To resolve all these free parameters by fitting would require a large amount of experimental data. On the other hand, complex heterogeneous dynamics of disordered molecules can be resolved by interpreting spin relaxation data directly using molecular dynamics (MD) simulations. However, this approach requires careful exploration of MD simulation models that reproduce experimental spin relaxation times with sufficient accuracy for the interpretation of experimental data. Here we present an approach to resolve the dynamic landscape of complexes formed by disordered biomolecules by interpreting spin relaxation data from solution state NMR experiments using MD simulations. This is demonstrated for six different types of peptides embedded in SDS micelles, a standard anionic membrane-mimicking environment. For the references, we resolved the dynamic landscape of two widely characterized peptides in the micellar environment: a model transmembrane GWALP peptide and an antimicrobial Magainin 2 peptide that is known to settle in a peripheral orientation parallel to membranes. To further demonstrate the usefulness of our approach, we studied tail-anchored peptides shown to target the mitochondrial outer membrane in yeast [eElaB(TA), eYqjD(TA), yFis1(TA)] or human [hMff(TA)] cells. The mechanism by which these tail-anchored peptides are inserted into lipid is still poorly understood. For these six peptides, we predicted experimental spin relaxation times directly, without any further fitting, from the MD simulation force field based on physical interaction parameters between atoms. Our predictions allowed detailed interpretation of the dynamic landscapes of peptides and a better understanding of their behaviour in lipid-like environments. Most importantly, the approach presented here can be generally applied to characterise dynamic landscapes of complexes formed by disordered biomolecules, including not only more realistic membrane mimicking systems, such as bicelles or nanodiscs, but also to other systems such as lipid droplets or membrane-less organelles.