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Fuzzy support vector machine model to predict human death domain protein–protein interactions

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Abstract

Proteins have crucial importance in every living system, and Protein–Protein Interactions (PPIs) play a pivotal role in regulation of virtually all biological processes such as DNA transcription, replication, metabolic cycles and signaling cascades. The PPIs play an important role in the complex process of cell death which mainly occurs via apoptosis and necrosis in eukaryotic cells. Apoptosis is an orderly cellular suicide program critical for the development and homeostasis of multicellular organism. Failure to control apoptosis can have catastrophic consequences. The cascades of amazing reactions carried out by proteins such as Caspase, CARD, NLRP, NOD, FADD, DEDD, POP, Myd88 etc. play important role in the process of cell death. The high throughput experimental methods for determining PPIs are time consuming, expensive and are generating huge amount of PPIs data. Therefore, there is need to develop computational methods to efficiently and accurately predict PPIs. In this work, an attempt has been made to develop a fuzzy support vector machine (F-SVM) model for predicting death domain (DD) PPIs based on sixteen physicochemical, biochemical and structural features of amino acids which are monomers of proteins. First, the protein primary sequences are encoded into sequential features represented by descriptors. Then, the Support Vector Machine and Sequential Minimal Optimization of WEKA software are employed to classify interacting and non-interacting protein pairs. The performance of SVM and F-SVM with various kernel functions has been evaluated and it was observed that libSVM with Polynomial kernel was found to be best with accuracy of 77.94 % via F-SVM which is optimum model in predicting human DD-PPIs. Validation is performed by tenfold cross-validation technique. The F-SVM performance measure is 2.94 % higher than SVM in terms of accuracy with the use of custom designed fuzzy membership function. The results obtained are in agreement with available experimental data. Such models can be useful in providing PPI information of DD proteins which can be useful in understanding the molecular mechanisms involved in death of cells taking place due to aging, programmed cell death and various diseases. It may through some light on the study of cancerous cell and gerontology.

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Abbreviations

PPIs:

Protein—protein interactions

F-SVM:

Fuzzy—support vector machine

SMO:

Sequential minimal optimization

MCC:

Mathew correlation coefficient

Apaf-1:

Apoptotic protease activating factor-1

ASC:

Apoptosis associated Spec-like protein containing a CARD

Bcl-10:

B cell lymphoma/leukemia 10

BinCARD:

Bcl-10 interacting CARD protein

CARD:

Caspase-recruitment domain

Caspase:

Cysteine aspartate-specific proteinase

CIAP:

Baculoviral IAP repeat containing protein

ANK3:

Ankyrin 3

DD:

Death domain

DAPK:

Death-associated protein kinase

DED:

Death effector domain

DR:

Tumor necrosis factor receptor superfamily member

FADD:

Fas-associated death domain

IRAK:

Interleukin receptor associated kinase

MAVS:

Mitochondrial antiviral signaling protein

Myd88:

Myeloid differentiation primary response protein MyD88

NLRP:

NACHT LRR and PYD domain containing protein

NOD:

Nucleotide binding oligomerization domain protein

POP:

Pyrin domain containing protein

RAIDD:

Death domain containing protein CRADD

RIG1:

Probable ATP dependent RNA helicase

RIPK:

Receptor interacting Ser/Thr protein kinase

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Acknowledgments

Authors are thankful to Bioinformatics Infrastructure Facility (BIF) of Department of Biotechnology (DBT), Ministry of Science and Technology, Government of India.

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Authors and Affiliations

  1. Department of Bioinformatics, Maulana Azad National Institute of Technology, Bhopal, 462003, Madhya Pradesh, India

    Prakash A. Nemade

  2. Department of Mathematics, Maulana Azad National Institute of Technology, Bhopal, 462003, Madhya Pradesh, India

    Kamal R. Pardasani

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Correspondence to Prakash A. Nemade.

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Nemade, P.A., Pardasani, K.R. Fuzzy support vector machine model to predict human death domain protein–protein interactions. Netw Model Anal Health Inform Bioinforma 4, 5 (2015). https://doi.org/10.1007/s13721-015-0078-1

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