PTGL: extension to graph-based topologies of cryo-EM data for large protein structures

Graph-theoretical prediction of biological modules in quaternary structures of large protein complexes

MOTIVATION

The functional complexity of biochemical processes is strongly related to the interplay of proteins and their assembly into protein complexes. In recent years, the discovery and characterization of protein complexes have substantially progressed through advances in cryo-electron microscopy, proteomics, and computational structure prediction. This development results in a strong need for computational approaches to analyse the data of large protein complexes for structural and functional characterization. Here, we aim to provide a suitable approach, which processes the growing number of large protein complexes, to obtain biologically meaningful information on the hierarchical organization of the structures of protein complexes.

RESULTS

We modelled the quaternary structure of protein complexes as undirected, labelled graphs called complex graphs. In complex graphs, the vertices represent protein chains and the edges spatial chain-chain contacts. We hypothesized that clusters based on the complex graph correspond to functional biological modules. To compute the clusters, we applied the Leiden clustering algorithm. To evaluate our approach, we chose the human respiratory complex I, which has been extensively investigated and exhibits a known biological module structure experimentally validated. Additionally, we characterized a eukaryotic group II chaperonin TRiC/CCT and the head of the bacteriophage Φ29. The analysis of the protein complexes correlated with experimental findings and indicated known functional, biological modules. Using our approach enables not only to predict functional biological modules in large protein complexes with characteristic features but also to investigate the flexibility of specific regions and coformational changes. The predicted modules can aid in the planning and analysis of experiments.

AVAILABILITY AND IMPLEMENTATION

Jupyter notebooks to reproduce the examples are available on our public GitHub repository: https://github.com/MolBIFFM/PTGLtools/tree/main/PTGLmodulePrediction.

Molecular dynamics simulations identify the topological weak spots of a protease CN2S8A

Thermophilic enzymes are highly desired in industrial applications due to their efficient catalytic activity at high temperature. However, most enzymes exhibit inferior thermostability and it remains challenging to identify the optimal sites for designing mutations to improve protein stability. To tackle this issue, we integrated topological analysis and all-atom molecular dynamics simulations to efficiently pinpoint the thermally-unstable regions in protein structures. Using a protease CN2S8A as the model, we analyzed the intramolecular hydrogen bonding interactions between adjacent secondary structure elements, and then identified the topological weak spots of CN2S8A where weak hydrogen bonding interactions were formed. To examine the role of these sites in protein structural stability, we designed three virtual mutations at different weak spots and characterized the effects of these mutations on the structural properties of CN2S8A. The results showed that all three mutations increased the protein structural stability. In conclusion, these findings provide a novel method to identify the topological weak spots of proteins, with implications in the rational design of biocatalysts with superior thermostability.

Evolution of Phage Tail Sheath Protein

Sheath proteins comprise a part of the contractile molecular machinery present in bacteriophages with myoviral morphology, contractile injection systems, and the type VI secretion system (T6SS) found in many Gram-negative bacteria. Previous research on sheath proteins has demonstrated that they share common structural features, even though they vary in their size and primary sequence. In this study, 112 contractile phage tail sheath proteins (TShP) representing different groups of bacteriophages and archaeal viruses with myoviral morphology have been modelled with the novel machine learning software, AlphaFold 2. The obtained structures have been analysed and conserved and variable protein parts and domains have been identified. The common core domain of all studied sheath proteins, including viral and T6SS proteins, comprised both N-terminal and C-terminal parts, whereas the other parts consisted of one or several moderately conserved domains, presumably added during phage evolution. The conserved core appears to be responsible for interaction with the tail tube protein and assembly of the phage tail. Additional domains may have evolved to maintain the stability of the virion or for adsorption to the host cell. Evolutionary relations between TShPs representing distinct viral groups have been proposed using a phylogenetic analysis based on overall structural similarity and other analyses.