Testing of the GROMOS Force-Field Parameter Set 54A8: Structural Properties of Electrolyte Solutions, Lipid Bilayers, and Proteins

Extension of the GROMOS 56a6CARBO/CARBO_R Force Field for Charged, Protonated, and Esterified Uronates

An extension of the GROMOS 56a6_{CARBO/CARBO_R} force field for hexopyranose-based carbohydrates is presented. The additional parameters describe the conformational properties of uronate residues. The three distinct chemical states of the carboxyl group are considered: deprotonated (negatively charged), protonated (neutral), and esterified (neutral). The parametrization procedure was based on quantum-chemical calculations, and the resulting parameters were tested in the context of (i) flexibility of the pyranose rings under different pH conditions, (ii) conformation of the glycosidic linkage of the (1 → 4)-type for uronates with different chemical states of carboxyl moieties, (iii) conformation of the exocyclic (i.e., carboxylate and lactol) moieties, and (iv) structure of the Ca²⁺-linked chain-chain complexes of uronates. The presently proposed parameters in combination with the 56a6_{CARBO/CARBO_R} set can be used to describe the naturally occurring polyuronates, composed either of homogeneous (e.g., glucuronans) or heterogeneous (e.g., pectins, alginates) segments. The results of simulations relying on the new set of parameters indicate that the conformation of glycosidic linkage is nearly unaffected by the chemical state of the carboxyl group, in contrary to the ring conformational equilibria. The calculations for the poly(α-d-galacturonate)-Ca²⁺ and poly(α-l-guluronate)-Ca²⁺ complexes show that both parallel and anitiparallel arrangements of uronate chains are possible but differ in several structural aspects.

Screening of potential lead molecules against prioritised targets of multi-drug-resistant-Acinetobacter baumannii - insights from molecular docking, molecular dynamic simulations and in vitro assays

Acinetobacter baumannii, an opportunistic pathogen, has become multi-drug resistant (MDR) to major classes of antibacterial and poses grave threat to public health. The current study focused to screen novel phytotherapeutics against prioritised targets of Acinetobacter baumannii by computational investigation. Fourteen potential drug targets were screened based on their functional role in various biosynthetic pathways and the 3D structures of 9 targets were retrieved from Protein Data Bank and others were computationally predicted. By extensive literature survey, 104 molecules from 48 herbal sources were screened and subjected to virtual screening. Ten clinical isolates of A. baumannii were tested for antibiotic susceptibility towards clinafloxacin, imipenem and polymyxin-E. Computational screening suggested that Ajmalicine ((19α)-16, 17-didehydro-19-methyloxayohimban-16-carboxylic acid methyl ester from Rauwolfia serpentina), Strictamin (Akuammilan-17-oic acid methyl ester from Alstonia scholaris) and Limonin (7, 16-dioxo-7, 16-dideoxylimondiol from Citrus sps) exhibited promising binding towards multiple drug targets of A. baumannii in comparison with the binding between standard drugs and their targets. Limonin displayed promising binding potential (binding energy -9.8 kcal/mol) towards diaminopimelate epimerase (DapF) and UDP-N-acetylglucosamine 1-carboxyvinyltransferase (MurA). Ajmalicine and Strictamin demonstrated good binding potential (-9.5, -8.5 kcal/mol, respectively) towards MurA and shikimate dehydrogenase (-7.8 kcal/mol). Molecular dynamic simulations further validated the docking results. In vitro assay suggested that the tested isolates exhibited resistance to clinafloxacin, imipenem and polymyxin-E and the herbal preparations (crude extract) demonstrated a significant antibacterial potential (p ≤ .05). The study suggests that the aforementioned lead candidates and targets can be used for structure-based drug screening towards MDR A. baumannii.

Saturation Mutagenesis by Efficient Free-Energy Calculation

Single-point mutations in proteins can greatly influence protein stability, binding affinity, protein function or its expression per se. Here, we present accurate and efficient predictions of the free energy of mutation of amino acids. We divided the complete mutational free energy into an uncharging step, which we approximate by a third-power fitting (TPF) approach, and an annihilation step, which we approximate using the one-step perturbation (OSP) method. As a diverse set of test systems, we computed the solvation free energy of all amino acid side chain analogues and obtained an excellent agreement with thermodynamic integration (TI) data. Moreover, we calculated mutational free energies in model tripeptides and established an efficient protocol involving a single reference state. Again, the approximate methods agreed excellently with the TI references, with a root-mean-square error of only 3.6 kJ/mol over 17 mutations. Our combined TPF+OSP approach does show not only a very good agreement but also a 2-fold higher efficiency than full blown TI calculations.

