Drug Discovery in Low Data Regimes: Leveraging a Computational Pipeline for the Discovery of Novel SARS-CoV-2 Nsp14-MTase Inhibitors (preprint)

The COVID-19 pandemic, caused by the SARS-CoV-2 virus, has led to significant global morbidity and mortality. A crucial viral protein, the non-structural protein 14 (nsp14), catalyzes the methylation of viral RNA and plays a critical role in viral genome replication and transcription. Due to the low mutation rate in the nsp region among various SARS-CoV-2 variants, nsp14 has emerged as a promising therapeutic target. However, discovering potential inhibitors remains a challenge. In this work, we introduce a computational pipeline for the rapid and efficient identification of potential nsp14 inhibitors by leveraging virtual screening and the NCI open compound collection, which contains 250,000 freely available molecules for researchers worldwide. The introduced pipeline provides a cost-effective and efficient approach for early-stage drug discovery by allowing researchers to evaluate promising molecules without incurring synthesis expenses. Our pipeline successfully identified seven promising candidates after experimentally validating only 40 compounds. Notably, we discovered NSC620333, a compound that exhibits a strong binding affinity to nsp14 with a dissociation constant of 427 {+/-} 84 nM. In addition, we gained new insights into the structure and function of this protein through molecular dynamics simulations. We identified new conformational states of the protein and determined that residues Phe367, Tyr368, and Gln354 within the binding pocket serve as stabilizing residues for novel ligand interactions. We also found that metal coordination complexes are crucial for the overall function of the binding pocket. Lastly, we present the solved crystal structure of the nsp14-MTase complexed with SS148, a potent inhibitor of methyltransferase activity at the nanomolar level (IC50 value of 70 {+/-} 6 nM). Our computational pipeline accurately predicted the binding pose of SS148, demonstrating its effectiveness and potential in accelerating drug discovery efforts against SARS-CoV-2 and other emerging viruses.

Disease diagnostics using machine learning of immune receptors (preprint)

ABSTRACT Clinical diagnoses rely on a wide variety of laboratory tests and imaging studies, interpreted alongside physical examination and documentation of symptoms and patient history. However, the tools of diagnosis make little use of the immune system’s internal record of specific disease exposures encoded by the antigen-specific receptors of memory B cells and T cells. We have combined extensive receptor sequence datasets with three different machine learning representations of the contents of immune repertoires to develop an interpretive framework, MAchine Learning for Immunological Diagnosis (Mal-ID) , that screens for multiple illnesses simultaneously. This approach can already reliably distinguish a wide range of disease states, including specific acute or chronic infections, and autoimmune or immunodeficiency disorders, and could contribute to identifying new infectious diseases as they emerge. Importantly, many features of the model of immune receptor sequences are human-interpretable. They independently recapitulate known biology of the responses to infection by SARS-CoV-2 or HIV, and reveal common features of autoreactive immune receptor repertoires, indicating that machine learning on immune repertoires can yield new immunological knowledge.