The primary structure of a protein, which is its sequence of amino acids, plays a fundamental role in determining its folding and 3D functional form. This complex process, although often assisted by cellular machinery, occurs rapidly, typically within a few milliseconds. From a mathematical and physics perspective, the speed at which the protein finds the “global minima” in the multi-dimensional energy landscape is quite remarkable.

One crucial factor that governs the speed and pathway of protein folding is the specific amino acid sequence. The arrangement of amino acids, not just their overall length, is critical in this process. Interestingly, not all amino acids in the sequence contribute equally to protein folding.

To understand the role of each amino acid in folding, we can analyze the ΔG (Gibbs free energy) of folding for individual residues where molecular dynamics simulation would be very useful. Such an analysis can shed light on whether “hydrophobic” or “hydrophilic” residues have a more significant impact on the folding process. The concept of Hydrophobic collapse suggests that non-polar residues tend to cluster together to avoid contact with water molecules, forming a core that aids in protein folding.

Studying the ΔG decomposition also provides insights into the importance of specific residues in the folding process, which can be valuable for protein design and understanding mutational effects.

Moreover, this approach enables us to explore protein aggregation from an energetic perspective, considering the positions of particular amino acids in the sequence. The primary sequence not only carries information about protein folding but also holds clues about protein misfolding tendencies, especially under non-physiological conditions. Each sequence possesses intrinsic aggregation characteristics, which differ across various proteins and are concealed within their primary sequences. Here, I believe that the machine learning approach can answer many questions that can allow us to go for understand the aggregation process and also in designing better therapeutic proteins.

In conclusion, the primary sequence of a protein plays a remarkable role in guiding its folding process and determining its functional form. Understanding the contribution of individual residues to the folding energy can have implications for protein engineering and uncovering the basis of misfolding diseases. Analyzing the sequence-based energetic features can provide valuable insights into protein behavior and aggregation propensity, contributing to advances in biotechnology and medicine.