The screening of enzymes for catalyzing specific substrate-product pairs is often constrained in the realms of metabolic engineering and synthetic biology. Existing tools based on substrate and reaction similarity predominantly rely on prior knowledge, demonstrating limited extrapolative capabilities and an inability to incorporate custom candidate-enzyme libraries. Addressing these limitations, we have developed the Substrate-product Pair-based Enzyme Promiscuity Prediction (SPEPP) model. This innovative approach utilizes transfer learning and transformer architecture to predict enzyme promiscuity, thereby elucidating the intricate interplay between enzymes and substrate-product pairs. SPEPP exhibited robust predictive ability, eliminating the need for prior knowledge of reactions and allowing users to define their own candidate-enzyme libraries. It can be seamlessly integrated into various applications, including metabolic engineering, de novo pathway design, and hazardous material degradation. To better assist metabolic engineers in designing and refining biochemical pathways, particularly those without programming skills, we also designed EnzyPick, an easy-to-use web server for enzyme screening based on SPEPP. EnzyPick is accessible at http://www.biosynther.com/enzypick/.

MOTIVATION: Despite low prevalence, rare diseases affect 300 million people worldwide. Research on pathogenesis and drug development lags due to limited commercial potential, insufficient epidemiological data, and a dearth of publications. The unique characteristics of rare diseases, including limited annotated data, intricate processes for extracting pertinent entity relationships, and difficulties in standardizing data, represent challenges for text mining. RESULTS: We developed a rare disease data acquisition framework using text mining and knowledge graphs and constructed the most comprehensive rare disease knowledge graph to date, Rare Disease Bridge (RDBridge). RDBridge offers search functions for genes, potential drugs, pathways, literature, and medical imaging data that will support mechanistic research, drug development, diagnosis, and treatment for rare diseases. AVAILABILITY AND IMPLEMENTATION: RDBridge is freely available at http://rdb.lifesynther.com/.

BACKGROUND: The rapid development of synthetic biology relies heavily on the use of databases and computational tools, which are also developing rapidly. While many tool registries have been created to facilitate tool retrieval, sharing, and reuse, no relatively comprehensive tool registry or catalog addresses all aspects of synthetic biology. RESULTS: We constructed SynBioTools, a comprehensive collection of synthetic biology databases, computational tools, and experimental methods, as a one-stop facility for searching and selecting synthetic biology tools. SynBioTools includes databases, computational tools, and methods extracted from reviews via SCIentific Table Extraction, a scientific table-extraction tool that we built. Approximately 57% of the resources that we located and included in SynBioTools are not mentioned in bio.tools, the dominant tool registry. To improve users' understanding of the tools and to enable them to make better choices, the tools are grouped into nine modules (each with subdivisions) based on their potential biosynthetic applications. Detailed comparisons of similar tools in every classification are included. The URLs, descriptions, source references, and the number of citations of the tools are also integrated into the system. CONCLUSIONS: SynBioTools is freely available at https://synbiotools.lifesynther.com/ . It provides end-users and developers with a useful resource of categorized synthetic biology databases, tools, and methods to facilitate tool retrieval and selection.

MOTIVATION: Digital pathological analysis is run as the main examination used for cancer diagnosis. Recently, deep learning-driven feature extraction from pathology images is able to detect genetic variations and tumor environment, but few studies focus on differential gene expression in tumor cells. RESULTS: In this paper, we propose a self-supervised contrastive learning framework, HistCode, to infer differential gene expression from whole slide images (WSIs). We leveraged contrastive learning on large-scale unannotated WSIs to derive slide-level histopathological features in latent space, and then transfer it to tumor diagnosis and prediction of differentially expressed cancer driver genes. Our experiments showed that our method outperformed other state-of-the-art models in tumor diagnosis tasks, and also effectively predicted differential gene expression. Interestingly, we found the genes with higher fold change can be more precisely predicted. To intuitively illustrate the ability to extract informative features from pathological images, we spatially visualized the WSIs colored by the attention scores of image tiles. We found that the tumor and necrosis areas were highly consistent with the annotations of experienced pathologists. Moreover, the spatial heatmap generated by lymphocyte-specific gene expression patterns was also consistent with the manually labeled WSIs.

SUMMARY: Advances in metabolic engineering have boosted the production of bulk chemicals, resulting in tons of production volumes of some bulk chemicals with very low prices. A decrease in the production cost and overproduction of bulk chemicals makes it necessary and desirable to explore the potential to synthesize higher-value products from them. It is also useful and important for society to explore the use of design methods involving synthetic biology to increase the economic value of these bulk chemicals. Therefore, we developed 'BioBulkFoundary', which provides an elaborate analysis of the biosynthetic potential of bulk chemicals based on the state-of-art exploration of pathways to synthesize value-added chemicals, along with associated comprehensive technology and economic database into a user-friendly framework. AVAILABILITY AND IMPLEMENTATION: Freely available on the web at http://design.rxnfinder.org/biobulkfoundary/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

