Extending the horizon: towards the efficient modeling of large biomolecular complexes in atomic detail

The application of evaluation of implicit solvent methods for the simulation of biomolecules is described. Detailed comparisons with explicit solvent are described for the modeling of peptide and proteins in continuum aqueous solvent. In addition, we are presenting new data on the simulation of DNA with implicit solvent and describe the development of a heterogeneous dielectric model for the simulation of integral membranes. The performance of implicit solvent simulations based on the GBMV generalized Born method is compared with explicit solvent simulations, and implications for the simulation of very large biomolecular complexes is discussed. We are anticipating that the work described herein will lead to new, efficient modeling tools that will allow the simulation of longer timescales and larger system sizes in order to meet current and future challenges by the experimental community.     

Volume-load capacity in fast-gradient liquid chromatography effect of sample solvent composition and injection volume on chromatographic performance

We studied the effects of sample solvent composition and injection volumes on the chromatographic performance of ODS-bonded silica columns under fast-gradient running conditions. Chromatographic performance is compromised as a function of both sample injection volume and sample solvent strength, with earlier-eluting analytes being much more affected than later-eluting ones. In general, when injecting samples dissolved in a strong solvent, performance was improved by diluting the strong injection solvent and injecting a proportionally larger volume. Volume loading capacity can be increased by using a longer column, or by using a column of equivalent length, but with a larger inner diameter. Data also suggest that sample solvent strength, not viscosity, is responsible for the noted effects.     

Velocity-scaling optimized replica exchange molecular dynamics of proteins in a hybrid explicit/implicit solvent

We propose a scheme for replica exchange molecular dynamics of proteins in explicit solvent that minimizes the number of required replicas using velocity rescaling. Our approach relies on a hybrid method where the protein evolves at each temperature in an explicit solvent, but replica exchange moves utilize an implicit solvent term. The two terms are coupled through the velocity rescaling. We test the efficiency of this approach for a common test case, the trp-cage protein.     

Simulation studies of the protein-water interface. I. Properties at the molecular resolution

We report molecular dynamics simulations of three globular proteins: ubiquitin, apo-calbindin D(9K), and the C-terminal SH2 domain of phospholipase C-gamma1 in explicit water. The proteins differ in their overall charge and fold type and were chosen to represent to some degree the structural variability found in medium-sized proteins. The length of each simulation was at least 15 ns, and larger than usual solvent boxes were used. We computed radial distribution functions, as well as orientational correlation functions about the surface residues. Two solvent shells could be clearly discerned about charged and polar amino acids. Near apolar amino acids the water density near such residues was almost devoid of structure. The mean residence time of water molecules was determined for water shells about the full protein, as well as for water layers about individual amino acids. In the dynamic properties, two solvent shells could be characterized as well. However, by comparison to simulations of pure water it could be shown that the influence of the protein reaches beyond 6 A, i.e., beyond the first two shells. In the first shell (r < or =3.5 A), the structural and dynamical properties of solvent waters varied considerably and depended primarily on the physicochemical properties of the closest amino acid side chain, with which the waters interact. By contrast, the solvent properties seem not to depend on the specifics of the protein studied (such as the net charge) or on the secondary structure element in which an amino acid is located. While differing considerably from the neat liquid, the properties of waters in the second solvation shell (3.5< r < or =6 A) are rather uniform; a direct influence from surface amino acids are already mostly shielded.     

Olefin metathesis in homogeneous aqueous media catalyzed by conventional ruthenium catalysts

Olefin metathesis in aqueous solvents is sought for applications in green chemistry and with the hydrophilic substrates of chemical biology, such as proteins and polysaccharides. Most demonstrations of metathesis in water, however, utilize exotic complexes. We have examined the performance of conventional catalysts in homogeneous water/organic mixtures, finding that the second-generation Hoveyda-Grubbs catalyst has extraordinary efficiency in aqueous dimethoxyethane and aqueous acetone. High (71-95%) conversions are achieved for ring-closing and cross metathesis of a variety of substrates in these solvent systems.     

Three-dimensional structure of the water-insoluble protein crambin in dodecylphosphocholine micelles and its minimal solvent-exposed surface

We chose crambin, a hydrophobic and water-insoluble protein originally isolated from the seeds of the plant Crambe abyssinica, as a model for NMR investigations of membrane-associated proteins. We produced isotopically labeled crambin(P22,L25) (variant of crambin containing Pro22 and Leu25) as a cleavable fusion with staphylococcal nuclease and refolded the protein by an approach that has proved successful for the production of proteins with multiple disulfide bonds. We used NMR spectroscopy to determine the three-dimensional structure of the protein in two membrane-mimetic environments: in a mixed aqueous-organic solvent (75%/25%, acetone/water) and in DPC micelles. With the sample in the mixed solvent, it was possible to determine (>NH...OC<) hydrogen bonds directly by the detection of (h3)J(NC)' couplings. H-bonds determined in this manner were utilized in the refinement of the NMR-derived protein structures. With the protein in DPC (dodecylphosphocholine) micelles, we used manganous ion as an aqueous paramagnetic probe to determine the surface of crambin that is shielded by the detergent. With the exception of the aqueous solvent exposed loop containing residues 20 and 21, the protein surface was protected by DPC. This suggests that the protein may be similarly embedded in physiological membranes. The strategy described here for the expression and structure determination of crambin should be applicable to structural and functional studies of membrane active toxins and small membrane proteins.     

