A Computational Pipeline to Identify and Characterize Binding Sites and Interacting Chemotypes in SARS-CoV-2
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Abstract
Minimizing the human and economic costs of the COVID-19 pandemic and of future pandemics requires the ability to develop and deploy effective treatments for novel pathogens as soon as possible after they emerge. To this end, we introduce a unique, computational pipeline for the rapid identification and characterization of binding sites in the proteins of novel viruses as well as the core chemical components with which these sites interact. We combine molecular-level structural modeling of proteins with clustering and cheminformatic techniques in a computationally efficient manner. Similarities between our results, experimental data, and other computational studies provide support for the effectiveness of our predictive framework. While we present here a demonstration of our tool on SARS-CoV-2, our process is generalizable and can be applied to any new virus, as long as either experimentally solved structures for its proteins are available or sufficiently accurate homology models can be constructed.
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SciScore for 10.1101/2022.03.24.485222: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
Software and Algorithms Sentences Resources 22 We then used the Gensim implementation of Word2vec to train a word embedding with 3,600 dimensions, a 6-token window of optimization, and a minimum token frequency of 5 instances within the corpus. Word2vecsuggested: (word2vec, RRID:SCR_014776)23 We compute a vector representing each PDBe ligand using the mean embedding of all synonyms for the chemical compound as listed in the PubChem database. PubChemsuggested: (PubChem, RRID:SCR_004284)Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nat…
SciScore for 10.1101/2022.03.24.485222: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
Software and Algorithms Sentences Resources 22 We then used the Gensim implementation of Word2vec to train a word embedding with 3,600 dimensions, a 6-token window of optimization, and a minimum token frequency of 5 instances within the corpus. Word2vecsuggested: (word2vec, RRID:SCR_014776)23 We compute a vector representing each PDBe ligand using the mean embedding of all synonyms for the chemical compound as listed in the PubChem database. PubChemsuggested: (PubChem, RRID:SCR_004284)Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.Results from TrialIdentifier: No clinical trial numbers were referenced.
Results from Barzooka: We did not find any issues relating to the usage of bar graphs.
Results from JetFighter: We did not find any issues relating to colormaps.
Results from rtransparent:- Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
- Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
- No protocol registration statement was detected.
Results from scite Reference Check: We found no unreliable references.
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