Occupational risk of COVID-19 in foreign-born employees in Denmark

BACKGROUND

Foreign-born workers in high-income countries experience higher rates of COVID-19 but the causes are only partially known.

AIMS

To examine if the occupational risk of COVID-19 in foreign-born workers deviates from the risk in native-born employees in Denmark.

METHODS

Within a registry-based cohort of all residents employed in Denmark (n = 2 451 542), we identified four-digit DISCO-08 occupations associated with an increased incidence of COVID-19-related hospital admission during 2020-21 (at-risk occupations). The sex-specific prevalence of at-risk employment in foreign born was compared with the prevalence in native born. Moreover, we examined if the country of birth modified the risk of a positive SARS-CoV-2 polymerase chain reaction (PCR) test and COVID-19-related hospital admission in at-risk occupations.

RESULTS

Workers born in low-income countries and male workers from Eastern Europe more often worked in at-risk occupations (relative risks between 1.16 [95% confidence interval {CI} 1.14-1.17] and 1.87 [95% CI 1.82-1.90]). Being foreign-born modified the adjusted risk of PCR test positivity (test for interaction P < 0.0001), primarily because of higher risk in at-risk occupations among men born in Eastern European countries (incidence rate ratio [IRR] 2.39 [95% CI 2.09-2.72] versus IRR 1.19 [95% CI 1.14-1.23] in native-born men). For COVID-19-related hospital admission, no overall interaction was seen, and in women, country of birth did not consistently modify the occupational risk.

CONCLUSIONS

Workplace viral transmission may contribute to an excess risk of COVID-19 in male workers born in Eastern Europe, but most foreign-born employees in at-risk occupations seem not to be at higher occupational risk than native born.

Uncertain of uncertainties? A comparison of uncertainty quantification metrics for chemical data sets

With the increasingly more important role of machine learning (ML) models in chemical research, the need for putting a level of confidence to the model predictions naturally arises. Several methods for obtaining uncertainty estimates have been proposed in recent years but consensus on the evaluation of these have yet to be established and different studies on uncertainties generally uses different metrics to evaluate them. We compare three of the most popular validation metrics (Spearman's rank correlation coefficient, the negative log likelihood (NLL) and the miscalibration area) to the error-based calibration introduced by Levi et al. (Sensors 2022, 22, 5540). Importantly, metrics such as the negative log likelihood (NLL) and Spearman's rank correlation coefficient bear little information in themselves. We therefore introduce reference values obtained through errors simulated directly from the uncertainty distribution. The different metrics target different properties and we show how to interpret them, but we generally find the best overall validation to be done based on the error-based calibration plot introduced by Levi et al. Finally, we illustrate the sensitivity of ranking-based methods (e.g. Spearman's rank correlation coefficient) towards test set design by using the same toy model ferent test sets and obtaining vastly different metrics (0.05 vs. 0.65).

Toward De Novo Catalyst Discovery: Fast Identification of New Catalyst Candidates for Alcohol-Mediated Morita-Baylis-Hillman Reactions

Recently we have demonstrated how a genetic algorithm (GA) starting from random tertiary amines can be used to discover a new and efficient catalyst for the alcohol-mediated Morita-Baylis-Hillman (MBH) reaction. In particular, the discovered catalyst was shown experimentally to be eight times more active than DABCO, commonly used to catalyze the MBH reaction. This represents a breakthrough in using generative models for catalyst optimization. However, the GA procedure, and hence discovery, relied on two important pieces of information; 1) the knowledge that tertiary amines catalyze the reaction and 2) the mechanism and reaction profile for the catalyzed reaction, in particular the transition state structure of the rate-determining step. Thus, truly de novo catalyst discovery must include these steps. Here we present such a method for discovering catalyst candidates for a specific reaction while simultaneously proposing a mechanism for the catalyzed reaction. We show that tertiary amines and phosphines are potential catalysts for the MBH reaction by screening 11 molecular templates representing common functional groups. The method relies on an automated reaction discovery workflow using meta-dynamics calculations. Combining this method for catalyst candidate discovery with our GA-based catalyst optimization method results in an algorithm for truly de novo catalyst discovery.

Computational Evolution Of New Catalysts For The Morita-Baylis-Hillman Reaction

We present a de novo discovery of an efficient catalyst of the Morita-Baylis-Hillman (MBH) reaction by searching chemical space for molecules that lower the estimated barrier of the rate-determining step using a genetic algorithm (GA) starting from randomly selected tertiary amines. We identify 435 candidates, virtually all of which contain an azetidine N as the catalytically active site, which is discovered by the GA. Two molecules are selected for further study based on their predicted synthetic accessibility and have predicted rate-determining barriers that are lower than that of a known catalyst. Azetidines have not been used as catalysts for the MBH reaction. One suggested azetidine is successfully synthesized and showed an eightfold increase in activity over a commonly used catalyst. We believe this is the first experimentally verified de novo discovery of an efficient catalyst using a generative model.

What the Heck?-Automated Regioselectivity Calculations of Palladium-Catalyzed Heck Reactions Using Quantum Chemistry

We present a quantum chemistry (QM)-based method that computes the relative energies of intermediates in the Heck reaction that relate to the regioselective reaction outcome: branched (α), linear (β), or a mix of the two. The calculations are done for two different reaction pathways (neutral and cationic) and are based on r ²SCAN-3c single-point calculations on GFN2-xTB geometries that, in turn, derive from a GFNFF-xTB conformational search. The method is completely automated and is sufficiently efficient to allow for the calculation of thousands of reaction outcomes. The method can mostly reproduce systematic experimental studies where the ratios of regioisomers are carefully determined. For a larger dataset extracted from Reaxys, the results are somewhat worse with accuracies of 63% for β-selectivity using the neutral pathway and 29% for α-selectivity using the cationic pathway. Our analysis of the dataset suggests that only the major or desired regioisomer is reported in the literature in many cases, which makes accurate comparisons difficult. The code is freely available on GitHub under the MIT open-source license: https://github.com/jensengroup/HeckQM.

Substituent Control of σ-Interference Effects in the Transmission of Saturated Molecules

The single-molecule conductance of saturated molecules can potentially be fully suppressed by destructive quantum interference in their σ-system. However, only few molecules with σ-interference have been identified, and the structure-property relationship remains to be elucidated. Here, we explore the role of substituents in modulating the electronic transmission of saturated molecules. In functionalized bicyclo[2.2.2]octanes, the transmission is suppressed by σ-interference when fluorine substituents are applied. For bicyclo[2.2.2]octasilane and -octagermanes, the transmission is suppressed when carbon-based substituents are used, and such molecules are likely to be highly insulating. For the carbon-based substituents, we find a strong correlation between the appropriate Hammett constants and the transmission. The substituent effect enables systematic optimization of the insulating properties of saturated molecular cores.

A Neural Network Approach for Property Determination of Molecular Solar Cell Candidates

The dihydroazulene/vinylheptafulvene (DHA/VHF) photocouple is a promising candidate for molecular solar heat batteries, storing and releasing energy in a closed cycle. Much work has been done on improving the energy storage capacity and the half-life of the high-energy isomer via substituent functionalization, but similarly important is keeping these improved properties in common polar solvents, along with being soluble in these, which is tied to the dipole properties. However, the number of possible derivatives makes an overview of this combinatorial space impossible both for experimental work and traditional computational chemistry. Due to the time-consuming nature of running many thousands of computations, we look to machine learning, which bears the advantage that once a model has been trained, it can be used to rapidly estimate approximate values for the given system. Applying a convolutional neural network, we show that it is possible to reach good agreement with traditional computations on a scale that allows us to rapidly screen tens of thousands of the DHA/VHF photocouple, eliminating bad candidates and allowing computational resources to be directed toward meaningful compounds.

