How researchers calculate students' grade point average in other courses has minimal impact.

Grade point average in "other" courses (GPAO) is an increasingly common measure used to control for prior academic performance and to predict future academic performance. In previous work, there are two distinct approaches to calculating GPAO, one based on only courses taken concurrently (term GPAO) and one based on all previous courses taken (cumulative GPAO). To our knowledge, no one has studied whether these methods for calculating the GPAO result in equivalent analyses and conclusions. As researchers often use one definition or the other without comment on why that choice was made, if the two calculations of GPAO are different, researchers might be inducing systematic error into their results and publishing potentially inaccurate conclusions. We looked at more than 3,700 courses at a public, research-intensive university over a decade and found limited evidence that the choice of GPAO calculation affects the conclusions. At most, one in seven courses could be affected. Further analysis suggests that there may be situations where one form of GPAO may be preferred over the other when it comes to examining inequity in courses or predicting student grades. However, we did not find sufficient evidence to universally recommend one form of GPAO over the other.

ABC transporters: human disease and pharmacotherapeutic potential.

Adenosine triphosphate (ATP)-binding cassette (ABC) transporters are a 48-member superfamily of membrane proteins that actively transport a variety of biological substrates across lipid membranes. Their functional diversity defines an expansive involvement in myriad aspects of human biology. At least 21 ABC transporters underlie rare monogenic disorders, with even more implicated in the predisposition to and symptomology of common and complex diseases. Such broad (patho)physiological relevance places this class of proteins at the intersection of disease causation and therapeutic potential, underlining them as promising targets for drug discovery, as exemplified by the transformative CFTR (ABCC7) modulator therapies for cystic fibrosis. This review will explore the growing relevance of ABC transporters to human disease and their potential as small-molecule drug targets.

I-TASSER-MTD: a deep-learning-based platform for multi-domain protein structure and function prediction.

Most proteins in cells are composed of multiple folding units (or domains) to perform complex functions in a cooperative manner. Relative to the rapid progress in single-domain structure prediction, there are few effective tools available for multi-domain protein structure assembly, mainly due to the complexity of modeling multi-domain proteins, which involves higher degrees of freedom in domain-orientation space and various levels of continuous and discontinuous domain assembly and linker refinement. To meet the challenge and the high demand of the community, we developed I-TASSER-MTD to model the structures and functions of multi-domain proteins through a progressive protocol that combines sequence-based domain parsing, single-domain structure folding, inter-domain structure assembly and structure-based function annotation in a fully automated pipeline. Advanced deep-learning models have been incorporated into each of the steps to enhance both the domain modeling and inter-domain assembly accuracy. The protocol allows for the incorporation of experimental cross-linking data and cryo-electron microscopy density maps to guide the multi-domain structure assembly simulations. I-TASSER-MTD is built on I-TASSER but substantially extends its ability and accuracy in modeling large multi-domain protein structures and provides meaningful functional insights for the targets at both the domain- and full-chain levels from the amino acid sequence alone.

A System for Standardizing and Combining U.S. Environmental Protection Agency Emissions and Waste Inventory Data.

The U.S. Environmental Protection Agency (USEPA) provides databases that agglomerate data provided by companies or states reporting emissions, releases, wastes generated, and other activities to meet statutory requirements. These databases, often referred to as inventories, can be used for a wide variety of environmental reporting and modeling purposes to characterize conditions in the United States. Yet, users are often challenged to find, retrieve, and interpret these data due to the unique schemes employed for data management, which could result in erroneous estimations or double-counting of emissions. To address these challenges, a system called Standardized Emission and Waste Inventories (StEWI) has been created. The system consists of four python modules that provide rapid access to USEPA inventory data in standard formats and permit filtering and combination of these inventory data. When accessed through StEWI, reported emissions of carbon dioxide to air and ammonia to water are reduced approximately two- and four-fold, respectively, to avoid duplicate reporting. StEWI will greatly facilitate the use of USEPA inventory data in chemical release and exposure modeling and life cycle assessment tools, among other things. To date, StEWI has been used to build the recent USEEIO model and the baseline electricity life cycle inventory database for the Federal LCA Commons.

PEPPI: Whole-proteome Protein-protein Interaction Prediction through Structure and Sequence Similarity, Functional Association, and Machine Learning.

