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    Education: FirstGlance in Jmol now on YouTube: Intro & Design Goals
    Submitted by Eric Martz; posted on Wednesday, March 24, 2021

    Submitter

    FirstGlance in Jmol is the easiest way to understand 3D structures of proteins. There is no command language to learn. It is much easier than PyMOL. It is easy for students, yet has plenty of power for researchers. Any molecular view in FirstGlance can be rendered as an animation ready to drop into a Powerpoint slide. It takes just a few mouse clicks. During the past year, FirstGlance was used >300 times/day on average.

    A new video on YouTube provides an introduction to FirstGlance in Jmol, and discusses the inspiration for its creation, and its design goals: www.youtube.com/watch?v=80og2ASrvnQ

    Slides showing examples of molecular animations created with FirstGlance: docs.google.com/pres[...]=id.p

    FirstGlance in Jmol: FirstGlance.Jmol.Org

    Submitter

    As posted recently by Jeff Bizzaro, in 2020, AlphaFold2 predicted protein structures with truly astonishing accuracy, certified by the bi-annual double-blind competition, CASP 14. Its predictions were based on the amino acid sequences of the target proteins, using massive artificial intelligence machine learning from sequence and structure databases. Predictions were made "blind", without access to empirical structures of the targets, and were judged later in 2020 when empirical structures became public. The judges did not know who made which prediction.

    AlphaFold2 was one of over 100 groups that submitted predictions for over 100 target single-chain domains. In most cases, AlphaFold2 made the best prediction, while the second best prediction was far less accurate. This was particularly impressive for "free modeling" targets, those for which no suitable homology modeling templates were available.

    I have briefly summarized the breakthrough here:

    proteopedia.org/w/Th[...]odels

    I have analyzed two free modeling cases in detail, with comparisons visualized in interactive 3D. One (92 amino acids) is the ORF8 virulence factor from SARS-CoV-2. Among the free modeling targets, it had the largest discrepancy between the best and 2nd best predictions. The second is a phage RNA polymerase, the longest free-modeling target domain (404 amino acids). See:

    proteopedia.org/w/Al[...]SP_14

    In January 2021, Degenics was launched as a blockchain-based, anonymous-first DNA testing platform, in collaboration with Blocksphere, a blockchain consulting company. Degenics intends to partner with projects in the Polkadot ecosystem, including KILT Protocol to incorporate credentials for the project's genetic lab & genetic products ecosystem.

    The platform provides a meeting place between genetic testing laboratories and privacy-conscious genetic testing users and includes sovereignty mechanisms that ensure that genetic test results produced will remain in full possession of each individual while providing incentive-flow mechanisms for the laboratories.

    Degenics calls for laboratories to register for the closed beta system test via Degenics.com.

    Article: ritzherald.com/dege[...]form/

    Submitter

    EXCERPT

    Pleiotropy analysis, which provides insight on how individual genes result in multiple characteristics, has become increasingly valuable as medicine continues to lean into mining genetics to inform disease treatments. Privacy stipulations, though, make it difficult to perform comprehensive pleiotropy analysis because individual patient data often can't be easily and regularly shared between sites. However, a statistical method called Sum-Share, developed at Penn Medicine, can pull summary information from many different sites to generate significant insights. In a test of the method, published in Nature Communications, Sum-Share's developers were able to detect more than 1,700 DNA-level variations that could be associated with five different cardiovascular conditions.
    Source: medicalxpress.com/news[...].html
    Article: doi.org/10.1[...]211-2

    Submitter

    EXCERPT

    DeepMind's AlphaFold is an AI system built to tackle this long-standing challenge. In 2018, the initial version of AlphaFold debuted at CASP (Critical Assessment of protein Structure Prediction), a biennial worldwide event for experimenting with state-of-the-art protein structuring technologies. AlphaFold achieved the highest accuracy of the participating technologies at CASP13 in 2018, but has now been developed further into what is being labeled a "stunning advance."

    The system was trained on publicly available data on around 170,000 protein structures and a large database of unknown protein structures ahead of its appearance at CASP14 this week. Technologies are graded from 0-100 for accuracy on what is known as the Global Distance test, which assesses what percentage of beads in the protein chain are within a threshold distance of the correct location. In results released today, AlphaFold scored 92.4 across all targets.
    Source: newatlas.com/biol[...]blem/

    More on AlphaFold: deepmind.com/blog[...]ology

    Submitter

    Congratulations to Dr. Xiaole Shirley Liu of the Dana-Farber Cancer Institute for receiving the 2020 Benjamin Franklin Award!

    Laureate presentation: www.youtube.com/watch?v=NcHBlYUmS0g

    Comparative metabolomics is aiming to find the biological meaningful common metabolites (biomarkers) from large-scale, high throughout metabolomics profiling data. Thus, approaches and tools towards detecting and aligning the potential same known and unknown metabolites across different samples, different biological experiments and even different instrument runs are desired.

    Inspired by the aims in LC/MS based comparative metabolomics, a series of approaches are developed and two tools entitled as MET-COFEA (bioinfo.noble.org/manu[...]ofea/) and MET-XAlign (bioinfo.noble.org/manu[...]lign/) are implemented.