Fast and automated identification of reactions with low barriers using meta-MD simulations

We test our meta-molecular dynamics (MD) based approach for finding low-barrier (<30 kcal/mol) reactions on uni- and bimolecular reactions extracted from the barrier dataset developed by Grambow, Pattanaik & Green (2020). For unimolecular reactions the meta-MD simulations identify 25 of the 26 products found by Grambow, Pattanaik & Green (2020), while the subsequent semiempirical screening eliminates an additional four reactions due to an overestimation of the reaction energies or estimated barrier heights relative to DFT. In addition, our approach identifies 36 reactions not found by Grambow, Pattanaik & Green (2020), 10 of which are <30 kcal/mol. For bimolecular reactions the meta-MD simulations identify 19 of the 20 reactions found by Grambow, Pattanaik & Green (2020), while the subsequent semiempirical screening eliminates an additional reaction. In addition, we find 34 new low-barrier reactions. For bimolecular reactions we found that it is necessary to “encourage” the reactants to go to previously undiscovered products, by including products found by other MD simulations when computing the biasing potential as well as decreasing the size of the molecular cavity in which the MD occurs, until a reaction is observed. We also show that our methodology can find the correct products for two reactions that are more representative of those encountered in synthetic organic chemistry. The meta-MD hyperparameters used in this study thus appear to be generally applicable to finding low-barrier reactions.

Quantitative Diffusion and Spectroscopic Neuroimaging Combined with a Novel Early-Developmental Assessment Improves Models for 1-Year Developmental Outcomes

BACKGROUND AND PURPOSE

Preterm infants are at risk for overt and silent CNS injury, with developmental consequences that are difficult to predict. The novel Specific Test of Early Infant Motor Performance, administered in preterm infants at term age, is indicative of later developmental gross motor and cognitive scores at 12 months. Here, we assessed whether functional performance on this early assessment correlates with CNS integrity via MR spectroscopy or diffusional kurtosis imaging and whether these quantitative neuroimaging methods improve predictions for future 12-month developmental scores.

MATERIALS AND METHODS

MR spectroscopy and quantitative diffusion MR imaging data were acquired in preterm infants (n = 16) at term. Testing was performed at term and 3 months using the Specific Test of Early Infant Motor Performance and the Bayley Scales of Infant and Toddler Development, Third Edition, at 12 months. We modeled the relationship of MR spectroscopy and diffusion MR imaging data with both test scores via multiple linear regression.

RESULTS

MR spectroscopy NAA ratios at a TE of 270 ms in the frontal WM and basal ganglia and kurtosis metrics in major WM tracts correlated strongly with total Specific Test of Early Infant Motor Performance scores. The addition of MR spectroscopy and diffusion separately improved the functional predictions of 12-month outcomes.

CONCLUSIONS

Microstructural integrity of the major WM tracts and metabolism in the basal ganglia and frontal WM strongly correlate with early developmental performance, suggesting that the Specific Test of Early Infant Motor Performance reflects CNS integrity after preterm birth. This study demonstrates that combining quantitative neuroimaging and early functional movement improves the prediction of 12-month outcomes in premature infants.

Virtual screening of norbornadiene-based molecular solar thermal energy storage systems using a genetic algorithm

We present a computational methodology for the screening of a chemical space of 10²⁵ substituted norbornadiene molecules for promising kinetically stable molecular solar thermal (MOST) energy storage systems with high energy densities that absorb in the visible part of the solar spectrum. We use semiempirical tight-binding methods to construct a dataset of nearly 34 000 molecules and train graph convolutional networks to predict energy densities, kinetic stability, and absorption spectra and then use the models together with a genetic algorithm to search the chemical space for promising MOST energy storage systems. We identify 15 kinetically stable molecules, five of which have energy densities greater than 0.45 MJ/kg, and the main conclusion of this study is that the largest energy density that can be obtained for a single norbornadiene moiety with the substituents considered here, while maintaining a long half-life and absorption in the visible spectrum, is around 0.55 MJ/kg.

Using a genetic algorithm to find molecules with good docking scores

A graph-based genetic algorithm (GA) is used to identify molecules (ligands) with high absolute docking scores as estimated by the Glide software package, starting from randomly chosen molecules from the ZINC database, for four different targets: Bacillus subtilis chorismate mutase (CM), human β2-adrenergic G protein-coupled receptor (β2AR), the DDR1 kinase domain (DDR1), and β-cyclodextrin (BCD). By the combined use of functional group filters and a score modifier based on a heuristic synthetic accessibility (SA) score our approach identifies between ca 500 and 6,000 structurally diverse molecules with scores better than known binders by screening a total of 400,000 molecules starting from 8,000 randomly selected molecules from the ZINC database. Screening 250,000 molecules from the ZINC database identifies significantly more molecules with better docking scores than known binders, with the exception of CM, where the conventional screening approach only identifies 60 compounds compared to 511 with GA+Filter+SA. In the case of β2AR and DDR1, the GA+Filter+SA approach finds significantly more molecules with docking scores lower than −9.0 and −10.0. The GA+Filters+SA docking methodology is thus effective in generating a large and diverse set of synthetically accessible molecules with very good docking scores for a particular target. An early incarnation of the GA+Filter+SA approach was used to identify potential binders to the COVID-19 main protease and submitted to the early stages of the COVID Moonshot project, a crowd-sourced initiative to accelerate the development of a COVID antiviral.

RegioSQM20: improved prediction of the regioselectivity of electrophilic aromatic substitutions

We present RegioSQM20, a new version of RegioSQM (Chem Sci 9:660, 2018), which predicts the regioselectivities of electrophilic aromatic substitution (EAS) reactions from the calculation of proton affinities. The following improvements have been made: The open source semiempirical tight binding program xtb is used instead of the closed source MOPAC program. Any low energy tautomeric forms of the input molecule are identified and regioselectivity predictions are made for each form. Finally, RegioSQM20 offers a qualitative prediction of the reactivity of each tautomer (low, medium, or high) based on the reaction center with the highest proton affinity. The inclusion of tautomers increases the success rate from 90.7 to 92.7%. RegioSQM20 is compared to two machine learning based models: one developed by Struble et al. (React Chem Eng 5:896, 2020) specifically for regioselectivity predictions of EAS reactions (WLN) and a more generally applicable reactivity predictor (IBM RXN) developed by Schwaller et al. (ACS Cent Sci 5:1572, 2019). RegioSQM20 and WLN offers roughly the same success rates for the entire data sets (without considering tautomers), while WLN is many orders of magnitude faster. The accuracy of the more general IBM RXN approach is somewhat lower: 76.3-85.0%, depending on the data set. The code is freely available under the MIT open source license and will be made available as a webservice (regiosqm.org) in the near future.

