Obituary: Dan S Tawfik (1955–2021)

Aharoni, Amir; Fleishman, Sarel J.

doi:10.1111/febs.16019

Cited by 3 publications

(2 citation statements)

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“…The critical importance of negative-design principles to counter misfolding and aggregation [ 8 , 23 , 94 , 95 ], the reliability of sequence-based “consensus” design [ 73 ] and ancestral sequence reconstruction [ 74 ], the modularity of so many of the most versatile protein folds [ 96 ], and the fact that most mutations are neutral or deleterious [ 18 , 71 ] have shaped our design strategy. We were also fortunate to have an intense and fruitful dialogue with one of the leaders of modern enzyme evolution and engineering, Dan Tawfik, who tragically died last year [ 97 ]. He made profound contributions to clarifying these principles [ 98 – 101 ] and insisted that design methods should be tested on real-world protein engineering challenges, collaborating with us to design large proteins that were intransigent even to the most reliable computational and experimental optimization strategies [ 68 , 69 , 80 ].…”

Section: What Have We Learned From Evolution-guided Atomistic Design?mentioning

confidence: 99%

What Have We Learned from Design of Function in Large Proteins?

Khersonsky

Fleishman

2022

BioDesign Res

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The overarching goal of computational protein design is to gain complete control over protein structure and function. The majority of sophisticated binders and enzymes, however, are large and exhibit diverse and complex folds that defy atomistic design calculations. Encouragingly, recent strategies that combine evolutionary constraints from natural homologs with atomistic calculations have significantly improved design accuracy. In these approaches, evolutionary constraints mitigate the risk from misfolding and aggregation, focusing atomistic design calculations on a small but highly enriched sequence subspace. Such methods have dramatically optimized diverse proteins, including vaccine immunogens, enzymes for sustainable chemistry, and proteins with therapeutic potential. The new generation of deep learning-based ab initio structure predictors can be combined with these methods to extend the scope of protein design, in principle, to any natural protein of known sequence. We envision that protein engineering will come to rely on completely computational methods to efficiently discover and optimize biomolecular activities.

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Section: What Have We Learned From Evolution-guided Atomistic Design?mentioning

confidence: 99%

What Have We Learned from Design of Function in Large Proteins?

Khersonsky

Fleishman

2022

BioDesign Res

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“…Danny made hugely significant contributions to the area of enzyme evolution, won numerous prestigious awards for his work and was a brilliant mentor to many young scientists who have gone on to establish their own laboratories. Amir Aharoni and Sarel Fleishman wrote a very touching tribute to Dan that conveys what a special scientist and person he was [2]. We will miss him.…”

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confidence: 99%