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Proteins perform their biological functions through motion. Although high throughput prediction of the three-dimensional static structures of proteins has proved feasible using deep-learning-based methods, predicting the conformational motions remains a challenge. Purely data-driven machine learning methods encounter difficulty for addressing such motions because available laboratory data on conformational motions are still limited. In this work, we develop a method for generating protein allosteric motions by integrating physical energy landscape information into deep-learning-based methods. We show that local energetic frustration, which represents a quantification of the local features of the energy landscape governing protein allosteric dynamics, can be utilized to empower AlphaFold2 (AF2) to predict protein conformational motions. Starting from ground state static structures, this integrative method generates alternative structures as well as pathways of protein conformational motions, using a progressive enhancement of the energetic frustration features in the input multiple sequence alignment sequences. For a model protein adenylate kinase, we show that the generated conformational motions are consistent with available experimental and molecular dynamics simulation data. Applying the method to another two proteins KaiB and ribose-binding protein, which involve large-amplitude conformational changes, can also successfully generate the alternative conformations. We also show how to extract overall features of the AF2 energy landscape topography, which has been considered by many to be black box. Incorporating physical knowledge into deep-learning-based structure prediction algorithms provides a useful strategy to address the challenges of dynamic structure prediction of allosteric proteins. 

Abstract Ferroptosis has been shown to play a crucial role in preventing cancer development, but the underlying mechanisms of dysregulated genes and genetic alternations driving cancer development by regulating ferroptosis remain unclear. Here, we showed that the synergistic role of ELF3 overexpression and PTEN deficiency in driving lung cancer development was highly dependent on the regulation of ferroptosis. HumanELF3(hELF3) overexpression in murine lung epithelial cells only caused hyperplasia with increased proliferation and ferroptosis. hELF3overexpression andPtengenetic disruption significantly induced lung tumor development with increased proliferation and inhibited ferroptosis. Mechanistically, we found it was due to the induction of SCL7A11, a typical ferroptosis inhibitor, and ELF3 directly and positively regulated SCL7A11 in the PTEN-deficient background. Erastin-mediated inhibition of SCL7A11 induced ferroptosis in cells with ELF3 overexpression and PTEN deficiency and thus inhibited cell colony formation and tumor development. Clinically, human lung tumors showed a negative correlation betweenELF3andPTENexpression and a positive correlation betweenELF3andSCL7A11in a subset of human lung tumors withPTEN-low expression.ELF3andSCL7A11expression levels were negatively associated with lung cancer patients’ survival rates. In summary, ferroptosis induction can effectively attenuate lung tumor development induced byELF3overexpression andPTENdownregulation or loss-of-function mutations.