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      A review of visualisations of protein fold networks and their relationship with sequence and function

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          ABSTRACT

          Proteins form arguably the most significant link between genotype and phenotype. Understanding the relationship between protein sequence and structure, and applying this knowledge to predict function, is difficult. One way to investigate these relationships is by considering the space of protein folds and how one might move from fold to fold through similarity, or potential evolutionary relationships. The many individual characterisations of fold space presented in the literature can tell us a lot about how well the current Protein Data Bank represents protein fold space, how convergence and divergence may affect protein evolution, how proteins affect the whole of which they are part, and how proteins themselves function. A synthesis of these different approaches and viewpoints seems the most likely way to further our knowledge of protein structure evolution and thus, facilitate improved protein structure design and prediction.

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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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            TM-align: a protein structure alignment algorithm based on the TM-score

            We have developed TM-align, a new algorithm to identify the best structural alignment between protein pairs that combines the TM-score rotation matrix and Dynamic Programming (DP). The algorithm is ∼4 times faster than CE and 20 times faster than DALI and SAL. On average, the resulting structure alignments have higher accuracy and coverage than those provided by these most often-used methods. TM-align is applied to an all-against-all structure comparison of 10 515 representative protein chains from the Protein Data Bank (PDB) with a sequence identity cutoff <95%: 1996 distinct folds are found when a TM-score threshold of 0.5 is used. We also use TM-align to match the models predicted by TASSER for solved non-homologous proteins in PDB. For both folded and misfolded models, TM-align can almost always find close structural analogs, with an average root mean square deviation, RMSD, of 3 Å and 87% alignment coverage. Nevertheless, there exists a significant correlation between the correctness of the predicted structure and the structural similarity of the model to the other proteins in the PDB. This correlation could be used to assist in model selection in blind protein structure predictions. The TM-align program is freely downloadable at .
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              Protein dynamism and evolvability.

              The traditional view that proteins possess absolute functional specificity and a single, fixed structure conflicts with their marked ability to adapt and evolve new functions and structures. We consider an alternative, "avant-garde view" in which proteins are conformationally dynamic and exhibit functional promiscuity. We surmise that these properties are the foundation stones of protein evolvability; they facilitate the divergence of new functions within existing folds and the evolution of entirely new folds. Packing modes of proteins also affect their evolvability, and poorly packed, disordered, and conformationally diverse proteins may exhibit high evolvability. This dynamic view of protein structure, function, and evolvability is extrapolated to describe hypothetical scenarios for the evolution of the early proteins and future research directions in the area of protein dynamism and evolution.
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                Author and article information

                Contributors
                janan.sykes@utas.edu.au
                Journal
                Biol Rev Camb Philos Soc
                Biol Rev Camb Philos Soc
                10.1111/(ISSN)1469-185X
                BRV
                Biological Reviews of the Cambridge Philosophical Society
                Blackwell Publishing Ltd (Oxford, UK )
                1464-7931
                1469-185X
                09 October 2022
                February 2023
                : 98
                : 1 ( doiID: 10.1111/brv.v98.1 )
                : 243-262
                Affiliations
                [ 1 ] School of Natural Sciences University of Tasmania Private Bag 37 Hobart Tasmania 7001 Australia
                Author notes
                [*] [* ] Author for correspondence (E‐mail: janan.sykes@ 123456utas.edu.au ).

                Author information
                https://orcid.org/0000-0003-0976-8895
                https://orcid.org/0000-0002-4628-7938
                https://orcid.org/0000-0001-8385-341X
                Article
                BRV12905
                10.1111/brv.12905
                10092621
                36210328
                300cf3a4-2ee5-4306-94ac-9c3980d0da59
                © 2022 The Authors. Biological Reviews published by John Wiley & Sons Ltd on behalf of Cambridge Philosophical Society.

                This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.

                History
                : 08 September 2022
                : 30 December 2021
                : 09 September 2022
                Page count
                Figures: 7, Tables: 0, Pages: 20, Words: 18501
                Funding
                Funded by: SET Research Training Program (RTP) Stipend
                Funded by: UTas Discipline of Mathematics.
                Categories
                Original Article
                Original Articles
                Custom metadata
                2.0
                February 2023
                Converter:WILEY_ML3GV2_TO_JATSPMC version:6.2.7 mode:remove_FC converted:12.04.2023

                Ecology
                protein folds,protein fold switches,protein similarity networks,protein structure networks,protein evolution

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