We present a new conceptual methodology for realizing encryption involving trap-door functions built from biological processes. Many standard encryption methods such as RSA security, for example, utilize functions that are easy to compute in one direction but the reverse is a computationally hard problem without a key. In biology, a trap-door like functions can be created from natural phenomena such as the process of creating protein sequences. A fragment of DNA can be transformed to protein easily however given a protein sequence, it is very hard to convert the protein information back to DNA. In essence, protein creation is a lossy function and if we keep certain side-information secret, then a trap-door like function can be constructed from this mechanism that is ideal for encryption. We propose sEncrypt (sequence Encrypt), a model inspired by the central dogma of biology to encode, encrypt, decrypt and decode plain text using publicly-available sequence data from bioinformatics research. We evaluate the entropy of the cipher text to show randomness of characters and show by autocorrelation tests that the encrypted text of our method contains no repetition which could form potential weaknesses. These tests and results show that the sEncrypt framework constitutes a good encryption framework for use in information exchange.