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ABSTRACT: Structural plasticity is often required for distinct microscopic steps during enzymatic reaction cycles. Adenylate kinase from Escherichia coli (AK(eco)) populates two major conformations in solution; the open (inactive) and closed (active) state, and the overall turnover rate is inversely proportional to the lifetime of the active conformation. Therefore, structural plasticity is intimately coupled to enzymatic turnover in AK(eco). Here, we probe the open to closed conformational equilibrium in the absence of bound substrate with NMR spectroscopy and molecular dynamics simulations. The conformational equilibrium in absence of substrate and, in turn, the turnover number can be modulated with mutational- and osmolyte-driven perturbations. Removal of one hydrogen bond between the ATP and AMP binding subdomains results in a population shift toward the open conformation and a resulting increase of k(cat). Addition of the osmolyte TMAO to AK(eco) results in population shift toward the closed conformation and a significant reduction of k(cat). The Michaelis constants (K(M)) scale with the change in k(cat), which follows from the influence of the population of the closed conformation for substrate binding affinity. Hence, k(cat) and K(M) are mutually dependent, and in the case of AK(eco), any perturbation that modulates k(cat) is mirrored with a proportional response in K(M). Thus, our results demonstrate that the equilibrium constant of a pre-existing conformational equilibrium directly affects enzymatic catalysis. From an evolutionary perspective, our findings suggest that, for AK(eco), there exists ample flexibility to obtain a specificity constant (k(cat)/K(M)) that commensurate with the exerted cellular selective pressure.
Journal of the American Chemical Society 09/2012; 134(40):16562-70. · 9.91 Impact Factor
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ABSTRACT: Evolution has selected a protein's sequence to be consistent with the native state geometry, as this configuration must be both thermodynamically stable and kinetically accessible to prevent misfolding and loss of function. In simple protein geometries, such as coiled-coil helical bundles, symmetry produces a competing, globally different, near mirror image with identical secondary structure and similar native contact interactions. Experimental techniques such as circular dichroism, which rely on probing secondary structure content, cannot readily distinguish these folds. Here, we want to clarify whether the native fold and mirror image are energetically competitive by investigating the free energy landscape of three-helix bundles. To prevent a bias from a specific computational approach, the present study employs the structure prediction forcefield PFF01/02, explicit solvent replica exchange molecular dynamics (REMD) with the Amber94 forcefield, and structure-based simulations based on energy landscape theory. We observe that the native fold and its mirror image have a similar enthalpic stability and are thermodynamically competitive. There is evidence that the mirror fold has faster folding kinetics and could function as a kinetic trap. All together, our simulations suggest that mirror images might not just be a computational annoyance but are competing folds that might switch depending on environmental conditions or functional considerations.
The Journal of Physical Chemistry B 04/2012; 116(23):6880-8. · 3.70 Impact Factor
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ABSTRACT: The presence of disulfide bonds in proteins has very important implications on the three-dimensional structure and folding of proteins. An adequate treatment of disulfide bonds in de-novo protein simulations is therefore very important. Here we present a computational study of a set of small disulfide-bridged proteins using an all-atom stochastic search approach and including various constraining potentials to describe the disulfide bonds. The proposed potentials can easily be implemented in any code based on all-atom force fields and employed in simulations to achieve an improved prediction of protein structure. Exploring different potential parameters and comparing the structures to those from unconstrained simulations and to experimental structures by means of a scoring function we demonstrate that the inclusion of constraining potentials improves the quality of final structures significantly. For some proteins (1KVG and 1PG1) the native conformation is visited only in simulations in presence of constraints. Overall, we found that the Morse potential has optimal performance, in particular for the β-sheet proteins.
Computational biology and chemistry 08/2011; 35(4):230-9. · 1.37 Impact Factor
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ABSTRACT: The presence of disulfide bonds leads to an interesting interplay between noncovalent intramolecular interactions and disulfide bond formation even in small proteins. Here we have investigated the folding mechanism of the 23-residue potassium channel blocker 1WQE and the 18-residue antimicrobial peptide protegrin-1 1PG1 , as two proteins containing disulfide bridges, in all-atom basin hopping simulations starting from completely extended conformations. The minimal-energy conformations deviate by only 2.1 and 1.2 A for 1WQE and 1PG1 , respectively, from their structurally conserved experimental conformations. A detailed analysis of their free energy surfaces demonstrates that the folding mechanism of disulfide-bridged proteins can vary dramatically from Levinthal's single-path scenario to a cooperative process consistent with the funnel paradigm of protein folding.
Biochemistry 09/2009; 48(34):8195-205. · 3.42 Impact Factor
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ABSTRACT: The presence of disulfide bonds leads to an interesting interplay between noncovalent intramolecular interactions and disulfide bond formation even in small proteins. Here we have investigated the folding mechanism of the 23-residue potassium channel blocker 1WQE and the 18-residue antimicrobial peptide protegrin-1 1PG1, as two proteins containing disulfide bridges, in all-atom basin hopping simulations starting from completely extended conformations. The minimal-energy conformations deviate by only 2.1 and 1.2 Å for 1WQE and 1PG1, respectively, from their structurally conserved experimental conformations. A detailed analysis of their free energy surfaces demonstrates that the folding mechanism of disulfide-bridged proteins can vary dramatically from Levinthal’s single-path scenario to a cooperative process consistent with the funnel paradigm of protein folding.
