PROFEAT: a web server for computing structural and physicochemical features of proteins and peptides from amino acid sequence. Sequence-derived structural and physicochemical features have frequently been used in the development of statistical learning models for predicting proteins and peptides of different structural, functional and interaction profiles. PROFEAT (Protein Features) is a web server for computing commonly-used structural and physicochemical features of proteins and peptides from amino acid sequence. It computes six feature groups composed of ten features that include 51 descriptors and 1447 descriptor values. The computed features include amino acid composition, dipeptide composition, normalized Moreau–Broto autocorrelation, Moran autocorrelation, Geary autocorrelation, sequence-order-coupling number, quasi-sequence-order descriptors and the composition, transition and distribution of various structural and physicochemical properties. In addition, it can also compute previous autocorrelations descriptors based on user-defined properties. Our computational algorithms were extensively tested and the computed protein features have been used in a number of published works for predicting proteins of functional classes, protein–protein interactions and MHC-binding peptides. PROFEAT is accessible at

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  1. Marrero-Ponce, Yovani; Teran, Julio E.; Contreras-Torres, Ernesto; García-Jacas, César R.; Perez-Castillo, Yunierkis; Cubillan, Nestor; Peréz-Giménez, Facundo; Valdés-Martini, José R.: LEGO-based generalized set of two linear algebraic 3D bio-macro-molecular descriptors: theory and validation by QSARs (2020)
  2. Carugo, Oliviero (ed.); Eisenhaber, Frank (ed.): Data mining techniques for the life sciences (2016)
  3. Marrero-Ponce, Yovani; Contreras-Torres, Ernesto; García-Jacas, César R.; Barigye, Stephen J.; Cubillán, Néstor; Alvarado, Ysaías J.: Novel 3D bio-macromolecular bilinear descriptors for protein science: predicting protein structural classes (2015)
  4. Bakhtiarizadeh, Mohammad Reza; Moradi-Shahrbabak, Mohammad; Ebrahimi, Mansour; Ebrahimie, Esmaeil: Neural network and SVM classifiers accurately predict lipid binding proteins, irrespective of sequence homology (2014)
  5. Han, Guo-Sheng; Yu, Zu-Guo; Anh, Vo: A two-stage SVM method to predict membrane protein types by incorporating amino acid classifications and physicochemical properties into a general form of Chou’s PseAAC (2014)
  6. Kavousi, Kaveh; Moshiri, Behzad; Sadeghi, Mehdi; Araabi, Babak N.; Moosavi-Movahedi, Ali Akbar: A protein fold classifier formed by fusing different modes of pseudo amino acid composition via PSSM (2011)
  7. Ahmad, Norashikin; Alahakoon, Damminda; Chau, Rowena: Cluster identification and separation in the growing self-organizing map: application in protein sequence classification (2010) ioport
  8. Lapinsh, Maris; Wikberg, Jarl E. S.: Kinome-wide interaction modelling using alignment-based and alignment-independent approaches for kinase description and linear and non-linear data analysis techniques (2010) ioport
  9. Xi, Lili; Li, Shuyan; Liu, Huanxiang; Li, Jiazhong; Lei, Beilei; Yao, Xiaojun: Global and local prediction of protein folding rates based on sequence autocorrelation information (2010)
  10. Faria, Daniel; Ferreira, António E. N.; Falcão, André O.: Enzyme classification with peptide programs: a comparative study (2009) ioport
  11. Shao, Xiaojian; Tian, Yingjie; Wu, Lingyun; Wang, Yong; Jing, Ling; Deng, Naiyang: Predicting DNA- and RNA-binding proteins from sequences with kernel methods (2009)
  12. Strömbergsson, Helena; Kleywegt, Gerard J.: A chemogenomics view on protein-ligand spaces (2009) ioport
  13. Chen, Chao; Chen, Li-Xuan; Zou, Xiao-Yong; Cai, Pei-Xiang: Predicting protein structural class based on multi-features fusion (2008)
  14. Li, Z. R.; Lin, H. H.; Han, L. Y.; Jiang, L.; Chen, X.; Chen, Yu Zong: PROFEAT: a web server for computing structural and physicochemical features of proteins and peptides from amino acid sequence. (2006) ioport