Mapping the Conformation Space of Wildtype and Mutant H-Ras with a Memetic, Cellular, and Multiscale Evolutionary Algorithm

An important goal in molecular biology is to understand functional changes upon single-point mutations in proteins. Doing so through a detailed characterization of structure spaces and underlying energy landscapes is desirable but continues to challenge methods based on Molecular Dynamics. In this paper we propose a novel algorithm, SIfTER, which is based instead on stochastic optimization to circumvent the computational challenge of exploring the breadth of a protein’s structure space. SIfTER is a data-driven evolutionary algorithm, leveraging experimentally-available structures of wildtype and variant sequences of a protein to define a reduced search space from where to efficiently draw samples corresponding to novel structures not directly observed in the wet laboratory. The main advantage of SIfTER is its ability to rapidly generate conformational ensembles, thus allowing mapping and juxtaposing landscapes of variant sequences and relating observed differences to functional changes. We apply SIfTER to variant sequences of the H-Ras catalytic domain, due to the prominent role of the Ras protein in signaling pathways that control cell proliferation, its well-studied conformational switching, and abundance of documented mutations in several human tumors. Many Ras mutations are oncogenic, but detailed energy landscapes have not been reported until now. Analysis of SIfTER-computed energy landscapes for the wildtype and two oncogenic variants, G12V and Q61L, suggests that these mutations cause constitutive activation through two different mechanisms. G12V directly affects binding specificity while leaving the energy landscape largely unchanged, whereas Q61L has pronounced, starker effects on the landscape. An implementation of SIfTER is made available at http://www.cs.gmu.edu/~ashehu/?q=OurTools. We believe SIfTER is useful to the community to answer the question of how sequence mutations affect the function of a protein, when there is an abundance of experimental structures that can be exploited to reconstruct an energy landscape that would be computationally impractical to do via Molecular Dynamics.
Author Summary Important human diseases are linked to mutations in proteins. One such protein, Ras, undergoes mutations in over 25% of human cancers. Its biological activity involves switching between two distinct states, and several oncogenic mutations affect this switching. Despite significant investigation in silico via methods based on Molecular Dynamics, details are missing on how mutations affect the ability of Ras to access the states it needs to perform its biological activity. In this paper we present an algorithm that is capable of providing such details by exploring the breadth of the structure space of a given protein. The algorithm leverages information gathered in the wet laboratory on long-lived structures of the healthy/wildtype and mutated versions of a protein to effectively explore its structure space and reconstruct the underlying energy landscape. We apply this algorithm to the wildtype H-Ras and two known oncogenic variants, G12V and Q61L. Comparison of the energy landscapes elucidates the detailed mechanism by which the oncogenic mutations affect biological activity. We provide the algorithm for the research community to allow further investigation of the open question on how mutations to the sequence of a protein affect biological activity.