A fast parallel clustering algorithm for molecular simulation trajectories

We implemented a GPU‐powered parallel k‐centers algorithm to perform clustering on the conformations of molecular dynamics (MD) simulations. The algorithm is up to two orders of magnitude faster than the CPU implementation. We tested our algorithm on four protein MD simulation datasets ranging from the small Alanine Dipeptide to a 370‐residue Maltose Binding Protein (MBP). It is capable of grouping 250,000 conformations of the MBP into 4000 clusters within 40 seconds. To achieve this, we effectively parallelized the code on the GPU and utilize the triangle inequality of metric spaces. Furthermore, the algorithm's running time is linear with respect to the number of cluster centers. In addition, we found the triangle inequality to be less effective in higher dimensions and provide a mathematical rationale. Finally, using Alanine Dipeptide as an example, we show a strong correlation between cluster populations resulting from the k‐centers algorithm and the underlying density. © 2012 Wiley Periodicals, Inc.     

Performance of density‐functional tight‐binding models in describing hydrogen‐bonded anionic‐water clusters

Density‐functional tight‐binding (DFTB) models are computationally efficient approximations to density‐functional theory that have been shown to predict reliable structural and energetic properties for various systems. In this work, the reliability and accuracy of the self‐consistent‐charge DFTB model and its recent extension(s) in predicting the structures, binding energies, charge distributions, and vibrational frequencies of small water clusters containing polyatomic anions of the Hofmeister series (carbonate, sulfate, hydrogen phosphate, acetate, nitrate, perchlorate, and thiocyanate) have been carefully and systematically evaluated on the basis of high‐level ab initio quantum‐chemistry [MP2/aug‐cc‐pVTZ and CCSD(T)/aug‐cc‐pVQZ] reference data. Comparison with available experimental data has also been made for further validation. The self‐consistent‐charge DFTB model, and even more so its recent extensions, are shown to properly account for the structural properties, energetics, intermolecular polarization, and spectral signature of hydrogen‐bonding in anionic water clusters at a fraction of the computational cost of ab initio quantum‐chemistry methods. This makes DFTB models candidates of choice for investigating much larger systems such as seeded water droplets, their structural properties, formation thermodynamics, and infrared spectra. © 2014 Wiley Periodicals, Inc.     

A Molecular‐Dynamics Simulation Study of the Conformational Preferences of Oligo(3‐hydroxyalkanoic acids) in Chloroform Solution

The GROMOS96 molecular‐dynamics (MD) program and force field was used to calculate the conformations at 298 K in CHCl₃ solution of two hexakis(3‐hydroxyalkanoic acids). One consists of (R)‐3‐hydroxybutanoate (HB) residues only: H−(OCH(Me)−CH₂−CO)₆−OH ( 1 ). The other one carries the side chains of valine, alanine, and leucine: H−(OCH(CHMe₂)CH₂−CO−O−CH(Me)−CH₂−CO−O−CH(CH₂ CHMe₂)−CH₂−CO)₂−OH ( 2 ), with homochiral 3‐hydroxyalkanoate (HA) moieties. In both cases, the conformational equilibria were sampled 2500 times for 25 ns. Other than clusters of arrangements with inter‐residual hydrogen bonding (between the O‐ and C‐terminal OH and COOH groups, and with chain‐bound ester carbonyl O‐atoms; Fig. 6), there are no preferred backbone conformations in which the molecules 1 and 2 spend more than 5% of the time. Specifically, neither the 2₁‐ nor the 3₁‐helical conformation of the oligoester backbone (found in stretched fibers, in lamellar crystallites, and in single crystals of polymers PHB and of oligomers OHB) is sampled to any significant extent (Fig. 8 and 9), in spite of the high population, in both oligomers, of the (−)‐synclinal conformation around the C(2)−C(3) bond (angle ϕ₂ in Fig. 2). In contrast to β‐oligopeptides, for which strongly preferred secondary structures are found after a few ns, and for which the number of conformations levels off with time, the number of conformational clusters of the corresponding oligoesters found by our force‐field MD calculations increases steadily over the observation time of 25 ns (Fig. 5). Thus, the conclusion from biological and physical‐chemical studies, according to which the PHB chain is extremely flexible, is confirmed by our computational investigation: in CHCl₃ solution, the hexakis(3‐hydroxyalkanoate) chain samples its conformational space randomly!     

