Three-dimensional structures of membrane proteins from genomic sequencing.

We show that amino acid covariation in proteins, extracted from the evolutionary sequence record, can be used to fold transmembrane proteins. We use this technique to predict previously unknown 3D structures for 11 transmembrane proteins (with up to 14 helices) from their sequences alone. The prediction method (EVfold_membrane) applies a maximum entropy approach to infer evolutionary covariation in pairs of sequence positions within a protein family and then generates all-atom models with the derived pairwise distance constraints. We benchmark the approach with blinded de novo computation of known transmembrane protein structures from 23 families, demonstrating unprecedented accuracy of the method for large transmembrane proteins. We show how the method can predict oligomerization, functional sites, and conformational changes in transmembrane proteins. With the rapid rise in large-scale sequencing, more accurate and more comprehensive information on evolutionary constraints can be decoded from genetic variation, greatly expanding the repertoire of transmembrane proteins amenable to modeling by this method.

A role for neuronal piRNAs in the epigenetic control of memory-related synaptic plasticity.

Small RNA-mediated gene regulation during development causes long-lasting changes in cellular phenotypes. To determine whether small RNAs of the adult brain can regulate memory storage, a process that requires stable and long-lasting changes in the functional state of neurons, we generated small RNA libraries from the Aplysia CNS. In these libraries, we discovered an unexpectedly abundant expression of a 28 nucleotide sized class of piRNAs in brain, which had been thought to be germline specific. These piRNAs have unique biogenesis patterns, predominant nuclear localization, and robust sensitivity to serotonin, a modulatory transmitter that is important for memory. We find that the Piwi/piRNA complex facilitates serotonin-dependent methylation of a conserved CpG island in the promoter of CREB2, the major inhibitory constraint of memory in Aplysia, leading to enhanced long-term synaptic facilitation. These findings provide a small RNA-mediated gene regulatory mechanism for establishing stable long-term changes in neurons for the persistence of memory.

The use of TASER devices in UK policing: an update for clinicians following the recent introduction of the TASER 7.

Taser was introduced into UK policing in 2003 to bridge the operational gap between use of incapacitant sprays and firearms. Use of force reporting in the UK indicates that Taser is relatively safe provided that it is used lawfully. Taser use can result in injuries and has been implicated in a small number of deaths. The latest version of the weapon, the TASER 7, has entered UK policing. The TASER 7 uses a novel probe that has implications for the medical community. A review of Taser medical effects and probe removal for TASER 7 are presented.

Stability of Prediction in Production ADMET Models as a Function of Version: Why and When Predictions Change.

As with other pharma companies, we maintain production QSAR models of ADMET end points and update them regularly. Here, for six ADMET end points, we examine the predictions of test set molecules on multiple versions of random forest models spanning a period of 10 years. For any given end point, the predictions for the majority of molecules are similar for all model versions. However, for a small minority of molecules, the prediction shifts substantially over the span of a few versions. For most molecules that shift, the prediction becomes more accurate at later times. This Perspective investigates metrics that can help indicate which molecules will shift substantially in prediction and when the shift will occur.

Prediction Accuracy of Production ADMET Models as a Function of Version: Activity Cliffs Rule.

As with many other institutions, our company maintains many quantitative structure-activity relationship (QSAR) models of absorption, distribution, metabolism, excretion, and toxicity (ADMET) end points and updates the models regularly. We recently examined version-to-version predictivity for these models over a period of 10 years. In this approach we monitor the goodness of prediction of new molecules relative to the training set of model version V before they are incorporated in the updated model V+1. Using a cell-based permeability assay (Papp) as an example, we illustrate how the QSAR models made from this data are generally predictive and can be utilized to enrich chemical designs and synthesis. Despite the obvious utility of these models, we turned up unexpected behavior in Papp and other ADMET activities for which the explanation is not obvious. One such behavior is that the apparent predictivity of the models as measured by root-mean-square-error can vary greatly from version to version and is sometimes very poor. One intuitively appealing explanation is that the observed activities of the new molecules fall outside the bulk of activities in the training set. Alternatively, one may think that the new molecules are exploring different regions of chemical space than the training set. However, the real explanation has to do with activity cliffs. If the observed activities of the new molecules are different than expected based on similar molecules in the training set, the predictions will be less accurate. This is true for all our ADMET end points.

Factors Associated With Mechanical Ventilation Duration in Pediatric Burn Patients in a Regional Burn Center in the United States.

