Biology subjects

Tan, P.

Publications and source records attributed to Tan, P..

At least 19 recordsLinked to original sources

bioRxiv2026

Development of the First Cytochrome Oxidase I Barcode and Evidence for a Single Haplotype Associated with the Recent United States Invasion of the Pasture Mealybug Heliococcus summervillei (Pseudococcidae, Hemiptera)

Heliococcus summervillei is an emerging invasive mealybug that causes severe dieback in grasses in pastures and turfgrass landscapes. It is widespread in Australia and has recently been detected across the Caribbean, Mexico, and the United States. Accurate identification of mealybugs is challenging due to cryptic morphology, overlapping diagnostic characters, and limited taxonomic expertise and literature, which makes molecular tools essential for regulatory diagnostics and management. We developed the first Cytochrome Oxidase I (COI) barcode for H. summervillei and used it to examine mitochondrial variation across available populations. COI sequences reveal approximately a 10.2% mitochondrial split between the Type A and Type B variants. Phylogenetic, haplotype network, and genetic distance analyses show that all invasive range populations share one haplotype associated with a recent invasion in the United States, Australia, Pakistan, and the Caribbean, whereas the Barbados lineage contains two closely related haplotypes that represent a historically stable mitochondrial variant. Together, these results establish the first COI reference library for H. summervillei, clarify mitochondrial lineage structure, and provide a practical barcode tool that enables rapid identification of invasive populations and supports timely regulatory and pest management responses. Recognizing mitochondrial variants also establishes a framework for resolving lineage-specific biological and management traits and strengthens reconstruction of introduction pathways central to regulatory decision-making and limiting further spread.

genetics↗

bioRxiv2026

Deep learning-guided design of hydrolases for crystalline PET depolymerization

Poly(ethylene terephthalate) (PET), a ubiquitous polyester used in packaging and textiles, persists in the environment due to its high crystallinity and stability, contributing substantially to global plastic pollution. Enzymatic depolymerization by PET hydrolase (PETase) offers a chemically precise and environmentally sustainable route to convert PET into its monomeric building blocks, enabling recycling. However, practical implementation remains hindered by the rigid, crystalline architecture of PET, which restricts enzyme access and necessitates energy-intensive pretreatment to enable efficient depolymerization. In addition, most PETases achieve only partial depolymerization with low terephthalic acid (TPA) yields and accumulate inhibitory intermediates, while limited thermostability and slow surface kinetics further restrict efficiency. To overcome these barriers, we report VenusPETase, an engineered variant of KbPETase designed using PET-Flow, a state-of-the-art computational framework for PETase engineering. Compared with KbPETase, VenusPETase exhibits a 3.4-fold increase in hydrolytic activity, up to a 27-fold improvement after heat treatment of protein, and a 12 {degrees}C enhancement in thermostability. VenusPETase exhibits rapid degradation across a wide crystallinity range (8%-50%) at 50 {degrees}C, effectively spanning the entire spectrum of commercial PET products. Moreover, VenusPETase outperformed nine high-performance PETases under their respective optimal conditions and degraded untreated PET substrates across 8%-50% crystallinity, producing TPA as over 95% of the released products with minimal intermediate accumulation. X-ray crystallography and molecular dynamics simulations suggest that dynamic modulation, elevated surface electrostatic potential enhance the interaction of VenusPETase with crystalline PET, thereby lowering the hydrolytic energy barrier and improving catalytic performance. We also demonstrate that untreated, postconsumer-PET from nine different products can all be degraded by VenusPETase. The recovered monomers can be directly repolymerized into virgin-quality PET, demonstrating a closed-loop enzymatic recycling process. In a 100 L bioreactor, VenusPETase completely depolymerizes post-consumer crystalline PET (28% crystallinity) within 24 h under 50 {degrees}C. These results establish VenusPETase as a robust biocatalyst that enables efficient, closed-loop recycling of crystalline PET under mild conditions.