On the use of the observation‐wise k‐fold operation in PCA cross‐validation

Cross‐validation (CV) is a common approach for determining the optimal number of components in a principal component analysis model. To guarantee the independence between model testing and calibration, the observation‐wise k‐fold operation is commonly implemented in each cross‐validation step. This operation renders the CV algorithm computationally intensive, and it is the main limitation to apply CV on very large data sets. In this paper, we carry out an empirical and theoretical investigation of the use of this operation in the element‐wise k‐fold (ekf) algorithm, the state‐of‐the‐art CV algorithm. We show that when very large data sets need to be cross‐validated and the computational time is a matter of concern, the observation‐wise k‐fold operation can be skipped. The theoretical properties of the resulting modified algorithm, referred to as column‐wise k‐fold (ckf) algorithm, are derived. Also, its performance is evaluated with several artificial and real data sets. We suggest the ckf algorithm to be a valid alternative to the standard ekf to reduce the computational time needed to cross‐validate a data set. Copyright © 2015 John Wiley & Sons, Ltd.     

Quantum and Classical Molecular Dynamics of Ionic Liquid Electrolytes for Na/Li‐based Batteries: Molecular Origins of the Conductivity Behavior

Compositional effects on the charge‐transport properties of electrolytes for batteries based on room‐temperature ionic liquids (RTILs) are well‐known. However, further understanding is required about the molecular origins of these effects, in particular regarding the replacement of Li by Na. In this work, we investigate the use of RTILs in batteries, by means of both classical molecular dynamics (MD), which provides information about structure and molecular transport, and ab initio molecular dynamics (AIMD), which provides information about structure. The focus has been placed on the effect of adding either Na⁺ or Li⁺ to 1‐methyl‐1‐butyl‐pyrrolidinium [C₄PYR]⁺ bis(trifluoromethanesulfonyl)imide [Tf₂N]^?. Radial distribution functions show excellent agreement between MD and AIMD, which ensures the validity of the force fields used in the MD. This is corroborated by the MD results for the density, the diffusion coefficients, and the total conductivity of the electrolytes, which reproduce remarkably well the experimental observations for all studied Na/Li concentrations. By extracting partial conductivities, it is demonstrated that the main contribution to the conductivity is that of [C₄PYR]⁺ and [Tf₂N]^?. However, addition of Na⁺/Li⁺, although not significant on its own, produces a dramatic decrease in the partial conductivities of the RTIL ions. The origin of this indirect effect can be traced to the modification of the microscopic structure of the liquid as observed from the radial distribution functions, owing to the formation of [Na(Tf₂N)_n]^(n?1)? and [Li(Tf₂N)_n]^(n?1)? clusters at high concentrations. This formation hinders the motion of the large ions, hence reducing the total conductivity. We demonstrate that this clustering effect is common to both Li and Na, showing that both ions behave in a similar manner at a microscopic level in spite of their distinct ionic radii. This is an interesting finding for extending Li‐ion and Li‐air technologies to their potentially cheaper Na‐based counterparts.     

