Hybrid computational methods combining experimental information with molecular dynamics.

A goal of structural biology is to understand how macromolecules carry out their biological roles by identifying their metastable states, mechanisms of action, pathways leading to conformational changes, and the thermodynamic and kinetic relationships between those states. Integrative modeling brings structural insights into systems where traditional structure determination approaches cannot help. We focus on the synergies and challenges of integrative modeling combining experimental data with molecular dynamics simulations.

An implementation of the Martini coarse-grained force field in OpenMM.

We describe a complete implementation of Martini 2 and Martini 3 in the OpenMM molecular dynamics software package. Martini is a widely used coarse-grained force field with applications in biomolecular simulation, materials, and broader areas of chemistry. It is implemented as a force field but makes extensive use of facilities unique to the GROMACS software, including virtual sites and bonded terms that are not commonly used in standard atomistic force fields. OpenMM is a flexible molecular dynamics package widely used for methods development and is competitive in speed on GPUs with other commonly used packages. OpenMM has facilities to easily implement new force field terms, external forces and fields, and other nonstandard features, which we use to implement all force field terms used in Martini 2 and Martini 3. This allows Martini simulations, starting with GROMACS topology files that are processed by custom scripts, with all the added flexibility of OpenMM. We provide a GitHub repository with test cases, compare accuracy and performance between GROMACS and OpenMM, and discuss the limitations of our implementation in terms of direct comparison with GROMACS. We describe a use case that implements the Modeling Employing Limited Data method to apply experimental constraints in a Martini simulation to efficiently determine the structure of a protein complex. We also discuss issues and a potential solution with the Martini 2 topology for cholesterol.

Development and Interrelation of Pain, Depression, and Anxiety in Music Students: Does Successful Treatment of Pain Have an Impact on Mental Health?

OBJECTIVE: Musculoskeletal pain is a common problem among professional musicians as well as music students. Studies have emphasized the effectiveness of music-specific physiotherapy for affected musicians. This study was designed to evaluate if physiotherapy treatment of pain-affected music students had an impact on pain perception as well as psychological well-being. To explore the possible development of musculoskeletal pain, depression, and anxiety, a second sample of pain-free music students, matched for age and gender, was examined twice at identical time intervals. METHODS: A convenience sample of 31 university music students with moderate to severe musculoskeletal pain and 31 pain-free music students, matched in age and gender, were included in the study. Both groups were examined physically and completed biographical, music-related, and psychological questionnaires. Perceived pain intensity was assessed with a visual-analogue scale (VAS), and depression and anxiety symptoms were assessed with the Beck Depression Inventory II (BDI-II) and the Hospital Anxiety and Depression Scale (HAD). Music students with pain received a series of 12 sessions of musician-specific physiotherapy, while controls waited for the same amount of time for retesting. RESULTS: On the 10-cm VAS, music students with pain reported an average improvement in pain intensity from a baseline of 6.25 (SD 1.95) to 2.7 (2.03) after the intervention, while the controls (music students without pain) did not change. Furthermore, music students with pain indicated higher depression and anxiety scores as compared to the control group before and after therapy. After intervention, music students with pain with higher BDI-II scores demonstrated clinical improvement concerning depression, but no significant improvement in mental health was found in the pain group taken as a whole. CONCLUSION: Physiotherapy was effective in reducing pain symptoms in music students affected by chronic musculoskeletal pain. However, physiotherapy did not improve mental health in pain-affected music students. Additional psychotherapeutic interventions may be needed to support music students with psychological comorbidities such as depression and anxiety.

A combined computational-biophysical approach to understanding fatty acid binding to FABP7.