Proteome-wide identification of protein-protein interactions is a formidable task which has yet to be sufficiently addressed by experimental methodologies. Many computational methods have been developed to predict proteome-wide interaction networks, but few leverage both the sensitivity of structural information and the wide availability of sequence data. We present PEPPI, a pipeline which integrates structural similarity, sequence similarity, functional association data, and machine learning-based classification through a naïve Bayesian classifier model to accurately predict protein-protein interactions at a proteomic scale. Through benchmarking against a set of 798 ground truth interactions and an equal number of non-interactions, we have found that PEPPI attains 4.5% higher AUROC than the best of other state-of-the-art methods. As a proteomic-scale application, PEPPI was applied to model the interactions which occur between SARS-CoV-2 and human host cells during coronavirus infection, where 403 high-confidence interactions were identified with predictions covering 73% of a gold standard dataset from PSICQUIC and demonstrating significant complementarity with the most recent high-throughput experiments. PEPPI is available both as a webserver and in a standalone version and should be a powerful and generally applicable tool for computational screening of protein-protein interactions.

Experimental evaluation of aerosol mitigation strategies in large open-plan dental clinics.

BACKGROUND: Aerosols are generated routinely during patient care in dentistry. Managing exposure risk requires understanding characteristics of aerosols created during procedures such as those performed using high-speed drills that operate at 200,000 revolutions per minute. METHODS: A trained dentist performed drilling procedures on a manikin's incisors (teeth nos. 8 and 9) using a high-speed drill and high-volume evacuator. The authors used high-speed imaging to visualize the formation and transport of aerosol clouds and particle sampling to measure aerosol concentration and size distribution at several locations. The authors studied several aerosol mitigation strategies. RESULTS: Aerosols produced during high-speed drilling were erratic and yielded high concentrations that were at least an order of magnitude above baseline. High-speed imaging showed aerosols initially travelled at 1 m per second. Owing to erratic behavior of aerosols, supplemental suction was not effective at collecting all aerosols; however, barriers were effective. CONCLUSIONS: Barriers are the most effective mitigation strategy. Other methods studied have limitations and risks. To the authors' knowledge, this article presents the first characterization of aerosols generated during high-speed drilling by a dentist. PRACTICAL IMPLICATIONS: With thorough preoperative planning and the use of this investigation's findings about effectiveness of mitigation strategies as a guide, dental offices may be able to return to prepandemic productivity.

Protein inter-residue contact and distance prediction by coupling complementary coevolution features with deep residual networks in CASP14.

This article reports and analyzes the results of protein contact and distance prediction by our methods in the 14th Critical Assessment of techniques for protein Structure Prediction (CASP14). A new deep learning-based contact/distance predictor was employed based on the ensemble of two complementary coevolution features coupling with deep residual networks. We also improved our multiple sequence alignment (MSA) generation protocol with wholesale meta-genome sequence databases. On 22 CASP14 free modeling (FM) targets, the proposed model achieved a top-L/5 long-range precision of 63.8% and a mean distance bin error of 1.494. Based on the predicted distance potentials, 11 out of 22 FM targets and all of the 14 FM/template-based modeling (TBM) targets have correctly predicted folds (TM-score >0.5), suggesting that our approach can provide reliable distance potentials for ab initio protein folding.

Folding non-homologous proteins by coupling deep-learning contact maps with I-TASSER assembly simulations.

Structure prediction for proteins lacking homologous templates in the Protein Data Bank (PDB) remains a significant unsolved problem. We developed a protocol, C-I-TASSER, to integrate interresidue contact maps from deep neural-network learning with the cutting-edge I-TASSER fragment assembly simulations. Large-scale benchmark tests showed that C-I-TASSER can fold more than twice the number of non-homologous proteins than the I-TASSER, which does not use contacts. When applied to a folding experiment on 8,266 unsolved Pfam families, C-I-TASSER successfully folded 4,162 domain families, including 504 folds that are not found in the PDB. Furthermore, it created correct folds for 85% of proteins in the SARS-CoV-2 genome, despite the quick mutation rate of the virus and sparse sequence profiles. The results demonstrated the critical importance of coupling whole-genome and metagenome-based evolutionary information with optimal structure assembly simulations for solving the problem of non-homologous protein structure prediction.

Protein structure prediction using deep learning distance and hydrogen-bonding restraints in CASP14.

