Periscope: quantitative prediction of soluble protein expression in the periplasm of Escherichia coli

Maximizing the recovery of the native p28 bacterial peptide with improved activity and maintained solubility and stability in Escherichia coli BL21 (DE3)

p28 is a natural bacterial product, which recently has attracted much attention as an efficient cell penetrating peptide (CPP) and a promising anticancer agent. Considering the interesting biological qualities of p28, maximizing its expression appears to be a prominent priority. The optimization of such bioprocesses might be facilitated by utilizing statistical approaches such as Design of Experiment (DoE). In this study, we aimed to maximize the expression of "biologically active" p28 in Escherichia coli BL21 (DE3) host by harnessing statistical tools and experimental methods. Using Minitab, Plackett-Burman and Box-Behnken Response Surface Methodology (RSM) designs were generated to optimize the conditions for the expression of p28. Each condition was experimentally investigated by assessing the biological activity of the purified p28 in the MCF-7 breast cancer cell line. Seven independent variables were investigated, and three of them including ethanol concentration, OD600 of the culture at the time of induction, and the post-induction temperature were demonstrated to significantly affect the p28 expression in E. coli. The cytotoxicity, penetration efficiency, and total process time were measured as dependent variables. The optimized expression conditions were validated experimentally, and the final products were investigated in terms of expression yield, solubility, and stability in vitro. Following the optimization, an 8-fold increase of the concentration of p28 expression was observed. In this study, we suggest an optimized combination of effective factors to produce soluble p28 in the E. coli host, a protocol that results in the production of a significantly high amount of the biologically active peptide with retained solubility and stability.

PERISCOPE-Opt: Machine learning-based prediction of optimal fermentation conditions and yields of recombinant periplasmic protein expressed in Escherichia coli

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The ensemble model considered both fermentation conditions and protein properties.

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Optimal fermentation conditions and periplasmic recombinant protein yield can be predicted.

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Predictor’s accuracy and Pearson correlation coefficient are 75% and 0.91, respectively.

Optimization of the fermentation process for recombinant protein production (RPP) is often resource-intensive. Machine learning (ML) approaches are helpful in minimizing the experimentations and find vast applications in RPP. However, these ML-based tools primarily focus on features with respect to amino-acid-sequence, ruling out the influence of fermentation process conditions. The present study combines the features derived from fermentation process conditions with that from amino acid-sequence to construct an ML-based model that predicts the maximal protein yields and the corresponding fermentation conditions for the expression of target recombinant protein in the Escherichia coli periplasm. Two sets of XGBoost classifiers were employed in the first stage to classify the expression levels of the target protein as high (>50 mg/L), medium (between 0.5 and 50 mg/L), or low (<0.5 mg/L). The second-stage framework consisted of three regression models involving support vector machines and random forest to predict the expression yields corresponding to each expression-level-class. Independent tests showed that the predictor achieved an overall average accuracy of 75% and a Pearson coefficient correlation of 0.91 for the correctly classified instances. Therefore, our model offers a reliable substitution of numerous trial-and-error experiments to identify the optimal fermentation conditions and yield for RPP. It is also implemented as an open-access webserver, PERISCOPE-Opt (http://periscope-opt.erc.monash.edu).

Artificial Intelligence, Machine Learning and Deep Learning in Ion Channel Bioinformatics

Ion channels are linked to important cellular processes. For more than half a century, we have been learning various structural and functional aspects of ion channels using biological, physiological, biochemical, and biophysical principles and techniques. In recent days, bioinformaticians and biophysicists having the necessary expertise and interests in computer science techniques including versatile algorithms have started covering a multitude of physiological aspects including especially evolution, mutations, and genomics of functional channels and channel subunits. In these focused research areas, the use of artificial intelligence (AI), machine learning (ML), and deep learning (DL) algorithms and associated models have been found very popular. With the help of available articles and information, this review provide an introduction to this novel research trend. Ion channel understanding is usually made considering the structural and functional perspectives, gating mechanisms, transport properties, channel protein mutations, etc. Focused research on ion channels and related findings over many decades accumulated huge data which may be utilized in a specialized scientific manner to fast conclude pinpointed aspects of channels. AI, ML, and DL techniques and models may appear as helping tools. This review aims at explaining the ways we may use the bioinformatics techniques and thus draw a few lines across the avenue to let the ion channel features appear clearer.

