Proteins are polypeptides consisting of twenty naturally occurring amino acids that are made to perform a variety of tasks in biological systems, including signaling, substance transport, structural support, mobility, defense, etc . The functionality of proteins heavily depends on their three-dimensional structure, known as tertiary structure, which itself is determined largely by the linear sequence of amino acids, known as primary structure. In other words, the primary structure of proteins carries all the necessary information required to determine the tertiary structure and, subsequently, their function. However, predicting the tertiary structure of proteins from microscopic principles remains a challenge to date . On the one hand, proteins are sufficiently small to feel the imprint of each particular individual amino acid on its tertiary structure. On the other hand, they are also large enough such that an exact ab initio characterization from fundamental quantum mechanical principles is computationally intractable. For this reason, the protein folding problem requires physical modeling across multiple length scales. To a large extent, popular physical models can be divided into three different length scales that coincide roughly with proteins' primary, secondary, and tertiary structures. The first and smallest level considers proteins (or parts of proteins) at the microscopic atomic and subatomic level, which requires ab initio modeling at the fundamental quantum mechanical level. At this level, the Born-Oppenheimer approximation is involved in describing the molecule as a collection of nuclei that are bonded together through the quantum mechanical electron distributions . The use of these models is crucial for the understanding of fine-grained interatomic interactions such as bond formation or docking processes . The second length scale makes abstraction of the microscopic electronic degrees of freedom and considers proteins as a collection of atoms that are held together by means of classically derivable forces. Although these models are mostly grounded in Newtonian physics, the forces that enter as input are physically informed either on a qualitative level by means of chemical intuition or on a quantitative level from ab initio first quantum mechanical principles . There has also been remarkable progress recently in machine-learned models trained on available experimental data . These models are mostly employed to model protein folding processes that involve global dynamics of medium-sized proteins that are beyond the capabilities of ab initio methods. We reach the realm of the course-grained models at the highest length scale. Here, several atoms are compounded together to form beads that act as individual entities. The course-graining of protein models becomes a necessity at the macromolecular level when the system exceeds the 100 kDa level . At each of the three levels, it is important to incorporate correlations between the substituent degrees of freedom at the lower level. For instance, in order to accurately describe tertiary structures, it is important to understand the correlations between the constituent amino acids, for which in turn, an understanding of the electronic properties is key . The twenty naturally occurring L-amino acids are distinguished by their different side chain structures and chemical composition. Covalent bonds, hydrogen bonds, salt bridges, and disulfide bonds all contribute to the stability of the 3D structure along with hydrophobic interactions, which are pivotal in the protein's core formation . Given the pivotal role of these interactions, employing a method that accurately describes the electronic structure of amino acids at a reasonable computational cost becomes imperative. Such methods provide a more realistic depiction of side-chain interactions compared to conventional force field methods, thereby enhancing our understanding of protein structure and function .