Concurrent Identification and Characterization of Protein Structure and Continuous Internal Dynamics with REDCRAFT.

Internal dynamics of proteins can play a critical role in the biological function of some proteins. Several well documented instances have been reported such as MBP, DHFR, hTS, DGCR8, and NSP1 of the SARS-CoV family of viruses. Despite the importance of internal dynamics of proteins, there currently are very few approaches that allow for meaningful separation of internal dynamics from structural aspects using experimental data. Here we present a computational approach named REDCRAFT that allows for concurrent characterization of protein structure and dynamics. Here, we have subjected DHFR (PDB-ID 1RX2), a 159-residue protein, to a fictitious, mixed mode model of internal dynamics. In this simulation, DHFR was segmented into 7 regions where 4 of the fragments were fixed with respect to each other, two regions underwent rigid-body dynamics, and one region experienced uncorrelated and melting event. The two dynamical and rigid-body segments experienced an average orientational modification of 7° and 12° respectively. Observable RDC data for backbone C'-N, N-HN, and C'-HN were generated from 102 uniformly sampled frames that described the molecular trajectory. The structure calculation of DHFR with REDCRAFT by using traditional Ramachandran restraint produced a structure with 29 Å of structural difference measured over the backbone atoms (bb-rmsd) over the entire length of the protein and an average bb-rmsd of more than 4.7 Å over each of the dynamical fragments. The same exercise repeated with context-specific dihedral restraints generated by PDBMine produced a structure with bb-rmsd of 21 Å over the entire length of the protein but with bb-rmsd of less than 3 Å over each of the fragments. Finally, utilization of the Dynamic Profile generated by REDCRAFT allowed for the identification of different dynamical regions of the protein and the recovery of individual fragments with bb-rmsd of less than 1 Å. Following the recovery of the fragments, our assembly procedure of domains (larger segments consisting of multiple fragments with a common dynamical profile) correctly assembled the four fragments that are rigid with respect to each other, categorized the two domains that underwent rigid-body dynamics, and identified one dynamical region for which no conserved structure could be defined. In conclusion, our approach was successful in identifying the dynamical domains, recovery of structure where it is meaningful, and relative assembly of the domains when possible.

Quantification of Smoking Characteristics Using Smartwatch Technology: Pilot Feasibility Study of New Technology.

BACKGROUND: While there have been many technological advances in studying the neurobiological and clinical basis of tobacco use disorder and nicotine addiction, there have been relatively minor advances in technologies for monitoring, characterizing, and intervening to prevent smoking in real time. Better understanding of real-time smoking behavior can be helpful in numerous applications without the burden and recall bias associated with self-report. OBJECTIVE: The goal of this study was to test the validity of using a smartwatch to advance the study of temporal patterns and characteristics of smoking in a controlled laboratory setting prior to its implementation in situ. Specifically, the aim was to compare smoking characteristics recorded by Automated Smoking PerceptIon and REcording (ASPIRE) on a smartwatch with the pocket Clinical Research Support System (CReSS) topography device, using video observation as the gold standard. METHODS: Adult smokers (N=27) engaged in a video-recorded laboratory smoking task using the pocket CReSS while also wearing a Polar M600 smartwatch. In-house software, ASPIRE, was used to record accelerometer data to identify the duration of puffs and interpuff intervals (IPIs). The recorded sessions from CReSS and ASPIRE were manually annotated to assess smoking topography. Agreement between CReSS-recorded and ASPIRE-recorded smoking behavior was compared. RESULTS: ASPIRE produced more consistent number of puffs and IPI durations relative to CReSS, when comparing both methods to visual puff count. In addition, CReSS recordings reported many implausible measurements in the order of milliseconds. After filtering implausible data recorded from CReSS, ASPIRE and CReSS produced consistent results for puff duration (R2=.79) and IPIs (R2=.73). CONCLUSIONS: Agreement between ASPIRE and other indicators of smoking characteristics was high, suggesting that the use of ASPIRE is a viable method of passively characterizing smoking behavior. Moreover, ASPIRE was more accurate than CReSS for measuring puffs and IPIs. Results from this study provide the foundation for future utilization of ASPIRE to passively and accurately monitor and quantify smoking behavior in situ.

REDCRAFT: A computational platform using residual dipolar coupling NMR data for determining structures of perdeuterated proteins in solution.

