Machine learning modeling for solubility prediction of recombinant antibody fragment in four different E. coli strains

Optimizing recombinant antibody fragment production: A comparison of artificial intelligence and statistical modeling

Maximizing the recombinant protein yield necessitates optimizing the production medium. This can be done using a variety of methods, including the conventional "one-factor-at-a-time" approach and more recent statistical and mathematical methods such as artificial neural network (ANN), genetic algorithm, etc. Every approach has advantages and disadvantages of its own, yet even when a technique has flaws, it is nevertheless used to get the best results. Here, one categorical variable and four numerical parameters, including post-induction time, inducer concentration, post-induction temperature, and pre-induction cell density, were optimized using the 232 experimental assays of the central composite design. The direct and indirect effects of factors on the yield of anti-epithelial cell adhesion molecule extracellular domain fragment antibody were examined using statistical methods. The analysis of variance results indicate that the response surface methodology (RSM) model is effective in predicting the amount of produced single-chain fragment variable (p-value = 0.0001 and R2 = 0.905). For ANN modeling, the evaluation using normalized root mean square error (NRMSE) and R2 values shows a good fit (R2 = 0.942) and accurate predictions (NRMSE = 0.145). The analysis of error parameters and R2 of a dataset, which contained 30 data points randomly selected from the complete dataset, showed that the ANN model had a higher R2 value (0.968) compared to the RSM model (0.932). Furthermore, the ANN model demonstrated stronger predictive ability with a lower NRMSE (0.048 vs. 0.064). Induction at the cell density of 0.7 and an isopropyl β-D-1-thiogalactopyranoside concentration of 0.6 mM for 32 h at 30°C in BW25113 was the ideal culture condition leading to the protein yield of 259.51 mg/L. Under the optimum conditions, the output values predicted by the ANN model (259.83 mg/L) were more in line with the experimental data (259.51 mg/L) than the RSM (276.13 mg/L) expected value. This outcome demonstrated that the ANN model outperforms the RSM in terms of prediction accuracy.

Artificial intelligence-driven design of the assembled major cat allergen Fel d 1 to improve its spatial folding and IgE-reactivity

BACKGROUND

Cat-derived allergens are considered as one of the most common causes of allergic diseases worldwide. Fel d 1 is a major cat allergen and plays an important role in immunoglobulin E (IgE)-reaction diagnosis. However, the two separate chains of Fel d 1 exhibited lower IgE-reactivity than its complete molecule of an assembled form, which makes it difficult to efficiently prepare and limits the application of Fel d 1 in molecular diagnosis of cat allergy.

METHODS

We first applied artificial intelligence (AI) based tool AlphaFold2 to build the 3-dimensional structures of Fel d 1 with different connection modes between two chains, which were evaluated by ERRAT program and were expressed in Escherichia coli. We then calculated the expression ratios of soluble form/inclusion bodies form of optimized Fel d 1. The Circular Dichroism (CD), High Performance Liquid Chromatography-Size Exclusion Chromatography (HPLC-SEC) and reducing/non-reducing SDS-PAGE were performed to characterize the folding status and dimerization of the optimized fusion Fel d 1. The improvement of specific-IgE reactivity to optimized fusion Fel d 1 was investigated by enzyme linked immunosorbent assay (ELISA).

RESULTS

Among several linkers, 2 × GGGGS got the highest scores, with an overall quality factor of 100. The error value of the residues around the junction of 2 × GGGGS was lower than others. It exhibited highest proportion of soluble protein than other Fel d 1 constructs with ERRAT (GGGGS, KK as well as direct fusion Fel d 1). The results of CD and HPLC-SEC showed the consistent folding and dimerization of two fused subunits between the optimized fusion Fel d 1 and previously well-defined direct fusion Fel d 1. The overall IgE-binding absorbance of optimized fusion Fel d 1 tested by ELISA was improved compared with that of the direct fusion Fel d 1.

CONCLUSION

We firstly provided an AI-design strategy to optimize the Fel d 1, which could spontaneously fold into its native-like structure without additional refolding process or eukaryotic folding factors. The improved IgE-binding activity and simplified preparation method could greatly facilitate it to be a robust allergen material for molecular diagnosis of cat allergy.

Accelerating antibody discovery and design with artificial intelligence: Recent advances and prospects

Therapeutic antibodies are the largest class of biotherapeutics and have been successful in treating human diseases. However, the design and discovery of antibody drugs remains challenging and time-consuming. Recently, artificial intelligence technology has had an incredible impact on antibody design and discovery, resulting in significant advances in antibody discovery, optimization, and developability. This review summarizes major machine learning (ML) methods and their applications for computational predictors of antibody structure and antigen interface/interaction, as well as the evaluation of antibody developability. Additionally, this review addresses the current status of ML-based therapeutic antibodies under preclinical and clinical phases. While many challenges remain, ML may offer a new therapeutic option for the future direction of fully computational antibody design.

Advancing structural biology through breakthroughs in AI

The past century has witnessed an exponential increase in our atomic-level understanding of molecular and cellular mechanisms from a structural perspective, with multiple landmark achievements contributing to the field. This, coupled with recent and continuing breakthroughs in artificial intelligence methods such as AlphaFold2, and enhanced computational power, is enabling our understanding of protein structure and function at unprecedented levels of accuracy and predictivity. Here, we describe some of the major recent advances across these fields, and describe, as these technologies coalesce, the potential to utilise our enhanced knowledge of intricate cellular and molecular systems to discover novel therapeutics to alleviate human suffering.

Assessing developability early in the discovery process for novel biologics

Beyond potency, a good developability profile is a key attribute of a biological drug. Selecting and screening for such attributes early in the drug development process can save resources and avoid costly late-stage failures. Here, we review some of the most important developability properties that can be assessed early on for biologics. These include the influence of the source of the biologic, its biophysical and pharmacokinetic properties, and how well it can be expressed recombinantly. We furthermore present in silico, in vitro, and in vivo methods and techniques that can be exploited at different stages of the discovery process to identify molecules with liabilities and thereby facilitate the selection of the most optimal drug leads. Finally, we reflect on the most relevant developability parameters for injectable versus orally delivered biologics and provide an outlook toward what general trends are expected to rise in the development of biologics.