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A computational approach to rapidly design peptides that detect SARS-CoV-2 surface protein S.

ABSTRACT: The coronavirus disease 19 (COVID-19) caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) prompted the development of diagnostic and therapeutic frameworks for timely containment of this pandemic. Here, we utilized our non-conventional computational algorithm, InSiPS, to rapidly design and experimentally validate peptides that bind to SARS-CoV-2 spike (S) surface protein. We previously showed that this method can be used to develop peptides against yeast proteins, however, the applicability of this method to design peptides against other proteins has not been investigated. In the current study, we demonstrate that two sets of peptides developed using InSiPS method can detect purified SARS-CoV-2 S protein via ELISA and Surface Plasmon Resonance (SPR) approaches, suggesting the utility of our strategy in real time COVID-19 diagnostics. Mass spectrometry-based salivary peptidomics shortlist top SARS-CoV-2 peptides detected in COVID-19 patients' saliva, rendering them attractive SARS-CoV-2 diagnostic targets that, when subjected to our computational platform, can streamline the development of potent peptide diagnostics of SARS-CoV-2 variants of concern. Our approach can be rapidly implicated in diagnosing other communicable diseases of immediate threat.

SUBMITTER: Hajikarimlou M

PROVIDER: S-EPMC9394169 | biostudies-literature | 2022 Sep

REPOSITORIES: biostudies-literature

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A computational approach to rapidly design peptides that detect SARS-CoV-2 surface protein S.

Hajikarimlou Maryam M Hooshyar Mohsen M Moutaoufik Mohamed Taha MT Aly Khaled A KA Azad Taha T Takallou Sarah S Jagadeesan Sasi S Phanse Sadhna S Said Kamaledin B KB Samanfar Bahram B Bell John C JC Dehne Frank F Babu Mohan M Golshani Ashkan A Golshani Ashkan A

NAR genomics and bioinformatics 20220822 3

The coronavirus disease 19 (COVID-19) caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) prompted the development of diagnostic and therapeutic frameworks for timely containment of this pandemic. Here, we utilized our non-conventional computational algorithm, InSiPS, to rapidly design and experimentally validate peptides that bind to SARS-CoV-2 spike (S) surface protein. We previously showed that this method can be used to develop peptides against yeast proteins, however, ...[more]

PMID: 36004308

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Project description:Proteins are typically represented by discrete atomic coordinates providing an accessible framework to describe different conformations. However, in some fields proteins are more accurately represented as near-continuous surfaces, as these are imprinted with geometric (shape) and chemical (electrostatics) features of the underlying protein structure. Protein surfaces are dependent on their chemical composition and, ultimately determine protein function, acting as the interface that engages in interactions with other molecules. In the past, such representations were utilized to compare protein structures on global and local scales and have shed light on functional properties of proteins. Here we describe RosettaSurf, a surface-centric computational design protocol, that focuses on the molecular surface shape and electrostatic properties as means for protein engineering, offering a unique approach for the design of proteins and their functions. The RosettaSurf protocol combines the explicit optimization of molecular surface features with a global scoring function during the sequence design process, diverging from the typical design approaches that rely solely on an energy scoring function. With this computational approach, we attempt to address a fundamental problem in protein design related to the design of functional sites in proteins, even when structurally similar templates are absent in the characterized structural repertoire. Surface-centric design exploits the premise that molecular surfaces are, to a certain extent, independent of the underlying sequence and backbone configuration, meaning that different sequences in different proteins may present similar surfaces. We benchmarked RosettaSurf on various sequence recovery datasets and showcased its design capabilities by generating epitope mimics that were biochemically validated. Overall, our results indicate that the explicit optimization of surface features may lead to new routes for the design of functional proteins.

Dataset Information

A computational approach to rapidly design peptides that detect SARS-CoV-2 surface protein S.

Publications

A computational approach to rapidly design peptides that detect SARS-CoV-2 surface protein S.

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