08/2009;
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ABSTRACT: All-atom free-energy methods offer a promising alternative to kinetic molecular mechanics simulations of protein folding and association. Here we report an accurate, transferable all-atom biophysical force field (PFF02) that stabilizes the native conformation of a wide range of proteins as the global optimum of the free-energy landscape. For 32 proteins of the ROSETTA decoy set and six proteins that we have previously folded with PFF01, we find near-native conformations with an average backbone RMSD of 2.14 A to the native conformation and an average Z-score of -3.46 to the corresponding decoy set. We used nonequilibrium sampling techniques starting from completely extended conformations to exhaustively sample the energy surface of three nonhomologous hairpin-peptides, a three-stranded beta-sheet, the all-helical 40 amino-acid HIV accessory protein, and a zinc-finger beta beta alpha motif, and find near-native conformations for the minimal energy for each protein. Using a massively parallel evolutionary algorithm, we also obtain a near-native low-energy conformation for the 54 amino-acid engrailed homeodomain. Our force field thus stabilized near-native conformations for a total of 20 proteins of all structure classes with an average RMSD of only 3.06 A to their respective experimental conformations.
Biophysical Journal 06/2009; 96(9):3483-94. · 3.65 Impact Factor
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ABSTRACT: Small beta hairpins have many distinct biological functions, including their involvement in chemokine and viral receptor recognition. The relevance of structural similarities between different hairpin loops with near homologous sequences is not yet understood, calling for the development of methods for de novo hairpin structure prediction and simulation. De novo folding of beta strands is more difficult than that of helical proteins because of nonlocal hydrogen bonding patterns that connect amino acids that are distant in the amino acid sequence and there is a large variety of possible hydrogen bond patterns. Here we use a greedy version of the basin hopping technique with our free-energy forcefield PFF02 to reproducibly and predictively fold the hairpin structure of a HIV-V3 loop. We performed 20 independent basin hopping runs for 500 cycles corresponding to 7.4 x 10(7) energy evaluations each. The lowest energy structure found in the simulation has a backbone root mean square deviation (bRMSD) of only 2.04 A to the native conformation. The lowest 9 out of the 20 simulations converged to conformations deviating less than 2.5 A bRMSD from native.
The Journal of Chemical Physics 04/2008; 128(10):105103. · 3.33 Impact Factor
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ABSTRACT: The search for efficient and predictive methods to describe the protein folding process at the all-atom level remains an important grand-computational challenge. The development of multi-teraflop architectures, such as the IBM BlueGene used in this study, has been motivated in part by the large computational requirements of such studies. Here we report the predictive all-atom folding of the forty-amino acid HIV accessory protein using an evolutionary stochastic optimization technique. We implemented the optimization method as a master-client model on an IBM BlueGene, where the algorithm scales near perfectly from 64 to 4096 processors in virtual processor mode. Starting from a completely extended conformation, we optimize a population of 64 conformations of the protein in our all-atom free-energy model PFF01. Using 2048 processors the algorithm predictively folds the protein to a near-native conformation with an RMS deviation of 3.43 A in < 24 h.
Journal of Computational Chemistry 01/2008; 28(16):2552-8. · 4.58 Impact Factor
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ABSTRACT: The reliable prediction of protein tertiary structure from the amino acid sequence remains challenging even for small proteins. We have developed an all-atom free-energy protein forcefield (PFF01) that we could use to fold several small proteins from completely extended conformations. Because the computational cost of de-novo folding studies rises steeply with system size, this approach is unsuitable for structure prediction purposes. We therefore investigate here a low-cost free-energy relaxation protocol for protein structure prediction that combines heuristic methods for model generation with all-atom free-energy relaxation in PFF01.
We use PFF01 to rank and cluster the conformations for 32 proteins generated by ROSETTA. For 22/10 high-quality/low quality decoy sets we select near-native conformations with an average Calpha root mean square deviation of 3.03 A/6.04 A. The protocol incorporates an inherent reliability indicator that succeeds for 78% of the decoy sets. In over 90% of these cases near-native conformations are selected from the decoy set. This success rate is rationalized by the quality of the decoys and the selectivity of the PFF01 forcefield, which ranks near-native conformations an average 3.06 standard deviations below that of the relaxed decoys (Z-score).
All-atom free-energy relaxation with PFF01 emerges as a powerful low-cost approach toward generic de-novo protein structure prediction. The approach can be applied to large all-atom decoy sets of any origin and requires no preexisting structural information to identify the native conformation. The study provides evidence that a large class of proteins may be foldable by PFF01.
BMC Structural Biology 02/2007; 7:12. · 2.48 Impact Factor
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Journal of Computational Chemistry. 01/2007; 28:2552-2558.