Accelerating Kohn‐Sham response theory using density fitting and the auxiliary‐density‐matrix method

An extension of the formulation of the atomic‐orbital‐based response theory of Larsen et al., JCP 113, 8909 (2000) is presented. This new framework has been implemented in LSDalton and allows for the use of Kohn‐Sham density‐functional theory with approximate treatment of the Coulomb and Exchange contributions to the response equations via the popular resolution‐of‐the‐identity approximation as well as the auxiliary‐density matrix method (ADMM). We present benchmark calculations of ground‐state energies as well as the linear and quadratic response properties: vertical excitation energies, polarizabilities, and hyperpolarizabilities. The quality of these approximations in a range of basis sets is assessed against reference calculations in a large aug‐pcseg‐4 basis. Our results confirm that density fitting of the Coulomb contribution can be used without hesitation for all the studied properties. The ADMM treatment of exchange is shown to yield high accuracy for ground‐state and excitation energies, whereas for polarizabilities and hyperpolarizabilities the performance gain comes at a cost of accuracy. Excitation energies of a tetrameric model consisting of units of the P700 special pigment of photosystem I have been studied to demonstrate the applicability of the code for a large system.     

Comprehensive understanding of size‐, shape‐, and composition‐dependent polarizabilities of SimCn (m,n = 1–4) clusters

We performed a comprehensive study of the size‐, shape‐, and composition‐dependent polarizabilities of Si_mC_n (m, n = 1–4) clusters on the basis of the density‐functional‐based coupled perturbed Hartree–Fock calculations. We found better correlations between the polarizabilities and both the binding energies (E_b) and change in charge distribution (Δq) than the energy gaps. The α values exhibit overall decreasing and increasing trends with increases in the E_b and Δq values, respectively. For isomers with the same E_b values and different polarizabilities, Δq can well explain the difference in polarizabilities. The π‐electron delocalization effect is the best factor for understanding the shape‐dependence. For a given m/n value, the linear clusters have an obviously larger polarizability than both the prolate and compact clusters, irrespective of the cluster size. We fit a quantitative expression [α = A ? (A ? B) × exp(?k(m/n))] to describe the composition‐dependent polarizabilities. © 2012 Wiley Periodicals, Inc.     

Conformational Selection in Glycomimetics: Human Galectin‐1 Only Recognizes syn‐Ψ‐Type Conformations of β‐1,3‐Linked Lactose and Its C‐Glycosyl Derivative

The human lectin galectin‐1 (hGal‐1) translates sugar signals, that is, β‐galactosides, into effects on the level of cells, for example, growth regulation, and has become a model for studying binding of biopharmaceutically relevant derivatives. Bound‐state conformations of Galβ‐C‐(1→3)‐Glcβ‐OMe ( 1 ) and its βGal‐(1→3)‐βGlc‐OMe disaccharide parent compound were studied by using NMR spectroscopy (transferred (TR)‐NOESY data), assisted by docking experiments and molecular dynamics (MD) simulations. The molecular recognition process involves a conformational selection event. Although free C‐glycoside access four distinct conformers in solution, hGal‐1 recognizes shape of a local minimum of compound 1 , the syn‐Φ/syn‐Ψ conformer, not the structure at global minimum. MD simulations were run to explain, in structural terms, the observed geometry of the complex.     

Classifying formulations of crosslinked polyethylene pipe by applying machine‐learning concepts to infrared spectra

Crosslinked polyethylene (PEX‐a) pipes are emerging as promising replacements for traditional metal or concrete pipes used for water, gas, and sewage transport. Understanding the relationship between pipe formulation and performance is critical to their proper design and implementation. We have developed a methodology using principal component analysis (PCA) and the machine learning techniques of k‐means clustering and support vector machines (SVM) to compare and classify different PEX‐a pipe formulations based on characteristic infrared (IR) spectroscopy absorbance peaks. The application of PCA revealed that a large percentage (89%) of the total variance could be explained by the first three principal components (PC1‐PC3), with distinct clustering of the data for each formulation. By examining the contribution of the individual IR bands to the PCs, we determined that PC1 could be attributed to different peroxide crosslinkers, whereas PC2 and PC3 could be attributed to differences in the additives. Using the PCA results as input to k‐means clustering and SVM resulted in very high accuracy of classifying the different pipe formulations. Our approach highlights the advantages of using PCA and machine learning techniques to characterize different formulations of PEX‐a pipes, which is important to achieve a detailed understanding of the pipe formulation and manufacturing process. © 2019 Wiley Periodicals, Inc. J. Polym. Sci., Part B: Polym. Phys. 2019 , 57, 1255–1262     

