A bioinformatics pipeline to build a knowledge database for in silico antibody engineering

The dispersion of fungal inocula such as the airborne spores of rust fungi (Pucciniales) can be monitored by metabarcoding the internal transcribed spacer 2 (ITS2) of the rRNA gene in environmental DNAs. This is largely dependent upon a high-quality reference database (refDB) and primers with proper taxonomic coverage and specificity. For this study, a curated ITS2 reference database (named CR-ITS2-refDB) comprising representatives of the major cereal rust fungi and phylogenetically related species was compiled. Inter- and intra-specific variation analyses suggested that the ITS2 region had reasonable discriminating power for the majority of the Puccinia species or species complexes in the database. In silico evaluation of nine forward and seven reverse ITS2 primers, including three newly designed, revealed marked variation in DNA amplification efficiency for the rusts. The theoretical assessment of rust-enhanced (Rust2inv/ITS4var_H) and universal fungal (ITS9F/ITS4) ITS2 primer pairs was validated by profiling the airborne rust fungal communities from environmental samples using a metabarcoding approach. Species or subspecific level identification of the rusts was improved by using CR-ITS2-refDB, and the Automated Oligonucleotide Design Pipeline (AODP), which identified all mutations distinguishing highly conserved DNA markers amongst close relatives. A generic bioinformatics pipeline was developed, including all steps employed in this study from in silico evaluation of primers to accurate identification of short metabarcodes at the level of interest for defining phytopathogens. The results highlighted the importance of primer selection, refDBs that are resolved to reflect phylogenetic relationships, and the use of AODP for improving the reliability of metabarcoding in phytopathogen biosurveillance.

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Kaleidoscope: A New Bioinformatics Pipeline Web Application for In Silico Hypothesis Exploration of Omics Signatures

10.1101/2020.05.01.070805 ◽

2020 ◽

Cited By ~ 4

Author(s):

Khaled Alganem ◽

Rammohan Shukla ◽

Hunter Eby ◽

Mackenzie Abel ◽

Xiaolu Zhang ◽

...

Keyword(s):

User Interface ◽

In Silico ◽

Web Application ◽

Single Gene ◽

Research Question ◽

Data Exploration ◽

Easy Access ◽

Bioinformatics Pipeline ◽

Medical Disorders ◽

Research Questions

AbstractBackgroundIn silico data exploration is a key first step of exploring a research question. There are many publicly available databases and tools that offer appealing features to help with such a task. However, many applications lack exposure or are constrained with unfriendly or outdated user interfaces. Thus, it follows that there are many resources that are relevant to investigation of medical disorders that are underutilized.ResultsWe developed an R Shiny web application, called Kaleidoscope, to address this challenge. The application offers access to several omics databases and tools to let users explore research questions in silico. The application is designed to be user- friendly with a unified user interface, while also scalable by offering the option of uploading user-defined datasets. We demonstrate the application features with a starting query of a single gene (Disrupted in schizophrenia 1, DISC1) to assess its protein-protein interactions network. We then explore expression levels of the gene network across tissues and cell types in the brain, as well as across 34 schizophrenia versus control differential gene expression datasets.ConclusionKaleidoscope provides easy access to several databases and tools under a unified user interface to explore research questions in silico. The web application is open-source and freely available at https://kalganem.shinyapps.io/Kaleidoscope/. This application streamlines the process of in silico data exploration for users and expands the efficient use of these tools to stakeholders without specific bioinformatics expertise.

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Affinity maturation of cross-reactive CR3022 antibody against the receptor binding domain of SARS-CoV-2 via in silico site-directed mutagenesis

10.21203/rs.3.rs-92745/v2 ◽

2021 ◽

Author(s):

Samvedna Saini ◽

Manusmriti Agarwal ◽

Amartya Pradhan ◽

Savitha Pareek ◽

Ashish K Singh ◽

...

