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EASAL: Algorithms and software for analyzing entropy-driven assembly landscapes

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  1. Tez No: 401674
  2. Yazar: AYŞEGÜL ÖZKAN
  3. Danışmanlar: PROF. MEERA SITHARAM
  4. Tez Türü: Doktora
  5. Konular: Bilgisayar Mühendisliği Bilimleri-Bilgisayar ve Kontrol, Computer Engineering and Computer Science and Control
  6. Anahtar Kelimeler: Belirtilmemiş.
  7. Yıl: 2014
  8. Dil: İngilizce
  9. Üniversite: University of Florida
  10. Enstitü: Yurtdışı Enstitü
  11. Ana Bilim Dalı: Bilgisayar Bilimleri ve Mühendisliği Ana Bilim Dalı
  12. Bilim Dalı: Belirtilmemiş.
  13. Sayfa Sayısı: 96

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Robust and spontaneous supramolecular and macromolecular self-assembly processes are poorly understood. These include helix packing, viral self-assembly, protein crystallization, prion aggregation, ligand and drug docking etc.. To elucidate the structure and geometric properties of molecular assembly configuration spaces, our new EASAL suite of algorithms and software combines classical concepts in algebraic topology and recent results in the theory of configuration spaces. These concepts and results formalize and allow leveraging the relative simplicity of assembly spaces compared to folding spaces. Specifically, EASAL builds an atlas (1) using a geometric-constraint representation to (2) extract a comprehensive degree-of-freedom-based stratification of the assembly landscape and encode a topological roadmap of neighborhood and boundary relationships between constant potential energy regions of varying effective dimensions; (3) parameterizes regions and their boundaries by a judicious, region-specific choice of Cayley (distance) coordinates, typically resulting in convex domains. These parameterizations reliably and efficiently isolate and enable sampling of crucial low-dimension regions, such as narrow regions with low potential energy. The underlying theory and a principled algorithm design provide a formal guarantee of correctness and efficiency. By sampling the assembly landscape of 2 TransMembrane Helices, with short-range pair potentials, this dissertation demonstrates that EASAL provides reasonable coverage of crucial but narrow regions of low effective dimension with much fewer samples and computational resources than traditional Monte Carlo or Molecular Dynamics based sampling. Promising avenues are discussed, for combining the complementary advantages of the two methods. Additionally, since accurate computation of configurational entropy and other integrals is required for estimation of both free energy and kinetics, it is essential to obtain uniform sampling in appropriate cartesian or moduli space parameterization. Standard adjustment of Cayley sampling via the Jacobian of the map between the two parameterizations is fraught with challenges stemming from an illconditioned Jacobian. This dissertation formalizes and analyzes these challenges to provide modifications to EASAL that secure the advantages of Cayley sampling while ensuring certain minimum distance and coverage relationships between sampled configurations - in Cartesian space. The modified EASALs performance is compared with the basic EASAL and the data are presented for Human and Rat Islet Amylin Polypeptide (HiAPP, PDB-2KJ7 and RiAPP PDB-2KB8) dimerization (the two differ in only 6 out of 37 residues, but the former aggregates into fibrils, while the latter does not). Finally, we discuss algorithmic future problems concerning the removal of exponential dependence on dimension.

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