| 1 | BLOSUM50 matrix | 2,3,4,5,6,8,10,12,15,18 | Simplified amino acid alphabets for protein fold recognition and implications for folding | 2000 |
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| 2 | BLOSUM40 matrix | 2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19 | Simplifying amino acid alphabets by means of a branch and bound algorithm and substitution matrices | 2002 |
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| 3 | artificial immune system(AIS) | 6,6,6,7,7,8,8,8,8,9 | Optimizing amino acid groupings for GPCR classification | 2008 |
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| 4 | The maximum information gain | 2,3,4,5,6,7,8,9,10,11,12,13,14 | Optimized Representations and Maximal Information in Proteins | 2000 |
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| 5 | Secondary-structure | 2,3,4,5,6,7,8,9,10,11,12,13,15,16,17,18,19 | Representation of Protein-Sequence Information by Amino Acid Subalphabet | 2004 |
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| 6 | Structure-Derived Matrix（SDM） | 2,3,4,6,7,8,9,10,11,12,13,14,16,17,19 | Structure-derived substitution matrices for alignment of distantly related sequences | 2000 |
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| 7 | GONNET matrix | 2,3,4,5,6,7,8,9,10,11,12,13,14,15,17 | Structure-derived substitution matrices for alignment of distantly related sequences | 2000 |
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| 8 | Miyazawa Jernigan (MJ) matrix | 2,3,4,5,6,7,8,9,10,11,12,13,14,15,16 | Simplified amino acid alphabets based on deviation of conditional probability from random background | 2002 |
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| 9 | BLOSUM50 matrix | 2,3,4,5,6,7,8,9,10,11,12,13,14,15,16 | Simplified amino acid alphabets based on deviation of conditional probability from random background | 2002 |
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| 10 | Genetic algorithm | 5 | Accuracy of Sequence Alignment and Fold Assessment Using Reduced Amino Acid Alphabets | 2006 |
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| 11 | Boltzmann relation/iteration | 2,3,4,5,6,7,8,9,10,14 | An iterative method for extracting energy-like quantities from protein structures | 1996 |
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| 12 | Distance matrix | 2,4,7,12 | Distance-dependent classification of amino acids by information theory | 2010 |
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| 13 | Distance matrix | 2,3,5,8,12 | Distance-dependent classification of amino acids by information theory | 2010 |
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| 14 | Miyazawa Jernigan (MJ) matrix | 4,8 | Amino acid partitioning using a Fiedler vector model | 2007 |
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| 15 | Protein blocks | 5,8,9,11,13 | A reduced amino acid alphabet for understanding and designing protein adaptation to mutation | 2007 |
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| 16 | Hierarchical clustering | 2,3,5,7,8,11,14 | A reduced amino acid alphabet for understanding and designing protein adaptation to mutation. | 2007 |
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| 17 | Clustering analysis | 4,4,5,5,5,5,5,5,5,5,5,5,5,5,5,6 | An information-theoretic classification of amino acids for the assessment of interfaces in protein-protein docking. | 2013 |
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| 18 | Physical-chemical property | 3,4,19 | Reduced amino acid alphabet is sufficient to accurately recognize intrinsically disordered protein. | 2004 |
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| 19 | Maximized mutual information | 2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19 | Amino acid alphabet reduction preserves fold information contained in contact interactions in proteins | 2015 |
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| 20 | variance maximization | 2,3,4,5,6,7,8,9,10,11,12,13,13,14,15,16,17,18,19 | Grouping of Amino Acid Types and Extraction of Amino Acid Properties from Multiple Sequence Alignments Using Variance Maximization | 2005 |
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| 21 | Extended Compact Genetic Algorithm(ECGA) | 2,3,4,5 | Automated Alphabet Reduction for Protein Datasets | 2009 |
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| 22 | Extended Compact Genetic Algorithm(ECGA) | 2,3,4 | Automated Alphabet Reduction for Protein Datasets | 2009 |
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| 23 | Extended Compact Genetic Algorithm(ECGA) | 2,3,4,5,7,9,11 | Automated Alphabet Reduction for Protein Datasets. | 2009 |
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| 24 | Physical-chemical property | 7,9,11 | Automated Alphabet Reduction for Protein Datasets | 2009 |
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| 25 | Extended compact genetic algorithm(ECGA) | 2,3,4,5 | Automated Alphabet Reduction for Protein Datasets | 2009 |
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| 26 | Extended Compact Genetic Algorithm(ECGA) | 2,3,3,4 | Automated Alphabet Reduction for Protein Datasets | 2009 |
