Abstract
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.
Highlights
Solubility is an important property for all drugs, including biologics
Many proteins and polypeptides are poorly soluble and those trespassing through cellular membranes, membrane proteins, are not in traditional sense soluble at all
Quite successful solubility prediction methods have been developed for small molecules that are most common as drugs
Summary
Solubility is an important property for all drugs, including biologics. Many proteins and polypeptides are poorly soluble and those trespassing through cellular membranes, membrane proteins, are not in traditional sense soluble at all. Quite successful solubility prediction methods have been developed for small molecules that are most common as drugs (see chapters in this volume). These methods, do not work for proteins. Methods to investigate the effects on solubility due to amino acid substitutions (variations) are discussed This topic is important for the design of changes for protein engineering e.g. to increase protein solubility, production etc. Several computational methods have been developed to predict protein solubility, especially in the context of heterologous protein overexpression These methods utilize different approaches, often in the field of machine learning. Methods in this category include ccSOL omics [10], DeepSol [11], PaRSnIP [12], Protein-Sol [13], SODA [14], SOLart [15], SOLpro [16], SWI [8] and others
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