Nuclear Magnetic Resonance (NMR) spectroscopy is one of the three primary experimental means of characterizing macromolecular structures, including protein structures. Structure determination by solution NMR spectroscopy has traditionally relied heavily on distance restraints derived from nuclear Overhauser effect (NOE) measurements. While structure determination of proteins from NOE-based restraints is well understood and broadly used, structure determination from Residual Dipolar Couplings (RDCs) is relatively less well developed. Here, we describe the new features of the protein structure modeling program REDCRAFT and focus on the new Adaptive Decimation (AD) feature. The AD plays a critical role in improving the robustness of REDCRAFT to missing or noisy data, while allowing structure determination of larger proteins from less data. In this report we demonstrate the successful application of REDCRAFT in structure determination of proteins ranging in size from 50 to 145 residues using experimentally collected data, and of larger proteins (145 to 573 residues) using simulated RDC data. In both cases, REDCRAFT uses only RDC data that can be collected from perdeuterated proteins. Finally, we compare the accuracy of structure determination from RDCs alone with traditional NOE-based methods for the structurally novel PF.2048.1 protein. The RDC-based structure of PF.2048.1 exhibited 1.0 Å BB-RMSD with respect to a high-quality NOE-based structure. Although optimal strategies would include using RDC data together with chemical shift, NOE, and other NMR data, these studies provide proof-of-principle for robust structure determination of largely-perdeuterated proteins from RDC data alone using REDCRAFT.

Increased usability, algorithmic improvements and incorporation of data mining for structure calculation of proteins with REDCRAFT software package.

BACKGROUND: Traditional approaches to elucidation of protein structures by Nuclear Magnetic Resonance spectroscopy (NMR) rely on distance restraints also known as Nuclear Overhauser effects (NOEs). The use of NOEs as the primary source of structure determination by NMR spectroscopy is time consuming and expensive. Residual Dipolar Couplings (RDCs) have become an alternate approach for structure calculation by NMR spectroscopy. In previous works, the software package REDCRAFT has been presented as a means of harnessing the information containing in RDCs for structure calculation of proteins. However, to meet its full potential, several improvements to REDCRAFT must be made. RESULTS: In this work, we present improvements to REDCRAFT that include increased usability, better interoperability, and a more robust core algorithm. We have demonstrated the impact of the improved core algorithm in the successful folding of the protein 1A1Z with as high as ±4 Hz of added error. The REDCRAFT computed structure from the highly corrupted data exhibited less than 1.0 Å with respect to the X-ray structure. We have also demonstrated the interoperability of REDCRAFT in a few instances including with PDBMine to reduce the amount of required data in successful folding of proteins to unprecedented levels. Here we have demonstrated the successful folding of the protein 1D3Z (to within 2.4 Å of the X-ray structure) using only N-H RDCs from one alignment medium. CONCLUSIONS: The additional GUI features of REDCRAFT combined with the NEF compliance have significantly increased the flexibility and usability of this software package. The improvements of the core algorithm have substantially improved the robustness of REDCRAFT in utilizing less experimental data both in quality and quantity.

Acute sleep deprivation during pregnancy in rats: Rapid elevation of placental and fetal inflammation and kynurenic acid.

The kynurenine pathway (KP) is the dominant pathway for tryptophan degradation in the mammalian body and emerging evidence suggests that acute episodes of sleep deprivation (SD) disrupt tryptophan metabolism via the KP. Increases in the neuroactive KP metabolite kynurenic acid (KYNA) during pregnancy may lead to a higher risk for disrupted neurodevelopment in the offspring. As pregnancy is a critical period during which several factors, including sleep disruptions, could disrupt the fetal environment, we presently explored the relationship between maternal SD and KP metabolism and immune pathways in maternal, placenta, and fetal tissues. Pregnant Wistar rat dams were sleep deprived by gentle handling for 5 h from zeitgeber time (ZT) 0 to ZT 5. Experimental cohorts included: i) controls, ii) one session of SD on embryonic day (ED) 18 or iii) three sessions of SD occurring daily on ED 16, ED 17 and ED 18. Maternal (plasma, brain), placental and fetal (plasma, brain) tissues were collected immediately after the last session of SD or after 24 h of recovery from SD. Respective controls were euthanized at ZT 5 on ED 18 or ED 19. Maternal plasma corticosterone and fetal brain KYNA were significantly elevated only after one session of SD on ED 18. Importantly, maternal plasma corticosterone levels correlated significantly with fetal brain KYNA levels. In addition, placental levels of the proinflammatory cytokines interleukin-1ß (IL-1ß) and interleukin-6 (IL-6) were increased following maternal SD, suggesting a relationship between placental immune response to SD and fetal brain KYNA accumulation. Collectively, our results demonstrate that sleep loss during the last week of gestation can adversely impact maternal stress, placental immune function, and fetal brain KYNA levels. We introduce KYNA as a novel molecular target influenced by sleep loss during pregnancy.