SDOVS: A solvent dipole ordering‐based method for virtual screening

We previously reported that solvent dipole ordering (SDO) at the ligand binding site of a protein indicates an outline of the preferred shape and binding pose of the ligands. We suggested that SDO‐mimetic pseudo‐molecules that mimic the 3D shape of the SDO region could be used as molecular queries with a shape similarity matching method in virtual screening. In this work, a virtual screening method based on SDO, named SDOVS, was proposed. This method was applied to virtual screening of ligands for four typical drug target proteins and the performance compared with that of FRED (well‐known rigid docking method); the efficiency of SDOVS was demonstrated to be better than FRED. © 2010 Wiley Periodicals, Inc. J Comput Chem, 2010     

多维离子交换色谱分离和串联质谱鉴定在小鼠肝脏膜蛋白质组分析中的应用

膜蛋白质在变性剂作用下能够较充分地溶解。根据这一特点,我们试图在变性剂溶液中采用串联离子交换色谱法分离小鼠肝脏膜蛋白质。将小鼠肝脏膜蛋白质溶解于含有4 mol/L尿素,20 mmol/L三羟甲基氨基甲烷(Tris)-盐酸缓冲液(pH 9.0)中,用Q-Sepharose FF和Sephacryl S-200HR树脂组成的色谱柱结合大部分溶解的膜蛋白质,然后采用氯化钠线性梯度洗脱蛋白质,分步收集后采用十二烷基硫酸钠-聚丙烯酰胺凝胶电泳(SDS-PAGE)进一步分离洗脱组分的蛋白质。利用胶内胰蛋白酶消化技术将SDS-PAGE胶内分离的蛋白质降解为相应的肽段,然后以反相高效液相色谱分离和离子阱质谱仪鉴定肽段。根据文献报道和蛋白质的功能分类,在所鉴定的392个蛋白质中有306个可能为膜蛋白质或膜结合蛋白质。蛋白质的疏水性计算表明,GRAVY(grand average of hydropathicity)得分大于或等于0.00的蛋白质有83个。综上所述,我们有理由认为本实验方法基本符合小鼠肝脏膜蛋白质组学研究的要求。     

How well does Poisson-Boltzmann implicit solvent agree with explicit solvent? A quantitative analysis

We have quantitatively studied the performance of a finite-difference Poisson-Boltzmann implicit solvent with respect to the TIP3P explicit solvent in a range of systems of biochemical interest. An overall agreement was found between the tested implicit and explicit solvents for hydrogen-bonding/salt-bridging dimers and peptide monomers and dimers of different conformations and different lengths. These comparative analyses also indicate a good transferability of empirically optimized parameters for the implicit solvent from small training molecules to large testing peptides. However, deviations between the two tested solvents are also apparent. Specifically, a consistent deviation was observed when hydrogen-bonding or salt-bridging dimers are within 4-6 A. The deviation reaches a maximum at about 5.5 A, the so-called water-bridging distance. The tested implicit solvent, even with optimized parameters, cannot capture the subtle fluctuation in the distance-dependent reaction field energy profiles, although smoothed profiles can still be obtained and are in overall agreement with those in the explicit solvent. Interestingly, the same mechanism underlining the above discrepancy is also responsible for the larger deviations of certain peptide conformations, such as parallel beta-strand dimers. It is likely that the observed discrepancy may cause improper conformational distributions in simulations with the implicit solvent when hydrogen-bonding or salt-bridging interactions are crucial, such as secondary structure populations in proteins. Validation of the implicit solvent with optimized parameters in dynamics simulations will be the next step to study the influences of the observed discrepancy at biological conditions.     

