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The Franck-Condon effect is considered and the vibrational structure of the πnbπ* transition of the peptide backbone is incorporated into matrix method calculations of the electronic circular dichroism (CD) spectra of proteins in the far-ultraviolet. We employ the state-averaged CASPT2 method to calculate the ground and πnbπ* excited state geometries and frequencies of N-methylacetamide (NMA), which represents the peptide chromophore. The results of these calculations are used to incorporate vibronic levels of the excited states into the matrix method calculation. The CD spectra of a set of 49 proteins, comprising a range of structural types, are calculated to assess the influence of the vibrational structure. The calculated spectra of α-helical proteins are better resolved using the vibronic parameters and correlation between the experimental and the calculated intensity of less regular β structure proteins improves over most wavelengths in the far-UV. No obvious improvement is observed in the calculated spectra of regular β-sheet proteins. Our high-level ab initio calculations of the vibronic structure of the πnbπ* transition in NMA have provided some further insight into the physical origins of the nature of protein CD spectra in the far-UV.

Traditionally, electrostatic interactions are modelled using Ewald techniques, which provide a good approximation, but are poorly suited to GPU architectures. We use the GPU versions of the LAMMPS MD package to implement and assess the Wolf summation method. We compute transport and structural properties of pure carbon dioxide and mixtures of carbon dioxide with either methane or difluoromethane. The diffusion of pure carbon dioxide is indistinguishable when using the Wolf summation method instead of PPPM on GPUs. The optimum value of the potential damping parameter, α, is 0.075. We observe a decrease in accuracy when the system polarity increases, yet the method is robust for mildly polar systems. We anticipate the method can be used for a number of techniques, and applied to a variety of systems. Substitution of PPPM can yield a two-fold decrease in the wall-clock time.

Here we provide an introduction and overview of current progress in the field of molecular simulation and visualization, touching on the following topics: (1) virtual and augmented reality for immersive molecular simulations; (2) advanced visualization and visual analytic techniques; (3) new developments in high performance computing; and (4) applications and model building.

The ileal lipid binding protein (ILBP or I-BABP) binds bile salts with positive cooperativity and has unusual site selectivity, whereby cholic acid binds preferentially in one site and chenodeoxycholic in another, despite both sites having an affinity for both ligands and the ligands only differing by a single hydroxyl group. Previous studies of the human variant have assumed that the ligand/protein binding ratio is 2:1, but we show, using electrospray ionization mass spectroscopy, that human ILBP binds bile acids with a 3:1 ratio, even at low protein and ligand concentrations. Docking calculations and molecular dynamics (MD) simulations identify an allosterically active binding site on the protein exterior that induces a change from a closed conformation to an open one, characterized by a movement of one of the α-helices by ~10° with respect to the β-clam shell. Additional independent MD simulations of several hundred nanoseconds implicate the change between conformations in the mechanisms of both cooperativity and ligand site selectivity.

Two-dimensional infrared (2DIR) spectroscopy is a rapidly developing nonlinear spectroscopy, which allows access to greater structural detail than traditional vibrational spectroscopies. The ability to gain extra structural insight is particularly relevant to the study of biomacromolecules, whose Fourier transform infrared (FTIR) spectra are often congested, due to the large number of vibrations. The subpicosecond timescale of the spectroscopy gives the opportunity to follow the fluctuations of a molecule in the time domain. Theoretical and experimental techniques are well developed for 2DIR, and they have already given insight into some of the fundamental aspects of the structure and dynamics of proteins and nucleic acids. This chapter reviews some of these recent studies and showcases the potential of the method., (Copyright © 2013 Elsevier Inc. All rights reserved.)

It is challenging to compute the partition function (Q) for systems with enormous configurational spaces, such as fluids. Recently, we developed a Monte Carlo technique (an energy partitioning method) for computing Q [ J. Chem. Phys. 2011 , 135 , 174105 ]. In this paper, we use this approach to compute the partition function of a binary fluid mixture (carbon dioxide + methane); this allows us to obtain the Helmholtz free energy (F) via F = -k(B)T ln Q and the Gibbs free energy (G) via G = F + pV. We then utilize G to obtain the coexisting mole fraction curves. The chemical potential of each species is also obtained. At the vapor-liquid equilibrium condition, the chemical potential of methane significantly increases, while that of carbon dioxide slightly decreases, as the pressure increases along an isotherm. Since Q is obtained from the density of states, which is independent of the temperature, equilibrium thermodynamic properties at any condition can be obtained by varying the total composition and volume of the system. Our methodology can be adapted to explore the free energies of other binary mixtures in general and of those containing CO(2) in particular. Since the method gives access to the free energy and chemical potentials, it will be useful in many other applications.

The partition function (Q) is a central quantity in statistical mechanics. All the thermodynamic properties can be derived from it. Here we show how the partition function of fluids can be calculated directly from simulations; this allows us to obtain the Helmholtz free energy (F) via F = -k(B)T ln Q. In our approach, we divide the density of states, assigning half of the configurations found in a simulation to a high-energy partition and half to a low-energy partition. By recursively dividing the low-energy partition into halves, we map out the complete density of states for a continuous system. The result allows free energy to be calculated directly as a function of temperature. We illustrate our method in the context of the free energy of water.