    MET-COFEA, being an analysis tool, can be used to extract and annotate each meaningful metabolite' associated chromatograph features from each LC-MS sample. For the extracted metabolite compound group with multiple fragment peaks, the neutral molecular mass can be deduced and the compound's representative retention time can be estimated, which can be considered as the common thing for the same metabolite across different samples and different experiment configurations, although the fragmentation pattern can vary from sample to sample, from experiment to experiment. MET-COFEA has already been successfully implemented as a pipeline tool with visualization.

    MET-XAlign, being as an alignment tool, has been dedicatedly developed based on the analysis results from MET-COFEA. It mainly includes an algorithm core and user interface. The identified compounds from MET-COFEA are characterized by compound retention time and neural molecular mass deduced by multiple associated fragments' m/z value, which are represented as its unique Compound_ID.

    Adopting this approach that combined MET-COFEA and MET-XAlign, the LC-MS based comparative metabolomics analysis including metabolite feature extraction and annotation for each sample and alignment across samples can be separated. Our previous experimental results show that the strategy combing MET-COFEA and MET-XAlign can efficiently realize the aim to find the biological meaningful biomarkers from LC/MS based comparative metabolomics data.

    The central dogma of biology is that the genome, transcriptome, proteome, and metabolome are cascading and connected to the end phenome. Recently, high-throughput sequencing technology, particularly the NGS (Next Generation Sequencing), made it capable to discover or measure million-scale genetic variants (such as SNPs) or expressed genes (such as transcripts). High performance mass spectrometry technology makes it possible to detect and measure a broad range of metabolites at a very high sensitivity. All these omics data are complex in nature due to the heterogeneous information and huge data size, but generally biologically correlated. Therefore, it's challenging yet necessary to develop efficient methods and tools to systematically interpret the biological insights behind multiple omics data.

    In classical GWAS (Genome Wide Association Studies), the genetic marker variants and the phenotypic traits can be connected through the canonical LMM (Linear Mixed Model). We extended the concept of genome-wide association into a broader 'ome'-wide association. Briefly, we proposed a novel LMM and developed an online tool named PATOWAS (bioinfo.noble.org/PATOWAS/), by which we can address not only GWAS, but also TWAS (Transcriptome wide association studies), and MWAS (Metabolome wide association studies) in one unified platform. To explain more the phenotypic variation and address the missing heritability, we also proposed another new LMM and developed another association tool named PEPIS (bioinfo.noble.org/PolyGenic_QTL/), by which the polygenic effect and epistasis from marker pairs can be accounted for. Using these tools, two-dimensional (2D) GWAS that accounts for epistatic genetic effects can be mapped, which are complementary to the one-dimensional (1D) GWAS mapping, and can provide more genetic information.

    In short, the combination of 2D GWAS analysis and multi-omics data integration can well address the challenging questions as GXG, GXE, which together can efficiently characterize the complex trait, such as crop yield.
    Education: Coronavirus spike protein and membrane fusion
    Submitted by Eric Martz; posted on Thursday, August 27, 2020

    Submitter

    Two new tutorials are available animating the latest findings about coronavirus spike protein:

    proteopedia.org/w/SA[...]furin
    proteopedia.org/w/SA[...]ation

    Each includes interactive morph animations, and downloadable Powerpoint-ready animations. These are based on recent cryo-EM structures of multiple conformations.

    A protease (typically furin, ample in the lungs) clips spike protein, inducing it to extend a receptor-binding surface to engage the ACE2 receptor on the host cell.

    The mechanism of membrane fusion, which deposits the viral RNA inside the host cell initiating infection, appears similar to that of influenza hemagglutinin. The spike protein "spears" the host cell membrane, capturing it with a "fusion peptide", and pulls the virus membrane very close. Fusion has been seen in molecular dynamics simulations (see Figure 2 in the fusion link). Spike protein has a cavity that is the target of drug screening (see Figure 4 in the fusion link). A cute graphical abstract shows a 3-legged folding stool as an analogy to spike protein, with the drug-target cavity between its legs (see Figure 3 in the fusion link).

    These tutorials have been cataloged at Merlot.Org: www.merlot.org/merl[...]eated

    These and all tutorials at Proteopedia.Org are offered under a Creative Commons Attribution Share Alike 3.0 license.

    Submitter

    Dear colleagues,

    We're writing to invite you to use ObjTables (objtables.org), a free and open-source toolkit, to create and reuse high-quality spreadsheets, such as supplementary tables to articles.

    Comparing and integrating data is essential to science. However, it is difficult to reuse many data sets, including spreadsheets, one of the most common formats. ObjTables makes spreadsheets reusable by combining spreadsheets with schemas, an object-relational mapping system, numerous data types for scientific information, and high-level software tools. First, ObjTables enables authors to use Excel and similar programs to create spreadsheets and use ObjTables to error check spreadsheets. Second, ObjTables extends the impact of sharing data by helping other investigators compare, merge, and translate spreadsheets into data structures that can be analyzed with tools such as Python. ObjTables is available as a web application, a command-line program, a web service, and a Python package.

    We hope that you join this initiative to make supplementary tables more reusable. Please contact us to share feedback or get involved. Together, we believe we can create a robust ecosystem of reusable data for research!

    Regards,

    Jonathan Karr
    Arthur Goldberg
    Icahn School of Medicine at Mount Sinai
    www.karrlab.org

    Wolfram Liebermeister
    INRAE, Université Paris-Saclay
    genome.jouy.inra.fr/~wli[...].html
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    Acknowledgments

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