Improving IDP theoretical chemical shift accuracy and efficiency through a combined MD/ADMA/DFT and machine learning approach.

This work extends the multi-scale computational scheme for the quantum mechanics (QM) calculations of Nuclear Magnetic Resonance (NMR) chemical shifts (CSs) in proteins that lack a well-defined 3D structure. The scheme couples the sampling of an intrinsically disordered protein (IDP) by classical molecular dynamics (MD) with protein fragmentation using the adjustable density matrix assembler (ADMA) and density functional theory (DFT) calculations. In contrast to our early investigation on IDPs (Pavlíková Precechtelová et al., J. Chem. Theory Comput., 2019, 15, 5642-5658) and the state-of-the art NMR calculations for structured proteins, a partial re-optimization was implemented on the raw MD geometries in vibrational normal mode coordinates to enhance the accuracy of the MD/ADMA/DFT computational scheme. In addition, machine-learning based cluster analysis was performed on the scheme to explore its potential in producing protein structure ensembles (CLUSTER ensembles) that yield accurate CSs at a reduced computational cost. The performance of the cluster-based calculations is validated against results obtained with conventional structural ensembles consisting of MD snapshots extracted from the MD trajectory at regular time intervals (REGULAR ensembles). CS calculations performed with the refined MD/ADMA/DFT framework employed the 6-311++G(d,p) basis set that outperformed IGLO-III calculations with the same density functional approximation (B3LYP) and both explicit and implicit solvation. The partial geometry optimization did not universally improve the agreement of computed CSs with the experiment but substantially decreased errors associated with the ensemble averaging. A CLUSTER ensemble with 50 structures yielded ensemble averages close to those obtained with a REGULAR ensemble consisting of 500 MD frames. The cluster based calculations thus required only a fraction of the computational time.

Boosting phase-contrast MRI performance in idiopathic normal pressure hydrocephalus diagnostics by means of machine learning approach.

OBJECTIVE: Phase-contrast MRI allows detailed measurements of various parameters of CSF motion. This examination is technically demanding and machine dependent. The literature on this topic is ambiguous. Machine learning (ML) approaches have already been successfully utilized in medical research, but none have yet been applied to enhance the results of CSF flowmetry. The aim of this study was to evaluate the possible contribution of ML algorithms in enhancing the utilization and results of MRI flowmetry in idiopathic normal pressure hydrocephalus (iNPH) diagnostics. METHODS: The study cohort consisted of 30 iNPH patients and 15 healthy controls examined on one MRI machine. All major phase-contrast parameters were inspected: peak positive, peak negative, and average velocities; peak amplitude; positive, negative, and average flow rates; and aqueductal area. The authors applied ML algorithms to 85 complex features calculated from a phase-contrast study. RESULTS: The most distinctive parameters with p < 0.005 were the peak negative velocity, peak amplitude, and negative flow. From the ML algorithms, the Adaptive Boosting classifier showed the highest specificity and best discrimination potential overall, with 80.4% ± 2.9% accuracy, 72.0% ± 5.6% sensitivity, 84.7% ± 3.8% specificity, and 0.812 ± 0.047 area under the receiver operating characteristic curve (AUC). The highest sensitivity was 85.7% ± 5.6%, reached by the Gaussian Naive Bayes model, and the best AUC was 0.854 ± 0.028 by the Extra Trees classifier. CONCLUSIONS: Feature extraction algorithms combined with ML approaches simplify the utilization of phase-contrast MRI. The highest-performing ML algorithm was Adaptive Boosting, which showed good calibration and discrimination on the testing data, with 80.4% accuracy, 72.0% sensitivity, 84.7% specificity, and 0.812 AUC. Phase-contrast MRI boosted by the ML approach can help to determine shunt-responsive iNPH patients.

First experiences with Miethke M.blue® valve in iNPH patients.

