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Algorithms for protein design
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DOI：10.1016/j.sbi.2016.03.006.png)
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Abstract
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Computational structure-based protein design programs are becoming an increasingly important tool in molecular biology. These programs compute protein sequences that are predicted to fold to a target structure and perform a desired function. The success of a program's predictions largely relies on two components: first, the input biophysical model, and second, the algorithm that computes the best sequence(s) and structure(s) according to the biophysical model. Improving both the model and the algorithm in tandem is essential to improving the success rate of current programs, and here we review recent developments in algorithms for protein design, emphasizing how novel algorithms enable the use of more accurate biophysical models. We conclude with a list of algorithmic challenges in computational protein design that we believe will be especially important for the design of therapeutic proteins and protein assemblies.
Keywords:
DEAD-END ELIMINATION
COMPUTATIONAL DESIGN
SIDE-CHAIN
OPTIMIZATION
PREDICTION
HIV-1
RESISTANCE
ANTIBODIES
FRAMEWORK
AFFINITY
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