Dataset Information

Thermometer: a webserver to predict protein thermal stability.

ABSTRACT:

Motivation

Thermal properties of proteins are of great importance for a number of theoretical and practical implications. Predicting the thermal stability of a protein is a difficult and still scarcely addressed task.

Results

Here, we introduce Thermometer, a webserver to assess the thermal stability of a protein using structural information. Thermometer is implemented as a publicly available, user-friendly interface.

Availability and implementation

Our server can be found at the following link (all major browser supported): http://service.tartaglialab.com/new_submission/thermometer_file.

Supplementary information

Supplementary data are available at Bioinformatics online.

SUBMITTER: Miotto M

PROVIDER: S-EPMC8963285 | biostudies-literature | 2022 Mar

REPOSITORIES: biostudies-literature

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Publications

Thermometer: a webserver to predict protein thermal stability.

Miotto Mattia M Armaos Alexandros A Di Rienzo Lorenzo L Ruocco Giancarlo G Milanetti Edoardo E Tartaglia Gian Gaetano GG

Bioinformatics (Oxford, England) 20220301 7

<h4>Motivation</h4>Thermal properties of proteins are of great importance for a number of theoretical and practical implications. Predicting the thermal stability of a protein is a difficult and still scarcely addressed task.<h4>Results</h4>Here, we introduce Thermometer, a webserver to assess the thermal stability of a protein using structural information. Thermometer is implemented as a publicly available, user-friendly interface.<h4>Availability and implementation</h4>Our server can be found ...[more]

PMID: 35020787

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Project description:MotivationProtein-protein interactions (PPIs) play an essential role in a great variety of cellular processes and are therefore of significant interest for the design of new therapeutic compounds as well as the identification of side effects due to unexpected binding. Here, we present ProteinPrompt, a webserver that uses machine learning algorithms to calculate specific, currently unknown PPIs. Our tool is designed to quickly and reliably predict contact propensities based on an input sequence in order to scan large sequence libraries for potential binding partners, with the goal to accelerate and assure the quality of the laborious process of drug target identification.ResultsWe collected and thoroughly filtered a comprehensive database of known binders from several sources, which is available as download. ProteinPrompt provides two complementary search methods of similar accuracy for comparison and consensus building. The default method is a random forest (RF) algorithm that uses the auto-correlations of seven amino acid scales. Alternatively, a graph neural network (GNN) implementation can be selected. Additionally, a consensus prediction is available. For each query sequence, potential binding partners are identified from a protein sequence database. The proteom of several organisms are available and can be searched for binders. To evaluate the predictive power of the algorithms, we prepared a test dataset that was rigorously filtered for redundancy. No sequence pairs similar to the ones used for training were included in this dataset. With this challenging dataset, the RF method achieved an accuracy rate of 0.88 and an area under the curve of 0.95. The GNN achieved an accuracy rate of 0.86 using the same dataset. Since the underlying learning approaches are unrelated, comparing the results of RF and GNNs reduces the likelihood of errors. The consensus reached an accuracy of 0.89.Availability and implementationProteinPrompt is available online at: http://proteinformatics.org/ProteinPrompt, where training and test data used to optimize the methods are also available. The server makes it possible to scan the human proteome for potential binding partners of an input sequence within minutes. For local offline usage, we furthermore created a ProteinPrompt Docker image which allows for batch submission: https://gitlab.hzdr.de/proteinprompt/ProteinPrompt. In conclusion, we offer a fast, accurate, easy-to-use online service for predicting binding partners from an input sequence.

Dataset Information

Thermometer: a webserver to predict protein thermal stability.

Motivation

Results

Availability and implementation

Supplementary information

Publications

Thermometer: a webserver to predict protein thermal stability.

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