The literature on hydrocephalus treatment shows support for adjustable valves and devices which prevent the so-called "siphon effect". In our study, 21 probable iNPH patients were indicated to shunt surgery with the Miethke M.blue® adjustable gravitational valve. Outcomes at three months were measured using the following tests: Dutch Gait Scale, International Consortium on Incontinence Questionnaire (ICIQ-UI SF), SF12V2-Health Survey, Kiefer Scale, 3T MRI, and a neuropsychological testing battery. Preoperative parameters were studied for any signs of overdrainage risk. Valves were set according to the manufacturer's recommendations. Significant improvement at three months was seen in the Dutch Gait Scale, ICIQ-UI SF, Kiefer Scale, Mental Health Component of the SF12V2-Health Survey (MCS-12) and three neuropsychological tests: Rey-Osterrieth complex figure test (ROCFT 30 min), auditory verbal learning test (AVLT I-V) and the NKP version of verbal fluency test. Seven patients needed more than one adjustment of the valve. This subgroup significantly improved only in Walking Score and Step Score but the trend was toward significant improvement in other variables. Eight patients had subdural effusions that were completely managed with adjustments until the 3-month control. BMI was significantly lower in patients with ≥2 adjustments compared to those with a maximum of one adjustment. Implantation had low complication rates and no mortality. Initial results are promising however more studies are needed to provide rationale for gravitational valves in iNPH. We recommend increasing the initial valve setting by 2-4 cm H2O above manufacturer's recommendation, especially in lean patients.

Prediction of Shunt Responsiveness in Suspected Patients With Normal Pressure Hydrocephalus Using the Lumbar Infusion Test: A Machine Learning Approach.

BACKGROUND: Machine learning (ML) approaches can significantly improve the classical Rout-based evaluation of the lumbar infusion test (LIT) and the clinical management of the normal pressure hydrocephalus. OBJECTIVE: To develop a ML model that accurately identifies patients as candidates for permanent cerebral spinal fluid shunt implantation using only intracranial pressure and electrocardiogram signals recorded throughout LIT. METHODS: This was a single-center cohort study of prospectively collected data of 96 patients who underwent LIT and 5-day external lumbar cerebral spinal fluid drainage (external lumbar drainage) as a reference diagnostic method. A set of selected 48 intracranial pressure/electrocardiogram complex signal waveform features describing nonlinear behavior, wavelet transform spectral signatures, or recurrent map patterns were calculated for each patient. After applying a leave-one-out cross-validation training-testing split of the data set, we trained and evaluated the performance of various state-of-the-art ML algorithms. RESULTS: The highest performing ML algorithm was the eXtreme Gradient Boosting. This model showed a good calibration and discrimination on the testing data, with an area under the receiver operating characteristic curve of 0.891 (accuracy: 82.3%, sensitivity: 86.1%, and specificity: 73.9%) obtained for 8 selected features. Our ML model clearly outperforms the classical Rout-based manual classification commonly used in clinical practice with an accuracy of 62.5%. CONCLUSION: This study successfully used the ML approach to predict the outcome of a 5-day external lumbar drainage and hence which patients are likely to benefit from permanent shunt implantation. Our automated ML model thus enhances the diagnostic utility of LIT in management.

From head micro-motions towards CSF dynamics and non-invasive intracranial pressure monitoring.

Continuous monitoring of the intracranial pressure (ICP) is essential in neurocritical care. There are a variety of ICP monitoring systems currently available, with the intraventricular fluid filled catheter transducer currently representing the "gold standard". As the placement of catheters is associated with the attendant risk of infection, hematoma formation, and seizures, there is a need for a reliable, non-invasive alternative. In the present study we suggest a unique theoretical framework based on differential geometry invariants of cranial micro-motions with the potential for continuous non-invasive ICP monitoring in conservative traumatic brain injury (TBI) treatment. As a proof of this concept, we have developed a pillow with embedded mechanical sensors and collected an extensive dataset (> 550 h on 24 TBI coma patients) of cranial micro-motions and the reference intraparenchymal ICP. From the multidimensional pulsatile curve we calculated the first Cartan curvature and constructed a "fingerprint" image (Cartan map) associated with the cerebrospinal fluid (CSF) dynamics. The Cartan map features maxima bands corresponding to a pressure wave reflection corresponding to a detectable skull tremble. We give evidence for a statistically significant and patient-independent correlation between skull micro-motions and ICP time derivative. Our unique differential geometry-based method yields a broader and global perspective on intracranial CSF dynamics compared to rather local catheter-based measurement and has the potential for wider applications.

Role of DESH, callosal angle and cingulate sulcus sign in prediction of gait responsiveness after shunting in iNPH patients.

