PredPS: Attention-based graph neural network for predicting stability of compounds in human plasma.

Stability of compounds in the human plasma is crucial for maintaining sufficient systemic drug exposure and considered an essential factor in the early stages of drug discovery and development. The rapid degradation of compounds in the plasma can result in poor in vivo efficacy. Currently, there are no open-source software programs for predicting human plasma stability. In this study, we developed an attention-based graph neural network, PredPS to predict the plasma stability of compounds in human plasma using in-house and open-source datasets. The PredPS outperformed the two machine learning and two deep learning algorithms that were used for comparison indicating its stability-predicting efficiency. PredPS achieved an area under the receiver operating characteristic curve of 90.1%, accuracy of 83.5%, sensitivity of 82.3%, and specificity of 84.6% when evaluated using 5-fold cross-validation. In the early stages of drug discovery, PredPS could be a helpful method for predicting the human plasma stability of compounds. Saving time and money can be accomplished by adopting an in silico-based plasma stability prediction model at the high-throughput screening stage. The source code for PredPS is available at https://bitbucket.org/krict-ai/predps and the PredPS web server is available at https://predps.netlify.app.

PredAOT: a computational framework for prediction of acute oral toxicity based on multiple random forest models.

BACKGROUND: Acute oral toxicity of drug candidates can lead to drug development failure; thus, predicting the acute oral toxicity of small compounds is important for successful drug development. However, evaluation of the acute oral toxicity of small compounds considered in the early stages of drug discovery is limited because of cost and time. Here, we developed a computational framework, PredAOT, that predicts the acute oral toxicity of small compounds in mice and rats. METHODS: PredAOT is based on multiple random forest models for the accurate prediction of acute oral toxicity. A total of 6226 and 6238 compounds evaluated in mice and rats, respectively, were used to train the models. RESULTS: PredAOT has the advantage of predicting acute oral toxicity in mice and rats simultaneously, and its prediction performance is similar to or better than that of existing tools. CONCLUSION: PredAOT will be a useful tool for the quick and accurate prediction of the acute oral toxicity of small compounds in mice and rats during drug development.

An interactive metabolic map of bio-based chemicals.

Metabolic engineering for the bio-based production of chemicals requires thorough understanding of metabolic reactions including enzymes, cofactors, reactants, and products. Here we present an interactive bio-based chemicals map that visualizes compounds, enzymes, and reaction pathways together with strategies for the production of chemicals by biological, chemical, and combined methods.

CDI Exerts Anti-Tumor Effects by Blocking the FoxM1-DNA Interaction.

The Forkhead box protein M1 (FoxM1) is an appealing target for anti-cancer therapeutics as this cell proliferation-associated transcription factor is overexpressed in most human cancers. FoxM1 is involved in tumor invasion, angiogenesis, and metastasis. To discover novel inhibitors that disrupt the FoxM1-DNA interaction, we identified CDI, a small molecule that inhibits the FoxM1-DNA interaction. CDI was identified through an assay based on the time-resolved fluorescence energy transfer response of a labeled consensus oligonucleotide that was bound to a recombinant FoxM1-dsDNA binding domain (FoxM1-DBD) protein and exhibited potent inhibitory activity against FoxM1-DNA interaction. CDI suppressed cell proliferation and induced apoptosis in MDA-MB-231 cells obtained from a breast cancer patient. Furthermore, it decreased not only the mRNA and protein expression of FoxM1 but also that of downstream targets such as CDC25b. Additionally, global transcript profiling of MDA-MB-231 cells by RNA-Seq showed that CDI decreases the expression of FoxM1-regulated genes. The docking and MD simulation results indicated that CDI likely binds to the DNA interaction site of FoxM1-DBD and inhibits the function of FoxM1-DBD. These results of CDI being a possible effective inhibitor of FoxM1-DNA interaction will encourage its usage in pharmaceutical applications.

Applications of artificial intelligence to enzyme and pathway design for metabolic engineering.