Background: The Elk-1 transcription factor is a member of a group of proteins called ternary complex factors, which serve as a paradigm for gene regulation in response to extracellular signals. Its deregulation has been linked to multiple human diseases including the development of tumours. The work herein aims to inform the design of potential peptidomimetic compounds that can inhibit the formation of the Elk-1 dimer, which is key to Elk-1 stability. We have conducted molecular dynamics simulations of the Elk-1 ETS domain followed by virtual screening., Results: We show the ETS dimerisation site undergoes conformational reorganisation at the α1β1 loop. Through exhaustive screening of di- and tri-peptide libraries against a collection of ETS domain conformations representing the dynamics of the loop, we identified a series of potential binders for the Elk-1 dimer interface. The di-peptides showed no particular preference toward the binding site; however, the tri-peptides made specific interactions with residues: Glu17, Gln18 and Arg49 that are pivotal to the dimer interface., Conclusions: We have shown molecular dynamics simulations can be combined with virtual peptide screening to obtain an exhaustive docking protocol that incorporates dynamic fluctuations in a receptor. Based on our findings, we suggest experimental binding studies to be performed on the 12 SILE ranked tri-peptides as possible compounds for the design of inhibitors of Elk-1 dimerisation. It would also be reasonable to consider the score-ranked tri-peptides as a comparative test to establish whether peptide size is a determinant factor of binding to the ETS domain.

The refrigerant 1-1-1-2-tetrafluoroethane (R134a) is being phased out in Europe from 2011. This requires the adoption of alternatives, and the mixture of R134a with carbon dioxide (CO(2)) is a promising candidate. However, limited experimental data currently stymie evaluation of its performance in industrial applications. In this paper, we employ atomistic force fields and the configurational-bias Monte Carlo technique to study the vapour-liquid equilibrium of this mixture. We also characterize the microscopic structure of the mixture, which is not readily available from experiments. At 272 K and 11.55 bar, the average coordination number of the first solvation shell of R134a is 11 and that of CO(2) is eight. CO(2) does not alter the structure of R134a, but its structure is slightly changed, due to the presence of R134a. All pair interactions are sensitive to pressure and are more structured at lower pressure. CO(2) prefers to form clusters of two in the mixture and highly extended or percolating clusters are not found., (This journal is © the Owner Societies 2011)

Water is pivotal in the stabilization of macromolecular biological structures, although the dynamic ensemble structure of water near to molecular surfaces has yet to be fully understood. We show, through molecular simulation and fluorescence measurements, that water at the membrane surface is substantially more ordered than bulk water, due to a loss of hydrogen bonding between water molecules, coupled with an alignment of lipid and water dipole moments. Ordering of the water leads to a gradient in the effective dielectric permittivity, which is evident in both the molecular simulations and the fluorescence measurements. A lower effective dielectric permittivity was correlated with a decreasing degree of hydrogen bonding over the same spatial range. The water molecules closest to the lipid headgroup oxygen atoms form hydrogen bonds which exhibit a mean lifetime of 6.3 ps, compared with a mean lifetime of water-water hydrogen bonds of less than 2 ps. Membranes made up purely of phosphatidylcholine (PC) were compared with those made with a PC/cholesterol ratio relevant to cell membranes. Clear differences were found between these membrane configurations. These observations point to molecular structural differences in the surface environments of membranes and may underlie regional differences in the surface biophysical properties of membrane microdomains., (Copyright © 2011 Wiley Periodicals, Inc.)

It is increasingly easy to develop software that exploits Graphics Processing Units (GPUs). The molecular dynamics simulation community has embraced this recent opportunity. Herein, we outline the current approaches that exploit this technology. In the context of biomolecular simulations, we discuss some of the algorithms that have been implemented and some of the aspects that distinguish the GPU from previous parallel environments. The ubiquity of GPUs and the ingenuity of the simulation community augur well for the scale and scope of future computational studies of biomolecules., (Copyright © 2011 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.)

Density functional theory based molecular dynamics simulations are used to study the structure, infrared (IR) spectroscopy, circular dichroism (CD) spectroscopy, and coupling between the amide I vibrations of a bridged cyclic diamide in the gas phase and in aqueous solution. IR spectra computed via the dipole moment time correlation function show a large red-shift of 30 cm(-1) in the amide I vibration in solution compared to the gas phase, and are in good agreement with experiment. Conformationally averaged CD spectra computed using the CIS(D) method are highly sensitive to the structures used, and structures sampled in the aqueous phase simulation are required to obtain qualitatively correct CD spectra. Analysis of the coupling between the amide I modes shows that in the aqueous phase there is an increased localization of the vibrations on the individual peptide groups and a reduction in the mode coupling parameter compared to the gas phase. Overall, the results illustrate the significance of incorporating molecular dynamics in the simulation of IR and CD spectra.

We have investigated the effects of explicit molecular interactions and the membrane dipole potential on the absorption and emission spectra of a widely used fluorescent probe, di-8-ANEPPS, in a dipalmitoylphosphatidylcholine (DPPC) and a mixed DPPC/cholesterol membrane bilayer. Ground-state and excited-state geometries were calculated with the complete active space self-consistent field (CASSCF) method. Interactions with up to 260 atoms of the membrane bilayer were explicitly incorporated using a decoupled quantum mechanics/molecular mechanics (QM/MM) approach, utilizing recent advances in time-dependent density functional theory (TDDFT). We find that no specific molecular interactions affect the fluorescence of di-8-ANEPPS; rather, the magnitude of the membrane dipole potential is key to the shifts observed in both of the two lowest excited states.

