Folding mechanisms of individual beta-hairpins in a Go model of Pin1 WW domain by all-atom molecular dynamics simulations

Molecular Dynamics Simulations for Rationalizing Polymer Bioconjugation Strategies: Challenges, Recent Developments, and Future Opportunities

The covalent modification of proteins with polymers is a well-established method for improving the pharmacokinetic properties of therapeutically valuable biologics. The conjugated polymer chains of the resulting hybrid represent highly flexible macromolecular structures. As the dynamics of such systems remain rather elusive for established experimental techniques from the field of protein structure elucidation, molecular dynamics simulations have proven as a valuable tool for studying such conjugates at an atomistic level, thereby complementing experimental studies. With a focus on new developments, this review aims to provide researchers from the polymer bioconjugation field with a concise and up to date overview of such approaches. After introducing basic principles of molecular dynamics simulations, as well as methods for and potential pitfalls in modeling bioconjugates, the review illustrates how these computational techniques have contributed to the understanding of bioconjugates and bioconjugation strategies in the recent past and how they may lead to a more rational design of novel bioconjugates in the future.

Probing conformational transitions of PIN1 from L. major during chemical and thermal denaturation

PIN1 proteins are a class of peptidyl prolyl cis-trans isomerases (PPIases), which have been implicated in numerous cellular functions like cell cycle progression, transcriptional control, signal transduction, promotion of oncogenesis and host-parasite interactions. In this work, the unfolding mechanism of a single domain PIN1 from Leishmania major (LmPIN1) has been characterized during thermal and denaturant-induced unfolding by differential scanning calorimetry (DSC), fluorescence and circular dichroism. Further, MD simulations have been performed to structurally probe the possible stages of its unfolding process. Both the fluorescence and CD data confirm classical two-state unfolding transitions for urea and GdnHCl. The thermal unfolding of LmPIN1, characterized by DSC, could optimally be fitted to a non two-state transition curve exhibiting two T_m's (53 °C and 57 °C) suggesting the possibility of an intermediate. Thermal unfolding of the modeled LmPIN1 by MD simulation shows that the unfolding process is initiated by increased fluctuations (dynamics) spanning residues 70-80, followed by perturbations in the sheet system and disjuncture of helix-sheet packing. Importantly, simulation and fluorescence quenching studies clearly suggest the possibility of the presence of residual structures of LmPIN1 even after complete denaturation.

Modeling the mechanism of CLN025 beta-hairpin formation

Beta-hairpins are substructures found in proteins that can lend insight into more complex systems. Furthermore, the folding of beta-hairpins is a valuable test case for benchmarking experimental and theoretical methods. Here, we simulate the folding of CLN025, a miniprotein with a beta-hairpin structure, at its experimental melting temperature using a range of state-of-the-art protein force fields. We construct Markov state models in order to examine the thermodynamics, kinetics, mechanism, and rate-determining step of folding. Mechanistically, we find the folding process is rate-limited by the formation of the turn region hydrogen bonds, which occurs following the downhill hydrophobic collapse of the extended denatured protein. These results are presented in the context of established and contradictory theories of the beta-hairpin folding process. Furthermore, our analysis suggests that the AMBER-FB15 force field, at this temperature, best describes the characteristics of the full experimental CLN025 conformational ensemble, while the AMBER ff99SB-ILDN and CHARMM22* force fields display a tendency to overstabilize the native state.

Implication of the cause of differences in 3D structures of proteins with high sequence identity based on analyses of amino acid sequences and 3D structures

BACKGROUND

Proteins that share a high sequence homology while exhibiting drastically different 3D structures are investigated in this study. Recently, artificial proteins related to the sequences of the GA and IgG binding GB domains of human serum albumin have been designed. These artificial proteins, referred to as GA and GB, share 98% amino acid sequence identity but exhibit different 3D structures, namely, a 3α bundle versus a 4β + α structure. Discriminating between their 3D structures based on their amino acid sequences is a very difficult problem. In the present work, in addition to using bioinformatics techniques, an analysis based on inter-residue average distance statistics is used to address this problem.

RESULTS

It was hard to distinguish which structure a given sequence would take only with the results of ordinary analyses like BLAST and conservation analyses. However, in addition to these analyses, with the analysis based on the inter-residue average distance statistics and our sequence tendency analysis, we could infer which part would play an important role in its structural formation.

CONCLUSIONS

The results suggest possible determinants of the different 3D structures for sequences with high sequence identity. The possibility of discriminating between the 3D structures based on the given sequences is also discussed.

