We have presented the building block folding model. The model postulates that protein folding is a hierarchical top-down process. The basic unit from which a fold is constructed, referred to as a hydrophobic folding unit, is the outcome of combinatorial assembly of a set of building blocks. Results obtained by the computational cutting procedure yield fragments that are in agreement with those obtained experimentally by limited proteolysis. We proposed a three-stage scheme for the prediction of a protein structure from its sequence. First, the sequence is cut to fragments that are each assigned a structure. Second, the assigned structures are combinatorially assembled to form the overall 3D organization. Third, highly ranked predicted arrangements are completed and refined. As expected, proteins from the same family give very similar building blocks. However, different proteins can also give building blocks that are similar in structure. In such cases the building blocks differ in sequence, stability, contacts with other building blocks, and in their 3D locations in the protein structure. This result, which we have repeatedly observed in many cases, led us to conclude that while a building block is influenced by its environment, nevertheless, it can be viewed as a stand-alone unit. With this conclusion in hand, it is possible to develop an algorithm that predicts the building block assignment of a protein sequence whose structure is unknown. Toward this goal, we have just updated our sequentially nonredundant database of building block sequences. A protein sequence can be aligned against these, in order to be matched to a set of potential building blocks. To address the first step, we have developed an assignment algorithm that selects optimal combinations to cover the protein sequence. Our results include proteins from different classes, with building blocks that are not necessarily assigned from the same protein class. These results are encouraging, indicating that folding by parts and part assembly may contribute to further progress in the protein-folding problem. Now the assignment has been automated and used to homology model protein structures. Toward the second step of this scheme, we developed CombDock, a combinatorial docking algorithm. CombDock gets an ordered set of protein sub-structures and predicts their overall organization. We reduce the combinatorial assembly to a graph-theory problem, and give a heuristic polynomial solution to this computationally hard problem. We tested CombDock using increasingly distorted input, where the native structural units were replaced by similarly folded units extracted from homologous proteins and, in the more difficult cases, from globally unrelated proteins. The algorithm is robust, showing low sensitivity to input distortion. Utilizing concepts of protein building blocks, we proposed a de novo computational algorithm that is similar to combinatorial shuffling experiments. Our goal is to engineer naturally occurring folds with low homology to existing proteins. A selected protein is first partitioned into its building blocks. The building blocks are substituted by fragments taken from other proteins with overall low sequence identity, but with a similar hydrophobic/hydrophilic pattern and a high structural similarity. The stabilities of the engineered proteins are tested by explicit water molecular dynamics simulations. The key in the design is using relatively stable fragments, with a high population time. We adopt a related (modified) strategy in nanodesign. Currently we are developing a CHARMM based scheme for an automated and efficient optimization of the nanotube. our goal is to carry out nanotube design using naturally occurring protein building blocks. We pick the nanotube geometry. Given this target geometry, our goal is to scan a library of candidate building block parts, combinatorially assembling them into the shape and testing the stability. Since self-assembly takes place on time scales not affordable for computations, we propose a strategy for the very first step in protein nanotube design: we map the candidate building blocks onto a planar sheet and wrap it around a cylinder with the target dimensions. Given the current limitations of the computational resources and the accuracy of the molecular mechanics force field, it is infeasible to carry out ab initio calculations in an attempt to self-assemble a nanostructure automatically. However, it is achievable to construct, rather than predict, an atomic model by incorporating any available experimental data such as images from electron microscopy (EM). On the technical side, the larger the wall thickness of the constructed nanotube and the building block size, and the smaller the tube diameter, the larger the design difficulty: the distortion of the interacting protein building blocks