We present pair potentials for fluorinated methanes and their dimers with CO(2) based on ab initio potential energy surfaces. These potentials reproduce the experimental second virial coefficients of the pure fluorinated methanes and their mixtures with CO(2) without adjustment. Ab initio calculations on trimers are used to model the effects of nonadditive dispersion and induction. Simulations using these potentials reproduce the experimental phase-coexistence properties of CH(3)F within 10% over a wide range of temperatures. The phase coexistence curve of the mixture of CH(2)F(2) and CO(2) is reproduced with an error in the mole fractions of both phases of less than 0.1. The potentials described here are based entirely on ab initio calculations, with no empirical fits to improve the agreement with experiment.

The PB1 domain of NBR1 folds via a single pathway mechanism involving two sequential energy barriers separated by a high-energy intermediate. The structural ensemble representing each of the two transition states (TS1 and TS2) has been calculated using experimental Φ values and biased molecular dynamics simulations. Both TS1 and TS2 represent compact states (β(TS1) = 0.71, and β(TS2) = 0.93) but are defined by quite different distributions of Φ values, degrees of structural heterogeneity, and nativelike secondary structure. TS1 forms a heterogeneous ensemble of dynamic structures, representing a global collapse of the polypeptide chain around a set of weak nativelike contacts. In contrast, TS2 has a high proportion of nativelike secondary structure, which is reflected in an extensive distribution of high Φ values. Two snapshots along the folding pathway of the PB1 domain reveal insights into the malleability, the solvent accessibility, and the timing of nativelike core packing that stabilizes the folded state.

Nisin is a polymacrocyclic peptide antimicrobial with high activity against Gram-positive bacteria. Lanthionine and methyllanthionine bridges, closing the macrocycles, are stabilized by thioether bonds, formed between cysteines and dehydrated serine or threonine. The role of polypeptide backbone conformation in the formation of macrocycles A and B within cysteine mutants of nisin residues 1−12 is investigated here by molecular dynamics simulations. Enantiomeric combinational space of Cys3 and Cys7 and of Cys8 and Cys11 is examined for the preference of disulfide bond formation over helical turn formation within this region. A clear preference for spontaneous disulfide formation and closure of rings 3,7 and 8,11 is demonstrated for the D-Cys3, D-Cys7, L-Cys8, L-Cys11 nisin homologue, while interlinked rings A and B are obtained through disulfide bridges between L-Cys3 and D-Cys8 and between D-Cys7 and D-Cys11. This study offers a simple designer approach to solid phase synthesis of macrocyclic peptides and lantibiotic analogues.

1-1-1-2-tetrafluoroethane (R134a) is one of the most commonly used refrigerants. Its thermophysical properties are important for evaluating the performance of refrigeration cycles. These can be obtained via computer simulation, with an insight into the microscopic structure of the liquid, which is not accessible to experiment. In this paper, vapour-liquid equilibrium properties of R134a and its liquid microscopic structure are investigated using coupled-decoupled configurational-bias Monte Carlo simulation in the Gibbs ensemble, with a recent potential [J. Phys. Chem. B 2009, 113, 178]. We find that the simulations agree well with the experimental data, except at the vicinity of the critical region. Liquid R134a packs like liquid argon, with a coordination number in the first solvation shell of 12 at 260 K. The nearest neighbours prefer to be localized in three different spaces around the central molecule, in such a manner that the dipole moments are in a parallel alignment. Analysis of the pair interaction energy shows clear association of R134a molecules, but no evidence for C-HF type hydrogen bonding is found. The above findings should be of relevance to a broad range of fluoroalkanes.

Revealing the structure and aggregation mechanism of amyloid fibrils is essential for the treatment of over 20 diseases related to protein misfolding. Coherent two-dimensional (2D) infrared spectroscopy is a novel tool that provides a wealth of new insight into the structure and dynamics of biomolecular systems. Recently developed ultrafast laser sources are extending multidimensional spectroscopy into the ultraviolet (UV) region, and this opens up new opportunities for probing fibrils. In a simulation study, we show that 2DUV spectra of the backbone of a 32-residue β-amyloid (Aβ(9-40)) fibril associated with Alzheimer's disease and two intermediate prefibrillar structures carry characteristic signatures of fibril size and geometry that could be used to monitor its formation kinetics. The dependence of these signals on the fibril size and geometry is explored. We demonstrate that the dominant features of the β-amyloid fibril spectra are determined by intramolecular interactions within a single Aβ(9-40), and intermolecular interactions at the "external interface" have clear signatures in the fine details of these signals.

Background: Beta-turns are secondary structure elements usually classified as coil. Their prediction is important, because of their role in protein folding and their frequent occurrence in protein chains., Results: We have developed a novel method that predicts beta-turns and their types using information from multiple sequence alignments, predicted secondary structures and, for the first time, predicted dihedral angles. Our method uses support vector machines, a supervised classification technique, and is trained and tested on three established datasets of 426, 547 and 823 protein chains. We achieve a Matthews correlation coefficient of up to 0.49, when predicting the location of beta-turns, the highest reported value to date. Moreover, the additional dihedral information improves the prediction of beta-turn types I, II, IV, VIII and "non-specific", achieving correlation coefficients up to 0.39, 0.33, 0.27, 0.14 and 0.38, respectively. Our results are more accurate than other methods., Conclusions: We have created an accurate predictor of beta-turns and their types. Our method, called DEBT, is available online at http://comp.chem.nottingham.ac.uk/debt/.

