Essentials of de novo protein design: Methods and applications
By Enrique Marcos, Daniel‐Adriano Silva. 2018
54 citations recorded by the source corpus.
The field of de novo protein design has undergone a rapid transformation in the last decade and now enables the accurate design of protein structures with exceptional stability and in a large variety of folds not necessarily restricted to those seen in nature. Before the existence of de novo protein design, traditional strategies to engineer proteins relied exclusively on modifying existing proteins already with a similar to desired function or, at least, a suitable geometry and enough stability to tolerate mutations needed for incorporating the desired functions. De novo computational protein design, instead, allows to completely overcome this limitation by permitting the access to a virtually infinite number of protein shapes that can be suitable candidates to engineer function. Recently, we have seen the first examples of such functionalization in the form of de novo proteins custom designed to bind specific targets or small molecules with novel medical and biotechnological applications. Despite this progress, the incursion on this nascent field can be difficult due to the plethora of approaches available and their constant evolution. Here, we review the most relevant computational methods for de novo protein design with the aim of compiling a comprehensive guide for researchers embarking on this field. We illustrate most of the concepts in the view of Rosetta, which is the most extensively developed software for de novo protein design, but we highlight relevant work with other protein modeling softwares. Finally, we give an overall view of the current challenges and future opportunities in the field.
Subfields: Materials Chemistry, Molecular Biology, Radiology, Nuclear Medicine and Imaging
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Citations in the corpus
- Identifying well-folded de novo proteins in the new era of accurate structure prediction (2022, background): “Fragment quality descriptors capture the local match between the designed sequence and structure, and proved to be particularly useful for filtering designs in previous studies (Koga et al., 2012; Marcos and Silva, 2018).”