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| Autores principales: | , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Materias: | |
| Acceso en línea: | https://arxiv.org/abs/2404.09370 |
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| _version_ | 1866929313761198080 |
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| author | Barletta, German P. Tandiana, Rika Soler, Miguel Fortuna, Sara Rocchia, Walter |
| author_facet | Barletta, German P. Tandiana, Rika Soler, Miguel Fortuna, Sara Rocchia, Walter |
| contents | Motivation: Engineering high-affinity binders targeting specific antigenic determinants remains a challenging and often daunting task, requiring extensive experimental screening. Computational methods have the potential to accelerate this process, reducing costs and time, but only if they demonstrate broad applicability and efficiency in exploring mutations, evaluating affinity, and pruning unproductive mutation paths. Results: In response to these challenges, we introduce a new computational platform for optimizing protein binders towards their targets. The platform is organized as a series of modules, performing mutation selection and application, molecular dynamics (MD) simulations to sample conformations around interaction poses, and mutation prioritization using suitable scoring functions. Notably, the platform supports parallel exploration of different mutation streams, enabling in silico high-throughput screening on HPC systems. Furthermore, the platform is highly customizable, allowing users to implement their own protocols. Availability and implementation: the source code is available at https://github. com/pgbarletta/locuaz and documentation is at https://locuaz.readthedocs.io/ |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_09370 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Locuaz: an in-silico platform for antibody fragments optimization Barletta, German P. Tandiana, Rika Soler, Miguel Fortuna, Sara Rocchia, Walter Biological Physics Motivation: Engineering high-affinity binders targeting specific antigenic determinants remains a challenging and often daunting task, requiring extensive experimental screening. Computational methods have the potential to accelerate this process, reducing costs and time, but only if they demonstrate broad applicability and efficiency in exploring mutations, evaluating affinity, and pruning unproductive mutation paths. Results: In response to these challenges, we introduce a new computational platform for optimizing protein binders towards their targets. The platform is organized as a series of modules, performing mutation selection and application, molecular dynamics (MD) simulations to sample conformations around interaction poses, and mutation prioritization using suitable scoring functions. Notably, the platform supports parallel exploration of different mutation streams, enabling in silico high-throughput screening on HPC systems. Furthermore, the platform is highly customizable, allowing users to implement their own protocols. Availability and implementation: the source code is available at https://github. com/pgbarletta/locuaz and documentation is at https://locuaz.readthedocs.io/ |
| title | Locuaz: an in-silico platform for antibody fragments optimization |
| topic | Biological Physics |
| url | https://arxiv.org/abs/2404.09370 |