Signal Recognition Particle (SRP) and SRP Receptor: A New Paradigm for Multistate Regulatory GTPases

The GTP-binding proteins or GTPases comprise a superfamily of proteins that provide molecular switches in numerous cellular processes. The “GTPase switch” paradigm, in which a GTPase acts as a bimodal switch that is turned “on” and “off” by external regulatory factors, has been used to interpret the regulatory mechanism of many GTPases for more than two decades. Nevertheless, recent work has unveiled an emerging class of “multistate” regulatory GTPases that do not adhere to this classical paradigm. Instead of relying on external nucleotide exchange factors or GTPase activating proteins to switch between the on and off states, these GTPases have the intrinsic ability to exchange nucleotides and to sense and respond to upstream and downstream factors. In contrast to the bimodal nature of the GTPase switch, these GTPases undergo multiple conformational rearrangements, allowing multiple regulatory points to be built into a complex biological process to ensure the efficiency and fidelity of the pathway. We suggest that these multistate regulatory GTPases are uniquely suited to provide spatial and temporal control of complex cellular pathways that require multiple molecular events to occur in a highly coordinated fashion

PatternGPT :A Pattern-Driven Framework for Large Language Model Text Generation

Large language models(LLMS) have shown excellent text generation capabilities,capable of generating fluent responses for many downstream tasks. However,applying large language models to real-world critical tasks remains challenging due to their susceptibility to hallucinations and inability to directly use external knowledge. To address the above challenges,this paper proposes PatternGPT, a pattern-driven text generation framework for large language models. First,the framework utilizes the extraction capabilities of large language models to generate rich and diverse patterns and later draws on the idea of federated learning. Using multiple agents to achieve sharing to obtain more diverse patterns. Finally, it searches for high-quality patterns using judgment criteria and optimization algorithms and uses the searched patterns to guide the model for generation. This framework has the advantages of generating diversified patterns, protecting data privacy,combining external knowledge, and improving the quality of generation, which provides an effective method to optimize the text generation capability of large language models,and make it better applied to the field of intelligent dialogue and content generation