To address this limitation, more insightful approaches incorporate quantum information or entanglement measures . These have proven particularly insightful for the quantum mechanical characterization of chemical concepts, such as bond formation , or provides the physical foundation for powerful numerical approaches. A notable example is the Density Matrix Renormalization Group (DMRG) algorithm, of which the underlying tensor network representation is physically motivated through the notion of locality of quantum correlations . Although the Coulomb interaction is a fundamentally non-local interaction, DMRG has found notable applications in quantum chemistry as well, mostly for multi-reference molecular systems that go significantly beyond the single-particle reference picture, e.g. in transition metal compounds . Here, quantum information theory can be efficiently employed as a post-analysis tool to elucidate the electronic structure and correlations in molecular systems . For instance, the mutual information index (MI) is a measure of the quantum correlations between any two individual subsystems within the molecule. A natural and popular choice of subsystem is the single-electron spin-orbital, given its foundational role within chemistry. Indeed, the MI between individual molecular or natural spin-orbitals provides a good measure for the multi-reference character of the molecular system. In one study it was shown that the nature of correlation energy, whether it is dynamic or static, can be reflected in the entanglement patterns of orbitals . In another work, some of us assessed the amount of entanglement between molecular orbitals (MOs) in diatomic systems to elucidate bond formation and dissociation processes . Furthermore, MI indices are also informative as optimization tools for practical electronic structure computations with increasing levels of sophistication. For instance, they can significantly help simplify the underlying tensor network wavefunction in DMRG by optimizing the ordering of the spin-orbitals such that quantum correlations are strongest in adjacent orbitals . However, through the optimization and reordering of molecular orbitals, a large portion of the intuitive spatial correlations within a molecule get obfuscated in the process, leading to a loss of the notion of locality. As such, one can argue that DMRG has not been applied to its potential in molecular systems: 1D extensive molecules with local quantum correlations. Examples of such systems that come naturally to mind are polymers and polypeptides or proteins. With this paper, we embark on a research project to capture the electronic structure of extensive effective 1D molecular systems within a tensor network formalism. Therefore, two major developments need to take place. First, the establishment of the local character of quantum correlations in polypeptides and polymers, and second the identification of the optimal tensor network architecture. In the present paper, we focus on the former aspect, evaluating and confirming the locality of quantum correlations in peptide systems using MI indices. More specifically, we compute the MI between local atomic orbitals (AO), rather than canonical molecular orbitals or localized orbitals (obtained by localizing molecular orbitals), as was done in previous works (see, for instance, ), and compound them to fully interatomic MI indices. Our results confirm the locality of quantum correlations in these systems. This is in line with recent work by Pekker et. al. which showed that the matrix elements of 1-and 2-RDMs decrease rapidly as a function of the distance and changes in local conformation only impact density matrices locally . Computations were performed at the DMRG, Hartree-Fock, and Density Functional Theory levels, from which density matrices are obtained from the Matrix Product State (MPS), Hartree-Fock and Kohn-Sham Slater determinant respectively. Interestingly, we observe a large degree of correlation and consistency between the results of these three vastly distinct methods.