Characterizing the switching transitions of an adsorbed peptide by mapping the potential energy surface

Peptide adsorption occurs across technology, medicine, and nature. The functions of adsorbed peptides are related to their conformation. In the past, molecular simulation methods such as molecular dynamics have been used to determine key conformations of adsorbed peptides. However, the transitions between these conformations often occur too slowly to be modeled reliably by such methods. This means such transitions are less well understood. In the study reported here, discrete path sampling is used for the first time to study the potential energy surface of an adsorbed peptide (polyalanine) and the transition pathways between various stable adsorbed conformations that have been identified in prior work by two of the authors [Mijajlovic, M.; Biggs, M. J. J. Phys. Chem. C 2007, 111, 15839−15847]. Mechanisms for the switching of adsorbed polyalanine between the stable conformations are elucidated along with the energetics of these switches

CHARMM: The biomolecular simulation program

CHARMM (Chemistry at HARvard Molecular Mechanics) is a highly versatile and widely used molecular simulation program. It has been developed over the last three decades with a primary focus on molecules of biological interest, including proteins, peptides, lipids, nucleic acids, carbohydrates, and small molecule ligands, as they occur in solution, crystals, and membrane environments. For the study of such systems, the program provides a large suite of computational tools that include numerous conformational and path sampling methods, free energy estimators, molecular minimization, dynamics, and analysis techniques, and model-building capabilities. The CHARMM program is applicable to problems involving a much broader class of many-particle systems. Calculations with CHARMM can be performed using a number of different energy functions and models, from mixed quantum mechanical-molecular mechanical force fields, to all-atom classical potential energy functions with explicit solvent and various boundary conditions, to implicit solvent and membrane models. The program has been ported to numerous platforms in both serial and parallel architectures. This article provides an overview of the program as it exists today with an emphasis on developments since the publication of the original CHARMM article in 1983. © 2009 Wiley Periodicals, Inc.J Comput Chem, 2009.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/63074/1/21287_ftp.pd

Solvation thermodynamics of organic molecules by the molecular integral equation theory : approaching chemical accuracy

The integral equation theory (IET) of molecular liquids has been an active area of academic research in theoretical and computational physical chemistry for over 40 years because it provides a consistent theoretical framework to describe the structural and thermodynamic properties of liquid-phase solutions. The theory can describe pure and mixed solvent systems (including anisotropic and nonequilibrium systems) and has already been used for theoretical studies of a vast range of problems in chemical physics / physical chemistry, molecular biology, colloids, soft matter, and electrochemistry. A consider- able advantage of IET is that it can be used to study speci fi c solute − solvent interactions, unlike continuum solvent models, but yet it requires considerably less computational expense than explicit solvent simulations

Testing recently-developed molecular dynamics protocols for structure prediction of polypeptides and prion proteins.

Two MD protocols were recently developed that allow a polypeptide to search the PE surface in search of the global PE minimum, which should correspond to the experimental structure. alpha-helical secondary structures have previously been tested. We tested an additional alpha helix (C-peptide of ribonuclease A), and we extended the tests with two beta-hairpin secondary structures (tryptophan zipper 2 and the B1 domain(41--56) of protein G). For the C-peptide of ribonuclease A, the a helix was the dominate secondary structure, but a beta hairpin was found, which to our knowledge had not previously been reported. For the tryptophan zipper 2 and the B1 domain(41--56) of protein G, the beta hairpin was reproduced but alternative conformations were also found. After these test cases, we simulated a small protein (betabetaalpha5) that contained both secondary structural motifs and an overall tertiary structure. The secondary structures were reproduced, but the tertiary structure was not maintained. Finally, we attempted to predict possible conformations for a 64-residue protein, Ure2p, which is implicated in amyloid diseases of yeast. We found that Ure2p(1--64) was dominated by helical conformations. The DIVE and DIP protocols will need to be tested further with different polypeptides and proteins and using more recent force fields. Ure2p(1--64) should be simulated from additional secondary structures such as a beta sheet or a combination of alpha helices and beta hairpins

Dynamic Docking, Path Analysis and Free Energy Computation in Protein-Ligand Binding