Solvent models for protein–ligand binding: Comparison of implicit solvent poisson and surface generalized born models with explicit solvent simulations

Solvent effects play a crucial role in mediating the interactions between proteins and their ligands. Implicit solvent models offer some advantages for modeling these interactions, but they have not been parameterized on such complex problems, and therefore, it is not clear how reliable they are. We have studied the binding of an octapeptide ligand to the murine MHC class I protein using both explicit solvent and implicit solvent models. The solvation free energy calculations are more than 10³ faster using the Surface Generalized Born implicit solvent model compared to FEP simulations with explicit solvent. For some of the electrostatic calculations needed to estimate the binding free energy, there is near quantitative agreement between the explicit and implicit solvent model results; overall, the qualitative trends in the binding predicted by the explicit solvent FEP simulations are reproduced by the implicit solvent model. With an appropriate choice of reference system based on the binding of the discharged ligand, electrostatic interactions are found to enhance the binding affinity because the favorable Coulomb interaction energy between the ligand and protein more than compensates for the unfavorable free energy cost of partially desolvating the ligand upon binding. Some of the effects of protein flexibility and thermal motions on charging the peptide in the solvated complex are also considered. © 2001 John Wiley & Sons, Inc. J Comput Chem 22: 591–607, 2001     

Ab initio folding of helix bundle proteins using molecular dynamics simulations

We have demonstrated that ab initio fast folding simulations at 400 K using a GB implicit solvent model with an all-atom based force field can describe the spontaneous formation of nativelike structures for the 36-residue villin headpiece and the 46-residue fragment B of Staphylococcal protein A. An implicit solvent model combined with high-temperature MD makes it possible to perform direct folding simulations of small- to medium-sized proteins by reducing the computational requirements tremendously. In the early stage of folding of the villin headpiece and protein A, initial hydrophobic collapse and rapid formation of helices were found to play important roles. For protein A, the third helix forms first in the early stage of folding and exhibits higher stability. The free energy profiles calculated from the folding simulations suggested that both of the helix-bundle proteins show a two-state thermodynamic behavior and protein A exhibits rather broad native basins.     

Simple models for nonpolar solvation: Parameterization and testing

Implicit solvent models are important for many biomolecular simulations. The polarity of aqueous solvent is essential and qualitatively captured by continuum electrostatics methods like Generalized Born (GB). However, GB does not account for the solvent‐induced interactions between exposed hydrophobic sidechains or solute‐solvent dispersion interactions. These “nonpolar” effects are often modeled through surface area (SA) energy terms, which lack realism, create mathematical singularities, and have a many‐body character. We have explored an alternate, Lazaridis–Karplus (LK) gaussian energy density for nonpolar effects and a dispersion (DI) energy term proposed earlier, associated with GB electrostatics. We parameterized several combinations of GB, SA, LK, and DI energy terms, to reproduce 62 small molecule solvation free energies, 387 protein stability changes due to point mutations, and the structures of 8 protein loops. With optimized parameters, the models all gave similar results, with GBLK and GBDILK giving no performance loss compared to GBSA, and mean errors of 1.7 kcal/mol for the stability changes and 2 Å deviations for the loop conformations. The optimized GBLK model gave poor results in MD of the Trpcage mini‐protein, but parameters optimized specifically for MD performed well for Trpcage and three other small proteins. Overall, the LK and DI nonpolar terms are valid alternatives to SA treatments for a range of applications. © 2017 Wiley Periodicals, Inc.     

Inference of the solvation energy parameters of amino acids using maximum entropy approach

We present a novel technique, based on the principle of maximum entropy, for deriving the solvation energy parameters of amino acids from the knowledge of the solvent accessible areas in experimentally determined native state structures as well as high quality decoys of proteins. We present the results of detailed studies and analyze the correlations of the solvation energy parameters with the standard hydrophobic scale. We study the ability of the inferred parameters to discriminate between the native state structures of proteins and their decoy conformations.     

Docking glycosaminoglycans to proteins: analysis of solvent inclusion

Glycosaminoglycans (GAGs) are anionic polysaccharides, which participate in key processes in the extracellular matrix by interactions with protein targets. Due to their charged nature, accurate consideration of electrostatic and water-mediated interactions is indispensable for understanding GAGs binding properties. However, solvent is often overlooked in molecular recognition studies. Here we analyze the abundance of solvent in GAG-protein interfaces and investigate the challenges of adding explicit solvent in GAG-protein docking experiments. We observe PDB GAG-protein interfaces being significantly more hydrated than protein–protein interfaces. Furthermore, by applying molecular dynamics approaches we estimate that about half of GAG-protein interactions are water-mediated. With a dataset of eleven GAG-protein complexes we analyze how solvent inclusion affects Autodock 3, eHiTs, MOE and FlexX docking. We develop an approach to de novo place explicit solvent into the binding site prior to docking, which uses the GRID program to predict positions of waters and to locate possible areas of solvent displacement upon ligand binding. To investigate how solvent placement affects docking performance, we compare these results with those obtained by taking into account information about the solvent position in the crystal structure. In general, we observe that inclusion of solvent improves the results obtained with these methods. Our data show that Autodock 3 performs best, though it experiences difficulties to quantitatively reproduce experimental data on specificity of heparin/heparan sulfate disaccharides binding to IL-8. Our work highlights the current challenges of introducing solvent in protein-GAGs recognition studies, which is crucial for exploiting the full potential of these molecules for rational engineering.     