Members of the fatty acid binding protein (FABP) family function as intracellular transporters of long-chain fatty acids and other hydrophobic molecules to different cellular compartments. Brain FABP (FABP7) exhibits ligand-directed differences in cellular transport. For example, when FABP7 binds to docosahexaenoic acid (DHA), the complex relocates to the nucleus and influences transcriptional activity, whereas FABP7 bound with monosaturated fatty acids remains in the cytosol. Preferential binding of FABP7 to polyunsaturated fatty acids like DHA has been previously observed and is thought to play a role in differential localization. However, we find that at 37°C, FABP7 does not display strong selectivity, suggesting that the conformational ensemble of FABP7 and its perturbation upon binding may be important. We use molecular dynamics simulations, NMR, and a variety of biophysical techniques to better understand the conformational ensemble of FABP7, how it is perturbed by fatty acid binding, and how this may be related to ligand-directed transport. We find that FABP7 has high degree of conformational heterogeneity that is substantially reduced upon ligand binding. We also observe substantial heterogeneity in ligand binding poses, which is consistent with our finding that ligand binding is resistant to mutations in key polar residues in the binding pocket. Our NMR experiments show that DHA binding leads to chemical shift perturbations in residues near the nuclear localization signal, which may point toward a mechanism of differential transport.

Understanding FABP7 binding to fatty acid micelles and membranes.

Fatty acid-binding proteins (FABPs) are chaperones that facilitate the transport of long-chain fatty acids within the cell and can provide cargo-dependent localization to specific cellular compartments. Understanding the nature of this transport is important because lipid signaling functions are associated with metabolic pathways impacting disease pathologies including cancer, autism, and schizophrenia. FABPs often associate with cell membranes to acquire and deliver their bound cargo as part of transport. We focus on brain FABP (FABP7), which demonstrates localization to the cytoplasm and nucleus, influencing transcription and fatty acid metabolism. We use a combined biophysical-computational approach to elucidate the interaction between FABP7 and model membranes. Specifically, we use multiple experiments to demonstrate that FABP7 can bind oleic acid and docosahexaenoic acid micelles. Data from NMR and multiscale molecular dynamics simulations reveal that the interaction with micelles is through FABP7's portal region residues. Simulations suggest that binding to membranes occurs through the same residues as micelles. Simulations also capture binding events where fatty acids dissociate from the membrane and enter FABP7's binding pocket. Overall, our data shed light on the interactions between FABP7 and OA or DHA micelles and provide insight into the transport of long-chain fatty acids.

Emergency button cannula vs. umbilical catheter as neonatal emergency umbilical vein access - a randomized cross-over pilot study.

OBJECTIVES: Establishing immediate intravenous access to a newborn is challenging even for trained neonatologists in an emergency situation. Correct placement of umbilical catheter or an intraosseous needle needs consistent training. We evaluated the time required to correctly place an emergency umbilical button cannula (EUC) or an umbilical catheter (UC) using the standard intersection (S-EUC or S-UC, respectively) or lateral umbilical cord incision (L-EUC) by untrained medical personnel. METHODS: Single-center cross-over pilot-study using a model with fresh umbilical cords. Video-based teaching of medical students before probands performed all three techniques after assignment to one of three cycles with different sequence, using a single umbilical cord divided in three pieces for each proband. RESULTS: Mean time required to establish L-EUC was 89.3 s, for S-EUC 82.2 s and for S-UC 115.1 s. Both application routes using the EUC were significantly faster than the UC technique. There was no significant difference between both application routes using EUC (p=0.54). CONCLUSIONS: Using an umbilical cannula is faster than an umbilical catheter, using a lateral incision of the umbilical vein is an appropriate alternative.

Cardiac Function Modifies the Impact of Heart Base Dose on Survival: A Voxel-Wise Analysis of Patients With Lung Cancer From the PET-Plan Trial.

INTRODUCTION: Heart dose has emerged as an independent predictor of overall survival in patients with NSCLC treated with radiotherapy. Several studies have identified the base of the heart as a region of enhanced dose sensitivity and a potential target for cardiac sparing. We present a dosimetric analysis of overall survival in the multicenter, randomized PET-Plan trial (NCT00697333) and for the first time include left ventricular ejection fraction (EF) at baseline as a metric of cardiac function. METHODS: A total of 205 patients with inoperable stage II or III NSCLC treated with 60 to 72 Gy in 2 Gy fractions were included in this study. A voxel-wise image-based data mining methodology was used to identify anatomical regions where higher dose was significantly associated with worse overall survival. Univariable and multivariable Cox proportional hazards models tested the association of survival with dose to the identified region, established prognostic factors, and baseline cardiac function. RESULTS: A total of 172 patients remained after processing and censoring for follow-up. At 2-years posttreatment, a highly significant region was identified within the base of the heart (p < 0.005), centered on the origin of the left coronary artery and the region of the atrioventricular node. In multivariable analysis, the number of positron emission tomography-positive nodes (p = 0.02, hazard ratio = 1.13, 95% confidence interval: 1.02-1.25) and mean dose to the cardiac subregion (p = 0.02, hazard ratio = 1.11 Gy-1, 95% confidence interval: 1.02-1.21) were significantly associated with overall survival. There was a significant interaction between EF and region dose (p = 0.04) for survival, with contrast plots revealing a larger effect of region dose on survival in patients with lower EF values. CONCLUSIONS: This work validates previous image-based data mining studies by revealing a strong association between dose to the base of the heart and overall survival. For the first time, an interaction between baseline cardiac health and heart base dose was identified, potentially suggesting preexisting cardiac dysfunction exacerbates the impact of heart dose on survival.

