- Email: [email protected]
A Predicted Structure of the Angiomotin Lipid Binding Domain
Sunday, February 12, 2017 275-Pos Board B40 Polarizable Amoeba Force Field Metadynamics with Minimization Predicts Missing Protein Loops Armin Avdic1, Mallory R. Tollefson2, Nicole Tatro2, Stephen D. LuCore2, Jacob M. Litman3, Timothy D. Fenn4, Michael J. Schnieders2,3. 1 Carver College of Medicine, The University of Iowa, Iowa City, IA, USA, 2 Biomedical Engineering Department, The University of Iowa, Iowa City, IA, USA, 3Biochemistry Department, The University of Iowa, Iowa City, IA, USA, 4Boehringer Ingelheim, Ridgefield, CT, USA. Computational methods developed to find the global free energy minimum of amino acid sequences are increasingly successful, but limitations in both accuracy and efficiency remain. Optimization algorithms are typically focused on proteins of modest size (i.e. of approximately 100 residues) and utilize potential energy functions based on fixed charged force fields, statistical or knowledge based potentials, and/or potentials incorporating experimental data. Although the aforementioned methods are widely used, known limitations include 1) search protocols that are inefficient or not deterministic due to rough energy landscapes characterized by large energy barriers between multiple minima and 2) use of a target function whose global minimum does not correspond to the actual free energy minimum. To overcome the first limitation, this work describes a global optimization approach based on metadynamics to drive the search of conformational space toward unexplored regions by adding a time-dependent bias to the objective function. To overcome the second limitation, a hybrid objective function is defined as the sum of the polarizable AMOEBA polarizable force field and an experimental X-ray crystallography target. As metadynamics drives the search, periodic quenching via local minimization is used to access structure quality via evaluation of Rwork. Thus, the overall method is called AMOEBA Metadynamics with Minimization (AMM), and is suitable for optimization of side-chains, ligand binding poses, protein loops or even protein complexes. Here we focus on characterizing the ability of AMM to elucidate the structural details of missing protein loops, which are often excluded from experimental X-ray crystallography structures due to conformational heterogeneity and/or limitations in the resolution of the data. We first show that the correlation between experimental data and AMOEBA structural minima is stronger than that for OPLS-AA/L (i.e. a fixed charge force field). Next, missing protein loops are optimized using 5 nsec of sampling for both AMM and simulated annealing with OPLS-AA/L. The AMM procedure provides more accurate structures in terms of both experimental (i.e. lower Rfree values) and structural metrics (i.e. MolProbity). In addition to providing more accurate loop conformations, AMM converged faster than the simulated annealing protocol. Overall, this work suggests that AMM is well-suited to refine or predict the coordinates of missing amino acid residues and/or protein loops due to both the increased accuracy of the target function relative to OPLS-AA/L and more rapid convergence of the metadynamics driven search compared to simulation annealing. 276-Pos Board B41 Improving 3D Structure Prediction of Beta-Barrel Membrane Proteins Wei Tian1, Hammad Naveed2, Jie Liang1. 1 University of Illinois at Chicago, Chicago, IL, USA, 2Toyota Technological Institute at Chicago, Chicago, IL, USA. Beta-barrel membrane proteins are found in the outer membrane of gramnegative bacteria, mitochondria, and chloroplasts. They carry out diverse biological functions, including pore formation, membrane anchoring, enzyme activity, and bacterial virulence. In addition, beta-barrel membrane proteins increasingly serve as scaffolds for bacterial surface display and nanoporebased DNA sequencing. Due to difficulties in experimental structure determination, they are sparsely represented in the protein structure databank and computational methods are required to understand their biophysical principles. We have developed a novel 3D structural template for the construction of betabarrel membrane proteins, which captures major geometric properties of these proteins. With the help of the prediction procedure of strand registers that we developed, we have achieved a high accuracy for the structure prediction of beta-barrel proteins. In addition, for the beta-barrel proteins with irregular barrel shape, we have further developed an elastic model, which characterizes bending and curvatures of the barrels, to improve the structure prediction of these proteins. 277-Pos Board B42 De Novo Protein Structure Prediction by Big Data and Deep Learning Sheng Wang, Jinbo Xu. Toyota Technological Institute at Chicago, Chicago, IL, USA. Recently ab initio protein folding using predicted contacts as restraints has made some progress, but it requires accurate contact prediction, which by existing methods can only be achieved on some large-sized protein families with
55a