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| 27 | Extended Compact Genetic Algorithm(ECGA) | 2,3,4,5 | Automated Alphabet Reduction for Protein Datasets. | 2009 |
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| 28 | Miyazawa and Jernigan (MJ) matrix | 2,3,5,5 | A computational approach to simplifying the protein folding alphabet | 1999 |
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| 29 | JTT rate matrix | 2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19 | On Reduced Amino Acid Alphabets for Phylogenetic Inference | 2007 |
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| 30 | Grantham Distance Matrix | 2,3,4,5,5,7,8,9,10,11,12,13,14,15,16,17,18,19 | On Reduced Amino Acid Alphabets for Phylogenetic Inference | 2007 |
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| 31 | hierarchical clustering | 2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,18,19 | Grouping of amino acids and recognition of protein structurally conserved regions by reduced alphabets of amino acids. | 2007 |
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| 32 | Hierarchical clustering | 2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19 | Grouping of amino acids and recognition of protein structurally conserved regions by reduced alphabets of amino acids | 2007 |
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| 33 | Hierarchical clustering | 2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19 | Grouping of amino acids and recognition of protein structurally conserved regions by reduced alphabets of amino acids. | 2007 |
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| 34 | Unweighted Pair-Group Method with Arithmetic (UPGMA) | 2,3,4,5,6,7,8,10,11,12,14,15,16,18,19 | Grouping of amino acids and recognition of protein structurally conserved regions by reduced alphabets of amino acids. | 2007 |
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| 35 |
Dynamic clustering | 2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18 | Grouping of amino acids and recognition of protein structurally conserved regions by reduced alphabets of amino acids. | 2007 |
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| 36 | Information theory | 8 | Conformational Properties of Amino Acid Residues in Globular Proteins. | 1976 |
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| 37 | Physical-chemical property | 6 | Universally Conserved Positions in Protein Folds:Reading Evolutionary Signals about Stability, Folding Kinetics and Function. | 1999 |
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| 38 | BLOSUM62 & Heuristic Monte Carlo (MC) method | 2,3,4,5,6,7,8,9,10,11,12,13,15,15,16,17,18,19 | Reduction of protein seqence complexity by residue grouping | 2003 |
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| 39 | Chemistry properties | 3,4,4,8,10 | Unearthing the Root of Amino Acid Similarity | 2013 |
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| 40 | Protein blocks | 4,8,9,10,10,13 | Unearthing the Root of Amino Acid Similarity | 2013 |
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| 41 | Sequence alignments | 5,5,6,10,10,10,10,10,13,16 | Unearthing the Root of Amino Acid Similarity | 2013 |
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| 42 | Structure alignments | 5,5,6,12,15,17 | Unearthing the Root of Amino Acid Similarity | 2013 |
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| 43 | Contact potential | 5,5,5,5 | Unearthing the Root of Amino Acid Similarity | 2013 |
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| 44 | Miyazawa Jernigan (MJ) & BLOSUM50& BLOSUM62 matrix | 13,14,19 | iDNA-Prot dis: Identifying DNA-Binding Proteins by Incorporating Amino Acid Distance-Pairs and Reduced Alphabet Profile into the General Pseudo Prediction of ketoacyl synthase family using reduced amino acid alphabets
| 2014 |
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| 45 | Contact potential | 4,7,7 | Contact potential that recognizes the correct folding of globular proteins | 1992 |
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| 46 | Miyazawa Jernigan (MJ) matrix and contact potential | 2,5,5 | Amino acid classes and the protein folding problem. | 2001 |
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| 47 | Physical-chemical property | 6 | Prediction of the subcellular location of apoptosis proteins | 2007 |
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| 48 | K-means | 8 | Deep Learning Improves Antimicrobial Peptide Recognition. | 2018 |
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| 49 | BLOSUM62 and Heuristic Monte Carlo (MC) | 2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19 | Reduction of protein seqence complexity by residue grouping | 2003 |
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| 50 | Dynamic Programming Alignments | 4,10 | Automatic generation of primary sequence patterns from sets of related protein sequences. | 1990 |
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| 51 | Unweighted variable group | 2,4,5,6,7,8,10,13,14,15,16,17,18,19 | Relations between Chemical Structure and Biological Activity in Peptides | 1966 |
