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Prediction of Protein Rigid Domains and Hinge Residues Based on Graph Theory and Elastic Network Model
Wednesday, March 2, 2016 2635-Pos Board B12 Investigation on Plexin Rho GTPase Binding Domain (RBD) Binding with Small Rho GTPases Using Molecular Dynamics Simulations Liqun Zhang1, Thomas Centa2, Matthias Buck3. 1 Chemical Engineering, Tennessee Technological University, Cookeville, TN, USA, 2University of Cincinnati, Cincinnati, OH, USA, 3Physiology and Biophysics, Case Western Reserve Univeristy, Cleveland, OH, USA. Plexins are a family of single pass transmembrane receptors with 9 family members: plexin-A1-4, B1-3, C1 and D1. Plexins receive the guidance cues of semaphoring ligands on the outside of the cell and transmit their signal through the lipid membrane. Because of their function, plexins can regulate cell migration and targeting processes, for example by controlling axon and blood vessel guidance. Misfunction of plexin enables serious diseases, including cancer metastasis. Up to now, it is believed that the binding of small Rho GTPases with the Rho GTPase Binding Domain (RBD) of plexin is necessary to the function of plexin. At the meanwhile, the same RBD can bind with different small Rho GTPases, such as Rac1 and Rnd1 GTPase, and different RBDs such plexin-A1-RBD, plexin-A2-RBD, plexin-B1-RBD, can bind with the same GTPase. Thus understanding the structure and dynamics of the free and bound forms of RBD and GTPases can help to understand the signal transduction processes of plexin. In this project, the complexes of RBDs bound with Rac1/Rnd1 GTPases, and the free form of RBDs and GTPases were investigated using miscrosecond length all atom molecular dynamics simulations. It is found that RBDs experience more structural changes than Rho-GTPases during the binding process. Different RBDs showed different structure fluctuation dependences on regions when binding with small Rho GTPases. Calculating the backbone dihedral angle covariance matrix, all the RBDs in the free states have similar correlations to their bound states, but the Rac1 GTPase in the free states has less correlations than their bound states. Mapping the highly correlated residues to the structure, it was expected that plexin-A1-RBD, plexin-B1-RBD, and plexinA2-RBD all having similar signal pathways, but different key residues taking the role in the process. 2636-Pos Board B13 Conformational Plasticity of the MAGE-A3 Protein as a Therapeutic Strategy in Multiple Myeloma Roman Osman1, Hearn J. Cho2, Anna H. Mei2, Joseph A. Newman3, Opher Gileadi3. 1 Structural and Chemical Biology, Icahn School of Medicine at Mount Sinai, New York, NY, USA, 2Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA, 3Structural Genomics Consortium, University of Oxford, Oxford, United Kingdom. Type I MAGE proteins interact with the RING domain protein Kap1 through their conserved MAGE Homology Domains (MHD) to form E3 ubiquitin (Ub) ligases, which ubiquitinylate p53 targeting it for proteasomal degradation. RNAi experiments demonstrated that MAGE-A3 inhibits p53-dependent and independent mechanisms of apoptosis and confers resistance to chemotherapy-induced apoptosis in human myeloma cell lines. Since MAGE expression correlates with progression of multiple myeloma (MM), preventing the interaction with Kap1 is a promising therapeutic intervention against MM. The MHD of MAGE proteins are made of two winged helix domains (WH) linked by a flexible b-hairpin. Structures of MAGE proteins suggest that the WH domains need to undergo a conformational change from a closed to an open form to interact with Kap1 and activate its Ub-ligase activity. Virtual screening on the closed form of MAGE-A3 identified two compounds that recapitulate the RNAi experiments, suggesting that they may inhibit the interaction of MAGE-A3 with Kap1 leading to the apoptosis of MM cells. Further experiments are being conducted to elucidate the nature of the observed effect. MD simulations of the complexes of MAGE-A3 with the small molecules show binding modes in the groove between the two WH domains. These may be responsible for inhibiting the conformational transition. Further simulations to estimate the effect of the small molecules on the conformational change as well as refinement of the initial leads to improve the affinity and selectivity of the compounds are under way. Supported by NIH R21 CA191898.
