Molecular dynamics (MD) simulations are a key computational chemistry technique that provide dynamic insight into the underlying atomic-level processes in the system under study. These insights not only improve our understanding of the molecular world, but also aid in the design of experiments and targeted interventions. Currently, MD is associated with several limitations, the most important of which are: insufficient sampling, inadequate accuracy of the atomistic models, and challenges with proper analysis and interpretation of the obtained trajectories. Although numerous efforts have been made to address these limitations, more effective solutions are still needed. The recent development of artificial intelligence, particularly machine learning (ML), offers exciting opportunities to address the challenges of MD. In this review we aim to familiarize readers with the basics of MD while highlighting its limitations. The main focus is on exploring the integration of deep learning with MD simulations. The advancements made by ML are systematically outlined, including the development of ML-based force fields, techniques for improved conformational space sampling, and innovative methods for trajectory analysis. Additionally, the challenges and implications associated with the integration of ML and artificial intelligence are discussed. While the potential of ML-MD fusion is clearly established, further applications are needed to confirm its superiority over traditional methods. This comprehensive overview of the new perspectives of MD, which ML has opened up, serves as a gentle introduction to the exciting phase of MD development.

As the world's population progressively ages, the burden on the socio-economic and health systems is escalating, demanding sustainable and lasting solutions. Cellular senescence, one of the hallmarks of ageing, is a state of irreversible cell cycle arrest that occurs in response to various genotoxic stressors and is considered an important factor in the development of many age-related diseases and therefore a potential therapeutic target. Here, the role of senescent cells in age-related diseases is discussed, focusing on their formation and main characteristics. The mechanisms leading to senescent cells are presented, including replicative and premature senescence as well as senescence that occurs in various physiological processes, such as wound healing. The second part comprises a comprehensive description of various biomarkers currently used for the detection of senescent cells along with the investigated therapeutic approaches, namely senolytics, senomorphics and the clearance of senescent cells by the immune system. Potential delivery systems suitable for such therapies and model organisms to study senescence are also briefly examined. This in-depth overview of cellular senescence contributes to a deeper understanding of a rapidly evolving area aimed to tackle the age-related diseases in a more mechanistic way, as well as highlights future research opportunities.

Intron removal from premature-mRNA (pre-mRNA splicing) is an essential part of gene expression and regulation that is required for the production of mature, protein-coding mRNA. The spliceosome (SPL), a majestic machine composed of five small nuclear RNAs and hundreds of proteins, behaves as an eminent transcriptome tailor, efficiently performing splicing as a protein-directed metallo-ribozyme. To select and excise long and diverse intronic sequences with single-nucleotide precision, the SPL undergoes a continuous compositional and conformational remodeling, forming eight distinct complexes throughout each splicing cycle. Splicing fidelity is of paramount importance to preserve the integrity of the proteome. Mutations in splicing factors can severely compromise the accuracy of this machinery, leading to aberrant splicing and altered gene expression. Decades of biochemical and genetic studies have provided insights into the SPL’s composition and function, but its complexity and plasticity have prevented an in-depth mechanistic understanding. Single-particle cryogenic electron microscopy techniques have ushered in a new era for comprehending eukaryotic gene regulation, providing several near-atomic resolution structures of the SPL from yeast and humans. Nevertheless, these structures represent isolated snapshots of the splicing process and are insufficient to exhaustively assess the function of each SPL component and to unravel particular facets of the splicing mechanism in a dynamic environment. In this Account, building upon our contributions in this field, we discuss the role of biomolecular simulations in uncovering the mechanistic intricacies of eukaryotic splicing in health and disease. Specifically, we showcase previous applications to illustrate the role of atomic-level simulations in elucidating the function of specific proteins involved in the architectural reorganization of the SPL along the splicing cycle. Moreover, molecular dynamics applications have uniquely contributed to decrypting the channels of communication required for critical functional transitions of the SPL assemblies. They have also shed light on the role of carcinogenic mutations in the faithful selection of key intronic regions and the molecular mechanism of splicing modulators. Additionally, we emphasize the role of quantum-classical molecular dynamics in unraveling the chemical details of pre-mRNA cleavage in the SPL and in its evolutionary ancestors, group II intron ribozymes. We discuss methodological pitfalls of multiscale calculations currently used to dissect the splicing mechanism, presenting future challenges in this field. The results highlight how atomic-level simulations can enrich the interpretation of experimental results. We envision that the synergy between computational and experimental approaches will aid in developing innovative therapeutic strategies and revolutionary gene modulation tools to fight the over 200 human diseases associated with splicing misregulation, including cancer and neurodegeneration.