High throughput virtual screening of 230 billion molecular solar heat battery candidates

The dihydroazulene/vinylheptafulvene (DHA/VHF) thermocouple is a promising candidate for thermal heat batteries that absorb and store solar energy as chemical energy without the need for insulation. However, in order to be viable the energy storage capacity and lifetime of the high energy form (i.e., the free energy barrier to the back reaction) of the canonical parent compound must be increased significantly to be of practical use. We use semiempirical quantum chemical methods, machine learning, and density functional theory to virtually screen over 230 billion substituted DHA molecules to identify promising candidates. We identify a molecule with a predicted energy density of 0.38 kJ/g, which is significantly larger than the 0.14 kJ/g computed for the parent compound. The free energy barrier to the back reaction is 11 kJ/mol higher than the parent compound, which should correspond to a half-life of about 10 days—4 months. This is considerably longer than the 3–39 h (depending on solvent) observed for the parent compound and sufficiently long for many practical applications. Our paper makes two main important contributions: (1) a novel and generally applicable methodological approach that makes screening of huge libraries for properties involving chemical reactivity with modest computational resources, and (2) a clear demonstration that the storage capacity of the DHA/VHF thermocouple cannot be increased to >0.5 kJ/g by combining simple substituents.

Fast and automatic estimation of transition state structures using tight binding quantum chemical calculations

We present a method for the automatic determination of transition states (TSs) that is based on Grimme’s RMSD-PP semiempirical tight binding reaction path method (J. Chem. Theory Comput. 2019, 15, 2847–2862), where the maximum energy structure along the path serves as an initial guess for DFT TS searches. The method is tested on 100 elementary reactions and located a total of 89 TSs correctly. Of the 11 remaining reactions, nine are shown not to be elementary reactions after all and for one of the two true failures the problem is shown to be the semiempirical tight binding model itself. Furthermore, we show that the GFN2-xTB RMSD-PP barrier is a good approximation for the corresponding DFT barrier for reactions with DFT barrier heights up to about 30 kcal/mol. Thus, GFN2-xTB RMSD-PP barrier heights, which can be estimated at the cost of a single energy minimisation, can be used to quickly identify reactions with low barriers, although it will also produce some false positives.

Chemical space exploration: how genetic algorithms find the needle in the haystack

We explain why search algorithms can find molecules with particular properties in an enormous chemical space (ca 1060 molecules) by considering only a tiny subset (typically 103−6 molecules). Using a very simple example, we show that the number of potential paths that the search algorithms can follow to the target is equally vast. Thus, the probability of randomly finding a molecule that is on one of these paths is quite high and from here a search algorithm can follow the path to the target molecule. A path is defined as a series of molecules that have some non-zero quantifiable similarity (score) with the target molecule and that are increasingly similar to the target molecule. The minimum path length from any point in chemical space to the target corresponds is on the order of 100 steps, where a step is the change of and atom- or bond-type. Thus, a perfect search algorithm should be able to locate a particular molecule in chemical space by screening on the order of 100s of molecules, provided the score changes incrementally. We show that the actual number for a genetic search algorithm is between 100 and several millions, and depending on the target property and its dependence on molecular changes, the molecular representation, and the number of solutions to the search problem.

A graph-based genetic algorithm and generative model/Monte Carlo tree search for the exploration of chemical space

This paper presents a comparison of a graph-based genetic algorithm (GB-GA) and machine learning (ML) results for the optimization of log P values with a constraint for synthetic accessibility and shows that the GA is as good as or better than the ML approaches for this particular property.

This paper presents a comparison of a graph-based genetic algorithm (GB-GA) and machine learning (ML) results for the optimization of log P values with a constraint for synthetic accessibility and shows that the GA is as good as or better than the ML approaches for this particular property. The molecules found by the GB-GA bear little resemblance to the molecules used to construct the initial mating pool, indicating that the GB-GA approach can traverse a relatively large distance in chemical space using relatively few (50) generations. The paper also introduces a new non-ML graph-based generative model (GB-GM) that can be parameterized using very small data sets and combined with a Monte Carlo tree search (MCTS) algorithm. The results are comparable to previously published results (Sci. Technol. Adv. Mater., 2017, 18, 972–976) using a recurrent neural network (RNN) generative model, and the GB-GM-based method is several orders of magnitude faster. The MCTS results seem more dependent on the composition of the training set than the GA approach for this particular property. Our results suggest that the performance of new ML-based generative models should be compared to that of more traditional, and often simpler, approaches such a GA.

The Bicyclo[2.2.2]octane Motif: A Class of Saturated Group 14 Quantum Interference Based Single-Molecule Insulators

The electronic transmission through σ-conjugated molecules can be fully suppressed by destructive quantum interference, which makes them potential candidates for single-molecule insulators. The first molecule with clear suppression of the single-molecule conductance due to σ-interference was recently found in the form of a functionalized bicyclo[2.2.2]octasilane. Here we continue the search for potential single-molecule insulators based on saturated group 14 molecules. Using a high-throughput screening approach, we assess the electron transport properties of the bicyclo[2.2.2]octane class by systematically varying the constituent atoms between carbon, silicon, and germanium, thus exploring the full chemical space of 771 different molecules. The majority of the molecules in the bicyclo[2.2.2]octane class are found to be highly insulating molecules. Though the all-silicon molecule is a clear-cut case of σ-interference, it is not unique within its class and there are many potential molecules that we predict to be more insulating. The finding of this class of quantum interference based single-molecule insulators indicates that a broad range of highly insulating saturated group 14 molecules are likely to exist.

First Accurate Normalization of the β-delayed α Decay of ^{16}N and Implications for the ^{12}C(α,γ)^{16}O Astrophysical Reaction Rate

The ^{12}C(α,γ)^{16}O reaction plays a central role in astrophysics, but its cross section at energies relevant for astrophysical applications is only poorly constrained by laboratory data. The reduced α width, γ_{11}, of the bound 1^{-} level in ^{16}O is particularly important to determine the cross section. The magnitude of γ_{11} is determined via sub-Coulomb α-transfer reactions or the β-delayed α decay of ^{16}N, but the latter approach is presently hampered by the lack of sufficiently precise data on the β-decay branching ratios. Here we report improved branching ratios for the bound 1^{-} level [b_{β,11}=(5.02±0.10)×10^{-2}] and for β-delayed α emission [b_{βα}=(1.59±0.06)×10^{-5}]. Our value for b_{βα} is 33% larger than previously held, leading to a substantial increase in γ_{11}. Our revised value for γ_{11} is in good agreement with the value obtained in α-transfer studies and the weighted average of the two gives a robust and precise determination of γ_{11}, which provides significantly improved constraints on the ^{12}C(α,γ) cross section in the energy range relevant to hydrostatic He burning.

Intermolecular interactions in the condensed phase: Evaluation of semi-empirical quantum mechanical methods

To facilitate further development of approximate quantum mechanical methods for condensed phase applications, we present a new benchmark dataset of intermolecular interaction energies in the solution phase for a set of 15 dimers, each containing one charged monomer. The reference interaction energy in solution is computed via a thermodynamic cycle that integrates dimer binding energy in the gas phase at the coupled cluster level and solute-solvent interaction with density functional theory; the estimated uncertainty of such calculated interaction energy is ±1.5 kcal/mol. The dataset is used to benchmark the performance of a set of semi-empirical quantum mechanical (SQM) methods that include DFTB3-D3, DFTB3/CPE-D3, OM2-D3, PM6-D3, PM6-D3H+, and PM7 as well as the HF-3c method. We find that while all tested SQM methods tend to underestimate binding energies in the gas phase with a root-mean-squared error (RMSE) of 2-5 kcal/mol, they overestimate binding energies in the solution phase with an RMSE of 3-4 kcal/mol, with the exception of DFTB3/CPE-D3 and OM2-D3, for which the systematic deviation is less pronounced. In addition, we find that HF-3c systematically overestimates binding energies in both gas and solution phases. As most approximate QM methods are parametrized and evaluated using data measured or calculated in the gas phase, the dataset represents an important first step toward calibrating QM based methods for application in the condensed phase where polarization and exchange repulsion need to be treated in a balanced fashion.

