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Notin, P. et al. TranceptEVE: Combining family-specific and family-agnostic models of protein sequences for improved fitness prediction. Learning Meaningful Representations of Life Workshop, NeurIPS (2022). Lin, Z. et al. Evolutionary-scale prediction of atomic-level protein structure with a lan...
enzyme activity20, substrate specificity, light sensitivity of channelrhodopsins31, in vivo titer in metabolic pathways32and adeno-associated virus capsid viability33, among others. Our initial work demonstrates a generalizable protein engineering platform whose scope and power will continuously expand with ...
Compared with mammalian hosts, microbial expression systems are characterized by being easy to work with, robust, and cost-effective, all highly desirable features in the context of biopharmaceutical production. In fact, microbial platforms are capable of delivering in a scalable and affordable manner ...
If you have a huge number of very short scaffolds in your genome assembly, those short scaffolds will likely increase runtime dramatically but will not increase prediction accuracy. Use simple scaffold names in the genome file (e.g. >contig1 will work better than >contig1my custom species ...
Proteins are responsible for a wide range of life activities in organisms, with up to three billion in human cells [1]. However, proteins cannot work alone in the body and must bind to other molecules, known as ligands [2,3]. These ligands interact with specific parts of proteins, known...
Generative Models in Protein Engineering: A Comprehensive Survey Chen Xinhui, Yiwen Yuan, Joseph Liu, Chak Tou Leong, Xiaoye Zhu, Jiaqi Chen Neurips 2024 Workshop A Survey of Deep Learning Methods in Protein Bioinformatics and its Impact on Protein Design Weihang Dai arXiv:2501.01477 The Promise ...
this work provides a novel approach of using lamellar inorganic solids with a brucite-like structure for controlling the release of protein therapeutics such as rhEPO in injectable hydrogels. The nanoengineered injectable system was formulated by incorporating two-dimensional layered double hydroxide (LDH)...
For initial node features, we follow the approach in [2,11], where an atom is represented by an 18-dimensional vector (refer to Table1in previous work). To distinguish between ligand and protein atoms, we encode an atom using a 36-dimensional vector, where the first half represents raw ...
We benchmarked DeepBLAST against three sequence alignment methods, Needleman–Wunsch52, BLAST1 and HMMER2, in addition to four structural alignment methods that work directly with the atomic coordinates, FAST19, TM-align15, Dali18 and Mammoth-local20 (Table 2). TM-align achieves global alignment...