Background: Random forest, an ensemble based supervised machine learning algorithm, is used to predict the SCOP structural classification for a target structure, based on the similarity of its structural descriptors to those of a template structure with an equal number of secondary structure elements (SSEs). An initial assessment of random forest is carried out for domains consisting of three SSEs. The usability of random forest in classifying larger domains is demonstrated by applying it to domains consisting of four, five and six SSEs., Results: Random forest, trained on SCOP version 1.69, achieves a predictive accuracy of up to 94% on an independent and non-overlapping test set derived from SCOP version 1.73. For classification to the SCOP Class, Fold, Super-family or Family levels, the predictive quality of the model in terms of Matthew's correlation coefficient (MCC) ranged from 0.61 to 0.83. As the number of constituent SSEs increases the MCC for classification to different structural levels decreases., Conclusions: The utility of random forest in classifying domains from the place-holder classes of SCOP to the true Class, Fold, Super-family or Family levels is demonstrated. Issues such as introduction of a new structural level in SCOP and the merger of singleton levels can also be addressed using random forest. A real-world scenario is mimicked by predicting the classification for those protein structures from the PDB, which are yet to be assigned to the SCOP classification hierarchy.

A generalized approach combining quantum mechanics (QM) and molecular mechanics (MM) calculations is developed to simulate the n --> pi* and pi --> pi* backbone transitions of proteins in aqueous solution. These transitions, which occur in the ultraviolet (UV) at 180-220 nm, provide a sensitive probe for secondary structures. The excitation Hamiltonian is constructed using high-level electronic structure calculations of N-methylacetamide (NMA). Its electrostatic fluctuations are modeled using a new algorithm, EHEF, which combines a molecular dynamics (MD) trajectory obtained with a MM forcefield and electronic structures of sampled MD snapshots calculated by QM. The lineshapes and excitation splittings induced by the electrostatic environment in the experimental UV linear absorption (LA) and circular dichroism (CD) spectra of several proteins in aqueous solution are reproduced by our calculations. The distinct CD features of alpha-helix and beta-sheet protein structures are observed in the simulations and can be assigned to different backbone geometries. The fine structure of the UV spectra is accurately characterized and enables us to identify signatures of secondary structures.

Amide n-pi* and pi-pi* excitations around 200 nm are prominent spectroscopic signatures of the protein backbone, which are routinely used in ultraviolet (UV) circular dichroism for structure characterization. Recently developed ultrafast laser sources may be used to extend these studies to two dimensions. We apply a new algorithm for modeling protein electronic transitions to simulate two-dimensional UV photon echo signals in this regime and to identify signatures of protein backbone secondary (and tertiary) structure. Simulated signals for a set of globular and fibrillar proteins and their specific regions reveal characteristic patterns of helical and sheet secondary structures. We investigate how these patterns vary and converge with the size of the structural motif. Specific chiral polarization configurations of the UV pulses are found to be sensitive to aspects of the protein structure. This information significantly augments that available from linear circular dichroism.

Interest in the design of peptide-based fibrous materials is growing because it opens possibilities to explore fundamental aspects of peptide self-assembly and to exploit the resulting structures--for example, as scaffolds for tissue engineering. Here we investigate the assembly pathway of self-assembling fibers, a rationally designed alpha-helical coiled-coil system comprising two peptides that assemble on mixing. The dimensions spanned by the peptides and final structures (nanometers to micrometers), and the timescale over which folding and assembly occur (seconds to hours), necessitate a multi-technique approach employing spectroscopy, analytical ultracentrifugation, electron and light microscopy, and protein design to produce a physical model. We show that fibers form via a nucleation and growth mechanism. The two peptides combine rapidly (in less than seconds) to form sticky ended, partly helical heterodimers. A lag phase follows, on the order of tens of minutes, and is concentration-dependent. The critical nucleus comprises six to eight partially folded dimers. Growth is then linear in dimers, and subsequent fiber growth occurs in hours through both elongation and thickening. At later times (several hours), fibers grow predominantly through elongation. This kinetic, biomolecular description of the folding-and-assembly process allows the self-assembling fiber system to be manipulated and controlled, which we demonstrate through seeding experiments to obtain different distributions of fiber lengths. This study and the resulting mechanism we propose provide a potential route to achieving temporal control of functional fibers with future applications in biotechnology and nanoscale science and technology., (Copyright 2010 Biophysical Society. Published by Elsevier Inc. All rights reserved.)

