Mối quan hệ cấu trúc thứ cấp và cấu trúc native trong miền protein

Exploring The Relationship Between Secondary Structure And Native Topology In Protein Domains by Haipeng Gong A dissertation submitted to Johns Hopkins University in conformity with the requirements for the degree of Doctor of Philosophy Baltimore, Maryland August, 2006 UMI Number: 3240716 INFORMATION TO USERS The quality of this reproduction is dependent upon the quality of the copy submitted. Broken or indistinct print, colored or poor quality illustrations and photographs, print bleed-through, substandard margins, and improper alignment can adversely affect reproduction. In the unlikely event that the author did not send a complete manuscript and there are missing pages, these will be noted. Also, if unauthorized copyright material had to be removed, a note will indicate the deletion.

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Box 1346 Ann Arbor, MI 48106-1346 Abstract Since the introduction of Pauling's groundbreaking model’, numerous experiments have shown that hydrogen-bonded secondary structure is an important factor in protein folding. Under folding conditions, the linear polypeptide chain can form marginally stable elements of secondary structure on a rapid time scale. Such elements, which are in dynamic equilibrium with their respective coil states, interact with one another, further organizing and stabilizing the protein. We hypothesize that this latter step is rate limiting in the folding of a protein domain.

To validate this idea, I tested whether the logarithm of the folding rate constant is linearly correlated with a protein's secondary structure content. The observed, large correlation coefficient is consistent with our hypothesis and underscores the importance of secondary structure elements in organizing the folding process. Przytycka and Rose proposed that the sequence of secondary structure elements is sufficient to capture a protein's native conformation, and they tested this proposal for a large collection of representative protein domains by showing that the hierarchic tree derived by aligning secondary structure sequences is almost identical to the one derived by direct three-dimensional structure comparison. To extend this idea, I developed a dynamic programming algorithm to compare domain structures by aligning mesostate sequences, where a mesostate is a coarse-grained ii representation of a backbone torsion angle.

Comparison of the performance of this algorithm against several existing fold recognition algorithms further supports the proposition that the sequence of secondary structure elements determines the protein's three-dimensional conformation. To retrieve the information about native conformation that is implicit in the mesostate sequence, I developed a fragment replacement Monte-Carlo algorithm that uses only this information to generate tertiary structure. Specifically, a crude potential including only hydrogen bonding, steric exclusion, and spatial confinement was sufficient to regenerate native-like backbone topology from the coarse-grained torsion angle restraints imposed by the native mesostate sequence. This dissertation is divided into three major parts, each of which corresponds to one of the three topics mentioned above.

Together, these three inter-related approaches highlight the central role that secondary structure plays in the protein folding process. Thesis Advisor: George D. Rose Second Reader: Douglas E. Barrick iti Thesis Committee: George D.

Barrick Bertrand Garcia-Moreno Thomas B. Cone iv Acknowledgement The work for this dissertation could not be completed without the help from many persons, only a few of whom I would name here. However, my acknowledgment should be given to all of these people who helped me. First, I thank my advisor George Rose, not only because all of the work were done under his direction, but also because I learned how to conduct scientific research from him.

From George, I learned how to partition a terribly huge problem into several small ones, which could be solved sequentially. On the other hand, his suggestion always kept me from sinking too much into the details of small projects to forget the physical meaning of the original huge problem. Additionally, his insight in both science and philosophy shaped my view of the world: Science should be simple and elegant; one should keep skeptic about any existing theory. George not only helped me as an advisor, but as a friend.

Neither my written English could be improved so much, nor could I be accustomed to American life so quickly without his help. I also thank the faculty members taking part in all the courses I took in Johns Hopkins University, especially Biophysics I and II, which convey important definitions, ideas, and experimental methods in modern biophysics. The faculty members participating my annual thesis review also helped me much both in expediting my research and in improving my presentation skills. My colleagues in Rose lab are also very helpful to me.

Rajgopal Srinivasan guided me through all the detailed implementation of LINUS simulation and helped me a lot in grasping the programming language Python. Teresa Przytycka and Rohit Puppu frequently gave me suggestions in mathematical and physical terms. Patrick Fleming not only taught me so much in molecular simulation, but also participate my project and wrote useful programs for me. Additionally, he is usually the first polisher of my written works.

Nicholas Fitzkee is always the encyclopedia for computer and programming language. Nicholas Panasik and Timothy Street usually supplied insightful suggestions in group meeting. I thank Ranice Crosby, Lisa Jia, Jerry Levins, and Ken Rutledge for their support in administration and patience. Ranice, who is usually my first consultant, has helped me much on daylife things even beyond regular administration.

I must thank my parents who are so supportive to me both personally and financially during the last six years. The money for my transportation and living expenses for my first couple of months in America is astronomical to them and could not be saved without ten years of diligent working. This dissertation is dedicated to my father, who is now in the convalescence of coronary heart disease. vi Table of Contents Abstract ii Acknowledgement V Table of Contents Vii Abbreviations xi List of Tables xii List of Figures xiii Chapter 1.1 Hierarchical definition of structures 1 1.2 Parameters to define topology 5 1.3 Classification of protein domain structures 7 1.2 Protein folding problem 14 1.1 Thermodynamics of the folding process 15 1.2 Dynamic view of folding 21 1.1 Polyproline II helix 29 vii 1.2 Native-like residual structure 31 1.3 Invalidity of Flory’s independent pair hypothesis 33 Chapter 2.