Validation Study for Non-Invasive Prediction of IDH Mutation Status in Patients with Glioma Using In Vivo 1H-Magnetic Resonance Spectroscopy and Machine Learning.

The isocitrate dehydrogenase (IDH) mutation status is an indispensable prerequisite for diagnosis of glioma (astrocytoma and oligodendroglioma) according to the WHO classification of brain tumors 2021 and is a potential therapeutic target. Usually, immunohistochemistry followed by sequencing of tumor tissue is performed for this purpose. In clinical routine, however, non-invasive determination of IDH mutation status is desirable in cases where tumor biopsy is not possible and for monitoring neuro-oncological therapies. In a previous publication, we presented reliable prediction of IDH mutation status employing proton magnetic resonance spectroscopy (1H-MRS) on a 3.0 Tesla (T) scanner and machine learning in a prospective cohort of 34 glioma patients. Here, we validated this approach in an independent cohort of 67 patients, for which 1H-MR spectra were acquired at 1.5 T between 2002 and 2007, using the same data analysis approach. Despite different technical conditions, a sensitivity of 82.6% (95% CI, 61.2-95.1%) and a specificity of 72.7% (95% CI, 57.2-85.0%) could be achieved. We concluded that our 1H-MRS based approach can be established in a routine clinical setting with affordable effort and time, independent of technical conditions employed. Therefore, the method provides a non-invasive tool for determining IDH status that is well-applicable in an everyday clinical setting.

Impact of radiotherapy protocol adherence in NSCLC patients treated with concurrent chemoradiation: RTQA results of the PET-Plan trial.

INTRODUCTION: The success of intensification and personalisation of the curative treatment of non-small cell lung cancer (NSCLC) is strongly associated with the precision in radiotherapy. Here, we evaluate the impact of radiotherapy protocol adherence in a prospective multicentre trial. METHODS: In the open-label, randomised, controlled PET-Plan trial, patients with inoperable NSCLC were randomized at a 1:1 ratio regarding the target volume delineation informed by 1F-FDG PET and CT plus elective nodal irradiation (arm A) or target volumes informed by PET alone (arm B) and received iso-toxically dose-escalated concurrent chemoradiation. The prospectively organised quality assurance program (RTQA) included individual case review by predefined criteria. For evaluation, protocol adherence was scored as per protocol (pP), with minor (miD), intermediate (inD) and major (maD) deviations. In order to exclude biases through patients who discontinued treatment, patients who received ≥60 Gy were additionally analysed. RESULTS: Between 05/2009-11/2016, 205 patients were randomized, 204 patients started treatment according to protocol of which 31 (15%) patients had maD. Patients with maD had an inferior overall survival (OS) (HR 2.9, 95% CI 1.8-4.4, p < 0.0001) and a higher risk of loco-regional progression (HR 5.7, 95% CI 2.7-11.1, p < 0.0001). These results were significant also in the subgroup of patients receiving ≥ 60 Gy. Patients with maD concerning normal tissue delineation and/or dose constraints had a worse OS (p = 0.006) although no higher incidence of grade ≥ 3 toxicities. CONCLUSIONS: Non-adherence to the radiotherapy protocol was associated with an inferior OS and loco-regional control. These results underline the importance of RTQA.

Synthetic single cell RNA sequencing data from small pilot studies using deep generative models.

