## 4 Discussion
### Highlighted Impressions and Overall Themes
#### 4.1.1 Advancements in Protein Structure Prediction  
Advancements in the field of protein structure prediction mark a significant leap in our understanding of biological processes and have profound implications for drug development and disease treatment. The papers in this category address the longstanding challenge of accurately predicting protein structures, a problem that has been a bottleneck in structural biology for decades. The advancements in protein structure prediction, as highlighted in the selected papers, address a critical and long-standing challenge in the field of structural biology: the accurate and efficient determination of protein structures. Before the advent of these new methods, understanding protein structures largely depended on experimental techniques like X-ray crystallography and NMR spectroscopy. These methods, while powerful, are time-consuming, resource-intensive, and often limited in their ability to resolve complex or dynamic protein structures. This limitation posed a significant barrier to a rapid and comprehensive understanding of protein functions and interactions and their implications for health and disease.  
The breakthroughs represented by these papers, particularly the development of AlphaFold by DeepMind, mark a paradigm shift. AlphaFold's success in protein structure prediction using deep learning algorithms has achieved a level of accuracy that is unprecedented [7]. This method stands out because it can predict protein structures quickly and with a high degree of precision, overcoming the limitations of traditional experimental approaches. This is especially critical for proteins that have eluded structure determination through experimental means, opening new possibilities for exploring the vast array of proteins critical to understanding biological processes and disease mechanisms.  
Researchers have built on AlphaFold's momentum, exploring network architectures that further refine the accuracy and efficiency of protein structure predictions [9]. Specifically, they developed a three-track neural network that can better encode information at the 1-D sequence level, the 2-D distance map level, and the 3-D coordinate level, which also allows for more rapid and accurate protein-protein complex models. Another group utilized AlphaFold for protein structure prediction for the human proteome, which resulted in a minimum increase of 19% of total residues identified, specifically from 17% to 36% [10]. To address the lingering challenge of structure prediction for proteins lacking homologous templates, another group used deep neural networks to create C-I-TASSER, which correctly folded 50% (4162/8266) domain families of unsolved Pfam families [11]. Another issue in the protein folding space is the loss of contrast at high frequencies for cryo-EM, used for protein structure modeling, which creates problems in utilization. Another group created DeepEMhancer, a deep learning solution for cryo-EM volume post-processing that was able to reduce noise levels and improve the quality of experimental maps [8].  
These studies underscore the shift from traditional, experiment-based approaches to AI-driven predictions. They highlight the role of advanced algorithms in deciphering protein interactions and conformations, essential for understanding cellular processes at a molecular level. This transition to AI-centric methods is a crucial advancement, as it not only speeds up the process of structure determination but also enhances the ability to predict the interactions and functions of proteins whose structures are not easily determined through experimental means.