Gibbs ensemble Monte Carlo simulations were used to study the vapor-liquid equilibrium of binary mixtures of carbon dioxide + methane and carbon dioxide + difluoromethane. The potential forcefields we employ are all atomistic models, and have not previously been mixed together to study the vapor-liquid equilibrium of the binary mixtures. In addition, we characterize the microscopic structure of these liquid mixtures. In carbon dioxide + methane at 230 K and 56 bar, the microscopic structure of carbon dioxide in the mixture is the same as that in the pure liquid. In carbon dioxide + difluoromethane at 283 K and 56 bar, the presence of carbon dioxide does not noticeably perturb the liquid structure of difluoromethane, but the structure of carbon dioxide is subtly changed, due to a strong interaction between it and difluoromethane. The simulations in the isobaric-isothermal (NPT) ensemble agree well with the experimental data, except at the two extreme regions of the pressure range. The good agreement of most simulated state points with experimental data encourages one to develop more accurate potentials for predicting the thermodynamic properties of these systems as well as other complicated systems, which are less amenable to measurement by experiment.

Actin-binding natural products have been identified as a potential basis for the design of cancer therapeutic agents. We report flexible docking and QSAR studies on aplyronine A analogues. Our findings show the macrolide 'tail' to be fundamental for the depolymerisation effect of actin-binding macrolides and that it is the tail which forms the initial interaction with the actin rather than the macrocycle, as previously believed. Docking energy scores for the compounds were highly correlated with actin depolymerisation activity. The 3D-QSAR models were predictive, with the best model giving a q(2) value of 0.85 and a r(2) of 0.94. Results from the docking simulations and the interpretation from QSAR "coeff*stdev" contour maps provide insight into the binding mechanism of each analogue and highlight key features that influence depolymerisation activity. The results herein may aid the design of a putative set of analogues that can help produce efficacious and tolerable anti-tumour agents. Finally, using the best QSAR model, we have also made genuine predictions for an independent set of recently reported aplyronine analogues.

Background: The topological maximum cross correlation (TMACC) descriptors are alignment-independent 2D descriptors for the derivation of QSARs. TMACC descriptors are generated using atomic properties determined by molecular topology. Previous validation (J Chem Inf Model 2007, 47: 626-634) of the TMACC descriptor suggests it is competitive with the current state of the art., Results: Here, we illustrate the interpretability of the TMACC descriptors, through the analysis of the QSARs of inhibitors of angiotensin converting enzyme (ACE) and dihydrofolate reductase (DHFR). In the case of the ACE inhibitors, the TMACC interpretation shows features specific to C-domain inhibition, which have not been explicitly identified in previous QSAR studies., Conclusions: The TMACC interpretation can provide new insight into the structure-activity relationships studied. Freely available, open source software for generating the TMACC descriptors can be downloaded from http://comp.chem.nottingham.ac.uk.

Background: The prediction of the secondary structure of a protein is a critical step in the prediction of its tertiary structure and, potentially, its function. Moreover, the backbone dihedral angles, highly correlated with secondary structures, provide crucial information about the local three-dimensional structure., Results: We predict independently both the secondary structure and the backbone dihedral angles and combine the results in a loop to enhance each prediction reciprocally. Support vector machines, a state-of-the-art supervised classification technique, achieve secondary structure predictive accuracy of 80% on a non-redundant set of 513 proteins, significantly higher than other methods on the same dataset. The dihedral angle space is divided into a number of regions using two unsupervised clustering techniques in order to predict the region in which a new residue belongs. The performance of our method is comparable to, and in some cases more accurate than, other multi-class dihedral prediction methods., Conclusions: We have created an accurate predictor of backbone dihedral angles and secondary structure. Our method, called DISSPred, is available online at http://comp.chem.nottingham.ac.uk/disspred/.

5-Hydroxytryptophan is a non-natural amino acid that has attracted a lot of recent interest as a fluorescent probe of protein structure, dynamics, and function. We have investigated its fluorescence in various protein environments, using a decoupled quantum mechanics/molecular mechanics approach. Classical, all-atom molecular dynamics simulations of several proteins containing single tryptophans were performed for both the wild-type and the 5-hydroxy derivatives. The excited state of the fluorophore was described using parameters from complete active space self-consistent field calculations. Time-dependent density functional theory calculations on 5-hydroxytryptophan and a significant portion of its explicit immediate surrounding environment, sampled by the simulations, show that the emission energies of 5-hydroxytryptophan shift, depending on the strength of hydrogen bonding and pi-pi stacking interactions. This quantitative description of how the fluorescence responds to different protein environments should enhance the insight that fluorescence studies using 5-hydroxytryptophan can provide at a molecular level.

Flow linear dichroism (LD) spectroscopy provides information on the orientation of molecules in solution and hence on the relative orientation of parts of molecules. Long molecules such as fibrous proteins can be aligned in Couette flow cells and characterized using LD. We have measured using Couette flow and calculated from first principles the LD of proteins representing prototypical secondary structure classes: a self-assembling fiber and tropomyosin (all-alpha-helical), FtsZ (an alphabeta protein), an amyloid fibril (beta-sheet), and collagen [poly(proline)II helices]. The combination of calculation and experiment allows elucidation of the protein orientation in the Couette flow and the orientation of chromophores within the protein fibers.