Free energy calculations on the stability of the 14-3-3ζ protein

Mutations of cysteine are often introduced to e.g. avoid formation of non-physiological inter-molecular disulfide bridges in in-vitro experiments, or to maintain specificity in labeling experiments. Alanine or serine is typically preferred, which usually do not alter the overall protein stability, when the original cysteine was surface exposed. However, selecting the optimal mutation for cysteines in the hydrophobic core of the protein is more challenging. In this work, the stability of selected Cys mutants of 14-3-3ζ was predicted by free-energy calculations and the obtained data were compared with experimentally determined stabilities. Both the computational predictions as well as the experimental validation point at a significant destabilization of mutants C94A and C94S. This destabilization could be attributed to the formation of hydrophobic cavities and a polar solvation of a hydrophilic side chain. A L12E, M78K double mutant was further studied in terms of its reduced dimerization propensity. In contrast to naïve expectations, this double mutant did not lead to the formation of strong salt bridges, which was rationalized in terms of a preferred solvation of the ionic species. Again, experiments agreed with the calculations by confirming the monomerization of the double mutants. Overall, the simulation data is in good agreement with experiments and offers additional insight into the stability and dimerization of this important family of regulatory proteins.

Ion-induced modification of the sucrose network and its impact on melting of freeze-dried liposomes. DSC and molecular dynamics study

Disaccharides play an important role in survival of anhydrobiotic organisms during extreme environmental conditions. A key protection feature is their capability to form the hydrogen bond (HB) network in a similar fashion as the one made by water. Since various ions also affect the HB network in completely hydrated systems, it is of a great interest to understand how they impact preservation when incorporated in a disaccharide network. To address this, we employ a combination of experimental and modeling techniques to study behavior of multilamellar 1,2-dimyristoyl-sn-glycero-3-phosphocholine (DMPC) liposomes freeze-dried with sucrose in presence of NaCl or NaH₂PO₄·H₂O at various concentrations (0.01-1M). Differential scanning calorimetry (DSC) was employed in order to determine the cooperative unit size (CUS), the number of lipid molecules that constitute a domain of cooperative motion in the liposome, and the melting temperature (T_m). In the absence of salt CUS was estimated to be 122±12, whereas in the presence of NaCl CUS increases more (347±34 for c=1M) than for NaH₂PO₄·H₂O (193±26 for 1M). When it comes to T_m, the situation is reversed; NaCl induces increase by about 1K, while NaH₂PO₄·H₂O by about 10K. These findings clearly demonstrate how different interaction forces-hydrogen bonding, charge pairing, and van der Waals interactions between acyl chains-affect CUS and T_m. Their interplay and contribution of particular interaction was further analyzed with molecular dynamics (MD) simulations. This analysis demonstrated that the HB network of DMPC and sucrose is partially disrupted in the presence of NaCl ions, and even to a greater extent in the case of NaH₂PO₄·H₂O ions. Notably, H₂PO₄^- ions outcompete and replace the sucrose molecules at the DMPC surface, which in turn alters the nature of the DMPC-surrounding interactions, from a weaker HB-dominated to a stronger CP-dominated interaction network.

Structural and Spectroscopic Properties of Assemblies of Self-Replicating Peptide Macrocycles

Self-replication at the molecular level is often seen as essential to the early origins of life. Recently a mechanism of self-replication has been discovered in which replicator self-assembly drives the process. We have studied one of the examples of such self-assembling self-replicating molecules to a high level of structural detail using a combination of computational and spectroscopic techniques. Molecular Dynamics simulations of self-assembled stacks of peptide-derived replicators provide insights into the structural characteristics of the system and serve as the basis for semiempirical calculations of the UV-vis, circular dichroism (CD) and infrared (IR) absorption spectra that reflect the chiral organization and peptide secondary structure of the stacks. Two proposed structural models are tested by comparing calculated spectra to experimental data from electron microscopy, CD and IR spectroscopy, resulting in a better insight into the specific supramolecular interactions that lead to self-replication. Specifically, we find a cooperative self-assembly process in which β-sheet formation leads to well-organized structures, while also the aromatic core of the macrocycles plays an important role in the stability of the resulting fibers.

