Proceedings of the International scientific and practical conference “Science in the Modern World” (January 19-21, 2026) / Publisher website: www.naukainfo.com. - Cambridge, United Kingdom, 2026. - 203 p.

9 has democratized access to structural information, enabling researchers and laboratories without specialized equipment to engage in advanced protein research and accelerating discovery across biotechnology and medicine [4, p. 1889]. AlphaFold offers three major advantages: accuracy, speed, and scalability. Protein structures that previously required months or years of experimental work can now be predicted within hours. The inclusion of per-residue confidence scores further allows researchers to assess prediction reliability and prioritize targets for experimental validation [3, p. 588]. These capabilities have already impacted drug discovery, where predicted structures support rational drug design, target identification, and binding-site analysis [4, p. 1889]. In molecular biology and protein engineering, AlphaFold facilitates functional annotation, enzyme redesign, and the study of disease-associated misfolding, highlighting its broad applicability. Despite its success, AlphaFold is not a complete solution to protein structure determination. A central limitation of the AlphaFold paradigm is the so-called “Dynamics Gap” - its inherent tendency to predict a single, high-confidence static structure rather than a biologically relevant ensemble of conformational states [1, pp. 3-5]. While this approach is sufficient for many globular proteins, it fails to capture conformational dynamics, allosteric regulation, transient interactions, and ligand- induced structural changes that are often essential for protein function. This limitation arises in part from AlphaFold’s training data. Because the model was trained predominantly on experimentally determined structures deposited in the Protein Data Bank (PDB) - which is biased toward stable, crystallizable conformations - it preferentially learns static structural solutions [3, p. 588]. As a result, functionally important motions central to signaling, regulation, and enzymatic activity are frequently underrepresented or absent in predictions. AlphaFold’s accuracy is further constrained by its reliance on evolutionary information derived from multiple sequence alignments (MSAs) [5, p.709]. Proteins lacking sufficient homologous sequences, including rare, rapidly evolving, lineage- specific, or synthetic proteins, often receive lower-confidence predictions due to the