IntEnzyDB: an Integrated Structure-Kinetics Enzymology Database

Data-driven modeling has emerged as a new paradigm for biocatalyst design and discovery. Biocatalytic databases that integrate enzyme structure and function data are in urgent need. Here, we described IntEnzyDB as an integrated structure-kinetics database for facile statistical modeling and machine learning. IntEnzyDB employs a relational architecture with flattened data structure, which allows rapid data operation. This architecture also makes it easy for IntEnzyDB to incorporate more types of enzyme function data. IntEnzyDB contains enzyme kinetics and structure data from six enzyme commission classes. Using 1019 enzyme structure-kinetics pairs, we investigated the efficiency-perturbing propensity for mutations that are close or distal to the active site. The statistical results show that efficiency-enhancing mutations are globally encoded; deleterious mutations are much more likely to occur in close mutations than in distal mutations. Finally, we described a web interface that allows public users to access enzymology data stored in IntEnzyDB. IntEnzyDB will provide a computational facility for data-driven modeling in biocatalysis and molecular evolution

Rate-enhancing Single Amino Acid Mutation for Hydrolases: A Statistical Profiling

We reported the statistical profiling for rate-enhancing mutant hydrolases with single amino acid substitution. We constructed an integrated structure-kinetics database, IntEnzyDB, which contains 3,907 experimentally characterized hydrolase kinetics and 2,715 hydrolase Protein Data Bank IDs. The hydrolase kinetics data involve 9% rate-enhancing mutations. Mutation to nonpolar residues with a hydrocarbon chain shows a stronger preference for rate acceleration than to polar or charged residues. To elucidate the structure-kinetics relationship for rate-enhancing mutations, we categorized each mutation into one of the three spatial shells of hydrolases. We defined the spatial shells by reference to either the active site or the center-of-mass of the enzyme. In either case, mutations in the first shell (i.e., closest to the reference point) appear on average more rate-deleterious than those in the other two shells (i.e., ~1.0 kcal/mol in ∆∆G‡ ). Under the active-site reference, mutations in the third shell (i.e., most distal to the active site) exhibit the highest likelihood of rate enhancement. This propensity is significant for larger-sized hydrolases. In contrast, under the center-of-mass reference, mutations in the second shell (i.e., 33.3th to 66.7th percentile rank of spatial proximity to the center-of-mass of the enzyme) show the highest likelihood of rate enhancement. This trend is significant for smaller-sized hydrolases. The studies reveal the statistical features for identifying rate-enhancing mutations in hydrolases, which will potentially guide hydrolase discovery in biocatalysis

A Meta-Analysis of Efficacy and Adverse Effects of Lobaplatin and Cisplatin in the Treatment of Malignant Pleural Effusion

Background and objective The aim of this study is to systematically evaluate the efficacy and adverse effects of Lobaplatin and Cisplatin in the treatment of malignant pleural effusion. Methods The databases of Medline (PubMed), Embase, Web of Science, Cochrane, Wanfang, CNKI and VIP were retrieved so as to search the studies about the randomized controlled clinical trials (RCT) that compared the Lobaplatin and Cisplatin for malignant pleural effusion. The main outcome indicators include objective response rate, complete response, partial response, nephrotoxicity, chest pain, gastrointestinal reaction, myelosuppression, fever response and hepatotoxicity. Relative risk was used as the effect size, which was expressed as 95% confidence interval. The meta-analysis was performed using Stata 14.0 statistical software. Results A total of 12 RCTs and 720 MPE patients were included. The results showed that the ORR (RR=1.27, 95%CI: 1.15-1.40, P<0.001), CR (RR=1.39, 95%CI: 1.09-1.78, P=0.007), PR (RR=1.21, 95%CI: 1.02-1.42, P=0.026) in LBP thoracic perfusion chemotherapy were significantly higher than those in DDP thoracic perfusion chemotherapy. The incidence of nephrotoxicity (RR=0.31, 95%CI: 0.13-0.71, P=0.005) and gastrointestinal reactions (RR=0.44, 95%CI: 0.31-0.62, P<0.001) in the LBP group were significantly lower than those in DDP group. Conclusion Compared with DDP pleural perfusion chemotherapy, the ORR, CR and PR of LBP pleural perfusion chemotherapy for MPE are significantly better than DDP, and its nephrotoxicity and gastrointestinal reactions are remarkably lower than DDP

Mutexa: A Computational Ecosystem for Intelligent Protein Engineering.