Critical Comparison of Biomembrane Force Fields: Protein-Lipid Interactions at the Membrane Interface

Molecular dynamics (MD) simulations offer the possibility to study biological processes at high spatial and temporal resolution often not reachable by experiments. Corresponding biomolecular force field parameters have been developed for a wide variety of molecules ranging from inorganic ligands and small organic molecules over proteins and lipids to nucleic acids. Force fields have typically been parametrized and validated on thermodynamic observables and structural characteristics of individual compounds, e.g. of soluble proteins or lipid bilayers. Less strictly, due to the added complexity and missing experimental data to compare to, force fields have hardly been tested on the properties of mixed systems, e.g. on protein-lipid systems. Their selection and combination for mixed systems is further complicated by the partially differing parametrization strategies. Additionally, the presence of other compounds in the system may shift the subtle balance of force field parameters. Here, we assessed the protein-lipid interactions as described in the four atomistic force fields GROMOS54a7, CHARMM36 and the two force field combinations Amber14sb/Slipids and Amber14sb/Lipid14. Four observables were compared, focusing on the membrane-water interface: the conservation of the secondary structure of transmembrane proteins, the positioning of transmembrane peptides relative to the lipid bilayer, the insertion depth of side chains of unfolded peptides absorbed at the membrane interface, and the ability to reproduce experimental insertion energies of Wimley-White peptides at the membrane interface. Significant differences between the force fields were observed that affect e.g. membrane insertion depths and tilting of transmembrane peptides.

Evaluation of solvation free energies for small molecules with the AMOEBA polarizable force field

The effects of electronic polarization in biomolecular interactions will differ depending on the local dielectric constant of the environment, such as in solvent, DNA, proteins, and membranes. Here the performance of the AMOEBA polarizable force field is evaluated under nonaqueous conditions by calculating the solvation free energies of small molecules in four common organic solvents. Results are compared with experimental data and equivalent simulations performed with the GAFF pairwise-additive force field. Although AMOEBA results give mean errors close to "chemical accuracy," GAFF performs surprisingly well, with statistically significantly more accurate results than AMOEBA in some solvents. However, for both models, free energies calculated in chloroform show worst agreement to experiment and individual solutes are consistently poor performers, suggesting non-potential-specific errors also contribute to inaccuracy. Scope for the improvement of both potentials remains limited by the lack of high quality experimental data across multiple solvents, particularly those of high dielectric constant. © 2016 The Authors. Journal of Computational Chemistry Published by Wiley Periodicals, Inc.

An extensive simulation study of lipid bilayer properties with different head groups, acyl chain lengths, and chain saturations

Previous MD simulations of six phosphocholine (PC) lipid bilayers demonstrated the accuracy of the CHARMM36 force field (C36FF) for PC bilayer simulation at varied temperatures (BBA-Biomembranes, 1838 (2014): 2520-2529). In this work, we further examine the accuracy of C36FF over a wide temperature range for a broader range of lipid types such as various head groups (phosphatidic acid (PA), PC, phosphoethanolamine (PE), phosphoglycerol (PG), and phosphoserine (PS)), and tails (saturated, mono-, mixed- and poly-unsaturated acyl chains with varied chain lengths). The structural properties (surface area per lipid (SA/lip), overall bilayer thickness, hydrophobic thickness, headgroup-to-headgroup thickness, deuterium order parameter (S_CD), and spin-lattice relaxation time (T₁)) obtained from simulations agree well with nearly all available experimental data. Our analyses indicate that PS lipids have the most inter-lipid hydrogen bonds, while PG lipids have the most intra-lipid hydrogen bonds, which play the main role in their low SA/lip in PS lipids and low thicknesses in PG lipids, respectively. PS, PE, and PA lipids have the largest contact clusters with on average 5-8 lipids per cluster, while PC and PG have clusters of 4 lipids based on a cutoff distance of 6.5Å. PS lipids have much slower lipid wobble (i.e., higher correlation time) than other head groups at a given temperature as the hydrogen bonded network significantly reduces a lipid's mobility, and the rate of lipid wobble increases dramatically as temperature increases. These in-depth analyses facilitate further understanding of lipid bilayers at the atomic level.

Extended Thermodynamic Integration: Efficient Prediction of Lambda Derivatives at Nonsimulated Points

Thermodynamic integration (TI) is one of the most commonly used free-energy calculation methods. The derivative of the Hamiltonian with respect to lambda, ⟨∂H/∂λ⟩, is determined at multiple λ-points. Because a numerical integration step is necessary, high curvature regions require simulations at densely spaced λ-points. Here, the principle of extended TI is introduced, where ⟨∂H/∂λ⟩ values are predicted at nonsimulated λ-points. On the basis of three model systems, it is shown that extended TI requires significantly fewer λ-points than regular TI to obtain similar accuracy.