Diblock‐Copolymer‐Mediated Self‐Assembly of Protein‐Stabilized Iron Oxide Nanoparticle Clusters for Magnetic Resonance Imaging

Superparamagnetic iron oxide nanoparticles (SPIONs) can be used as efficient transverse relaxivity (T₂) contrast agents in magnetic resonance imaging (MRI). Organizing small (D<10 nm) SPIONs into large assemblies can considerably enhance their relaxivity. However, this assembly process is difficult to control and can easily result in unwanted aggregation and precipitation, which might further lead to lower contrast agent performance. Herein, we present highly stable protein–polymer double‐stabilized SPIONs for improving contrast in MRI. We used a cationic–neutral double hydrophilic poly(N‐methyl‐2‐vinyl pyridinium iodide‐block‐poly(ethylene oxide) diblock copolymer (P2QVP‐b‐PEO) to mediate the self‐assembly of protein‐cage‐encapsulated iron oxide (γ‐Fe₂O₃) nanoparticles (magnetoferritin) into stable PEO‐coated clusters. This approach relies on electrostatic interactions between the cationic N‐methyl‐2‐vinylpyridinium iodide block and magnetoferritin protein cage surface (pI≈4.5) to form a dense core, whereas the neutral ethylene oxide block provides a stabilizing biocompatible shell. Formation of the complexes was studied in aqueous solvent medium with dynamic light scattering (DLS) and cryogenic transmission electron microcopy (cryo‐TEM). DLS results indicated that the hydrodynamic diameter (D_h) of the clusters is approximately 200 nm, and cryo‐TEM showed that the clusters have an anisotropic stringlike morphology. MRI studies showed that in the clusters the longitudinal relaxivity (r₁) is decreased and the transverse relaxivity (r₂) is increased relative to free magnetoferritin (MF), thus indicating that clusters can provide considerable contrast enhancement.     

A critical assessment of hidden markov model sub‐optimal sampling strategies applied to the generation of peptide 3D models

Hidden Markov Model derived structural alphabets are a probabilistic framework in which the complete conformational space of a peptidic chain is described in terms of probability distributions that can be sampled to identify conformations of largest probabilities. Here, we assess how three strategies to sample sub‐optimal conformations—Viterbi k‐best, forward backtrack and a taboo sampling approach—can lead to the efficient generation of peptide conformations. We show that the diversity of sampling is essential to compensate biases introduced in the estimates of the probabilities, and we find that only the forward backtrack and a taboo sampling strategies can efficiently generate native or near‐native models. Finally, we also find such approaches are as efficient as former protocols, while being one order of magnitude faster, opening the door to the large scale de novo modeling of peptides and mini‐proteins. © 2016 Wiley Periodicals, Inc.     

Density functional study of structural and electronic properties of maximum‐spin n+1Aun−1Ag clusters

The structures and relative stabilities of high‐spin ⁿ⁺¹Au_n?1Ag and ⁿAu_n?1Ag⁺ (n = 2–8) clusters have been studied with density functional calculation. We predicted the existence of a number of previously unknown isomers. Our results revealed that all structures of high‐spin neutral or cationic Au_n?1Ag clusters can be understood as a substitution of an Au atom by an Ag atom in the high‐spin neutral or cationic Au_n clusters. The properties of mixed gold–silver clusters are strongly sized and structural dependence. The high‐spin bimetallic clusters tend to be holding three‐dimensional geometry rather than planar form represented in their low‐spin situations. Silver atom prefers to occupy those peripheral positions until to n = 8 for high‐spin clusters, which is different from its position occupied by light atom in the low‐spin situations. Our theoretical calculations indicated that in various high‐spin Au_n?1Ag neutral and cationic species, ⁵Au₃Ag, ³AuAg and ⁵Au₄Ag⁺ hold high stability, which can be explained by valence bond theory. © 2009 Wiley Periodicals, Inc. Int J Quantum Chem, 2009     