Local Secondary Structure Content Predicts Folding Rate for Simple, Two-state Proteins 36 2. Does Secondary Structure Determine Tertiary Structure in Proteins? 51 3.3 Materials and Methods 54 3.3 Mesostate and Secondary Structure Assignment 56 3.4 SCOP benchmark test 57 viii 3.4 Results and Discussion 59 3. Building native protein conformation from highly approximate backbone torsion angles 71 4.1 Fragment Library Construction 76 4.2 Fragment Replacement Criteria 76 4.3 Fragment Assembly by Monte Carlo Simulation 78 4.6 Test Protein Set 80 4.6 Acknowledgements 85 Reference 100 Vita 122 Abbreviations Cơ a-Carbon Extended conformation a-Helical conformation Ink Logarithm of folding rate constant PDB Protein Data Bank PPII Polyproline II conformation RCO Relative contact order Rg Radius of gyration RMSD Root mean square distance Tight B-turns Backbone phi torsion angle Backbone psi torsion angle xi List of Tables Table 2. Predicted folding rates for four recently characterized proteins 50 Table 3.

Programs used in SCOP benchmark tests. Examples of Structural Similarities Identified by Meso_Align But Not by Other Methods 66 Table 4. Protein test set. Backbone rmsd of the most stable conformation.

Topological clusters from each ensemble. 99 xi List of Figures Figure 1. Mesostate definition in dihedral angle space. Correlation between folding rate and DSSP secondary structure.

Correlation between folding rates and secondary structure prediction. Sensitivity curve of SCOP family benchmark test. Sensitivity curve of SCOP superfamily benchmark test. Sensitivity curve of SCOP fold benchmark test.

Distribution of Rg. Superposition of simulation and native conformations. Superposition of simulation and native conformations. Superposition of simulation and native conformations.

Distribution of energy potentials for 2GB1. Distribution of energy potentials for 1UBQ. Distribution of energy potentials for 1C9OA. Distribution of energy potentials for 1IFB.

Distribution of energy potentials for 1 VII. Distribution of energy potentials of 1R69.1 Protein structure Proteins, one of the key macromolecules in living organisms are involved in almost all aspects of biological activity. A protein could not perform its normal biological function without folding into a specific three-dimensional structure, called the native conformation, although natively unfolded proteins may be an exception. The first protein structure to be solved by x-ray crystallography was myoglobin, and its conformation revealed that globular proteins are not repetitive structures like DNA; rather, they are compact objects with complex topologies.

Kendrew solved the structure of myoglobin almost a half-century ago.” Since that time, many more protein structures have been solved by x-ray diffraction and NMR, and the number of protein structures in the protein database increases exponentially with each passing year.1 Hierarchical definition of structures Protein molecules are linear polymers of amino acid residues, covalently joined via peptide bonds. This linear sequence of residues is called the primary structure. These residues self-organize into specific hydrogen-bonded spatial arrangements called secondary structures, which include a-helices, strands of B-sheet, and tight turns. Recent studies have shown that a significant population of polyproline II conformation (PP) is also found in both folded and unfolded proteins.

PPII is a sterically forced conformation for polyproline peptides in aqueous solution.? Consecutive residues in PPII conformation form PPII helices, which are left-handed, all-trans extended helices with average backbone torsion angles of (Ø,ø) = (—75°,+145°). With exactly three residues per turn of helix, PPII helix is more extended than œ-helix. PPII helical conformation is observed frequently in collagen, where three left-handed PPII helices intertwine to form a right-handed, coiled-coil collagen helix. Despite its name, there is no restriction on the residue composition of a PPI helix; other residues besides proline can adopt this conformation.

As discussed below, polyalanine has a marked propensity to form PPII helices at room temperature in water, and PPII conformation is observed frequently in the unfolded state of proteins. Additionally, PPII conformation is also observed frequently in folded globular proteins. Sreerama and Woody estimated that about 10% of individual amino acid residues in proteins are found in PPII conformation.* Owing to the absence of intrachain backbone hydrogen bonds, PPII helices are important in binding and recognition motifs where ligand:protein recognition may involve hydrogen bonding with unsatisfied backbone hydrogen bond donors and acceptors in PPII helices. Studies have shown that PPII conformation is the common binding motif in both WW domains and SH3 domains.” Both sequential and non-sequential regions of the protein can interact to form compact, independently stable structural and/or functional domains.

Many isolated domains can fold into their unique three-dimensional conformation independently. From an evolutionary point of view, domain swapping is a possible path for generating new proteins. Thus, an early step in protein structure analysis is usually domain decomposition because direct comparison of multi-domain proteins might lead to spurious results in structure clustering and classification.® Additionally, domain decomposition is necessary when predicting protein structure by “threading” because homologs are retained at the domain level, not the protein level.’ Structural domains are defined as compact structures in which there is a tendency for hydrophobic residues to be buried in the interior and hydrophilic residues to be exposed to polar solvent at the surface.® The most authoritative database of existing protein domains is SCOP, which codifies and classifies domains by several criteria, some of them subjective.” In view of the exponential increase of known protein structures, it is desirable to develop domain decomposition algorithms that can be run automatically and do not rely on human intervention. Several algorithms, such as PUU, DETECTIVE, and DOMAK, have been developed for domain decomposition based on the operational premise that residues within the same domain will experience more intra-domain than inter-domain contacts.

However, according to Jones et al., none of these automatic methods have an accuracy that exceeds 80%.