The source code of Seq-InSite is optimized for high-throughput predictions and does not share the website's restriction of 10 sequences per run.
Seq-InSite is freely available as a web server at http://seq-insite.csd.uwo.ca/.
S. Hosseini, G.B. Golding, L. Ilie, Seq-InSite: sequence supersedes structure for protein interaction site prediction, Bioinformatics (2024) 40(1) btad738.
Contact: SeyedMohsen Hosseini (shosse59@uwo.ca) Lucian Ilie (ilie@uwo.ca)
Seq-InSite is developed under Linux environment with python 3.8.
Recommended RAM for testing: > 24GB and for training: >110 GB The RAM requirement mainly depends on the length of the input sequence.
Recommended GPU for testing: p100 with 12 GB of memory and for training: t4 with 16 GB of memory.
- clone the source code of Seq-InSite
mkdir -p Src && cd Src
git clone [Seq-InSite git link]
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Install msa-transformer in order to calculate msa-transformer embbedings
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Install bio_embeddings packege in order to calculate T5 embeddings
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install dependencies
There are 8 different model weights have been released, each corresponding to a different model, as described below:
- LSTM_T5_MSA_without*.h5 -LSTM architecture was trained using both T5 and msa-transformer's embeddings as input. This particular model was trained on data that does not share similarity with Dset_* where * is 60, 70, 315.
- MLP_T5_MSA_without*.h5 -MLP architecture was trained using both T5 and msa-transformer's embeddings as input. This particular model was trained on data that does not share similarity with Dset_* where * is 60, 70, 315.
- LSTM_T5_MSA.h5 -LSTM architecture was trained using both T5 and msa-transformer's embeddings as input. This particular model was trained on data that does not share similarity with Dset_* where * is 72, 164, 186, 448.
- MLP_T5_MSA.h5 -MLP architecture was trained using both T5 and msa-transformer's embeddings as input. This particular model was trained on data that does not share similarity with Dset_* where * is 72, 164, 186, 448.
Note: At the moment, we have only shared 'LSTM_T5_MSA_without60.h5' and 'MLP_T5_MSA_without60.h5' via the provided link, while the rest of the weights can be obtained by the address provided below. This temporary measure is necessary due to limitations with GitFront, but once our GitHub repository becomes public, all the weights will be readily accessible. https://drive.google.com/drive/folders/1CxrIpyBnPNWFuSNkE8fkKQd-DQzce7Gq
In order to run Seq-InSite use the following command
bash Seq-InSite.sh [Fasta file directory]
Assuming that a file named "dataset.txt" is present in the given directory, this script will create the required files and directories for calculating alignments, embeddings, and the appropriate output directory. Finally, it will execute the "predict_ENS.py" file to predict the interactions. By default, the code will execute the ensemble version of Seq-InSite on the dataset that dissimilar from dset_60. If you desire to run a specific architecture of Seq-InSite, you must alter the predict file and provide the corresponding weights for that model.
If you already possess the appropriate embeddings, you may utilize the following command:
python predict_ENS.py /path/to/dataset /path/to/msa-embeddings /path/to/t5-embeddings /path/to/output
Please note that the accepted naming convention for embedding files is "PDBID.embd". Each line of the embedding file must begin with the one-letter code for the corresponding amino acid, followed by a colon (:) symbol. The embedding representation of the amino acid should then be divided by spaces e.g.:
M:0.30833972 -0.17879489 -0.019303203 ...
A:0.32114908 -0.01173505 -0.1363031 ...
L:0.23623097 -0.295787 0.056586854 ...
In order to retrain the model you should use 'train_T5_MSA_LSTM.py' and 'train_T5_MSA_MLP.py'. If you have the embedding stored in the desired directory, you can employ the subsequent command to train each branch of the model.
python train_T5_MSA_MLP.py
python train_T5_MSA_LSTM.py
The Results directory encompass the predictions utilized in this paper. This directory contains the outputs of the methods employed in the research study, which were then utilized to draw conclusions. By putting the predictions in the Results directory, the researchers can easily access and analyze them for further investigation and evaluation.