A fast method for large‐scale De Novo peptide and miniprotein structure prediction

Although peptides have many biological and biomedical implications, an accurate method predicting their equilibrium structural ensembles from amino acid sequences and suitable for large‐scale experiments is still missing. We introduce a new approach—PEP‐FOLD—to the de novo prediction of peptides and miniproteins. It first predicts, in the terms of a Hidden Markov Model‐derived structural alphabet, a limited number of local conformations at each position of the structure. It then performs their assembly using a greedy procedure driven by a coarse‐grained energy score. On a benchmark of 52 peptides with 9–23 amino acids, PEP‐FOLD generates lowest‐energy conformations within 2.8 and 2.3 Å Cα root‐mean‐square deviation from the full nuclear magnetic resonance structures (NMR) and the NMR rigid cores, respectively, outperforming previous approaches. For 13 miniproteins with 27–49 amino acids, PEP‐FOLD reaches an accuracy of 3.6 and 4.6 Å Cα root‐mean‐square deviation for the most‐native and lowest‐energy conformations, using the nonflexible regions identified by NMR. PEP‐FOLD simulations are fast—a few minutes only—opening therefore, the door to in silico large‐scale rational design of new bioactive peptides and miniproteins. © 2009 Wiley Periodicals, Inc. J Comput Chem, 2010     

Estimation of the breakthrough volume of selected steroids for C‐18 solid‐phase extraction sorbent using retention data from micro‐thin layer chromatography

SPE method is a very popular technique, and is commonly used for the prepurification, concentration, and isolation of different organic compounds from variable matrices. In this work, the optimization of SPE process was carried out. The breakthrough volume of solid sorbents based on octadecylsilane was determined and three methods were compared: (1) calculation one – the breakthrough volume was calculated using retention factor k determined with micro‐TLC method, frontal analysis – (2) breakthrough volume was determined as volume of whole elution peak, and (3) breakthrough volume was determined as the center of peak gravity. For calculation method, the k values of key estrogens and progestogens were derived from the micro‐TLC experiment reported previously. By combining these three methods, we can point the start of elution, the maximum concentration of analyte in eluate, and the whole eluent volume, which is necessary to achieve an appropriate selectivity and high extraction recovery. Proposed calculation method allows to estimate the beginning of the steroid peak, when the analyte appears in the eluate flowing from the sorbent. Such observation advances the SPE optimization protocol that was described before and was based on the correlation between raw k_SPE and k_micro‐TLC data.     

Conformational Analysis of the Cyclic Pentadepsipeptide Cyclo(Tro‐Aib‐Aib‐Aib‐Aib) in the Solid State and in Solution

The cyclic 16‐membered pentadepsipeptide cyclo(Tro‐Aib‐Aib‐Aib‐Aib) ( 1 ) was crystallized from MeOH/AcOEt/CH₂Cl₂, and its structure was established by X‐ray crystallography (Fig. 1). There are two symmetry‐independent molecules with different conformations in the asymmetric unit. Two intramolecular H‐bonds stabilize two β‐turns in each molecule. On the other hand, two of the four Aib residues are forced to assume a nonfavorable nonhelical conformation in each of the symmetry‐independent molecules (Table 1). The conformational study in CDCl₃ solution by NMR spectroscopy and molecular dynamics (MD) simulations indicate that the averaged structure (Fig. 3) is almost the same as in the solid state.     

