Implementation of a Design of Experiments to Improve Periplasmic Yield of Functional ScFv Antibodies in a Phage Display Platform

Purpose: Production of functional recombinant antibody fragments in the periplasm of E. coli is a prerequisite step to achieve sufficient reagent for preclinical studies. Thus, the cost-effective and lab-scale production of antibody fragments demands the optimization of culture conditions. Methods: The culture conditions such as temperature, optical density (OD600) at induction, induction time, and IPTG concentration were investigated to optimize the functional expression of a phage-derived scFv molecule using a design of experiment (DoE). Additionally, the effects of different culture media and osmolyte supplements on the expression yield of scFv were examined. Results: The developed 2FI regression model indicated the significant linear effect of the incubation temperature, the induction time, and the induction OD600 on the expression yield of functional scFv. Besides, the statistical analysis indicated that two significant interactions of the temperature/induction time and the temperature/induction OD600 significantly interplay to increase the yield. Further optimization showed that the expression level of functional scFv was the most optimal when the cultivation was undertaken either in the TB medium or in the presence of media supplements of 0.5 M sorbitol or 100 mM glycine betaine. Conclusion: In the present study, for the first time, we successfully implemented DoE to comprehensively optimize the culture conditions for the expression of scFv molecules in a phage antibody display setting, where scFv molecules can be isolated from a tailor-made phage antibody library known as "Human Single Fold scFv Library I."

Pervasive fold switching in a ubiquitous protein superfamily

Fold-switching proteins challenge the one-sequence-one-structure paradigm by adopting multiple stable folds. Nevertheless, it is uncertain whether fold switchers are naturally pervasive or rare exceptions to the well-established rule. To address this question, we developed a predictive method and applied it to the NusG superfamily of >15,000 transcription factors. We predicted that a substantial population (25%) of the proteins in this family switch folds. Circular dichroism and nuclear magnetic resonance spectroscopies of 10 sequence-diverse variants confirmed our predictions. Thus, we leveraged family-wide predictions to determine both conserved contacts and taxonomic distributions of fold-switching proteins. Our results indicate that fold switching is pervasive in the NusG superfamily and that the single-fold paradigm significantly biases structure-prediction strategies.

MTDDI: a graph convolutional network framework for predicting Multi-Type Drug-Drug Interactions

Although the polypharmacy has both higher therapeutic efficacy and less drug resistance in combating complex diseases, drug-drug interactions (DDIs) may trigger unexpected pharmacological effects, such as side effects, adverse reactions, or even serious toxicity. Thus, it is crucial to identify DDIs and explore its underlying mechanism (e.g., DDIs types) for polypharmacy safety. However, the detection of DDIs in assays is still time-consuming and costly, due to the need of experimental search over a large drug combinational space. Machine learning methods have been proved as a promising and efficient method for preliminary DDI screening. Most shallow learning-based predictive methods focus on whether a drug interacts with another or not. Although deep learning (DL)-based predictive methods address a more realistic screening task for identifying the DDI types, they only predict the DDI types of known DDI, ignoring the structural relationship between DDI entries, and they also cannot reveal the knowledge about the dependence between DDI types. Thus, here we proposed a novel end-to-end deep learning-based predictive method (called MTDDI) to predict DDIs as well as its types, exploring the underlying mechanism of DDIs. MTDDI designs an encoder derived from enhanced deep relational graph convolutional networks to capture the structural relationship between multi-type DDI entries, and adopts the tensor-like decoder to uniformly model both single-fold interactions and multi-fold interactions to reflect the relation between DDI types. The results show that our MTDDI is superior to other state-of-the-art deep learning-based methods. For predicting the multi-type DDIs with unknown DDIs in case of both single-fold DDIs and multi-fold DDIs, we validated the effectiveness and the practical capability of our MTDDI. More importantly, MTDDI can reveal the dependency between DDI types. These crucial observations are beneficial to uncover the mechanism and regularity of DDIs.

Sampling of Structure and Sequence Space of Small Protein Folds

Nature only samples a small fraction in sequence space, yet many more amino acid combinations can fold into stable proteins. Furthermore, small structural variations in a single fold, which may only be a few amino acids different from the next homolog, define their molecular function. Hence, to design proteins with novel molecular functionalities, such as molecular recognition, methods to control and sample shape diversity are necessary. To explore this space, we developed and experimentally validated a computational platform that can design a wide variety of small protein folds while sampling high shape diversity. We designed and evaluated about 30,000 de novo protein designs of 7 different folds. Among these designs, about 6,200 stable proteins were identified, with predicted structures having first-of-its-kind minimalized thioredoxin. Obtained data revealed more protein folding rules, such as helix connecting loops, which were in nature. Beyond providing a resource database for protein engineering, our data presents a large training data set for machine learning. We developed a high-accuracy classifier to predict the stability of our designed proteins. The methods and the wide range of new protein shapes provide a basis for the design of new protein function without compromising stability.