Random versus Systematic Errors in Reaction Enthalpies Computed Using Semiempirical and Minimal Basis Set Methods

The connectivity-based hierarchy (CBH) protocol for computing accurate reaction enthalpies developed by Sengupta and Raghavachari is tested for fast ab initio methods (PBEh-3c, HF-3c, and HF/STO-3G), tight-binding density functional theory (DFT) methods (GFN-xTB, DFTB, and DFTB-D3), and neglect-of-diatomic-differential-overlap (NDDO)-based semiempirical methods (AM1, PM3, PM6, PM6-DH+, PM6-D2, PM6-D3H+, PM6-D3H4X, PM7, and OM2) using the same set of 25 reactions as in the original study. For the CBH-2 scheme, which reflects the change in the immediate chemical environment of all of the heavy atoms, the respective mean unsigned error relative to G4 for PBEh-3c, HF-3c, HF/STO-3G, GFN-xTB, DFTB-D3, DFTB, PM3, AM1, PM6, PM6-DH+, PM6-D3, PM6-D3H+, PM6-D3H4X, PM7, and OM2 are 1.9, 2.4, 3.0, 3.9, 3.7, 4.5, 4.8, 5.5, 5.4, 5.3, 5,4, 6.5, 5.3, 5.2, and 5.9 kcal/mol, with a single outlier removed for HF-3c, PM6, PM6-DH+, PM6-D3, PM6-D3H4X, and PM7. The increase in accuracy for the NDDO-based methods is relatively modest due to the random errors in predicted heats for formation.

Fast and accurate prediction of the regioselectivity of electrophilic aromatic substitution reactions

While computational prediction of chemical reactivity is possible it usually requires expert knowledge and there are relatively few computational tools that can be used by a bench chemist to help guide synthesis. The RegioSQM method for predicting the regioselectivity of electrophilic aromatic substitution reactions of heteroaromatic systems is presented in this paper. RegioSQM protonates all aromatic C-H carbon atoms and identifies those with the lowest free energies in chloroform using the PM3 semiempirical method as the most nucleophilic center. These positions are found to correlate qualitatively with the regiochemical outcome in a retrospective analysis of 96% of more than 525 literature examples of electrophilic aromatic halogenation reactions. The method is automated and requires only a SMILES string of the molecule of interest, which can easily be generated using chemical drawing programs such as ChemDraw. The computational cost is 1-10 minutes per molecule depending on size, using relatively modest computational resources and the method is freely available via a web server at ; http://www.regiosqm.org. RegioSQM should therefore be of practical use in the planning of organic synthesis.

pICalculax: Improved Prediction of Isoelectric Point for Modified Peptides

The isoelectric point of a peptide is a physicochemical property that can be accurately predicted from the sequence of the peptide when the peptide is built from natural amino acids. Peptides can however have chemical modifications, such as phosphorylations, amidations, and unnatural amino acids, which can result in erroneous predictions if not accounted for. Here we report on an open source program, pICalculax, which in an extensible way can handle pI calculations of modified peptides. Tests on a database of modified peptides and experimentally determined pI values show an improvement in pI predictions when taking the modifications into account. The correlation coefficient improves from 0.45 to 0.91, and the root-mean-square deviation likewise improves from 3.3 to 0.9. The program is available at https://github.com/EBjerrum/pICalculax.

Diffusional Kurtosis Imaging of the Corticospinal Tract in Multiple Sclerosis: Association with Neurologic Disability

BACKGROUND AND PURPOSE

Multiple sclerosis is an autoimmune disorder resulting in progressive neurologic disability. Our aim was to evaluate the associations between diffusional kurtosis imaging-derived metrics for the corticospinal tract and disability in multiple sclerosis.

MATERIALS AND METHODS

Forty patients with MS underwent brain MR imaging including diffusional kurtosis imaging. After we masked out T2 hyperintense lesions, the fractional anisotropy, mean diffusivity, radial diffusivity, axial diffusivity, mean kurtosis, radial kurtosis, and axial kurtosis were estimated for the corticospinal tract. Disability was quantified by using the Expanded Disability Status Scale at the time of MR imaging and 12 months post-MR imaging. The Pearson correlation coefficient and linear regression analyses were conducted to evaluate the associations between diffusion metrics and disability.

RESULTS

Significant correlations were found between the Expanded Disability Status Scale scores during the baseline visit and age (r = 0.47), T2 lesion volume (r = 0.38), corticospinal tract mean diffusivity (r = 0.41), radial diffusivity (r = 0.41), axial diffusivity (r = 0.34), fractional anisotropy (r = -0.36), and radial kurtosis (r = -0.42). Significant correlations were also found between the Expanded Disability Status Scale scores at 12-month follow-up and age (r = 0.38), mean diffusivity (r = 0.45), radial diffusivity (r = 0.41), axial diffusivity (r = 0.45), mean kurtosis (r = -0.42), radial kurtosis (r = -0.56), and axial kurtosis (r = -0.36). Linear regression analyses demonstrated significant associations among radial kurtosis, age, and Expanded Disability Status Scale score during the baseline visit, while radial kurtosis was the only variable associated with Expanded Disability Status Scale score for the 12-month follow-up.

CONCLUSIONS

Radial kurtosis of the corticospinal tract may have an association with neurologic disability in MS.

Diffusional Kurtosis Imaging and Motor Outcome in Acute Ischemic Stroke

BACKGROUND AND PURPOSE

Motor impairment is the most common deficit after stroke. Our aim was to evaluate whether diffusional kurtosis imaging can detect corticospinal tract microstructural changes in the acute phase for patients with first-ever ischemic stroke and motor impairment and to assess the correlations between diffusional kurtosis imaging-derived diffusion metrics for the corticospinal tract and motor impairment 3 months poststroke.

MATERIALS AND METHODS

We evaluated 17 patients with stroke who underwent brain MR imaging including diffusional kurtosis imaging within 4 days after the onset of symptoms. Neurologic evaluation included the Fugl-Meyer Upper Extremity Motor scale in the acute phase and 3 months poststroke. For the corticospinal tract in the lesioned and contralateral hemispheres, we estimated with diffusional kurtosis imaging both pure diffusion metrics, such as the mean diffusivity and mean kurtosis, and model-dependent quantities, such as the axonal water fraction. We evaluated the correlations between corticospinal tract diffusion metrics and the Fugl-Meyer Upper Extremity Motor scale at 3 months.

RESULTS

Among all the diffusion metrics, the largest percentage signal changes of the lesioned hemisphere corticospinal tract were observed with axial kurtosis, with an average 12% increase compared with the contralateral corticospinal tract. The strongest associations between the 3-month Fugl-Meyer Upper Extremity Motor scale score and diffusion metrics were found for the lesioned/contralateral hemisphere corticospinal tract mean kurtosis (ρ = -0.85) and axial kurtosis (ρ = -0.78) ratios.

CONCLUSIONS

This study was designed to be one of hypothesis generation. Diffusion metrics related to kurtosis were found to be more sensitive than conventional diffusivity metrics to early poststroke corticospinal tract microstructural changes and may have potential value in the prediction of motor impairment at 3 months.

