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      Structure and Protein-Protein Interactions of Ice Nucleation Proteins Drive Their Activity

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          Abstract

          Microbially-produced ice nucleating proteins (INpro) are unique molecular structures with the highest known catalytic efficiency for ice formation. Airborne microorganisms utilize these proteins to enhance their survival by reducing their atmospheric residence times. INpro also have critical environmental effects including impacts on the atmospheric water cycle, through their role in cloud and precipitation formation, as well as frost damage on crops. INpro are ubiquitously present in the atmosphere where they are emitted from diverse terrestrial and marine environments. Even though bacterial genes encoding INpro have been discovered and sequenced decades ago, the details of how the INpro molecular structure and oligomerization foster their unique ice-nucleation activity remain elusive. Using machine-learning based software AlphaFold 2 and trRosetta, we obtained and analysed the first ab initio structural models of full length and truncated versions of bacterial INpro. The modeling revealed a novel beta-helix structure of the INpro central repeat domain responsible for ice nucleation activity. This domain consists of repeated stacks of two beta strands connected by two sharp turns. One beta-strand is decorated with a TxT amino acid sequence motif and the other strand has an SxL[T/I] motif. The core formed between the stacked beta helix-pairs is unusually polar and very distinct from previous INpro models. Using synchrotron radiation circular dichroism, we validated the β-strand content of the central repeat domain in the model. Combining the structural model with functional studies of purified recombinant INpro, electron microscopy and modeling, we further demonstrate that the formation of dimers and higher-order oligomers is key to INpro activity. Using computational docking of the new INpro model based on rigid-body algorithms we could reproduce a previously proposed homodimer structure of the INpro CRD with an interface along a highly conserved tyrosine ladder and show that the dimer model agrees with our functional data. The parallel dimer structure creates a surface where the TxT motif of one monomer aligns with the SxL[T/I] motif of the other monomer widening the surface that interacts with water molecules and therefore enhancing the ice nucleation activity. This work presents a major advance in understanding the molecular foundation for bacterial ice-nucleation activity.

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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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            The HADDOCK2.2 Web Server: User-Friendly Integrative Modeling of Biomolecular Complexes.

            The prediction of the quaternary structure of biomolecular macromolecules is of paramount importance for fundamental understanding of cellular processes and drug design. In the era of integrative structural biology, one way of increasing the accuracy of modeling methods used to predict the structure of biomolecular complexes is to include as much experimental or predictive information as possible in the process. This has been at the core of our information-driven docking approach HADDOCK. We present here the updated version 2.2 of the HADDOCK portal, which offers new features such as support for mixed molecule types, additional experimental restraints and improved protocols, all of this in a user-friendly interface. With well over 6000 registered users and 108,000 jobs served, an increasing fraction of which on grid resources, we hope that this timely upgrade will help the community to solve important biological questions and further advance the field. The HADDOCK2.2 Web server is freely accessible to non-profit users at http://haddock.science.uu.nl/services/HADDOCK2.2.
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              Improved protein structure prediction using predicted interresidue orientations

              The prediction of interresidue contacts and distances from coevolutionary data using deep learning has considerably advanced protein structure prediction. Here, we build on these advances by developing a deep residual network for predicting interresidue orientations, in addition to distances, and a Rosetta-constrained energy-minimization protocol for rapidly and accurately generating structure models guided by these restraints. In benchmark tests on 13th Community-Wide Experiment on the Critical Assessment of Techniques for Protein Structure Prediction (CASP13)- and Continuous Automated Model Evaluation (CAMEO)-derived sets, the method outperforms all previously described structure-prediction methods. Although trained entirely on native proteins, the network consistently assigns higher probability to de novo-designed proteins, identifying the key fold-determining residues and providing an independent quantitative measure of the “ideality” of a protein structure. The method promises to be useful for a broad range of protein structure prediction and design problems.
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                Author and article information

                Contributors
                Journal
                Front Microbiol
                Front Microbiol
                Front. Microbiol.
                Frontiers in Microbiology
                Frontiers Media S.A.
                1664-302X
                17 June 2022
                2022
                : 13
                : 872306
                Affiliations
                [1] 1Institute for Tropospheric Research , Leipzig, Germany
                [2] 2Department of Biology, Microbiology Section, Aarhus University , Aarhus, Denmark
                [3] 3Department of Physics and Astronomy, Stellar Astrophysics Centre, Aarhus University , Aarhus, Denmark
                [4] 4Department of Molecular Biology and Genetics, Section for Protein Science, Aarhus University , Aarhus, Denmark
                [5] 5Department of Earth and Planetary Sciences, Weizmann Institute of Science , Rehovot, Israel
                [6] 6Department of Physics and Astronomy, The Institute for Storage Ring Facilities, Aarhus University , Aarhus, Denmark
                [7] 7Interdisciplinary Nanoscience Center and Center for Electromicrobiology, Aarhus University , Aarhus, Denmark
                Author notes

                Edited by: Philippe M. Oger, Adaptation et Pathogenie (MAP), France

                Reviewed by: Konrad Meister, University of Alaska Southeast, United States; Samuel Sathyanesan, University of South Dakota, United States

                *Correspondence: Tina Šantl-Temkiv, temkiv@ 123456bio.au.dk

                These authors have contributed equally to this work

                This article was submitted to Extreme Microbiology, a section of the journal Frontiers in Microbiology

                Article
                10.3389/fmicb.2022.872306
                9247515
                35783412
                f24184bc-1e5b-461e-afd9-058c7e37906b
                Copyright © 2022 Hartmann, Ling, Dreyer, Zipori, Finster, Grawe, Jensen, Borck, Reicher, Drace, Niedermeier, Jones, Hoffmann, Wex, Rudich, Boesen and Šantl-Temkiv.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 09 February 2022
                : 16 May 2022
                Page count
                Figures: 5, Tables: 0, Equations: 3, References: 84, Pages: 16, Words: 12960
                Funding
                Funded by: Danish National Research Foundation , doi 10.13039/501100001732;
                Award ID: DNRF106
                Funded by: Villum Fonden , doi 10.13039/100008398;
                Award ID: 23175
                Award ID: 37435
                Funded by: Novo Nordisk , doi 10.13039/501100004191;
                Award ID: NNF19OC0056963
                Categories
                Microbiology
                Original Research

                Microbiology & Virology
                ice-nucleating proteins,protein structure,atmospheric bacteria,protein–protein interactions,protein activity

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