AlphaFold3, the latest release of AlphaFold developed by Google DeepMind and Isomorphic Labs, was designed to predict protein structures with remarkable accuracy. AlphaFold3 enhances our ability to model not only single protein structures but also complex biomolecular interactions, including protein–protein interactions, protein–ligand docking, and protein-nucleic acid complexes. Herein, we provide a detailed examination of AlphaFold3’s capabilities, emphasizing its applications across diverse biological fields and its effectiveness in complex biological systems. The strengths of the new AI model are also highlighted, including its ability to predict protein structures in dynamic systems, multi-chain assemblies, and complicated biomolecular complexes that were previously challenging to depict. We explore its role in advancing drug discovery, epitope prediction, and the study of disease-related mutations. Despite its significant improvements, the present review also addresses ongoing obstacles, particularly in modeling disordered regions, alternative protein folds, and multi-state conformations. The limitations and future directions of AlphaFold3 are discussed as well, with an emphasis on its potential integration with experimental techniques to further refine predictions. Lastly, the work underscores the transformative contribution of the new model to computational biology, providing new insights into molecular interactions and revolutionizing the fields of accelerated drug design and genomic research.

Amyotrophic lateral sclerosis (ALS) is a rapidly progressive disease that affects motor neurons, leading to paralysis and death usually 3–5 years after the onset of symptoms. The investigation of both sporadic and familial ALS highlighted four main genes that contribute to the pathogenesis of the disease: SOD1, FUS, TARDBP and C9orf72. This study aims to provide a comprehensive investigation of genetic variants found in SOD1, FUS and TARDBP genes in Greek sporadic ALS (sALS) cases. Our sequencing analysis of the coding regions of the abovementioned genes that include the majority of the variants that lead to ALS in 32 sALS patients and 3 healthy relatives revealed 6 variants in SOD1, 19 variants in FUS and 37 variants in TARDBP, of which the SOD1 p.D90A and the FUS c.*356G>A (rs886051940) variants have been previously associated with ALS, while two novel nonsense pathogenic variants were also identified, namely FUS p.R241* and TDP-43 p.Y214*. Our study contributes to the worldwide effort toward clarifying the genetic basis of sALS to better understand the disease’s molecular pathology.

Protein functionality depends on its proper folding, making protein misfolding crucial for the function of proteins and, by extension, cells and the whole organism. Increasing evidence supports the role of protein misfolding in the pathogenesis of neurodegenerative diseases, such as amyotrophic lateral sclerosis (ALS). ALS is a rapidly progressive disease diagnosed at a prevalence of 5 cases per 100,000, with approximately 2–3 patients per 100,000 diagnosed each year. To date, there is no cure, and the disease usually leads to death within 2 to 5 years from diagnosis. There are two types of the disorder: familial ALS (fALS), accounting for approximately 10% of cases, and sporadic (sALS), accounting for the remaining 90%. The hallmark of ALS, regardless of type, is the protein aggregates found in patients’ tissues. This suggests that the disruption of proteostasis plays a critical role in the development of the disease. Herein, we stress the distinct factors that lead to protein misfolding and aggregate formation in ALS. Specifically, we highlight several triggering factors affecting protein misfolding, namely mutations, errors in the processes of protein production and trafficking, and failures of folding and chaperone machinery. Gaining a deeper understanding of protein aggregation will improve our comprehension of disease pathogenesis and potentially uncover new therapeutic approaches.