Predictive methods using protein sequences

Simply put, DNA encodes the instructions for life, while proteins constitute the machinery of life. DNA is transcribed into RNA and from there information is delivered into the amino acid sequence of a protein. This simplified version of the “central dogma of molecular biology” formulated by Francis Crick (1958) essentially remains valid, although new discoveries have extended our view (Elbarbary et al. 2016). Furthermore, epigenetic studies have demonstrated that chromatin contains more complex information than just a one-dimensional (1D) string of letters, with the heritability of epigenetic traits having a profound effect on gene expression (Allis and Jenuwein 2016). Nonetheless, the 1D protein sequence ultimately determines the three-dimensional (3D) structure into which the protein will fold (Anfinsen 1973), where it will reside in the cell, with which other molecules it will interact, its biochemical and physiological function, and when and how it will eventually be broken down and reduced back into its building blocks. In sum, the function (or, in the case of a disease, the malfunction) of every protein is encoded in the sequence of amino acids. The central dogma suggests that everything about a protein can be inferred from its DNA sequence–so, why then analyze protein sequences? It turns out that computationally converting DNA to protein sequence is challenging and we still do not understand exactly how to identify the structure of a protein based on the DNA that encodes it. It is even more difficult to predict transcripts from DNA. Fortunately, many experimental approaches, including proteomics methods, can be used to deduce protein