In this article, we report 3D structure prediction results by two of our best server groups ("Zhang-Server" and "QUARK") in CASP14. These two servers were built based on the D-I-TASSER and D-QUARK algorithms, which integrated four newly developed components into the classical protein folding pipelines, I-TASSER and QUARK, respectively. The new components include: (a) a new multiple sequence alignment (MSA) collection tool, DeepMSA2, which is extended from the DeepMSA program; (b) a contact-based domain boundary prediction algorithm, FUpred, to detect protein domain boundaries; (c) a residual convolutional neural network-based method, DeepPotential, to predict multiple spatial restraints by co-evolutionary features derived from the MSA; and (d) optimized spatial restraint energy potentials to guide the structure assembly simulations. For 37 FM targets, the average TM-scores of the first models produced by D-I-TASSER and D-QUARK were 96% and 112% higher than those constructed by I-TASSER and QUARK, respectively. The data analysis indicates noticeable improvements produced by each of the four new components, especially for the newly added spatial restraints from DeepPotential and the well-tuned force field that combines spatial restraints, threading templates, and generic knowledge-based potentials. However, challenges still exist in the current pipelines. These include difficulties in modeling multi-domain proteins due to low accuracy in inter-domain distance prediction and modeling protein domains from oligomer complexes, as the co-evolutionary analysis cannot distinguish inter-chain and intra-chain distances. Specifically tuning the deep learning-based predictors for multi-domain targets and protein complexes may be helpful to address these issues.

Deducing high-accuracy protein contact-maps from a triplet of coevolutionary matrices through deep residual convolutional networks.

The topology of protein folds can be specified by the inter-residue contact-maps and accurate contact-map prediction can help ab initio structure folding. We developed TripletRes to deduce protein contact-maps from discretized distance profiles by end-to-end training of deep residual neural-networks. Compared to previous approaches, the major advantage of TripletRes is in its ability to learn and directly fuse a triplet of coevolutionary matrices extracted from the whole-genome and metagenome databases and therefore minimize the information loss during the course of contact model training. TripletRes was tested on a large set of 245 non-homologous proteins from CASP 11&12 and CAMEO experiments and outperformed other top methods from CASP12 by at least 58.4% for the CASP 11&12 targets and 44.4% for the CAMEO targets in the top-L long-range contact precision. On the 31 FM targets from the latest CASP13 challenge, TripletRes achieved the highest precision (71.6%) for the top-L/5 long-range contact predictions. It was also shown that a simple re-training of the TripletRes model with more proteins can lead to further improvement with precisions comparable to state-of-the-art methods developed after CASP13. These results demonstrate a novel efficient approach to extend the power of deep convolutional networks for high-accuracy medium- and long-range protein contact-map predictions starting from primary sequences, which are critical for constructing 3D structure of proteins that lack homologous templates in the PDB library.

Protein structure and sequence re-analysis of 2019-nCoV genome does not indicate snakes as its intermediate host or the unique similarity between its spike protein insertions and HIV-1.

As the infection of 2019-nCoV coronavirus is quickly developing into a global pneumonia epidemic, careful analysis of its transmission and cellular mechanisms is sorely needed. In this report, we re-analyzed the computational approaches and findings presented in two recent manuscripts by Ji et al. (https://doi.org/10.1002/jmv.25682) and by Pradhan et al. (https://doi.org/10.1101/2020.01.30.927871), which concluded that snakes are the intermediate hosts of 2019-nCoV and that the 2019-nCoV spike protein insertions shared a unique similarity to HIV-1. Results from our re-implementation of the analyses, built on larger-scale datasets using state-of-the-art bioinformatics methods and databases, do not support the conclusions proposed by these manuscripts. Based on our analyses and existing data of coronaviruses, we concluded that the intermediate hosts of 2019-nCoV are more likely to be mammals and birds than snakes, and that the "novel insertions" observed in the spike protein are naturally evolved from bat coronaviruses.

Protein Structure and Sequence Reanalysis of 2019-nCoV Genome Refutes Snakes as Its Intermediate Host and the Unique Similarity between Its Spike Protein Insertions and HIV-1.

As the infection of 2019-nCoV coronavirus is quickly developing into a global pneumonia epidemic, the careful analysis of its transmission and cellular mechanisms is sorely needed. In this Communication, we first analyzed two recent studies that concluded that snakes are the intermediate hosts of 2019-nCoV and that the 2019-nCoV spike protein insertions share a unique similarity to HIV-1. However, the reimplementation of the analyses, built on larger scale data sets using state-of-the-art bioinformatics methods and databases, presents clear evidence that rebuts these conclusions. Next, using metagenomic samples from Manis javanica, we assembled a draft genome of the 2019-nCoV-like coronavirus, which shows 73% coverage and 91% sequence identity to the 2019-nCoV genome. In particular, the alignments of the spike surface glycoprotein receptor binding domain revealed four times more variations in the bat coronavirus RaTG13 than in the Manis coronavirus compared with 2019-nCoV, suggesting the pangolin as a missing link in the transmission of 2019-nCoV from bats to human.

EDock: blind protein-ligand docking by replica-exchange monte carlo simulation.