Protein Structure, Prediction, and Design 2637-Pos Board B14 Enhancing the Coevolutionary Signal Travis A. Hoppe, Pengfei Tian, Robert Best. NIDDK, LCP, National Institutes of Health, Bethesda, MD, USA. Popular coevolutionary methods for predicting residue-residue contacts in 3D proteins structure from aligned sequences use an arbitrary cutoff to separate
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the signal from the noise. These methods, like GREMLIN and PSICOV, rely on a fixed cutoff value from a rank-sorted list of potential contacts. We show that by considering the local signal within the GREMLIN or PSICOV score matrix, we can increase both the accuracy and the number of correctly predicted contacts. Our approach uses a random forest classification scheme and reveals that even a simple Gaussian kernel can improve the contact prediction. 2638-Pos Board B15 Alternative Approach to Protein Structure Prediction Based on Sequential Similarity of Physical Properties Yi He1, S. Rackovsky1,2, Yanping Yin1, Harold A. Scheraga1. 1 Chemistry and Chemical Biology, Cornell University, ITHACA, NY, USA, 2 Department of Pharmacology and Systems Therapeutics, The Icahn School of Medicine at Mount Sinai, New York, NY, USA. Protein structure similarity arises from physical property similarity of the amino acid sequences. The traditional approaches to alignment, however, are based on evolutionary preconceptions. Traditional substitution matrices are able to capture only a small part of the overall influence of physical properties, because they are derived from statistical analysis of sequences which are believed a priori on structural grounds to be related. An alternative method is therefore proposed to identify homologs, which is based on pairwise physical property similarities of sequences. This approach, the property factor method (PFM), is based entirely on the physics of amino acids, and devoid of evolutionary assumptions. It uses physical property factors derived by Kidera et al (1985), based on an exhaustive statistical analysis of amino acid property sets. A comparison is made between our method and PSI BLAST. We demonstrate that traditionally defined sequence similarity can be very low for pairs of sequences (which therefore cannot be identified using PSI BLAST), but similarity of physical property distributions results in almost identical 3D structures. 2639-Pos Board B16 Protein Rethreading Salem Faham, Sandra Poulos, Austin Yu, Sayeh Agah. University of Virginia, Charlottesville, VA, USA. Protein engineering is an important tool for the design of proteins with novel and desirable features. Templates from the protein databank (PDB) are often relied on to serve as initial models that can be modified to introduce new properties. We examine whether it is possible to reconnect a protein in a manner that generates a new fold yet preserves its structural integrity. Here, we describe the rethreading of dihydrofolate reductase (DHFR) from E. coli. The rethreading process performed involved the removal of three native loops, and the introduction of three new loops with alternate connec˚ . The tions. The structure of the rethreaded DHFR was determined to 1.55A structure demonstrated the success of the rethreading process. Protein rethreading can be a powerful tool for the design of a large array of novel protein folds. 2640-Pos Board B17 Prediction of Protein Rigid Domains and Hinge Residues Based on Graph Theory and Elastic Network Model Julian Lee1, Jun Sim2, Jaehyun Sim3, Eunsung Park4. 1 Soongsil University, Seoul, Korea, Republic of, 2Dept. of Bioinformatics and Life Science, Soongsil University, Seoul, Korea, Republic of, 3Seoul National University, Seoul, Korea, Republic of, 4Apsun Dental Hospital, Seoul, Korea, Republic of. Many proteins undergo large-scale motions where relatively rigid domains move against each other. The prediction of rigid domains, as well as the hinge residues important for their relative movements, is important for various applications. We developed a novel method for protein rigid domain identification, DAGR (Domain Analysis base on GRaph theory), based on an exhaustive enumeration of maximal rigid domains, the rigid domains not fully contained within other domains. The computation is performed by mapping the problem to that of finding maximal cliques in a graph. A minimal set of rigid domains are then selected, which cover most of the protein with minimal overlap. Combining DAGR with elastic network model, we construct a method for predicting the rigid domains and the hinge regions of a protein. 2641-Pos Board B18 Characterizing the Statistical Properties of Protein Surfaces Ji Hyun Bak, Anne-Florence Bitbol, William Bialek. Princeton University, Princeton, NJ, USA. Proteins and their interactions form the body of the signaling transduction pathway in many living systems. In order to ensure the accuracy as well as the specificity of signaling, it is crucial that proteins recognize their correct
