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The last exorcism. Part II
At a New Orleans halfway house for girls, Nell Sweetzer attempts to recover from her trauma, but the demon that possessed her returns with an even more horrific plan.
DVD
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.
Journal Article
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 protocol is described for predicting the structures and functions of multi-domain proteins using the freely available deep-learning-based web platform I-TASSER-MTD.
Journal Article
The Andromeda galaxy’s most important merger about 2 billion years ago as M32’s likely progenitor
Although the proximity of the Andromeda galaxy (M31) offers an opportunity to understand how mergers affect galaxies
1
, uncertainty remains about M31’s most important mergers. Previous studies focused individually on the giant stellar stream
2
or the impact of M32 on M31’s disk
3
,
4
, thereby suggesting many substantial satellite interactions
5
. Yet models of M31’s disk heating
6
and the similarity between the stellar populations of different tidal substructures in M31’s outskirts
7
both suggested a single large merger. M31’s stellar halo (its outer low-surface-brightness regions) is built up from the tidal debris of satellites
5
and provides information about its important mergers
8
. Here we use cosmological models of galaxy formation
9
,
10
to show that M31’s massive
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and metal-rich
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stellar halo, containing intermediate-age stars
7
, dramatically narrows the range of allowed interactions, requiring a single dominant merger with a large galaxy (with stellar mass about 2.5 × 10
10
solar masses,
M
☉
the third largest member of the Local Group) about 2 billion years (Gyr) ago. This single event explains many observations that were previously considered separately: M31’s compact and metal-rich satellite M32
13
is likely to be the stripped core of the disrupted galaxy, its rotating inner stellar halo
14
contains most of the merger debris, and the giant stellar stream
15
is likely to have been thrown out during the merger. This interaction may explain M31’s global burst of star formation about 2 Gyr ago
16
in which approximately a fifth of its stars were formed. Moreover, M31’s disk and bulge were already in place, suggesting that mergers of this magnitude need not dramatically affect galaxy structure.
M31’s massive and metal-rich stellar halo appears to indicate that a single dominant merger with a large galaxy took place about 2 Gyr ago, co-temporal with M31’s global burst of star formation. M32 is likely to be the stripped core of the disrupted galaxy.
Journal Article
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.
Journal Article
Modeling the carbon footprint of fresh produce: effects of transportation, localness, and seasonality on US orange markets
Agriculture is one of the most impactful ways that we interact with the environment. Food demand is expected to increase 70% by 2050 as a result of population growth and the emergence of the global middle class. Meeting the expected demand in a sustainable manner will require an integrated systems-level approach to food production and supply. We present a conceptual framework for estimating the cradle-to-market life-cycle seasonal greenhouse gas emissions impact of fresh produce commodities, including the production, post-harvest processing, packaging, and transportation stages. Using oranges as a case study, we estimate the carbon footprint per kilogram of fruit delivered to wholesale market in New York City, Los Angeles, Chicago, and Atlanta and assess the relative importance of transportation mode, transportation distance (i.e. localness), and seasonality. We find that the cradle-to-market carbon footprint of oranges delivered to US cities can vary by more than a factor of two, depending on the production origin (e.g. 0.3 kgCO2e/kg for Californian oranges delivered to New York City versus 0.7 kgCO2e/kg for Mexican oranges delivered to New York City). The transportation mode was found to have a significant impact on the results; transportation-related greenhouse gas emissions associated with oranges trucked from Mexico to New York City were found to be six times higher than those transported by containership from Chile, in spite of traveling less than half the distance. Seasonality had a moderate impact on the results and varied depending on the destination city; based on our cradle-to-market analysis, the average carbon footprint of 'out-of-season' oranges relative to 'in-season' oranges increased by 51%, 46%, 14%, and 24% for Atlanta, Chicago, Los Angeles, and New York City, respectively. This study highlights the value of regionally-specific carbon footprinting for fresh produce and the need for a consistent and standardized data reporting framework for agricultural systems.
Journal Article
Exploring how complex multiple-choice questions could contribute to inequity in introductory physics
High-stakes exams significantly impact introductory physics students' final grades and have been shown to be inequitable, often to the detriment of students identifying with groups historically marginalized in physics. Certain types of exam questions may contribute more than other types to the observed equity gaps.
The primary objective of this study was to determine whether complex multiple-choice (CMC) questions may be a potential cause of inequity.
We used four years of data from Problem Roulette, an online, not-for-credit exam preparation program, to address our objective. This data set included 951 Physics II (Electricity and Magnetism) questions, each of which we categorized as CMC or non-CMC. We then compared student performance on each question type and created a multi-level logistic regression model to control individual student and question differences.
Students performed 7.9 percentage points worse on CMC questions than they did on non-CMC questions. We find minimal additional performance differences based on student performance in the course. The results from mixed-effects models suggest that CMC questions may be contributing to the observed equity gaps, especially for male and female students, though more evidence is needed.
We found CMC questions are more difficult for everyone. Future research should examine the source of this difficulty and whether that source is functionally related to learning and assessment. Our data does not support using CMC questions instead of non-CMC questions as a way to differentiate top-performing students from everyone else.
Journal Article
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.
Journal Article