Keyword(s):

Binding Affinity ◽

In Silico ◽

Neutralizing Antibodies ◽

Neutralizing Antibody ◽

Affinity Maturation ◽

Antibody Engineering ◽

Site Directed Mutagenesis ◽

Dynamics Simulation ◽

Directed Mutagenesis ◽

Human Antibody

Abstract Introduction: Computational antibody engineering, affinity maturation, and screening greatly aid in vaccine and therapeutic antibody development by increasing the speed and accuracy of predictions. This study presents a protocol for designing affinity enhancing mutants of antibodies through in silico mutagenesis. A SARS-CoV-2 cross-reactive neutralizing antibody, CR3022, is considered as a case study.Methods: Our study aimed at generating antibody candidates from the human antibody CR3022 (derived from convalescent SARS patient) against the RBD of SARS-CoV-2 via in silico affinity maturation using site-directed mutagenesis in mutation hotspots. We optimized the paratope of the CR3022 antibody towards the RBD of SARS-CoV-2 for better binding affinity and stability, employing molecular modeling, docking, dynamics simulations, and molecular mechanics energies combined with generalized Born and surface area (MM-GBSA). Results: Nine antibody candidates were generated post in silico site-directed mutagenesis followed by preliminary screening. Molecular dynamics simulation of 100 nanoseconds and MM-GBSA analysis confirmed L-K45S as a lead antibody with the highest binding affinity against the RBD compared to wild-type and mutant counterparts. Three out of the remaining mutants were also found to have distinct epitopes and binding, possessing a potential to be developed against emerging SARS-CoV-2 variants of concern. Conclusion: The study demonstrates the use of an integrative antibody engineering protocol for enhancing affinity and neutralization potential through mutagenesis using robust open-source computational tools and predictors. This study highlights unique scoring and ranking methods for evaluating docking efficiency. It also underscores the importance of framework mutations for developing broadly neutralizing antibodies.

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Insights into the mutation T1117I in the spike and the lineage B.1.1.389 of SARS-CoV-2 circulating in Costa Rica

10.1101/2021.07.08.451640 ◽

2021 ◽

Author(s):

Jose Arturo Molina-Mora

Keyword(s):

Immune Response ◽

Molecular Docking ◽

Natural Selection ◽

Costa Rica ◽

In Silico ◽

Spike Protein ◽

Evolutionary Models ◽

Bioinformatics Pipeline ◽

Predominant Genotype ◽

Adaptive Selection

Emerging mutations and genotypes of the SARS-CoV-2 virus, responsible for the COVID-19 pandemic, have been reported globally. In Costa Rica during the year 2020, a predominant genotype carrying the mutation T1117I in the spike (S:T1117I) was previously identified. To investigate the possible effects of this mutation on the function of the spike, i.e. the biology of the virus, different bioinformatic pipelines based on phylogeny, natural selection and co-evolutionary models, molecular docking and epitopes prediction were implemented. Results of the phylogeny of sequences carrying the S:T1117I worldwide showed a polyphyletic group, with the emergency of local lineages. In Costa Rica, the mutation is found in the lineage B.1.1.389 and it is suggested to be a product of positive/adaptive selection. Different changes in the function of the spike protein and more stable interaction with a ligand (nelfinavir drug) were found. Only one epitope out 742 in the spike was affected by the mutation, with some different properties, but suggesting scarce changes in the immune response and no influence on the vaccine effectiveness. Jointly, these results suggest a partial benefit of the mutation for the spread of the virus with this genotype during the year 2020 in Costa Rica, although possibly not strong enough with the introduction of new lineages during early 2021 which became predominant later. In addition, the bioinformatics pipeline offers an integrative and exhaustive in silico strategy to eventually study other mutations of interest for the SARS-CoV-2 virus and other pathogens.

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Application of an integrated computational antibody engineering platform to design SARS-CoV-2 neutralizers

Antibody Therapeutics ◽

10.1093/abt/tbab011 ◽

2021 ◽

Author(s):

Saleh Riahi ◽

Jae Hyeon Lee ◽

Shuai Wei ◽

Robert Cost ◽

Alessandro Masiero ◽

...

Keyword(s):

Binding Affinity ◽

Receptor Binding ◽

In Silico ◽

Half Life ◽

Neutralizing Antibodies ◽

Binding Domain ◽

Antibody Engineering ◽

Receptor Binding Domain ◽

Effector Functions

Abstract As the COVID-19 pandemic continues to spread, hundreds of new initiatives including studies on existing medicines are running to fight the disease. To deliver a potentially immediate and lasting treatment to current and emerging SARS-CoV-2 variants, new collaborations and ways of sharing are required to create as many paths forward as possible. Here we leverage our expertise in computational antibody engineering to rationally design/engineer three previously reported SARS-CoV neutralizing antibodies and share our proposal towards anti-SARS-CoV-2 biologics therapeutics. SARS-CoV neutralizing antibodies, m396, 80R, and CR-3022 were chosen as templates due to their diversified epitopes and confirmed neutralization potency against SARS-CoV (but not SARS-CoV-2 except for CR3022). Structures of variable fragment (Fv) in complex with receptor binding domain (RBD) from SARS-CoV or SARS-CoV-2 were subjected to our established in silico antibody engineering platform to improve their binding affinity to SARS-CoV-2 and developability profiles. The selected top mutations were ensembled into a focused library for each antibody for further screening. In addition, we convert the selected binders with different epitopes into the trispecific format, aiming to increase potency and to prevent mutational escape. Lastly, to avoid antibody induced virus activation or enhancement, we suggest application of NNAS and DQ mutations to the Fc region to eliminate effector functions and extend half-life.