Metabolic engineering for developing industrial strains capable of overproducing bioproducts requires good understanding of cellular metabolism, including metabolic reactions and enzymes. However, metabolic pathways and enzymes involved are still unknown for many products of interest, which presents a key challenge in their biological production. This challenge can be partly overcome by constructing novel biosynthetic pathways through enzyme and pathway design approaches. With the increase in bio-big data, data-driven approaches using artificial intelligence (AI) techniques are allowing more advanced protein and pathway design. In this paper, we review recent studies on AI-aided protein engineering and design, focusing on directed evolution that uses AI approaches to efficiently construct mutant libraries. Also, recent works of AI-aided pathway design strategies, including template-based and template-free approaches, are discussed.

Drugs repurposed for COVID-19 by virtual screening of 6,218 drugs and cell-based assay.

The COVID-19 pandemic caused by SARS-CoV-2 is an unprecedentedly significant health threat, prompting the need for rapidly developing antiviral drugs for the treatment. Drug repurposing is currently one of the most tangible options for rapidly developing drugs for emerging and reemerging viruses. In general, drug repurposing starts with virtual screening of approved drugs employing various computational methods. However, the actual hit rate of virtual screening is very low, and most of the predicted compounds are false positives. Here, we developed a strategy for virtual screening with much reduced false positives through incorporating predocking filtering based on shape similarity and postdocking filtering based on interaction similarity. We applied this advanced virtual screening approach to repurpose 6,218 approved and clinical trial drugs for COVID-19. All 6,218 compounds were screened against main protease and RNA-dependent RNA polymerase of SARS-CoV-2, resulting in 15 and 23 potential repurposed drugs, respectively. Among them, seven compounds can inhibit SARS-CoV-2 replication in Vero cells. Three of these drugs, emodin, omipalisib, and tipifarnib, show anti-SARS-CoV-2 activities in human lung cells, Calu-3. Notably, the activity of omipalisib is 200-fold higher than that of remdesivir in Calu-3. Furthermore, three drug combinations, omipalisib/remdesivir, tipifarnib/omipalisib, and tipifarnib/remdesivir, show strong synergistic effects in inhibiting SARS-CoV-2. Such drug combination therapy improves antiviral efficacy in SARS-CoV-2 infection and reduces the risk of each drug's toxicity. The drug repurposing strategy reported here will be useful for rapidly developing drugs for treating COVID-19 and other viruses.

Production of Carminic Acid by Metabolically Engineered Escherichia coli.

Carminic acid is an aromatic polyketide found in scale insects (i.e., Dactylopius coccus) and is a widely used natural red colorant. It has long been produced by the cumbersome farming of insects followed by multistep purification processes. Thus, there has been much interest in producing carminic acid by the fermentation of engineered bacteria. Here we report the complete biosynthesis of carminic acid from glucose in engineered Escherichia coli. We first optimized the type II polyketide synthase machinery from Photorhabdus luminescens, enabling a high-level production of flavokermesic acid upon coexpression of the cyclases ZhuI and ZhuJ from Streptomyces sp. R1128. To discover the enzymes responsible for the remaining two reactions (hydroxylation and C-glucosylation), biochemical reaction analyses were performed by testing enzyme candidates reported to perform similar reactions. The two identified enzymes, aklavinone 12-hydroxylase (DnrF) from Streptomyces peucetius and C-glucosyltransferase (GtCGT) from Gentiana triflora, could successfully perform hydroxylation and C-glucosylation of flavokermesic acid, respectively. Then, homology modeling and docking simulations were performed to enhance the activities of these two enzymes, leading to the generation of beneficial mutants with 2-5-fold enhanced conversion efficiencies. In addition, the GtCGT mutant was found to be a generally applicable C-glucosyltransferase in E. coli, as was showcased by the successful production of aloesin found in Aloe vera. Simple metabolic engineering followed by fed-batch fermentation resulted in 0.63 ± 0.02 mg/L of carminic acid production from glucose. The strategies described here will be useful for the design and construction of biosynthetic pathways involving unknown enzymes and consequently the production of diverse industrially important natural products.

