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      Rules of Physical Mathematics Govern Intrinsically Disordered Proteins

      1 , 2 , 1 , 1 , 2
      Annual Review of Biophysics
      Annual Reviews

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          Abstract

          In stark contrast to foldable proteins with a unique folded state, intrinsically disordered proteins and regions (IDPs) persist in perpetually disordered ensembles. Yet an IDP ensemble has conformational features—even when averaged—that are specific to its sequence. In fact, subtle changes in an IDP sequence can modulate its conformational features and its function. Recent advances in theoretical physics reveal a set of elegant mathematical expressions that describe the intricate relationships among IDP sequences, their ensemble conformations, and the regulation of their biological functions. These equations also describe the molecular properties of IDP sequences that predict similarities and dissimilarities in their functions and facilitate classification of sequences by function, an unmet challenge to traditional bioinformatics. These physical sequence-patterning metrics offer a promising new avenue for advancing synthetic biology at a time when multiple novel functional modes mediated by IDPs are emerging.

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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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            Biomolecular condensates: organizers of cellular biochemistry

            In addition to membrane-bound organelles, eukaryotic cells feature various membraneless compartments, including the centrosome, the nucleolus and various granules. Many of these compartments form through liquid–liquid phase separation, and the principles, mechanisms and regulation of their assembly as well as their cellular functions are now beginning to emerge.
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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

                Journal
                Annual Review of Biophysics
                Annu. Rev. Biophys.
                Annual Reviews
                1936-122X
                1936-1238
                May 09 2022
                May 09 2022
                : 51
                : 1
                : 355-376
                Affiliations
                [1 ]Department of Physics and Astronomy, University of Denver, Denver, Colorado, USA;
                [2 ]Molecular and Cellular Biophysics Program, University of Denver, Denver, Colorado, USA
                Article
                10.1146/annurev-biophys-120221-095357
                9190209
                35119946
                e8c5a051-c6b8-4fdf-992d-416b7c3f367f
                © 2022
                History

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