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Parallel Computing: Architectures, Algorithms and Applications, ParCo 2007, Forschungszentrum Jülich and RWTH Aachen University, Germany, 4-7 September 2007; 01/2007
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ABSTRACT: The performances of three different stochastic optimization methods for all-atom protein structure prediction are investigated and compared. We use the recently developed all-atom free-energy force field (PFF01), which was demonstrated to correctly predict the native conformation of several proteins as the global optimum of the free energy surface. The trp-cage protein (PDB-code 1L2Y) is folded with the stochastic tunneling method, a modified parallel tempering method, and the basin-hopping technique. All the methods correctly identify the native conformation, and their relative efficiency is discussed.
ChemPhysChem 01/2006; 6(12):2640-6. · 3.41 Impact Factor
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ABSTRACT: We recently developed an all-atom free energy force field (PFF01) for protein structure prediction with stochastic optimization
methods. We demonstrated that PFF01 correctly predicts the native conformation of several proteins as the global optimum of
the free energy surface. Here we review recent folding studies, which permitted the reproducible all-atom folding of the 20
amino-acid trp-cage protein, the 40-amino acid three-helix HIV accessory protein and the sixty amino acid bacterial ribosomal
protein L20 with a variety of stochastic optimization methods. These results demonstrate that all-atom protein folding can
be achieved with present day computational resources for proteins of moderate size.
12/2005: pages 557-572;
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Abhinav Verma
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ABSTRACT: Proteins are the workhorses of all cellular life. They constitute the building blocks and the machinery of all cells and typically function in specific three-dimensional conformations into which each protein folds. Currently over one million protein sequences are known, compared to about 40,000 structures deposited in the Protein Data Bank (the world-wide database of protein structures). Reliable theoretical methods for protein structure prediction could help to reduce the gap between sequence and structural databases and elucidate the biological information in structurally unresolved sequences. In this thesis we explore an approach for protein structure prediction and folding that is based on the Anfinsen’s hypothesis that most proteins in their native state are in thermodynamic equilibrium with their environment. We have developed a free energy forcefield (PFF02) that locates the native conformation of many proteins from all structural classes at the global minimum of the free-energy model. We have validated the forcefield against a large decoy set (Rosetta). The average root mean square deviation (RMSD) for the lowest energy structure for the 32 proteins of the decoy set was only 2.14 from the experimental conformation. We have successfully implemented and used stochastic optimization methods, such as the basin hopping technique and evolutionary algorithms for all atom protein structure prediction. The evolutionary algorithm performs exceptionally well on large supercomputational architectures, such as BlueGene and MareNostrum. Using the PFF02 forcefield, we were able to fold 13 proteins (12-56 amino acids), which include helix, sheet and mixed secondary structure. On average the predicted structure of these proteins deviated from their experimental conformation by only 2.89 RMSD. Proteine sind die nano-skaligen Maschinen der Zelle. Sie sind Bausteine und Funktionseinheiten aller Zellen und funktionieren typischerweise in spezifischen dreidimensionalen Konformationen, die sie als Endpunkt eines komplexen Faltungsprozesses annehmen. Gegenwärtig sind über eine Million Proteinsequenzen bekannt, es konnten jedoch nur etwa 40.000 Strukturen von Proteinen aufgelöst und in der Proteindatenbank hinterlegt werden. Verlässliche theoretische Methoden zu Proteinstrukturvorhersage könnten helfen, diese Lücke zwischen den Sequenz- und den strukturellen Datenbanken zu schließen und die biologische Information in den bislang strukturell unbekannten Proteinen zu entschlüsseln. In dieser Dissertation untersuchten wir einen Ansatz zur Proteinstrukturvorhersage und -faltung, der auf Anfinsons thermodynamischer Hypothese aufbaut, nach der sich Proteine in ihrem nativen Zustand im Gleichgewicht mit ihrer Umgebung befinden. Wir entwickelten daher ein Kraftfeld für die freie Energie von Proteinen (PFF02), das die nativen Konformationen vieler Proteine aller bekannten Strukturklassen als das globale Minimum des Modells der freien Energie beschreibt. Wir haben dieses Kraftfeld gegen die Strukturen des Rosetta Testdatensatzes getestet und fanden, dass die Strukturen mit der jeweils niedrigsten Energie für 32 Proteine dieses Datensatzes im Mittel nur 2,14 Å von der assoziierten experimentellen Konformation abwichen. Wir haben darüber hinaus stochastische Optimierungsverfahren, unter anderem die Basin-Hopping Methode und evolutionären Algorithmen, für die Proteinstrukturvorhersage und - faltung mit atomarer Auflösung entwickelt. Insbesondere der evolutionäre Algorithmus lieferte auf großen Supercomputern, wie zum Beispiel den BlueGene oder MareMonstrum Supercomputer- Clustern, hervorragende Ergebnisse. Mit dem PFF02 Kraftfeld waren wir in der Lage, 13 Proteine mit 12-56 Aminosäuren Länge mit helikaler, Faltblatt- oder gemischter Sekundärstruktur zu falten. Im Mittel wichen dabei die vorhergesagten Strukturen von den jeweiligen experimentell bekannten Strukturen dieser Proteine um nur 2,89 Å RMSD ab.
