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Center for Nonlinear Studies |
Energy landscape theory has been a powerful approach to study protein folding dynamics and function. The discovery that an accurate estimate of the joint probability distribution of amino acid occupancies in protein families provides insights about residue-residue coevolution and concrete details about protein folding landscapes has also advanced structural biophysics. Our realization that the collection of couplings and local fields as parameters of such distribution is inherently connected with the thermodynamics of sequence selection towards folding and function demonstrates the importance of coevolutionary methods to understand stability and function of biomolecules. The synergy between structure based models and coevolutionary information has spearheaded the field of structure prediction, including protein and RNA, as well as accelerating the discovery of functional structural states and the prediction of protein complexes. Coevolution signals can also be used to create protein recognition metrics, which led to successful experimental efforts, and the uncovering of novel molecular interactions. This idea has opened the door to encode recognition in protein pairs. Coevolved interfaces can also be combined with small molecule hot spot estimation methods to improve the discovery of druggable interfaces. Recently this approach has been used to predict extremely large protein assemblies consisting of structural maintenance of chromosomes (SMC) and kleisin subunits which are essential for the process of chromosome segregation across all domains of life. While limited structural data exist for the proteins that comprise the (SMC)–kleisin complex, using an integrative approach combining both crystallographic data and coevolutionary information, we have predicted an atomic-scale structure of the whole condensing complex in prokaryotes.
*supported by the NSF and the Welch Foundation
Host: Angel Garcia