Solubility of proteins

Solubility is a fundamental protein property that has important connotations for therapeutics and use in diagnosis. Solubility of many proteins is low and affect heterologous overexpression of proteins, formulation of products and their stability. Two processes are related to soluble and solid phase relations. Solubility refers to the process where proteins have correctly folded structure, whereas aggregation is related to the formation of fibrils, oligomers or amorphous particles. Both processes are related to some diseases. Amyloid fibril formation is one of the characteristic features in several neurodegenerative diseases, but it is related to many other diseases, including cancers. Severe complex V deficiency and cataract are examples of diseases due to reduced protein solubility. Methods and approaches are described for prediction of protein solubility and aggregation, as well as predictions of consequences of amino acid substitutions. Finally, protein engineering solutions are discussed. Protein solubility can be increased, although such alterations are relatively rare and can lead to trade-off with some other properties. The aggregation prediction methods mainly aim to detect aggregation-prone sequence patches and then making them more soluble. The solubility predictors utilize a wide spectrum of features.</p

Differential Precipitation and Solubilisation of Proteins

Differential protein precipitation is a rapid and economical step in protein purification and is based on exploiting the inherent physicochemical properties of the polypeptide. Precipitation of recombinant proteins, lysed from the host cell, is commonly used to concentrate the protein of choice before further polishing steps with more selective purification columns (e.g., His-Tag, Size Exclusion, etc.). Recombinant proteins can also precipitate naturally as inclusion bodies due to various influences during overexpression in the host cell. Although this phenomenon permits easier initial separation from native proteins, these inclusion bodies must carefully be differentially solubilized so as to reform functional, correctly folded proteins. Here, appropriate bioinformatics tools to aid in understanding a protein’s propensity to aggregate and solubilize are explored as a backdrop for a typical protein extraction, precipitation, and selective resolubilization procedure, based on a recombinantly expressed protein

Codon usage clusters correlation: Towards protein solubility prediction in heterologous expression systems in E. coli

Production of soluble recombinant proteins is crucial to the development of industry and basic research. However, the aggregation due to the incorrect folding of the nascent polypeptides is still a mayor bottleneck. Understanding the factors governing protein solubility is important to grasp the underlying mechanisms and improve the design of recombinant proteins. Here we show a quantitative study of the expression and solubility of a set of proteins from Bizionia argentinensis. Through the analysis of different features known to modulate protein production, we defined two parameters based on the %MinMax algorithm to compare codon usage clusters between the host and the target genes. We demonstrate that the absolute difference between all %MinMax frequencies of the host and the target gene is significantly negatively correlated with protein expression levels. But most importantly, a strong positive correlation between solubility and the degree of conservation of codons usage clusters is observed for two independent datasets. Moreover, we evince that this correlation is higher in codon usage clusters involved in less compact protein secondary structure regions. Our results provide important tools for protein design and support the notion that codon usage may dictate translation rate and modulate co-Translational folding.Fil: Pellizza Pena, Leonardo Agustín. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigaciones Bioquímicas de Buenos Aires. Fundación Instituto Leloir. Instituto de Investigaciones Bioquímicas de Buenos Aires; ArgentinaFil: Smal, Clara. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigaciones Bioquímicas de Buenos Aires. Fundación Instituto Leloir. Instituto de Investigaciones Bioquímicas de Buenos Aires; ArgentinaFil: Rodrigo, Guido. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigaciones Bioquímicas de Buenos Aires. Fundación Instituto Leloir. Instituto de Investigaciones Bioquímicas de Buenos Aires; ArgentinaFil: Aran, Martin. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigaciones Bioquímicas de Buenos Aires. Fundación Instituto Leloir. Instituto de Investigaciones Bioquímicas de Buenos Aires; Argentin

Protein Extraction and Purification by Differential Solubilization

The preparation of purified soluble proteins for biochemical studies is essential and the solubility of a protein of interest in various media is central to this process. Selectively altering the solubility of a protein is a rapid and economical step in protein purification and is based on exploiting the inherent physicochemical properties of a polypeptide. Precipitation of proteins, released from cells upon lysis, is often used to concentrate a protein of interest before further purification steps (e.g., ion exchange chromatography, size exclusion chromatography etc). Recombinant proteins may be expressed in host cells as insoluble inclusion bodies due to various influences during overexpression. Such inclusion bodies can often be solubilized to be reconstituted as functional, correctly folded proteins. In this chapter, we examine strategies for extraction/precipitation/solubilization of proteins for protein purification. We also present bioinformatic tools to aid in understanding a protein’s propensity to aggregate/solubilize that will be a useful starting point for the development of protein extraction, precipitation, and selective re-solubilization procedures

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

Progress and challenges for the machine learning-based design of fit-for-purpose monoclonal antibodies

