At BIT-LLM, we are pioneering the future of protein engineering by harnessing the power of Large Language Models. Our mission is to intelligently design and optimize protein sequences, unlocking enhanced functionalities for a better world.
Traditional protein engineering is a slow, trial-and-error process.
For decades, enhancing protein functions like enzyme activity or stability has relied on random mutagenesis and directed evolution. While powerful, these methods are labor-intensive and explore only a fraction of the vast sequence space. This bottleneck limits the speed at which we can develop novel proteins for medicine, industry, and environmental solutions.
We treat proteins as a language, and our LLM is the fluent speaker.
Our project, "LLM-ProteinForge," leverages a custom-trained Large Language Model that understands the complex "grammar" of protein sequences. By feeding it data on protein structure and function, our model learns to predict mutations that will enhance specific properties, drastically accelerating the design cycle and improving the probability of success.
Our workflow begins by selecting a target protein. The LLM analyzes its sequence and, based on the desired enhancement (e.g., increased thermostability), proposes a set of optimized sequences. These sequences are then synthesized and tested in the wet lab, with the results feeding back into the model to make it even smarter. This creates a powerful, self-improving design-build-test-learn cycle.
By reducing the need for extensive wet-lab screening, our computational approach significantly lowers the cost and resources required for protein engineering.
This technology has vast potential, from creating more effective therapeutic enzymes to designing robust biocatalysts for green manufacturing.
Our AI-driven method dramatically increases the efficiency of discovering beneficial mutations, turning a months-long process into weeks or even days.