Validation of the GROMOS 54A7 Force Field Regarding Mixed α/β‐Peptide Molecules

A molecular‐dynamics (MD) simulation study of two heptapeptides containing α‐ and β‐amino acid residues is presented. According to NMR experiments, the two peptides differ in dominant fold when solvated in MeOH: peptide 3 adopts predominantly β‐hairpin‐like conformations, while peptide 8 adopts a 14/15‐helical fold. The MD simulations largely reproduce the experimental data. Application of NOE atom? atom distance restraining improves the agreement with experimental data, but reduces the conformational sampling. Peptide 3 shows a variety of conformations, while still agreeing with the NOE and ³J‐coupling data, whereas the conformational ensemble of peptide 8 is dominated by one helical conformation. The results confirm the suitability of the GROMOS 54A7 force field for simulation or structure refinement of mixed α/β‐peptides in MeOH.     

Structure‐Reactivity Relationships as Probes to Acetylcholinesterase Inhibition Mechanisms by Aryl Carbamates. II. Hammett‐Taft Cross‐Interaction Correlations

Substituted phenyl‐N‐butyl carbamates ( 1 ) and p‐nitrophenyl‐N‐substituted carbamates ( 2 ) are characterized as “pseudo‐pseudo‐substrate” inhibitors of acetylcholinesterase. Since the inhibitors protonate in pH 7.0 buffer solution, the virtual inhibition constants (K_i's) of the protonated inhibitors can be calculated from the equation, ‐logK_i' = ‐logK_i ‐ pK_a + 14. The ‐logK_i' and logk_c values for acetylcholinesterase inhibitions by carbamates 1 correlate with the Hammett equation (log(k/k₀) = ρσ); moreover, those by carbamates 2 correlate with the Taft equation (log(k/k₀) = ρ* σ*). With modified Hammett‐Taft cross‐interaction variations, multiple linear regressions of the ‐logK_i' and logk_c values of carbamates 1 and 2 give good correlations, and the cross‐interaction constants (ρ_XR) are 0.5 and 0.0, respectively. The ρ_XR value of 0.5 indicates that the carbamate O‐C(O)‐N‐R geometries for the transition states that lead to enzyme‐carbamate tetrahedral intermediates are all pseudo‐trans conformations. Therefore, the carbamate moiety of the inhibitors stretches along the active site gorge of the enzyme but does not bind in the acyl binding site pocket of the enzyme. Overall, the carbamate O‐C(O)‐N‐R geometries for carbamates 1 and 2 , protonated carbamates 1 and 2 , and the tetrahedral intermediate are all retained in pseudo‐trans conformations. The ρ_XR value of 0.0 suggests that the transition states that lead to the carbamyl enzymes are breaking C‐O bonds and are excluding the leaving groups, substituted phenols.     

Kinetic investigation of gas‐phase reactions between the OH‐radical and o‐, m‐, p‐ethyltoluene and n‐nonane in air

We have carried out relative rate experiments (T = 294 ± 2 K, atmospheric pressure) to investigate the OH‐oxidation of o‐, m‐, and p‐ethyltoluene and n‐nonane (k₁, k₂, k₃, and k₄ respectively). The experiments were performed in a 2‐m³ smog chamber with Teflon coated walls. The rate constants obtained are (in cm³ molecule^?1 s^?1 with two sigma uncertainties): k₁ = (1.36 ± 0.07) × 10^?11; k₂ = (2.12 ± 0.26) × 10^?11; k₃ = (1.47 ± 0.04) × 10^?11, and k₄ = (0.95 ± 0.02) × 10^?11. The measured rate constants are in accordance with previously published data, so that a coherent group of values for the compounds studied can be established. Atmospheric implications, ozone, and particle production are discussed. In addition, we have determined the amount of o‐, m‐, and p‐ethyltoluenes in different types of gasoline. © 2004 Wiley Periodicals, Inc. Int J Chem Kinet 36: 367–378 2004