Protein Structure, Prediction, and Design 2637-Pos Board B14 Enhancing the Coevolutionary Signal Travis A. Hoppe, Pengfei Tian, Robert Best. NIDDK, LCP, National Institutes of Health, Bethesda, MD, USA. Popular coevolutionary methods for predicting residue-residue contacts in 3D proteins structure from aligned sequences use an arbitrary cutoff to separate
535a
the signal from the noise. These methods, like GREMLIN and PSICOV, rely on a fixed cutoff value from a rank-sorted list of potential contacts. We show that by considering the local signal within the GREMLIN or PSICOV score matrix, we can increase both the accuracy and the number of correctly predicted contacts. Our approach uses a random forest classification scheme and reveals that even a simple Gaussian kernel can improve the contact prediction. 2638-Pos Board B15 Alternative Approach to Protein Structure Prediction Based on Sequential Similarity of Physical Properties Yi He1, S. Rackovsky1,2, Yanping Yin1, Harold A. Scheraga1. 1 Chemistry and Chemical Biology, Cornell University, ITHACA, NY, USA, 2 Department of Pharmacology and Systems Therapeutics, The Icahn School of Medicine at Mount Sinai, New York, NY, USA. Protein structure similarity arises from physical property similarity of the amino acid sequences. The traditional approaches to alignment, however, are based on evolutionary preconceptions. Traditional substitution matrices are able to capture only a small part of the overall influence of physical properties, because they are derived from statistical analysis of sequences which are believed a priori on structural grounds to be related. An alternative method is therefore proposed to identify homologs, which is based on pairwise physical property similarities of sequences. This approach, the property factor method (PFM), is based entirely on the physics of amino acids, and devoid of evolutionary assumptions. It uses physical property factors derived by Kidera et al (1985), based on an exhaustive statistical analysis of amino acid property sets. A comparison is made between our method and PSI BLAST. We demonstrate that traditionally defined sequence similarity can be very low for pairs of sequences (which therefore cannot be identified using PSI BLAST), but similarity of physical property distributions results in almost identical 3D structures. 2639-Pos Board B16 Protein Rethreading Salem Faham, Sandra Poulos, Austin Yu, Sayeh Agah. University of Virginia, Charlottesville, VA, USA. Protein engineering is an important tool for the design of proteins with novel and desirable features. Templates from the protein databank (PDB) are often relied on to serve as initial models that can be modified to introduce new properties. We examine whether it is possible to reconnect a protein in a manner that generates a new fold yet preserves its structural integrity. Here, we describe the rethreading of dihydrofolate reductase (DHFR) from E. coli. The rethreading process performed involved the removal of three native loops, and the introduction of three new loops with alternate connec˚ . The tions. The structure of the rethreaded DHFR was determined to 1.55A structure demonstrated the success of the rethreading process. Protein rethreading can be a powerful tool for the design of a large array of novel protein folds. 2640-Pos Board B17 Prediction of Protein Rigid Domains and Hinge Residues Based on Graph Theory and Elastic Network Model Julian Lee1, Jun Sim2, Jaehyun Sim3, Eunsung Park4. 1 Soongsil University, Seoul, Korea, Republic of, 2Dept. of Bioinformatics and Life Science, Soongsil University, Seoul, Korea, Republic of, 3Seoul National University, Seoul, Korea, Republic of, 4Apsun Dental Hospital, Seoul, Korea, Republic of. Many proteins undergo large-scale motions where relatively rigid domains move against each other. The prediction of rigid domains, as well as the hinge residues important for their relative movements, is important for various applications. We developed a novel method for protein rigid domain identification, DAGR (Domain Analysis base on GRaph theory), based on an exhaustive enumeration of maximal rigid domains, the rigid domains not fully contained within other domains. The computation is performed by mapping the problem to that of finding maximal cliques in a graph. A minimal set of rigid domains are then selected, which cover most of the protein with minimal overlap. Combining DAGR with elastic network model, we construct a method for predicting the rigid domains and the hinge regions of a protein. 2641-Pos Board B18 Characterizing the Statistical Properties of Protein Surfaces Ji Hyun Bak, Anne-Florence Bitbol, William Bialek. Princeton University, Princeton, NJ, USA. Proteins and their interactions form the body of the signaling transduction pathway in many living systems. In order to ensure the accuracy as well as the specificity of signaling, it is crucial that proteins recognize their correct