OBJECTIVES: Among burned children who arrive at a burn center and require invasive mechanical ventilation (IMV), some may have prolonged IMV needs. This has implications for patient-centered outcomes as well as triage and resource allocation decisions. Our objective was to identify factors associated with the duration of mechanical ventilation in pediatric patients with acute burn injury in this setting. DESIGN: Single-center, retrospective cohort study. SETTING: Registry data from a regional, pediatric burn center in the United States. PATIENTS: Children less than or equal to 18 years old admitted with acute burn injury who received IMV between January 2005 and December 2020. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Ventilator days were defined as any full or partial day having received IMV via an endotracheal tube or tracheostomy, not inclusive of time spent ventilated for procedures. Of 5,766 admissions for acute burn care, 4.3% ( n = 249) required IMV with a median duration of 10 days. A multivariable model for freedom from mechanical ventilation showed that the presence of inhalational injury (subhazard ratio [sHR], 0.62; 95% CI, 0.46-0.85) and burns to the head and neck region (sHR, 0.94; 95% CI, 0.90-0.98) were associated with increased risk of remaining mechanically ventilated at any time point. Older (sHR, 1.03; 95% CI, 1.01-1.04) and male children (sHR, 1.39; 95% CI, 1.05-1.84) were more likely to discontinue mechanical ventilation. A majority of children (94.8%) survived to hospital discharge. CONCLUSIONS: The presence of inhalational injury and burns to the head and neck region were associated with a longer duration of mechanical ventilation. Older age and male gender were associated with a shorter duration of mechanical ventilation. These factors should help clinicians better estimate a burned child's expected trajectory and resource-intensive needs upon arrival to a burn center.

Health professionals' perceptions of maggot debridement therapy.

BACKGROUND: Maggot debridement therapy (MDT), or the use of maggots in dead tissue removal, has been shown to be beneficial in wound healing. Yet MDT in the US is often only used once conventional debridement methods have failed. METHOD: In this study, nine health professionals, experienced in MDT, were interviewed in order to identify and analyse the perceived societal barriers to MDT acceptance and usage in the US. RESULTS: Through qualitative analysis, using the grounded theory framework, this study found that among those interviewed, insurance reimbursement restrictions and stigmatisation of medicinal maggots were the factors driving resistance to MDT use. CONCLUSION: Specifically, the 'yuck' factor and the perception of MDT as an 'ancient' modality contributed towards MDT stigma; in addition, lack of outpatient insurance coverage deterred MDT use. These findings provide useful information regarding the perceptual and systemic barriers that prevent greater acceptance of MDT. Ultimately, these barriers must be understood if we are to facilitate MDT implementation and improve MDT usage in the future.

QSAR without borders.

Prediction of chemical bioactivity and physical properties has been one of the most important applications of statistical and more recently, machine learning and artificial intelligence methods in chemical sciences. This field of research, broadly known as quantitative structure-activity relationships (QSAR) modeling, has developed many important algorithms and has found a broad range of applications in physical organic and medicinal chemistry in the past 55+ years. This Perspective summarizes recent technological advances in QSAR modeling but it also highlights the applicability of algorithms, modeling methods, and validation practices developed in QSAR to a wide range of research areas outside of traditional QSAR boundaries including synthesis planning, nanotechnology, materials science, biomaterials, and clinical informatics. As modern research methods generate rapidly increasing amounts of data, the knowledge of robust data-driven modelling methods professed within the QSAR field can become essential for scientists working both within and outside of chemical research. We hope that this contribution highlighting the generalizable components of QSAR modeling will serve to address this challenge.

Correction: QSAR without borders.

Correction for 'QSAR without borders' by Eugene N. Muratov et al., Chem. Soc. Rev., 2020, DOI: 10.1039/d0cs00098a.

The EVcouplings Python framework for coevolutionary sequence analysis.

SUMMARY: Coevolutionary sequence analysis has become a commonly used technique for de novo prediction of the structure and function of proteins, RNA, and protein complexes. We present the EVcouplings framework, a fully integrated open-source application and Python package for coevolutionary analysis. The framework enables generation of sequence alignments, calculation and evaluation of evolutionary couplings (ECs), and de novo prediction of structure and mutation effects. The combination of an easy to use, flexible command line interface and an underlying modular Python package makes the full power of coevolutionary analyses available to entry-level and advanced users. AVAILABILITY AND IMPLEMENTATION: https://github.com/debbiemarkslab/evcouplings.

Nearest Neighbor Gaussian Process for Quantitative Structure-Activity Relationships.

