Eigenvector Centrality Distribution for Characterization of Protein Allosteric Pathways (1706.02327v2)

Abstract: Determining the principal energy pathways for allosteric communication in biomolecules, that occur as a result of thermal motion, remains challenging due to the intrinsic complexity of the systems involved. Graph theory provides an approach for making sense of such complexity, where allosteric proteins can be represented as networks of amino acids. In this work, we establish the eigenvector centrality metric in terms of the mutual information, as a mean of elucidating the allosteric mechanism that regulates the enzymatic activity of proteins. Moreover, we propose a strategy to characterize the range of the physical interactions that underlie the allosteric process. In particular, the well known enzyme, imidazol glycerol phosphate synthase (IGPS), is utilized to test the proposed methodology. The eigenvector centrality measurement successfully describes the allosteric pathways of IGPS, and allows to pinpoint key amino acids in terms of their relevance in the momentum transfer process. The resulting insight can be utilized for refining the control of IGPS activity, widening the scope for its engineering. Furthermore, we propose a new centrality metric quantifying the relevance of the surroundings of each residue. In addition, the proposed technique is validated against experimental solution NMR measurements yielding fully consistent results. Overall, the methodologies proposed in the present work constitute a powerful and cost effective strategy to gain insight on the allosteric mechanism of proteins.

Eigenvector Centrality Distribution for Characterization of Protein Allosteric Pathways

The paper presents an advanced methodology for characterizing the allosteric pathways within proteins through the application of eigenvector centrality (EC) derived from mutual information metrics in network analysis. This work focuses specifically on elucidating the complex mechanisms by which proteins such as enzymes mediate energy transfer for allosteric communications. These mechanisms are crucial for understanding biological regulatory processes that impact enzymatic activity at distal sites.

Summary of Eigenvector Centrality Approach

The authors employ graph theory to model proteins as networks of nodes, where each node represents an amino acid, and edges signify the strength of the correlation between them, established using mutual information. Eigenvector centrality is introduced as a metric to prioritize nodes, elucidating their importance in transmitting momentum within the protein, which is vital for understanding the allosteric mechanisms that regulate enzymatic activity.

Using imidazole glycerol phosphate synthase (IGPS) as a model enzyme, the eigenvector centrality framework reveals the allosteric communication pathways crucial to its function. This approach not only identifies key amino acids involved in these pathways but also introduces a controlled damping factor in the adjacency matrix that allows for distinguishing local and non-local interaction contributions to allosteric signaling.

Implications and Future Directions

The paper significantly contributes to the understanding of protein dynamics, particularly in differentiating short-range from long-range communication influences on protein function. Such techniques can enhance computational efficiency in simulating protein dynamics and pave the way for targeted enzyme engineering, where modifying specific residues could modulate enzymatic activity.

The results, especially the validated predictions of allosteric pathways and the considerable agreement with experimental NMR data, reinforce the viability of this approach in broader applications, from designing new therapeutics to improving understanding of fundamental biological processes. This methodology offers a computationally tractable and theoretically potent tool for protein engineering and drug discovery efforts aiming to modulate allosteric sites or pathways.

Conclusion

The paper exemplifies how computational methods adapted from network analysis and graph theory can enrich the understanding of complex biological processes like allosteric regulation. By demonstrating the reliability of eigenvector centrality for predicting allosteric pathways, this work not only extends our current methodological toolbox but also underlines the potential for insights into protein dynamics leading to innovative applications in biotechnology and pharmacology. The reiteration of experimental consistency supports its robustness and applicability for future developments in protein dynamics research and engineering.