thousands of sequence homologs. To improve contact prediction for smallsized protein families, we employ the emerging deep learning technique from Computer Science, a powerful technique that can learn complex patterns from large datasets and has revolutionized object and speech recognition, machine translation and the GO game. Our deep learning model for contact prediction is formed by two deep residual neural networks. The first one learns relationship between contacts and sequential features (residue conservation and predicted secondary structure) from thousands of protein families, while the second one learns the occurring patterns of contacts and their relationship with pairwise features such as contact potential, residue co-evolution strength and the output of the first network. Experimental results suggest that our deep learning method greatly improves contact prediction and contact-assisted folding, especially for small-sized protein families. Tested on 579 proteins dissimilar to training proteins, the average top L (L is sequence length) long-range prediction accuracy of our method, the representative direct evolutionary coupling method CCMpred and the CASP11 winner MetaPSICOV is 0.47, 0.21 and 0.30, respectively; their average top L/10 long-range accuracy is 0.77, 0.47 and 0.59, respectively. Even without using force fields, our predicted contacts allow us to correctly fold 203 test proteins, while MetaPSICOV and CCMpred contacts can do only 79 and 62 proteins, respectively. In the three weeks of blind test with the weekly benchmark CAMEO (http://www. cameo3d.org/), our method successfully folded three large hard targets with a new fold and only 1.3L-2.3L sequence homologs while all template-based methods failed. 278-Pos Board B43 Next Generation Evolutionary Sampling and Energy Function Guided ab initio Protein Structure Prediction Avdesh Mishra, Md Tamjidul Hoque. Computer Science Department, University of New Orleans, New Orleans, LA, USA. The conformation of a protein is vital to understand its function. Homology and ab initio modeling are the two major strategies to solve the protein structure prediction from sequence. The homology methods are not applicable in the absence of homologous sequences. This makes the ab initio modeling unavoidable. The development of ab initio method hinges on the effective conformational space sampling and the accurate energy function to guide the search process. In addition, recent studies demonstrates that it is possible to sample and predict good quality protein structure without using native fragments from known protein structures. Towards this goal, we developed an ab initio method that applies a memory assisted Evolutionary Algorithm (EA) to sample the energy hyper-surface of the protein folding process, looking for the global minimum or the native fold of the protein. Sampling of the energy hypersurface of the protein is achieved by novel mutation and crossover operations based on angular rotation and translation capabilities. Furthermore, the crossover operations in current generation are enhanced by the use of the best parent selected from previous generation. In addition, we developed and employed a knowledge-based novel energy function, called 3DIGARS, which can differentiate the native structure that corresponds to the most thermodynamically stable state, compared to the possible decoy structures most effectively. The 3DIGARS energy function is an optimized combination of crucial properties such as hydrophobic versus hydrophilic properties, sequence-specific predicted accessibility and ubiquitous phi-psi angular characterization. The samples obtained from the sampling of the energy hyper-surface are scored using 3DIGARS and passed through the EA operators for natural selection and processing. The merits of the proposed approach has been rigorously examined and found to be effective. 279-Pos Board B44 A Predicted Structure of the Angiomotin Lipid Binding Domain Ann C. Kimble-Hill, Cameron J. Peck, Piiamaria S. Virtanen. Biochemistry & Molecular Biology, Indiana University School of Medicine, Indianapolis, IN, USA. Amots are a family of adapter proteins that modulate cellular polarity, differentiation, proliferation, and migration. Amot family members also have a characteristic lipid-binding domain, the coiled coil homology (ACCH) Domain that selectively targets the protein to membranes, which has been directly linked to its regulatory role in the cell. Therefore, we endeavored to understand the structure-function relationship of this domain with the desire to find ways to modulate these signaling pathways. After many failed attempts to crystallize the ACCH domain of each of the Amot family members for structural analysis, we decided to pursue homologous models that could be refined using small angle x-ray scattering data. Theoretical models were produced using the Zhang suite programs I-TASSER and LOMETS and then refined and analyzed using Coot and PyMol modeling software based on RMSD, C-score, TM-score, and
56a
Sunday, February 12, 2017