Machine learning algorithms for predicting protein folding rates and stability of mutant proteins: comparison with statistical methods

Machine learning algorithms have wide range of applications in bioinformatics and computational biology such as prediction of protein secondary structures, solvent accessibility, binding site residues in protein complexes, protein folding rates, stability of mutant proteins, and discrimination of proteins based on their structure and function. In this work, we focus on two aspects of predictions: (i) protein folding rates and (ii) stability of proteins upon mutations. We briefly introduce the concepts of protein folding rates and stability along with available databases, features for prediction methods and measures for prediction performance. Subsequently, the development of structure based parameters and their relationship with protein folding rates will be outlined. The structure based parameters are helpful to understand the physical basis for protein folding and stability. Further, basic principles of major machine learning techniques will be mentioned and their applications for predicting protein folding rates and stability of mutant proteins will be illustrated. The machine learning techniques could achieve the highest accuracy of predicting protein folding rates and stability. In essence, statistical methods and machine learning algorithms are complimenting each other for understanding and predicting protein folding rates and the stability of protein mutants. The available online resources on protein folding rates and stability will be listed.     

Soft energy function and generic evolutionary method for discriminating native from nonnative protein conformations

We have developed a soft energy function, termed GEMSCORE, for the protein structure prediction, which is one of emergent issues in the computational biology. The GEMSORE consists of the van der Waals, the hydrogen-bonding potential and the solvent potential with 12 parameters which are optimized by using a generic evolutionary method. The GEMSCORE is able to successfully identify 86 native proteins among 96 target proteins on six decoy sets from more 70,000 near-native structures. For these six benchmark datasets, the predictive performance of the GEMSCORE, based on native structure ranking and Z-scores, was superior to eight other energy functions. Our method is based solely on a simple and linear function and thus is considerably faster than other methods that rely on the additional complex calculations. In addition, the GEMSCORE recognized 17 and 2 native structures as the first and the second rank, respectively, among 21 targets in CASP6 (Critical Assessment of Techniques for Protein Structure Prediction). These results suggest that the GEMSCORE is fast and performs well to discriminate between native and nonnative structures from thousands of protein structure candidates. We believe that GEMSCORE is robust and should be a useful energy function for the protein structure prediction.     

Study of the chromatographic parameters of ultra‐high‐performance supercritical fluid chromatography and method development using a design of experiments approach for the quantification of pesticides in lettuce

We examine the potential of ultra‐high‐performance supercritical fluid chromatography for multiresidue quantification of ten pesticides commonly applied to lettuce and compares it to ultra‐high‐performance liquid chromatography. Initially, a thorough study of the stationary and mobile phase composition and injection solvent was carried out. In a second step, a chemometric approach based on design of experiments was used to simultaneously study the influence of temperature, pressure, and percentage of ethanol on the retention, resolution and symmetry of the peaks. Using this approach, it was possible to obtain the Design Space, a robust region where complete separation of the analytes was achieved, with acceptable peak shape. Both methods were validated according to the figures of merit: selectivity, linearity, quantification limit, accuracy (in terms of recovery), and precision (repeatability and intermediate precision) and used to quantify the pesticides in lettuce samples. Comparing both techniques, it was concluded that the limits of quantification, accuracy, and precision were similar. However, in supercritical fluid chromatography, a reduced volume of organic solvent was used, the method was faster and generated lower amounts of residues.     

Effects of Cosolvents and pH on Protein Adsorption on Polystyrene Latex: A Dynamic Light Scattering Study

Dynamic light scattering was used to study the adsorption of two proteins with different surface properties (IgG and HSA) on negatively charged polystyrene latex. The proteins were adsorbed from water and from water/methanol and water/glycerol mixtures at various pH. Some striking differences between the adsorption behaviors of the proteins were observed. Whereas the thickness of the adsorbed layer of HSA was extremely sensitive to pH and solvent composition, that of IgG was not. IgG mainly showed an end-on orientation on polystyrene whereas several different surface orientations are suggested for HSA under different conditions. The addition of methanol inhibited the adsorption of HSA on the latex, but it did not affect the adsorption of IgG. In contrast, the addition of glycerol increased the thickness of the adsorbed layers of both proteins. So, the orientation of IgG on the latex is insensitive to pH but is a function of the kind of solvent whereas both pH and solvent strongly affect the adsorption of HSA. This is a puzzling result since both cosolvents should equally affect the adsorption of both proteins if the dominant forces for adsorption are the same. Therefore, we concluded that, whereas hydrophobic interactions are the dominant force in the adsorption behavior of HSA, van der Waals forces are the main forces involved in the attachment of IgG to the lattices. Copyright 2000 Academic Press.