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Davide Sala et al.
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Carter J. Wilson et al.
Summary: The development of AlphaFold2 has brought a paradigm shift in structural biology and has shown excellent performance in predicting disordered regions. The accuracy of the constructed disorder predictor from AlphaFold2 structures varies, with the naive assumption of assigning residues to ordered or disordered based on helices, strands, and H-bond stabilized turns leading to a significant overestimation of disorder. The predicted local distance difference test (pLDDT) provides a reliable measure of residue-wise disorder and shows an interesting relationship with secondary structure.
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Ruth Nussinov et al.
Summary: AlphaFold is a powerful algorithm that leverages biological sequence data and artificial intelligence. It has the potential for vast impacts in the life sciences, but also faces challenges in certain areas such as protein folding mechanisms.
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Davide Sala et al.
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Soumil Y. Joshi et al.
Summary: Coarse-grained molecular dynamics has become an efficient way to model large systems, but developing models and accurate interaction potentials remains challenging. Traditional parameterisation techniques, while tedious, are still widely used, and advanced optimization methods and machine learning can help develop models with higher transferability and accuracy more quickly.
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Georgi K. Kanev et al.
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Mohammed AlQuraishi
Summary: Prediction of protein structure from sequence has made significant progress in the past two years, driven by the increasing use of neural networks in structure prediction pipelines. These neural networks have optimized the previous energy models and sampling procedures, resulting in algorithms that can now predict protein structures with a median accuracy of 2.1 angstroms.
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