template redundancy. Top models were then compared to SAXS data for further model selection and refinement. As a result, we present a theoretical model of the domain that is driven by alpha helices and short random coil regions. These alpha helical regions form a classic dimer interface followed by two wide spread legs that we predict to be the lipid binding interface. Finally, we validate the model presented with several lipid binding assays, which leads to a suggested mechanism that links ACCH lipid binding, membrane deformation, and vesicle fusion functions. 280-Pos Board B45 Self-Association and Conformational Stability of NAMPT Protein Trivikram R. Molugu1, Udeep Chawla1, Annie Huang1, Radu C. Oita2, Ting Wang2, Michael F. Brown1,3, Joe G.N. Garcia2. 1 Chemistry and Biochemistry, University of Arizona, Tucson, AZ, USA, 2 Department of Medicine, University of Arizona, Tucson, AZ, USA, 3 Department of Physics, University of Arizona, Tucson, AZ, USA. Nicotinamide phosphoribosyltransferase (NAMPT), also known as Visfatin and Pre B-cell colony enhancing factor (PBEF), is a rate-limiting enzyme in the salvage pathway required for nicotinamide adenine dinucleotide (NAD) biosynthesis. It is a highly conserved 52-kDa protein, found in living species from bacteria to humans [1]. The protein serves as a cytokine and is involved in cellular regulation influencing cancer, ischemia, obesity, and type-II diabetes [2, 3]. Despite being involved in many regulatory pathways, there is a paucity of information concerning the function of NAMPT, due to the limitations of in vivo assays, and lack of expression systems for the protein. Here, we successfully expressed the NAMPT protein using the pET-SUMO expression vector in E.coli strain SHuffle containing a hexa-His tag for protein purification. Activity assays demonstrated functionality of the protein. Moreover, initial biophysical characterization of the protein using circular dichroism revealed secondary structural elements consistent with crystallographic data. Dynamic light scattering showed the protein exists as large oligomeric units potentially involved in the NAMPT signal amplification pathway. Hydropathy analysis indicated possible hydrophobic patches on the protein surface that explains the native oligomeric state. Most striking, we discovered that NAMPT can be solubilized in n-dodecyl-b-D-maltopyranoside detergent in monomeric form. These findings open opportunities for further structural and functional investigations. Presently we are optimizing conditions for NMR experiments on NAMPT protein. These methods [4] are complementary to X-ray crystallography, and provide valuable information on the structure and dynamics, offering an important tool for understanding biological functioning. [1] T. Wang et al. (2006) Nat. Struct. Mol. Biol 13, 661. [2] S. M. Camp et al. (2015) Sci. Rep. 5, 13135. [3] A. Fukuhara et al. (2005) Science 307, 426. [4] T. R. Molugu et al. (2016) Chem. Rev. (in press). 281-Pos Board B46 The Interval Branch-And-Prune Algorithm for the Protein Structure Determination The´re`se E. Malliavin1, Bradley Worley1, Benjamin Bardiaux1, Guillaume Bouvier1, Mohamed Machat1, Andrea Cassioli2, Carlile Lavor3, Leo Liberti2, Michael Nilges1. 1 Institut Pasteur, Paris, France, 2Ecole Polytechnique, Paris, France, 3 University of Campinas, Campinas, Brazil. A general trend of structural biology is the switch of a rigid description of protein structures, as in the first X-ray structures in the 50’s, to a flexible description, in which each protein populate several distinct conformations or even a continuum of conformations. This flexibility was shown in numerous cases to have a crucial importance in the function of proteins. Most of the methods for bio-molecular structure calculations are, up to now, based on a combination of sampling and optimization, which does not allow a systematic exploration of the conformational space. But, in the case of highly flexible bio-molecules, a systematic (or global) exploration of the conformational space would be very welcome. On the other hand, the interval branch-and-prune (iBP) approach has been proposed as a method for allowing a global optimization of molecular structure under distance restraints, the so-called Distance Geometry problem. A recursive implementation of this algorithm has permitted to apply this approach on small structures of proteins in alpha-bundles for which few long-range distance restraints were known. Here, we are going to present the results obtained on a set of protein structures with sizes from 24 to 100 residues, displaying various secondary structures and topology. The distance restraints present on these structures were chosen to contain exclusively short-range information. The results obtained with various sets of distance restraints, including exact values and interval of values, will be presented, in order to experimentally eval-
uate the complexity of the algorithm on real-case of protein structure determination. Several procedures of acceleration will be used in order to allow a complete exploration of the tree describing the molecular Distance Geometry problem. The efficiency of the exploration will be evaluated using the selforganizing map (SOM) clustering approach, and the quality of obtained conformations with respect to Ramachandran plots and steric clashes will be evaluated. References: Cassioli A, Bardiaux B, Bouvier G, Mucherino A, Alves R, Liberti L, Nilges M, Lavor C and Malliavin TE. An algorithm to enumerate all possible protein conformations verifying a set of distance constraints. BMC Bioinformatics, 28;16:23 (2015). Lavor C., Alves R., Figueiredo W., Petraglia A., Maculan N. Clifford Algebra and the Discretizable Molecular Distance Geometry Problem. Adv. Appl. Clifford Algebras 25 (2015), 925-942.
