# PREFMoDeL: A Systematic Review and Proposed Taxonomy of Biomolecular Features for Deep Learning
## 6 Discussion
### Directions for Future Work
#### 6.2.4 Potential New Features for Molecular Deep Learning  
This current meta-analysis of biomolecular features for deep learning presents a broad perspective on specific features utilized in publications across the field of biochemistry. The primary goal for developing our taxonomy was to organize the current state of the art of biomolecular features for deep learning to strengthen future implementations of deep learning models to the wide variety of biochemical, molecular interaction, and biomedicalcal questions being studied by research investigators worldwide. After our analysis, we discovered that the resulting taxonomy of features also served to highlight several noticeable voids in the molecular feature space, especially with respect to dynamic structural features. A few examples of potential new features are described below. Furthermore, these features should be, in principle, relatively straightforward to implement using modern Python libraries. 1. **Kinetics-Weighted and Multi-Transition State Sequences**. The consensus structure method described by [PERSON] et al. [58] is an excellent advance in enzyme design, but can be slightly modified to incorporate more dynamic detail. Each transition state's contribution can be weighted by the inverse of the experimentally determined forward kinetic constant for that particular reaction step. This adjustment would enable the slowest reaction barriers to exert the greatest influence on the consensus structure. Secondly, multiple transition states might be considered rather than creating a consensus structure. This can be accomplished by optimizing multiple states and their transitions by using fast protein motions, such as sidechain rearrangements, cofactor vibrations, and other atom-scale dynamics. These two features, coupled with methods developed to utilize them to the fullest extent, would enhance structure- and dynamics-based enzyme engineering efforts. 2. **Bond strain vector**. A bond strain vector would describe bond strain for every bond in a protein or ligand from theoretically ideal values. Such a feature can be visualized as a one-dimensional vector where each index in the vector holds the energy of the indexed bond in kiljoules per mole. Through application of [PERSON]'s law and idealized bond geometries, bond energy deviations are easily computed. Then, they can be visualized on a protein structure, or for a ligand, bound in an active site. Such a feature is extensible to analogues for angular strain (between three atoms) or torsional strain (between four atoms) which together describe the system in more dynamic and mechanical detail. This feature would illustrate where strain is present in a protein or a ligand structure and may prove important in deep learning tasks concerning function such as recognition, binding, and catalysis [59]. 3. **Amino acid sidechain normal modes**. Conformational plasticity, from high-frequency rate-promoting vibrations to sidechain rotamer sampling to large domain motion, is often required to understand protein function [60; 61; 62; 63]. This is especially relevant on the scale of individual amino acid sidechains within a binding pocket, as they are often subject to rapid structural rearrangements to enable precise biochemical function. [PERSON] et al. simulated enzyme active site dynamics with restricted conformational mobility to determine which structural features activate catalysis [64]. The ability to compute other such elementary features may lead to widely interpretable insight when applied to novel systems. For example, single amino acid motions are critical for the substrate IP7 to enter the active site of PPIP5K2. Here, a single residue glutamine-192 utilizes spontaneous motion and the irreversibility of reverse substrate diffusion to ratchet IP7 from a capture site to the active site [65]. These case studies demonstrate the value of the automatic extraction of this detail to sense amino acid motions for analysis or design purposes.