Although the therapeutic efficacy and commercial success of monoclonal antibodies (mAbs) are tremendous, the design and discovery of new candidates remain a time and cost-intensive endeavor. In this regard, progress in the generation of data describing antigen binding and developability, computational methodology, and artificial intelligence may pave the way for a new era of in silico on-demand immunotherapeutics design and discovery. Here, we argue that the main necessary machine learning (ML) components for an in silico mAb sequence generator are: understanding of the rules of mAb-antigen binding, capacity to modularly combine mAb design parameters, and algorithms for unconstrained parameter-driven in silico mAb sequence synthesis. We review the current progress toward the realization of these necessary components and discuss the challenges that must be overcome to allow the on-demand ML-based discovery and design of fit-for-purpose mAb therapeutic candidates

Design of selective peptide inhibitors of anti-apoptotic Bfl-1 using experimental screening, structure-based design, and data-driven modeling

Thesis: Ph. D., Massachusetts Institute of Technology, Department of Biology, 2018.Cataloged from PDF version of thesis.Includes bibliographical references.Protein-protein interactions are central to all biological processes. Designer reagents that selectively bind to proteins and inhibit their interactions can be used to probe protein interaction networks, discover druggable targets, and generate potential therapeutic leads. Current technology makes it possible to engineer proteins and peptides with desirable interaction profiles using carefully selected sets of experiments that are customized for each design objective. There is great interest in improving the protein design pipeline to create protein binders more efficiently and against a wider array of targets. In this thesis, I describe the design and development of selective peptide inhibitors of anti-apoptotic BcI-2 family proteins, with an emphasis on targeting Bfl-1. Anti-apoptotic Bcl-2 family proteins bind to short, pro-apoptotic BH3 motifs to support cellular survival. Overexpression of BfI-1 has been shown to promote cancer cell survival and the development of chemoresistance. Prior work suggests that selective inhibition of Bfl-1 can induce cell death in Bfl-1 overexpressing cancer cells without compromising healthy cells that also rely on anti-apoptotic BcI-2 proteins for survival. Thus, Bfl-1-selective BH3 mimetic peptides are potentially valuable for diagnosing Bfl-1 dependence and can serve as leads for therapeutic development. In this thesis, I describe three distinct approaches to designing potent and selective Bfl-1 inhibitors. First, I describe the design and screening of libraries of variants of BH3 peptides. I show that peptides from this screen bind in a previously unobserved BH3 binding mode and have large margins of specificity for Bfl-1 when tested in vitro and in cultured cells. Second, I describe a computational model of the specificity landscape of three anti-apoptotic Bcl-2 proteins including Bfl-1. This model was derived from high-throughput affinity measurement of thousands of peptides from BH3 libraries. I show that this model is useful for designing peptides with desirable interaction profiles within a family of related proteins. Third, I describe the use of a scoring potential built on the amino acid frequencies from well-defined structural motifs complied from the Protein Data Bank to design novel BH3 peptides targeting Bfl-1.by Justin Michael Jenson.Ph. D

Engineering Novel Rhodopsins for Neuroscience

The overarching goal of my PhD research has been engineering proteins capable of controlling and reading out neural activity to advance neuroscience research. I engineered light-gated microbial rhodopsins, primarily focusing on the algal derived, light-gated channel, channelrhodopsin (ChR), which can be used to modulate neuronal activity with light. This work has required overcoming three major challenges. First, rhodopsins are trans-membrane proteins, which are inherently difficult to engineer because the sequence and structural determinants of membrane protein expression and plasma membrane localization are highly constrained and poorly understood (Chapter 3-5). Second, protein properties of interest for neuroscience applications are assayed using very low throughput patch-clamp electrophysiology preventing the use of high-throughput assays required for directed evolution experiments (Chapter 2, 5-6). And third, in vivo application of these improved tools require either retention or optimization of multiple protein properties in a single protein tool; for example, we must optimize expression and localization of these algal membrane proteins in mammalian cells while at the same time optimizing kinetic and functional properties (Chapter 5-6). These challenges restricted the field to low-throughput, conservative methods for discovery of improved ChRs, e.g., structure-guided mutagenesis and testing of natural ChR variants. I used an alternative approach: data-driven machine learning to model the fitness landscape of ChRs for different properties of interest and applying these models to select ChR sequences with optimal combinations of properties (Chapters 5-6). ChR variants identified from this work have unprecedented conductance properties and light sensitivity that could enable non-invasive activation of populations of cells throughout the nervous system. These ChRs have the potential to change how optogenetics experiments are done. This work is a convincing demonstration of the power of machine learning guided protein engineering for a class of proteins that present multiple engineering challenges. A component of the novel application of these new ChR tools relies on recent advances in gene delivery throughout the nervous system facilitated by engineered AAVs (Chapter 7). And finally, I developed a behavioral tracking system to monitor behavior and demonstrate sleep behavior in the jellyfish Cassiopea, the most primitive organism to have this behavior formally characterized (Chapter 8).</p