While Gaussian process models are typically restricted to smaller data sets, we propose a variation which extends its applicability to the larger data sets common in the industrial drug discovery space, making it relatively novel in the quantitative structure-activity relationship (QSAR) field. By incorporating locality-sensitive hashing for fast nearest neighbor searches, the nearest neighbor Gaussian process model makes predictions with time complexity that is sub-linear with the sample size. The model can be efficiently built, permitting rapid updates to prevent degradation as new data is collected. Given its small number of hyperparameters, it is robust against overfitting and generalizes about as well as other common QSAR models. Like the usual Gaussian process model, it natively produces principled and well-calibrated uncertainty estimates on its predictions. We compare this new model with implementations of random forest, light gradient boosting, and k-nearest neighbors to highlight these promising advantages. The code for the nearest neighbor Gaussian process is available at https://github.com/Merck/nngp.

Experimental Error, Kurtosis, Activity Cliffs, and Methodology: What Limits the Predictivity of Quantitative Structure-Activity Relationship Models?

Given a particular descriptor/method combination, some quantitative structure-activity relationship (QSAR) datasets are very predictive by random-split cross-validation while others are not. Recent literature in modelability suggests that the limiting issue for predictivity is in the data, not the QSAR methodology, and the limits are due to activity cliffs. Here, we investigate, on in-house data, the relative usefulness of experimental error, distribution of the activities, and activity cliff metrics in determining how predictive a dataset is likely to be. We include unmodified in-house datasets, datasets that should be perfectly predictive based only on the chemical structure, datasets where the distribution of activities is manipulated, and datasets that include a known amount of added noise. We find that activity cliff metrics determine predictivity better than the other metrics we investigated, whatever the type of dataset, consistent with the modelability literature. However, such metrics cannot distinguish real activity cliffs due to large uncertainties in the activities. We also show that a number of modern QSAR methods, and some alternative descriptors, are equally bad at predicting the activities of compounds on activity cliffs, consistent with the assumptions behind "modelability." Finally, we relate time-split predictivity with random-split predictivity and show that different coverages of chemical space are at least as important as uncertainty in activity and/or activity cliffs in limiting predictivity.

Deep Dive into Machine Learning Models for Protein Engineering.

Protein redesign and engineering has become an important task in pharmaceutical research and development. Recent advances in technology have enabled efficient protein redesign by mimicking natural evolutionary mutation, selection, and amplification steps in the laboratory environment. For any given protein, the number of possible mutations is astronomical. It is impractical to synthesize all sequences or even to investigate all functionally interesting variants. Recently, there has been an increased interest in using machine learning to assist protein redesign, since prediction models can be used to virtually screen a large number of novel sequences. However, many state-of-the-art machine learning models, especially deep learning models, have not been extensively explored. Moreover, only a small selection of protein sequence descriptors has been considered. In this work, the performance of prediction models built using an array of machine learning methods and protein descriptor types, including two novel, single amino acid descriptors and one structure-based three-dimensional descriptor, is benchmarked. The predictions were evaluated on a diverse collection of public and proprietary data sets, using a variety of evaluation metrics. The results of this comparison suggest that Convolution Neural Network models built with amino acid property descriptors are the most widely applicable to the types of protein redesign problems faced in the pharmaceutical industry.

Antegrade Foreheadplasty: A Novel Surgical Approach for Upper Eyelid Contracture Deformities in Pediatric Patients.

The intricate and delicate structure of the periorbital region, particularly in pediatric patients, presents challenges to eyelid reconstruction. Much like the more common lower eyelid ectropion, upper eyelid ectropion can result from lack of tissue, scar contracture, or over-resection as in blepharoplasty. In burns and trauma, the cause of cicatricial ectropion is typically direct scar contracture from injuries to the eyelid. However, in some cases, extrinsic wounds involving contracture to the forehead or eyebrow can result in upper eyelid cicatricial ectropion. Direct reconstruction and skin grafting of the eyelid present complex challenges, especially in the acute inflammatory phase of traumatic injury and burn care. Furthermore, in many of these cases the periorbital and lamellae anatomy is preserved, but rather severely displaced due to scar contracture forces. The authors discuss our experience with treatment of extrinsic upper eyelid cicatricial ectropion in a series of 4 pediatric patients with burns or trauma to the forehead and periorbital regions. In all 4 cases, the antegrade foreheadplasty procedure helped to provide globe coverage, while avoiding skin matching difficulties and the intrinsic risks of operating on the eyelid during the acute phase of recovery. There is currently very limited data for the use of this technique to correct such defects. With this study, the authors hope to establish the antegrade foreheadplasty as a reconstructive option for a select patient population.

Radiation Dose Monitoring for Fluoroscopically Guided Interventional Procedures: Effect on Patient Radiation Exposure.

