The rise of big data: deep sequencing-driven computational methods are transforming the landscape of synthetic antibody design
Abstract Synthetic antibodies (Abs) represent a category of artificial proteins capable of closely emulating the functions of natural Abs. Their in vitro production eliminates the need for an immunological response, streamlining the process of Ab discovery, engineering, and development. These artifi...
| Main Author: | |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
BMC
2024-03-01
|
| Series: | Journal of Biomedical Science |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12929-024-01018-5 |
| _version_ | 1797259096622104576 |
|---|---|
| author | Eugenio Gallo |
| author_facet | Eugenio Gallo |
| author_sort | Eugenio Gallo |
| collection | DOAJ |
| description | Abstract Synthetic antibodies (Abs) represent a category of artificial proteins capable of closely emulating the functions of natural Abs. Their in vitro production eliminates the need for an immunological response, streamlining the process of Ab discovery, engineering, and development. These artificially engineered Abs offer novel approaches to antigen recognition, paratope site manipulation, and biochemical/biophysical enhancements. As a result, synthetic Abs are fundamentally reshaping conventional methods of Ab production. This mirrors the revolution observed in molecular biology and genomics as a result of deep sequencing, which allows for the swift and cost-effective sequencing of DNA and RNA molecules at scale. Within this framework, deep sequencing has enabled the exploration of whole genomes and transcriptomes, including particular gene segments of interest. Notably, the fusion of synthetic Ab discovery with advanced deep sequencing technologies is redefining the current approaches to Ab design and development. Such combination offers opportunity to exhaustively explore Ab repertoires, fast-tracking the Ab discovery process, and enhancing synthetic Ab engineering. Moreover, advanced computational algorithms have the capacity to effectively mine big data, helping to identify Ab sequence patterns/features hidden within deep sequencing Ab datasets. In this context, these methods can be utilized to predict novel sequence features thereby enabling the successful generation of de novo Ab molecules. Hence, the merging of synthetic Ab design, deep sequencing technologies, and advanced computational models heralds a new chapter in Ab discovery, broadening our comprehension of immunology and streamlining the advancement of biological therapeutics. |
| first_indexed | 2024-04-24T23:04:00Z |
| format | Article |
| id | doaj.art-b434980509e24be2871ca33b2faae13e |
| institution | Directory Open Access Journal |
| issn | 1423-0127 |
| language | English |
| last_indexed | 2024-04-24T23:04:00Z |
| publishDate | 2024-03-01 |
| publisher | BMC |
| record_format | Article |
| series | Journal of Biomedical Science |
| spelling | doaj.art-b434980509e24be2871ca33b2faae13e2024-03-17T12:34:09ZengBMCJournal of Biomedical Science1423-01272024-03-0131111910.1186/s12929-024-01018-5The rise of big data: deep sequencing-driven computational methods are transforming the landscape of synthetic antibody designEugenio Gallo0Department of Medicinal Chemistry, Avance BiologicalsAbstract Synthetic antibodies (Abs) represent a category of artificial proteins capable of closely emulating the functions of natural Abs. Their in vitro production eliminates the need for an immunological response, streamlining the process of Ab discovery, engineering, and development. These artificially engineered Abs offer novel approaches to antigen recognition, paratope site manipulation, and biochemical/biophysical enhancements. As a result, synthetic Abs are fundamentally reshaping conventional methods of Ab production. This mirrors the revolution observed in molecular biology and genomics as a result of deep sequencing, which allows for the swift and cost-effective sequencing of DNA and RNA molecules at scale. Within this framework, deep sequencing has enabled the exploration of whole genomes and transcriptomes, including particular gene segments of interest. Notably, the fusion of synthetic Ab discovery with advanced deep sequencing technologies is redefining the current approaches to Ab design and development. Such combination offers opportunity to exhaustively explore Ab repertoires, fast-tracking the Ab discovery process, and enhancing synthetic Ab engineering. Moreover, advanced computational algorithms have the capacity to effectively mine big data, helping to identify Ab sequence patterns/features hidden within deep sequencing Ab datasets. In this context, these methods can be utilized to predict novel sequence features thereby enabling the successful generation of de novo Ab molecules. Hence, the merging of synthetic Ab design, deep sequencing technologies, and advanced computational models heralds a new chapter in Ab discovery, broadening our comprehension of immunology and streamlining the advancement of biological therapeutics.https://doi.org/10.1186/s12929-024-01018-5Antibody engineeringAntibody library designMachine learningSynthetic antibodiesDeep sequencingNext-generation sequencing |
| spellingShingle | Eugenio Gallo The rise of big data: deep sequencing-driven computational methods are transforming the landscape of synthetic antibody design Journal of Biomedical Science Antibody engineering Antibody library design Machine learning Synthetic antibodies Deep sequencing Next-generation sequencing |
| title | The rise of big data: deep sequencing-driven computational methods are transforming the landscape of synthetic antibody design |
| title_full | The rise of big data: deep sequencing-driven computational methods are transforming the landscape of synthetic antibody design |
| title_fullStr | The rise of big data: deep sequencing-driven computational methods are transforming the landscape of synthetic antibody design |
| title_full_unstemmed | The rise of big data: deep sequencing-driven computational methods are transforming the landscape of synthetic antibody design |
| title_short | The rise of big data: deep sequencing-driven computational methods are transforming the landscape of synthetic antibody design |
| title_sort | rise of big data deep sequencing driven computational methods are transforming the landscape of synthetic antibody design |
| topic | Antibody engineering Antibody library design Machine learning Synthetic antibodies Deep sequencing Next-generation sequencing |
| url | https://doi.org/10.1186/s12929-024-01018-5 |
| work_keys_str_mv | AT eugeniogallo theriseofbigdatadeepsequencingdrivencomputationalmethodsaretransformingthelandscapeofsyntheticantibodydesign AT eugeniogallo riseofbigdatadeepsequencingdrivencomputationalmethodsaretransformingthelandscapeofsyntheticantibodydesign |