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In silico directed evolution in antibody engineering: a promising approach to improvement antitumor biopharmaceuticals

Anais do IV International Symposium on Immunobiological e VII Seminário Anual Científico e Tecnológico de Bio-Manguinhos ◽

10.35259/isi.sact.2019_32699 ◽

2019 ◽

Author(s):

Thiago Chaves ◽

Beatriz Chaves ◽

Fernando Conte ◽

Rodrigo Silva ◽

Patrícia Neves

Keyword(s):

Directed Evolution ◽

In Silico ◽

Antibody Engineering

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Application of an integrated computational antibody engineering platform to design SARS-CoV-2 neutralizers

10.1101/2021.03.23.436613 ◽

2021 ◽

Author(s):

Saleh Riahi ◽

Jae Hyeon Lee ◽

Shuai Wei ◽

Robert Cost ◽

Alessandro Masiero ◽

...

Keyword(s):

Binding Affinity ◽

Receptor Binding ◽

In Silico ◽

Half Life ◽

Neutralizing Antibodies ◽

Binding Domain ◽

Antibody Engineering ◽

Receptor Binding Domain ◽

Effector Functions

As the COVID-19 pandemic continues to spread, hundreds of new initiatives including studies on existing medicines are running to fight the disease. To deliver a potentially immediate and lasting treatment to current and emerging SARS-CoV-2 variants, new collaborations and ways of sharing are required to create as many paths forward as possible. Here we leverage our expertise in computational antibody engineering to rationally design/optimize three previously reported SARS-CoV neutralizing antibodies and share our proposal towards anti-SARS-CoV-2 biologics therapeutics. SARS-CoV neutralizing antibodies, m396, 80R, and CR-3022 were chosen as templates due to their diversified epitopes and confirmed neutralization potency against SARS. Structures of variable fragment (Fv) in complex with receptor binding domain (RBD) from SARS-CoV or SARS-CoV2 were subjected to our established in silico antibody engineering platform to improve their binding affinity to SARS-CoV2 and developability profiles. The selected top mutations were ensembled into a focused library for each antibody for further screening. In addition, we convert the selected binders with different epitopes into the trispecific format, aiming to increase potency and to prevent mutational escape. Lastly, to avoid antibody induced virus activation or enhancement, we applied NNAS and DQ mutations to the Fc region to eliminate effector functions and extend half-life.

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In silico Techniques for Prospecting and Characterizing Monoclonal Antibodies

10.5772/intechopen.94366 ◽

2020 ◽

Author(s):

Tania M. Manieri ◽

Carolina G. Magalhaes ◽

Daniela Y. Takata ◽

João V. Batalha-Carvalho ◽

Ana M. Moro

Keyword(s):

Monoclonal Antibodies ◽

Binding Affinity ◽

In Silico ◽

Fusion Proteins ◽

Antibody Engineering ◽

Bispecific Antibodies ◽

Antibody Complex ◽

Antigen Antibody ◽

Key Residues ◽

In Silico Methods

In the past few years, improvement in computational approaches provided faster and less expensive outcomes on the identification, development, and optimization of monoclonal antibodies (mAbs). In silico methods, such as homology modeling, to predict antibody structures, identification of epitope-paratope interactions, and molecular docking are useful to generate 3D structures of the antibody–antigen complexes. It helps identify the key residues involved in the antigen–antibody complex and enable modifications to enhance the antibody binding affinity. Recent advances in computational tools for redesigning antibodies are significant resources to improve antibody biophysical properties, such as binding affinity, solubility, stability, decreasing the timeframe and costs during antibody engineering. The immunobiological market grows continuously with new molecules, both natural and new molecular formats, such as bispecific antibodies, Fc-antibody fusion proteins, and mAb fragments, requiring novel methods for designing, screening, and analyzing. Algorithms and software set the in silico techniques on the innovation frontier.

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