Molecular dynamics insights into protein-glycosaminoglycan systems from microsecond-scale simulations

Heparin is a key player in cell signaling via its physical interactions with protein targets in the extracellular matrix. However, basic molecular level understanding of these highly biologically relevant intermolecular interactions is still incomplete. In this study, for the first time, microsecond-scale MD simulations are reported for a complex between fibroblast growth factor 1 and heparin. We rigorously analyze this molecular system in terms of the conformational space, structural, energetic, and dynamic characteristics. We reveal that the conformational selection mechanism of binding denotes a recognition specificity determinant. We conclude that the length of the simulation could be crucial for evaluation of some of the analyzed parameters. Our data provide novel significant insights into the interactions in the fibroblast growth factor 1 complex with heparin, in particular, and into the physical-chemical nature of protein-glycosaminoglycan systems in general, which have potential applicability for biomaterials development in the area of regenerative medicine.     

Combined use of periodic reaction field and coarse-grained molecular dynamics simulations. I. phospholipid monolayer systems

Abstract

A periodic reaction field based on a linear-combination-based isotropic periodic sum (LIPS) method was applied for coarse-grained molecular dynamics simulations of zwitterionic lipid systems. In phospholipid monolayer systems with various number of lipid molecules, the density profile, lipid orientation and surface tension were mainly calculated using the periodic reaction field and Ewald sum. The results from the periodic reaction field were almost equal to that from the Ewald sum. It is concluded that the periodic reaction field method has a great possibility to provide a high accuracy in determining coarse-grained zwitterionic lipid systems.     

Behavior of the ATP grasp domain of biotin carboxylase monomers and dimers studied using molecular dynamics simulations

The enzyme biotin carboxylase (BC) uses adenosine triphosphate (ATP) to carboxylate biotin and is involved in fatty acid synthesis. Structural evidence suggests that the B domain of BC undergoes a large hinge motion of ～45° when binding and releasing substrates. Escherichia coli BC can function as a natural homodimer and as a mutant monomer. Using molecular dynamics simulations, we evaluate the free energy profile along a closure angle of the B domain of E. coli BC for three cases: a monomer without bound Mg(2)ATP, a monomer with bound Mg(2)ATP, and a homodimer with bound Mg(2)ATP in one subunit. The simulation results show that a closed state is the most probable for the monomer with or without bound Mg(2)ATP. For the dimer with Mg(2)ATP in one of its subunits, communication between the two subunits was observed. Specifically, in the dimer, the opening of the subunit without Mg(2)ATP caused the other subunit to open, and hysteresis was observed upon reclosing it. The most stable state of the dimer is one in which the B domain of both subunits is closed; however, the open state for the B domain without Mg(2)ATP is only approximately 2k(B)T higher in free energy than the closed state. A simple diffusion model indicates that the mean times for opening and closing of the B domain in the monomer with and without Mg(2)ATP are much smaller than the overall reaction time, which is on the order of seconds.     

Fully differentiable coarse-grained and all-atom knowledge-based potentials for RNA structure evaluation

RNA molecules play integral roles in gene regulation, and understanding their structures gives us important insights into their biological functions. Despite recent developments in template-based and parameterized energy functions, the structure of RNA--in particular the nonhelical regions--is still difficult to predict. Knowledge-based potentials have proven efficient in protein structure prediction. In this work, we describe two differentiable knowledge-based potentials derived from a curated data set of RNA structures, with all-atom or coarse-grained representation, respectively. We focus on one aspect of the prediction problem: the identification of native-like RNA conformations from a set of near-native models. Using a variety of near-native RNA models generated from three independent methods, we show that our potential is able to distinguish the native structure and identify native-like conformations, even at the coarse-grained level. The all-atom version of our knowledge-based potential performs better and appears to be more effective at discriminating near-native RNA conformations than one of the most highly regarded parameterized potential. The fully differentiable form of our potentials will additionally likely be useful for structure refinement and/or molecular dynamics simulations.     