Primary endpoint of this single-centre, prospective consecutive cohort study was to evaluate DESH score, CA, CSS and Evans index of suspected iNPH patients against the reference standard of lumbar infusion test (LIT) and external lumbar drainage (ELD) in prediction of gait response after VP shunt implantation in patients with idiopathic normal pressure hydrocephalus (iNPH). Patients were assigned to NPH and non-NPH groups based on LIT and ELD results. Age-matched controls were added for group comparison. 32 NPH, 46 non-NPH and 15 control subjects were enrolled in the study. There were significant differences in mean preoperative DESH scores of NPH, non-NPH and control groups (6.3 ± 2.3 ([±SD]) (range 2-10) vs 4.5 ± 2.4 (range 0-10) vs 1.0 ± 1.2 (range 0-4)). Differences in mean CA and Evans index were not significant between NPH and non-NPH groups. CSS showed 62.5% sensitivity, 60.87% specificity, 52.63% PPV and 70% NPV for differentiation of NPH and non-NPH groups. A CA of 68 degrees had 48.49% sensitivity, 76.09% specificity, 59.26% PPV 67.31% NPV and DESH score of 4 had 93.75% sensitivity, 41.30% specificity, 52.63% PPV and 90.48% NPV for differentiation between NPH and non-NPH groups. The groups of probable iNPH patients with gait impairment diagnosed by high DESH score or positive functional testing did not overlap and DESH score did not correlate with gait improvement after ELD. DESH score should not be used as a simple diagnostic or prognostic marker of iNPH and we could not confirm the benefit of measurement of callosal angle and cingulate sulcus sign.

Structural volumetry in NPH diagnostics and treatment-future or dead end?

To assess automated volumetric analysis as a potential presurgical diagnostic tool or as a method to potentially shed light on normal pressure hydrocephalus (NPH) pathophysiology. MRI imaging according to our protocol was performed in 29 NPH patients, 45 non-NPH (but suspected) patients and 15 controls. Twenty patients underwent a second MRI 3 months after ventriculoperitoneal (VP) shunt surgery. All structures relevant to NPH diagnosis were automatically segmented using commercial software. The results were subsequently tested using ANOVA analysis. Significant differences in the volumes of the corpus callosum, left hippocampus, internal globus pallidus, grey and white matter and ventricular volumes were observed between NPH group and healthy controls. However, the differences between NPH and non-NPH groups were non-significant. Three months after, VP shunt insertion decreased ventricular volume was the only clearly significant result (p value 0.0001). Even though a detailed volumetric study shows several significant differences, volumetric analysis as a standalone method does not provide a simple diagnostic biomarker, nor does it shed a light on an unknown NPH aetiology.

Choice of Force Field for Proteins Containing Structured and Intrinsically Disordered Regions.

Biomolecular force fields optimized for globular proteins fail to properly reproduce properties of intrinsically disordered proteins. In particular, parameters of the water model need to be modified to improve applicability of the force fields to both ordered and disordered proteins. Here, we compared performance of force fields recommended for intrinsically disordered proteins in molecular dynamics simulations of three proteins differing in the content of ordered and disordered regions (two proteins consisting of a well-structured domain and of a disordered region with and without a transient helical motif and one disordered protein containing a region of increased helical propensity). The obtained molecular dynamics trajectories were used to predict measurable parameters, including radii of gyration of the proteins and chemical shifts, residual dipolar couplings, paramagnetic relaxation enhancement, and NMR relaxation data of their individual residues. The predicted quantities were compared with experimental data obtained within this study or published previously. The results showed that the NMR relaxation parameters, rarely used for benchmarking, are particularly sensitive to the choice of force-field parameters, especially those defining the water model. Interestingly, the TIP3P water model, leading to an artificial structural collapse, also resulted in unrealistic relaxation properties. The TIP4P-D water model, combined with three biomolecular force-field parameters for the protein part, significantly improved reliability of the simulations. Additional analysis revealed only one particular force field capable of retaining the transient helical motif observed in NMR experiments. The benchmarking protocol used in our study, being more sensitive to imperfections than the commonly used tests, is well suited to evaluate the performance of newly developed force fields.

Normal pressure hydrocephalus-an overview of pathophysiological mechanisms and diagnostic procedures.