SUMMARY: The field of synthetic biology lacks a comprehensive knowledgebase for selecting synthetic target molecules according to their functions, economic applications and known biosynthetic pathways. We implemented ChemHub, a knowledgebase containing >90 000 chemicals and their functions, along with related biosynthesis information for these chemicals that was manually extracted from >600 000 published studies by more than 100 people over the past 10 years. AVAILABILITY AND IMPLEMENTATION: Multiple algorithms were implemented to enable biosynthetic pathway design and precursor discovery, which can support investigation of the biosynthetic potential of these functional chemicals. ChemHub is freely available at: http://www.rxnfinder.org/chemhub/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MOTIVATION: Rapid advances in sequencing technology have resulted huge increases in the accessibility of sequencing data. Moreover, researchers are focusing more on organisms that lack a reference genome. However, few easy-to-use web servers focusing on annotations of enzymatic functions are available. Accordingly, in this study, we describe Transcriptor, a novel platform for annotating transcripts encoding enzymes. RESULTS: The transcripts were evaluated using more than 300 000 in-house enzymatic reactions through bridges of Enzyme Commission numbers. Transcriptor also enabled ontology term identification and along with associated enzymes, visualization and prediction of domains and annotation of regulatory structure, such as long noncoding RNAs, which could facilitate the discovery of new functions in model or nonmodel species. Transcriptor may have applications in elucidation of the roles of organs transcriptomes and secondary metabolite biosynthesis in organisms lacking a reference genome. AVAILABILITY AND IMPLEMENTATION: Transcriptor is available at http://design.rxnfinder.org/transcriptor/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MOTIVATION: The 2019 novel coronavirus outbreak has significantly affected global health and society. Thus, predicting biological function from pathogen sequence is crucial and urgently needed. However, little work has been conducted to identify viruses by the enzymes that they encode, and which are key to pathogen propagation. RESULTS: We built a comprehensive scientific resource, SARS2020, which integrates coronavirus-related research, genomic sequences and results of anti-viral drug trials. In addition, we built a consensus sequence-catalytic function model from which we identified the novel coronavirus as encoding the same proteinase as the severe acute respiratory syndrome virus. This data-driven sequence-based strategy will enable rapid identification of agents responsible for future epidemics. AVAILABILITYAND IMPLEMENTATION: SARS2020 is available at http://design.rxnfinder.org/sars2020/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Mixed land use has been widely used as a planning tool to improve the functionality of cities. However, depicting mixed land use is rather difficult due to its complexities. Previous studies have decomposed urban land areas using either remote sensing images or geospatial big data. Few studies have combined these two data sources because of the lack of methodologies. This article proposed an end-to-end two-stream convolutional neural network (CNN) for combining features (CF-CNN) to estimate the proportion of mixed land use by integrating high spatial resolution (HSR) images and geospatial big data of real-time Tencent user density (RTUD) data. Two deep learning networks, one for image information extraction and other for human activity-related information extraction, are used to construct two branches of CF-CNN. The mixed land use can be described by calculating the proportions of each land use type at the street-block level. Compared with methods for using single-source data, CF-CNN obtained the highest classification accuracy. We further applied the Shannon diversity index (SHDI) to quantify the agglomerated urban mixed land use. The Spearman correlation coefficients among the SHDI, community distance, and neighborhood vibrancy were calculated to verify the effectiveness of the mixed land use composition. Our framework provided an alternative way of identifying mixed land use structures by integrating multisource data.

MOTIVATION: Molecular scaffolds are useful in medicinal chemistry to describe, discuss and visualize series of chemical compounds, biochemical transformations and associated biological properties. RESULTS: Here, we present RxnBLAST as a web-based tool for analyzing scaffold transformations and reactive chemical environment features in bioreactions. RxnBLAST extracts chemical features from bioreactions including atom-atom mapping, reaction centers, rules and functional groups to help understand chemical compositions and reaction patterns. Core-to-Core is proposed, which can be utilized in scaffold networks and for constructing a reaction space, as well as providing guidance for subsequent biosynthesis efforts. AVAILABILITY AND IMPLEMENTATION: RxnBLAST is available at: http://design.rxnfinder.org/rxnblast/.

SUMMARY: The biosynthetic ability of living organisms has important applications in producing bulk chemicals, biofuels and natural products. Based on the most comprehensive biosynthesis knowledgebase, a computational system, BCSExplorer, is proposed to discover the unexplored chemical space using nature's biosynthetic potential. BCSExplorer first integrates the most comprehensive biosynthetic reaction database with 280 000 biochemical reactions and 60 000 chemicals biosynthesized globally over the past 130 years. Second, in this study, a biosynthesis tree is computed for a starting chemical molecule based on a comprehensive biotransformation rule library covering almost all biosynthetic possibilities, in which redundant rules are removed using a new algorithm. Moreover, biosynthesis feasibility, drug-likeness and toxicity analysis of a new generation of compounds will be pursued in further studies to meet various needs. BCSExplorer represents a novel method to explore biosynthetically available chemical space. AVAILABILITY AND IMPLEMENTATION: BCSExplorer is available at: http://www.rxnfinder.org/bcsexplorer/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MOTIVATION: Build a web-based 3D molecular structure viewer focusing on interactive structural analysis. RESULTS: iCn3D (I-see-in-3D) can simultaneously show 3D structure, 2D molecular contacts and 1D protein and nucleotide sequences through an integrated sequence/annotation browser. Pre-defined and arbitrary molecular features can be selected in any of the 1D/2D/3D windows as sets of residues and these selections are synchronized dynamically in all displays. Biological annotations such as protein domains, single nucleotide variations, etc. can be shown as tracks in the 1D sequence/annotation browser. These customized displays can be shared with colleagues or publishers via a simple URL. iCn3D can display structure-structure alignments obtained from NCBI's VAST+ service. It can also display the alignment of a sequence with a structure as identified by BLAST, and thus relate 3D structure to a large fraction of all known proteins. iCn3D can also display electron density maps or electron microscopy (EM) density maps, and export files for 3D printing. The following example URL exemplifies some of the 1D/2D/3D representations: https://www.ncbi.nlm.nih.gov/Structure/icn3d/full.html?mmdbid=1TUP&showanno=1&show2d=1&showsets=1. AVAILABILITY AND IMPLEMENTATION: iCn3D is freely available to the public. Its source code is available at https://github.com/ncbi/icn3d. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.