How much of protein sequence space has been explored by life on Earth?

We suggest that the vastness of protein sequence space is actually completely explorable during the populating of the Earth by life by considering upper and lower limits for the number of organisms, genome size, mutation rate and the number of functionally distinct classes of amino acids. We conclude that rather than life having explored only an infinitesimally small part of sequence space in the last 4 Gyr, it is instead quite plausible for all of functional protein sequence space to have been explored and that furthermore, at the molecular level, there is no role for contingency.

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Deep learning enables therapeutic antibody optimization in mammalian cells by deciphering high-dimensional protein sequence space

10.1101/617860 ◽

2019 ◽

Cited By ~ 12

Author(s):

Derek M Mason ◽

Simon Friedensohn ◽

Cédric R Weber ◽

Christian Jordi ◽

Bastian Wagner ◽

...

Keyword(s):

Deep Learning ◽

Sequence Space ◽

Protein Sequence ◽

In Silico ◽

Mammalian Cells ◽

Therapeutic Antibody ◽

Quality Data ◽

High Dimensional ◽

Antigen Specificity ◽

Protein Sequence Space

ABSTRACTTherapeutic antibody optimization is time and resource intensive, largely because it requires low-throughput screening (103 variants) of full-length IgG in mammalian cells, typically resulting in only a few optimized leads. Here, we use deep learning to interrogate and predict antigen-specificity from a massively diverse sequence space to identify globally optimized antibody variants. Using a mammalian display platform and the therapeutic antibody trastuzumab, rationally designed site-directed mutagenesis libraries are introduced by CRISPR/Cas9-mediated homology-directed repair (HDR). Screening and deep sequencing of relatively small libraries (104) produced high quality data capable of training deep neural networks that accurately predict antigen-binding based on antibody sequence. Deep learning is then used to predict millions of antigen binders from an in silico library of ~108 variants, where experimental testing of 30 randomly selected variants showed all 30 retained antigen specificity. The full set of in silico predicted binders is then subjected to multiple developability filters, resulting in thousands of highly-optimized lead candidates. With its scalability and capacity to interrogate high-dimensional protein sequence space, deep learning offers great potential for antibody engineering and optimization.

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Exploring the limits of protein sequence space

Science Signaling ◽

10.1126/scisignal.aaa8618 ◽

2015 ◽

Vol 8 (363) ◽

pp. ec32-ec32

Author(s):

Guy Riddihough

Keyword(s):

Sequence Space ◽

Protein Sequence ◽

Protein Sequence Space

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Experimental Rugged Fitness Landscape in Protein Sequence Space

PLoS ONE ◽

10.1371/journal.pone.0000096 ◽

2006 ◽

Vol 1 (1) ◽

pp. e96 ◽

Cited By ~ 37

Author(s):

Yuuki Hayashi ◽

Takuyo Aita ◽

Hitoshi Toyota ◽

Yuzuru Husimi ◽

Itaru Urabe ◽

...

Keyword(s):

Sequence Space ◽

Protein Sequence ◽

Fitness Landscape ◽

Protein Sequence Space

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Pervasive degeneracy and epistasis in a protein-protein interface

Science ◽

10.1126/science.1257360 ◽

2015 ◽

Vol 347 (6222) ◽

pp. 673-677 ◽

Cited By ~ 106

Author(s):

Anna I. Podgornaia ◽

Michael T. Laub

Keyword(s):

Escherichia Coli ◽

Sequence Space ◽

Protein Sequence ◽

Difficult Problem ◽

Protein Interface ◽

Functional Variants ◽

Key Residues ◽

Protein Sequence Space ◽

Step Selection ◽

Generation Sequencing

Mapping protein sequence space is a difficult problem that necessitates the analysis of 20N combinations for sequences of length N. We systematically mapped the sequence space of four key residues in the Escherichia coli protein kinase PhoQ that drive recognition of its substrate PhoP. We generated a library containing all 160,000 variants of PhoQ at these positions and used a two-step selection coupled to next-generation sequencing to identify 1659 functional variants. Our results reveal extensive degeneracy in the PhoQ-PhoP interface and epistasis, with the effect of individual substitutions often highly dependent on context. Together, epistasis and the genetic code create a pattern of connectivity of functional variants in sequence space that likely constrains PhoQ evolution. Consequently, the diversity of PhoQ orthologs is substantially lower than that of functional PhoQ variants.

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