 2 17 • Interchangeable side chains • Can make different sequences of amino acids on-the-fly
  42. 42. PeppyChains 18 PeppyChains (with Ala-Val-Gly sequence) • 3D-printed as a single unit • No assembly required • Without bias magnets • next step! • Interchangeable side chains
  43. 43. Physical Biomodeling: a new field of exploration 19 Chakraborty, PhD Dissertation, 2014. Example: BioTable Example: Peppytide Example: MD in grid* *Chakraborty, Jha, Katz, Phil.Trans. R. Soc A, 2009 @CCT, LSU
  44. 44. Physical Biomodeling: a new field of exploration • Precision biomodels as scientific tool for computational modeling 20 Chakraborty, PhD Dissertation, 2014.
  45. 45. Physical Biomodeling and CAD 21 • Enabling CAD- Bioplatform-3DPrint • Design→ 3dPrint → Fold paradigm Thanks:Autodesk funding and collaborationChakraborty, PhD Dissertation, 2014.
  46. 46. Possibilities 22 • a different approach to study the same problem • protein-protein interaction • CAD-cum-biocomputation platform does not exist yet • Study of misfolded proteins and aggregates • Exploring other types of polymers Chakraborty, PhD Dissertation, 2014. • Enabling CAD-Bioplatform-3DPrint • Design→ 3dPrint → Fold paradigm • A viable input device for molecular chains • A viable output device for molecular chains • Self-folding
  47. 47. Envisioning the future of dynamic macromolecules Imagine a world! 23
  48. 48. 40! How to make them? Open source! Make Magazine -- Projects! Peppytides
  49. 49. Acknowledgments and Contacts • QuezyLab • Collaboration with UCSF Science Health Education Partnership • Collaboration with Foothill College, Los Altos 25 • Shantenu Jha, Daniel Katz (CCT, LSU, now at Rutgers U. and Argonne/U. Chicago respectively) • Deborah Tatar, Steve Harrison, Francis Quek (VT) • Ronald Zuckermann (LBNL), DoE (Office of Basic Energy Sciences), Defense Threat Reduction Agency (DTRA) • Alexey Onufriev (VT), Joseph DeRisi (UCSF) • Molecular Foundry, Lawrence Berkeley National Lab • Virginia Tech, Dept. of Computer Sc. • Lawrence Hall of Science Museum • Industry Collaboration and support by Autodesk Inc. www.quezylab.com promita@quezylab.com
  50. 50. Peppytide videoshoot Joe DeRisi’s lab, UCSF Berkeley Lab booth, Berkeley Solano Fest, 2013 Bay Area 2014 Prof. Robert Stroud testing for beta-turns, UCSF, Dec 2013 Congress Offices, Capitol Hill,Washington DC Jun 2014 LBL team with Congressman Jerry McNerney Nanobio Summer Camp 2014, Foothill College
  51. 51. Extras 27
  52. 52. Existing models 28 CPK models Dreiding stereomodel Ball-and-stick Center for BioMolecular Modeling, Milwaukee School of Engineering http://www. 3dmoleculardesigns.com/ Scripps Physical Model Service, Scripps Research Institute http://models.scripps.edu/ beta sheet alpha helix DNA double helix
  53. 53. Trade off: Van der Waals radius Vs. Covalent radius Benzene 0.1 VDWr 0.35 VDWr 0.5 VDWr 0.65 VDWr 0.7 VDWr 0.8 VDWr 0.9 VDWr 1.0 VDWr Version # *VDWr Ver1 0.5 / 0.35 Ver2.1 0.8 Ver2.2 0.7 Bondi Radius 29
  54. 54. 30 3D printing
  55. 55. • beta beta alpha motif • pid: 1FSD 28 amino acids identities (36 to 39 percent) and P values scores that no sequence information from any protein motif was used in our sequence scoring function. In order to examine the robustness of the computed sequence, we used the sequence of FSD-1 as the starting point of a Monte Carlo simulated annealing run. The Monte Carlo search revealed high scoring, suboptimal se- quences in the neighborhood of the optimal solution (4). The energy spread from the ground-state solution to the 1000th most stable sequence is about 5 kcal/mol, an indi- cation that the density of states is high. The amino acids comprising the core of the mol- ecule, with the exception of position 7, are essentially invariant (Fig. 1). Almost all of the sequence variation occurs at surface po- sitions, and typically involves conservative changes. Asn14 , which is predicted to form a stabilizing hydrogen bond to the helix back- D-1 structures. (A) Stereoview of the second zinc ues and zinc binding site. (B) Stereoview of the 1. For clarity, only side chains from residues 3, 5, 8, re created with MOLMOL (38). traints, structural statistics, and atomic root-mean- annealing structures, SA is the average structure onAugust15,2012www.sciencemag.orgdedfrom binding His positions of Zif268, are more than 80 percent buried, and the Ala at position 5 is 100 percent buried but the Lys at position 8 is more than 60 percent ex- posed to solvent (Fig. 2). The other bound- ary positions demonstrate the steric con- straints on buried residues by packing similar side chains in an arrangement similar to that of Zif268 (Fig. 2). The calculated optimal configuration for core and boundary residues buries 1150 Å2 of nonpolar surface area. On the helix surface, the algorithm places Asn14 with a hydrogen bond between its side-chain carbonyl oxygen and the back- bone amide proton of residue 16. The eight charged residues on the helix form three pairs of hydrogen bonds, although in our coiled-coil designs, helical surface hydrogen the overall helix propensity of the sequenc (5). Positions 4 and 11 on the exposed she surface were selected by the program to b Thr, one of the best -sheet forming res dues (21). Alignment of the sequences for FSD and Zif268 (Fig. 1) indicates that only 6 the 28 residues (21 percent) are identic and only 11 (39 percent) are similar. Four the identities are in the buried cluster, whic is consistent with the expectation that bu ied residues are more conserved than so vent-exposed residues for a given motif (22 A BLAST (23) search of the FSD-1 s quence against the nonredundant prote sequence database of the National Cent for Biotechnology Information did not r veal any zinc finger protein sequences. Fu N-term C-term Dahiyat, B.I. and S.L. Mayo, De Novo Protein Design: Fully Automated Sequence Selection. Science, 1997. 278(82)31 FSD-1 denovo structure

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