Background: Classification of newly resolved protein structures is important in understanding their architectural, evolutionary and functional relatedness to known protein structures. Among various efforts to improve the database of Structural Classification of Proteins (SCOP), automation has received particular attention. Herein, we predict the deepest SCOP structural level that an unclassified protein shares with classified proteins with an equal number of secondary structure elements (SSEs)., Results: We compute a coefficient of dissimilarity (Omega) between proteins, based on structural and sequence-based descriptors characterising the respective constituent SSEs. For a set of 1,661 pairs of proteins with sequence identity up to 35%, the performance of Omega in predicting shared Class, Fold and Super-family levels is comparable to that of DaliLite Z score and shows a greater than four-fold increase in the true positive rate (TPR) for proteins sharing the Family level. On a larger set of 600 domains representing 200 families, the performance of Z score improves in predicting a shared Family, but still only achieves about half of the TPR of Omega. The TPR for structures sharing a Super-family is lower than in the first dataset, but Omega performs slightly better than Z score. Overall, the sensitivity of Omega in predicting common Fold level is higher than that of the DaliLite Z score., Conclusion: Classification to a deeper level in the hierarchy is specific and difficult. So the efficiency of Omega may be attractive to the curators and the end-users of SCOP. We suggest Omega may be a better measure for structure classification than the DaliLite Z score, with the caveat that currently we are restricted to comparing structures with equal number of SSEs.

Amino acid derivatives of naphthalenediimide (NDI) form non-covalent polymers, which assemble into helical nanotubes through hydrogen bonding. The two enantiomers possess distinct circular dichroism (CD) spectra, but the bands could not be entirely ascribed to the effects of the monomer or a supramolecular structure. We calculate the CD of oligomers, using the (exciton) matrix method, based on ab initio results for the monomer. Several features in the experimental spectrum could be reproduced well and allow assignment of the electronic states of the oligomeric structure. The calculations provide evidence that the oligomer structures in the solid state and the solution phase are equivalent. The calculated spectra show a dependence on the oligomer length, which potentially could be exploited for the experimental characterization of the length of the helical nanotubes.

We explore automation of protein structural classification using supervised machine learning methods on a set of 11,360 pairs of protein domains (up to 35% sequence identity) consisting of three secondary structure elements. Fifteen algorithms from five categories of supervised algorithms are evaluated for their ability to learn for a pair of protein domains, the deepest common structural level within the SCOP hierarchy, given a one-dimensional representation of the domain structures. This representation encapsulates evolutionary information in terms of sequence identity and structural information characterising the secondary structure elements and lengths of the respective domains. The evaluation is performed in two steps, first selecting the best performing base learners and subsequently evaluating boosted and bagged meta learners. The boosted random forest, a collection of decision trees, is found to be the most accurate, with a cross-validated accuracy of 97.0% and F-measures of 0.97, 0.85, 0.93 and 0.98 for classification of proteins to the Class, Fold, Super-Family and Family levels in the SCOP hierarchy. The meta learning regime, especially boosting, improved performance by more accurately classifying the instances from less populated classes.

In this review, we highlight recent applications of machine learning to virtual screening, focusing on the use of supervised techniques to train statistical learning algorithms to prioritize databases of molecules as active against a particular protein target. Both ligand-based similarity searching and structure-based docking have benefited from machine learning algorithms, including naïve Bayesian classifiers, support vector machines, neural networks, and decision trees, as well as more traditional regression techniques. Effective application of these methodologies requires an appreciation of data preparation, validation, optimization, and search methodologies, and we also survey developments in these areas.

We have investigated the absorption and emission spectrum of 5-hydroxyindole in the gas phase and in various solvents. 5-Hydroxyindole is the fluorophore of the non-natural amino acid 5-hydroxytryptophan, which has attracted recent interest as a novel intrinsic probe for protein structure, dynamics, and function. Gas-phase and implicit solvent calculations were performed with multiconfigurational perturbation theory (CASPT2). An explicit solvent model was calculated using a decoupled quantum mechanics/molecular mechanics approach, utilizing recent advances in time-dependent density functional theory. The importance of hydrogen bonding is shown by comparing the implicit solvent model calculations with the explicit solvent calculations and experimental results. In line with other indole systems, the order of the 1L state peaks in 5-hydroxindole is 1L(b) at lower energy than 1L(a), with the emitting state being 1L(a).

Motivation: Circular dichroism (CD) is widely used in studies of protein folding. The CD spectrum of a protein can be estimated from its structure alone, using the well-established matrix method. In the last decade, a related spectroscopy, linear dichroism (LD), has been increasingly applied to study the orientation of proteins in solution. However, matrix method calculations of LD spectra have not been presented before. DichroCalc makes both CD and LD calculations available in an easy-to-use fashion., Results: DichroCalc can be used without registration and calculates CD and LD spectra using a variety of matrix method parameters. PDB files can be uploaded as input or retrieved via their PDB code and a Perl-based parser is offered for easy handling of PDB files., Availability: http://comp.chem.nottingham.ac.uk/dichrocalc and http://comp.chem.nottingham.ac.uk/parsepdb.