Normal pressure hydrocephalus (NPH) is an important differential diagnosis of neurodegenerative diseases. The prevalence of dementia is increasing in line with the worldwide increase in life expectancy. NPH can be divided into idiopathic (iNPH) and secondary (sNPH) which is important in terms of clinical symptoms, future progress, and the outcome of possible treatment. The full clinical triad is not prevalent in all of the cases and the pathophysiology of iNPH remains unclear. Diagnosis is based on the evaluation of clinical symptoms (Hakim's triad) combined with an MRI assessment, evaluation of CSF dynamic parameters by different methods such as a tap test, lumbar infusion test (LIT), and external lumbar drainage (ELD). Despite the development of diagnostic techniques and strategies in management, NPH remains to be a challenge for the specialists despite more than 50 years of research. However, results of this research have brought new opportunities in the diagnosis, therapy, and quality of life as well as survival time of NPH patients with improved symptoms. The aim of this article is to present the pathophysiological hypotheses of NPH and an overview of the diagnostic techniques used for the evaluation of NPH patients.

Quantum Chemical Calculations of NMR Chemical Shifts in Phosphorylated Intrinsically Disordered Proteins.

Quantum mechanics (QM) calculations are applied to examine 1H, 13C, 15N, and 31P chemical shifts of two phosphorylation sites in an intrinsically disordered protein region. The QM calculations employ a combination of (1) structural ensembles generated by molecular dynamics, (2) a fragmentation technique based on the adjustable density matrix assembler, and (3) density functional methods. The combined computational approach is used to obtain chemical shifts (i) in the S19 and S40 residues of the nonphosphorylated and (ii) in the pS19 and pS40 residues of the doubly phosphorylated human tyrosine hydroxylase 1 as the system of interest. We study the effects of conformational averaging and explicit solvent sampling as well as the effects of phosphorylation on the computed chemical shifts. Good to great quantitative agreement with the experiment is achieved for all nuclei, provided that the systematic error cancellation is optimized by the choice of a suitable NMR standard. The effect of the standard reference on the computed 15N and 31P chemical shifts is demonstrated by employing three different referencing methods. Error bars associated with the statistical averaging of the computed 31P chemical shifts are larger than the difference between the 31P chemical shift of pS19 and pS40. The sequence trend of 31P shifts therefore could not be reliably reproduced. On the contrary, the calculations correctly predict the change of the 13C chemical shift for CB induced by the phosphorylation of the serine residues. The present work demonstrates that QM calculations coupled with molecular dynamics simulations and fragmentation techniques can be used as an alternative to empirical prediction tools in the structure characterization of intrinsically disordered proteins.

Development of In-Browser Simulators for Medical Education: Introduction of a Novel Software Toolchain.

BACKGROUND: Simulators used in teaching are interactive applications comprising a mathematical model of the system under study and a graphical user interface (GUI) that allows the user to control the model inputs and visualize the model results in an intuitive and educational way. Well-designed simulators promote active learning, enhance problem-solving skills, and encourage collaboration and small group discussion. However, creating simulators for teaching purposes is a challenging process that requires many contributors including educators, modelers, graphic designers, and programmers. The availability of a toolchain of user-friendly software tools for building simulators can facilitate this complex task. OBJECTIVE: This paper aimed to describe an open-source software toolchain termed Bodylight.js that facilitates the creation of browser-based client-side simulators for teaching purposes, which are platform independent, do not require any installation, and can work offline. The toolchain interconnects state-of-the-art modeling tools with current Web technologies and is designed to be resilient to future changes in the software ecosystem. METHODS: We used several open-source Web technologies, namely, WebAssembly and JavaScript, combined with the power of the Modelica modeling language and deployed them on the internet with interactive animations built using Adobe Animate. RESULTS: Models are implemented in the Modelica language using either OpenModelica or Dassault Systèmes Dymola and exported to a standardized Functional Mock-up Unit (FMU) to ensure future compatibility. The C code from the FMU is further compiled to WebAssembly using Emscripten. Industry-standard Adobe Animate is used to create interactive animations. A new tool called Bodylight.js Composer was developed for the toolchain that enables one to create the final simulator by composing the GUI using animations, plots, and control elements in a drag-and-drop style and binding them to the model variables. The resulting simulators are stand-alone HyperText Markup Language files including JavaScript and WebAssembly. Several simulators for physiology education were created using the Bodylight.js toolchain and have been received with general acclaim by teachers and students alike, thus validating our approach. The Nephron, Circulation, and Pressure-Volume Loop simulators are presented in this paper. Bodylight.js is licensed under General Public License 3.0 and is free for anyone to use. CONCLUSIONS: Bodylight.js enables us to effectively develop teaching simulators. Armed with this technology, we intend to focus on the development of new simulators and interactive textbooks for medical education. Bodylight.js usage is not limited to developing simulators for medical education and can facilitate the development of simulators for teaching complex topics in a variety of different fields.

