Coevolutionary constraints in the sequence-space of macromolecular complexes reflect their self-assembly pathways
Saurav Mallik1,2, Sudip Kundu1,2
1Department of Biophysics, Molecular Biology and Bioinformatics, University of Calcutta, Kolkata, West Bengal, India.
Proteins
|March 26, 2017
Summary
The order of biomolecular subunit assembly into complexes is encoded in coevolutionary constraints. Stronger constraints at early assembly interfaces correlate with higher binding affinity and surface complementarity.
Area of Science:
- Biochemistry
- Structural Biology
- Bioinformatics
Background:
- Macromolecular complexes are essential for cellular function.
- Understanding the self-assembly order of these complexes is crucial.
- The relationship between sequence, structure, and assembly dynamics remains an active research area.
Purpose of the Study:
- To determine if the sequence space contains information about the temporal order of macromolecular complex self-assembly.
- To investigate the predictive power of coevolutionary constraints on assembly dynamics.
- To explore the link between early-forming interfaces and binding properties.
Main Methods:
- Analysis of residue-level coevolutionary constraints.
- Integration of structural attributes from crystal structure data.
- Statistical analysis of interface properties in relation to assembly order.
Main Results:
- Coevolutionary constraints effectively capture the temporal order of macromolecular complex assembly.
- This predictive power is independent of complex classification, stoichiometry, and quaternary structure.
- Stronger coevolutionary constraints at early assembly interfaces are associated with higher binding affinity, larger surface area, and increased complementarity.
Conclusions:
- Sequence coevolution provides a powerful, generalizable method for predicting macromolecular assembly order.
- Early-forming interfaces in the assembly hierarchy likely evolve under stronger coevolutionary pressures to ensure high-affinity binding.
- This finding offers insights into the evolutionary mechanisms driving the formation of complex biological machinery.
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