Protein-protein interactions in paralogues: Electrostatics modulates specificity on a conserved steric scaffold

Unveiling the influence of factor VIII physicochemical properties on hemophilia A phenotype through an in silico methodology

BACKGROUND AND OBJECTIVES

Hemophilia A (HA) is an X-linked blood disorder. It is caused by pathogenic F8 gene variants, among which missense mutations are the most prevalent. The resulting amino acid substitutions may have different impacts on physicochemical properties and, consequently, on protein functionality. Regular prediction tools do not include structural elements and their physiological significance, which hampers our ability to functionally link variants to disease phenotype, opening an ample field for investigation. The present study aims to elucidate how physicochemical changes generated by substitutions in different protein domains relate to HA, and which of these features are more consequential to protein function and its impact on HA phenotype.

METHODS

An in silico evaluation of 71 F8 variants found in patients with different HA phenotypes (mild, moderate, severe) was performed to understand protein modifications and functional impact. Homology modeling was used for the structural analysis of physicochemical changes including electrostatic potential, hydrophobicity, solvent-accessible/excluded surface areas, disulfide disruptions, and substitutions indexes. These variants and properties were analyzed by hierarchical clustering analysis (HCA) and principal component analysis (PCA), independently and in combination, to investigate their relative contribution.

RESULTS

About 69% of variants show electrostatic changes, and almost all show hydrophobicity and surface area modifications. HCA combining all physicochemical properties analyzed was better in reflecting the impact of different variants in disease severity, more so than the single feature analysis. On the other hand, PCA led to the identification of prominent properties involved in the clustering results for variants of different domains.

CONCLUSIONS

The methodology developed here enables the assessment of structural features not available in other prediction tools (e.g., surface distribution of electrostatic potential), evaluating what kind of physicochemical changes are involved in FVIII functional disruption. HCA results allow distinguishing substitutions according to their properties, and yielded clusters which were more homogeneous in phenotype. All evaluated properties are involved in determining disease severity. The nature, as well as the position of the variants in the protein, were shown to be relevant for physicochemical changes, demonstrating that all these aspects must be collectively considered to fine-tune an approach to predict HA severity.

Predicting the Specificity- Determining Positions of Receptor Tyrosine Kinase Axl

Owing to its clinical significance, modulation of functionally relevant amino acids in protein-protein complexes has attracted a great deal of attention. To this end, many approaches have been proposed to predict the partner-selecting amino acid positions in evolutionarily close complexes. These approaches can be grouped into sequence-based machine learning and structure-based energy-driven methods. In this work, we assessed these methods’ ability to map the specificity-determining positions of Axl, a receptor tyrosine kinase involved in cancer progression and immune system diseases. For sequence-based predictions, we used SDPpred, Multi-RELIEF, and Sequence Harmony. For structure-based predictions, we utilized HADDOCK refinement and molecular dynamics simulations. As a result, we observed that (i) sequence-based methods overpredict partner-selecting residues of Axl and that (ii) combining Multi-RELIEF with HADDOCK-based predictions provides the key Axl residues, covered by the extensive molecular dynamics simulations. Expanding on these results, we propose that a sequence-structure-based approach is necessary to determine specificity-determining positions of Axl, which can guide the development of therapeutic molecules to combat Axl misregulation.

Cellular polyamines condense hyperphosphorylated Tau, triggering Alzheimer's disease

Many gaps in our understanding of Alzheimer's disease remain despite intense research efforts. One such prominent gap is the mechanism of Tau condensation and fibrillization. One viewpoint is that positively charged Tau is condensed by cytosolic polyanions. However, this hypothesis is likely based on an overestimation of the abundance and stability of cytosolic polyanions and an underestimation of crucial intracellular constituents - the cationic polyamines. Here, we propose an alternative mechanism grounded in cellular biology. We describe extensive molecular dynamics simulations and analysis on physiologically relevant model systems, which suggest that it is not positively charged, unmodified Tau that is condensed by cytosolic polyanions but negatively charged, hyperphosphorylated Tau that is condensed by cytosolic polycations. Our work has broad implications for anti-Alzheimer's research and drug development and the broader field of tauopathies in general, potentially paving the way to future etiologic therapies.

Bridging solvent molecules mediate RNase A - Ligand binding

Due to its high catalytic activity and readily available supply, ribonuclease A (RNase A) has become a pivotal enzyme in the history of protein science. Moreover, this great interest has carried over to computational chemistry and molecular dynamics, where RNase A has become a model system for various types of studies, all the while being an important drug design target in its own right. Here, we present a detailed molecular dynamics study of RNase–ligand binding involving 22 compounds, spanning nearly five orders of magnitude in affinity, and totaling 8.8 μs of sampling with the standard Amber parameters and an additional 8.8 μs of sampling with a modified potential. We show that short-lived, solvent-mediated bridging interactions are crucial to RNase–ligand binding. We characterize the behavior of bridging solvent molecules, uncovering a power-law dependence between the lifetime of a solvent bridge and the probability of its occurrence. We also demonstrate that from an energetic perspective, bridging solvent in RNase A–ligand binding behaves like part of the enzyme, rather than the ligands. Moreover, we describe an automated pipeline for the detection and processing of bridging interactions, and offer an independent assessment of the performance of the state-of-the-art fixed-charge force fields. Thus, our work has broad implications for drug design and computational chemistry in general.

Protein/ Hormone Based Nanoparticles as Carriers for Drugs Targeting Protein-Protein Interactions

BACKGROUND

In this review, protein-protein interactions (PPIs) were defined, and their behaviors in normal in disease conditions are discussed. Their status at nuclear, molecular and cellular level was underscored, as for their interference in many diseases. Finally, the use of protein nanoscale structures as possible carriers for drugs targeting PPIs was highlighted.

OBJECTIVE

The objective of this review is to suggest a novel approach for targeting PPIs. By using protein nanospheres and nanocapsules, a promising field of study can be emerged.

METHODS

To solidify this argument, PPIs and their biological significance was discussed, same as their role in hormone signaling.

RESULTS

We shed the light on the drugs that targets PPI and we suggested the use of nanovectors to encapsulate these drugs to possibly achieve better results.

CONCLUSION

Protein based nanoparticles, due to their advantages, can be suitable carriers for drugs targeting PPIs. This can open a new opportunity in the emerging field of multifunctional therapeutics.