Therapeutic antibody development requires selection and engineering of molecules with high affinity and other drug-like biophysical properties. Co-optimization of multiple antibody properties remains a difficult and time-consuming process that impedes drug development. Here we evaluate the use of machine learning to simplify antibody co-optimization for a clinical-stage antibody (emibetuzumab) that displays high levels of both on-target (antigen) and off-target (non-specific) binding. We mutate sites in the antibody complementarity-determining regions, sort the antibody libraries for high and low levels of affinity and non-specific binding, and deep sequence the enriched libraries. Interestingly, machine learning models trained on datasets with binary labels enable predictions of continuous metrics that are strongly correlated with antibody affinity and non-specific binding. These models illustrate strong tradeoffs between these two properties, as increases in affinity along the co-optimal (Pareto) frontier require progressive reductions in specificity. Notably, models trained with deep learning features enable prediction of novel antibody mutations that co-optimize affinity and specificity beyond what is possible for the original antibody library. These findings demonstrate the power of machine learning models to greatly expand the exploration of novel antibody sequence space and accelerate the development of highly potent, drug-like antibodies.

Angiotensin I-converting enzyme (ACE) peptides are bioactive peptides that have important value in terms of research and application in the prevention and treatment of hypertension. While widespread literature is concentrated on casein or whey protein for production of ACE-inhibitory peptides, relatively little information is available on selecting the proper proteases to hydrolyze the protein. In this study, skimmed cow and goat milk were hydrolyzed by four commercial proteases, including alkaline protease, trypsin, bromelain, and papain. Angiotensin I-converting enzyme-inhibitory peptides and degree of hydrolysis (DH) of hydrolysates were measured. Moreover, we compared the difference in ACE-inhibitory activity between cow and goat milk. The results indicated that the DH increased with the increase in hydrolysis time. The alkaline protease-treated hydrolysates exhibited the highest DH value and ACE-inhibitory activity. Additionally, the ACE-inhibitory activity of hydrolysates from goat milk was higher than that of cow milk-derived hydrolysates. Therefore, goat milk is a good source to obtain bioactive peptides with ACE-inhibitory activity, as compared with cow milk. A proper enzyme to produce ACE-inhibitory peptides is important for the development of functional milk products and will provide the theoretical basis for industrial production.

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Background. The efficacy of Lactobacillus delbrueckii subsp. bulgaricus as starter cultures for the dairy industry depends largely on the number of viable and active cells. Freeze-drying is the most convenient and successful method to preserve the bacterial cells. However, not all strains survived during freeze-drying. Methods. The effects of six substances including NaCl, sorbitol, mannitol, mannose, sodium glutamate, betaine added to the MRS medium on the growth and freeze-drying survival rate and viable counts of Lb. delbrueckii subsp. bulgaricus were studied through a single-factor test and Plackett-Burman design. Subsequently, the optimum freeze-drying conditions of Lb. delbrueckii subsp. bulgaricus were determined. Results. Lb. delbrueckii subsp. bulgaricus survival rates were up to the maximum of 42.7%, 45.4%, 23.6%, while the concentrations of NaCl, sorbitol, sodium glutamate were 0.6%, 0.15%, 0.09%, respectively. In the optimum concentration, the viable counts in broth is 6.1, 6.9, 5.13 (×10 8 CFU/mL), respectively; the viable counts in freeze-drying power are 3.09, 5.2, 2.7 (×10 10 CFU/g), respectively. Conclusion. Three antifreeze factors including NaCl, sorbitol, sodium glutamate have a positive effect on the growth and freeze-drying of Lb. delbrueckii subsp. bulgaricus. The results are beneficial for developing Lb. delbrueckii subsp. bulgaricus.

The effects of kefir grains from different regions in China on fermentation and bioactivity were studied by using pH value, acidity degree, protein hydrolysis degree, antioxidant activity, angiotensin converting enzyme (ACE, EC 3.4.15.1) inhibition rate and sensory evaluation as indexes to select the most suitable kefir grains as starter for goat milk. The parameters of kefir fermented goat milk were optimized by single factor experiment constantly. The results showed that kefir grain K1, which performed better in antioxidant activity, ACE inhibitory activity than the other four kefir grains and sensory evaluation was inferior to kefir grain K5 only, was suitable for the fermentation of goat milk. And the optimum fermentation conditions were found to be as 3% inoculation size at 25 °C for 22h.

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