Surfactant Like Peptides (SLPs) are the peptide sequences resembling surfactants, comprising of a stretch of hydrophobic residues on one end and a stretch of hydrophilic residues on the other. Depending on its amino acid sequence, they can self-assemble into ordered nanostructures. SLPs are experimentally determined to form nanostructures like membrane, vesicle, nanotubes, nano-sheets and nano rods. The kinetics of formation of the nanostructure is also influenced by the change in solvent conditions like pH and temperature. One such SLP is A 6 K, which is known to form a nanotube with potential application in drug delivery. Like A 6 K, numerous peptide sequences can also form stable nanostructures. Identification of all those possible combinations experimentally is an insurmountable task. Hence, we propose computational methods to obtain insights on the mechanism of self-assembly of peptides into nanostructures and determine the stable molecular architecture that could result into ordered nanostructure. This knowledge will help us quickly predict the feasibility of a given peptide sequence to form a stable nanostructure. We also aim to investigate the effect of solvent conditions on the self-assembly of SLPs and the stability of the resultant nanostructures.