This server is designed for the prediction of bioluminescent proteins (BLPs). It will generate an email message containing numeric score for each protein in your submission that quantifies putative propensity to make bioluminescence. Putative annotations (a given protein is predicted either as a BLP or non-BLP) displayed on the result page are based on the judgement threshold input in this page. All comments, suggestions or requests regarding our work are welcome. For more information please contact biocomputinglab@sina.com.

Please follow the four steps below to make a prediction

STEP ONE Paste your protein sequence with standard FASTA format:

  Click Here to Use Example Protein Sequence

STEP TWO Select a prediction model:

STEP THREE Set a judgement threshold:

STEP FOUR Provide you email address:

HELP

PredBLP can accept fifteen protein sequences for a sigle prediction. You should input the protein sequence in standard FASTA format, which begins with a symbol ">". The format of the input protein sequence is as follows:

    Line 1: > PDB ID or UniProtKB ID or any other annotation(s)

    Line 2: protein sequence (1-letter amino acid encoding)

    Line 3: > PDB ID or UniProtKB ID or any other annotation(s)

    Line 4: protein sequence (1-letter amino acid encoding)

    Line 5: ...

The judgement threshold that input in STEP THREE will be used to generate putative annotations. Proteins gaining numeric score higher the threshold will be regard as a putative BLP.

We will send a message reporting the detailed prediction result to the email address you input once the current prediction is finished. For more information in respect to its content, please make a try and see the result.

Materials

BLP-General: dataset used for general BLPs Download

BLP-Bacteria: dataset used for bacteria BLPs Download

BLP-Eukaryota: dataset used for eukaryota BLPs Download

BLP-Archaea: dataset used for archaea BLPs Download

Kandaswamy et al.'s dataset: dataset used for general BLPs Download

Citation

Upon the usage the users are requested to use the following citation:

Jian Zhang, Haiting Chai, Guifu Yang and Zhiqiang Ma, 2017, Prediction of bioluminescent proteins by using sequence-derived features and lineage-specific scheme, BMC bioinformatics, DOI: 10.1186/S12859-017-1709-6, PMID: 28583090 link

Acknowledgments

We acknowledge with thanks the following databases and software used as a part of this server:

UniProtKB - UniProt Knowledgebase

LIBSVM - A Library for Support Vector Machines