Prediction of pKa Values for Druglike Molecules Using Semiempirical Quantum Chemical Methods

Rapid yet accurate pK_a prediction for druglike molecules is a key challenge in computational chemistry. This study uses PM6-DH+/COSMO, PM6/COSMO, PM7/COSMO, PM3/COSMO, AM1/COSMO, PM3/SMD, AM1/SMD, and DFTB3/SMD to predict the pK_a values of 53 amine groups in 48 druglike compounds. The approach uses an isodesmic reaction where the pK_a value is computed relative to a chemically related reference compound for which the pK_a value has been measured experimentally or estimated using a standard empirical approach. The AM1- and PM3-based methods perform best with RMSE values of 1.4-1.6 pH units that have uncertainties of ±0.2-0.3 pH units, which make them statistically equivalent. However, for all but PM3/SMD and AM1/SMD the RMSEs are dominated by a single outlier, cefadroxil, caused by proton transfer in the zwitterionic protonation state. If this outlier is removed, the RMSE values for PM3/COSMO and AM1/COSMO drop to 1.0 ± 0.2 and 1.1 ± 0.3, whereas PM3/SMD and AM1/SMD remain at 1.5 ± 0.3 and 1.6 ± 0.3/0.4 pH units, making the COSMO-based predictions statistically better than the SMD-based predictions. For pK_a calculations where a zwitterionic state is not involved or proton transfer in a zwitterionic state is not observed, PM3/COSMO or AM1/COSMO is the best pK_a prediction method; otherwise PM3/SMD or AM1/SMD should be used. Thus, fast and relatively accurate pK_a prediction for 100-1000s of druglike amines is feasible with the current setup and relatively modest computational resources.

Protein structure refinement using a quantum mechanics-based chemical shielding predictor

We show that a QM-based predictor of a protein backbone and CB chemical shifts is of comparable accuracy to empirical chemical shift predictors after chemical shift-based structural refinement that removes small structural errors (errors in chemical shifts shown in red).

The accurate prediction of protein chemical shifts using a quantum mechanics (QM)-based method has been the subject of intense research for more than 20 years but so far empirical methods for chemical shift prediction have proven more accurate. In this paper we show that a QM-based predictor of a protein backbone and CB chemical shifts (ProCS15, PeerJ, 2016, 3, e1344) is of comparable accuracy to empirical chemical shift predictors after chemical shift-based structural refinement that removes small structural errors. We present a method by which quantum chemistry based predictions of isotropic chemical shielding values (ProCS15) can be used to refine protein structures using Markov Chain Monte Carlo (MCMC) simulations, relating the chemical shielding values to the experimental chemical shifts probabilistically. Two kinds of MCMC structural refinement simulations were performed using force field geometry optimized X-ray structures as starting points: simulated annealing of the starting structure and constant temperature MCMC simulation followed by simulated annealing of a representative ensemble structure. Annealing of the CHARMM structure changes the CA-RMSD by an average of 0.4 Å but lowers the chemical shift RMSD by 1.0 and 0.7 ppm for CA and N. Conformational averaging has a relatively small effect (0.1–0.2 ppm) on the overall agreement with carbon chemical shifts but lowers the error for nitrogen chemical shifts by 0.4 ppm. If an amino acid specific offset is included the ProCS15 predicted chemical shifts have RMSD values relative to experiments that are comparable to popular empirical chemical shift predictors. The annealed representative ensemble structures differ in CA-RMSD relative to the initial structures by an average of 2.0 Å, with >2.0 Å difference for six proteins. In four of the cases, the largest structural differences arise in structurally flexible regions of the protein as determined by NMR, and in the remaining two cases, the large structural change may be due to force field deficiencies. The overall accuracy of the empirical methods are slightly improved by annealing the CHARMM structure with ProCS15, which may suggest that the minor structural changes introduced by ProCS15-based annealing improves the accuracy of the protein structures. Having established that QM-based chemical shift prediction can deliver the same accuracy as empirical shift predictors we hope this can help increase the accuracy of related approaches such as QM/MM or linear scaling approaches or interpreting protein structural dynamics from QM-derived chemical shift.

Prediction of pKa values using the PM6 semiempirical method

The PM6 semiempirical method and the dispersion and hydrogen bond-corrected PM6-D3H+ method are used together with the SMD and COSMO continuum solvation models to predict pKa values of pyridines, alcohols, phenols, benzoic acids, carboxylic acids, and phenols using isodesmic reactions and compared to published ab initio results. The pKa values of pyridines, alcohols, phenols, and benzoic acids considered in this study can generally be predicted with PM6 and ab initio methods to within the same overall accuracy, with average mean absolute differences (MADs) of 0.6-0.7 pH units. For carboxylic acids, the accuracy (0.7-1.0 pH units) is also comparable to ab initio results if a single outlier is removed. For primary, secondary, and tertiary amines the accuracy is, respectively, similar (0.5-0.6), slightly worse (0.5-1.0), and worse (1.0-2.5), provided that di- and tri-ethylamine are used as reference molecules for secondary and tertiary amines. When applied to a drug-like molecule where an empirical pKa predictor exhibits a large (4.9 pH unit) error, we find that the errors for PM6-based predictions are roughly the same in magnitude but opposite in sign. As a result, most of the PM6-based methods predict the correct protonation state at physiological pH, while the empirical predictor does not. The computational cost is around 2-5 min per conformer per core processor, making PM6-based pKa prediction computationally efficient enough to be used for high-throughput screening using on the order of 100 core processors.

Mapping the Orientation of White Matter Fiber Bundles: A Comparative Study of Diffusion Tensor Imaging, Diffusional Kurtosis Imaging, and Diffusion Spectrum Imaging

BACKGROUND AND PURPOSE

White matter fiber tractography relies on fiber bundle orientation estimates from diffusion MR imaging. However, clinically feasible techniques such as DTI and diffusional kurtosis imaging use assumptions, which may introduce error into in vivo orientation estimates. In this study, fiber bundle orientations from DTI and diffusional kurtosis imaging are compared with diffusion spectrum imaging as a criterion standard to assess the performance of each technique.

MATERIALS AND METHODS

For each subject, full DTI, diffusional kurtosis imaging, and diffusion spectrum imaging datasets were acquired during 2 independent sessions, and fiber bundle orientations were estimated by using the specific theoretic assumptions of each technique. Angular variability and angular error measures were assessed by comparing the orientation estimates. Tractography generated with each of the 3 reconstructions was also examined and contrasted.

RESULTS

Orientation estimates from all 3 techniques had comparable angular reproducibility, but diffusional kurtosis imaging decreased angular error throughout the white matter compared with DTI. Diffusion spectrum imaging and diffusional kurtosis imaging enabled the detection of crossing-fiber bundles, which had pronounced effects on tractography relative to DTI. Diffusion spectrum imaging had the highest sensitivity for detecting crossing fibers; however, the diffusion spectrum imaging and diffusional kurtosis imaging tracts were qualitatively similar.

CONCLUSIONS

Fiber bundle orientation estimates from diffusional kurtosis imaging have less systematic error than those from DTI, which can noticeably affect tractography. Moreover, tractography obtained with diffusional kurtosis imaging is qualitatively comparable with that of diffusion spectrum imaging. Because diffusional kurtosis imaging has a shorter typical scan time than diffusion spectrum imaging, diffusional kurtosis imaging is potentially more suitable for a variety of clinical and research applications.

