AAindex: amino acid index database, progress report 2008

By Shuichi Kawashima, Piotr Pokarowski, M. Pokarowska, Andrzej Kolinski, Toshiaki Katayama, Minoru Kanehisa. Nucleic Acids Res., 2007

1,112 citations recorded by the source corpus.

AAindex is a database of numerical indices representing various physicochemical and biochemical properties of amino acids and pairs of amino acids. We have added a collection of protein contact potentials to the AAindex as a new section. Accordingly AAindex consists of three sections now: AAindex1 for the amino acid index of 20 numerical values, AAindex2 for the amino acid substitution matrix and AAindex3 for the statistical protein contact potentials. All data are derived from published literature. The database can be accessed through the DBGET/LinkDB system at GenomeNet (http://www.genome.jp/dbget-bin/www_bfind?aaindex) or downloaded by anonymous FTP (ftp://ftp.genome.jp/pub/db/community/aaindex/).

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/501fffd8c5b8f95c7d9c990855ae8f46d6bea043

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Citations in the corpus

Use of Proteomics and Peptidomics Methods in Food Bioactive Peptide Science and Engineering (2012, neutral): “jp/aaindex/ [91] 234 Food Eng Rev (2012) 4:224–243”
The genetic landscape of a physical interaction (2018, methodology): “Prediction of single mutant effects from amino acid physic-chemical features Fos and Jun single mutant effects were predicted at each of the 32 mutated positions from amino acid features retrieved from the amino acid index database (Kawashima et al., 2008).”
Computational identification of MoRFs in protein sequences (2015, methodology): “First, an SVM with a linear kernel determines a MoRF propensity score based on nine sets of features: physiochemical properties of amino acids from the Amino Acid Index (Kawashima et al., 2008), conservation information in the form of Position Specific Scoring Matrices (PSSM) generated with PSI_BLAS”
Pse-in-One: a web server for generating various modes of pseudo components of DNA, RNA, and protein sequences (2015, methodology): “The PC-PseAAC-General approach (14) cannot only incorporate comprehensive built-in indices (Table 5) extracted from AAindex (15), but also allow the users to upload their own indices to generate the PC-PseAAC-General feature vector.”
Distance Matrix-Based Approach to Protein Structure Prediction (2009, methodology): “We used AAindex database of over 500 amino acid indices collected by Kanehisa(Kawashima et al., 2000;Kawashima et al., 2008) http://www.genome.jp/aaindex/ and have found that all matrices of contact potentials can be approximated with correlation 0.9 by hydrophobicities and isoelectric points of…”
Machine learning in protein engineering (2018, background): “AAIndex (Kawashima et al., 2007) attempts to systematically collect descriptors of protein sequences.”
Properties of MHC Class I Presented Peptides That Enhance Immunogenicity (2013, methodology): “[47], the Spearman Rank correlation with enrichment scores in immunogenic pMHCs was determined.”
The in silico human surfaceome (2018, background): “Details of the selected features from AAindex1 and UniProt are discussed in Methods.”
SBSM-Pro: support bio-sequence machine for proteins (2023, background): “Furthermore, every alphabet in the sequence (amino acids, nucleotides) can be numerically represented, thereby contributing to the global feature of the sequence [8, 9].”
SuSPect: Enhanced Prediction of Single Amino Acid Variant (SAV) Phenotype Using Network Features (2014, positive): “Finally, a number of features describing the wild typeand themutant aminoacidswere used—BLOSUM[54], change in charge, mutation to/from glycine/proline and changes to the values of the five principle components calculated by Atchley et al. [55] from the 494 amino acid indices in the Amino Acid Index ”
Genome‐scale prediction of proteins with long intrinsically disordered regions (2014, methodology): “We collected all 544 indices from version 9.1 of AAindex55,56 to comprehensively cover physicochemical properties of AAs; 13 indices were removed since they include unknown/missing values.”
BioSeq-Analysis2.0: an updated platform for analyzing DNA, RNA and protein sequences at sequence level and residue level based on machine learning approaches (2019, methodology): “The second category is PP that represents residues using the 547 amino acid physicochemical indices from AAindex (26), and users can select some physicochemical properties from the index boxes to use.”
NRPSpredictor2—a web server for predicting NRPS adenylation domain specificity (2011, methodology): “NRPSpredictor2 makes use of two feature encodings for amino acids: one is the original encoding proposed by Rausch et al. based on 12 AAindex (14) descriptors and the other is a reduced encoding based on three z-scales descriptors devised by Wold et al. (15).”
Machine-learning-guided directed evolution for protein engineering (2018, background): “AAIndex 65 and ProFET 66 have large collections of physical descriptors for protein sequences.”