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      Computational methods in glaucoma research: Current status and future outlook

      , , ,
      Molecular Aspects of Medicine
      Elsevier BV

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          AlphaFold Protein Structure Database: massively expanding the structural coverage of protein-sequence space with high-accuracy models

          The AlphaFold Protein Structure Database (AlphaFold DB, https://alphafold.ebi.ac.uk ) is an openly accessible, extensive database of high-accuracy protein-structure predictions. Powered by AlphaFold v2.0 of DeepMind, it has enabled an unprecedented expansion of the structural coverage of the known protein-sequence space. AlphaFold DB provides programmatic access to and interactive visualization of predicted atomic coordinates, per-residue and pairwise model-confidence estimates and predicted aligned errors. The initial release of AlphaFold DB contains over 360,000 predicted structures across 21 model-organism proteomes, which will soon be expanded to cover most of the (over 100 million) representative sequences from the UniRef90 data set.
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            Is Open Access

            The Ensembl Variant Effect Predictor

            The Ensembl Variant Effect Predictor is a powerful toolset for the analysis, annotation, and prioritization of genomic variants in coding and non-coding regions. It provides access to an extensive collection of genomic annotation, with a variety of interfaces to suit different requirements, and simple options for configuring and extending analysis. It is open source, free to use, and supports full reproducibility of results. The Ensembl Variant Effect Predictor can simplify and accelerate variant interpretation in a wide range of study designs.
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              Accurate prediction of protein structures and interactions using a 3-track neural network

              DeepMind presented remarkably accurate predictions at the recent CASP14 protein structure prediction assessment conference. We explored network architectures incorporating related ideas and obtained the best performance with a 3-track network in which information at the 1D sequence level, the 2D distance map level, and the 3D coordinate level is successively transformed and integrated. The 3-track network produces structure predictions with accuracies approaching those of DeepMind in CASP14, enables the rapid solution of challenging X-ray crystallography and cryo-EM structure modeling problems, and provides insights into the functions of proteins of currently unknown structure. The network also enables rapid generation of accurate protein-protein complex models from sequence information alone, short circuiting traditional approaches which require modeling of individual subunits followed by docking. We make the method available to the scientific community to speed biological research.
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                Author and article information

                Contributors
                (View ORCID Profile)
                Journal
                Molecular Aspects of Medicine
                Molecular Aspects of Medicine
                Elsevier BV
                00982997
                December 2023
                December 2023
                : 94
                : 101222
                Article
                10.1016/j.mam.2023.101222
                f348fe28-f638-4993-a2f8-aa626384688d
                © 2023

                https://www.elsevier.com/tdm/userlicense/1.0/

                https://www.elsevier.com/legal/tdmrep-license

                http://www.elsevier.com/open-access/userlicense/1.0/

                https://doi.org/10.15223/policy-017

                https://doi.org/10.15223/policy-037

                https://doi.org/10.15223/policy-012

                https://doi.org/10.15223/policy-029

                https://doi.org/10.15223/policy-004

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