Protein Modelling: What Happened to the “Protein Structure Gap”?

By Torsten Schwede. 2013

125 citations recorded by the source corpus.

Computational modeling and prediction of three-dimensional macromolecular structures and complexes from their sequence has been a long standing vision in structural biology as it holds the promise to bypass part of the laborious process of experimental structure solution. Over the last two decades, a paradigm shift has occurred: starting from a situation where the “structure knowledge gap” between the huge number of protein sequences and small number of known structures has hampered the widespread use of structure-based approaches in life science research, today some form of structural information – either experimental or computational – is available for the majority of amino acids encoded by common model organism genomes. Template based homology modeling techniques have matured to a point where they are now routinely used to complement experimental techniques. With the scientific focus of interest moving towards larger macromolecular complexes and dynamic networks of interactions, the integration of computational modeling methods with low-resolution experimental techniques allows studying large and complex molecular machines. Computational modeling and prediction techniques are still facing a number of challenges which hamper the more widespread use by the non-expert scientist. For example, it is often difficult to convey the underlying assumptions of a computational technique, as well as the expected accuracy and structural variability of a specific model. However, these aspects are crucial to understand the limitations of a model, and to decide which interpretations and conclusions can be supported.

Subfields: Materials Chemistry, Molecular Biology

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Citations in the corpus

AlphaFold2 models indicate that protein sequence determines both structure and dynamics (2022, methodology): “We used the AF2-scores, as a reversed normalization of the pLDDT scores (see Methods section), and found that the AF2(2) B = (”
QMEANDisCo—distance constraints applied on model quality estimation (2019, background): “This is a major concern as the range of applications for which the model can be used directly depends on its quality (Baker and Sali, 2001; Schwede, 2013), hence the importance of quality estimation methods.”