Protein structure prediction assisted with sparse NMR data in CASP13.

CASP13 has investigated the impact of sparse NMR data on the accuracy of protein structure prediction. NOESY and 15 N-1 H residual dipolar coupling data, typical of that obtained for 15 N,13 C-enriched, perdeuterated proteins up to about 40 kDa, were simulated for 11 CASP13 targets ranging in size from 80 to 326 residues. For several targets, two prediction groups generated models that are more accurate than those produced using baseline methods. Real NMR data collected for a de novo designed protein were also provided to predictors, including one data set in which only backbone resonance assignments were available. Some NMR-assisted prediction groups also did very well with these data. CASP13 also assessed whether incorporation of sparse NMR data improves the accuracy of protein structure prediction relative to nonassisted regular methods. In most cases, incorporation of sparse, noisy NMR data results in models with higher accuracy. The best NMR-assisted models were also compared with the best regular predictions of any CASP13 group for the same target. For six of 13 targets, the most accurate model provided by any NMR-assisted prediction group was more accurate than the most accurate model provided by any regular prediction group; however, for the remaining seven targets, one or more regular prediction method provided a more accurate model than even the best NMR-assisted model. These results suggest a novel approach for protein structure determination, in which advanced prediction methods are first used to generate structural models, and sparse NMR data is then used to validate and/or refine these models.

Comparing an on-site nurse practitioner with telemedicine physician support hospitalist programme with a traditional physician hospitalist programme.

INTRODUCTION: Since 2010, more than 75 rural hospitals have closed in the USA and more than one-third are at risk of closure due to lower patient volumes, lower funding levels, decreased hospital revenue and lower physician employment pools. Telemedicine can provide new models of care delivery that maintain quality and reduce cost of healthcare in rural populations. The purpose of this project was to evaluate a cross-organizational pilot program by comparing a NP/telemedicine physician hospitalist programme with a traditional physician hospitalist model to assess effects on length of patient stay, mortality rates, readmission rate, Hospital Consumer Assessment of Healthcare Providers and Systems (HCAHPS) ratings of provider communication, and total hospital costs. METHODS: The Standard for Quality Improvement Reporting Excellence (SQUIRE) guidelines were followed. Using a one-year retrospective chart review, average length of stay, mortality rates, 30-day readmission rates and provider communication ratings were compared between hospitalists that were nurse practitioners working with physicians through telemedicine support and physicians alone. RESULTS: There was no statistically significant variance in average length of stay, mortality rates, 30-day readmission rates, or provider communication ratings on HCAHPS surveys compared to the NP or physician hospitalist. DISCUSSION: This new model of care demonstrates that telemedicine can be used to provide safe and efficient physician support from a regional hub medical centre to nurse practitioners practising as hospitalists in rural Critical Access Hospitals at up to 58% cost savings while maintaining quality of care and increasing access to community-based physicians.

Detecting Smoking Events Using Accelerometer Data Collected Via Smartwatch Technology: Validation Study.

BACKGROUND: Smoking is the leading cause of preventable death in the world today. Ecological research on smoking in context currently relies on self-reported smoking behavior. Emerging smartwatch technology may more objectively measure smoking behavior by automatically detecting smoking sessions using robust machine learning models. OBJECTIVE: This study aimed to examine the feasibility of detecting smoking behavior using smartwatches. The second aim of this study was to compare the success of observing smoking behavior with smartwatches to that of conventional self-reporting. METHODS: A convenience sample of smokers was recruited for this study. Participants (N=10) recorded 12 hours of accelerometer data using a mobile phone and smartwatch. During these 12 hours, they engaged in various daily activities, including smoking, for which they logged the beginning and end of each smoking session. Raw data were classified as either smoking or nonsmoking using a machine learning model for pattern recognition. The accuracy of the model was evaluated by comparing the output with a detailed description of a modeled smoking session. RESULTS: In total, 120 hours of data were collected from participants and analyzed. The accuracy of self-reported smoking was approximately 78% (96/123). Our model was successful in detecting 100 of 123 (81%) smoking sessions recorded by participants. After eliminating sessions from the participants that did not adhere to study protocols, the true positive detection rate of the smartwatch based-detection increased to more than 90%. During the 120 hours of combined observation time, only 22 false positive smoking sessions were detected resulting in a 2.8% false positive rate. CONCLUSIONS: Smartwatch technology can provide an accurate, nonintrusive means of monitoring smoking behavior in natural contexts. The use of machine learning algorithms for passively detecting smoking sessions may enrich ecological momentary assessment protocols and cessation intervention studies that often rely on self-reported behaviors and may not allow for targeted data collection and communications around smoking events.

