Browsing by Subject "Protein structure predictions"

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    3D variability analysis reveals a hidden conformational change controlling ammonia transport in human asparagine synthetase
    (Springer Nature, 2024-12-03) Coricello, Adriana; Nardone, Alanya J.; Lupia, Antonio; Gratteri, Carmen; Vos, Matthijn; Chaptal, Vincent; Alcaro, Stefano; Zhu, Wen; Takagi, Yuichiro; Richards, Nigel G. J.; Biochemistry and Molecular Biology, School of Medicine
    Advances in X-ray crystallography and cryogenic electron microscopy (cryo-EM) offer the promise of elucidating functionally relevant conformational changes that are not easily studied by other biophysical methods. Here we show that 3D variability analysis (3DVA) of the cryo-EM map for wild-type (WT) human asparagine synthetase (ASNS) identifies a functional role for the Arg-142 side chain and test this hypothesis experimentally by characterizing the R142I variant in which Arg-142 is replaced by isoleucine. Support for Arg-142 playing a role in the intramolecular translocation of ammonia between the active site of the enzyme is provided by the glutamine-dependent synthetase activity of the R142 variant relative to WT ASNS, and MD simulations provide a possible molecular mechanism for these findings. Combining 3DVA with MD simulations is a generally applicable approach to generate testable hypotheses of how conformational changes in buried side chains might regulate function in enzymes.
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    Critical assessment of protein intrinsic disorder prediction
    (Springer Nature, 2021) Necci, Marco; Piovesan, Damiano; CAID Predictors; DisProt Curators; Tosatto, Silvio C. E.; Biochemistry and Molecular Biology, School of Medicine
    Intrinsically disordered proteins, defying the traditional protein structure–function paradigm, are a challenge to study experimentally. Because a large part of our knowledge rests on computational predictions, it is crucial that their accuracy is high. The Critical Assessment of protein Intrinsic Disorder prediction (CAID) experiment was established as a community-based blind test to determine the state of the art in prediction of intrinsically disordered regions and the subset of residues involved in binding. A total of 43 methods were evaluated on a dataset of 646 proteins from DisProt. The best methods use deep learning techniques and notably outperform physicochemical methods. The top disorder predictor has Fmax = 0.483 on the full dataset and Fmax = 0.792 following filtering out of bona fide structured regions. Disordered binding regions remain hard to predict, with Fmax = 0.231. Interestingly, computing times among methods can vary by up to four orders of magnitude.