Quantum chemical benchmark study on 46 RNA backbone families using a dinucleotide unit.

We have created a benchmark set of quantum chemical structure-energy data denoted as UpU46, which consists of 46 uracil dinucleotides (UpU), representing all known 46 RNA backbone conformational families. Penalty-function-based restrained optimizations with COSMO TPSS-D3/def2-TZVP ensure a balance between keeping the target conformation and geometry relaxation. The backbone geometries are close to the clustering-means of their respective RNA bioinformatics family classification. High-level wave function methods (DLPNO-CCSD(T) as reference) and a wide-range of dispersion-corrected or inclusive DFT methods (DFT-D3, VV10, LC-BOP-LRD, M06-2X, M11, and more) are used to evaluate the conformational energies. The results are compared to the Amber RNA bsc0χOL3 force field. Most dispersion-corrected DFT methods surpass the Amber force field significantly in accuracy and yield mean absolute deviations (MADs) for relative conformational energies of ∼0.4-0.6 kcal/mol. Double-hybrid density functionals represent the most accurate class of density functionals. Low-cost quantum chemical methods such as PM6-D3H+, HF-3c, DFTB3-D3, as well as small basis set calculations corrected for basis set superposition errors (BSSEs) by the gCP procedure are also tested. Unfortunately, the presently available low-cost methods are struggling to describe the UpU conformational energies with satisfactory accuracy. The UpU46 benchmark is an ideal test for benchmarking and development of fast methods to describe nucleic acids, including force fields.

Energies and 2'-Hydroxyl Group Orientations of RNA Backbone Conformations. Benchmark CCSD(T)/CBS Database, Electronic Analysis, and Assessment of DFT Methods and MD Simulations.

Sugar-phosphate backbone is an electronically complex molecular segment imparting RNA molecules high flexibility and architectonic heterogeneity necessary for their biological functions. The structural variability of RNA molecules is amplified by the presence of the 2'-hydroxyl group, capable of forming multitude of intra- and intermolecular interactions. Bioinformatics studies based on X-ray structure database revealed that RNA backbone samples at least 46 substates known as rotameric families. The present study provides a comprehensive analysis of RNA backbone conformational preferences and 2'-hydroxyl group orientations. First, we create a benchmark database of estimated CCSD(T)/CBS relative energies of all rotameric families and test performance of dispersion-corrected DFT-D3 methods and molecular mechanics in vacuum and in continuum solvent. The performance of the DFT-D3 methods is in general quite satisfactory. The B-LYP-D3 method provides the best trade-off between accuracy and computational demands. B3-LYP-D3 slightly outperforms the new PW6B95-D3 and MPW1B95-D3 and is the second most accurate density functional of the study. The best agreement with CCSD(T)/CBS is provided by DSD-B-LYP-D3 double-hybrid functional, although its large-scale applications may be limited by high computational costs. Molecular mechanics does not reproduce the fine energy differences between the RNA backbone substates. We also demonstrate that the differences in the magnitude of the hyperconjugation effect do not correlate with the energy ranking of the backbone conformations. Further, we investigated the 2'-hydroxyl group orientation preferences. For all families, we conducted a QM and MM hydroxyl group rigid scan in gas phase and solvent. We then carried out set of explicit solvent MD simulations of folded RNAs and analyze 2'-hydroxyl group orientations of different backbone families in MD. The solvent energy profiles determined primarily by the sugar pucker match well with the distribution data derived from the simulations. The QM and MM energy profiles predict the same 2'-hydroxyl group orientation preferences. Finally, we demonstrate that the high energy of unfavorable and rarely sampled 2'-hydroxyl group orientations can be attributed to clashes between occupied orbitals.

Ion Binding to Quadruplex DNA Stems. Comparison of MM and QM Descriptions Reveals Sizable Polarization Effects Not Included in Contemporary Simulations.