Structure Calculation and Reconstruction of Discrete-State Dynamics from Residual Dipolar Couplings.

Residual dipolar couplings (RDCs) acquired by nuclear magnetic resonance (NMR) spectroscopy are an indispensable source of information in investigation of molecular structures and dynamics. Here, we present a comprehensive strategy for structure calculation and reconstruction of discrete-state dynamics from RDC data that is based on the singular value decomposition (SVD) method of order tensor estimation. In addition to structure determination, we provide a mechanism of producing an ensemble of conformations for the dynamical regions of a protein from RDC data. The developed methodology has been tested on simulated RDC data with ±1 Hz of error from an 83 residue α protein (PDB ID 1A1Z ) and a 213 residue α/ß protein DGCR8 (PDB ID 2YT4 ). In nearly all instances, our method reproduced the structure of the protein including the conformational ensemble to within less than 2 Å. On the basis of our investigations, arc motions with more than 30° of rotation are identified as internal dynamics and are reconstructed with sufficient accuracy. Furthermore, states with relative occupancies above 20% are consistently recognized and reconstructed successfully. Arc motions with a magnitude of 15° or relative occupancy of less than 10% are consistently unrecognizable as dynamical regions within the context of ±1 Hz of error.

A reinvestigation of seroconversion rates in hepatits B-vaccinated individuals.

The purpose of this study was to examine whether the hepatitis B seroconversion rates in a small sample of subjects reflected that of the published rates, regardless of HIV status, after a hepatitis B vaccination series was completed while following strict vaccine protocols. The study, which had a nonexperimental, correlational cross-sectional design, took place at a community clinic in Laguna Beach, CA. Participants comprised 100, predominantly White, men aged 18-65 years (mean 39.2 years) who were hepatitis B naive, regardless of immune status. After receiving a primary vaccine series for hepatitis B, participants were tested for seroconversion to immune status. Seroconversion to the immune state occurred in 78.6% of the non-immunocompromised, HIV-negative participants (n = 50), who had received the standard three-dose regimen of the vaccine, and in 77% of the immunocompromised, HIV-positive participants (n = 48), who had received the recommended modified three-double-dose regimen. The manufacturer-published rates of seroconversion are 90-100%, depending upon the population. These findings highlight a need for further study to validate or reveal deficits in current vaccine protocols for individuals who are vaccinated against hepatitis B, including health care workers, the immune-compromised and other high-risk populations.

Improvements to REDCRAFT: a software tool for simultaneous characterization of protein backbone structure and dynamics from residual dipolar couplings.

Within the past two decades, there has been an increase in the acquisition of residual dipolar couplings (RDC) for investigations of biomolecular structures. Their use however is still not as widely adopted as the traditional methods of structure determination by NMR, despite their potential for extending the limits in studies that examine both the structure and dynamics of biomolecules. This is in part due to the difficulties associated with the analysis of this information-rich data type. The software analysis tool REDCRAFT was previously introduced to address some of these challenges. Here we describe and evaluate a number of additional features that have been incorporated in order to extend its computational and analytical capabilities. REDCRAFT's more traditional enhancements integrate a modified steric collision term, as well as structural refinement in the rotamer space. Other, non-traditional improvements include: the filtering of viable structures based on relative order tensor estimates, decimation of the conformational space based on structural similarity, and forward/reverse folding of proteins. Utilizing REDCRAFT's newest features we demonstrate de-novo folding of proteins 1D3Z and 1P7E to within less than 1.6 Å of the corresponding X-ray structures, using as many as four RDCs per residue and as little as two RDCs per residue, in two alignment media. We also show the successful folding of a structure to less than 1.6 Å of the X-ray structure using {C(i-1)-N(i), N(i)-H(i), and C(i-1)-H(i)} RDCs in one alignment medium, and only {N(i)-H(i)} in the second alignment medium (a set of data which can be collected on deuterated samples). The program is available for download from our website at http://ifestos.cse.sc.edu .