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      Small-angle X-ray scattering studies of enzymes

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      Current Opinion in Chemical Biology
      Elsevier BV

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          Abstract

          <p class="first" id="d4654545e83">Enzyme function requires conformational changes to achieve substrate binding, domain rearrangements, and interactions with partner proteins, but these movements are difficult to observe. Small-angle X-ray scattering (SAXS) is a versatile structural technique that can probe such conformational changes under solution conditions that are physiologically relevant. Although it is generally considered a low-resolution structural technique, when used to study conformational changes as a function of time, ligand binding, or protein interactions, SAXS can provide rich insight into enzyme behavior, including subtle domain movements. In this perspective, we highlight recent uses of SAXS to probe structural enzyme changes upon ligand and partner-protein binding and discuss tools for signal deconvolution of complex protein solutions. </p>

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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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            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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              PRIMUS: a Windows PC-based system for small-angle scattering data analysis

              A program suite for one-dimensional small-angle scattering data processing running on IBM-compatible PCs under Windows 9 x /NT/2000/XP is presented. The main program, PRIMUS , has a menu-driven graphical user interface calling computational modules to perform data manipulation and analysis. Experimental data in binary OTOKO format can be reduced by calling the program SAPOKO , which includes statistical analysis of time frames, averaging and scaling. Tools to generate the angular axis and detector response files from diffraction patterns of calibration samples, as well as binary to ASCII transformation programs, are available. Several types of ASCII files can be directly imported into PRIMUS , in particular, sasCIF or ILL-type files are read without modification. PRIMUS provides basic data manipulation functions (averaging, background subtraction, merging of data measured in different angular ranges, extrapolation to zero sample concentration, etc. ) and computes invariants from Guinier and Porod plots. Several external modules coupled with PRIMUS via pop-up menus enable the user to evaluate the characteristic functions by indirect Fourier transformation, to perform peak analysis for partially ordered systems and to find shape approximations in terms of three-parametric geometrical bodies. For the analysis of mixtures, PRIMUS enables model-independent singular value decomposition or linear fitting if the scattering from the components is known. An interface is also provided to the general non-linear fitting program MIXTURE , which is designed for quantitative analysis of multicomponent systems represented by simple geometrical bodies, taking shape and size polydispersity as well as interparticle interference effects into account.
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                Author and article information

                Contributors
                Journal
                Current Opinion in Chemical Biology
                Current Opinion in Chemical Biology
                Elsevier BV
                13675931
                February 2023
                February 2023
                : 72
                : 102232
                Article
                10.1016/j.cbpa.2022.102232
                36462455
                520d06aa-1039-4a4c-a1ba-0afcd74e2758
                © 2023

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

                http://creativecommons.org/licenses/by/4.0/

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