Deep generative models, such as variational autoencoders (VAEs) or deep Boltzmann machines (DBMs), can generate an arbitrary number of synthetic observations after being trained on an initial set of samples. This has mainly been investigated for imaging data but could also be useful for single-cell transcriptomics (scRNA-seq). A small pilot study could be used for planning a full-scale experiment by investigating planned analysis strategies on synthetic data with different sample sizes. It is unclear whether synthetic observations generated based on a small scRNA-seq dataset reflect the properties relevant for subsequent data analysis steps. We specifically investigated two deep generative modeling approaches, VAEs and DBMs. First, we considered single-cell variational inference (scVI) in two variants, generating samples from the posterior distribution, the standard approach, or the prior distribution. Second, we propose single-cell deep Boltzmann machines (scDBMs). When considering the similarity of clustering results on synthetic data to ground-truth clustering, we find that the [Formula: see text] variant resulted in high variability, most likely due to amplifying artifacts of small datasets. All approaches showed mixed results for cell types with different abundance by overrepresenting highly abundant cell types and missing less abundant cell types. With increasing pilot dataset sizes, the proportions of the cells in each cluster became more similar to that of ground-truth data. We also showed that all approaches learn the univariate distribution of most genes, but problems occurred with bimodality. Across all analyses, in comparing 10[Formula: see text] Genomics and Smart-seq2 technologies, we could show that for 10[Formula: see text] datasets, which have higher sparsity, it is more challenging to make inference from small to larger datasets. Overall, the results show that generative deep learning approaches might be valuable for supporting the design of scRNA-seq experiments.

Deep generative models in DataSHIELD.

BACKGROUND: The best way to calculate statistics from medical data is to use the data of individual patients. In some settings, this data is difficult to obtain due to privacy restrictions. In Germany, for example, it is not possible to pool routine data from different hospitals for research purposes without the consent of the patients. METHODS: The DataSHIELD software provides an infrastructure and a set of statistical methods for joint, privacy-preserving analyses of distributed data. The contained algorithms are reformulated to work with aggregated data from the participating sites instead of the individual data. If a desired algorithm is not implemented in DataSHIELD or cannot be reformulated in such a way, using artificial data is an alternative. Generating artificial data is possible using so-called generative models, which are able to capture the distribution of given data. Here, we employ deep Boltzmann machines (DBMs) as generative models. For the implementation, we use the package "BoltzmannMachines" from the Julia programming language and wrap it for use with DataSHIELD, which is based on R. RESULTS: We present a methodology together with a software implementation that builds on DataSHIELD to create artificial data that preserve complex patterns from distributed individual patient data. Such data sets of artificial patients, which are not linked to real patients, can then be used for joint analyses. As an exemplary application, we conduct a distributed analysis with DBMs on a synthetic data set, which simulates genetic variant data. Patterns from the original data can be recovered in the artificial data using hierarchical clustering of the virtual patients, demonstrating the feasibility of the approach. Additionally, we compare DBMs, variational autoencoders, generative adversarial networks, and multivariate imputation as generative approaches by assessing the utility and disclosure of synthetic data generated from real genetic variant data in a distributed setting with data of a small sample size. CONCLUSIONS: Our implementation adds to DataSHIELD the ability to generate artificial data that can be used for various analyses, e.g., for pattern recognition with deep learning. This also demonstrates more generally how DataSHIELD can be flexibly extended with advanced algorithms from languages other than R.

Synthetic observations from deep generative models and binary omics data with limited sample size.

Deep generative models can be trained to represent the joint distribution of data, such as measurements of single nucleotide polymorphisms (SNPs) from several individuals. Subsequently, synthetic observations are obtained by drawing from this distribution. This has been shown to be useful for several tasks, such as removal of noise, imputation, for better understanding underlying patterns, or even exchanging data under privacy constraints. Yet, it is still unclear how well these approaches work with limited sample size. We investigate such settings specifically for binary data, e.g. as relevant when considering SNP measurements, and evaluate three frequently employed generative modeling approaches, variational autoencoders (VAEs), deep Boltzmann machines (DBMs) and generative adversarial networks (GANs). This includes conditional approaches, such as when considering gene expression conditional on SNPs. Recovery of pair-wise odds ratios (ORs) is considered as a primary performance criterion. For simulated as well as real SNP data, we observe that DBMs generally can recover structure for up to 300 variables, with a tendency of over-estimating ORs when not carefully tuned. VAEs generally get the direction and relative strength of pairwise relations right, yet with considerable under-estimation of ORs. GANs provide stable results only with larger sample sizes and strong pair-wise relations in the data. Taken together, DBMs and VAEs (in contrast to GANs) appear to be well suited for binary omics data, even at rather small sample sizes. This opens the way for many potential applications where synthetic observations from omics data might be useful.

