The field of protein research is witnessing significant advancements with the integration of machine learning techniques. A notable direction is the development of generative models that can create novel protein sequences, such as epitopes, which are crucial for immunotherapies and vaccine development. These models have the potential to bypass traditional screening methods, making the discovery process faster and more cost-effective. Another area of focus is the improvement of protein foundation models, with an emphasis on addressing potential biological safety risks. Red-teaming frameworks are being developed to systematically test these models and identify vulnerabilities. Furthermore, researchers are exploring the use of personas in automated red-teaming to uncover a wider range of potential risks. The application of Bayesian optimization in embedding space is also showing promise in directed evolution of proteins, leading to more efficient and effective screening processes. Noteworthy papers include: epiGPTope, which presents a machine learning-based epitope generator and classifier, and SafeProtein, which introduces a red-teaming framework for protein foundation models. PersonaTeaming is also notable for its novel approach to incorporating personas in automated red-teaming. Directed Evolution of Proteins via Bayesian Optimization in Embedding Space is another significant contribution, demonstrating improved performance in machine-learning-assisted directed evolution of proteins.