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      Reconstitution of an active human CENP-E motor

      research-article
      , ,
      Open Biology
      The Royal Society
      motor, mitosis, microtubule, motility, CENP-E, kinetochore

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          Abstract

          Abstract

          CENP-E is a large kinesin motor protein which plays pivotal roles in mitosis by facilitating chromosome capture and alignment, and promoting microtubule flux in the spindle. So far, it has not been possible to obtain active human CENP-E to study its molecular properties. Xenopus CENP-E motor has been characterized in vitro and is used as a model motor; however, its protein sequence differs significantly from human CENP-E. Here, we characterize human CENP-E motility in vitro. Full-length CENP-E exhibits an increase in run length and longer residency times on microtubules when compared to CENP-E motor truncations, indicating that the C-terminal microtubule-binding site enhances the processivity when the full-length motor is active. In contrast with constitutively active human CENP-E truncations, full-length human CENP-E has a reduced microtubule landing rate in vitro, suggesting that the non-motor coiled-coil regions self-regulate motor activity. Together, we demonstrate that human CENP-E is a processive motor, providing a useful tool to study the mechanistic basis for how human CENP-E drives chromosome congression and spindle organization during human cell division.

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          Most cited references50

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          Highly accurate protein structure prediction with AlphaFold

          Proteins are essential to life, and understanding their structure can facilitate a mechanistic understanding of their function. Through an enormous experimental effort 1 – 4 , the structures of around 100,000 unique proteins have been determined 5 , but this represents a small fraction of the billions of known protein sequences 6 , 7 . Structural coverage is bottlenecked by the months to years of painstaking effort required to determine a single protein structure. Accurate computational approaches are needed to address this gap and to enable large-scale structural bioinformatics. Predicting the three-dimensional structure that a protein will adopt based solely on its amino acid sequence—the structure prediction component of the ‘protein folding problem’ 8 —has been an important open research problem for more than 50 years 9 . Despite recent progress 10 – 14 , existing methods fall far short of atomic accuracy, especially when no homologous structure is available. Here we provide the first computational method that can regularly predict protein structures with atomic accuracy even in cases in which no similar structure is known. We validated an entirely redesigned version of our neural network-based model, AlphaFold, in the challenging 14th Critical Assessment of protein Structure Prediction (CASP14) 15 , demonstrating accuracy competitive with experimental structures in a majority of cases and greatly outperforming other methods. Underpinning the latest version of AlphaFold is a novel machine learning approach that incorporates physical and biological knowledge about protein structure, leveraging multi-sequence alignments, into the design of the deep learning algorithm. AlphaFold predicts protein structures with an accuracy competitive with experimental structures in the majority of cases using a novel deep learning architecture.
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            NIH Image to ImageJ: 25 years of image analysis

            For the past twenty five years the NIH family of imaging software, NIH Image and ImageJ have been pioneers as open tools for scientific image analysis. We discuss the origins, challenges and solutions of these two programs, and how their history can serve to advise and inform other software projects.
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              High-efficiency yeast transformation using the LiAc/SS carrier DNA/PEG method.

              Here we describe a high-efficiency version of the lithium acetate/single-stranded carrier DNA/PEG method of transformation of Saccharomyces cerevisiae. This method currently gives the highest efficiency and yield of transformants, although a faster protocol is available for small number of transformations. The procedure takes up to 1.5 h, depending on the length of heat shock, once the yeast culture has been grown. This method is useful for most transformation requirements.
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                Author and article information

                Contributors
                Role: ConceptualizationRole: Formal analysisRole: InvestigationRole: ValidationRole: Writing – original draftRole: Writing – review & editing
                Role: Methodology
                Role: ConceptualizationRole: Formal analysisRole: Funding acquisitionRole: Project administrationRole: SupervisionRole: ValidationRole: Writing – original draftRole: Writing – review & editing
                Journal
                Open Biol
                Open Biol
                RSOB
                royopenbio
                Open Biology
                The Royal Society
                2046-2441
                March 9, 2022
                March 2022
                March 9, 2022
                : 12
                : 3
                : 210389
                Affiliations
                Wellcome Trust Centre for Cell Biology, School of Biological Sciences, University of Edinburgh, , Edinburgh, Scotland EH9 3BF, UK
                Author notes
                [ † ]

                Present address: Department of Anatomy and Cell Biology, 3640 Rue University, Montreal, McGill University, QC, Canada H3A 0C7.

                Electronic supplementary material is available online at https://doi.org/10.6084/m9.figshare.c.5873245.

                Author information
                http://orcid.org/0000-0002-5440-6060
                Article
                rsob210389
                10.1098/rsob.210389
                8905165
                35259950
                5222b940-0d2c-4a39-84e9-d4f61bcd979d
                © 2022 The Authors.

                Published by the Royal Society under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/4.0/, which permits unrestricted use, provided the original author and source are credited.

                History
                : December 22, 2021
                : Feburary 15, 2022
                Funding
                Funded by: Wellcome Trust, http://dx.doi.org/10.13039/100010269;
                Award ID: 203149
                Award ID: 207430
                Funded by: Biotechnology and Biological Sciences Research Council, http://dx.doi.org/10.13039/501100000268;
                Award ID: BB/M010996/1
                Categories
                30
                15
                Methods and Techniques

                Life sciences
                motor,mitosis,microtubule,motility,cenp-e,kinetochore
                Life sciences
                motor, mitosis, microtubule, motility, cenp-e, kinetochore

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