Background: We investigate automated and generic alphabet reduction techniques for protein structure prediction datasets. Reducing alphabet cardinality without losing key biochemical information opens the door to potentially faster machine learning, data mining and optimization applications in structural bioinformatics. Furthermore, reduced but informative alphabets often result in, e.g., more compact and human-friendly classification/clustering rules. In this paper we propose a robust and sophisticated alphabet reduction protocol based on mutual information and state-of-the-art optimization techniques., Results: We applied this protocol to the prediction of two protein structural features: contact number and relative solvent accessibility. For both features we generated alphabets of two, three, four and five letters. The five-letter alphabets gave prediction accuracies statistically similar to that obtained using the full amino acid alphabet. Moreover, the automatically designed alphabets were compared against other reduced alphabets taken from the literature or human-designed, outperforming them. The differences between our alphabets and the alphabets taken from the literature were quantitatively analyzed. All the above process had been performed using a primary sequence representation of proteins. As a final experiment, we extrapolated the obtained five-letter alphabet to reduce a, much richer, protein representation based on evolutionary information for the prediction of the same two features. Again, the performance gap between the full representation and the reduced representation was small, showing that the results of our automated alphabet reduction protocol, even if they were obtained using a simple representation, are also able to capture the crucial information needed for state-of-the-art protein representations., Conclusion: Our automated alphabet reduction protocol generates competent reduced alphabets tailored specifically for a variety of protein datasets. This process is done without any domain knowledge, using information theory metrics instead. The reduced alphabets contain some unexpected (but sound) groups of amino acids, thus suggesting new ways of interpreting the data.

Background: Post translational modifications (PTMs) occur in the vast majority of proteins and are essential for function. Prediction of the sequence location of PTMs enhances the functional characterisation of proteins. Glycosylation is one type of PTM, and is implicated in protein folding, transport and function., Results: We use the random forest algorithm and pairwise patterns to predict glycosylation sites. We identify pairwise patterns surrounding glycosylation sites and use an odds ratio to weight their propensity of association with modified residues. Our prediction program, GPP (glycosylation prediction program), predicts glycosylation sites with an accuracy of 90.8% for Ser sites, 92.0% for Thr sites and 92.8% for Asn sites. This is significantly better than current glycosylation predictors. We use the trepan algorithm to extract a set of comprehensible rules from GPP, which provide biological insight into all three major glycosylation types., Conclusion: We have created an accurate predictor of glycosylation sites and used this to extract comprehensible rules about the glycosylation process. GPP is available online at http://comp.chem.nottingham.ac.uk/glyco/.

Optimisation problems pervade structural bioinformatics. In this review, we describe recent work addressing a selection of bioinformatics challenges. We begin with a discussion of research into protein structure comparison, and highlight the utility of Kolmogorov complexity as a measure of structural similarity. We then turn to research into de novo protein structure prediction, in which structures are generated from first principles. In this endeavour, there is a compromise between the detail of the model and the extent to which the conformational space of the protein can be sampled. We discuss some developments in this area, including off-lattice structure prediction using the great deluge algorithm. One strategy to reduce the size of the search space is to restrict the protein chain to sites on a regular lattice. In this context, we highlight the use of memetic algorithms, which combine genetic algorithms with local optimisation, to the study of simple protein models on the two-dimensional square lattice and the face-centred cubic lattice.

Motivation: We introduce a new method for designating the location of residues in folded protein structures based on the recursive convex hull (RCH) of a point set of atomic coordinates. The RCH can be calculated with an efficient and parameterless algorithm., Results: We show that residue RCH class contains information complementary to widely studied measures such as solvent accessibility (SA), residue depth (RD) and to the distance of residues from the centroid of the chain, the residues' exposure (Exp). RCH is more conserved for related structures across folds and correlates better with changes in thermal stability of mutants than the other measures. Further, we assess the predictability of these measures using three types of machine-learning technique: decision trees (C4.5), Naive Bayes and Learning Classifier Systems (LCS) showing that RCH is more easily predicted than the other measures. As an exemplar application of predicted RCH class (in combination with other measures), we show that RCH is potentially helpful in improving prediction of residue contact numbers (CN).

The ubiquitin associated domain of p62 is a small three-helix bundle of approximately 50 residues that mediates the recognition of polyubiquitin chains and ubiquitylated substrates. The solution structure of a 52 residue construct containing this domain has been characterized using heteronuclear nuclear magnetic resonance (NMR) methods. The resulting ensemble of NMR-derived structures was used in molecular dynamics (MD) simulations to investigate the equilibrium conformation and dynamics of this domain. NOE and (15)N relaxation data have been used to validate the structural ensemble produced by the MD simulations and show a good correlation for residues in regions of secondary structure. A similar approach was taken using an ensemble of structures from the MD simulations to calculate electronic circular dichroism (CD) and IR spectra from first principles with an encouraging correlation with the experimental CD and IR data., ((c) 2007 Wiley-Liss, Inc.)

Circular dichroism (CD) is widely used in the structural characterization and secondary structure determination of proteins. The vacuum UV region (below 190 nm), where charge-transfer transitions have an influence on the CD spectra, can be accessed using synchrotron radiation circular dichroism (SRCD) spectroscopy. Recently, charge-transfer transitions in a conformationally diverse set of dipeptides have been characterized ab initio using complete active space self-consistent field calculations, and the relevant charge distributions have been parametrized for use in the matrix method for calculations of protein CD. Here, we present calculations of the vacuum UV CD spectra of 71 proteins, for which experimental SRCD spectra and X-ray crystal structures are available. The theoretical spectra are calculated considering charge-transfer and side chain transitions. This significantly improves the agreement with experiment, raising the Spearman correlation coefficient between the calculated and the experimental intensity at 175 nm from 0.12 to 0.79. The influence of the conformation on charge-transfer transitions is analyzed in detail, showing that the n --> pi* charge-transfer transitions are most important in alpha-helical proteins, whereas in beta strand proteins the pi --> pi* charge-transfer transition along the chain in the amino- to carboxy-end direction is most dominant.