Insights into the Direct Oxidative Repair of Etheno Lesions: MD and QM/MM Study on the Substrate Scope of ALKBH2 and AlkB.

E. coli AlkB and human ALKBH2 belong to the AlkB family enzymes, which contain several α-ketoglutarate (α-KG)/Fe(II)-dependent dioxygenases that repair alkylated DNA. Specifically, the AlkB enzymes catalyze decarboxylation of α-KG to generate a high-valent Fe(IV)-oxo species that oxidizes alkyl groups on DNA adducts. AlkB and ALKBH2 have been reported to differentially repair select etheno adducts, with preferences for 1,N6-ethenoadenine (1,N6-εA) and 3,N4-ethenocytosine (3,N4-εC) over 1,N2-ethenoguanine (1,N2-εG). However, N2,3-ethenoguanine (N2,3-εG), the most common etheno adduct, is not repaired by the AlkB enzymes. Unfortunately, a structural understanding of the differential activity of E. coli AlkB and human ALKBH2 is lacking due to challenges acquiring atomistic details for a range of substrates using experiments. This study uses both molecular dynamics (MD) simulations and ONIOM(QM:MM) calculations to determine how the active site changes upon binding each etheno adduct and characterizes the corresponding catalytic impacts. Our data reveal that the preferred etheno substrates (1,N6-εA and 3,N4-εC) form favorable interactions with catalytic residues that situate the lesion near the Fe(IV)-oxo species and permit efficient oxidation. In contrast, although the damage remains correctly aligned with respect to the Fe(IV)-oxo moiety, repair of 1,N2-εG is mitigated by increased solvation of the active site and a larger distance between Fe(IV)-oxo and the aberrant carbons. Binding of non-substrate N2,3-εG in the active site disrupts key DNA-enzyme interactions, and positions the aberrant carbon atoms even further from the Fe(IV)-oxo species, leading to prohibitively high barriers for oxidative catalysis. Overall, our calculations provide the first structural insight required to rationalize the experimentally-reported substrate specificities of AlkB and ALKBH2 and thereby highlight the roles of several active site residues in the repair of etheno adducts that directly correlates with available experimental data. These proposed catalytic strategies can likely be generalized to other α-KG/Fe(II)-dependent dioxygenases that play similar critical biological roles, including epigenetic and post-translational regulation.

Efficacy and Toxicity of Different Chemotherapy Protocols for Concurrent Chemoradiation in Non-Small Cell Lung Cancer-A Secondary Analysis of the PET Plan Trial.

(1) Background: The optimal chemotherapy (CHT) regimen for concurrent chemoradiation (cCRT) is not well defined. In this secondary analysis of the international randomized PET-Plan trial, we evaluate the efficacy of different CHT. (2) Methods: Patients with inoperable NSCLC were randomized at a 1:1 ratio regarding the target volume definition and received isotoxically dose-escalated cCRT using cisplatin 80 mg/m2 (day 1, 22) and vinorelbin 15 mg/m2 (day 1, 8, 22, 29) (P1) or cisplatin 20 mg/m2 (day 1-5, 29-33) and vinorelbin 12.5 mg/m2 (day 1, 8, 15, 29, 36, 43) (P2) or carboplatin AUC1 (day 1-5, 29-33) and vinorelbin 12.5 mg/m2 (day 1, 8, 15, 29, 36, 43) (P3) or other CHT at the treating physician's discretion. (3) Results: Between 05/2009 and 11/2016, 205 patients were randomized and 172 included in the per-protocol analysis. Patients treated in P1 or P2 had a better overall survival (OS) compared to P3 (p = 0.015, p = 0.01, respectively). Patients treated with carboplatin had a worse OS compared to cisplatin (HR 1.78, p = 0.03), but the difference did not remain significant after adjusting for age, ECOG, cardiac function creatinine and completeness of CHT. (4) Conclusions: Carboplatin doublets show no significant difference compared to cisplatin, after adjusting for possibly relevant factors, probably due to existing selection bias.