Protein-ligand docking is an important approach for virtual screening and protein function annotation. Although many docking methods have been developed, most require a high-resolution crystal structure of the receptor and a user-specified binding site to start. This information is, however, not available for the majority of unknown proteins, including many pharmaceutically important targets. Developing blind docking methods without predefined binding sites and working with low-resolution receptor models from protein structure prediction is thus essential. In this manuscript, we propose a novel Monte Carlo based method, EDock, for blind protein-ligand docking. For a given protein, binding sites are first predicted by sequence-profile and substructure-based comparison searches with initial ligand poses generated by graph matching. Next, replica-exchange Monte Carlo (REMC) simulations are performed for ligand conformation refinement under the guidance of a physical force field coupled with binding-site distance constraints. The method was tested on two large-scale datasets containing 535 protein-ligand pairs. Without specifying binding pockets on the experimental receptor structures, EDock achieves on average a ligand RMSD of 2.03 Å, which compares favorably with state-of-the-art docking methods including DOCK6 (2.68 Å) and AutoDock Vina (3.92 Å). When starting with predicted models from I-TASSER, EDock still generates reasonable docking models, with a success rate 159% and 67% higher than DOCK6 and AutoDock Vina, respectively. Detailed data analyses show that the major advantage of EDock lies in reliable ligand binding site predictions and extensive REMC sampling, which allows for the implementation of multiple van der Waals weightings to accommodate different levels of steric clashes and cavity distortions and therefore enhances the robustness of low-resolution docking with predicted protein structures.

Recombinant Penicillium oxalicum 16 ß-Glucosidase 1 Displays Comprehensive Inhibitory Resistance to Several Lignocellulose Pretreatment Products, Ethanol, and Salt.

ß-Glucosidase (BGL) is a rate-limiting enzyme of lignocellulose hydrolysis for second-generation bioethanol production, but its inhibition by lignocellulose pretreatment products, ethanol, and salt is apparent. Here, the recombinant Penicillium oxalicum 16 BGL 1 (rPO16BGL1) from Pichia pastoris GS115 kept complete activity at 0.2-1.4 mg/mL furan derivatives and phenolic compounds, 50 mg/mL sodium chloride (potassium chloride), or 100 mg/mL ethanol at 40 °C. rPO16BGL1 retained above 50% residual activity at 30 mg/mL organic acid sodium, and 60% residual activity at 40 °C with 300 mg/mL ethanol. Sodium chloride and potassium chloride had a complicated effect on rPO16BGL1, which resulted in activation or inhibition. The inhibition kinetics of the enzyme reaction demonstrated that organic acids and organic acid sodium were non-competitive inhibitors and that ethanol was a competitive inhibitor at < 1.5 mg/mL salicin. Moreover, substrate inhibition of the enzyme was found at > 2 mg/mL salicin, and the Km/KI and Km/KSI average values revealed that the inhibitory strength was ranked as salicin-organic acids > organic acids > salicin-organic acid sodium salt > organic acid sodium salt > salicin > salicin-KCl > salicin-NaCl > salicin-ethanol > ethanol.

Ensembling multiple raw coevolutionary features with deep residual neural networks for contact-map prediction in CASP13.

We report the results of residue-residue contact prediction of a new pipeline built purely on the learning of coevolutionary features in the CASP13 experiment. For a query sequence, the pipeline starts with the collection of multiple sequence alignments (MSAs) from multiple genome and metagenome sequence databases using two complementary Hidden Markov Model (HMM)-based searching tools. Three profile matrices, built on covariance, precision, and pseudolikelihood maximization respectively, are then created from the MSAs, which are used as the input features of a deep residual convolutional neural network architecture for contact-map training and prediction. Two ensembling strategies have been proposed to integrate the matrix features through end-to-end training and stacking, resulting in two complementary programs called TripletRes and ResTriplet, respectively. For the 31 free-modeling domains that do not have homologous templates in the PDB, TripletRes and ResTriplet generated comparable results with an average accuracy of 0.640 and 0.646, respectively, for the top L/5 long-range predictions, where 71% and 74% of the cases have an accuracy above 0.5. Detailed data analyses showed that the strength of the pipeline is due to the sensitive MSA construction and the advanced strategies for coevolutionary feature ensembling. Domain splitting was also found to help enhance the contact prediction performance. Nevertheless, contact models for tail regions, which often involve a high number of alignment gaps, and for targets with few homologous sequences are still suboptimal. Development of new approaches where the model is specifically trained on these regions and targets might help address these problems.

I-TASSER gateway: A protein structure and function prediction server powered by XSEDE.