Microscopic Characterization of Membrane Transporter Function by In Silico Modeling and Simulation

Membrane transporters mediate one of the most fundamental processes in biology. They are the main gatekeepers controlling active traffic of materials in a highly selective and regulated manner between different cellular compartments demarcated by biological membranes. At the heart of the mechanism of membrane transporters lie protein conformational changes of diverse forms and magnitudes, which closely mediate critical aspects of the transport process, most importantly the coordinated motions of remotely located gating elements and their tight coupling to chemical processes such as binding, unbinding and translocation of transported substrate and cotransported ions, ATP binding and hydrolysis, and other molecular events fueling uphill transport of the cargo. An increasing number of functional studies have established the active participation of lipids and other components of biological membranes in the function of transporters and other membrane proteins, often acting as major signaling and regulating elements. Understanding the mechanistic details of these molecular processes require methods that offer high spatial and temporal resolutions. Computational modeling and simulations technologies empowered by advanced sampling and free energy calculations have reached a sufficiently mature state to become an indispensable component of mechanistic studies of membrane transporters in their natural environment of the membrane. In this article, we provide an overview of a number of major computational protocols and techniques commonly used in membrane transporter modeling and simulation studies. The article also includes practical hints on effective use of these methods, critical perspectives on their strengths and weak points, and examples of their successful applications to membrane transporters, selected from the research performed in our own laboratory.

Biological Membranes in Extreme Conditions: Simulations of Anionic Archaeal Tetraether Lipid Membranes

In contrast to the majority of organisms that have cells bound by di-ester phospholipids, archaeal membranes consist of di- and tetraether phospholipids. Originating from organisms that withstand harsh conditions (e.g., low pH and a wide range of temperatures) such membranes have physical properties that make them attractive materials for biological research and biotechnological applications. We developed force-field parameters based on the widely used Generalized Amber Force Field (GAFF) to enable the study of anionic tetraether membranes of the model archaean Sulfolobus acidocaldarius by computer simulations. The simulations reveal that the physical properties of these unique membranes depend on the number of cyclopentane rings included in each lipid unit, and on the size of cations that are used to ensure charge neutrality. This suggests that the biophysical properties of Sulfolobus acidocaldarius cells depend not only on the compositions of their membranes but also on the media in which they grow.

Molecular electrometer and binding of cations to phospholipid bilayers

The molecular electrometer – reorientation of lipid head due to bound charge – allows direct quantitative vetting of simulations against noninvasive NMR experiments; most simulation models overestimated lipid–cation affinities.

Integrating atomistic molecular dynamics simulations, experiments, and network analysis to study protein dynamics: strength in unity

In the last years, we have been observing remarkable improvements in the field of protein dynamics. Indeed, we can now study protein dynamics in atomistic details over several timescales with a rich portfolio of experimental and computational techniques. On one side, this provides us with the possibility to validate simulation methods and physical models against a broad range of experimental observables. On the other side, it also allows a complementary and comprehensive view on protein structure and dynamics. What is needed now is a better understanding of the link between the dynamic properties that we observe and the functional properties of these important cellular machines. To make progresses in this direction, we need to improve the physical models used to describe proteins and solvent in molecular dynamics, as well as to strengthen the integration of experiments and simulations to overcome their own limitations. Moreover, now that we have the means to study protein dynamics in great details, we need new tools to understand the information embedded in the protein ensembles and in their dynamic signature. With this aim in mind, we should enrich the current tools for analysis of biomolecular simulations with attention to the effects that can be propagated over long distances and are often associated to important biological functions. In this context, approaches inspired by network analysis can make an important contribution to the analysis of molecular dynamics simulations.

Improving the description of interactions between Ca2+ and protein carboxylate groups, including γ-carboxyglutamic acid: revised CHARMM22* parameters

We show that the CHARMM22* force-field over-binds the interaction between aqueous carboxylates and Ca²⁺, and introduce a modification that can recover experimentally-determined binding free energies for these systems.

