US 11,670,399 B2
Systems and methods for predicting glycosylation on proteins
Philipp N. Spahn, San Diego, CA (US); and Nathan E. Lewis, San Diego, CA (US)
Assigned to The Regents of the University of California, Oakland, CA (US)
Appl. No. 15/573,239
Filed by The Regents of the University of California, Oakland, CA (US)
PCT Filed May 18, 2016, PCT No. PCT/US2016/033136
§ 371(c)(1), (2) Date Nov. 10, 2017,
PCT Pub. No. WO2016/187341, PCT Pub. Date Nov. 24, 2016.
Claims priority of provisional application 62/162,901, filed on May 18, 2015.
Prior Publication US 2018/0101643 A1, Apr. 12, 2018
Int. Cl. G01N 33/48 (2006.01); G16B 40/00 (2019.01); G01N 33/68 (2006.01); G01N 33/50 (2006.01); C12P 21/00 (2006.01); G16C 20/60 (2019.01); G16B 5/20 (2019.01); G16B 35/10 (2019.01); G16H 50/50 (2018.01); G06N 7/08 (2006.01)
CPC G16B 40/00 (2019.02) [C12P 21/005 (2013.01); G01N 33/50 (2013.01); G01N 33/68 (2013.01); G06N 7/08 (2013.01); G16B 5/20 (2019.02); G16B 35/10 (2019.02); G16C 20/60 (2019.02); G16H 50/50 (2018.01); Y02A 90/10 (2018.01)] 21 Claims
 
1. A method comprising:
generating, by a computing device, using a known initial structure and a plurality of known glycosylation enzymes and/or reactions, a generic glycosylation reaction network comprising a plurality of glycans;
receiving, at the computing device, information relating to a measured glycoprofile of a particular protein, the measured glycoprofile comprising a plurality of glycoprofile glycans, each having an associated relative frequency;
tailoring the generic glycosylation reaction network to the measured glycoprofile to provide a tailored network, the tailored network comprising a reduced set of the plurality of glycans, the reduced set including the glycoprofile glycans;
transforming the tailored network into a Markov chain, the Markov chain representing a stochastic network wherein each glycoprofile glycan in the reduced set is regarded as a state in the stochastic network that can transition to another state in the stochastic network with a particular transition probability;
determining reaction conditions that approximate the particular transition probabilities that define a set of desired states in the stochastic network; and
culturing cells under the reaction conditions that approximate said particular transition probabilities, thereby producing a desired glycoprofile glycan.