Background: We introduce the decision support system for Protein (Structure) Comparison, Knowledge, Similarity and Information (ProCKSI). ProCKSI integrates various protein similarity measures through an easy to use interface that allows the comparison of multiple proteins simultaneously. It employs the Universal Similarity Metric (USM), the Maximum Contact Map Overlap (MaxCMO) of protein structures and other external methods such as the DaliLite and the TM-align methods, the Combinatorial Extension (CE) of the optimal path, and the FAST Align and Search Tool (FAST). Additionally, ProCKSI allows the user to upload a user-defined similarity matrix supplementing the methods mentioned, and computes a similarity consensus in order to provide a rich, integrated, multicriteria view of large datasets of protein structures., Results: We present ProCKSI's architecture and workflow describing its intuitive user interface, and show its potential on three distinct test-cases. In the first case, ProCKSI is used to evaluate the results of a previous CASP competition, assessing the similarity of proposed models for given targets where the structures could have a large deviation from one another. To perform this type of comparison reliably, we introduce a new consensus method. The second study deals with the verification of a classification scheme for protein kinases, originally derived by sequence comparison by Hanks and Hunter, but here we use a consensus similarity measure based on structures. In the third experiment using the Rost and Sander dataset (RS126), we investigate how a combination of different sets of similarity measures influences the quality and performance of ProCKSI's new consensus measure. ProCKSI performs well with all three datasets, showing its potential for complex, simultaneous multi-method assessment of structural similarity in large protein datasets. Furthermore, combining different similarity measures is usually more robust than relying on one single, unique measure., Conclusion: Based on a diverse set of similarity measures, ProCKSI computes a consensus similarity profile for the entire protein set. All results can be clustered, visualised, analysed and easily compared with each other through a simple and intuitive interface.ProCKSI is publicly available at http://www.procksi.net for academic and non-commercial use.

By the use of x-ray structures and flexible docking, we have developed the first in silico ligand-based view of the structural determinants of the binding of small molecule mimics of gelsolin, natural products bound to actin. Our technique highlights those residues on the actin binding site forming important hydrophobic and hydrogen-bonding interactions with the ligands. Significantly, through the flexible docking of toxin fragments, we have also identified potential residues on the actin binding site that have yet to be exploited. Guided by these observations, we have demonstrated that kabiramide C can be modified to produce a structure with a predicted binding energy increased by 20% while the molecular mass is reduced by 20%, clearly indicating the potential for future elaboration of structures targeting this important component of the cytoskeleton.

Circular dichroism (CD) is an important technique in the structural characterisation of proteins, and especially for secondary structure determination. The CD of proteins can be calculated from first principles using the so-called matrix method, with an accuracy which is almost quantitative for helical proteins. Thus, for proteins of unknown structure, CD calculations and experimental data can be used in conjunction to aid structure analysis. Linear dichroism (LD) can be calculated using analogous methodology and has been used to establish the relative orientations of subunits in proteins and protein orientation in an environment such as a membrane. However, simple analysis of LD data is not possible, due to overlapping transitions. So coupling the calculations and experiment is an important strategy. In this paper, the use of LD for the determination of protein orientation and how these data can be interpreted with the aid of calculations, are discussed. We review methods for the calculation of CD spectra, focusing on semiempirical and ab initio parameter sets used in the matrix method. Lastly, a new web interface for online CD and LD calculation is presented.

We report CASPT2 calculations on the electronic excited states of several ureas. For monoureas, we find an electric dipole forbidden n --> pi* transition between 180 and 210 nm, dependent on the geometry and substituents of the urea. We find two intense pinb --> pi* transitions between 150 and 210 nm, which account for the absorptions seen in the experimental spectra. The n' --> pi* and pib --> pi* transitions are at wavelengths below 125 nm, which is below the lower limit of the experimental spectra. Parameter sets modeling the charge densities of the electronic transitions have been derived and permit calculations on larger oligoureas, using the exciton matrix method. For glycouril, a urea dimer, both the CASPT2 method and the matrix method yield similar results. Calculations of the electronic circular dichroism spectrum of an oligourea containing eight urea groups indicate that the experimental spectrum cannot be reproduced without the inclusion of electronic excitations involving the side chains. These calculations are one of the first attempts to understand the relationship between the structure and excited states of this class of macromolecule.

Highly predictive topological maximum cross correlation (TMACC) descriptors for the derivation of quantitative structure-activity relationships (QSARs) are presented, based on the widely used autocorrelation method. They require neither the calculation of three-dimensional conformations nor an alignment of structures. We have validated the TMACC descriptors across eight literature data sets, ranging in size from 66 to 361 molecules. In combination with partial least-squares regression, they perform competitively with a current state-of-the-art 2D QSAR methodology, hologram QSAR (HQSAR), yielding larger leave-one-out cross-validated coefficient of determination values (LOO q2) for five data sets. Like HQSAR, these descriptors are also interpretable but do not require hashing. The interpretation both enables the automated extraction of SARs and can give a description in qualitative agreement with more time-consuming 3D and 4D QSAR methods. Open source software for generating the TMACC descriptors is freely available from our Web site.