Exploring sequence characteristics related to high-level production of secreted proteins in Aspergillus niger

ProG-SOL: Predicting Protein Solubility Using Protein Embeddings and Dual-Graph Convolutional Networks

Solubility is a key biophysical property of proteins and is essential for evaluating the effectiveness of proteins in biochemical engineering. In recent years, the prediction method of protein solubility has received extensive attention in the protein engineering research community. Many methods have been developed to predict protein solubility, but the generalization performance of existing prediction methods on independent test sets must be improved. In addition, solubility prediction methods do not work well when they are used for regression tasks. To address these issues, we developed a new method, ProG-SOL, an innovative sequence-based dual-graph convolutional network that simultaneously exploits the protein pretrained graph and the protein evolutionary graph for assessing solubility. Compared with other methods, ProG-SOL achieves better classification and regression results for different independent test sets at the same time. The model framework of our method may also be used to predict other properties of proteins such as protein function, protein-protein interaction, protein folding, and drug design, which provide broad application prospects in protein engineering.

Amino acid sequence encodes protein abundance shaped by protein stability at reduced synthesis cost

Understanding what drives protein abundance is essential to biology, medicine, and biotechnology. Driven by evolutionary selection, an amino acid sequence is tailored to meet the required abundance of a proteome, underscoring the intricate relationship between sequence and functional demand. Yet, the specific role of amino acid sequences in determining proteome abundance remains elusive. Here we show that the amino acid sequence alone encodes over half of protein abundance variation across all domains of life, ranging from bacteria to mouse and human. With an attempt to go beyond predictions, we trained a manageable-size Transformer model to interpret latent factors predictive of protein abundances. Intuitively, the model's attention focused on the protein's structural features linked to stability and metabolic costs related to protein synthesis. To probe these relationships, we introduce MGEM (Mutation Guided by an Embedded Manifold), a methodology for guiding protein abundance through sequence modifications. We find that mutations which increase predicted abundance have significantly altered protein polarity and hydrophobicity, underscoring a connection between protein structural features and abundance. Through molecular dynamics simulations we revealed that abundance-enhancing mutations possibly contribute to protein thermostability by increasing rigidity, which occurs at a lower synthesis cost.

Improving glucose oxidase catalysis in Aspergillus niger via Vitreoscilla hemoglobin fusion protein

Oxygen is crucial for converting glucose to gluconic acid catalyzed by glucose oxidase (Gox). However, industrial gluconic acid production faces oxygen supply limitations. To enhance Gox efficiency, Vitreoscilla hemoglobin (VHb) has been considered as an efficient oxygen transfer carrier. This study identified GoxA, a specific isoform of Gox in the industrial gluconic acid-producing strain of Aspergillus niger. Various forms of VHb expression in A. niger were tested to improve GoxA's catalytic efficiency. Surprisingly, the expression of free VHb, both intracellularly and extracellularly, did not promote gluconic acid production during shake flask fermentation. Then, five fusion proteins were constructed by linking Gox and VHb using various methods. Among these, VHb-GS1-GoxA, where VHb's C-terminus connected to GoxA's N-terminus via the flexible linker GS1, demonstrated a significantly higher Kcat/Km value (96% higher) than GoxA. Unfortunately, the expression of VHb-GS1-GoxA in A. niger was limited, resulting in a low gluconic acid production of 3.0 g/L. To overcome the low expression problem, single- and dual-strain systems were designed with tools of SpyCatcher/SpyTag and SnoopCatcher/SnoopTag. In these systems, Gox and VHb were separately expressed and then self-assembled into complex proteins. Impressively, the single-strain system outperformed the GoxA overexpression strain S1971, resulting in 23% and 9% higher gluconic acid production under 0.6 vvm and 1.2 vvm aeration conditions in the bioreactor fermentation, respectively. The successful construction of Gox and VHb fusion or complex proteins, as proposed in this study, presents promising approaches to enhance Gox catalytic efficiency and lower aerodynamic costs in gluconic acid production. KEY POINTS: • Overexpressing free VHb in A. niger did not improve the catalytic efficiency of Gox • The VHb-GS1-GoxA showed an increased Kcat/Km value by 96% than GoxA • The single-strain system worked better in the gluconic acid bioreactor fermentation.

