  Large language models (LLMs) have garnered considerable attention for their
proficiency in tackling intricate tasks, particularly leveraging their
capacities for zero-shot and in-context learning. However, their utility has
been predominantly restricted to general tasks due to an absence of
domain-specific knowledge. This constraint becomes particularly pertinent in
the realm of protein engineering, where specialized expertise is required for
tasks such as protein function prediction, protein evolution analysis, and
protein design, with a level of specialization that existing LLMs cannot
furnish. In response to this challenge, we introduce \textsc{ProteinEngine}, a
human-centered platform aimed at amplifying the capabilities of LLMs in protein
engineering by seamlessly integrating a comprehensive range of relevant tools,
packages, and software via API calls. Uniquely, \textsc{ProteinEngine} assigns
three distinct roles to LLMs, facilitating efficient task delegation,
specialized task resolution, and effective communication of results. This
design fosters high extensibility and promotes the smooth incorporation of new
algorithms, models, and features for future development. Extensive user
studies, involving participants from both the AI and protein engineering
communities across academia and industry, consistently validate the superiority
of \textsc{ProteinEngine} in augmenting the reliability and precision of deep
learning in protein engineering tasks. Consequently, our findings highlight the
potential of \textsc{ProteinEngine} to bride the disconnected tools for future
research in the protein engineering domain.