A TOMM40/APOE allele encoding APOE-E3 predicts high likelihood of late-onset Alzheimer's disease in autopsy cases.

BACKGROUND: The APOE-ε4 allele is an established risk factor for Alzheimer's disease (AD). TOMM40 located adjacent to APOE has also been implicated in AD but reports of TOMM40 associations with AD that are independent of APOE-ε4 are at variance. METHODS: We investigated associations of AD with haplotypes defined by three TOMM40 and two APOE single nucleotide polymorphisms in 73 and 71 autopsy cases with intermediate and high likelihood of AD (defined by BRAAK stages 0.02. The two haplotypes encoding APOE-E4 showed strong associations with AD that did not differ between intermediate and high likelihood AD. In contrast, a TOMM40 haplotype encoding APOE-E3 was identified as risk haplotype of high- (p = .0186), but not intermediate likelihood AD (p = .7530). Furthermore, the variant allele of rs2075650 located in intron 2 of TOMM40, increased the risk of high-, but not intermediate likelihood AD on the APOE-ε3/ε3 background (p = .0230). CONCLUSION: The striking association of TOMM40 only with high likelihood AD may explain some contrasting results for TOMM40 in clinical studies and may reflect an association with more advanced disease and/or suggest a role of TOMM40 in the pathogenesis of neurofibrillary tangles.

DFT Study on the Deglycosylation of Methylated, Oxidized, and Canonical Pyrimidine Nucleosides in Water: Implications for Epigenetic Regulation and DNA Repair.

Density functional theory (B3LYP) was used to characterize the kinetics and thermodynamics of the (nonenzymatic) deglycosylation in water for a variety of 2'-deoxycytidine (dC) and 2'-deoxyuridine (dU) nucleoside derivatives that differ in methylation and subsequent oxidation of the C5 substituent. A range of computational models are considered that combine implicit and explicit solvation of the nucleophile and nucleobase. Regardless of the model implemented, our calculations reveal that the glycosidic bond in dC is inherently more stable than that in dU. Furthermore, C5 methylation of either pyrimidine and subsequent oxidation of the methyl group yield overall small changes to the Gibbs reaction energy profiles and thereby preserve lower deglycosylation barriers for the dC compared to those for the dU nucleoside derivatives. However, hydrolytic deglycosylation becomes significantly more energetically favorable when 5-methyl-dC (5m-dC) undergoes two or three rounds of oxidation, with the Gibbs energy barrier decreasing and the reaction becoming more exergonic by up to 40 kJ/mol. In fact, two or three oxidation reactions from 5m-dC result in a deglycosylation barrier similar to that for dU, as well as those for the associated C5-methylated (2'-deoxythymidine) and oxidized (5-hydroxymethyl-dU) derivatives. These predicted trends in the inherent deglycosylation energetics in water directly correlate with the previously reported activity of thymine DNA glycosylase (TDG), which cleaves the glycosidic bond in select dC nucleosides as part of epigenetic regulation and in dU variants as part of DNA repair. Thus, our data suggests that fundamental differences in the intrinsic reactivity of the pyrimidine nucleosides help regulate the function of human enzymes that maintain cellular integrity.

DNA repair enzymes ALKBH2, ALKBH3, and AlkB oxidize 5-methylcytosine to 5-hydroxymethylcytosine, 5-formylcytosine and 5-carboxylcytosine in vitro.

5-Methylcytosine (5mC) in DNA CpG islands is an important epigenetic biomarker for mammalian gene regulation. It is oxidized to 5-hydroxymethylcytosine (5hmC), 5-formylcytosine (5fC), and 5-carboxylcytosine (5caC) by the ten-eleven translocation (TET) family enzymes, which are α-ketoglutarate (α-KG)/Fe(II)-dependent dioxygenases. In this work, we demonstrate that the epigenetic marker 5mC is modified to 5hmC, 5fC, and 5caC in vitro by another class of α-KG/Fe(II)-dependent proteins-the DNA repair enzymes in the AlkB family, which include ALKBH2, ALKBH3 in huamn and AlkB in Escherichia coli. Theoretical calculations indicate that these enzymes may bind 5mC in the syn-conformation, placing the methyl group comparable to 3-methylcytosine, the prototypic substrate of AlkB. This is the first demonstration of the AlkB proteins to oxidize a methyl group attached to carbon, instead of nitrogen, on a DNA base. These observations suggest a broader role in epigenetics for these DNA repair proteins.

