Application of N-Terminal Site-Specific Biotin and Digoxigenin Conjugates to Clinical Anti-drug Antibody Assay Development.

Biotin- and digoxigenin (DIG)-conjugated therapeutic drugs are critical reagents used for the development of anti-drug antibody (ADA) assays for the assessment of immunogenicity. The current practice of generating biotin and DIG conjugates is to label a therapeutic antibody with biotin or DIG via primary amine groups on lysine or N-terminal residues. This approach modifies lysine residues nonselectively, which can impact the ability of an ADA assay to detect those ADAs that recognize epitopes located at or near the modified lysine residue(s). The impact of the lysine modification is considered greater for therapeutic antibodies that have a limited number of lysine residues, such as the variable heavy domain of heavy chain (VHH) antibodies. In this paper, for the first time, we report the application of site-specifically conjugated biotin- and DIG-VHH reagents to clinical ADA assay development using a model molecule, VHHA. The site-specific conjugation of biotin or DIG to VHHA was achieved by using an optimized reductive alkylation approach, which enabled the majority of VHHA molecules labeled with biotin or DIG at the desirable N-terminus, thereby minimizing modification of the protein after labeling and reducing the possibility of missing detection of ADAs. Head-to-head comparison of biophysical characterization data revealed that the site-specific biotin and DIG conjugates demonstrated overall superior quality to biotin- and DIG-VHHA prepared using the conventional amine coupling method, and the performance of the ADA assay developed using site-specific biotin and DIG conjugates met all acceptance criteria. The approach described here can be applied to the production of other therapeutic-protein- or antibody-based critical reagents that are used to support ligand binding assays.

Characteristics of Highland Barley-Wheat Composite Flour and Its Effect on the Properties of Coating Batter and Deep-Fried Meat.

Highland barley flour-based coating batter has rarely been reported, although highland barley flour is promising due to its high ß-glucan and amylose content. In this study, highland barley flour was used to substitute 40% to 80% of wheat flour to form a highland barely-wheat composite flour used in the coating batter. The characteristics of the highland barley-wheat composite flour and its effect on the properties of coating batter and deep-fried meat were analyzed. Results showed that the composite flour significantly improved water holding capacity, oil absorbing capacity, and water solubility index. In contrast, no significant change was observed in the water absorption index or swelling power. The incorporation of highland barley flour significantly changed the pasting properties of the composite flour. Compared with the wheat flour, the viscosity and the pickup of the coating batter made with composite flour increased from 4905 Pa·s and 0.53% to more than 12,252 Pa·s and 0.63%, respectively, and its water mobility decreased. These changes were closely related to the substitution rate of highland barley flour. The composite flour significantly increased the moisture content from 27.73% to more than 33.03% and decreased the oil content of the crust from 19.15% to lower than 16.44%, respectively. It decreased L* and increased a* of the crust and decreased the hardness, adhesiveness, and springiness of the deep-fried meat. A spongy inner structure with a flatter surface was formed in all composite flour-based crusts, and the substitution rate influenced the flatness of the crust. Thus, highland barley flour could be used for batter preparation with partial substitution, enhancing the quality of deep-fried meat and acting as an oil barrier-forming ingredient for fried batter foods.

Determination and Analysis of Composition, Structure, and Properties of Teff Protein Fractions.

To develop teff-based food products with acceptable quality, the composition, structure, and properties of teff protein fractions should be better understood. In this study, teff proteins were extracted, and their protein composition, structure, and properties were calculated, analyzed, and compared with those of wheat gliadin and glutenin. Results showed that teff flour contained 9.07% protein, with prolamin as its main protein fraction. The isoelectric points of albumin, globulin, prolamin, and glutelin were at pH 3.6, 3.0, 4.4, and 3.4, respectively. Teff prolamin and glutelin showed a significant difference in amino acids and free energy of hydration compared to wheat gliadins and glutenins. The protein chain length of teff prolamins was smaller than that of wheat gliadins, and teff glutelins lacked high molecular weight glutelin subunits. Teff prolamin had the highest α-helices content (27.08%), whereas no random coils were determined, which is different from wheat gliadin. Teff glutelin had a lower content of ß-turn than wheat glutenin, and no α-helices were determined in it. Teff prolamin and glutelin had lower disulfide bond content and surface hydrophobicity. Teff prolamin had significantly higher thermal stability than wheat gliadin, whereas the thermal stability of teff glutelin was significantly lower than that of wheat glutenin.

In Silico Prediction of Hemolytic Toxicity on the Human Erythrocytes for Small Molecules by Machine-Learning and Genetic Algorithm.

Hemolytic toxicity of small molecules, as one of the important ADMET end points, can cause the lysis of erythrocytes membrane and leaking of hemoglobin into the blood plasma, which leads to various side effects. Thus, it is very crucial to assess the hemolytic potential of small molecules during the early stage of drug development process. However, so far there is no computational model to predict the human hemolytic toxicity of small molecules. To this end, we manually curate the hemolytic toxicity data set for the small molecules experimentally evaluated on the human erythrocytes, develop the first machine-learning (ML) based models to predict the human hemolytic toxicity of small molecules, harness the genetic algorithm (GA) and ML based model to optimize human hemolytic toxicity based on the molecular fingerprint to derive "optimal virtual fingerprints (OVFs)" with the desired hemolytic/nonhemolytic property, and finally implement a free software for the users to predict/optimize the human hemolytic toxicity with ML and GA in the automatic manner.