Molecular mechanical (MM) force fields are commonly employed for biomolecular simulations. Despite their success, the nonpolarizable nature of contemporary additive force fields limits their performance, especially in long simulations and when strong polarization effects are present. Guanine quadruplex D(R)NA molecules have been successfully studied by MM simulations in the past. However, the G-stems are stabilized by a chain of monovalent cations that create sizable polarization effects. Indeed, simulation studies revealed several problems that have been tentatively attributed to the lack of polarization. Here, we provide a detailed comparison between quantum chemical (QM) DFT-D3 and MM potential energy surfaces of ion binding to G-stems and assess differences that may affect MM simulations. We suggest that MM describes binding of a single ion to the G-stem rather well. However, polarization effects become very significant when a second ion is present. We suggest that the MM approximation substantially limits accuracy of description of energy and dynamics of multiple ions inside the G-stems and binding of ions at the stem-loop junctions. The difference between QM and MM descriptions is also explored using symmetry-adapted perturbation theory and quantum theory of atoms in molecules analyses, which reveal a delicate balance of electrostatic and induction effects.

Nature and magnitude of aromatic base stacking in DNA and RNA: Quantum chemistry, molecular mechanics, and experiment.

Base stacking is a major interaction shaping up and stabilizing nucleic acids. During the last decades, base stacking has been extensively studied by experimental and theoretical methods. Advanced quantum-chemical calculations clarified that base stacking is a common interaction, which in the first approximation can be described as combination of the three most basic contributions to molecular interactions, namely, electrostatic interaction, London dispersion attraction and short-range repulsion. There is not any specific π-π energy term associated with the delocalized π electrons of the aromatic rings that cannot be described by the mentioned contributions. The base stacking can be rather reasonably approximated by simple molecular simulation methods based on well-calibrated common force fields although the force fields do not include nonadditivity of stacking, anisotropy of dispersion interactions, and some other effects. However, description of stacking association in condensed phase and understanding of the stacking role in biomolecules remain a difficult problem, as the net base stacking forces always act in a complex and context-specific environment. Moreover, the stacking forces are balanced with many other energy contributions. Differences in definition of stacking in experimental and theoretical studies are explained.

Relative stability of different DNA guanine quadruplex stem topologies derived using large-scale quantum-chemical computations.

We provide theoretical predictions of the intrinsic stability of different arrangements of guanine quadruplex (G-DNA) stems. Most computational studies of nucleic acids have applied Molecular Mechanics (MM) approaches using simple pairwise-additive force fields. The principle limitation of such calculations is the highly approximate nature of the force fields. In this study, we for the first time apply accurate QM computations (DFT-D3 with large atomic orbital basis sets) to essentially complete DNA building blocks, seven different folds of the cation-stabilized two-quartet G-DNA stem, each having more than 250 atoms. The solvent effects are approximated by COSMO continuum solvent. We reveal sizable differences between MM and QM descriptions of relative energies of different G-DNA stems, which apparently reflect approximations of the DNA force field. Using the QM energy data, we propose correction to earlier free energy estimates of relative stabilities of different parallel, hybrid, and antiparallel G-stem folds based on classical simulations. The new energy ranking visibly improves the agreement between theory and experiment. We predict the 5'-anti-anti-3' GpG dinucleotide step to be the most stable one, closely followed by the 5'-syn-anti-3' step. The results are in good agreement with known experimental structures of 2-, 3-, and 4-quartet G-DNA stems. Besides providing specific results for G-DNA, our study highlights basic limitations of force field modeling of nucleic acids. Although QM computations have their own limitations, mainly the lack of conformational sampling and the approximate description of the solvent, they can substantially improve the quality of calculations currently relying exclusively on force fields.

How to understand quantum chemical computations on DNA and RNA systems? A practical guide for non-specialists.

In this review primarily written for non-experts we explain basic methodological aspects and interpretation of modern quantum chemical (QM) computations applied to nucleic acids. We introduce current reference QM computations on small model systems consisting of dozens of atoms. Then we comment on recent advance of fast and accurate dispersion-corrected density functional theory methods, which will allow computations of small but complete nucleic acids building blocks in the near future. The qualitative difference between QM and molecular mechanics (MM, force field) computations is discussed. We also explain relation of QM and molecular simulation computations to experiments.

Benchmark quantum-chemical calculations on a complete set of rotameric families of the DNA sugar-phosphate backbone and their comparison with modern density functional theory.