Protein Stability 282-Pos Board B47 Biological Roles of Protein Hyperstability: Implications for Biotechnology Wilfredo Colo´n, Ke Xia, Jennifer Church, Jayeeta Sen, Jane Thibeault, Hannah S. Trasatti. Chemistry and Chemical Biology, Rensselaer Polytechnic Institute, Troy, NY, USA. Although most proteins in nature are marginally stable, some proteins are hyperstable, as indicated by their resistance to degradation, even under relatively harsh conditions. The hyperstability of such proteins is usually under kinetic control due to a high-energy barrier for unfolding that virtually traps them in a specific conformation. Although the protective role of protein kinetic stability is well established, relatively little is known about the extent of biological roles related to this biophysical property. We have demonstrated a correlation between kinetic stability and a protein’s resistance to the denaturing detergent SDS, and have developed electrophoresis assays that in combination with proteomics analysis allow the identification of kinetically stable proteins in complex biological samples. We have applied these methods to discover hyperstable proteins in diverse systems, including mesophilic and thermophilic bacteria, beans, and human plasma. The results of these studies have revealed novel insight about the biological significance and roles of protein kinetic stability. In addition, the analysis of a growing list of SDS-resistant proteins generated from these studies is enhancing our understanding of the structural basis of protein kinetic stability. 283-Pos Board B48 Volumetrically Derived Thermodynamic Profile of Interactions of Urea with a Native Protein Ikbae Son, Tigran Chalikian. University of Toronto, Toronto, ON, Canada. We report the first experimental characterization of the thermodynamic profile of urea binding to a native protein. We measured the volumetric parameters of lysozyme at pH 7.0 as a function of urea within a temperature range of 18 to 45 C. At neutral pH, lysozyme retains its native conformation between 0 and 8 M urea over the entire temperature range studied. Consequently, our measured volumetric properties solely reflect the interactions of urea with the native protein and do not involve contributions from urea-induced conformational transitions. We treated our data within the framework of a statistical thermodynamic analytical model in which ureaprotein interactions are viewed as solvent exchange in the vicinity of the protein. The van’t Hoff analysis of the temperature dependence of the equilibrium constant, k, for the urea-protein binding reaction produced changes in free energy, DG , enthalpy, DH , and entropy, DS , accompanying the binding. The thermodynamic profile of urea-protein interactions, in conjunction with published MD simulation results, is consistent with the picture in which urea molecules, being underhydrated in the bulk, form strong, enthalpically favorable interactions with the surface protein groups while paying a high entropic price. We discuss ramifications of our results for providing insights into the combined effect of urea, temperature, and pressure on the conformational preferences of proteins. 284-Pos Board B49 Sub-State Conformations of the Mesophilic and Psychrophilic Lactate Dehydrogenases Preceding Irreversible Thermal Inactivation Sergei Khrapunov, Eric Chang, Robert Callender. Biochemistry, Albert Einstein College of Medicine, Bronx, NY, USA. The thermodynamics of oxamate binding and the temperature stability of the glycolytic enzyme lactate dehydrogenase from porcine heart, phLDH (mesophilic Sus scrofa) and from mackerel icefish, cgLDH (psychrophilic
55a
thousands of sequence homologs. To improve contact prediction for smallsized protein families, we employ the emerging deep learning technique from Computer Science, a powerful technique that can learn complex patterns from large datasets and has revolutionized object and speech recognition, machine translation and the GO game. Our deep learning model for contact prediction is formed by two deep residual neural networks. The first one learns relationship between contacts and sequential features (residue conservation and predicted secondary structure) from thousands of protein families, while the second one learns the occurring patterns of contacts and their relationship with pairwise features such as contact potential, residue co-evolution strength and the output of the first network. Experimental results suggest that our deep learning method greatly improves contact prediction and contact-assisted folding, especially for small-sized protein families. Tested on 579 proteins dissimilar to training proteins, the average top L (L is sequence length) long-range prediction accuracy of our method, the representative direct evolutionary coupling method CCMpred and the CASP11 winner MetaPSICOV is 0.47, 0.21 and 0.30, respectively; their average top L/10 long-range accuracy is 0.77, 0.47 and 0.59, respectively. Even without using force fields, our predicted contacts allow us to correctly fold 203 test proteins, while MetaPSICOV and CCMpred contacts can do only 79 and 62 proteins, respectively. In the three weeks of blind test with the weekly benchmark CAMEO (http://www. cameo3d.org/), our method successfully folded three large hard targets with a new fold and only 1.3L-2.3L sequence homologs while all template-based methods failed. 