AAindex: Amino Acid Index Database

By Shuichi Kawashima, Hiroyuki Ogata, Minoru Kanehisa. 1999

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Citations in the corpus

Better prediction of functional effects for sequence variants (2015, neutral): “For the sequence-only network (SNAP2 noali ) the following features where included: amino acid composition, protein length, sequence and variant profiles, contact potential profiles and delta, volume and hydrophobicity along with the corresponding delta features as well as several amino acid indices”
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 ami”
AAindex: amino acid index database, progress report 2008 (2007, methodology): “The AAindex database is continuously updated by the present authors (3,4).”
Hidden Markov Models and their Applications in Biological Sequence Analysis (2009, methodology): “These features are extracted by performing a spectral analysis of a number of selected `amino acid indices' [46] and using principal component analysis (PCA) to reduce the redundancy in the resulting signal.”
Computational prediction of proteotypic peptides for quantitative proteomics (2007, methodology): “To identify the properties governing a peptide's proteotypic propensity, we evaluated 494 numeric physicochemical property scales for amino acids, including charge, secondary structure propensity and hydrophobicity ( Supplementary Tables 4 , 5 , 6 , 7 for complete list”
Specificity prediction of adenylation domains in nonribosomal peptide synthetases (NRPS) using transductive support vector machines (TSVMs) (2005, methodology): “We standardized the values in such a way that the interval of ±1 SD (calculated from the value distribution of each AAindex ﬁle) is projected onto the interval of ±1.”
ASH structure alignment package: Sensitivity and selectivity in domain classification (2007, result): “To further investigate the similarity between the proposed substitution matrix and previous work by other groups, our matrix was compared to all 98 entries in the AAindex, a database of substitution matrices [20-22].”
The pairwise energy content estimated from amino acid composition discriminates between folded and intrinsically unstructured proteins. (2005, background): “94) among more than 400 different amino acid propensities collected in the AAIndex database.(31) Interestingly, the same scale among hydrophobicities was found to be the best for discriminating structured proteins from IUPs in a systematic search among 265 amino acid properties.”
Extensive feature detection of N-terminal protein sorting signals (2002, background): “The AAindex database (Kawashima and Kanehisa, 2000) is a compilation of 434 of such indices.”
Benchmarking of protein descriptor sets in proteochemometric modeling (part 1): comparative study of 13 amino acid descriptor sets (2013, methodology): “This is a principal difference to several other AA descriptor sets, where also non-natural AAs were taken into account [38].”
Update of PROFEAT: a web server for computing structural and physicochemical features of proteins and peptides from amino acid sequence (2006, background): “Autocorrelation features describe the level of correlation between two objects (protein or peptide sequences) in terms of their specific structural or physicochemical property (28), which are defined based on the distribution of amino acid properties along the sequence ( 29 ).”
Computational methods and tools for binding site recognition between proteins and small molecules: from classical geometrical approaches to modern machine learning strategies (2019, methodology): “The feature selected for the prediction are derived from the AAindex database which contains 544 properties [63].”
Prediction‐based fingerprints of protein–protein interactions (2006, background): “54 In particular, hydrophobicity (AAIndex ID ¼ ARGP820101) and the expected number of contacts within 14 A˚ sphere (AAIndex ID ¼ NISK860101) were found to be most informative.”
Maximum Allowed Solvent Accessibilites of Residues in Proteins (2012, methodology): “As of this writing, the AAIndex database [36] contains over 40 scales related to amino acid hydrophobicity or polarity.”
propy: a tool to generate various modes of Chou's PseAAC (2013, methodology): “Apart from these descriptors, it can also compute previous descriptors based on user-defined properties, which are easily accessible from the AAindex database (Kawashima and Kanehisa, 2000).”