Coupling of the guanosine glycosidic bond conformation and the ribonucleotide cleavage reaction: implications for barnase catalysis

To examine the possible relationship of guanine-dependent GpA conformations with ribonucleotide cleavage, two potential of mean force (PMF) calculations were performed in aqueous solution. In the first calculation, the guanosine glycosidic (Gchi) angle was used as the reaction coordinate, and computations were performed on two GpA ionic species: protonated (neutral) or deprotonated (negatively charged) guanosine ribose O2 '. Similar energetic profiles featuring two minima corresponding to the anti and syn Gchi regions were obtained for both ionic forms. For both forms the anti conformation was more stable than the syn, and barriers of approximately 4 kcal/mol were obtained for the anti --> syn transition. Structural analysis showed a remarkable sensitivity of the phosphate moiety to the conformation of the Gchi angle, suggesting a possible connection between this conformation and the mechanism of ribonucleotide cleavage. This hypothesis was confirmed by the second PMF calculations, for which the O2 '--P distance for the deprotonated GpA was used as reaction coordinate. The computations were performed from two selected starting points: the anti and syn minima determined in the first PMF study of the deprotonated guanosine ribose O2'. The simulations revealed that the O2 ' attack along the syn Gchi was more favorable than that along the anti Gchi: energetically, significantly lower barriers were obtained in the syn than in the anti conformation for the O--P bond formation; structurally, a lesser O2 '--P initial distance, and a better suited orientation for an in-line attack was observed in the syn relative to the anti conformation. These results are consistent with the catalytically competent conformation of barnase-ribonucleotide complex, which requires a guanine syn conformation of the substrate to enable abstraction of the ribose H2 ' proton by the general base Glu73, thereby suggesting a coupling between the reactive substrate conformation and enzyme structure and mechanism.     

The dominant interaction between peptide and urea is electrostatic in nature: a molecular dynamics simulation study

The conformational equilibrium of a blocked valine peptide in water and aqueous urea solution is studied using molecular dynamics simulations. Pair correlation functions indicate enhanced concentration of urea near the peptide. Stronger hydrogen bonding of urea-peptide compared to water-peptide is observed with preference for helical conformation. The potential of mean force, computed using umbrella sampling, shows only small differences between urea and water solvation that are difficult to quantify. The changes in solvent structure around the peptide are explained by favorable electrostatic interactions (hydrogen bonds) of urea with the peptide backbone. There is no evidence for significant changes in hydrophobic interactions in the two conformations of the peptide in urea solution. Our simulations suggest that urea denatures proteins by preferentially forming hydrogen bonds to the peptide backbone, reducing the barrier for exposing protein residues to the solvent, and reaching the unfolded state.     

Computer simulations aimed at structure prediction of supersecondary motifs in proteins

It is well established that protein structures are more conserved than protein sequences. One-third of all known protein structures can be classified into ten protein folds, which themselves are composed mainly of alpha-helical hairpin, beta hairpin, and betaalphabeta supersecondary structural elements. In this study, we explore the ability of a recent Monte Carlo-based procedure to generate the 3D structures of eight polypeptides that correspond to units of supersecondary structure and three-stranded antiparallel beta sheet. Starting from extended or misfolded compact conformations, all Monte Carlo simulations show significant success in predicting the native topology using a simplified chain representation and an energy model optimized on other structures. Preliminary results on model peptides from nucleotide binding proteins suggest that this simple protein folding model can help clarify the relation between sequence and topology.     

Simulating the free energy of passive membrane permeation for small molecules

Abstract

Computer simulations of passive membrane permeation provide important microscopic insights into the molecular mechanism of this important biological process that are complementary to experimental data. Our review focuses on the main approaches for calculating the free energy, or potential of mean force, for permeation of small molecules through lipid bilayers. The theoretical background for most currently used methods for potential of mean force calculation is described, including particle insertion, thermodynamic integration, umbrella sampling, metadynamics, adaptive biasing force and milestoning. A brief comparison of strengths and weaknesses of the competing approaches is presented. This is followed by a survey of results obtained by the different methods, with special attention to describing the mechanistic insights generated by modelling and illustrating capabilities of the different techniques. We conclude with a discussion of recent advances and future directions in modelling membrane permeation, including latest methodological enhancements, consideration of multiple slow variables and memory effects.     