278-Pos Board B43 Next Generation Evolutionary Sampling and Energy Function Guided ab initio Protein Structure Prediction Avdesh Mishra, Md Tamjidul Hoque. Computer Science Department, University of New Orleans, New Orleans, LA, USA. The conformation of a protein is vital to understand its function. Homology and ab initio modeling are the two major strategies to solve the protein structure prediction from sequence. The homology methods are not applicable in the absence of homologous sequences. This makes the ab initio modeling unavoidable. The development of ab initio method hinges on the effective conformational space sampling and the accurate energy function to guide the search process. In addition, recent studies demonstrates that it is possible to sample and predict good quality protein structure without using native fragments from known protein structures. Towards this goal, we developed an ab initio method that applies a memory assisted Evolutionary Algorithm (EA) to sample the energy hyper-surface of the protein folding process, looking for the global minimum or the native fold of the protein. Sampling of the energy hypersurface of the protein is achieved by novel mutation and crossover operations based on angular rotation and translation capabilities. Furthermore, the crossover operations in current generation are enhanced by the use of the best parent selected from previous generation. In addition, we developed and employed a knowledge-based novel energy function, called 3DIGARS, which can differentiate the native structure that corresponds to the most thermodynamically stable state, compared to the possible decoy structures most effectively. The 3DIGARS energy function is an optimized combination of crucial properties such as hydrophobic versus hydrophilic properties, sequence-specific predicted accessibility and ubiquitous phi-psi angular characterization. The samples obtained from the sampling of the energy hyper-surface are scored using 3DIGARS and passed through the EA operators for natural selection and processing. The merits of the proposed approach has been rigorously examined and found to be effective. 279-Pos Board B44 A Predicted Structure of the Angiomotin Lipid Binding Domain Ann C. Kimble-Hill, Cameron J. Peck, Piiamaria S. Virtanen. Biochemistry & Molecular Biology, Indiana University School of Medicine, Indianapolis, IN, USA. Amots are a family of adapter proteins that modulate cellular polarity, differentiation, proliferation, and migration. Amot family members also have a characteristic lipid-binding domain, the coiled coil homology (ACCH) Domain that selectively targets the protein to membranes, which has been directly linked to its regulatory role in the cell. Therefore, we endeavored to understand the structure-function relationship of this domain with the desire to find ways to modulate these signaling pathways. After many failed attempts to crystallize the ACCH domain of each of the Amot family members for structural analysis, we decided to pursue homologous models that could be refined using small angle x-ray scattering data. Theoretical models were produced using the Zhang suite programs I-TASSER and LOMETS and then refined and analyzed using Coot and PyMol modeling software based on RMSD, C-score, TM-score, and
56a
Sunday, February 12, 2017
template redundancy. Top models were then compared to SAXS data for further model selection and refinement. As a result, we present a theoretical model of the domain that is driven by alpha helices and short random coil regions. These alpha helical regions form a classic dimer interface followed by two wide spread legs that we predict to be the lipid binding interface. Finally, we validate the model presented with several lipid binding assays, which leads to a suggested mechanism that links ACCH lipid binding, membrane deformation, and vesicle fusion functions. 280-Pos Board B45 Self-Association and Conformational Stability of NAMPT Protein Trivikram R. Molugu1, Udeep Chawla1, Annie Huang1, Radu C. Oita2, Ting Wang2, Michael F. Brown1,3, Joe G.N. Garcia2. 