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Connecting the sequence dots: shedding light on the genesis of antibodies reported to be designed in silico.

ABSTRACT: Two recent publications out of the same research laboratory report on structure-based in silico design of antibodies against viral targets without sequence disclosure. Cross-referencing the published data to patent databases, we established the sequence identity of said computationally designed antibodies. In both cases, the antibodies align with high sequence identity to previously reported antibodies of the same specificity. This clear underlying sequence relationship, which is far closer than the antibody templates reported to seed the computational design, suggests an alternative origin of the computationally designed antibodies. The lack of both reproducible computational algorithms and of output sequences in the initial publications obscures the relationship to previously reported antibodies, and sows doubt as to the genesis narrative described therein.

SUBMITTER: Vasquez M

PROVIDER: S-EPMC6601546 | biostudies-literature | 2019 Jul

REPOSITORIES: biostudies-literature

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Connecting the sequence dots: shedding light on the genesis of antibodies reported to be designed in silico.

Vásquez Maximiliano M Krauland Eric E Walker Laura L Wittrup Dane D Gerngross Tillman T

mAbs 20190520 5

Two recent publications out of the same research laboratory report on structure-based in silico design of antibodies against viral targets without sequence disclosure. Cross-referencing the published data to patent databases, we established the sequence identity of said computationally designed antibodies. In both cases, the antibodies align with high sequence identity to previously reported antibodies of the same specificity. This clear underlying sequence relationship, which is far closer than ...[more]

PMID: 31107637

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Project description:Angiotensin-converting enzyme 2 (ACE2) is the host cellular receptor that locks onto the surface spike protein of the 2002 SARS coronavirus (SARS-CoV-1) and of the novel, highly transmissible and deadly 2019 SARS-CoV-2, responsible for the COVID-19 pandemic. One strategy to avoid the virus infection is to design peptides by extracting the human ACE2 peptidase domain α1-helix, which would bind to the coronavirus surface protein, preventing the virus entry into the host cells. The natural α1-helix peptide has a stronger affinity to SARS-CoV-2 than to SARS-CoV-1. Another peptide was designed by joining α1 with the second portion of ACE2 that is far in the peptidase sequence yet grafted in the spike protein interface with ACE2. Previous studies have shown that, among several α1-based peptides, the hybrid peptidic scaffold is the one with the highest/strongest affinity for SARS-CoV-1, which is comparable to the full-length ACE2 affinity. In this work, binding and folding dynamics of the natural and designed ACE2-based peptides were simulated by the well-known coarse-grained structure-based model, with the computed thermodynamic quantities correlating with the experimental binding affinity data. Furthermore, theoretical kinetic analysis of native contact formation revealed the distinction between these processes in the presence of the different binding partners SARS-CoV-1 and SARS-CoV-2 spike domains. Additionally, our results indicate the existence of a two-state folding mechanism for the designed peptide en route to bind to the spike proteins, in contrast to a downhill mechanism for the natural α1-helix peptides. The presented low-cost simulation protocol demonstrated its efficiency in evaluating binding affinities and identifying the mechanisms involved in the neutralization of spike-ACE2 interaction by designed peptides. Finally, the protocol can be used as a computer-based screening of more potent designed peptides by experimentalists searching for new therapeutics against COVID-19.

Dataset Information

Connecting the sequence dots: shedding light on the genesis of antibodies reported to be designed in silico.

Publications

Connecting the sequence dots: shedding light on the genesis of antibodies reported to be designed in silico.

Similar Datasets

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