Purpose To analyze the clinical effect of continuous dose monitoring and patient follow-up for fluoroscopically guided vascular interventional procedures over 8 years. Materials and Methods In this retrospective study, an in-house semiautomated system was developed for fluoroscopic dose monitoring. The quarterly number of procedures from January 2010 to December 2017 was analyzed with count time series to estimate quarterly change rate. Technologists recorded four dose surrogates in custom fields of institutional dictation software through a Web interface. Radiation doses were transferred automatically to the radiology report and a centralized dose database when the radiologist initiated procedure dictation. A medical physicist reported weekly on procedures with air kerma at the reference point (Ka,r) of 2 Gy or higher to a division-designated radiologist and hospital radiation safety committee who required the attending radiologist to set up follow-up appointments for patients who underwent procedures with a Ka,r greater than or equal to 5 Gy. Results There were a total of 41 585 procedures; 1553 (3.7%) procedures had a Ka,r of 2-5 Gy. Among 240 procedures with Ka,r greater than 5 Gy, 22 had Ka,r greater than 9 Gy. The percentage of high Ka,r procedures decreased over time, going from 5.9% in 2010 to 2.0% in 2017 for procedures with Ka,r of 2-5 Gy and from 1.0% in 2010 to 0.13% in 2017 for procedures with Ka,r greater than or equal to 5 Gy. Relative reduction per quarter was approximately 2.7% (95% confidence interval: 1.5%, 3.8%) for Ka,r of 2-5 Gy and 4.5% (95% confidence interval: 1.5%, 7.6%) for Ka,r greater than or equal to 5 Gy. Conclusion Eight-year temporal trends show three- to eightfold reduction in the number of high-dose procedures. © RSNA, 2019 Online supplemental material is available for this article. See also the editorial by Balter in this issue.

G2S: a web-service for annotating genomic variants on 3D protein structures.

Motivation: Accurately mapping and annotating genomic locations on 3D protein structures is a key step in structure-based analysis of genomic variants detected by recent large-scale sequencing efforts. There are several mapping resources currently available, but none of them provides a web API (Application Programming Interface) that supports programmatic access. Results: We present G2S, a real-time web API that provides automated mapping of genomic variants on 3D protein structures. G2S can align genomic locations of variants, protein locations, or protein sequences to protein structures and retrieve the mapped residues from structures. G2S API uses REST-inspired design and it can be used by various clients such as web browsers, command terminals, programming languages and other bioinformatics tools for bringing 3D structures into genomic variant analysis. Availability and implementation: The webserver and source codes are freely available at https://g2s.genomenexus.org. Contact: g2s@genomenexus.org. Supplementary information: Supplementary data are available at Bioinformatics online.

Interpretation of QSAR Models by Coloring Atoms According to Changes in Predicted Activity: How Robust Is It?

Most chemists would agree that the ability to interpret a quantitative structure-activity relationship (QSAR) model is as important as the ability of the model to make accurate predictions. One type of interpretation is coloration of atoms in molecules according to the contribution of each atom to the predicted activity, as in "heat maps". The ability to determine which parts of a molecule increase the activity in question and which decrease it should be useful to chemists who want to modify the molecule. For that type of application, we would hope the coloration to not be particularly sensitive to the details of model building. In this Article, we examine a number of aspects of coloration against 20 combinations of descriptors and QSAR methods. We demonstrate that atom-level coloration is much less robust to descriptor/method combinations than cross-validated predictions. Even in ideal cases where the contribution of individual atoms is known, we cannot always recover the important atoms for some descriptor/method combinations. Thus, model interpretation by atom coloration may not be as simple as it first appeared.

Building Quantitative Structure-Activity Relationship Models Using Bayesian Additive Regression Trees.

Quantitative structure-activity relationship (QSAR) is a very commonly used technique for predicting the biological activity of a molecule using information contained in the molecular descriptors. The large number of compounds and descriptors and the sparseness of descriptors pose important challenges to traditional statistical methods and machine learning (ML) algorithms (such as random forest (RF)) used in this field. Recently, Bayesian Additive Regression Trees (BART), a flexible Bayesian nonparametric regression approach, has been demonstrated to be competitive with widely used ML approaches. Instead of only focusing on accurate point estimation, BART is formulated entirely in a hierarchical Bayesian modeling framework, allowing one to also quantify uncertainties and hence to provide both point and interval estimation for a variety of quantities of interest. We studied BART as a model builder for QSAR and demonstrated that the approach tends to have predictive performance comparable to RF. More importantly, we investigated BART's natural capability to analyze truncated (or qualified) data, generate interval estimates for molecular activities as well as descriptor importance, and conduct model diagnosis, which could not be easily handled through other approaches.