Computational modeling of membrane proteins

The determination of membrane protein (MP) structures has always trailed that of soluble proteins due to difficulties in their overexpression, reconstitution into membrane mimetics, and subsequent structure determination. The percentage of MP structures in the protein databank (PDB) has been at a constant 1-2% for the last decade. In contrast, over half of all drugs target MPs, only highlighting how little we understand about drug-specific effects in the human body. To reduce this gap, researchers have attempted to predict structural features of MPs even before the first structure was experimentally elucidated. In this review, we present current computational methods to predict MP structure, starting with secondary structure prediction, prediction of trans-membrane spans, and topology. Even though these methods generate reliable predictions, challenges such as predicting kinks or precise beginnings and ends of secondary structure elements are still waiting to be addressed. We describe recent developments in the prediction of 3D structures of both α-helical MPs as well as β-barrels using comparative modeling techniques, de novo methods, and molecular dynamics (MD) simulations. The increase of MP structures has (1) facilitated comparative modeling due to availability of more and better templates, and (2) improved the statistics for knowledge-based scoring functions. Moreover, de novo methods have benefited from the use of correlated mutations as restraints. Finally, we outline current advances that will likely shape the field in the forthcoming decade.

Mathematical and computational modeling in biology at multiple scales

A variety of topics are reviewed in the area of mathematical and computational modeling in biology, covering the range of scales from populations of organisms to electrons in atoms. The use of maximum entropy as an inference tool in the fields of biology and drug discovery is discussed. Mathematical and computational methods and models in the areas of epidemiology, cell physiology and cancer are surveyed. The technique of molecular dynamics is covered, with special attention to force fields for protein simulations and methods for the calculation of solvation free energies. The utility of quantum mechanical methods in biophysical and biochemical modeling is explored. The field of computational enzymology is examined.

ff14ipq: A Self-Consistent Force Field for Condensed-Phase Simulations of Proteins

We present the ff14ipq force field, implementing the previously published IPolQ charge set for simulations of complete proteins. Minor modifications to the charge derivation scheme and van der Waals interactions between polar atoms are introduced. Torsion parameters are developed through a generational learning approach, based on gas-phase MP2/cc-pVTZ single-point energies computed of structures optimized by the force field itself rather than the quantum benchmark. In this manner, we sacrifice information about the true quantum minima in order to ensure that the force field maintains optimal agreement with the MP2/cc-pVTZ benchmark for the ensembles it will actually produce in simulations. A means of making the gas-phase torsion parameters compatible with solution-phase IPolQ charges is presented. The ff14ipq model is an alternative to ff99SB and other Amber force fields for protein simulations in programs that accommodate pair-specific Lennard-Jones combining rules. The force field gives strong performance on α-helical and β-sheet oligopeptides as well as globular proteins over microsecond time scale simulations, although it has not yet been tested in conjunction with lipid and nucleic acid models. We show how our choices in parameter development influence the resulting force field and how other choices that may have appeared reasonable would actually have led to poorer results. The tools we developed may also aid in the development of future fixed-charge and even polarizable biomolecular force fields.

Practical Aspects of Free-Energy Calculations: A Review

Free-energy calculations in the framework of classical molecular dynamics simulations are nowadays used in a wide range of research areas including solvation thermodynamics, molecular recognition, and protein folding. The basic components of a free-energy calculation, that is, a suitable model Hamiltonian, a sampling protocol, and an estimator for the free energy, are independent of the specific application. However, the attention that one has to pay to these components depends considerably on the specific application. Here, we review six different areas of application and discuss the relative importance of the three main components to provide the reader with an organigram and to make nonexperts aware of the many pitfalls present in free energy calculations.

Dihedral-based segment identification and classification of biopolymers I: proteins

A new structure classification scheme for biopolymers is introduced, which is solely based on main-chain dihedral angles. It is shown that by dividing a biopolymer into segments containing two central residues, a local classification can be performed. The method is referred to as DISICL, short for Dihedral-based Segment Identification and Classification. Compared to other popular secondary structure classification programs, DISICL is more detailed as it offers 18 distinct structural classes, which may be simplified into a classification in terms of seven more general classes. It was designed with an eye to analyzing subtle structural changes as observed in molecular dynamics simulations of biomolecular systems. Here, the DISICL algorithm is used to classify two databases of protein structures, jointly containing more than 10 million segments. The data is compared to two alternative approaches in terms of the amount of classified residues, average occurrence and length of structural elements, and pair wise matches of the classifications by the different programs. In an accompanying paper (Nagy, G.; Oostenbrink, C. Dihedral-based segment identification and classification of biopolymers II: Polynucleotides. J. Chem. Inf. Model. 2013, DOI: 10.1021/ci400542n), the analysis of polynucleotides is described and applied. Overall, DISICL represents a potentially useful tool to analyze biopolymer structures at a high level of detail.