The DNA sugar-phosphate backbone has a substantial influence on the DNA structural dynamics. Structural biology and bioinformatics studies revealed that the DNA backbone in experimental structures samples a wide range of distinct conformational substates, known as rotameric DNA backbone conformational families. Their correct description is essential for methods used to model nucleic acids and is known to be the Achilles heel of force field computations. In this study we report the benchmark database of MP2 calculations extrapolated to the complete basis set of atomic orbitals with aug-cc-pVTZ and aug-cc-pVQZ basis sets, MP2(T,Q), augmented by ΔCCSD(T)/aug-cc-pVDZ corrections. The calculations are performed in the gas phase as well as using a COSMO solvent model. This study includes a complete set of 18 established and biochemically most important families of DNA backbone conformations and several other salient conformations that we identified in experimental structures. We utilize an electronically sufficiently complete DNA sugar-phosphate-sugar (SPS) backbone model system truncated to prevent undesired intramolecular interactions. The calculations are then compared with other QM methods. The BLYP and TPSS functionals supplemented with Grimme's D3(BJ) dispersion term provide the best tradeoff between computational demands and accuracy and can be recommended for preliminary conformational searches as well as calculations on large model systems. Among the tested methods, the best agreement with the benchmark database has been obtained for the double-hybrid DSD-BLYP functional in combination with a quadruple-ζ basis set, which is, however, computationally very demanding. The new hybrid density functionals PW6B95-D3 and MPW1B95-D3 yield outstanding results and even slightly outperform the computationally more demanding PWPB95 double-hybrid functional. B3LYP-D3 is somewhat less accurate compared to the other hybrids. Extrapolated MP2(D,T) calculations are not as accurate as the less demanding DFT-D3 methods. Preliminary force field tests using several charge sets reveal an almost order of magnitude larger deviations from the reference QM data compared to modern DFT-D3, underlining the challenges facing force field simulations of nucleic acids. As expected, inclusion of the solvent environment approximated by a continuum approach has a large impact on the relative stabilities of different backbone substates and is important when comparing the QM data with structural bioinformatics and other experimental data.

Structural and energetic factors controlling the enantioselectivity of dinucleotide formation under prebiotic conditions.

Recently, it has been reported that the montmorillonite-catalyzed oligomerization of activated nucleotides exhibits remarkable enantioselectivity. In the current paper we investigate the structures and intrinsic energies of homochiral and heterochiral cyclic dinucleotides by means of accurate quantum chemical calculations in gas-phase and in bulk water. The steric effect of the clay is represented with geometrical constraints. Our computations reveal that the heterochiral dimer geometries are systematically less stable than their homochiral counterparts due to steric clashes inside the sugar-phosphate ring geometry. Thus we suggest that the homochiral selectivity observed in the cyclic dinucleotide formation in confined spaces may arise from the energetic destabilization of the heterochiral ring geometries as compared to their homochiral analogues. In the present paper we provide the first model of the 3D structure of d,l cyclic dinucleotides, which until now has eluded experimental observation.

On the road from formamide ices to nucleobases: IR-spectroscopic observation of a direct reaction between cyano radicals and formamide in a high-energy impact event.

The formamide-based synthesis of nucleic acids is considered as a nonaqueous scenario for the emergence of biomolecules from inorganic matter. In the current study, we scrutinized the chemical composition of formamide ices mixed with an FeNi meteorite material treated with laser-induced dielectric breakdown plasma created in nitrogen buffer gas. These experiments aimed to capture the first steps of those chemical transformations that may lead to the formation of nucleobases during the impact of an extraterrestrial icy body containing formamide on an early Earth atmosphere. High-resolution FT-IR spectroscopy combined with quantum chemical calculations was used to analyze the volatile fraction of the products formed during such an event. We have found that the spectrum of the evaporated formamide ices is dominated by the spectral signatures of the dimeric form of formamide. Upon exposure to laser sparks, new well-defined bands appear in the spectrum centered at ~820, ~995, and ~1650 cm(-1). On the basis of quantum chemical calculations, these bands can be assigned to the absorptions of 2-amino-2-hydroxy-acetonitrile and to 2-amino-2-hydroxy-malononitrile, which are formed in a direct reaction between formamide and CN(•) radicals upon the high-energy impact event. We also show that there is an exergonic reaction route via these intermediates leading to diaminomaleonitrile, which is generally considered to play a key role in the synthesis of nucleobases.