biochemistry↗

bioRxiv2026

Identifying a cooperative catalytic network for efficient esterase catalysis

Active-site redesign frequently yields modest improvements because residues controlling physical steps like substrate binding and product release lie outside the active site. Efficient catalysis requires a cooperative catalytic network of residues that support both the chemical and physical steps of catalysis. Using ancestral hydroxynitrile lyase HNL1, an /{beta}-hydrolase with poor esterase activity, we tested this framework directly. Matching all active-site residues to a proficient esterase improved KM five-fold but left kcat unchanged, confirming that chemical machinery alone is insufficient. Activity-weighted sequence comparison (SigniSite) across ten homologous HNLs and esterases identified 38 positions disfavoring esterase activity. Experimental refinement yielded a minimal set of fifteen substitutions (HNL1-15) with [~]60-fold higher kcat and [~]400-fold higher kcat/KM. Single-substitution reversion analysis confirmed that all fifteen substitutions are essential and provided evidence for strong cooperativity between them. X-ray crystal structures of HNL1 and HNL1-15 reveal three coordinated structural changes: reshaping the substrate-binding pocket to favor productive ester binding, restoring access to the oxyanion hole, and opening an additional tunnel for product egress and water entry. These changes arise through backbone rearrangements and altered flexibility rather than direct active-site contacts, explaining why the responsible positions escape conservation-based detection. Because cooperativity masks individual contributions, engineering such networks may require step-specific assays -- measuring binding, acylation, or product release directly -- rather than screening composite kcat.

biochemistry↗

bioRxiv2026

TIM3+ Tumor Associated M2 Macrophages Impair Antitumor T Cell Immunity and Promote Gastric Cancer Progression and Peritoneal Metastasis

Peritoneal metastases (PM) are the leading cause of cancer-related death in gastric cancer (GC) patients with survival typically < 9 months. Here, we demonstrate that TIM3 and its ligands are increased along the GC continuum and associated with poor survival. Integrated omics analyses and functional studies revealed highly enriched TIM3 in CD163+ tumor associated M2 immunosuppressive macrophages significantly promote tumor cell invasion and tumor growth in vivo, while TIM3 depletion in macrophages reduced tumor cell malignant attributes and increased T cell immunity from PBMCs or CD45+ immune cells of malignant ascites in co-culture system. By cytokine and kinase arrays, we discovered that depletion of TIM3 in macrophages reduced the production of notable secretome of cytokines/chemokines from M2 macrophages; and the protumor function of TIM3+ macrophages rely on the p90RSK1/2/CCL20 axis. Finally, we reveal that TIM3 blockage or genetic KO had superior antitumor activity in combination with anti-PD1 immunotherapy and mitomycin C (MMC) chemotherapy. Together, this study uncovers an important role for TIM3 in tumor associated M2 macrophages and underscores the potential of TIM3 blockage in GC patients with PM. Statement of significanceIn this study, we show TIM3 increases along GC continuum, and highly enriched on tumor associated M2 macrophages that fuel tumor growth; and suppress T cell function via p90RSK1/2/CCL20 axis. TIM3 depletion restores T-cell immunity and curbs tumor growth. TIM3 blockade combined with anti-PD1 and mitomycin C provide a novel therapeutic strategy for GC patients with PM.