Fold recognition and ab initio structure predictions using hidden markov models and β-strand pair potentials

Protein structure predictions were submitted for 9 of the target sequences in the competition that ran during 1994. Targets sequences were selected that had no known homology with any sequence of known structure and were members of a reasonably sized family of related but divergent sequences. The objective was either to recognize a compatible fold for the target sequence in the database of known structures or to predict ab initio its rough 3D topology. The main tools used were Hidden Markov models (HMM) for fold recognition, a β- strand pair potential to predict β-sheet topology, and the PHD server for secondary structure prediction. Compatible folds were correctly identified in a number of cases and the β-strand pair potential was shown to be a useful tool for ab initio topology prediction. © 1995 Wiley-Liss, Inc.     

Free energy simulations for protein ligand binding and stability

Abstract

We summarize several computational techniques to determine relative free energies for condensed-phase systems. The focus is on practical considerations which are capable of making direct contact with experiments. Particular applications include the thermodynamic stability of apo- and holo-myoglobin, insulin dimerization free energy, ligand binding in lysozyme, and ligand diffusion in globular proteins. In addition to provide differential free energies between neighboring states, converged umbrella sampling simulations provide insight into migration barriers and ligand dissociation barriers and analysis of the trajectories yield additional insight into the structural dynamics of fundamental processes. Also, such simulations are useful tools to quantify relative stability changes for situations where experiments are difficult. This is illustrated for NO-bound myoglobin. For the dissociation of benzonitrile from lysozyme it is found that long umbrella sampling simulations are required to approximately converge the free energy profile. Then, however, the resulting differential free energy between the bound and unbound state is in good agreement with estimates from molecular mechanics with generalized Born surface area simulations. Furthermore, comparing the barrier height for ligand escape suggests that ligand dissociation contains a non-equilibrium component.     

Sensitivity of molecular dynamics simulations to the choice of the X-ray structure used to model an enzymatic reaction

A subject of great practical importance that has not received much attention is the question of the sensitivity of molecular dynamics simulations to the initial X-ray structure used to set up the calculation. We have found two cases in which seemingly similar structures lead to quite different results, and in this article we present a detailed analysis of these cases. The first case is acyl-CoA dehydrogenase, and the chief difference of the two structures is attributed to a slight shift in a backbone carbonyl that causes a key residue (the proton-abstracting base) to be in a bad conformation for reaction. The second case is xylose isomerase, and the chief difference of the two structures appears to be the ligand sphere of a Mg2+ metal cofactor that plays an active role in catalysis.     

Simple theoretical models for biochemical systems,with applications to DNA

A set oflogically connected models, to study chemical systems ofbiological interest, is presented. The sequence in the set is dictated by a progressive reduction of details with a corresponding enlargement of the field of application. The exposition starts with models suitable for interactions among a finite number of molecules, passes then to models considering also solvent effects and ends with models specialized for DNA containing systems.     

Local quality assessment in homology models using statistical potentials and support vector machines

In this study, we address the problem of local quality assessment in homology models. As a prerequisite for the evaluation of methods for predicting local model quality, we first examine the problem of measuring local structural similarities between a model and the corresponding native structure. Several local geometric similarity measures are evaluated. Two methods based on structural superposition are found to best reproduce local model quality assessments by human experts. We then examine the performance of state-of-the-art statistical potentials in predicting local model quality on three qualitatively distinct data sets. The best statistical potential, DFIRE, is shown to perform on par with the best current structure-based method in the literature, ProQres. A combination of different statistical potentials and structural features using support vector machines is shown to provide somewhat improved performance over published methods.     

