Comprehensive synthesis: The systematic review and meta-analysis aim to provide a comprehensive synthesis of available evidence on the structural and functional consequences of SARS-CoV-2 spike protein mutations.
Meta-analytic approach: The review will use a meta-analytic approach to examine overall effect sizes of spike protein mutations on structure and function, as well as potential sources of heterogeneity.
Insights into implications: The review is expected to provide valuable insights into the potential implications of SARS-CoV-2 spike protein mutations for the development of effective treatments and vaccines.
Identification of gaps: The review will identify gaps in the existing literature and highlight areas for future research, which can inform the design and prioritization of future in silico studies.
Identification of drug targets: The review will identify potential drug targets based on the structural and functional consequences of SARS-CoV-2 spike protein mutations, and analyze the impact of these mutations on vaccine efficacy and development.
3.1. Search Strategy
We will conduct a comprehensive search of multiple electronic databases, including: PubMed, Cochrane, MEDLINE via EBSCOhost, ScienceDirect, Web of Science, Scopus, Google Scholar, Cumulative Index to Nursing and Allied Health Literature (CINAHL), Cochrane Library, and the WHO Global Health Library for studies published from January 2020 to the present day. We will use Medical Subject Headings (MeSH) terms and keywords related to SARS-CoV-2 and spike protein mutations. Search terms will include a combination of the following keywords: "SARS-CoV-2", "spike protein", "mutations", "in silico studies", "structural consequences", "variants", "in silico studies"."functional consequences", "binding affinity", "ACE2 receptor", "systematic review", and "meta-analysis". The search results will then be exported to EndNote Version 20 for further analysis. We will also search for preprints in bioRxiv and medRxiv, as they may contain relevant studies that are not yet published in peer-reviewed journals.
Our search strategy will be developed in consultation with an experienced medical librarian to ensure that it is comprehensive and appropriate for the selected databases. The search will be conducted independently by two reviewers, with any discrepancies resolved through discussion or consultation with a third reviewer, if necessary. We will also review the reference lists of relevant articles to identify additional studies that meet our inclusion criteria. The search will be conducted in English language publications. No geographical or publication status restriction will be applied.
3.2. Identification of Eligible Studies
The identification of eligible studies is a critical step in our systematic review and meta-analysis of in silico studies assessing the structural and functional consequences of SARS-CoV-2 spike protein mutations. Our search strategy will be developed based on the research question and the inclusion and exclusion criteria. We will conduct a comprehensive search of electronic databases, including PubMed, Scopus, and Web of Science, for studies published from January 2020 to the present day. We will also search for preprints in bioRxiv and medRxiv, as they may contain relevant studies that are not yet published in peer-reviewed journals.
Our search terms will include combinations of keywords related to SARS-CoV-2 spike protein mutations, structural and functional consequences, and in silico studies. We will also review the reference lists of relevant articles to identify additional studies that meet our inclusion criteria. Two independent reviewers will screen the titles and abstracts of all identified studies for eligibility based on our pre-defined inclusion and exclusion criteria. Any discrepancies will be resolved through discussion or consultation with a third reviewer if necessary.
The full text of potentially eligible studies will be assessed for final inclusion in the review. We will record reasons for exclusion for studies that are deemed ineligible. The study selection process will be documented in a PRISMA flowchart, and reasons for study exclusions will be recorded. The identification of eligible studies will follow a comprehensive and rigorous search strategy and rigorous screening process, ensuring that all relevant studies are identified and included in the systematic review and meta-analysis.
3.3. Patient and Public Involvement
No patients are involved.
3.4. Data management
3.4.1. Study Records and Data Extraction
Identification of relevant studies will begin with a comprehensive search of electronic databases such as PubMed, Scopus, and Web of Science. We will use a combination of search terms and Boolean operators to identify relevant articles published in English from the beginning of the pandemic through the search date. We will also manually search reference lists of included articles and relevant review articles to identify any additional studies that may have been missed in the electronic search.
Two reviewers will independently screen the titles and abstracts of identified articles to determine eligibility for inclusion in the review. Full-text articles will then be reviewed for inclusion according to the pre-defined eligibility criteria. Any discrepancies between the two reviewers will be resolved through discussion or consultation with a third reviewer. Data extraction will be performed independently by two reviewers using a pre-designed data extraction form. Our data extraction form will include information such as the study design, sample size, mutation type, protein region, and outcomes measured. We will also extract data on the methods used to assess the impact of mutations on protein stability, binding affinity, and immunogenicity.
The form will include information on study design, population characteristics, mutation types, modeling methods, outcomes assessed, and key findings. Any discrepancies between the two reviewers will be resolved through discussion or consultation with a third reviewer. Data synthesis will involve the use of appropriate statistical methods to analyze and summarize the extracted data. This will include the calculation of effect sizes, such as odds ratios or standardized mean differences, as well as the use of forest plots and subgroup analyses to explore sources of heterogeneity. We will also perform sensitivity analyses to assess the robustness of our findings to variations in study selection criteria and statistical methods. To ensure the accuracy and completeness of our data extraction process, we will pilot-test our data extraction form on a subset of studies and make any necessary adjustments before proceeding with the full data extraction process.
