The biosynthetic logic and enzymatic machinery of approved fungi-derived pharmaceuticals and agricultural biopesticides.

Covering: 2000 to 2023The kingdom Fungi has become a remarkably valuable source of structurally complex natural products (NPs) with diverse bioactivities. Since the revolutionary discovery and application of the antibiotic penicillin from Penicillium, a number of fungi-derived NPs have been developed and approved into pharmaceuticals and pesticide agents using traditional "activity-guided" approaches. Although emerging genome mining algorithms and surrogate expression hosts have brought revolutionary approaches to NP discovery, the time and costs involved in developing these into new drugs can still be prohibitively high. Therefore, it is essential to maximize the utility of existing drugs by rational design and systematic production of new chemical structures based on these drugs by synthetic biology. To this purpose, there have been great advances in characterizing the diversified biosynthetic gene clusters associated with the well-known drugs and in understanding the biosynthesis logic mechanisms and enzymatic transformation processes involved in their production. We describe advances made in the heterogeneous reconstruction of complex NP scaffolds using fungal polyketide synthases (PKSs), non-ribosomal peptide synthetases (NRPSs), PKS/NRPS hybrids, terpenoids, and indole alkaloids and also discuss mechanistic insights into metabolic engineering, pathway reprogramming, and cell factory development. Moreover, we suggest pathways for expanding access to the fungal chemical repertoire by biosynthesis of representative family members via common platform intermediates and through the rational manipulation of natural biosynthetic machineries for drug discovery.

Biomimetic, Highly Reusable and Hydrophobic Graphene/Polyvinyl Alcohol/Cellulose Nanofiber Aerogels as Oil-Removing Absorbents.

Aerogels have great potential in oil absorption applications; however, many reported aerogels have the drawbacks of a low oil-recovery rate and poor mechanical properties, which limit their application. In this study, highly reusable graphene oxide (GO)/TEMPO-oxidized cellulose nanofiber (TOCN)/polyvinyl alcohol (PVA) aerogels with excellent mechanical properties and with an architecture similar to that of Thalia dealbata stems were fabricated through a three-step process of bidirectional-freezing, freeze-drying, and chemical vapor deposition (CVD) modification. After CVD modification, the modified GTPA (MGTPA) accorded hydrophobicity. The synergistic effects of the three components and the unique biomimetic structure conferred biomimetic-MGTPA (b-MGTPA) with excellent compressible properties. As an adsorbent, b-MGTPA showed a high adsorption capacity (75-151 g/g) for various types of organic solvents. In addition, its high compressibility enables b-MGTPA to have fast and highly efficient recovery of absorbed oil through simple mechanical squeezing and it possesses excellent reusable stability (the oil recovery rate and oil retention rate reached 80% and 91.5%, respectively, after 10 repeated absorption-compression cycles).

Predicting MHC-peptide binding affinity by differential boundary tree.

MOTIVATION: The prediction of the binding between peptides and major histocompatibility complex (MHC) molecules plays an important role in neoantigen identification. Although a large number of computational methods have been developed to address this problem, they produce high false-positive rates in practical applications, since in most cases, a single residue mutation may largely alter the binding affinity of a peptide binding to MHC which cannot be identified by conventional deep learning methods. RESULTS: We developed a differential boundary tree-based model, named DBTpred, to address this problem. We demonstrated that DBTpred can accurately predict MHC class I binding affinity compared to the state-of-art deep learning methods. We also presented a parallel training algorithm to accelerate the training and inference process which enables DBTpred to be applied to large datasets. By investigating the statistical properties of differential boundary trees and the prediction paths to test samples, we revealed that DBTpred can provide an intuitive interpretation and possible hints in detecting important residue mutations that can largely influence binding affinity. AVAILABILITY AND IMPLEMENTATION: The DBTpred package is implemented in Python and freely available at: https://github.com/fpy94/DBT. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

A machine learning-based framework for modeling transcription elongation.

RNA polymerase II (Pol II) generally pauses at certain positions along gene bodies, thereby interrupting the transcription elongation process, which is often coupled with various important biological functions, such as precursor mRNA splicing and gene expression regulation. Characterizing the transcriptional elongation dynamics can thus help us understand many essential biological processes in eukaryotic cells. However, experimentally measuring Pol II elongation rates is generally time and resource consuming. We developed PEPMAN (polymerase II elongation pausing modeling through attention-based deep neural network), a deep learning-based model that accurately predicts Pol II pausing sites based on the native elongating transcript sequencing (NET-seq) data. Through fully taking advantage of the attention mechanism, PEPMAN is able to decipher important sequence features underlying Pol II pausing. More importantly, we demonstrated that the analyses of the PEPMAN-predicted results around various types of alternative splicing sites can provide useful clues into understanding the cotranscriptional splicing events. In addition, associating the PEPMAN prediction results with different epigenetic features can help reveal important factors related to the transcription elongation process. All these results demonstrated that PEPMAN can provide a useful and effective tool for modeling transcription elongation and understanding the related biological factors from available high-throughput sequencing data.

Dynamic tubulation of mitochondria drives mitochondrial network formation.

Mitochondria form networks. Formation of mitochondrial networks is important for maintaining mitochondrial DNA integrity and interchanging mitochondrial material, whereas disruption of the mitochondrial network affects mitochondrial functions. According to the current view, mitochondrial networks are formed by fusion of individual mitochondria. Here, we report a new mechanism for formation of mitochondrial networks through KIF5B-mediated dynamic tubulation of mitochondria. We found that KIF5B pulls thin, highly dynamic tubules out of mitochondria. Fusion of these dynamic tubules, which is mediated by mitofusins, gives rise to the mitochondrial network. We further demonstrated that dynamic tubulation and fusion is sufficient for mitochondrial network formation, by reconstituting mitochondrial networks in vitro using purified fusion-competent mitochondria, recombinant KIF5B, and polymerized microtubules. Interestingly, KIF5B only controls network formation in the peripheral zone of the cell, indicating that the mitochondrial network is divided into subzones, which may be constructed by different mechanisms. Our data not only uncover an essential mechanism for mitochondrial network formation, but also reveal that different parts of the mitochondrial network are formed by different mechanisms.