OPENPichia: licence-free Komagataella phaffii chassis strains and toolkit for protein expression.

The industrial yeast Komagataella phaffii (formerly named Pichia pastoris) is commonly used to synthesize recombinant proteins, many of which are used as human therapeutics or in food. However, the basic strain, named NRRL Y-11430, from which all commercial hosts are derived, is not available without restrictions on its use. Comparative genome sequencing leaves little doubt that NRRL Y-11430 is derived from a K. phaffii type strain deposited in the UC Davis Phaff Yeast Strain Collection in 1954. We analysed four equivalent type strains in several culture collections and identified the NCYC 2543 strain, from which we started to develop an open-access Pichia chassis strain that anyone can use to produce recombinant proteins to industry standards. NRRL Y-11430 is readily transformable, which we found to be due to a HOC1 open-reading-frame truncation that alters cell-wall mannan. We introduced the HOC1 open-reading-frame truncation into NCYC 2543, which increased the transformability and improved secretion of some but not all of our tested proteins. We provide our genome-sequenced type strain, the hoc1tr derivative that we named OPENPichia as well as a synthetic, modular expression vector toolkit under liberal end-user distribution licences as an unencumbered OPENPichia resource for the microbial biotechnology community.

Integrating artificial intelligence-based epitope prediction in a SARS-CoV-2 antibody discovery pipeline: caution is warranted.

BACKGROUND: SARS-CoV-2-neutralizing antibodies (nABs) showed great promise in the early phases of the COVID-19 pandemic. The emergence of resistant strains, however, quickly rendered the majority of clinically approved nABs ineffective. This underscored the imperative to develop nAB cocktails targeting non-overlapping epitopes. METHODS: Undertaking a nAB discovery program, we employed a classical workflow, while integrating artificial intelligence (AI)-based prediction to select non-competing nABs very early in the pipeline. We identified and in vivo validated (in female Syrian hamsters) two highly potent nABs. FINDINGS: Despite the promising results, in depth cryo-EM structural analysis demonstrated that the AI-based prediction employed with the intention to ensure non-overlapping epitopes was inaccurate. The two nABs in fact bound to the same receptor-binding epitope in a remarkably similar manner. INTERPRETATION: Our findings indicate that, even in the Alphafold era, AI-based predictions of paratope-epitope interactions are rough and experimental validation of epitopes remains an essential cornerstone of a successful nAB lead selection. FUNDING: Full list of funders is provided at the end of the manuscript.

Massively parallel interrogation of protein fragment secretability using SECRiFY reveals features influencing secretory system transit.

While transcriptome- and proteome-wide technologies to assess processes in protein biogenesis are now widely available, we still lack global approaches to assay post-ribosomal biogenesis events, in particular those occurring in the eukaryotic secretory system. We here develop a method, SECRiFY, to simultaneously assess the secretability of >105 protein fragments by two yeast species, S. cerevisiae and P. pastoris, using custom fragment libraries, surface display and a sequencing-based readout. Screening human proteome fragments with a median size of 50-100 amino acids, we generate datasets that enable datamining into protein features underlying secretability, revealing a striking role for intrinsic disorder and chain flexibility. The SECRiFY methodology generates sufficient amounts of annotated data for advanced machine learning methods to deduce secretability patterns. The finding that secretability is indeed a learnable feature of protein sequences provides a solid base for application-focused studies.

An affinity-enhanced, broadly neutralizing heavy chain-only antibody protects against SARS-CoV-2 infection in animal models.

Broadly neutralizing antibodies are an important treatment for individuals with coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Antibody-based therapeutics are also essential for pandemic preparedness against future Sarbecovirus outbreaks. Camelid-derived single domain antibodies (VHHs) exhibit potent antimicrobial activity and are being developed as SARS-CoV-2­neutralizing antibody-like therapeutics. Here, we identified VHHs that neutralize both SARS-CoV-1 and SARS-CoV-2, including now circulating variants. We observed that the VHHs bound to a highly conserved epitope in the receptor binding domain of the viral spike protein that is difficult to access for human antibodies. Structure-guided molecular modeling, combined with rapid yeast-based prototyping, resulted in an affinity enhanced VHH-human immunoglobulin G1 Fc fusion molecule with subnanomolar neutralizing activity. This VHH-Fc fusion protein, produced in and purified from cultured Chinese hamster ovary cells, controlled SARS-CoV-2 replication in prophylactic and therapeutic settings in mice expressing human angiotensin converting enzyme 2 and in hamsters infected with SARS-CoV-2. These data led to affinity-enhanced selection of the VHH, XVR011, a stable anti­COVID-19 biologic that is now being evaluated in the clinic.