Boltzmann-Aligned Inverse Folding Model as a Predictor of Mutational Effects on Protein-Protein Interactions

Predicting the change in binding free energy ($\Delta \Delta G$) is crucial for understanding and modulating protein-protein interactions, which are critical in drug design. Due to the scarcity of experimental $\Delta \Delta G$ data, existing methods focus on pre-training, while neglecting the importance of alignment. In this work, we propose the Boltzmann Alignment technique to transfer knowledge from pre-trained inverse folding models to $\Delta \Delta G$ prediction. We begin by analyzing the thermodynamic definition of $\Delta \Delta G$ and introducing the Boltzmann distribution to connect energy with protein conformational distribution. However, the protein conformational distribution is intractable; therefore, we employ Bayes' theorem to circumvent direct estimation and instead utilize the log-likelihood provided by protein inverse folding models for $\Delta \Delta G$ estimation. Compared to previous inverse folding-based methods, our method explicitly accounts for the unbound state of protein complex in the $\Delta \Delta G$ thermodynamic cycle, introducing a physical inductive bias and achieving both supervised and unsupervised state-of-the-art (SoTA) performance. Experimental results on SKEMPI v2 indicate that our method achieves Spearman coefficients of 0.3201 (unsupervised) and 0.5134 (supervised), significantly surpassing the previously reported SoTA values of 0.2632 and 0.4324, respectively. Futhermore, we demonstrate the capability of our method on binding energy prediction, protein-protein docking and antibody optimization tasks.

Automated Lane Change Behavior Prediction and Environmental Perception Based on SLAM Technology

In addition to environmental perception sensors such as cameras, radars, etc. in the automatic driving system, the external environment of the vehicle is perceived, in fact, there is also a perception sensor that has been silently dedicated in the system, that is, the positioning module. This paper explores the application of SLAM (Simultaneous Localization and Mapping) technology in the context of automatic lane change behavior prediction and environment perception for autonomous vehicles. It discusses the limitations of traditional positioning methods, introduces SLAM technology, and compares LIDAR SLAM with visual SLAM. Real-world examples from companies like Tesla, Waymo, and Mobileye showcase the integration of AI-driven technologies, sensor fusion, and SLAM in autonomous driving systems. The paper then delves into the specifics of SLAM algorithms, sensor technologies, and the importance of automatic lane changes in driving safety and efficiency. It highlights Tesla's recent update to its Autopilot system, which incorporates automatic lane change functionality using SLAM technology. The paper concludes by emphasizing the crucial role of SLAM in enabling accurate environment perception, positioning, and decision-making for autonomous vehicles, ultimately enhancing safety and driving experience.