immunology↗

bioRxiv2026

Hunting for microsatellite instability in long-read data with Owl

Microsatellite instability (MSI) is a key biomarker of mismatch repair deficiency and response to immunotherapy, yet most existing genomic detection methods are optimized for short-read sequencing and rely on small panels of homopolymer markers, limiting the ability to characterize genome-wide and motif-specific patterns of instability. Here we present Owl, a bioinformatic tool for quantifying MSI from long-read (PacBio) genomic data. Owl leverages a genome-wide marker set of more than 140,000 microsatellite repeats ranging from 1-6 bp in length to measure MSI across a phased genome. Using a wrap-around alignment algorithm, Owl constructs repeat-length distributions at each marker site and flags somatic instability using the coefficient of variation. We applied Owl to screen for markers with stable coverage, phasing, and baseline variation across 131 diverse genomes from the Human Pangenome Reference Consortium, where Owl scores ranged from 1.4% to 5.4% of markers exceeding the instability threshold. When applied to 19 cancer cell lines and one diffuse astrocytoma tumor-normal pair, Owl identified five MSI-high genomes with 15-18% unstable markers and showed close concordance with an Illumina DRAGEN MSI assay for the astrocytoma sample. Motif-level analyses revealed shared enrichment of short homopolymer and dinucleotide (A- and AT-rich) repeats across MSI-high cancers, and additionally uncovered a distinct pattern of elevated GGAA microsatellite instability in Ewing sarcoma cell lines, consistent with the known role of the EWS::FLI1 fusion protein at GGAA-rich regulatory elements. Owl is implemented in Rust and integrated into the PacBio HiFi Somatic workflow, providing a scalable framework for MSI analysis from long-read sequencing focused on repeat instability specifically in tumor samples.

bioinformatics↗

bioRxiv2026

Reaction-Conditioned Enzyme Discovery with Multimodal Deep Learning

The precise mapping between chemical transformations and enzymatic catalysts underpins the complexity of metabolic networks. Conventional discovery methods, tethered to sequence homology or structural alignment, are inherently blind to new reactions. Here we present VenusRXN, a multimodal deep learning framework that shatters this limitation by enabling reaction-conditioned enzyme discovery. By seamlessly unifying a pre-trained reaction encoder with a protein language model, VenusRXN achieves a fine-grained, high-dimensional alignment of chemical and biological representations. On benchmarks to discover enzymes which catalyze reactions not seen in the training dataset, it surpasses state-of-the-art baselines with a top-20 retrieval hit rate of 76.5%. Most critically, we demonstrate VenusRXNs capabilities in a zero-shot discovery. As verified by the wet-lab experiments, it successfully identified enzymes to catalyze the chemical reactions never reported, including the one to catalyze the synthesis route for a type 2 diabetes drug intermediate using a non-natural substrate. With surprising precision, the model pinpointed active candidates within the top 10 sequences directly from a global search space of over 300 million proteins, which can hardly be achieved by structure-based enzyme discovery algorithm. Thus, VenusRXN unlocks the capacity to interrogate the vast, unanno-tated "dark matter" of the protein universe with affordable computational cost for everyone. This work signals a definitive paradigm shift, establishing the chemical reaction itself, rather than homology, as the primary functional descriptor for the de novo discovery of biocatalysts.

synthetic biology↗

bioRxiv2026

In Utero CFTR Modulation Alleviates Disease in G551D Cystic Fibrosis Pigs

Previous studies indicate that pigs with CFTR-null and CFTR-{Delta}F508 mutations develop multiorgan disease similar to that in people with cystic fibrosis (CF). At birth, their airways exhibit host defense defects that predispose to airway infection, inflammation, and mucus accumulation. The CFTR-G551D mutation causes CF by producing CFTR channels that localize correctly but have reduced channel activity. Ivacaftor (VX-770) is a small molecule drug developed to potentiate CFTR activity. To test the phenotype of the CFTR-G551D mutation in pigs and determine whether ivacaftor can rescue CF abnormalities, we developed CFTRG551D/G551D (CF-G551D) pigs through homologous recombination in fetal fibroblasts and somatic cell nuclear transfer. Newborn CF-G551D piglets exhibited phenotypes similar to CF-null piglets, including meconium ileus, exocrine pancreatic destruction, micro-gallbladder, vas deferens destruction, and airway structural abnormalities. Compared to wild-type pigs, CF-G551D pigs had reduced forskolin-stimulated short-circuit current in airway and intestinal tissues. Ivacaftor increased the single-channel open state probability of CFTR-G551D and increased short-circuit current to near wild-type levels. Similar to our other CF pig models, we found that 100% of CF-G551D pigs were born with meconium ileus. To test whether in utero ivacaftor treatment could prevent or alleviate meconium ileus, pregnant sows were treated with ivacaftor beginning at day 35 of gestation and continuing until delivery. This treatment rescued the pancreas, gallbladder, and vas deferens phenotype in the majority of CF-G551D pigs. Animals that were spared from meconium ileus were able to survive without ivacaftor treatment. Airway disease developed similar to other CF pig models. These findings indicate that this model may be useful for studies in which CFTR function can be reversed, for investigating in utero CFTR correction strategies, and for longitudinal studies in CF pigs.