DeepSol: a deep learning framework for sequence-based protein solubility prediction

Motivation

Protein solubility plays a vital role in pharmaceutical research and production yield. For a given protein, the extent of its solubility can represent the quality of its function, and is ultimately defined by its sequence. Thus, it is imperative to develop novel, highly accurate in silico sequence-based protein solubility predictors. In this work we propose, DeepSol, a novel Deep Learning-based protein solubility predictor. The backbone of our framework is a convolutional neural network that exploits k-mer structure and additional sequence and structural features extracted from the protein sequence.

Results

DeepSol outperformed all known sequence-based state-of-the-art solubility prediction methods and attained an accuracy of 0.77 and Matthew's correlation coefficient of 0.55. The superior prediction accuracy of DeepSol allows to screen for sequences with enhanced production capacity and can more reliably predict solubility of novel proteins.

Availability and implementation

DeepSol's best performing models and results are publicly deposited at https://doi.org/10.5281/zenodo.1162886 (Khurana and Mall, 2018).

Supplementary information

Supplementary data are available at Bioinformatics online.

Synthetic biology for the directed evolution of protein biocatalysts: navigating sequence space intelligently

The amino acid sequence of a protein affects both its structure and its function. Thus, the ability to modify the sequence, and hence the structure and activity, of individual proteins in a systematic way, opens up many opportunities, both scientifically and (as we focus on here) for exploitation in biocatalysis. Modern methods of synthetic biology, whereby increasingly large sequences of DNA can be synthesised de novo, allow an unprecedented ability to engineer proteins with novel functions. However, the number of possible proteins is far too large to test individually, so we need means for navigating the 'search space' of possible protein sequences efficiently and reliably in order to find desirable activities and other properties. Enzymologists distinguish binding (Kd) and catalytic (kcat) steps. In a similar way, judicious strategies have blended design (for binding, specificity and active site modelling) with the more empirical methods of classical directed evolution (DE) for improving kcat (where natural evolution rarely seeks the highest values), especially with regard to residues distant from the active site and where the functional linkages underpinning enzyme dynamics are both unknown and hard to predict. Epistasis (where the 'best' amino acid at one site depends on that or those at others) is a notable feature of directed evolution. The aim of this review is to highlight some of the approaches that are being developed to allow us to use directed evolution to improve enzyme properties, often dramatically. We note that directed evolution differs in a number of ways from natural evolution, including in particular the available mechanisms and the likely selection pressures. Thus, we stress the opportunities afforded by techniques that enable one to map sequence to (structure and) activity in silico, as an effective means of modelling and exploring protein landscapes. Because known landscapes may be assessed and reasoned about as a whole, simultaneously, this offers opportunities for protein improvement not readily available to natural evolution on rapid timescales. Intelligent landscape navigation, informed by sequence-activity relationships and coupled to the emerging methods of synthetic biology, offers scope for the development of novel biocatalysts that are both highly active and robust.

The Cell Factory Aspergillus Enters the Big Data Era: Opportunities and Challenges for Optimising Product Formation

Living with limits. Getting more from less. Producing commodities and high-value products from renewable resources including waste. What is the driving force and quintessence of bioeconomy outlines the lifestyle and product portfolio of Aspergillus, a saprophytic genus, to which some of the top-performing microbial cell factories belong: Aspergillus niger, Aspergillus oryzae and Aspergillus terreus. What makes them so interesting for exploitation in biotechnology and how can they help us to address key challenges of the twenty-first century? How can these strains become trimmed for better growth on second-generation feedstocks and how can we enlarge their product portfolio by genetic and metabolic engineering to get more from less? On the other hand, what makes it so challenging to deduce biological meaning from the wealth of Aspergillus -omics data? And which hurdles hinder us to model and engineer industrial strains for higher productivity and better rheological performance under industrial cultivation conditions? In this review, we will address these issues by highlighting most recent findings from the Aspergillus research with a focus on fungal growth, physiology, morphology and product formation. Indeed, the last years brought us many surprising insights into model and industrial strains. They clearly told us that similar is not the same: there are different ways to make a hypha, there are more protein secretion routes than anticipated and there are different molecular and physical mechanisms which control polar growth and the development of hyphal networks. We will discuss new conceptual frameworks derived from these insights and the future scientific advances necessary to create value from Aspergillus Big Data.