Comprehending how drugs interact with biological macromolecules to form a complex with consequent biological response is particularly relevant in drug design to guide a rational design of new active compounds. The establishment and the duration of the protein-ligand binding complex is principally determined by thermodynamics and kinetics of the dynamical process of molecular recognition. Thus, an accurate characterization of the free-energy governing the formation of the protein-ligand complex is of fundamental importance to deeply understand each contribution to the establishment of the molecular complex. Experimental biophysical techniques proved to be efficient in characterizing both thermodynamics and kinetics of protein-ligand binding. However, a detailed description of the whole binding process on a mechanistic level is not possible since only a quantitative estimation is allowed. Conversely, from the computational point of view, plain molecular dynamics, which has been increasingly considered as the method of choice to investigate the entire dynamic process upon complex formation and to predict the associated thermodynamic and kinetic observables, cannot be applied in a routinely drug discovery pipeline because of the high computational cost. In this context, this PhD thesis wants to address specific aspects of the protein-ligand binding process. In particular, it will deal with dynamic docking, thermodynamics and kinetics of protein-ligand binding by devising respectively three different computational protocols. We developed a dynamic docking protocol based on potential-scaled (sMD) simulations, in which the protein and the ligand are let completely flexible in order to predict the protein-ligand binding pose within a reasonable computational time. Then, we investigated the applicability of sMD in describing the kinetic behavior of a series of drug-like molecules and we devised a fully automated method to analyze the unbinding trajectories. Finally, we develop a semi-automated protocol based on path collective variables combined with well-tempered metadynamics to estimate free-energies along a binding path

Monte Carlo simulation studies of DNA hybridization and DNA-directed nanoparticle assembly

A coarse-grained lattice model of DNA oligonucleotides is proposed to investigate how fundamental thermodynamic processes are encoded by the nucleobase sequence at the microscopic level, and to elucidate the general mechanisms by which single-stranded oligonucleotides hybridize to their complements either in solution or when tethered to nanoparticles. Molecular simulations based on a high-coordination cubic lattice are performed using the Monte Carlo method. The dependence of the model's thermal stability on sequence complementarity is shown to be qualitatively consistent with experiment and statistical mechanical models. From the analysis of the statistical distribution of base-paired states and of the associated free-energy landscapes, two general hybridization scenarios are found. For sequences that do not follow a two-state process, hybridization is weakly cooperative and proceeds in multiple sequential steps involving stable intermediates with increasing number of paired bases. In contrast, sequences that conform to two-state thermodynamics exhibit moderately rough landscapes, in which multiple metastable intermediates appear over broad free-energy barriers. These intermediates correspond to duplex species that bridge the configurational and energetic gaps between duplex and denatured states with minimal loss of conformational entropy, and lead to a strongly cooperative hybridization. Remarkably, two-state thermodynamic signatures are generally observed in both scenarios. The role of cooperativity in the assembly of nanoparticles tethered with model DNA oligonucleotides is similarly addressed with the Monte Carlo method, where nanoparticles are represented as finely discretized hard-core spheres on a cubic lattice. The energetic and structural mechanisms of self-assembling are investigated by simulating the aggregation of small "satellite" particles from the bulk onto a large "core" particle. A remarkable enhancement of the system's thermal stability is attained by increasing the number of strands per satellite particle available to hybridize with those on the core particle. This cooperative process is driven by the formation of multiple bridging duplexes under favorable conditions of reduced translational entropy and the resultant energetic compensation; this behavior rapidly weakens above a certain threshold of linker strands per satellite particle. Cooperativity also enhances the structural organization of the assemblies by systematically narrowing the radial distribution of the satellite particles bound the core

Statistical approaches to the study of protein folding and energetics

The determination of protein structure and the exploration of protein folding landscapes are two of the key problems in computational biology. In order to address these challenges, both a protein model that accurately captures the physics of interest and an efficient sampling algorithm are required. The first part of this thesis documents the continued development of CRANKITE, a coarse-grained protein model, and its energy landscape exploration using nested sampling, a Bayesian sampling algorithm. We extend CRANKITE and optimize its parameters using a maximum likelihood approach. The efficiency of our procedure, using the contrastive divergence approximation, allows a large training set to be used, producing a model which is transferable to proteins not included in the training set. We develop an empirical Bayes model for the prediction of protein β-contacts, which are required inputs for CRANKITE. Our approach couples the constraints and prior knowledge associated with β-contacts to a maximum entropy-based statistic which predicts evolutionarily-related contacts. Nested sampling (NS) is a Bayesian algorithm shown to be efficient at sampling systems which exhibit a first-order phase transition. In this work we parallelize the algorithm and, for the first time, apply it to a biophysical system: small globular proteins modelled using CRANKITE. We generate energy landscape charts, which give a large-scale visualization of the protein folding landscape, and we compare the efficiency of NS to an alternative sampling technique, parallel tempering, when calculating the heat capacity of a short peptide. In the final part of the thesis we adapt the NS algorithm for use within a molecular dynamics framework and demonstrate the application of the algorithm by calculating the thermodynamics of allatom models of a small peptide, comparing results to the standard replica exchange approach. This adaptation will allow NS to be used with more realistic force fields in the future