There is an increasing gap between the number of known protein sequences and the number of proteins with experimentally characterized structure and function. To alleviate this issue, we have developed the I-TASSER gateway, an online server for automated and reliable protein structure and function prediction. For a given sequence, I-TASSER starts with template recognition from a known structure library, followed by full-length atomic model construction by iterative assembly simulations of the continuous structural fragments excised from the template alignments. Functional insights are then derived from comparative matching of the predicted model with a library of proteins with known function. The I-TASSER pipeline has been recently integrated with the XSEDE Gateway system to accommodate pressing demand from the user community and increasing computing costs. This report summarizes the configuration of the I-TASSER Gateway with the XSEDE-Comet supercomputer cluster, together with an overview of the I-TASSER method and milestones of its development.

Correction to "Selective PPARδ Modulators Improve Mitochondrial Function: Potential Treatment for Duchenne Muscular Dystrophy (DMD)".

[This corrects the article DOI: 10.1021/acsmedchemlett.8b00287.].

DockRMSD: an open-source tool for atom mapping and RMSD calculation of symmetric molecules through graph isomorphism.

Comparison of ligand poses generated by protein-ligand docking programs has often been carried out with the assumption of direct atomic correspondence between ligand structures. However, this correspondence is not necessarily chemically relevant for symmetric molecules and can lead to an artificial inflation of ligand pose distance metrics, particularly those that depend on receptor superposition (rather than ligand superposition), such as docking root mean square deviation (RMSD). Several of the commonly-used RMSD calculation algorithms that correct for molecular symmetry do not take into account the bonding structure of molecules and can therefore result in non-physical atomic mapping. Here, we present DockRMSD, a docking pose distance calculator that converts the symmetry correction to a graph isomorphism searching problem, in which the optimal atomic mapping and RMSD calculation are performed by an exhaustive and fast matching search of all isomorphisms of the ligand structure graph. We show through evaluation of docking poses generated by AutoDock Vina on the CSAR Hi-Q set that DockRMSD is capable of deterministically identifying the minimum symmetry-corrected RMSD and is able to do so without significant loss of computational efficiency compared to other methods. The open-source DockRMSD program can be conveniently integrated with various docking pipelines to assist with accurate atomic mapping and RMSD calculations, which can therefore help improve docking performance, especially for ligand molecules with complicated structural symmetry.

PPARδ modulation rescues mitochondrial fatty acid oxidation defects in the mdx model of muscular dystrophy.

Duchenne muscular dystrophy (DMD) is a recessive, fatal X-linked disease that is characterized by progressive skeletal muscle wasting due to the absence of dystrophin, which is an a essential protein that bridges the inner cytoskeleton and extra-cellular matrix. This study set out to characterize the mitochondria in primary muscle satellite cell derived myoblasts from mdx mice and wild type control mice. Compared to wild type derived cells the mdx derived cells have reduced mitochondrial bioenergetics and have fewer mitochondria. Here, we demonstrate that a novel PPARδ modulator improves mitochondrial function in the mdx mice, which supports that modulating PPARδ may be therapeutically beneficial in DMD patients.

Identification of inhibitors of the E. coli chaperone SurA using in silico and in vitro techniques.

SurA is a gram-negative, periplasmic chaperone protein involved in the proper folding of outer membrane porins (OMPs), which protect bacteria against toxins in the extracellular environment by selectively regulating the passage of nutrients into the cell. Previous studies demonstrated that deletion of SurA renders bacteria more sensitive to toxins that compromise the integrity of the outer membrane. Inhibitors of SurA will perturb the folding of OMPs, leading to disruption of the outer membrane barrier and making the cell more vulnerable to toxic insults. The discovery of novel SurA inhibitors is therefore of great importance for developing alternative strategies to overcome antibiotic resistance. Our laboratory has screened over 10,000,000 compoundsin silicoby computationally docking these compounds onto the crystal structure of SurA. Through this screen and a screen of fragment compounds (molecular weight less than 250 g/mol), we found twelve commercially readily available candidate compounds that bind to the putative client binding site of SurA. We confirmed binding to SurA by developing and employing a competitive fluorescence anisotropy-based binding assay. Our results show that one of these compounds, Fmoc-ß-(2-quinolyl)-d-alanine, binds the client binding site with high micromolar affinity. Using this compound as a lead, we also discovered that Fmoc-l-tryptophan and Fmoc-l-phenylalanine, but not Fmoc-l-tyrosine, bind SurA with similar micromolar affinity. To our knowledge, this is the first report of a competitive fluorescence anisotropy assay developed for the identification of inhibitors of the chaperone SurA, and the identification of three small molecules that bind SurA at its client binding site.