Microbial production of multiple short-chain primary amines via retrobiosynthesis.

Bio-based production of many chemicals is not yet possible due to the unknown biosynthetic pathways. Here, we report a strategy combining retrobiosynthesis and precursor selection step to design biosynthetic pathways for multiple short-chain primary amines (SCPAs) that have a wide range of applications in chemical industries. Using direct precursors of 15 target SCPAs determined by the above strategy, Streptomyces viridifaciens vlmD encoding valine decarboxylase is examined as a proof-of-concept promiscuous enzyme both in vitro and in vivo for generating SCPAs from their precursors. Escherichia coli expressing the heterologous vlmD produces 10 SCPAs by feeding their direct precursors. Furthermore, metabolically engineered E. coli strains are developed to produce representative SCPAs from glucose, including the one producing 10.67 g L-1 of iso-butylamine by fed-batch culture. This study presents the strategy of systematically designing biosynthetic pathways for the production of a group of related chemicals as demonstrated by multiple SCPAs as examples.

A deep learning approach to evaluate the feasibility of enzymatic reactions generated by retrobiosynthesis.

Retrobiosynthesis allows the designing of novel biosynthetic pathways for the production of chemicals and materials through metabolic engineering, but generates a large number of reactions beyond the experimental feasibility. Thus, an effective method that can reduce a large number of the initially predicted enzymatic reactions has been needed. Here, we present Deep learning-based Reaction Feasibility Checker (DeepRFC) to classify the feasibility of a given enzymatic reaction with high performance and speed. DeepRFC is designed to receive Simplified Molecular-Input Line-Entry System (SMILES) strings of a reactant pair, which is defined as a substrate and a product of a reaction, as an input, and evaluates whether the input reaction is feasible. A deep neural network is selected for DeepRFC as it leads to better classification performance than five other representative machine learning methods examined. For validation, the performance of DeepRFC is compared with another in-house reaction feasibility checker that uses the concept of reaction similarity. Finally, the use of DeepRFC is demonstrated for the retrobiosynthesis-based design of novel one-carbon assimilation pathways. DeepRFC will allow retrobiosynthesis to be more practical for metabolic engineering applications by efficiently screening a large number of retrobiosynthesis-derived enzymatic reactions. DeepRFC is freely available at https://bitbucket.org/kaistsystemsbiology/deeprfc.

Systematic and Comparative Evaluation of Software Programs for Template-Based Modeling of Protein Structures.

Modeling protein structures is critical for understanding protein functions in various biological and biotechnological studies. Among representative protein structure modeling approaches, template-based modeling (TBM) is by far the most reliable and most widely used approach to model protein structures. However, it still remains as a challenge to select appropriate software programs for pairwise alignments and model building, two major steps of the TBM. In this paper, pairwise alignment methods for TBM are first compared with respect to the quality of structure models built using these methods. This comparative study is conducted using comprehensive datasets, which cover 6185 domain sequences from Structural Classification of Proteins extended for soluble proteins, and 259 Protein Data Bank entries (whole protein sequences) from Orientations of Proteins in Membranes database for membrane proteins. Overall, a profile-based method, especially PSI-BLAST, consistently shows high performance across the datasets and model evaluation metrics used. Next, use of two model building programs, MODELLER and SWISS-MODEL, does not seem to significantly affect the quality of protein structure models built except for the Hard group (a group of relatively less homologous proteins) of membrane proteins. The results presented in this study will be useful for more accurate implementation of TBM.

A safe and sustainable bacterial cellulose nanofiber separator for lithium rechargeable batteries.