Investigating the Release of a Hydrophobic Peptide from Matrices of Biodegradable Polymers: An Integrated Method Approach

The objectives of this work were: (1) to select suitable compositions of tyrosine-derived polycarbonates for controlled delivery of voclosporin, a potent drug candidate to treat ocular diseases, (2) to establish a structure-function relationship between key molecular characteristics of biodegradable polymer matrices and drug release kinetics, and (3) to identify factors contributing in the rate of drug release. For the first time, the experimental study of polymeric drug release was accompanied by a hierarchical sequence of three computational methods. First, suitable polymer compositions used in subsequent neural network modeling were determined by means of response surface methodology (RSM). Second, accurate artificial neural network (ANN) models were built to predict drug release profiles for fifteen polymers located outside the initial design space. Finally, thermodynamic properties and hydrogen-bonding patterns of model drug-polymer complexes were studied using molecular dynamics (MD) technique to elucidate a role of specific interactions in drug release mechanism. This research presents further development of methodological approaches to meet challenges in the design of polymeric drug delivery systems.

Construction of an intermediate-resolution lattice model and re-examination of the helix-coil transition: a dynamic Monte Carlo simulation

In protein modeling, spatial resolution and computational efficiency are always incompatible. As a compromise, an intermediate-resolution lattice model has been constructed in the present work. Each residue is decomposed into four basic units, i.e. the α-carbon group, the carboxyl group, the imino group, and the side-chain group, and each basic coarse-grained unit is represented by a minimum cubic box with eight lattice sites. The spacing of the lattice is about 0.56 Å, holding the highest spatial resolution for the present lattice protein models. As the first report of this new model, the helix-coil transition of a polyalanine chain was examined via dynamic Monte Carlo simulation. The period of formed α-helix was about 3.68 residues, close to that of a natural α-helix. The resultant backbone motion was found to be in the realistic regions of the conformational space in the Ramachandran plot. Helix propagation constant and nucleation constant were further determined through the dynamic hydrogen bonding process and torsional angle variation, and the results were used to make comparison between classical Zimm-Bragg theory and Lifson-Roig theory based on the Qian-Schellman relationship. The simulation results confirmed that our lattice model can reproduce the helix-coil transition of polypeptide and construct a moderately fine α-helix conformation without significantly weakening the priority in efficiency for a lattice model.

Raising the speed limit for β-hairpin formation

Understanding the folding of the β-hairpin is a crucial step in studying how β-rich proteins fold. We have studied CLN025, an optimized ten residue synthetic peptide, which adopts a compact, well-structured β-hairpin conformation. Formation of the component β-sheet and β-turn structures of CLN025 was probed independently using a combination of equilibrium Fourier transform infrared spectroscopy and laser-induced temperature jump coupled with time-resolved infrared and fluorescence spectroscopies. We find that CLN025 is an ultrafast folder due to its small free energy barrier to folding and that it exceeds the predicted speed limit for β-hairpin formation by an order of magnitude. We also find that the folding mechanism cannot be described by a simple two-state model, but rather is a heterogeneous process involving two independent parallel processes. Formation of stabilizing cross-strand hydrophobic interactions and turn alignment occur competitively, with relaxation lifetimes of 82 ± 10 and 124 ± 10 ns, respectively, at the highest probed temperature. The ultrafast and heterogeneous folding kinetics observed for CLN025 provide evidence for folding on a nearly barrierless free energy landscape, and recalibrate the speed limit for the formation of a β-hairpin.

Biomolecular dynamics: order-disorder transitions and energy landscapes

While the energy landscape theory of protein folding is now a widely accepted view for understanding how relatively weak molecular interactions lead to rapid and cooperative protein folding, such a framework must be extended to describe the large-scale functional motions observed in molecular machines. In this review, we discuss (1) the development of the energy landscape theory of biomolecular folding, (2) recent advances toward establishing a consistent understanding of folding and function and (3) emerging themes in the functional motions of enzymes, biomolecular motors and other biomolecular machines. Recent theoretical, computational and experimental lines of investigation have provided a very dynamic picture of biomolecular motion. In contrast to earlier ideas, where molecular machines were thought to function similarly to macroscopic machines, with rigid components that move along a few degrees of freedom in a deterministic fashion, biomolecular complexes are only marginally stable. Since the stabilizing contribution of each atomic interaction is on the order of the thermal fluctuations in solution, the rigid body description of molecular function must be revisited. An emerging theme is that functional motions encompass order-disorder transitions and structural flexibility provides significant contributions to the free energy. In this review, we describe the biological importance of order-disorder transitions and discuss the statistical-mechanical foundation of theoretical approaches that can characterize such transitions.

