Locuaz: an in-silico platform for antibody fragments optimization
arxiv(2024)
摘要
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/
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