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| 52 | The Point Accepted Mutation(PAM )matrix | 2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19 |
A new criterion and method for amino acid classification | 2004 |
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| 53 | Whelan and Goldman（WAG ）matrix | 2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19 |
A new criterion and method for amino acid classification | 2004 |
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| 54 | Physical-chemical property | 3,3,3,3,3,3,3 | PROFEAT:a web server for computing structural and physicochemical features of proteins and peptides from amino acid sequence | 2006 |
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| 55 | hydrophobic and polar (HP) model | 4 | Chaos game representation of protein sequences based on the detailed HP model and their multifractal and correlation analyses | 2004 |
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| 56 | Unweighted pair group | 2,3,4,5,6,7,8,9,10,11,12,13,14,16,18,19 | Solving the protein sequence metric problem | 2004 |
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| 57 | Unweighted pair group | 2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19 | Solving the protein sequence metric problem | 2004 |
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| 58 | Unweighted pair group method | 2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19 | Solving the protein sequence metric problem | 2004 |
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| 59 | Unweighted pair group method | 2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19 | Solving the protein sequence metric problem | 2004 |
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| 60 | Particle swarm optimization (PSO) | 5,6,8 | Improved prediction of residue flexibility by embedding optimized amino acid grouping into RSA-based linear models. | 2014 |
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| 61 | Euclidean distance | 3,3,4 | A New Approach to Clustering the Amino Acids | 1996 |
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| 62 | Physical-chemical property | 4 | Predicting Disordered Regions in Proteins Based on Decision Trees of Reduced Amino Acid Composition | 2006 |
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| 63 | Miyazawa Jernigan (MJ) matrix | 2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,18,19 | A general clustering approach with application to the Miyazawa-Jernigan potentials for amino acids | 2004 |
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| 64 | Chemistry space | 5 |
Testing for adaptive signatures of amino acid alphabet evolution using chemistry space | 2014 |
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| 65 | Hierarchical clustering | 2,3,4,5,6,7,8,9,10 | Helix-helix packing and interfacial pairwise interactions of residues in membrane proteins | 2001 |
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| 66 | Hierachical clustering | 2,3,5,6,7,10,11,13,15,16,18 | Simplicial edge representation of protein structures and alpha contact potential with con?dence measure | 2008 |
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| 67 | Fuzzy clustering technique and matrices | 2,4,5,15,19 | Use of fuzzy clustering technique and matrices to classify amino acids and its impact to Chou's pseudo amino acid composition. | 2009 |
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| 68 | Fuzzy clustering technique and matrices | 2,3,5,7,14,19 | Use of fuzzy clustering technique and matrices to classify amino acids and its impact to Chou's pseudo amino acid composition. | 2009 |
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| 69 | Fuzzy clustering technique and matrices | 2,4,5,14,19 | Use of fuzzy clustering technique and matrices to classify amino acids and its impact to Chou's pseudo amino acid composition. | 2009 |
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| 70 | Fuzzy clustering technique and matrices | 5,7,8,19 | Use of fuzzy clustering technique and matrices to classify amino acids and its impact to Chou's pseudo amino acid composition. | 2009 |
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| 71 | Fuzzy clustering technique and matrices | 5,10,13,14,16,19 | Use of fuzzy clustering technique and matrices to classify amino acids and its impact to Chou's pseudo amino acid composition. | 2009 |
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| 72 | Fuzzy clustering technique and matrices | 2,3,5,7,14,19 | Use of fuzzy clustering technique and matrices to classify amino acids and its impact to Chou's pseudo amino acid composition. | 2009 |
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| 73 | Fuzzy clustering technique and matrices | 6,10,13,14,17,19 | Use of fuzzy clustering technique and matrices to classify amino acids and its impact to Chou's pseudo amino acid composition. | 2009 |
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| 74 | Fuzzy clustering technique and matrices | 5,11,15,18,19 | Use of fuzzy clustering technique and matrices to classify amino acids and its impact to Chou's pseudo amino acid composition. | 2009 |
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