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The molecular weight (MW) of an enzyme is a critical parameter in enzyme-constrained models (ecModels). It is determined by two factors: the presence of subunits and the abundance of each subunit. Although the number of subunits (NS) can potentially be obtained from UniProt, this information is not readily available for most proteins. In this study, we addressed this gap by extracting and curating subunit information from the UniProt database to establish a robust benchmark dataset. Subsequently, we propose a novel model named DeepSub, which leverages the protein language model and Bi-directional Gated Recurrent Unit (GRU), to predict NS in homo-oligomers solely based on protein sequences. DeepSub demonstrates remarkable accuracy, achieving an accuracy rate as high as 0.967, surpassing the performance of QUEEN. To validate the effectiveness of DeepSub, we performed predictions for protein homo-oligomers that have been reported in the literature but are not documented in the UniProt database. Examples include homoserine dehydrogenase from Corynebacterium glutamicum, Matrilin-4 from Mus musculus and Homo sapiens, and the Multimerins protein family from M. musculus and H. sapiens. The predicted results align closely with the reported findings in the literature, underscoring the reliability and utility of DeepSub. [ABSTRACT FROM AUTHOR]

In the area of recommendation systems, one of the fundamental tasks is rating prediction. Most existing neural network methods independently extract user's and item's review features utilizing a parallel convolutional neural network(CNN) and use them as the representation of users and items to predict rating scores. There are two main drawbacks of these methods: (1) They typically only leverage user or item reviews but ignore the latent information provided by user-item interactions. (2) The historical rating scores are not integrated into the representation of users and items, they are simply used as labels to train models. Thus the rating information is not adequately utilized, leading to the prediction performance of these methods is not superior. To remedy these drawbacks mentioned above, in this paper, we build a unified graph convolutional network(GCN) to capture the interaction information between user and item, also obtain additional information provided by reviews and rating scores. As both reviews and ratings carry interactive messages among users and items, they would magnify the learning performance of user-item features. Specifically, we first construct a multi-attributed bipartite graph(MA-bipartite graph) to represent users, items, and their interactions through reviews and ratings. Then we divide the MA-bipartite graph into two sub-graphs according to the attributes of the edge types which represent the user-item interaction in review domain and item domain respectively. Next, an attributed GCN model is explicitly designed to learn latent features of users and items by incorporating review embeddings and rating score weights. Finally, the attention mechanism is carried to fuse user and item features dynamically to conduct the rating prediction. We conduct our experiments on two real-world datasets. The results demonstrate that the proposed model achieved the state-of-the-art performance, which increases the prediction accuracy by more than 3%, compared with baseline methods. [ABSTRACT FROM AUTHOR]

Revolutionary breakthroughs in artificial intelligence (AI) and machine learning (ML) have had a profound impact on a wide range of scientific disciplines, including the development of artificial cell factories for biomanufacturing. In this paper, we review the latest studies on the application of data-driven methods for the design of new proteins, pathways, and strains. We first briefly introduce the various types of data and databases relevant to industrial biomanufacturing, which are the basis for data-driven research. Different types of algorithms, including traditional ML and more recent deep learning methods, are also presented. We then demonstrate how these data-based approaches can be applied to address various issues in cell factory development using examples from recent studies, including the prediction of protein function, improvement of metabolic models, and estimation of missing kinetic parameters, design of non-natural biosynthesis pathways, and pathway optimization. In the last section, we discuss the current limitations of these data-driven approaches and propose that data-driven methods should be integrated with mechanistic models to complement each other and facilitate the development of synthetic strains for industrial biomanufacturing., Competing Interests: The authors declare that they have no conflicts of interest related to the publication of this manuscript., (Copyright © 2022 Zhenkun Shi et al.)