Abhinav Verma

Basin hopping simulations for all-atom protein folding.

We investigate different protocols of the basin hopping technique for de novo protein folding. Using the protein free-energy force field PFF01 we report the reproducible all-atom folding of the 20-amino-acid tryptophan-cage protein [Protein Data Bank (PDB) code: 112y] and of the recently discovered 26-amino-acid potassium channel blocker (PDB code: 1wqc), which exhibits an unusual fold. We find that simulations with increasing cycle length and random starting temperatures perform best in comparison with other parametrizations. The basin hopping technique emerges as a simple but very efficient and robust workhorse for all-atom protein folding.

Modulation of a pre-existing conformational equilibrium tunes adenylate kinase activity

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.

Mirror Images as Naturally Competing Conformations in Protein Folding

Evolution has selected a proteins 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.

Protein structure prediction by all-atom free-energy refinement

BackgroundThe 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.ResultsWe 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 Cαroot mean square deviation of 3.03 Å/6.04 Å. 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).ConclusionAll-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.

A Free-Energy Approach for All-Atom Protein Simulation

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.

Investigation of the parallel tempering method for protein folding

We investigate the suitability and efficiency of an adapted version of the parallel tempering method for all-atom protein folding. We have recently developed an all-atom free energy force field (PFF01) for protein structure prediction with stochastic optimization methods. Here we report reproducible folding of the 20-amino-acid trp-cage protein and the conserved 40-amino-acid three-helix HIV accessory protein with an adapted parallel tempering method. We find that the native state, for both proteins, is correctly predicted to 2 A backbone root mean square deviation and analyse the efficiency of the simulation approach.

eSBMTools 1.0: enhanced native structure-based modeling tools

MOTIVATION Molecular dynamics simulations provide detailed insights into the structure and function of biomolecular systems. Thus, they complement experimental measurements by giving access to experimentally inaccessible regimes. Among the different molecular dynamics techniques, native structure-based models (SBMs) are based on energy landscape theory and the principle of minimal frustration. Typically used in protein and RNA folding simulations, they coarse-grain the biomolecular system and/or simplify the Hamiltonian resulting in modest computational requirements while achieving high agreement with experimental data. eSBMTools streamlines running and evaluating SBM in a comprehensive package and offers high flexibility in adding experimental- or bioinformatics-derived restraints. RESULTS We present a software package that allows setting up, modifying and evaluating SBM for both RNA and proteins. The implemented workflows include predicting protein complexes based on bioinformatics-derived inter-protein contact information, a standardized setup of protein folding simulations based on the common PDB format, calculating reaction coordinates and evaluating the simulation by free-energy calculations with weighted histogram analysis method or by phi-values. The modules interface with the molecular dynamics simulation program GROMACS. The package is open source and written in architecture-independent Python2. AVAILABILITY http://sourceforge.net/projects/esbmtools/. CONTACT alexander.schug@kit.edu. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.

All-atom de novo protein folding with a scalable evolutionary algorithm.

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 Å in <24 h.

Predictive and reproducible de novo all-atom folding of a β-hairpin loop in an improved free-energy forcefield

We have recently improved our free-energy forcefield for all-atom de novo protein folding to address the folding of proteins with beta-sheet secondary structure. The folding of beta strands is nevertheless more difficult than that of helical proteins, because of non-local interactions between regions of the protein chain that are not consecutive in the amino acid sequence. Here we use a greedy version of the basin-hopping technique with our free-energy forcefield PFF02 to reproducibly and predictively fold the structure of a /3-hairpin loop. The lowest energy structure found in the simulation has a backbone root mean square deviation of only 2.62 A to the native conformation. The side-chain alignment is also correctly predicted, as are four of the five backbone hydrogen bonds found in the native structure.

Folding path and funnel scenarios for two small disulfide-bridged proteins.

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 Levinthals single-path scenario to a cooperative process consistent with the funnel paradigm of protein folding.