iSNO-PseAAC: Predict Cysteine S-Nitrosylation Sites in Proteins by Incorporating Position Specific Amino Acid Propensity into Pseudo Amino Acid Composition

Open Access

Peer-reviewed

Research Article

Yan Xu ,

* E-mail: yxu@gordonlifescience.org (YX); kcchou@gordonlifescience.org (K-CC)

Affiliations Department of Information and Computer Science, University of Science and Technology Beijing, Beijing, China, Gordon Life Science Institute, San Diego, California, United States of America
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Jun Ding,

Affiliation Department of Information and Computer Science, University of Science and Technology Beijing, Beijing, China
⨯
Ling-Yun Wu,

Affiliation Institute of Applied Mathematics, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, China
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Kuo-Chen Chou

* E-mail: yxu@gordonlifescience.org (YX); kcchou@gordonlifescience.org (K-CC)

Affiliation Gordon Life Science Institute, San Diego, California, United States of America
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Forecasting
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Protein sequencing
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DNA-binding proteins
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Outer membrane proteins
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Protein structure prediction
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Drug research and development
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Cysteine
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S-nitrosylation
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