Complementarity of stability patches at the interfaces of protein complexes: Implication for the structural organization of energetic hot spots

Modeling the Dynamics of Protein–Protein Interfaces, How and Why?

Protein–protein assemblies act as a key component in numerous cellular processes. Their accurate modeling at the atomic level remains a challenge for structural biology. To address this challenge, several docking and a handful of deep learning methodologies focus on modeling protein–protein interfaces. Although the outcome of these methods has been assessed using static reference structures, more and more data point to the fact that the interaction stability and specificity is encoded in the dynamics of these interfaces. Therefore, this dynamics information must be taken into account when modeling and assessing protein interactions at the atomistic scale. Expanding on this, our review initially focuses on the recent computational strategies aiming at investigating protein–protein interfaces in a dynamic fashion using enhanced sampling, multi-scale modeling, and experimental data integration. Then, we discuss how interface dynamics report on the function of protein assemblies in globular complexes, in fuzzy complexes containing intrinsically disordered proteins, as well as in active complexes, where chemical reactions take place across the protein–protein interface.

The Subtle Trade-Off between Evolutionary and Energetic Constraints in Protein-Protein Interactions

Life machinery, although overwhelmingly complex, is rooted on a rather limited number of molecular processes. One of the most important is protein-protein interaction. Metabolic regulation, protein folding control, and cellular motility are examples of processes based on the fine-tuned interaction of several protein partners. The region on the protein surface devoted to the recognition of a specific partner is essential for the function of the protein and is, therefore, likely to be conserved during evolution. On the other hand, the physical chemistry of amino acids underlies the mechanism of interactions. Both evolutionary and energetic constraints can then be used to build scoring functions capable of recognizing interaction sites. Our working hypothesis is that residues within the interaction interface tend at the same time to be evolutionarily conserved (to preserve their function) and to provide little contribution to the internal stabilization of the structure of their cognate protein, to facilitate conformational adaptation to the partner. Here, we show that for some classes of protein partners (for example, those involved in signal transduction and in enzymes) evolutionary constraints play the key role in defining the interaction surface. In contrast, energetic constraints emerge as more important in protein partners involved in immune response, in inhibitor proteins, and in structural proteins. Our results indicate that a general-purpose scoring function for protein-protein interaction should not be agnostic of the biological function of the partners.

An Engineered Variant of the B1 Domain of Protein G Suppresses the Aggregation and Toxicity of Intra- and Extracellular Aβ42

Intra- and extraneuronal deposition of amyloid β (Aβ) peptides have been linked to Alzheimer's disease (AD). While both intra- and extraneuronal Aβ deposits affect neuronal cell viability, the molecular mechanism by which these Aβ structures, especially when intraneuronal, do so is still not entirely understood. This makes the development of inhibitors challenging. To prevent the formation of toxic Aβ structural assemblies so as to prevent neuronal cell death associated with AD, we used a combination of computational and combinatorial-directed evolution approaches to develop a variant of the HTB1 protein (HTB1_M2). HTB1_M2 inhibits in vitro self-assembly of Aβ42 peptide and shifts the Aβ42 aggregation pathway to the formation of oligomers that are nontoxic to neuroblastoma SH-SY5Y cells overexpressing or treated with Aβ42 peptide. This makes HTB1_M2 a potential therapeutic lead in the development of AD-targeted drugs and a tool for elucidating conformational changes in the Aβ42 peptide.