Towards a barrier height benchmark set for biologically relevant systems

We have collected computed barrier heights and reaction energies (and associated model structures) for five enzymes from studies published by Himo and co-workers. Using this data, obtained at the B3LYP/6- 311+G(2d,2p)[LANL2DZ]//B3LYP/6-31G(d,p) level of theory, we then benchmark PM6, PM7, PM7-TS, and DFTB3 and discuss the influence of system size, bulk solvation, and geometry re-optimization on the error. The mean absolute differences (MADs) observed for these five enzyme model systems are similar to those observed for PM6 and PM7 for smaller systems (10-15 kcal/mol), while DFTB results in a MAD that is significantly lower (6 kcal/mol). The MADs for PMx and DFTB3 are each dominated by large errors for a single system and if the system is disregarded the MADs fall to 4-5 kcal/mol. Overall, results for the condensed phase are neither more or less accurate relative to B3LYP than those in the gas phase. With the exception of PM7-TS, the MAD for small and large structural models are very similar, with a maximum deviation of 3 kcal/mol for PM6. Geometry optimization with PM6 shows that for one system this method predicts a different mechanism compared to B3LYP/6-31G(d,p). For the remaining systems, geometry optimization of the large structural model increases the MAD relative to single points, by 2.5 and 1.8 kcal/mol for barriers and reaction energies. For the small structural model, the corresponding MADs decrease by 0.4 and 1.2 kcal/mol, respectively. However, despite these small changes, significant changes in the structures are observed for some systems, such as proton transfer and hydrogen bonding rearrangements. The paper represents the first step in the process of creating a benchmark set of barriers computed for systems that are relatively large and representative of enzymatic reactions, a considerable challenge for any one research group but possible through a concerted effort by the community. We end by outlining steps needed to expand and improve the data set and how other researchers can contribute to the process.

Predicting accurate absolute binding energies in aqueous solution: thermodynamic considerations for electronic structure methods

Recent predictions of absolute binding free energies of host-guest complexes in aqueous solution using electronic structure theory have been encouraging for some systems, while other systems remain problematic. In this paper I summarize some of the many factors that could easily contribute 1-3 kcal mol(-1) errors at 298 K: three-body dispersion effects, molecular symmetry, anharmonicity, spurious imaginary frequencies, insufficient conformational sampling, wrong or changing ionization states, errors in the solvation free energy of ions, and explicit solvent (and ion) effects that are not well-represented by continuum models. While I focus on binding free energies in aqueous solution the approach also applies (with minor adjustments) to any free energy difference such as conformational or reaction free energy differences or activation free energies in any solvent.

ProCS15: a DFT-based chemical shift predictor for backbone and Cβ atoms in proteins

We present ProCS15: a program that computes the isotropic chemical shielding values of backbone and Cβ atoms given a protein structure in less than a second. ProCS15 is based on around 2.35 million OPBE/6-31G(d,p)//PM6 calculations on tripeptides and small structural models of hydrogen-bonding. The ProCS15-predicted chemical shielding values are compared to experimentally measured chemical shifts for Ubiquitin and the third IgG-binding domain of Protein G through linear regression and yield RMSD values of up to 2.2, 0.7, and 4.8 ppm for carbon, hydrogen, and nitrogen atoms. These RMSD values are very similar to corresponding RMSD values computed using OPBE/6-31G(d,p) for the entire structure for each proteins. These maximum RMSD values can be reduced by using NMR-derived structural ensembles of Ubiquitin. For example, for the largest ensemble the largest RMSD values are 1.7, 0.5, and 3.5 ppm for carbon, hydrogen, and nitrogen. The corresponding RMSD values predicted by several empirical chemical shift predictors range between 0.7–1.1, 0.2–0.4, and 1.8–2.8 ppm for carbon, hydrogen, and nitrogen atoms, respectively.

Effect of mutations on the thermostability of Aspergillus aculeatus β-1,4-galactanase

New variants of β-1,4-galactanase from the mesophilic organism Aspergillus aculeatus were designed using the structure of β-1,4-galactanase from the thermophile organism Myceliophthora thermophila as a template. Some of the variants were generated using PROPKA 3.0, a validated pKa prediction tool, to test its usefulness as an enzyme design tool. The PROPKA designed variants were D182N and S185D/Q188T, G104D/A156R. Variants Y295F and G306A were designed by a consensus approach, as a complementary and validated design method. D58N was a stabilizing mutation predicted by both methods. The predictions were experimentally validated by measurements of the melting temperature (Tm ) by differential scanning calorimetry. We found that the Tm is elevated by 1.1 °C for G306A, slightly increased (in the range of 0.34 to 0.65 °C) for D182N, D58N, Y295F and unchanged or decreased for S185D/Q188T and G104D/A156R. The Tm changes were in the range predicted by PROPKA. Given the experimental errors, only the D58N and G306A show significant increase in thermodynamic stability. Given the practical importance of kinetic stability, the kinetics of the irreversible enzyme inactivation process were also investigated for the wild-type and three variants and found to be biphasic. The half-lives of thermal inactivation were approximately doubled in G306A, unchanged for D182N and, disappointingly, a lot lower for D58N. In conclusion, this study tests a new method for estimating Tm changes for mutants, adds to the available data on the effect of substitutions on protein thermostability and identifies an interesting thermostabilizing mutation, which may be beneficial also in other galactanases.

Bayesian inference of protein structure from chemical shift data

Protein chemical shifts are routinely used to augment molecular mechanics force fields in protein structure simulations, with weights of the chemical shift restraints determined empirically. These weights, however, might not be an optimal descriptor of a given protein structure and predictive model, and a bias is introduced which might result in incorrect structures. In the inferential structure determination framework, both the unknown structure and the disagreement between experimental and back-calculated data are formulated as a joint probability distribution, thus utilizing the full information content of the data. Here, we present the formulation of such a probability distribution where the error in chemical shift prediction is described by either a Gaussian or Cauchy distribution. The methodology is demonstrated and compared to a set of empirically weighted potentials through Markov chain Monte Carlo simulations of three small proteins (ENHD, Protein G and the SMN Tudor Domain) using the PROFASI force field and the chemical shift predictor CamShift. Using a clustering-criterion for identifying the best structure, together with the addition of a solvent exposure scoring term, the simulations suggests that sampling both the structure and the uncertainties in chemical shift prediction leads more accurate structures compared to conventional methods using empirical determined weights. The Cauchy distribution, using either sampled uncertainties or predetermined weights, did, however, result in overall better convergence to the native fold, suggesting that both types of distribution might be useful in different aspects of the protein structure prediction.

Altered microstructure in temporal lobe epilepsy: a diffusional kurtosis imaging study

BACKGROUND AND PURPOSE

Temporal lobe epilepsy is associated with regional abnormalities in tissue microstructure, as demonstrated by DTI. However, the full extent of these abnormalities has not yet been defined because DTI conveys only a fraction of the information potentially accessible with diffusion MR imaging. In this study, we assessed the added value of diffusional kurtosis imaging, an extension of DTI, to evaluate microstructural abnormalities in patients with temporal lobe epilepsy.

MATERIALS AND METHODS

Thirty-two patients with left temporal lobe epilepsy and 36 matched healthy subjects underwent diffusion MR imaging. To evaluate abnormalities in patients, we performed voxelwise analyses, assessing DTI-derived mean diffusivity, fractional anisotropy, and diffusional kurtosis imaging-derived mean diffusional kurtosis, as well as diffusional kurtosis imaging and DTI-derived axial and radial components, comparing patients with controls.