1 Chemistry and Biochemistry, University of Arizona, Tucson, AZ, USA, 2 Department of Medicine, University of Arizona, Tucson, AZ, USA, 3 Department of Physics, University of Arizona, Tucson, AZ, USA. Nicotinamide phosphoribosyltransferase (NAMPT), also known as Visfatin and Pre B-cell colony enhancing factor (PBEF), is a rate-limiting enzyme in the salvage pathway required for nicotinamide adenine dinucleotide (NAD) biosynthesis. It is a highly conserved 52-kDa protein, found in living species from bacteria to humans [1]. The protein serves as a cytokine and is involved in cellular regulation influencing cancer, ischemia, obesity, and type-II diabetes [2, 3]. Despite being involved in many regulatory pathways, there is a paucity of information concerning the function of NAMPT, due to the limitations of in vivo assays, and lack of expression systems for the protein. Here, we successfully expressed the NAMPT protein using the pET-SUMO expression vector in E.coli strain SHuffle containing a hexa-His tag for protein purification. Activity assays demonstrated functionality of the protein. Moreover, initial biophysical characterization of the protein using circular dichroism revealed secondary structural elements consistent with crystallographic data. Dynamic light scattering showed the protein exists as large oligomeric units potentially involved in the NAMPT signal amplification pathway. Hydropathy analysis indicated possible hydrophobic patches on the protein surface that explains the native oligomeric state. Most striking, we discovered that NAMPT can be solubilized in n-dodecyl-b-D-maltopyranoside detergent in monomeric form. These findings open opportunities for further structural and functional investigations. Presently we are optimizing conditions for NMR experiments on NAMPT protein. These methods [4] are complementary to X-ray crystallography, and provide valuable information on the structure and dynamics, offering an important tool for understanding biological functioning. [1] T. Wang et al. (2006) Nat. Struct. Mol. Biol 13, 661. [2] S. M. Camp et al. (2015) Sci. Rep. 5, 13135. [3] A. Fukuhara et al. (2005) Science 307, 426. [4] T. R. Molugu et al. (2016) Chem. Rev. (in press). 281-Pos Board B46 The Interval Branch-And-Prune Algorithm for the Protein Structure Determination The´re`se E. Malliavin1, Bradley Worley1, Benjamin Bardiaux1, Guillaume Bouvier1, Mohamed Machat1, Andrea Cassioli2, Carlile Lavor3, Leo Liberti2, Michael Nilges1. 1 Institut Pasteur, Paris, France, 2Ecole Polytechnique, Paris, France, 3 University of Campinas, Campinas, Brazil. A general trend of structural biology is the switch of a rigid description of protein structures, as in the first X-ray structures in the 50’s, to a flexible description, in which each protein populate several distinct conformations or even a continuum of conformations. This flexibility was shown in numerous cases to have a crucial importance in the function of proteins. Most of the methods for bio-molecular structure calculations are, up to now, based on a combination of sampling and optimization, which does not allow a systematic exploration of the conformational space. But, in the case of highly flexible bio-molecules, a systematic (or global) exploration of the conformational space would be very welcome. On the other hand, the interval branch-and-prune (iBP) approach has been proposed as a method for allowing a global optimization of molecular structure under distance restraints, the so-called Distance Geometry problem. A recursive implementation of this algorithm has permitted to apply this approach on small structures of proteins in alpha-bundles for which few long-range distance restraints were known. Here, we are going to present the results obtained on a set of protein structures with sizes from 24 to 100 residues, displaying various secondary structures and topology. The distance restraints present on these structures were chosen to contain exclusively short-range information. The results obtained with various sets of distance restraints, including exact values and interval of values, will be presented, in order to experimentally eval-
uate the complexity of the algorithm on real-case of protein structure determination. Several procedures of acceleration will be used in order to allow a complete exploration of the tree describing the molecular Distance Geometry problem. The efficiency of the exploration will be evaluated using the selforganizing map (SOM) clustering approach, and the quality of obtained conformations with respect to Ramachandran plots and steric clashes will be evaluated. References: Cassioli A, Bardiaux B, Bouvier G, Mucherino A, Alves R, Liberti L, Nilges M, Lavor C and Malliavin TE. An algorithm to enumerate all possible protein conformations verifying a set of distance constraints. BMC Bioinformatics, 28;16:23 (2015). Lavor C., Alves R., Figueiredo W., Petraglia A., Maculan N. Clifford Algebra and the Discretizable Molecular Distance Geometry Problem. Adv. Appl. Clifford Algebras 25 (2015), 925-942.