Discriminative ability with respect to amino acid types: assessing the performance of knowledge-based potentials without threading

We present a novel method designed to analyze the discriminative ability of knowledge-based potentials with respect to the 20 residue types. The method is based on the preference of amino acids for specific types of protein environment, and uses a virtual mutagenesis experiment to estimate how much information a given potential can provide about environments of each amino acid type. This allows one to test and optimize the performance of real potentials at the level of individual amino acids, using actual data on residue environments from a dataset of known protein structures. We have applied our method to long-range and medium-range pairwise distance-dependent potentials. The results of our study indicate that these potentials are only able to discriminate between a very limited number of residue types, and that discriminative ability is extremely sensitive to the choice of parameters used to construct the potentials, and even to the size of the training dataset. We also show that different types of pairwise distance potentials are dominated by different types of interactions. These dominant interactions strongly depend on the type of approximation used to define residue position. For each potential, our methodology is able to identify a potential-specific amino acid distance matrix and a reduced amino acid alphabet of any specified size, which may have implications for sequence alignment and multibody models.     

Quantitative prediction of protein–protein binding affinity with a potential of mean force considering volume correction

Quantitative prediction of protein–protein binding affinity is essential for understanding protein–protein interactions. In this article, an atomic level potential of mean force (PMF) considering volume correction is presented for the prediction of protein–protein binding affinity. The potential is obtained by statistically analyzing X‐ray structures of protein–protein complexes in the Protein Data Bank. This approach circumvents the complicated steps of the volume correction process and is very easy to implement in practice. It can obtain more reasonable pair potential compared with traditional PMF and shows a classic picture of nonbonded atom pair interaction as Lennard‐Jones potential. To evaluate the prediction ability for protein–protein binding affinity, six test sets are examined. Sets 1–5 were used as test set in five published studies, respectively, and set 6 was the union set of sets 1–5, with a total of 86 protein–protein complexes. The correlation coefficient (R) and standard deviation (SD) of fitting predicted affinity to experimental data were calculated to compare the performance of ours with that in literature. Our predictions on sets 1–5 were as good as the best prediction reported in the published studies, and for union set 6, R = 0.76, SD = 2.24 kcal/mol. Furthermore, we found that the volume correction can significantly improve the prediction ability. This approach can also promote the research on docking and protein structure prediction.     

A three‐dimensional potential of mean force to improve backbone and sidechain hydrogen bond geometry in Xplor‐NIH protein structure determination

We introduce a new hydrogen bonding potential of mean force generated from high‐quality crystal structures for use in Xplor‐NIH structure calculations. This term applies to hydrogen bonds involving both backbone and sidechain atoms. When used in structure refinement calculations of 10 example protein systems with experimental distance, dihedral and residual dipolar coupling restraints, we demonstrate that the new term has superior performance to the previously developed hydrogen bonding potential of mean force used in Xplor‐NIH.     

A physical reference state unifies the structure-derived potential of mean force for protein folding and binding

Extracting knowledge-based statistical potential from known structures of proteins is proved to be a simple, effective method to obtain an approximate free-energy function. However, the different compositions of amino acid residues at the core, the surface, and the binding interface of proteins prohibited the establishment of a unified statistical potential for folding and binding despite the fact that the physical basis of the interaction (water-mediated interaction between amino acids) is the same. Recently, a physical state of ideal gas, rather than a statistically averaged state, has been used as the reference state for extracting the net interaction energy between amino acid residues of monomeric proteins. Here, we find that this monomer-based potential is more accurate than an existing all-atom knowledge-based potential trained with interfacial structures of dimers in distinguishing native complex structures from docking decoys (100% success rate vs. 52% in 21 dimer/trimer decoy sets). It is also more accurate than a recently developed semiphysical empirical free-energy functional enhanced by an orientation-dependent hydrogen-bonding potential in distinguishing native state from Rosetta docking decoys (94% success rate vs. 74% in 31 antibody-antigen and other complexes based on Z score). In addition, the monomer potential achieved a 93% success rate in distinguishing true dimeric interfaces from artificial crystal interfaces. More importantly, without additional parameters, the potential provides an accurate prediction of binding free energy of protein-peptide and protein-protein complexes (a correlation coefficient of 0.87 and a root-mean-square deviation of 1.76 kcal/mol with 69 experimental data points). This work marks a significant step toward a unified knowledge-based potential that quantitatively captures the common physical principle underlying folding and binding. A Web server for academic users, established for the prediction of binding free energy and the energy evaluation of the protein-protein complexes, may be found at http://theory.med.buffalo.edu.