Stable de Novo Protein Design via Joint Conformational Landscape and Sequence Optimization @NatureComms 1. This study presents a novel approach to protein design by jointly optimizing sequence-to-structure and structure-to-sequence mappings, demonstrating superior stability in designed proteins through large-scale experimental validation. 2. The joint model integrates both P(sequence|structure) and P(structure|sequence) to model the conformational landscape, preventing sequences from folding into alternative states and achieving a global minimum energy state. 3. Sequences generated by the joint model exhibit higher frequencies of hydrophilic interactions, which help maintain secondary structure registry and pairing, contributing to enhanced stability. 4. The study validates the joint optimization approach using a cDNA display-based proteolysis method, showing that joint model sequences have higher folding stability compared to single-objective models. 5. Hybrid scoring methods combining sequence and structure-based models achieve better correlation with experimental folding stability, highlighting the importance of joint optimization for accurate stability prediction. 6. This work provides a large-scale comparison of different protein design methods and offers insights into improving protein stability through joint modeling of sequence and structure. 💻Code: github.com/yehlincho/Joint_M… 📜Paper: nature.com/articles/s41467-0… #ProteinDesign #JointOptimization #Stability #ComputationalBiology

#highlycitedpaper Joint Beamforming, Power Allocation, and Splitting Control for SWIPT-Enabled IoT Networks with Deep Reinforcement Learning and Game Theory mdpi.com/1424-8220/22/6/2328 #JointOptimization #DeepReinforcementLearning #IoT #Beamforming

Joint optimization vs Pattern training youtu.be/wBzH0oxy8Gw #FunctionalRangeSystems #Jointoptimization