Report / Institute für Physik

The 2016 Report of the Physics Institutes of the Universität Leipzig presents a hopefully interesting overview of our research activities in the past year. It is also testimony of our scientific interaction with colleagues and partners worldwide. We are grateful to our guests for enriching our academic year with their contributions in the colloquium and within our work groups

Characterizing Structure and Free Energy Landscape of Proteins by NMR-guided Metadynamics

In the last two decades, a series of experimental and theoretical advances has made it possible to obtain a detailed understanding of the molecular mechanisms underlying the folding process of proteins. With the increasing power of computer technology, as well as with the improvements in force fields, atomistic simulations are also becoming increasingly important because they can generate highly detailed descriptions of the motions of proteins. A supercomputer specifically designed to integrate the Newton's equations of motion of proteins, Anton, has been recently able to break the millisecond time barrier. This achievement has allowed the direct calculation of repeated folding events for several fast-folding proteins and to characterize the molecular mechanisms underlying protein dynamics and function. However these exceptional resources are available only to few research groups in the world and moreover the observation of few event of a specific process is usually not enough to provide a statistically significant picture of the phenomenon. In parallel, it has also been realized that by bringing together experimental measurements and computational methods it is possible to expand the range of problems that can be addressed. For example, by incorporating structural informations as structural restraints in molecular dynamics simulations it is possible to obtain structural models of these transiently populated states, as well as of native and non-native intermediates explored during the folding process. By applying this strategy to structural parameters measured by nuclear magnetic resonance (NMR) spectroscopy, one can determine the atomic-level structures and characterize the dynamics of proteins. In these approaches the experimental information is exploited to create an additional term in the force field that penalizes the deviations from the measured values, thus restraining the sampling of the conformational space to regions close to those observed experimentally. In this thesis we propose an alternative strategy to exploit experimental information in molecular dynamics simulations. In this approach the measured parameters are not used as structural restraints in the simulations, but rather to build collective variables within metadynamics calculations. In metadynamics , the conformational sampling is enhanced by constructing a time-dependent potential that discourages the explorations of regions already visited in terms of specific functions of the atomic coordinates called collective variables. In this work we show that NMR chemical shifts can be used as collective variables to guide the sampling of conformational space in molecular dynamics simulations. Since the method that we discuss here enables the conformational sampling to be enhanced without modifying the force field through the introduction of structural restraints, it allows estimating reliably the statistical weights corresponding to the force field used in the molecular dynamics simulations. In the present implementation we used the bias exchange metadynamics method, an enhanced sampling technique that allows reconstructing the free energy as a simultaneous function of several variables. By using this approach, we have been able to compute the free energy landscape of two different proteins by explicit solvent molecular dynamics simulations. In the application to a well-structured globular protein, the third immunoglobulin-binding domain of streptococcal protein G (GB3), our calculation predicts the native fold as the lowest free energy minimum, identifying also the presence of an on-pathway compact intermediate with non-native topological elements. In addition, we provide a detailed atomistic picture of the structure at the folding barrier, which shares with the native state only a fraction of the secondary structure elements. The further application to the case of the 40-residue form of Amyloid beta, allows us another remarkable achievement: the quantitative description of the free energy landscape for an intrinsically disordered protein. This kind of proteins are indeed characterized by the absence of a well-defined three-dimensional structure under native conditions and are therefore hard to investigate experimentally. We found that the free energy landscape of this peptide has approximately inverted features with respect to normal globular proteins. Indeed, the global minimum consists of highly disordered structures while higher free energy regions correspond to partially folded conformations. These structures are kinetically committed to the disordered state, but they are transiently explored even at room temperature. This makes our findings particularly relevant since this protein is involved in the Alzheimer's disease because it is prone to aggregate in oligomers determined by the interaction of the monomer in extended beta-strand organization, toxic for the cells. Our structural and energetic characterization allows defining a library of possible metastable states which are involved in the aggregation process. These results have been obtained using relatively limited computational resources. The total simulation time required to reconstruct the thermodynamics of GB3 for example is about three orders of magnitude less than the typical timescale of folding of similar proteins, simulated also by Anton. We thus anticipate that the technique introduced in this thesis will allow the determination of the free energy landscapes of wide range of proteins for which NMR chemical shifts are available. Finally, since chemical shifts are the only external information used to guide the folding of the proteins, our methods can be also successfully applied to the challenging purpose of NMR structure determination, as we have demonstrated in a blind prediction test on the last CASD-NMR target