Exploring the limits of protein sequence space

2015 ◽  
Vol 8 (363) ◽  
pp. ec32-ec32
Author(s):  
Guy Riddihough
Keyword(s):  
Sequence Space ◽  
Protein Sequence ◽  
Protein Sequence Space

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

2019 ◽  
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.

scholarly journals Experimental Rugged Fitness Landscape in Protein Sequence Space

PLoS ONE ◽  
2006 ◽  
Vol 1 (1) ◽  
pp. e96 ◽  
Author(s):  
Yuuki Hayashi ◽  
Takuyo Aita ◽  
Hitoshi Toyota ◽  
Yuzuru Husimi ◽  
Itaru Urabe ◽  
...  
Keyword(s):  
Sequence Space ◽  
Protein Sequence ◽  
Fitness Landscape ◽  
Protein Sequence Space

scholarly journals Pervasive degeneracy and epistasis in a protein-protein interface

Science ◽  
2015 ◽  
Vol 347 (6222) ◽  
pp. 673-677 ◽  
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.

Experimental sketch of landscapes in protein sequence space

1995 ◽  
Vol 79 (2) ◽  
pp. 107-118 ◽  
Author(s):  
Savitr Trakulnaleamsai ◽  
Tetsuya Yomo ◽  
Masako Yoshikawa ◽  
Satoshi Aihara ◽  
Itaru Urabe
Keyword(s):  
Sequence Space ◽  
Protein Sequence ◽  
Protein Sequence Space

Exploring the limits of protein sequence space

Science ◽  
2015 ◽  
Vol 347 (6222) ◽  
pp. 623.16-625
Author(s):  
Guy Riddihough
Keyword(s):  
Sequence Space ◽  
Protein Sequence ◽  
Protein Sequence Space

scholarly journals PairsDB atlas of protein sequence space

2007 ◽  
Vol 36 (suppl_1) ◽  
pp. D276-D280 ◽  
Author(s):  
Andreas Heger ◽  
Eija Korpelainen ◽  
Taavi Hupponen ◽  
Kimmo Mattila ◽  
Vesa Ollikainen ◽  
...  
Keyword(s):  
Sequence Space ◽  
Protein Sequence ◽  
Protein Sequence Space
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