Developing allosteric drugs, a novel class of therapeutics mimicking a ubiquitous phenomenon in biology, requires selecting the active protein substate conformation. However, there is a gap in knowledge to link how allosteric effectors select particular conformational substates; this is the key information needed to engineer efficacious allosteric drugs selecting for active substates. The long-term goal is to develop allosteric drugs to modulate and/or restore protein function. The overall objective of this application is to develop a mechanistic understanding of how allosteric effectors select specific conformational substates to control function of the p53 protein. The central hypothesis is that casting the energy landscape as vector tensors provides critical information for rationally designing allosteric effectors to select substates corresponding to desired protein function. The approach will link the allosteric effector to its effects in the energy landscape, leading to substate selection. The three aims of the project will enable allosteric control of proteins. First, the free energy landscape of the protein by residue will be captured in a vector tensor model. The interaction energies between a residue and its neighbors will be computed, and the magnitude and direction of the net force will be mapped to its alpha carbon. Repeating for all residues will produce a field of vectors reporting on the energy landscape. Preliminary results indicate the method is capable of identifying the major conformational substate of a test case. Second, the utility of the vector approach for elucidating functional substates of proteins will be demonstrated using an example of a known allosteric effector of the tumor suppressor protein p53. MD simulations and MD-Markov State Models will identify the desired active conformational substates, and the vector tensors will point out the forces as the drug restores a mutant to a functional conformation. Third, drugs based on alpha helical peptides will bind to allosteric control sites p53 from MD Sectors to rescue cancerous hotspot mutations. The vectors will identify which interactions need to be changed by modifying the side chain composition of alpha helical allosteric effectors to steer the protein into the desired active conformation as identified by Molecular Dynamics-Markov State Models. Using chemical principles, the functional group side chains of the peptide will be iteratively refined to steer the protein following the energy vectors into the desired active substate, thereby selecting functionality. This work will significantly advance the development of a new class of allosteric drugs by creating an algorithm to steer non-functional mutant proteins into an active conformation through the iterative refinement of the functional R-groups of the drug. This new drug design takes the innovative strategy of capturing the free energy landscape itself in a field of vector tensors that will directly point out t...