will increase. We study examples of protein nanotubes in atomistic model detail for which there are experimental data: so far two peptides and one protein. A constructed atomic nanostructure serves two main purposes. First, we may ask if a constructed nanostructure truly supports all available experimental data. If yes, the established atomic model will be valuable for further design toward a nanodevice. If no, the discrepancy between the constructed model and experimental data will be useful for the next round of construction. Second, in order to design a nanostructure with a specified geometry, the capability of constructing such a designed nanostructure is the very first step. Peter Grodzinski who was interested in our tube formation, also made the suggestion of experimenting with drugs. Currently experiment is checking some of our predicted synthetic residues in the nano structure stabilization scheme. The mechanism of amyloid toxicity is poorly understood and there are two schools of thought in this hotly debated field: the first favors membrane destabilization by intermediate-to-large amyloid oligomers, with consequent thinning and non-specific ion leakage;the second favors ion-specific permeable channels lined by small amyloid oligomers. Published results currently support both mechanisms. However, the amyloidbeta (Abeta) peptide has recently been shown to form a U-shaped 'beta-strand-turn-beta-strand'structure. This structure and the available physiological data present a challenge for computational biology--to provide candidate models consistent with the experimental data. Modeling based on small Abeta oligomers containing extramembranous N-termini predicts channels with shapes and dimensions consistent with experimentally derived channel structures. These results support the hypothesis that small Abeta oligomers can form ion channels. Molecular dynamics modeling can provide blueprints of 3D structural conformations for many other amyloids whose membrane association is key to their toxicity. We modeled the Alzheimer beta-peptide ion channel with the goal of obtaining insight into the mechanism of amyloid toxicity. The models are built based on NMR data of the oligomers, with the universal U-shaped (strand-turn-strand) motif. After 30-ns simulations in the bilayer, the channel dimensions, shapes and subunit organization are in good agreement with atomic force microscopy (AFM). We have also designed toxic amyloid channels in the lipid bilayer for the Alzheimer A-beta protein consistent with experiment.

Agency
National Institute of Health (NIH)
Institute
National Cancer Institute (NCI)
Type
Investigator-Initiated Intramural Research Projects (ZIA)
Project #
1ZIABC010440-07
Application #
7965317
Study Section
Project Start
Project End
Budget Start
Budget End
Support Year
7
Fiscal Year
2009
Total Cost
$651,363
Indirect Cost
Name
National Cancer Institute Division of Basic Sciences
Department
Type
DUNS #
City
State
Country
Zip Code
Jang, Hyunbum; Muratcioglu, Serena; Gursoy, Attila et al. (2016) Membrane-associated Ras dimers are isoform-specific: K-Ras dimers differ from H-Ras dimers. Biochem J 473:1719-32
Lu, Shaoyong; Jang, Hyunbum; Nussinov, Ruth et al. (2016) The Structural Basis of Oncogenic Mutations G12, G13 and Q61 in Small GTPase K-Ras4B. Sci Rep 6:21949
Baram, Michal; Atsmon-Raz, Yoav; Ma, Buyong et al. (2016) Amylin-A? oligomers at atomic resolution using molecular dynamics simulations: a link between Type 2 diabetes and Alzheimer's disease. Phys Chem Chem Phys 18:2330-8
Nussinov, Ruth; Muratcioglu, Serena; Tsai, Chung-Jung et al. (2016) K-Ras4B/calmodulin/PI3K?: A promising new adenocarcinoma-specific drug target? Expert Opin Ther Targets 20:831-42
Guven-Maiorov, Emine; Keskin, Ozlem; Gursoy, Attila et al. (2016) TRAF3 signaling: Competitive binding and evolvability of adaptive viral molecular mimicry. Biochim Biophys Acta 1860:2646-55
Datta, Siddhartha A K; Clark, Patrick K; Fan, Lixin et al. (2016) Dimerization of the SP1 Region of HIV-1 Gag Induces a Helical Conformation and Association into Helical Bundles: Implications for Particle Assembly. J Virol 90:1773-87
Lu, Shaoyong; Jang, Hyunbum; Gu, Shuo et al. (2016) Drugging Ras GTPase: a comprehensive mechanistic and signaling structural view. Chem Soc Rev 45:4929-52
Jang, Hyunbum; Banerjee, Avik; Chavan, Tanmay S et al. (2016) The higher level of complexity of K-Ras4B activation at the membrane. FASEB J 30:1643-55
Chakrabarti, Mayukh; Jang, Hyunbum; Nussinov, Ruth (2016) Comparison of the Conformations of KRAS Isoforms, K-Ras4A and K-Ras4B, Points to Similarities and Significant Differences. J Phys Chem B 120:667-79
Xu, Liang; Nussinov, Ruth; Ma, Buyong (2016) Allosteric stabilization of the amyloid-? peptide hairpin by the fluctuating N-terminal. Chem Commun (Camb) 52:1733-6

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