In the first, we used the latest version of Rosetta (3.10) to design Method Recovery (%) Speed (residues/s)Rosetta 3.10 fixbb 17.9 4.88 10 Thank you for these great suggestions. Thank you for this suggestion. (Figure 1). Benchmarking against Rosetta Please see our response to reviewer # 1.

In the first, we used the latest version of Rosetta (3.10) to design

After AlphaFold won the Nobel Prize, protein prediction with deep learning once again became a hot topic. We comprehensively explore advanced deep learning methods applied to protein structure prediction and design. It begins by examining recent innovations in prediction architectures, with detailed discussions on improvements such as diffusion based frameworks and novel pairwise attention modules. The text analyses key components including structure generation, evaluation metrics, multiple sequence alignment processing, and network architecture, thereby illustrating the current state of the art in computational protein modelling. Subsequent chapters focus on practical applications, presenting case studies that range from individual protein predictions to complex biomolecular interactions. Strategies for enhancing prediction accuracy and integrating deep learning techniques with experimental validation are thoroughly explored. The later sections review the industry landscape of protein design, highlighting the transformative role of artificial intelligence in biotechnology and discussing emerging market trends and future challenges. Supplementary appendices provide essential resources such as databases and open source tools, making this volume a valuable reference for researchers and students.

A day after recognizing former Google vice president and engineering fellow Geoffrey Hinton for his contributions to the field of physics, the Royal Swedish Academy of Sciences has honored a pair of current Google employees. On Wednesday, DeepMind CEO Demis Hassabis and senior research scientist John Jumper won half of the 2024 Nobel Prize in Chemistry, with the other half going to David Baker, a professor at the University of Washington. Baker, Hassabis and Jumper all advanced our understanding of those essential building blocks of life that are responsible for functions both inside and outside the human body. The Nobel Committee cited Baker's seminal work in computational protein design. Since 2003, Baker and his research team have been using amino acids and computers to design entirely new proteins.

What if… you could join an organization that is discovering and developing an entirely new class of medicines, one that leverages both chemistry and biology, to overcome many of the challenges faced by current therapeutics today? Flagship Labs 83, Inc. (FL83) is a privately held, early-stage biotechnology company on a mission to make biology better through chemistry. We are pioneering the development of a transformational new class of medicines, called Artificial BiologicsTM, that is unlocked by novel synthetic and computational technologies. We seek an exceptional Computational Scientist to join our entrepreneurial and innovation-driven organization. FL83 was founded in Flagship Pioneering's venture creation engine, where companies such as Moderna Therapeutics (NASDAQ: MRNA), Rubius Therapeutics (NASDAQ: RUBY) and Generate Biomedicines were conceived and created.

Learning on 3D structures of large biomolecules is emerging as a distinct area in machine learning, but there has yet to emerge a unifying network architecture that simultaneously leverages the graph-structured and geometric aspects of the problem domain. To address this gap, we introduce geometric vector perceptrons, which extend standard dense layers to operate on collections of Euclidean vectors. Graph neural networks equipped with such layers are able to perform both geometric and relational reasoning on efficient and natural representations of macromolecular structure. We demonstrate our approach on two important problems in learning from protein structure: model quality assessment and computational protein design. Our approach improves over existing classes of architectures, including state-of-the-art graph-based and voxel-based methods.

The computation of the global minimum energy conformation (GMEC) is an important and challenging topic in structure-based computational protein design. In this paper, we propose a new protein design algorithm based on the AND/OR branch-and-bound (AOBB) search, which is a variant of the traditional branch-and-bound search algorithm, to solve this combinatorial optimization problem. By integrating with a powerful heuristic function, AOBB is able to fully exploit the graph structure of the underlying residue interaction network of a backbone template to significantly accelerate the design process. Tests on real protein data show that our new protein design algorithm is able to solve many prob- lems that were previously unsolvable by the traditional exact search algorithms, and for the problems that can be solved with traditional provable algorithms, our new method can provide a large speedup by several orders of magnitude while still guaranteeing to find the global minimum energy conformation (GMEC) solution.