Protein Stability 282-Pos Board B47 Biological Roles of Protein Hyperstability: Implications for Biotechnology Wilfredo Colo´n, Ke Xia, Jennifer Church, Jayeeta Sen, Jane Thibeault, Hannah S. Trasatti. Chemistry and Chemical Biology, Rensselaer Polytechnic Institute, Troy, NY, USA. Although most proteins in nature are marginally stable, some proteins are hyperstable, as indicated by their resistance to degradation, even under relatively harsh conditions. The hyperstability of such proteins is usually under kinetic control due to a high-energy barrier for unfolding that virtually traps them in a specific conformation. Although the protective role of protein kinetic stability is well established, relatively little is known about the extent of biological roles related to this biophysical property. We have demonstrated a correlation between kinetic stability and a protein’s resistance to the denaturing detergent SDS, and have developed electrophoresis assays that in combination with proteomics analysis allow the identification of kinetically stable proteins in complex biological samples. We have applied these methods to discover hyperstable proteins in diverse systems, including mesophilic and thermophilic bacteria, beans, and human plasma. The results of these studies have revealed novel insight about the biological significance and roles of protein kinetic stability. In addition, the analysis of a growing list of SDS-resistant proteins generated from these studies is enhancing our understanding of the structural basis of protein kinetic stability. 283-Pos Board B48 Volumetrically Derived Thermodynamic Profile of Interactions of Urea with a Native Protein Ikbae Son, Tigran Chalikian. University of Toronto, Toronto, ON, Canada. We report the first experimental characterization of the thermodynamic profile of urea binding to a native protein. We measured the volumetric parameters of lysozyme at pH 7.0 as a function of urea within a temperature range of 18 to 45 C. At neutral pH, lysozyme retains its native conformation between 0 and 8 M urea over the entire temperature range studied. Consequently, our measured volumetric properties solely reflect the interactions of urea with the native protein and do not involve contributions from urea-induced conformational transitions. We treated our data within the framework of a statistical thermodynamic analytical model in which ureaprotein interactions are viewed as solvent exchange in the vicinity of the protein. The van’t Hoff analysis of the temperature dependence of the equilibrium constant, k, for the urea-protein binding reaction produced changes in free energy, DG , enthalpy, DH , and entropy, DS , accompanying the binding. The thermodynamic profile of urea-protein interactions, in conjunction with published MD simulation results, is consistent with the picture in which urea molecules, being underhydrated in the bulk, form strong, enthalpically favorable interactions with the surface protein groups while paying a high entropic price. We discuss ramifications of our results for providing insights into the combined effect of urea, temperature, and pressure on the conformational preferences of proteins. 284-Pos Board B49 Sub-State Conformations of the Mesophilic and Psychrophilic Lactate Dehydrogenases Preceding Irreversible Thermal Inactivation Sergei Khrapunov, Eric Chang, Robert Callender. Biochemistry, Albert Einstein College of Medicine, Bronx, NY, USA. The thermodynamics of oxamate binding and the temperature stability of the glycolytic enzyme lactate dehydrogenase from porcine heart, phLDH (mesophilic Sus scrofa) and from mackerel icefish, cgLDH (psychrophilic