Bacterial cellulose nanofiber (BCNF) with high thermal stability produced by an ecofriendly process has emerged as a promising solution to realize safe and sustainable materials in the large-scale battery. However, an understanding of the actual thermal behavior of the BCNF in the full-cell battery has been lacking, and the yield is still limited for commercialization. Here, we report the entire process of BCNF production and battery manufacture. We systematically constructed a strain with the highest yield (31.5%) by increasing metabolic flux and improved safety by introducing a Lewis base to overcome thermochemical degradation in the battery. This report will open ways of exploiting the BCNF as a "single-layer" separator, a good alternative to the existing chemical-derived one, and thus can greatly contribute to solving the environmental and safety issues.

Genomic and metabolic analysis of Komagataeibacter xylinus DSM 2325 producing bacterial cellulose nanofiber.

Bacterial cellulose nanofiber (CNF) is a polymer with a wide range of potential industrial applications. Several Komagataeibacter species, including Komagataeibacter xylinus as a model organism, produce CNF. However, the industrial application of CNF has been hampered by inefficient CNF production, necessitating metabolic engineering for the enhanced CNF production. Here, we present complete genome sequence and a genome-scale metabolic model KxyMBEL1810 of K. xylinus DSM 2325 for metabolic engineering applications. Genome analysis of this bacterium revealed that a set of genes associated with CNF biosynthesis and regulation were present in this bacterium, which were also conserved in another six representative Komagataeibacter species having complete genome information. To better understand the metabolic characteristics of K. xylinus DSM 2325, KxyMBEL1810 was reconstructed using genome annotation data, relevant computational resources and experimental growth data generated in this study. Random sampling and correlation analysis of the KxyMBEL1810 predicted pgi and gnd genes as novel overexpression targets for the enhanced CNF production. Among engineered K. xylinus strains individually overexpressing heterologous pgi and gnd genes, either from Escherichia coli or Corynebacterium glutamicum, batch fermentation of a strain overexpressing the E. coli pgi gene produced 3.15 g/L of CNF in a complex medium containing glucose, which was the best CNF concentration achieved in this study, and 115.8% higher than that (1.46 g/L) obtained from the control strain. Genome sequence data and KxyMBEL1810 generated in this study should be useful resources for metabolic engineering of K. xylinus for the enhanced CNF production.

Systems Metabolic Engineering Strategies: Integrating Systems and Synthetic Biology with Metabolic Engineering.

Metabolic engineering allows development of microbial strains efficiently producing chemicals and materials, but it requires much time, effort, and cost to make the strains industrially competitive. Systems metabolic engineering, which integrates tools and strategies of systems biology, synthetic biology, and evolutionary engineering with traditional metabolic engineering, has recently been used to facilitate development of high-performance strains. The past decade has witnessed this interdisciplinary strategy continuously being improved toward the development of industrially competitive overproducer strains. In this article, current trends in systems metabolic engineering including tools and strategies are reviewed, focusing on recent developments in selection of host strains, metabolic pathway reconstruction, tolerance enhancement, and metabolic flux optimization. Also, future challenges and prospects are discussed.

Bacterial cellulose as an example product for sustainable production and consumption.

Life cycle of bacterial cellulose. Sustainable production and consumption of bio-based products are showcased using bacterial cellulose as an example.

Discovery of Tyk2 inhibitors via the virtual site-directed fragment-based drug design.

In this study, we synthesized compound 12 with potent Tyk2 inhibitory activity from FBDD study and carried out a cell-based assay for Tyk2/STAT3 signaling activation upon IFNα5 stimulation. Compound 12 completely suppressed the IFNα5-mediated Tyk2/STAT3 signaling pathway as well as the basal levels of pSTAT3. Stimulation with IFNα/ß leads to the tyrosine phosphorylation of the JAK1 and Tyk2 receptor-associated kinases with subsequent STATs activation, transmitting signals from the cell surface receptor to the nucleus. In conclusion, the potency of compound 12 to interrupt the signal transmission of Tyk2/STAT3 appeared to be equivalent or superior to that of the reference compound.