20 research outputs found
Machine learning-guided directed evolution for protein engineering
Machine learning (ML)-guided directed evolution is a new paradigm for
biological design that enables optimization of complex functions. ML methods
use data to predict how sequence maps to function without requiring a detailed
model of the underlying physics or biological pathways. To demonstrate
ML-guided directed evolution, we introduce the steps required to build ML
sequence-function models and use them to guide engineering, making
recommendations at each stage. This review covers basic concepts relevant to
using ML for protein engineering as well as the current literature and
applications of this new engineering paradigm. ML methods accelerate directed
evolution by learning from information contained in all measured variants and
using that information to select sequences that are likely to be improved. We
then provide two case studies that demonstrate the ML-guided directed evolution
process. We also look to future opportunities where ML will enable discovery of
new protein functions and uncover the relationship between protein sequence and
function.Comment: Made significant revisions to focus on aspects most relevant to
applying machine learning to speed up directed evolutio
Evaluating the Potential of Leading Large Language Models in Reasoning Biology Questions
Recent advances in Large Language Models (LLMs) have presented new
opportunities for integrating Artificial General Intelligence (AGI) into
biological research and education. This study evaluated the capabilities of
leading LLMs, including GPT-4, GPT-3.5, PaLM2, Claude2, and SenseNova, in
answering conceptual biology questions. The models were tested on a
108-question multiple-choice exam covering biology topics in molecular biology,
biological techniques, metabolic engineering, and synthetic biology. Among the
models, GPT-4 achieved the highest average score of 90 and demonstrated the
greatest consistency across trials with different prompts. The results
indicated GPT-4's proficiency in logical reasoning and its potential to aid
biology research through capabilities like data analysis, hypothesis
generation, and knowledge integration. However, further development and
validation are still required before the promise of LLMs in accelerating
biological discovery can be realized