Multi-domain proteins are not only formed through natural evolution but
can also be generated by recombinant DNA technology. Because many fusion
proteins can enhance the selectivity of cell targeting, these
artificially produced molecules, called multi-specific biologics, are
promising drug candidates, especially for immunotherapy. Moreover, the
rational design of domain linkers in fusion proteins is becoming an
essential step toward a quantitative understanding of the dynamics in
these biopharmaceutics. We developed a computational framework to
characterize the impacts of peptide linkers on the dynamics of
multi-specific biologics. We constructed a benchmark containing six
types of linkers that represent various lengths and degrees of
flexibility and used them to connect two natural proteins as a test
system. The microsecond dynamics of these proteins generated from Anton
were projected onto a coarse-grained conformational space. The
similarity of dynamics among different proteins in this low-dimensional
space was further analyzed by a neural network model. Finally,
hierarchical clustering was applied to place linkers into different
subgroups based on the neural network classification results. The
clustering results suggest that the length of linkers used to spatially
separate different functional modules plays the most important role in
regulating the dynamics of this fusion protein. Given the same number of
amino acids, linker flexibility functions as a regulator of protein
dynamics. In summary, we illustrated that a new computational strategy
can be used to study the dynamics of multi-domain fusion proteins by a
combination of long timescale molecular dynamics simulation,
coarse-grained modeling, and artificial intelligence.