Protein engineering holds immense promise in shaping the future of biomedicine and biotechnology. This Review focuses on our ongoing development of Mutexa, a computational ecosystem designed to enable intelligent protein engineering. In this vision, researchers will seamlessly acquire sequences of protein variants with desired functions as biocatalysts, therapeutic peptides, and diagnostic proteins through a finely-tuned computational machine, akin to Amazon Alexas role as a versatile virtual assistant. The technical foundation of Mutexa has been established through the development of a database that combines and relates enzyme structures and their respective functions (e.g., IntEnzyDB), workflow software packages that enable high-throughput protein modeling (e.g., EnzyHTP and LassoHTP), and scoring functions that map the sequence-structure-function relationship of proteins (e.g., EnzyKR and DeepLasso). We will showcase the applications of these tools in benchmarking the convergence conditions of enzyme functional descriptors across mutants, investigating protein electrostatics and cavity distributions in SAM-dependent methyltransferases, and understanding the role of nonelectrostatic dynamic effects in enzyme catalysis. Finally, we will conclude by addressing the future steps and fundamental challenges in our endeavor to develop new Mutexa applications that assist the identification of beneficial mutants in protein engineering

Mutexa: A Computational Ecosystem for Intelligent Protein Engineering

Protein engineering holds immense promise in shaping the future of biomedicine and biotechnology. This review focuses on our ongoing development of Mutexa, a computational ecosystem designed to enable "intelligent protein engineering". In this vision, researchers can seamlessly acquire sequences of protein variants with desired functions as biocatalysts, therapeutic peptides, and diagnostic proteins by interacting with a computational machine, similar to how we use Amazon Alexa in these days. The technical foundation of Mutexa has been established through the development of database that integrates enzyme structures with their respective functions (e.g., IntEnzyDB), workflow software packages that enable high-throughput protein modeling (e.g., EnzyHTP and LassoHTP), and scoring functions that map the sequence-structure-function relationship of proteins (e.g., EnzyKR and DeepLasso). We will showcase the applications of these tools in benchmarking the convergence conditions of enzyme functional descriptors across mutants, investigating protein electrostatics and cavity distributions in SAM-dependent methyltransferases, and understanding the role of non-electrostatic dynamic effects in enzyme catalysis. Finally, we will conclude by addressing the future steps and challenges in our endeavor to develop new Mutexa applications that facilitate the selection of beneficial mutants in enzyme engineering

Molecular Mechanism of Xixin-Ganjiang Herb Pair Treating Chronic Obstructive Pulmonary Disease-Integrated Network Pharmacology and Molecular Docking

Background. Chronic obstructive pulmonary disease (COPD) is characterized by high morbidity, disability, and mortality, which seriously threatens human life and health. Xixin and Ganjiang are classic herb pairs of Zhongjing Zhang, which are often used to treat COPD in China. However, the substance basis and mechanism of action of Xixin-Ganjiang herb pair (XGHP) in the treatment of COPD remain unclear. Methods. On the website of TCMSP and the DrugBank, effective compounds and targets of XGHP were found. COPD targets were obtained from GeneCards, DisGeNET, and GEO gene chips. Intersecting these databases resulted in a library of drug targets for COPD. Then, intersection targets were used for protein-protein interaction (PPI) and pathway enrichment analysis. Finally, the binding activity between compounds and core genes was evaluated by molecular docking to verify the expression level of PTGS2 and PPARG in rats. Results. Twelve effective compounds and 104 core genes were found in the intersection library, and kaempferol, sesamin, β-sitosterol, PTGS2, and PPARG were particularly prominent in the network analysis. A total of 113 pathways were obtained and enrichment of the TNF signaling pathway, IL-17 signaling pathway, and C-type lectin receptor signaling pathway was particularly obvious. Molecular docking indicated that kaempferol, sesamin, and β-sitosterol were closely related to PTGS2 and PPARG and were superior to aminophylline. Key compounds in XGHP could restrict the expression of PTGS2 in the lung tissues of COPD rats and promote the expression of PPARG. Conclusion. Inhibition of the expression of inflammatory factor PTGS2 and promotion of the expression of PPARG may be an effective target of XGHP in the treatment of COPD

αPD-1-mesoCAR-T cells partially inhibit the growth of advanced/refractory ovarian cancer in a patient along with daily apatinib

Case presentation Here we report a case of refractory EOC in a patient who had relapsed after multiline chemotherapy. The patient received autologous T cells that contained sequences encoding single-chain variable fragments specific for MSLN and full-length antibody for PD-1 (αPD-1). The modified T cells were called αPD-1-mesoCAR-T cells. After infusion, the copy number and PD-1 antibody secretion of the CAR-T cells were increased in the blood. By application of multimodality tumor tracking, MRI of the liver showed shrinkage of metastatic nodules from average diameter of 71.3–39.1 mm at month 2. The patient achieved partial response and survived more than 17 months. IL-6 levels in the patient fluctuated from the baseline to 2–4-folds after treatment, but side effects were mild with only grade 1 hypertension and fatigue.Conclusion αPD-1-mesoCAR-T cell therapy combined with apatinib demonstrates a potential therapeutic effect on advanced refractory ovarian cancer.Trial registration number NCT03615313