Method for efficient soluble expression and purification of recombinant human interleukin-15

Periplasmic expression of recombinant proteins ensures the production of biologically active proteins in a correctly folded state with several key advantages. This research focused on the in-frame cloning of rhIL-15 in pET-20 (+) vector with pelB-leader sequence to direct the protein to the bacterial periplasm. The target construct periplasmic expression was evaluated in four strains, BL21 (DE3), BL21 (DE3) pLysS, Rosetta 2 (DE3) and Rosetta-gami 2 (DE3). Soluble periplasmic expression of IL-15 was highest in Rosetta-gami 2 (DE3) followed by Rossetta 2 (DE3) whereas negligible expression was observed with rest of two expression host. Best expression clone was selected for purification by dye ligand affinity chromatography. Purified rhIL-15 was characterized by SDS-PAGE, Western blotting and SEC-HPLC. This is the first report of functional recombinant human interleukin-15 being expressed and purified with yield of 120 mg/L in the periplasmic space of E. coli.

iProt-Sub: a comprehensive package for accurately mapping and predicting protease-specific substrates and cleavage sites

Regulation of proteolysis plays a critical role in a myriad of important cellular processes. The key to better understanding the mechanisms that control this process is to identify the specific substrates that each protease targets. To address this, we have developed iProt-Sub, a powerful bioinformatics tool for the accurate prediction of protease-specific substrates and their cleavage sites. Importantly, iProt-Sub represents a significantly advanced version of its successful predecessor, PROSPER. It provides optimized cleavage site prediction models with better prediction performance and coverage for more species-specific proteases (4 major protease families and 38 different proteases). iProt-Sub integrates heterogeneous sequence and structural features and uses a two-step feature selection procedure to further remove redundant and irrelevant features in an effort to improve the cleavage site prediction accuracy. Features used by iProt-Sub are encoded by 11 different sequence encoding schemes, including local amino acid sequence profile, secondary structure, solvent accessibility and native disorder, which will allow a more accurate representation of the protease specificity of approximately 38 proteases and training of the prediction models. Benchmarking experiments using cross-validation and independent tests showed that iProt-Sub is able to achieve a better performance than several existing generic tools. We anticipate that iProt-Sub will be a powerful tool for proteome-wide prediction of protease-specific substrates and their cleavage sites, and will facilitate hypothesis-driven functional interrogation of protease-specific substrate cleavage and proteolytic events.

Learned protein embeddings for machine learning

Motivation

Machine-learning models trained on protein sequences and their measured functions can infer biological properties of unseen sequences without requiring an understanding of the underlying physical or biological mechanisms. Such models enable the prediction and discovery of sequences with optimal properties. Machine-learning models generally require that their inputs be vectors, and the conversion from a protein sequence to a vector representation affects the model's ability to learn. We propose to learn embedded representations of protein sequences that take advantage of the vast quantity of unmeasured protein sequence data available. These embeddings are low-dimensional and can greatly simplify downstream modeling.

Results

The predictive power of Gaussian process models trained using embeddings is comparable to those trained on existing representations, which suggests that embeddings enable accurate predictions despite having orders of magnitude fewer dimensions. Moreover, embeddings are simpler to obtain because they do not require alignments, structural data, or selection of informative amino-acid properties. Visualizing the embedding vectors shows meaningful relationships between the embedded proteins are captured.

Availability and implementation

The embedding vectors and code to reproduce the results are available at https://github.com/fhalab/embeddings_reproduction/.

Supplementary information

Supplementary data are available at Bioinformatics online.

Machine learning to design integral membrane channelrhodopsins for efficient eukaryotic expression and plasma membrane localization

There is growing interest in studying and engineering integral membrane proteins (MPs) that play key roles in sensing and regulating cellular response to diverse external signals. A MP must be expressed, correctly inserted and folded in a lipid bilayer, and trafficked to the proper cellular location in order to function. The sequence and structural determinants of these processes are complex and highly constrained. Here we describe a predictive, machine-learning approach that captures this complexity to facilitate successful MP engineering and design. Machine learning on carefully-chosen training sequences made by structure-guided SCHEMA recombination has enabled us to accurately predict the rare sequences in a diverse library of channelrhodopsins (ChRs) that express and localize to the plasma membrane of mammalian cells. These light-gated channel proteins of microbial origin are of interest for neuroscience applications, where expression and localization to the plasma membrane is a prerequisite for function. We trained Gaussian process (GP) classification and regression models with expression and localization data from 218 ChR chimeras chosen from a 118,098-variant library designed by SCHEMA recombination of three parent ChRs. We use these GP models to identify ChRs that express and localize well and show that our models can elucidate sequence and structure elements important for these processes. We also used the predictive models to convert a naturally occurring ChR incapable of mammalian localization into one that localizes well.

A protein’s amino acid sequence determines how it will fold, traffic to subcellular locations, and carry out specific functions within the cell. Understanding this process would enable the design of protein sequences capable of useful functions; unfortunately, we cannot predict in detail how sequence encodes function. However, machine-learning models have the potential to infer the complex protein sequence-function relationship by identifying patterns or features that are important for function from sequences with known functions. We used machine learning to learn about and design membrane proteins (MPs). To function, a MP must be expressed, correctly folded in a lipid membrane, and trafficked to the proper cellular location. We built predictive, machine-learning models for this complex process from a set of >200 chimeric MPs and used them to design new sequences with optimal performance on the challenging task of membrane localization. This general approach to understanding and designing MPs could be broadly useful for important pharmaceutical and engineering MP targets.