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Protein-Sol: a web tool for predicting protein solubility from sequence.

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TLDR
The model returns a predicted solubility and an indication of the features which deviate most from average values, and the utility of these additional features is demonstrated with the example of thioredoxin.
Abstract
Motivation Protein solubility is an important property in industrial and therapeutic applications Prediction is a challenge, despite a growing understanding of the relevant physicochemical properties Results Protein-Sol is a web server for predicting protein solubility Using available data for Escherichia coli protein solubility in a cell-free expression system, 35 sequence-based properties are calculated Feature weights are determined from separation of low and high solubility subsets The model returns a predicted solubility and an indication of the features which deviate most from average values Two other properties are profiled in windowed calculation along the sequence: fold propensity, and net segment charge The utility of these additional features is demonstrated with the example of thioredoxin Availability and implementation The Protein-Sol webserver is available at http://protein-solmanchesteracuk Contact jimwarwicker@manchesteracuk

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Computational approaches to therapeutic antibody design: established methods and emerging trends

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An in silico deep learning approach to multi-epitope vaccine design: a SARS-CoV-2 case study.

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Machine learning with physicochemical relationships: solubility prediction in organic solvents and water

TL;DR: A successful approach to solubility prediction in organic solvents and water is reported using a combination of machine learning (ANN, SVM, RF, ExtraTrees, Bagging and GP) and computational chemistry.
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Generating functional protein variants with variational autoencoders.

TL;DR: It is shown that variational autoencoders trained on a dataset of almost 70000 luciferase-like oxidoreductases can be used to generate novel, functional variants of the luxA bacterial Luciferase.
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Generating functional protein variants with variational autoencoders

TL;DR: The feasibility of using deep generative models to explore the space of possible protein sequences and generate useful variants is demonstrated, providing a method complementary to rational design and directed evolution approaches.
References
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Journal ArticleDOI

A simple method for displaying the hydropathic character of a protein

TL;DR: A computer program that progressively evaluates the hydrophilicity and hydrophobicity of a protein along its amino acid sequence has been devised and its simplicity and its graphic nature make it a very useful tool for the evaluation of protein structures.
Journal ArticleDOI

UniProt: the Universal Protein knowledgebase

TL;DR: The Swiss-Prot, TrEMBL and PIR protein database activities have united to form the Universal Protein Knowledgebase (UniProt), which is to provide a comprehensive, fully classified, richly and accurately annotated protein sequence knowledgebase, with extensive cross-references and query interfaces.
Journal ArticleDOI

Why are "natively unfolded" proteins unstructured under physiologic conditions?

TL;DR: Analysis of amino acid sequences, based on the normalized net charge and mean hydrophobicity, has been applied to two sets of proteins and shows that “natively unfolded” proteins are specifically localized within a unique region of charge‐hydrophobia phase space.
Journal ArticleDOI

GlobPlot: exploring protein sequences for globularity and disorder

TL;DR: A new tool for discovery of unstructured, or disordered regions within proteins, and examples with known proteins where it successfully identifies inter-domain segments containing linear motifs, and also apparently ordered regions that do not contain any recognised domain are presented.
Journal ArticleDOI

Crystal structure of thioredoxin from Escherichia coli at 1.68 A resolution.

TL;DR: The crystal structure of thioredoxin from Escherichia coli has been refined by the stereochemically restrained least-squares procedure to a crystallographic R-factor of 0.165 at 1.68 A resolution.
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Trending Questions (1)
Tell me the relationship among helical wheel.and protein solubility. Is there any methods for predicting protein solubility?

The relationship between helical wheel and protein solubility is not mentioned in the provided information. However, the Protein-Sol web server is a method for predicting protein solubility.