Coarse-grain Protein Models

Coarse-graining is a powerful approach for modeling biomolecules that, over the last few decades, has been extensively applied to proteins. Coarse-grain models offer access to large systems and to slow processes without becoming computationally unmanageable. In addition, they are very versatile, enabling both the protein representation and the energy function to be adapted to the biological problem in hand. This review concentrates on modeling soluble proteins and their assemblies. It presents an overview of the coarse-grain representations, of the associated interaction potentials, and of the optimization procedures used to define them. It then shows how coarse-grain models have been used to understand processes involving proteins, from their initial folding to their functional properties, their binary interactions, and the assembly of large complexes.

Is there an en route folding intermediate for Cold shock proteins?

Cold shock proteins (Csps) play an important role in cold shock response of a diverse number of organisms ranging from bacteria to humans. Numerous studies of the Csp from various species showed that a two-state folding mechanism is conserved and the transition state (TS) appears to be very compact. However, the atomic details of the folding mechanism of Csp remain unclear. This study presents the folding mechanism of Csp in atomic detail using an all-atom Go model-based simulations. Our simulations predict that there may exist an en route intermediate, in which β strands 1-2-3 are well ordered and the contacts between β1 and β4 are almost developed. Such an intermediate might be too unstable to be detected in the previous fluorescence energy transfer experiments. The transition state ensemble has been determined from the P(fold) analysis and the TS appears even more compact than the intermediate state.

The ensemble folding kinetics of the FBP28 WW domain revealed by an all-atom Monte Carlo simulation in a knowledge-based potential

In this work, we apply a detailed all-atom model with a transferable knowledge-based potential to study the folding kinetics of Formin-Binding protein, FBP28, which is a canonical three-stranded β-sheet WW domain. Replica exchange Monte Carlo simulations starting from random coils find native-like (Cα RMSD of 2.68 Å) lowest energy structure. We also study the folding kinetics of FBP28 WW domain by performing a large number of ab initio Monte Carlo folding simulations. Using these trajectories, we examine the order of formation of two β-hairpins, the folding mechanism of each individual β-hairpin, and transition state ensemble (TSE) of FBP28 WW domain and compare our results with experimental data and previous computational studies. To obtain detailed structural information on the folding dynamics viewed as an ensemble process, we perform a clustering analysis procedure based on graph theory. Further, a rigorous P(fold) analysis is used to obtain representative samples of the TSEs showing good quantitative agreement between experimental and simulated Φ values. Our analysis shows that the turn structure between first and second β strands is a partially stable structural motif that gets formed before entering the TSE in FBP28 WW domain and there exist two major pathways for the folding of FBP28 WW domain, which differ in the order and mechanism of hairpin formation.

Common folding mechanism of a beta-hairpin peptide via non-native turn formation revealed by unbiased molecular dynamics simulations

The folding of a 15-residue beta-hairpin peptide (Peptide 1) is characterized using multiple unbiased, atomistic molecular dynamics (MD) simulations. Fifteen independent MD trajectories, each 2.5 micros-long for a total of 37.5 micros, are performed of the peptide in explicit solvent, at room temperature, and without the use of enhanced sampling techniques. The computed folding time of 1-1.5 micros obtained from the simulations is in good agreement with experiment [Xu, Y.; et al. J. Am. Chem. Soc. 2003, 125, 15388-15394]. A common folding mechanism is observed, in which the turn is always found to be the major determinant in initiating the folding process, followed by cooperative formation of the interstrand hydrogen bonds and the side-chain packing. Furthermore, direct transition to the folded state from fully unstructured conformations does not take place. Instead, the peptide is always observed to form partially structured conformations involving a non-native (ESYI) turn from which the native (NPDG) turn forms, triggering the folding to the beta-hairpin.

Insights from coarse-grained Gō models for protein folding and dynamics

Exploring the landscape of large scale conformational changes such as protein folding at atomistic detail poses a considerable computational challenge. Coarse-grained representations of the peptide chain have therefore been developed and over the last decade have proved extremely valuable. These include topology-based Gō models, which constitute a smooth and funnel-like approximation to the folding landscape. We review the many variations of the Gō model that have been employed to yield insight into folding mechanisms. Their success has been interpreted as a consequence of the dominant role of the native topology in folding. The role of local contact density in determining protein dynamics is also discussed and is used to explain the ability of Gō-like models to capture sequence effects in folding and elucidate conformational transitions.