de novo;
design principles;
protein design;
protein function;
protein structure;
Rosetta;
COMPUTATIONAL DESIGN;
STRUCTURE PREDICTION;
RATIONAL DESIGN;
REPEAT PROTEINS;
ACCURATE DESIGN;
HOMO-OLIGOMERS;
COILED-COILS;
ROSETTA;
BUNDLE;
MACROMOLECULES;
D O I:
10.1002/wcms.1374
中图分类号:
O6 [化学];
学科分类号:
0703 ;
摘要:
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. This article is categorized under: Computer and Information Science > Computer Algorithms and Programming Structure and Mechanism > Computational Biochemistry and Biophysics Software > Molecular Modeling Structure and Mechanism > Molecular Structures
机构:
Univ Calif San Francisco, Dept Bioengn & Therapeut Sci, San Francisco, CA 94143 USA
Univ Calif San Francisco, UC Berkeley UCSF Grad Program Bioengn, San Francisco, CA 94143 USAUniv Calif San Francisco, Dept Bioengn & Therapeut Sci, San Francisco, CA 94143 USA
Pan, Xingjie
Kortemme, Tanja
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机构:
Univ Calif San Francisco, Dept Bioengn & Therapeut Sci, San Francisco, CA 94143 USA
Univ Calif San Francisco, UC Berkeley UCSF Grad Program Bioengn, San Francisco, CA 94143 USA
Univ Calif San Francisco, Quantitat Biosci Inst QBI, San Francisco, CA 94143 USAUniv Calif San Francisco, Dept Bioengn & Therapeut Sci, San Francisco, CA 94143 USA
机构:
Syracuse Univ, Dept Chem, 111 Coll Pl, Syracuse, NY 13244 USASyracuse Univ, Dept Chem, 111 Coll Pl, Syracuse, NY 13244 USA
Korendovych, Ivan, V
DeGrado, William F.
论文数: 0引用数: 0
h-index: 0
机构:
Univ Calif San Francisco, Dept Pharmaceut Chem, 555 Mission Bay Blvd, San Francisco, CA 94158 USA
Univ Calif San Francisco, Cardiovasc Res Inst, 555 Mission Bay Blvd, San Francisco, CA 94158 USASyracuse Univ, Dept Chem, 111 Coll Pl, Syracuse, NY 13244 USA