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Principal Investigator: Eva-Maria Strauch
Organization: WASHINGTON UNIVERSITY
Fiscal Year: 2024
Award: $785,407
Funding agency: National Institute of Allergy and Infectious Diseases
Abstract
With the advent of small, highly stable miniproteins and the ability to generate easily more, methods and
prototypes for their applications both computationally as well as experimentally are needed.
As part 1, we will investigate the potential of miniprotein-based fusion inhibitors. We will target multiple class I
fusion proteins and evaluate their potential as treatment using two animal models. Many pathogenic viruses,
including influenza, Ebola, coronaviruses and the Pneumoviridae, rely on class I fusion proteins to fuse the viral
and cellular membranes. Their fusion proteins change from the metastable pre- to the more energetically
favorable post-fusion state which is thought to drive membrane merging. The postfusion structure gives insight
into how a potential intermediate state can be inhibited. We propose the development of a platform that takes
advantage of this phenomenon and extracts information from the post-fusion state structure to develop fusion
inhibitors. We are targeting CoV-2, Respiratory Syncytial Virus (RSV), Nipah virus and Middle East Respiratory
Syndrome (MERS). We have generated several candidates against CoV-2 and RSV. We will evaluate their
neutralization potential first in cell culture and then analyze the most potent version in animals. We will explore
treatment window, dosage, and delivery methods, linkage to co-localizers and their immunogenicity. We have
established a proof-of-concept illustrating that we can computationally design inhibitors, verify, and optimize
these binding using yeast surface display: preliminary data demonstrates, an inhibitor generated with this
pipeline neutralizes the live CoV-2 virus in cell culture. We believe the proposed framework will be applicable for
more class I fusion viruses and provide a general protocol for the development or optimization of new biologics
based on miniproteins. Furthermore, our platform will provide technology for pandemic preparedness.
Second, we are proposing the development of two independent deep learning-based protein design methods to
improve stability and to design protein-protein interactions from scratch. For the generation of de novo protein-
protein interactions (PPIs), we have previously developed a prototype algorithm. Sequence and interface
recovery indicate high accuracies for predicting hotspot interactions, both, for their surface locations, as well as
their identities. We previously established a stability predictor based on the evaluation of 31,000 designed
miniproteins. We aim to expand on our success and built a more complex neural network-based algorithm that
can guide re-design. We will integrate other published dataset, evolutionary information while also feeding back
any information obtained while optimizing for stability under strenuous conditions (e.g. low pH, high temperature,
high protease concentrations and in serum) of any of our designed proteins. We will ensure an iterative coupling
between computation and experimental evaluation.
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