Protein Structure and Folding: AlphaFold and Beyond

Dear Colleagues,

The prediction of the three-dimensional structure of proteins from their sequences has represented for decades the Holy Grail of structural biology. For more than half a century, despite intense research activities in this field, the number of successful attempts has been marginal. This situation has rapidly changed in the last couple of years with the development of machine learning approaches culminating in the AlphaFold release by DeepMind. This approach has led to the rapid and generally reliable structure  prediction of the proteome of many highly studied organisms, and it is expected to generate, in the near feature, three-dimensional models for essentially all proteins with a known sequence. Such a huge, and somehow unexpected, scientific triumph will heavily affect the approach of the structural biology community to the problem of protein structure and folding prediction. 

This Special Issue invites reviews and novel research results in which the characterization of protein structure and folding is studied by considering this novel scenario. In particular, studies corroborating AlphaFold models or highlighting discrepancies between newly determined structures with the predicted ones are welcome. Moreover, computational studies describing innovative aspects of protein structure and folding as well as investigations whose findings could be exploited in the future for improving the predictions approach will also be favorably considered.

We look forward to your contributions to this issue.

Dr. Luigi Vitagliano
Dr. Nicole Balasco
Dr. Luciana Esposito
Guest Editors

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