All details of the screening and data extraction process will be reported in Supplementary Table.
Overall, our rigorous and standardized data extraction process will enable us to systematically and comprehensively extract relevant data from each included study and facilitate the synthesis of the results in our meta-analysis.
3.4.2. Data Simplification
After completing the data extraction process, the extracted data will be simplified and organized to facilitate data synthesis and meta-analysis. This process involves converting the extracted data into a standardized format and grouping it according to specific outcomes of interest (17). A data extraction spreadsheet will be used to organize the extracted data, where each row will represent a specific study and each column will represent a specific outcome or characteristic. Descriptive statistics, such as means, standard deviations, and ranges, will be used to summarize the extracted data, as appropriate.
To simplify the data further, we may convert continuous data into categorical data or calculate effect sizes to enable comparison between studies. Any data conversions or simplifications will be documented and justified in the final report. Simplifying and organizing the extracted data in a systematic and standardized manner will enable effective synthesis of the results across studies and facilitate the comparison of findings between different studies. This will allow for the drawing of more robust and reliable conclusions from the meta-analysis.
3.5. Risk of bias
The Newcastle-Ottawa Scale will be used to assess the risk of bias (18). This tool will assess the risk of bias across several domains, including selection bias, performance bias, detection bias, attrition bias, reporting bias, and other sources of bias (19). To ensure consistency and accuracy in the risk of bias assessment, two independent reviewers will assess each study using the appropriate tool, with any disagreements resolved through discussion and consensus. We will assign a low, unclear, or high risk of bias for each domain based on the information provided in the study (18, 19). Studies with a high risk of bias in one or more domains will not be excluded from the analysis, but the risk of bias will be considered in the interpretation of the findings.
We will present the risk of bias assessment for each study in a summary table and in a risk of bias graph, which will provide an overview of the risk of bias across all studies included in the meta-analysis. This will enable readers to evaluate the quality and reliability of the evidence presented in the review and to determine the extent to which the findings are subject to potential bias.
3.6. Data Synthesis
Our approach will be guided by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines (14). For the meta-analysis of reported data, a Review Manager version 5.4 software Forrest plot will be used (20). Firstly, we will extract relevant data from each included study, such as study design, sample size, mutation type, protein stability, binding affinity, and immunogenicity. We will then evaluate the quality of each study using established criteria. We will use a random-effects model to synthesize the data and calculate pooled effect sizes for the outcomes of interest. This will allow us to estimate the overall effect of SARS-CoV-2 spike protein mutations on protein stability, binding affinity, and immunogenicity. We will also perform subgroup analyses to explore potential sources of heterogeneity, such as study design, mutation type, and protein region.
In addition to quantitative synthesis, we will also perform a qualitative synthesis of the included studies. This will involve summarizing the main findings of each study and identifying common themes and trends. We will use thematic analysis to identify key factors that influence the effects of mutations on protein stability, binding affinity, and immunogenicity.
3.7. Sensitivity Analysis
The sensitivity analysis will involve testing the effect of various factors on the results of the meta-analysis (21). We will evaluate the impact of exclusion or inclusion of studies with high risk of bias, studies with small sample sizes, studies using different software or methods, and studies with different mutation types or protein regions. We will perform the sensitivity analysis using established statistical methods such as leave-one-out analysis and meta-regression (22). Leave-one-out analysis involves removing one study at a time and re-analyzing the data to evaluate the impact of each study on the overall results. Meta-regression involves evaluating the relationship between study characteristics and effect sizes to identify sources of heterogeneity. The RevMan software will be used to automatically calculate heterogeneity, as depicted in the forest plot (23). Greater homogeneity will be indicated by a larger degree of overlap between confidence intervals (24). The I2 statistic will be calculated using the forest plot, yielding a value between 0% and 100%. A value below 25% will indicate strong homogeneity, whereas a value exceeding 75% will indicate strong heterogeneity. A value of 50% will be considered average (25). In addition to these methods, we will also perform visual inspections of funnel plots and the Egger's test as well as evaluate the results of publication bias tests to identify potential sources of bias and assess the reliability of the meta-analysis results (26).
3.8. Assessment of Strength of Evidence
Two reviewers will independently assess the quality of the included studies. To assess the quality of evidence, we will use established criteria GRADE (Grading of Recommendations, Assessment, Development and Evaluations) system (26). This criteria will consider factors such as study design, sample size, blinding, and statistical methods to determine the risk of bias and the overall quality of the evidence. However, the quality of evidence will ultimately depend on the number and quality of studies included in our final analysis (27). To assess consistency and precision of findings, we will use statistical methods such as meta-regression, subgroup analysis, and sensitivity analysis. These methods will help us identify sources of heterogeneity and potential confounding factors, and evaluate the robustness of our findings. Any discrepancies will be resolved through discussion and, if necessary, consultation with a third reviewer.
3.9. Ethics and dissemination
This study does not involve human participants or data and therefore does not require ethical approval. The findings of this study will be disseminated through publication in a peer-reviewed journal and conference presentations. The data will be made available upon request.