# Revisiting Chameleon Sequences in the Protein Data Bank
## 1 Introduction  
It is the accepted wisdom of structural biology that the amino acid sequence of a protein determines its three-dimensional structure. This is rather remarkable since individual peptides are quite flexible, as can be deduced from the fact that large areas of the Ramachandran [1] plot represent literally observed conformations, this being confirmed by many simulations [2]. This apparent conundrum can be resolved by assuming that for a sequence to be able to determine the structure it has to be long enough. The first attempt at clarifying this issue originated from the Paracelsus challenge of [PERSON] and [PERSON] [3] where they posed the challenge of changing less than 50% of the sequence to cause a change in the fold (i.e., the tertiary structure). Not only was the challenge soon met, recent work found examples whereby changing only 10% of the sequence still changed the fold [4]. The complexity of the relationship between sequence and structure is further highlighted by the discovery of misfolded proteins; the importance of understanding misfolding, in some cases combined with (or helped by(7)) aggregation, stems from the fact that they are often involved in serious diseases like mad-cow disease [5] and Alzheimer's [6], as well as Parkinson's [7]. Another issue that somewhat muddies the sequence-structure relationship is the discovery of intrinsically disordered proteins (IDP), which highlights the important role the protein's environment plays in forming its structure. However, the prediction of the protein structure is a 'Hard' problem [8]. Thus, initial attention has been focused on the problem of predicting the secondary structure from the sequence [9]. Clearly, successful prediction of the secondary structure is a necessary, but not sufficient, condition of successfully predicting the tertiary structure. The usefulness of the ability to predict secondary structure from sequence is further highlighted by the hierarchical nature of protein folding [10], i.e., during folding, secondary structures form locally as the first step. Given the known flexibility of peptides (vide supra), it is not surprising that the reliability of such prediction is limited [11]. One limit on the reliability of sequence-based structure prediction is the existence of chameleon sequences, i.e., sequences that can form different secondary structures in different environments. In particular, the more reliable such predictions are, the less likely that a given peptide sequence, to be a chameleon. Given the fact that conformational promiscuity is often involved in diseases, the characterization of chameleon sequences can contribute both to the field of structure prediction and to the fight against misfolding-related diseases. The Protein Data Bank (PDB) [12] has been searched for chameleons several times, starting with the work of [PERSON] and [PERSON] [13], and as the number of structures in the PDB grew several repeat searches where conducted (including one by this author) [14; 15; 16; 17; 18; 19]. The longest chameleon sequences found involved 10 residues. As the number of structures in the PDB grew, the searches were restricted [17; 19] to non-redundant subsets like The Structural Classification of Proteins (SCOP) [20] or the set at NCBI ([[https://blast.ncbi.nlm.nih.gov/Blast.cgi](https://blast.ncbi.nlm.nih.gov/Blast.cgi)]([https://blast.ncbi.nlm.nih.gov/Blast.cgi](https://blast.ncbi.nlm.nih.gov/Blast.cgi))). This sped up the search and also eliminated the problem of giving large weight to proteins whose structural variants (and their homologs) occur repeatedly. It has the drawback of possibly missing chameleons that only occur in a variant/homolog that was not included in the non-redundant set. The aim of this paper is to present a fast search algorithm, apply it to the proteins in the current PDB, and analyze the resulting chameleons.