. Computational structure-based protein design is a transformative field with exciting prospects for advancing both basic science and translational medical research. My laboratory has developed new protein design algorithms and used them to predict MRSA resistance to new antibiotics; design a broadly neutralizing antibody VRC07-523LS against HIV with unprecedented breadth and potency that is now in clinical trials; design protein-peptide interactions to treat cystic fibrosis; perform antigenicity-guided structural design of HIV gp140 envelope protein (Env) trimer constructs to delineate mechanism and fix conformation; and design a new antigenic membrane-bound membrane proximal external region (MPER) trimer for examining immunogenic responses to the HIV viral coat protein gp41. Central to protein design methodology is the need to optimize the amino acid sequence, placement of side chains, and backbone conformations in protein structures. By developing advanced search and scoring algorithms for combinatorial optimization of protein and ligand structure and sequence, we showed that desired structure, affinity, and activity can be designed by (a) modeling improved molecular flexibility and (b) exploiting ensembles of structures for accurate predictions. Our suite of algorithms has mathematical guarantees on the solution quality (up to the accuracy of the input model, which includes the initial structures, molecular flexibility to be modeled, and an empirical molecular mechanics energy function). Specifically, our algorithms guarantee to compute the global minimum energy conformation (GMEC), a gap-free list of sequences and structures in order of predicted energy, and a provably-good approximation to the binding affinity by bounding partition functions over molecular ensembles. We propose to build on our foundation of protein design algorithms, called OSPREY, and apply them in areas of biochemical and pharmacological importance. We will (1) predict future resistance mutations in protein targets of novel drugs; (2) design inhibitors of protein:protein interactions to target today?s ?undruggable? proteins; and (3) use OSPREY to redesign and improve broadly neutralizing HIV antibodies. Improvements to our protein design algorithms will be implemented to improve accuracy and scope, and we will advance the state of the art in protein design by making algorithmic and modeling improvements to accomplish the Aims (1- 3) above, including: the modeling of more protein/ligand flexibility and improved energy functions during large- scale design; new combinatorial optimization and energy-fitting methods to accelerate the design search; and design of affinity and specificity using novel multi-state design algorithms that model thermodynamic molecular ensembles. We will test our design predictions prospectively, by making novel predicted mutant proteins and performing biochemical, biological, and structural studies. We will also validate our algorithms retrospectively, using existing structures and data. All software will be released open-source.
Statement. We propose computational structure-based protein design algorithms that could revolutionize therapeutic treatment. Our algorithms will enable the design of proteins and other molecules to act on today's undruggable proteins and tomorrow's drug-resistant diseases. In the next grant period, we will develop novel protein design algorithms and software, and use them to (1) predict future resistance mutations to new drugs in pathogens responsible for deadly nosocomial and community-acquired infections: methicillin-resistant Staphylococcus aureus (MRSA), Escherichia coli (E. coli), vancomycin-resistant Enterococcus (VRE), and Candida glabrata; (2a) design inhibitors of protein:protein interactions (PPIs) that address the underlying genetic defect in cystic fibrosis patients and alleviate their symptoms; (2b) design a KRas PPI inhibitor to ameliorate mutations that potentiate pancreatic ductal adenocarcinoma (PDAC); and (3) design and improve broadly neutralizing antibodies against Human immunodeficiency virus (HIV).
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