A review of machine learning methods to predict the solubility of overexpressed recombinant proteins in Escherichia coli

Background

Over the last 20 years in biotechnology, the production of recombinant proteins has been a crucial bioprocess in both biopharmaceutical and research arena in terms of human health, scientific impact and economic volume. Although logical strategies of genetic engineering have been established, protein overexpression is still an art. In particular, heterologous expression is often hindered by low level of production and frequent fail due to opaque reasons. The problem is accentuated because there is no generic solution available to enhance heterologous overexpression. For a given protein, the extent of its solubility can indicate the quality of its function. Over 30% of synthesized proteins are not soluble. In certain experimental circumstances, including temperature, expression host, etc., protein solubility is a feature eventually defined by its sequence. Until now, numerous methods based on machine learning are proposed to predict the solubility of protein merely from its amino acid sequence. In spite of the 20 years of research on the matter, no comprehensive review is available on the published methods.

Results

This paper presents an extensive review of the existing models to predict protein solubility in Escherichia coli recombinant protein overexpression system. The models are investigated and compared regarding the datasets used, features, feature selection methods, machine learning techniques and accuracy of prediction. A discussion on the models is provided at the end.

Conclusions

This study aims to investigate extensively the machine learning based methods to predict recombinant protein solubility, so as to offer a general as well as a detailed understanding for researches in the field. Some of the models present acceptable prediction performances and convenient user interfaces. These models can be considered as valuable tools to predict recombinant protein overexpression results before performing real laboratory experiments, thus saving labour, time and cost.

SPiCE: a web-based tool for sequence-based protein classification and exploration

Background

Amino acid sequences and features extracted from such sequences have been used to predict many protein properties, such as subcellular localization or solubility, using classifier algorithms. Although software tools are available for both feature extraction and classifier construction, their application is not straightforward, requiring users to install various packages and to convert data into different formats. This lack of easily accessible software hampers quick, explorative use of sequence-based classification techniques by biologists.

Results

We have developed the web-based software tool SPiCE for exploring sequence-based features of proteins in predefined classes. It offers data upload/download, sequence-based feature calculation, data visualization and protein classifier construction and testing in a single integrated, interactive environment. To illustrate its use, two example datasets are included showing the identification of differences in amino acid composition between proteins yielding low and high production levels in fungi and low and high expression levels in yeast, respectively.

Conclusions

SPiCE is an easy-to-use online tool for extracting and exploring sequence-based features of sets of proteins, allowing non-experts to apply advanced classification techniques. The tool is available at http://helix.ewi.tudelft.nl/spice.

How to achieve high-level expression of microbial enzymes: strategies and perspectives

Microbial enzymes have been used in a large number of fields, such as chemical, agricultural and biopharmaceutical industries. The enzyme production rate and yield are the main factors to consider when choosing the appropriate expression system for the production of recombinant proteins. Recombinant enzymes have been expressed in bacteria (e.g., Escherichia coli, Bacillus and lactic acid bacteria), filamentous fungi (e.g., Aspergillus) and yeasts (e.g., Pichia pastoris). The favorable and very advantageous characteristics of these species have resulted in an increasing number of biotechnological applications. Bacterial hosts (e.g., E. coli) can be used to quickly and easily overexpress recombinant enzymes; however, bacterial systems cannot express very large proteins and proteins that require post-translational modifications. The main bacterial expression hosts, with the exception of lactic acid bacteria and filamentous fungi, can produce several toxins which are not compatible with the expression of recombinant enzymes in food and drugs. However, due to the multiplicity of the physiological impacts arising from high-level expression of genes encoding the enzymes and expression hosts, the goal of overproduction can hardly be achieved, and therefore, the yield of recombinant enzymes is limited. In this review, the recent strategies used for the high-level expression of microbial enzymes in the hosts mentioned above are summarized and the prospects are also discussed. We hope this review will contribute to the development of the enzyme-related research field.