RESULTS

We replicated findings from previous studies demonstrating a reduction in fractional anisotropy and an increase in mean diffusivity preferentially affecting, but not restricted to, the temporal lobe ipsilateral to seizure onset. We also noted a pronounced pattern of diffusional kurtosis imaging abnormalities in gray and white matter tissues, often extending into regions that were not detected as abnormal by DTI measures.

CONCLUSIONS

Diffusional kurtosis is a sensitive and complementary measure of microstructural compromise in patients with temporal lobe epilepsy. It provides additional information regarding the anatomic distribution and degree of damage in this condition. Diffusional kurtosis imaging may be used as a biomarker for disease severity, clinical phenotypes, and treatment monitoring in epilepsy.

Non-Gaussian diffusion MRI of gray matter is associated with cognitive impairment in multiple sclerosis

BACKGROUND

Non-Gaussian diffusion imaging by using diffusional kurtosis imaging (DKI) allows assessment of isotropic tissue as of gray matter (GM), an important limitation of diffusion tensor imaging (DTI).

OBJECTIVE

In this study, we describe DKI and DTI metrics of GM in multiple sclerosis (MS) patients and their association with cognitive deficits.

METHODS

Thirty-four patients with relapsing-remitting MS and 17 controls underwent MRI on a 3T scanner including a sequence for DKI with 30 diffusion directions and 3b values for each direction. Mean kurtosis (MK), mean diffusivity and fractional anisotropy (FA) of cortical and subcortical GM were measured using histogram analysis. Spearman rank correlations were used to characterize associations among imaging measures and clinical/neuropsychological scores.

RESULTS

In cortical GM, a significant decrease of MK (0.68 vs. 0.73; p < 0.001) and increase of FA (0.16 vs. 0.13; p < 0.001) was found in patients compared to controls. Decreased cortical MK was correlated with poor performance on the Delis-Kaplan Executive Function System test (r = 0.66, p = 0.01).

CONCLUSION

Mean kurtosis is sensitive to abnormality in GM of MS patients and can provide information that is complementary to that of conventional DTI-derived metrics. The association between MK and cognitive deficits suggests that DKI might serve as a clinically relevant biomarker for cortical injury.

A third-generation dispersion and third-generation hydrogen bonding corrected PM6 method: PM6-D3H+

We present new dispersion and hydrogen bond corrections to the PM6 method, PM6-D3H+, and its implementation in the GAMESS program. The method combines the DFT-D3 dispersion correction by Grimme et al. with a modified version of the H+ hydrogen bond correction by Korth. Overall, the interaction energy of PM6-D3H+ is very similar to PM6-DH2 and PM6-DH+, with RMSD and MAD values within 0.02 kcal/mol of one another. The main difference is that the geometry optimizations of 88 complexes result in 82, 6, 0, and 0 geometries with 0, 1, 2, and 3 or more imaginary frequencies using PM6-D3H+ implemented in GAMESS, while the corresponding numbers for PM6-DH+ implemented in MOPAC are 54, 17, 15, and 2. The PM6-D3H+ method as implemented in GAMESS offers an attractive alternative to PM6-DH+ in MOPAC in cases where the LBFGS optimizer must be used and a vibrational analysis is needed, e.g., when computing vibrational free energies. While the GAMESS implementation is up to 10 times slower for geometry optimizations of proteins in bulk solvent, compared to MOPAC, it is sufficiently fast to make geometry optimizations of small proteins practically feasible.

In silico prediction of mutant HIV-1 proteases cleaving a target sequence

HIV-1 protease represents an appealing system for directed enzyme re-design, since it has various different endogenous targets, a relatively simple structure and it is well studied. Recently Chaudhury and Gray (Structure (2009) 17: 1636–1648) published a computational algorithm to discern the specificity determining residues of HIV-1 protease. In this paper we present two computational tools aimed at re-designing HIV-1 protease, derived from the algorithm of Chaudhuri and Gray. First, we present an energy-only based methodology to discriminate cleavable and non cleavable peptides for HIV-1 proteases, both wild type and mutant. Secondly, we show an algorithm we developed to predict mutant HIV-1 proteases capable of cleaving a new target substrate peptide, different from the natural targets of HIV-1 protease. The obtained in silico mutant enzymes were analyzed in terms of cleavability and specificity towards the target peptide using the energy-only methodology. We found two mutant proteases as best candidates for specificity and cleavability towards the target sequence.

FragBuilder: an efficient Python library to setup quantum chemistry calculations on peptides models

We present a powerful Python library to quickly and efficiently generate realistic peptide model structures. The library makes it possible to quickly set up quantum mechanical calculations on model peptide structures. It is possible to manually specify a specific conformation of the peptide. Additionally the library also offers sampling of backbone conformations and side chain rotamer conformations from continuous distributions. The generated peptides can then be geometry optimized by the MMFF94 molecular mechanics force field via convenient functions inside the library. Finally, it is possible to output the resulting structures directly to files in a variety of useful formats, such as XYZ or PDB formats, or directly as input files for a quantum chemistry program. FragBuilder is freely available at https://github.com/jensengroup/fragbuilder/ under the terms of the BSD open source license.

Hybrid RHF/MP2 geometry optimizations with the effective fragment molecular orbital method

The frozen domain effective fragment molecular orbital method is extended to allow for the treatment of a single fragment at the MP2 level of theory. The approach is applied to the conversion of chorismate to prephenate by Chorismate Mutase, where the substrate is treated at the MP2 level of theory while the rest of the system is treated at the RHF level. MP2 geometry optimization is found to lower the barrier by up to 3.5 kcal/mol compared to RHF optimzations and ONIOM energy refinement and leads to a smoother convergence with respect to the basis set for the reaction profile. For double zeta basis sets the increase in CPU time relative to RHF is roughly a factor of two.

Protein structure validation and refinement using amide proton chemical shifts derived from quantum mechanics

We present the ProCS method for the rapid and accurate prediction of protein backbone amide proton chemical shifts--sensitive probes of the geometry of key hydrogen bonds that determine protein structure. ProCS is parameterized against quantum mechanical (QM) calculations and reproduces high level QM results obtained for a small protein with an RMSD of 0.25 ppm (r = 0.94). ProCS is interfaced with the PHAISTOS protein simulation program and is used to infer statistical protein ensembles that reflect experimentally measured amide proton chemical shift values. Such chemical shift-based structural refinements, starting from high-resolution X-ray structures of Protein G, ubiquitin, and SMN Tudor Domain, result in average chemical shifts, hydrogen bond geometries, and trans-hydrogen bond ((h3)J(NC')) spin-spin coupling constants that are in excellent agreement with experiment. We show that the structural sensitivity of the QM-based amide proton chemical shift predictions is needed to obtain this agreement. The ProCS method thus offers a powerful new tool for refining the structures of hydrogen bonding networks to high accuracy with many potential applications such as protein flexibility in ligand binding.

Diffusional kurtosis imaging of the developing brain

BACKGROUND AND PURPOSE

Diffusional kurtosis imaging is an extension of DTI but includes non-Gaussian diffusion effects, allowing more comprehensive characterization of microstructural changes during brain development. Our purpose was to use diffusional kurtosis imaging to measure age-related microstructural changes in both the WM and GM of the developing human brain.

MATERIALS AND METHODS

Diffusional kurtosis imaging was performed in 59 subjects ranging from birth to 4 years 7 months of age. Diffusion metrics, fractional anisotropy, and mean kurtosis were collected from VOIs within multiple WM and GM structures and subsequently analyzed with respect to age. Diffusional kurtosis tractography images at various stages of development were also generated.

