Protein Fold Recognition Based Upon the Amino Acid Occurrence

We have investigated the relative performance of amino acid occurrence and other features, such as predicted secondary structure, hydrophobicity, normalized van der Waals volume, polarity, polarizability, and real/predicted contact information of residues, for recognizing protein folds. We observed that the improvement over other features is only marginal compared with amino acid occurrence. This is because amino acid occurrence, indirectly, can consider varieties of physical properties which are useful to discriminate protein folds. If we consider only proteins which are well aligned structurally with each other, the accuracy of discrimination is drastically improved. In order to discriminate protein folds more accurately, we need to consider anything other than structure alignment.

[1]  A. Valencia,et al.  Improving contact predictions by the combination of correlated mutations and other sources of sequence information. , 1997, Folding & design.

[2]  Hongyi Zhou,et al.  Fold recognition by combining sequence profiles derived from evolution and from depth‐dependent structural alignment of fragments , 2004, Proteins.

[3]  M. Gromiha,et al.  Important amino acid properties for enhanced thermostability from mesophilic to thermophilic proteins. , 1999, Biophysical chemistry.

[4]  R. Casadio,et al.  A neural network based predictor of residue contacts in proteins. , 1999, Protein engineering.

[5]  James E. Bray,et al.  The CATH database: an extended protein family resource for structural and functional genomics , 2003, Nucleic Acids Res..

[6]  Robert M. MacCallum,et al.  Striped sheets and protein contact prediction , 2004, ISMB/ECCB.

[7]  M. Michael Gromiha,et al.  Importance of Native-State Topology for Determining the Folding Rate of Two-State Proteins , 2003, J. Chem. Inf. Comput. Sci..

[8]  A G Murzin,et al.  SCOP: a structural classification of proteins database for the investigation of sequences and structures. , 1995, Journal of molecular biology.

[9]  M. Michael Gromiha.  Importance of Native-State Topology for Determining the Folding Rate of Two-State Proteins. , 2003 .

[10]  M. Gromiha,et al.  Relationship Between Amino Acid Properties and Protein Stability: Buried Mutations , 1999, Journal of protein chemistry.

[11]  Marimuthu Palaniswami,et al.  Protein topology classification using two-stage support vector machines. , 2006, Genome informatics. International Conference on Genome Informatics.

[12]  Shigeki Mitaku,et al.  SOSUI: classification and secondary structure prediction system for membrane proteins , 1998, Bioinform..

[13]  Kian-Lee Tan,et al.  Matalign: Precise Protein Structure Comparison by Matrix Alignment , 2006, J. Bioinform. Comput. Biol..

[14]  M Michael Gromiha,et al.  Importance of mutant position in Ramachandran plot for predicting protein stability of surface mutations. , 2002, Biopolymers.

[15]  M. Gromiha,et al.  Comparison of amino acid occurrence and composition for predicting protein folds , 2006, q-bio/0609037.

[16]  T L Blundell,et al.  FUGUE: sequence-structure homology recognition using environment-specific substitution tables and structure-dependent gap penalties. , 2001, Journal of molecular biology.

[17]  Chris H. Q. Ding,et al.  Multi-class protein fold recognition using support vector machines and neural networks , 2001, Bioinform..

[18]  Pierre Baldi,et al.  A machine learning information retrieval approach to protein fold recognition. , 2006, Bioinformatics.

[19]  K C Chou,et al.  Prediction of protein structural classes and subcellular locations. , 2000, Current protein & peptide science.

[20]  M. Michael Gromiha,et al.  A Statistical Method for Predicting Protein Unfolding Rates from Amino Acid Sequence. , 2006 .

[21]  M. Michael Gromiha,et al.  A simple statistical method for discriminating outer membrane proteins with better accuracy , 2005, Bioinform..