A Queue-Poll Extension and DataSHIELD: Standardised, Monitored, Indirect and Secure Access to Sensitive Data.

Analyzing data across hospitals and institutions without the data leaving the hospitals and adding institutions to a trusted network is an important part of privacy preserving data analysis. This work implements a queue-poll extension and integrates with DataSHIELD to allow for a standardized, monitored, indirect and secure access to data. The extension was created using the HTTP protocol and requests are not pushed into a participating institution but are sent to a server outside an institutional network. These requests are then pulled into the institution from within, executed and the response sent back to the outside server, which relays the request back to the request sender. We found that the requests were slower than a direct push request, but also that the integration of new institutions into the network was easily achieved. We propose that future work should focus on optimizing the monitoring and speed of the service. The service created here could reduce the barriers to entry for institutions to form an analysis network and can be used not only to drive analysis but also the sharing of resulting information and models.

Structural explanation for the tunable substrate specificity of an E. coli nucleoside hydrolase: insights from molecular dynamics simulations.

Parasitic protozoa rely on nucleoside hydrolases that play key roles in the purine salvage pathway by catalyzing the hydrolytic cleavage of the N-glycosidic bond that connects nucleobases to ribose sugars. Cytidine-uridine nucleoside hydrolase (CU-NH) is generally specific toward pyrimidine nucleosides; however, previous work has shown that replacing two active site residues with Tyr, specifically the Thr223Tyr and Gln227Tyr mutations, allows CU-NH to process inosine. The current study uses molecular dynamics (MD) simulations to gain atomic-level insight into the activity of wild-type and mutant E. coli CU-NH toward inosine. By examining systems that differ in the identity and protonation states of active site catalytic residues, key enzyme-substrate interactions that dictate the substrate specificity of CU-NH are identified. Regardless of the wild-type or mutant CU-NH considered, our calculations suggest that inosine binding is facilitated by interactions of the ribose moiety with active site residues and Ca2+, and π-interactions between two His residues (His82 and His239) and the nucleobase. However, the lack of observed activity toward inosine for wild-type CU-NH is explained by no residue being correctly aligned to stabilize the departing nucleobase. In contrast, a hydrogen-bonding network between hypoxanthine and a newly identified general acid (Asp15) is present when the two Tyr mutations are engineered into the active site. Investigation of the single CU-NH mutants reveals that this hydrogen-bonding network is only maintained when both Tyr mutations are present due to a π-interaction between the residues. These results rationalize previous experiments that show the single Tyr mutants are unable to efficiently hydrolyze inosine and explain how the Tyr residues work synergistically in the double mutant to stabilize the nucleobase leaving group during hydrolysis. Overall, our simulations provide a structural explanation for the substrate specificity of nucleoside hydrolases, which may be used to rationally develop new treatments for kinetoplastid diseases.

Modeling Activity Tracker Data Using Deep Boltzmann Machines.

Commercial activity trackers are set to become an essential tool in health research, due to increasing availability in the general population. The corresponding vast amounts of mostly unlabeled data pose a challenge to statistical modeling approaches. To investigate the feasibility of deep learning approaches for unsupervised learning with such data, we examine weekly usage patterns of Fitbit activity trackers with deep Boltzmann machines (DBMs). This method is particularly suitable for modeling complex joint distributions via latent variables. We also chose this specific procedure because it is a generative approach, i.e., artificial samples can be generated to explore the learned structure. We describe how the data can be preprocessed to be compatible with binary DBMs. The results reveal two distinct usage patterns in which one group frequently uses trackers on Mondays and Tuesdays, whereas the other uses trackers during the entire week. This exemplary result shows that DBMs are feasible and can be useful for modeling activity tracker data.