RESULTS

Fractional anisotropy and mean kurtosis both showed age-related increases in all WM regions, reflecting progression of diffusional anisotropy throughout development, predominantly in the first 2 years of life (eg, 70% and 157% increase in fractional anisotropy and mean kurtosis, respectively, from birth to 2 years for the splenium). However, mean kurtosis detected continued microstructural changes in WM past the fractional anisotropy plateau, accounting for more delayed isotropic changes (eg, 90% of maximum fractional anisotropy was reached at 5 months, whereas 90% of maximum mean kurtosis occurred at 18 months for the external capsule). Mean kurtosis may also provide greater characterization of GM maturation (eg, the putamen showed no change in fractional anisotropy but an 81% change in mean kurtosis from birth to 4 years 7 months).

CONCLUSIONS

Mean kurtosis detects significant microstructural changes consistent with known patterns of brain maturation. In comparison with fractional anisotropy, mean kurtosis may offer a more comprehensive evaluation of age-related microstructural changes in both WM and GM and is potentially a valuable technique for studying brain development.

Novel white matter tract integrity metrics sensitive to Alzheimer disease progression

BACKGROUND AND PURPOSE

Along with cortical abnormalities, white matter microstructural changes such as axonal loss and myelin breakdown are implicated in the pathogenesis of Alzheimer disease. Recently, a white matter model was introduced that relates non-Gaussian diffusional kurtosis imaging metrics to characteristics of white matter tract integrity, including the axonal water fraction, the intra-axonal diffusivity, and the extra-axonal axial and radial diffusivities.

MATERIALS AND METHODS

This study reports these white matter tract integrity metrics in subjects with amnestic mild cognitive impairment (n = 12), Alzheimer disease (n = 14), and age-matched healthy controls (n = 15) in an effort to investigate their sensitivity, diagnostic accuracy, and associations with white matter changes through the course of Alzheimer disease.

RESULTS

With tract-based spatial statistics and region-of-interest analyses, increased diffusivity in the extra-axonal space (extra-axonal axial and radial diffusivities) in several white matter tracts sensitively and accurately discriminated healthy controls from those with amnestic mild cognitive impairment (area under the receiver operating characteristic curve = 0.82-0.95), while widespread decreased axonal water fraction discriminated amnestic mild cognitive impairment from Alzheimer disease (area under the receiver operating characteristic curve = 0.84). Additionally, these white matter tract integrity metrics in the body of the corpus callosum were strongly correlated with processing speed in amnestic mild cognitive impairment (r = |0.80-0.82|, P < .001).

CONCLUSIONS

These findings have implications for the course and spatial progression of white matter degeneration in Alzheimer disease, suggest the mechanisms by which these changes occur, and demonstrate the viability of these white matter tract integrity metrics as potential neuroimaging biomarkers of the earliest stages of Alzheimer disease and disease progression.

Hot topics in functional neuroradiology

Functional neuroradiology represents a relatively new and ever-growing subspecialty in the field of neuroradiology. Neuroradiology has evolved beyond anatomy and basic tissue signal characteristics and strives to understand the underlying physiologic processes of central nervous system disease. The American Society of Functional Neuroradiology sponsors a yearly educational and scientific meeting, and the educational committee was asked to suggest a few cutting-edge functional neuroradiology techniques (hot topics). The following is a review of several of these topics and includes "Diffusion Tensor Imaging of the Pediatric Spinal Cord"; "Diffusional Kurtosis Imaging"; "From Standardization to Quantification: Beyond Biomarkers toward Bioscales as Neuro MR Imaging Surrogates of Clinical End Points"; Resting-State Functional MR Imaging"; and "Current Use of Cerebrovascular Reserve Imaging."

In silico screening of 393 mutants facilitates enzyme engineering of amidase activity in CalB

Our previously presented method for high throughput computational screening of mutant activity (Hediger et al., 2012) is benchmarked against experimentally measured amidase activity for 22 mutants of Candida antarctica lipase B (CalB). Using an appropriate cutoff criterion for the computed barriers, the qualitative activity of 15 out of 22 mutants is correctly predicted. The method identifies four of the six most active mutants with ≥3-fold wild type activity and seven out of the eight least active mutants with ≤0.5-fold wild type activity. The method is further used to screen all sterically possible (386) double-, triple- and quadruple-mutants constructed from the most active single mutants. Based on the benchmark test at least 20 new promising mutants are identified.

A computational method for the systematic screening of reaction barriers in enzymes: searching for Bacillus circulans xylanase mutants with greater activity towards a synthetic substrate

We present a semi-empirical (PM6-based) computational method for systematically estimating the effect of all possible single mutants, within a certain radius of the active site, on the barrier height of an enzymatic reaction. The intent of this method is not a quantitative prediction of the barrier heights, but rather to identify promising mutants for further computational or experimental study. The method is applied to identify promising single and double mutants of Bacillus circulans xylanase (BCX) with increased hydrolytic activity for the artificial substrate ortho-nitrophenyl β-xylobioside (ONPX2). The estimated reaction barrier for wild-type (WT) BCX is 18.5 kcal/mol, which is in good agreement with the experimental activation free energy value of 17.0 kcal/mol extracted from the observed k cat using transition state theory (Joshi et al., 2001). The PM6 reaction profiles for eight single point mutations are recomputed using FMO-MP2/PCM/6-31G(d) single points. PM6 predicts an increase in barrier height for all eight mutants while FMO predicts an increase for six of the eight mutants. Both methods predict that the largest change in barrier occurs for N35F, where PM6 and FMO predict a 9.0 and 15.8 kcal/mol increase, respectively. We thus conclude that PM6 is sufficiently accurate to identify promising mutants for further study. We prepared a set of all theoretically possible (342) single mutants in which every amino acid of the active site (except for the catalytically active residues E78 and E172) was mutated to every other amino acid. Based on results from the single mutants we construct a set of 111 double mutants consisting of all possible pairs of single mutants with the lowest barrier for a particular position and compute their reaction profile. None of the mutants have, to our knowledge, been prepared experimentally and therefore present experimentally testable predictions.

Interface of the polarizable continuum model of solvation with semi-empirical methods in the GAMESS program

An interface between semi-empirical methods and the polarized continuum model (PCM) of solvation successfully implemented into GAMESS following the approach by Chudinov et al (Chem. Phys. 1992, 160, 41). The interface includes energy gradients and is parallelized. For large molecules such as ubiquitin a reasonable speedup (up to a factor of six) is observed for up to 16 cores. The SCF convergence is greatly improved by PCM for proteins compared to the gas phase.

PHAISTOS: a framework for Markov chain Monte Carlo simulation and inference of protein structure

We present a new software framework for Markov chain Monte Carlo sampling for simulation, prediction, and inference of protein structure. The software package contains implementations of recent advances in Monte Carlo methodology, such as efficient local updates and sampling from probabilistic models of local protein structure. These models form a probabilistic alternative to the widely used fragment and rotamer libraries. Combined with an easily extendible software architecture, this makes PHAISTOS well suited for Bayesian inference of protein structure from sequence and/or experimental data. Currently, two force-fields are available within the framework: PROFASI and OPLS-AA/L, the latter including the generalized Born surface area solvent model. A flexible command-line and configuration-file interface allows users quickly to set up simulations with the desired configuration. PHAISTOS is released under the GNU General Public License v3.0. Source code and documentation are freely available from http://phaistos.sourceforge.net. The software is implemented in C++ and has been tested on Linux and OSX platforms.