I'm currently looking for a method or tool that predicts linker sequences. There's no shortage of AI that predict protein structure from their amino acid sequence, but does a tool or method that does the opposite exist?
Basically, (flexible) linkers are intrinsically disordered regions connecting two structured domains - once you have identified the structured regions in a protein sequence, what is left is flexible tails (at the termini) and linkers (situated between two structured regions).
e.g. https://en.wikipedia.org/wiki/List_of_disorder_prediction_software
While predicting linker sequences is a complex task, the use of bioinformatics tools, machine learning algorithms, and statistical models can aid in making informed predictions and advancing your understanding of linker regions in proteins. Here are a few methods commonly employed for predicting linker sequences:
- Sequence Alignment: By aligning a target protein sequence with known linker sequences, one can identify conserved regions or motifs that indicate potential linker regions. This method relies on the assumption that linker sequences may share similarities with previously characterized linkers.
- Machine Learning Algorithms: Supervised machine learning algorithms can be trained on a dataset of known linker sequences to predict novel linker regions. Features such as amino acid composition, physicochemical properties, and structural characteristics can be used to train the model. Examples of machine learning algorithms include support vector machines (SVM), random forests, and deep learning approaches like recurrent neural networks (RNN) or convolutional neural networks (CNN).
- Hidden Markov Models (HMM): HMMs are probabilistic models that can be trained on a dataset of known linker sequences. These models capture the statistical properties of linker regions and can predict potential linker sequences in a given protein sequence based on these properties.
- Rule-Based Approaches: Rule-based methods utilize predefined rules or patterns to predict linker sequences. These rules can be based on knowledge about the specific domain or function of the protein or general characteristics of linker regions.
It's important to note that predicting linker sequences accurately can be challenging due to the diversity and variability of linker regions in different proteins. Therefore, a combination of approaches and careful evaluation of predictions is often necessary. Additionally, experimental validation is crucial to confirm the predicted linker sequences and their functionality in the intended application
I could not find a specific tool dedicated solely to predicting linker sequences. you can utilize various bioinformatics tools and resources to aid in the design and analysis of linker sequences. Analyze the amino acid sequences of known linker regions in proteins with similar functions or structures. Look for common patterns, motifs, or composition preferences that can guide the design of linker sequences. Tools such as multiple sequence alignment (MSA) and motif discovery algorithms can aid in this analysis.
Here are some commonly used tools:
Multiple Sequence Alignment (MSA) Tools:
Clustal Omega (https://www.ebi.ac.uk/Tools/msa/clustalo/) MAFFT (https://mafft.cbrc.jp/alignment/server/) Motif Discovery Tools:
MEME Suite (http://meme-suite.org/) HMMER (http://hmmer.org/)
Hi Andrew Bridson,
I think these are what you are looking for:
1) DFLpred: High-throughput prediction of disordered flexible linker regions in protein sequences.
https://pubmed.ncbi.nlm.nih.gov/27307636/
http://biomine.cs.vcu.edu/servers/DFLpred/
2) APOD: accurate sequence-based predictor of disordered flexible linkers
https://pubmed.ncbi.nlm.nih.gov/33381830/
https://yanglab.nankai.edu.cn/APOD/
3) TransDFL: Identification of Disordered Flexible Linkers in Proteins by Transfer Learning
https://pubmed.ncbi.nlm.nih.gov/36272675/
http://bliulab.net/TransDFL/
Best,
Tamas
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