A sequence-based method for predicting extant fold switchers that undergo α-helix β-strand transitions

Extant fold-switching proteins remodel their secondary structures and change their functions in response to cellular stimuli, regulating biological processes and affecting human health. In spite of their biological importance, these proteins remain understudied. Few representative examples of fold switchers are available in the Protein Data Bank, and they are difficult to predict. In fact, all 96 experimentally validated examples of extant fold switchers were stumbled upon by chance. Thus, predictive methods are needed to expedite the process of discovering and characterizing more of these shapeshifting proteins. Previous approaches require a solved structure or all-atom simulations, greatly constraining their use. Here, we propose a high-throughput sequence-based method for predicting extant fold switchers that transition from α-helix in one conformation to β-strand in the other. This method leverages two previous observations: (1) α-helix <-> β-strand prediction discrepancies from JPred4 are a robust predictor of fold switching, and (2) the fold-switching regions (FSRs) of some extant fold switchers have different secondary structure propensities when expressed in isolation (isolated FSRs) than when expressed within the context of their parent protein (contextualized FSRs). Combining these two observations, we ran JPred4 on the sequences of isolated and contextualized FSRs from 14 known extant fold switchers and found α-helix <->β-strand prediction discrepancies in every case. To test the overall robustness of this finding, we randomly selected regions of proteins not expected to switch folds (single-fold proteins) and found significantly fewer α-helix <-> β-strand prediction discrepancies (p < 4.2*10−20, Kolmogorov-Smirnov test). Combining these discrepancies with the overall percentage of predicted secondary structure, we developed a classifier that often robustly identifies extant fold switchers (Matthews Correlation Coefficient of 0.70). Although this classifier had a high false negative rate (6/14), its false positive rate was very low (1/211), suggesting that it can be used to predict a subset of extant fold switchers from billions of available genomic sequences.

Exploring the Sequence Fitness Landscape of a Bridge Between Protein Folds

Most foldable protein sequences adopt only a single native fold. Recent protein design studies have, however, created protein sequences which fold into different structures apon changes of environment, or single point mutation, the best characterized example being the switch between the folds of the GA and GB binding domains of streptococcal protein G. To obtain further insight into the design of sequences which can switch folds, we have used a computational model for the fitness landscape of a single fold, built from the observed sequence variation of protein homologues. We have recently shown that such coevolutionary models can be used to design novel foldable sequences. By appropriately combining two of these models to describe the joint fitness landscape of GA and GB, we are able to describe the propensity of a given sequence for each of the two folds. We have successfully tested the combined model against the known series of designed GA/GB hybrids. Using Monte Carlo simulations on this landscape, we are able to identify pathways of mutations connecting the two folds. In the absence of a requirement for domain stability, the most frequent paths go via sequences in which neither domain is stably folded, reminiscent of the propensity for certain intrinsically disordered proteins to fold into different structures according to context. Even if the folded state is required to be stable, we find that there is nonetheless still a wide range of sequences which are close to the transition region and therefore likely fold switches, consistent with recent estimates that fold switching may be more widespread than had been thought.Author SummaryWhile most proteins self-assemble (or “fold”) to a unique three-dimensional structure, a few have been identified that can fold into two distinct structures. These so-called “metamorphic” proteins that can switch folds have attracted a lot of recent interest, and it has been suggested that they may be much more widespread than currently appreciated. We have developed a computational model that captures the propensity of a given protein sequence to fold into either one of two specific structures (GA and GB), in order to investigate which sequences are able to fold to both GA and GB (“switch sequences”), versus just one of them. Our model predicts that there is a large number of switch sequences that could fold into both structures, but also that the most likely such sequences are those for which the folded structures have low stability, in agreement with available experimental data. This also suggests that intrinsically disordered proteins which can fold into different structures on binding may provide an evolutionary path in sequence space between protein folds.

Folding of Thick Origami Through Regionally Sandwiched Compliant Sheets

The regional sandwiching of compliant sheets (ReCS) technique presented in this work creates flat-foldable, rigid-foldable, and self-deploying thick origami-based mechanisms. Regional sandwiching of the compliant sheet is used to create mountain-valley assignments for each fold about a vertex, constraining motion to a single branch of folding. Strain energy in deflected flexible members is used to enable self-deployment. This work presents the methods to design origami-based mechanisms using the ReCS technique, including volume trimming at the vertex of the compliant sheet and of the panels used in the sandwich. Three physical models, a simple single fold mechanism, a degree-four vertex mechanism, and a full tessellation, are presented to demonstrate the ReCS technique using acrylic panels with spring and low-carbon steels. Consideration is given to the risk of yielding of the compliant sheet due to parasitic motion with possible mitigation of yielding by decreasing the thickness of the sheet.

Thick Folding Through Regionally-Sandwiched Compliant Sheets

The regional-sandwiching of compliant sheets (ReCS) technique presented in this work creates flat-foldable, rigid-foldable, and self-deploying thick origami-based mechanisms. Regional-sandwiching of the compliant sheet is used to create mountain/valley assignments for each fold about a vertex, constraining motion to a single branch of folding. Strain energy in deflected flexible members is used to enable self-deployment. This work presents the methods to design origami-based mechanisms using the ReCS technique, including volume trimming at the vertex of the compliant sheet and of the panels used in the sandwich. Physical models of a simple single fold mechanism and a degree-four vertex mechanism are presented to demonstrate the ReCS technique using acrylic panels and spring steel. Consideration is given to the risk of yielding of the compliant sheet due to parasitic motion with possible mitigation of yielding by decreasing the thickness of the sheet.

Beam-Based Vibration Energy Harvesters Tunable Through Folding

We present a novel beam-based vibration energy harvester, and use a structural tailoring concept to tune its natural frequencies. Using a solution of the Euler–Bernoulli beam theory equations, verified with finite element (FE) solutions of shell theory equations, we show that introducing folds or creases along the span of a slender beam, varying the fold angle at a crease, and changing the crease location helps tune the beam natural frequencies to match an external excitation frequency and maximize the energy harvested. For a beam clamped at both ends, the first frequency can be increased by 175% with a single fold. With two folds, selective frequencies can be tuned, leaving others unchanged. The number of folds, their locations, and the fold angles act as tuning parameters that provide high sensitivity and controllability of the frequency response of the harvester. The analytical model can be used to quickly optimize designs with multiple folds for anticipated external frequencies.