Human proteins rarely act alone. Their function is most often achieved in concert with cellular partners, including other proteins, RNA and DNA. Typical examples include the regulation of enzymatic reactions in the synaptic transmission or tumor suppressing proteins. In the frequent case that complexes involve many components, it may be very hard to dissect the role of the single proteins for the overall cellular function.

The very recent Nobel Prize in Chemistry has highlighted the relevance of computational structural biology. Within this framework, machine learning based, multiscale simulations methods can be of great help not only to uncover how these gigantic biomolecular machines work but also to uncover the role of disease-linked mutations and of ligands in affecting the overall function, greatly assisting drug design campaigns.

Recent technological advances, like the development of exascale computers (hardware) and coarse-grained models created using machine learning (ML, software), have significantly expanded our ability to predict the structure, dynamics and energetics of large biomolecular assemblies. This is further boosted by the emergence of structural data by experimental techniques such as cryo-EM and computational approaches including the artificial intelligence (AI) models such as alpha-fold. In this research topic we welcome computational approaches from experts in method developments, from structural predictions, to multi-scale molecular dynamics, ML-assisted enhanced sampling or ML optimized force fields, along with outstanding applications in the field of molecular medicine and pharmacology, from the impact of disease-linked mutations for cell derangement to the use of protein/protein interactions inhibitors as therapeutics. The huge variety of new modelling strategies and broad availability exascale resources, the explosion of AI algorithms and the timid but unstoppable rise of quantum computing, urge for an overview of the state of the art of advanced modelling of bio-systems and especially of a perspective on the near future roadmap.

We will collect contributions in the following fields:

• Coarse-grained models and potentials, even based on ML approaches

• Dynamics and energetics of large macromolecular assemblies

• Predictions of pose and affinities of protein/protein and protein/DNA-RNA inhibitors

• Advanced techniques for conformational/sequence/parameters space sampling

• Drug design targeting large biomolecular complexes

• Integrating experimental information for the investigation of protein/protein interactions in large complexes

• Role of new computer architectures (such as exascale, quantum computing and neuromorphic computing) for the investigation of large biomolecular complexes

• Multiscale simulations (QM/MM, MM/coarse grained, meso-scale and continuum models.

We are especially interested in reviews and perspectives including (but not limited to) one or more of the listed points, but we will also consider original research papers, especially if bringing new insights in the complex landscape of biomolecular modeling.

Are you interested in contributing a manuscript to this Research Topic?

Function and Dysfunction of Large Bio-Molecules Assemblies: Insights from Multidisciplinary Computational Approaches