An open source in silico workflow to assist in the design of fusion proteins.

Fusion proteins have the potential to become the new norm for targeted therapeutic treatments. Highly specific payload delivery can be achieved by combining custom targeting moieties, such as V _HH domains, with active parts of proteins that have a particular activity not naturally targeted to the intended cells. Conversely, novel drug products may make use of the highly specific targeting properties of naturally occurring proteins and combine them with custom payloads. When designing such a product, there is rarely a known structure for the final construct which makes it difficult to assess molecular behaviour that may ultimately impact therapeutic outcome. Considering the time and cost of expressing a construct, optimising the purification procedure, obtaining sufficient quantities for biophysical characterisation, and performing structural studies in vitro, there is an enormous benefit to conduct in silico studies ahead of wet lab work. By following a repeatable, streamlined, and fast workflow of molecular dynamics assessment, it is possible to eliminate low-performing candidates from costly experimental work. There are, however, many aspects to consider when designing a novel fusion protein and it is crucial not to overlook some elements. In this work, we suggest a set of user-friendly, open-source methods which can be used to screen fusion protein candidates from the sequence alone. We used the light chain and translocation domain of botulinum toxin A (BoNT/A) fused with a selected V _HH domain, termed here LC-H _N -V _HH , as a case study for a general approach to designing, modelling, and simulating fusion proteins. Its behaviour in silico correlated well with initial in vitro work, with SEC HPLC showing multiple protein states in solution and a dynamic protein shifting between these states over time without loss of material.
Competing Interests: Declaration of Competing Interest The authors declare that they have no competing interests to declare. This research was sponsored by Ipsen Bioinnovation. S.M. Liu was an employee of Ipsen at the time of the work & K.A. Bunting is an employee of Ipsen.
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