Prediction of Hemolytic Toxicity for Saponins by Machine-Learning Methods.

Saponins are a type of compounds bearing a hydrophobic steroid/triterpenoid moiety and hydrophilic carbohydrate branches. The majority of the saponins demonstrate a broad range of prominent pharmacological activities. Nevertheless, many saponins also possess harmful hemolytic toxicity, which can cause the lysis of erythrocytes and thereby hamper their applications in medicine. As such, the organic synthesis of diverse saponins with versatile therapeutic effects and without hemolytic toxicity has gained considerable interests among medicinal/organic chemists. To date, the non-hemolytic saponins of interests have usually been designed by the traditional trial-and-error method or discovered by serendipity. It would be more efficient to develop an in silico method to rationally design promising saponins without hemolytic toxicity prior to the laborious organic synthesis, despite the fact that there is, so far, no computational model to predict the hemolytic toxicity of saponins. To this end, we manually curate 331 hemolytic and 121 non-hemolytic saponins from the literature for the first time and build the first machine-learning-based hemolytic toxicity classification model for the saponins, which provides encouraging performance with 95% confidence intervals for accuracy (0.906 ± 0.009), precision (0.904 ± 0.012), specificity (0.711 ± 0.039), sensitivity (0.978 ± 0.010), F1-score (0.939 ± 0.006), and Matthews correlation coefficient (0.756 ± 0.025) on the test set by averaging over 19 different random data-partitioning schemes. Moreover, we have developed a free program called "e-Hemolytic-Saponin" for the automatic prediction and design of hemolytic/non-hemolytic saponins. To the best of our knowledge, we herein compile the first comprehensive saponin dataset focused on hemolytic toxicity, build the first informative model of hemolytic toxicity for the saponins, and implement the first convenient software that will enable organic/medicinal chemists to automatically predict and design the saponins of interests.

e-Sweet: A Machine-Learning Based Platform for the Prediction of Sweetener and Its Relative Sweetness.

Artificial sweeteners (AS) can elicit the strong sweet sensation with the low or zero calorie, and are widely used to replace the nutritive sugar in the food and beverage industry. However, the safety issue of current AS is still controversial. Thus, it is imperative to develop more safe and potent AS. Due to the costly and laborious experimental-screening of AS, in-silico sweetener/sweetness prediction could provide a good avenue to identify the potential sweetener candidates before experiment. In this work, we curate the largest dataset of 530 sweeteners and 850 non-sweeteners, and collect the second largest dataset of 352 sweeteners with the relative sweetness (RS) from the literature. In light of these experimental datasets, we adopt five machine-learning methods and conformational-independent molecular fingerprints to derive the classification and regression models for the prediction of sweetener and its RS, respectively via the consensus strategy. Our best classification model achieves the 95% confidence intervals for the accuracy (0.91 ± 0.01), precision (0.90 ± 0.01), specificity (0.94 ± 0.01), sensitivity (0.86 ± 0.01), F1-score (0.88 ± 0.01), and NER (Non-error Rate: 0.90 ± 0.01) on the test set, which outperforms the model (NER = 0.85) of Rojas et al. in terms of NER, and our best regression model gives the 95% confidence intervals for the R2(test set) and ΔR2 [referring to |R2(test set)- R2(cross-validation)|] of 0.77 ± 0.01 and 0.03 ± 0.01, respectively, which is also better than the other works based on the conformation-independent 2D descriptors (e.g., 2D Dragon) according to R2(test set) and ΔR2. Our models are obtained by averaging over nineteen data-splitting schemes, and fully comply with the guidelines of Organization for Economic Cooperation and Development (OECD), which are not completely followed by the previous relevant works that are all on the basis of only one random data-splitting scheme for the cross-validation set and test set. Finally, we develop a user-friendly platform "e-Sweet" for the automatic prediction of sweetener and its corresponding RS. To our best knowledge, it is a first and free platform that can enable the experimental food scientists to exploit the current machine-learning methods to boost the discovery of more AS with the low or zero calorie content.

Computational Prediction of a New ADMET Endpoint for Small Molecules: Anticommensal Effect on Human Gut Microbiota.

The human gut microbiota (HGM), which are evolutionarily commensal in the human gastrointestinal system, are crucial to our health. However, HGM can be broadly shaped by multifaceted factors such as intake of drugs. About one-quarter of the existing drugs for humans, which are designed to target human cells rather than HGM, can notably alter the composition of HGM. Therefore, the anticommensal effect of human drugs should be avoided to the maximum extent possible in the drug discovery and development process. Nevertheless, the anticommensal effect of small molecules is a new ADMET (absorption, distribution, metabolism, excretion, and toxicity) end point, which was never predicted with the computational method before. In this work, we present the first machine-learning based consensus classification model with the accuracy (0.811 ± 0.012), precision (0.759 ± 0.032), specificity (0.901 ± 0.019), sensitivity (0.628 ± 0.036), F1-score (0.687 ± 0.023), and AUC (0.814 ± 0.030) respectively on the test set. Furthermore, we develop an easy-to-use "e-Commensal" program for the automatic prediction. Based on this program, virtual-screening of the food-constituent database (FooDB) indicates that 5888 of 23 202 food-relevant compounds are forecasted to possess an anticommensal effect on HGM. Several top-ranked anticommensal compounds in our prediction are further scrutinized and confirmed by experiments in the existing literature. To the best of our knowledge, this is the first classification model and stand-alone software for the prediction of commensal or anticommensal compounds impacting HGM.