physiology↗

bioRxiv2025

High-throughput plasma proteomic platforms: Insights from a multi-ethnic Asian cohort

We compared three leading affinity-based and mass-spectrometry-based proteomic platforms (SomaScan11K, Olink Explore HT, Orbitrap Astral with Seer Proteograph [MS-Seer]) in a multi-ethnic Asian cohort, to inform biomarker discovery and functional genomic studies of global populations. We found limited overlap (1,740 proteins) across the three platforms, with modest correlations (0.34-0.10). SomaScan had lower missingness (<1%) and CV (<10%), compared to Olink (51%, 23%) and MS-Seer (12%, 19%). The new assays in Olink Explore HT (absent in Explore3072) primarily drove its higher missingness and CV. The number of phenotypic associations varied by trait, while the number of genetic associations (cis-pQTLs at P<5e-08) were similar for Olink and SomaScan. Protein levels differed between ethnicities, with SomaScan identifying more ethnicity-differentiated proteins than Olink (FDR<0.05). Finally, SomaScan ANML normalization attenuated biologically relevant associations in our study. These findings underscore the importance of platform evaluation and data normalization strategies for application in large-scale, diverse population cohorts.

bioinformatics↗

bioRxiv2025

Gene-Family Encoding Boosts Domain-Adapted Single-Cell Language Models

Transformer-based single-cell foundation models often rely on ranked-gene (RG) sequences where genes, ranked by expression, are often not functionally related, weakening next-token learning and the structure of learned embeddings. Here, we introduce gene-family (GF) encoding, where expressed genes are grouped into functionally defined families, and ranking performed within each family. Using 100,000 gastric-cancer (GC) cells, we domain-adapted 8-billion-parameter Llama and Qwen backbones with either RG or GF sentences and benchmarked zero-shot performance on embedding-based and generative tasks. GF models outperformed RG models across both tasks and backbones. We scaled GF-Llama to 1.3 million GC cells to obtain GF-Llama-GC and applied it to two applications: resolving fine-grained cellular heterogeneity and discovering cell populations associated with disease progression. GF-Llama-GC revealed immune-cell subclusters not resolved by standard expression-based analyses. Applying in-silico cell removal/transplantation on a chemotherapy responder/non-responder scRNA-seq dataset, GF-Llama-GC highlighted not only epithelial cells but also neutrophils as key cells associated with chemotherapy response.

bioinformatics↗

bioRxiv2025

A Complete Telomere-to-Telomere Diploid Reference Genome for Indian Population

Human reference genomes have been instrumental in advancing genomic and biomedical research, but South and Southeast Asian populations are underrepresented, despite accounting for a large proportion of world population. As a part of effort on generating reference genomes for these populations, we present the first gapless, telomere-to-telomere (T2T) diploid genome assembly created by using a trio sample set of Indian ancestry (I002C), with NG50 of 154.89 Mb and 146.27 Mb for the maternal and paternal haplotypes, including the fully assembled rDNA array for the maternal chromosome 21 and Y chromosome. With the Merqury QVs of 82.05, 83.08 and 82.64 for the maternal, paternal and haploid assemblies respectively, I002C represents the highest-quality human genome assembled in both diploid and haploid forms to date. Compared to CHM13, the I002C genome displays substantial sequence diversity, resulting in 14,943 structural variants, including 3,236 novel variants absent from public databases. Analysis of trio-phased haplotypes further revealed elevated inter-haplotype divergence within centromeric and subtelomeric regions, along with identification of differentially methylated regions (DMRs) as candidates for novel imprinting loci. As a result of substantial SVs between them, I002C is a more suitable reference than CHM13 for the genomic analysis of South Asian samples with less reference bias and better performance in mapping and variant calling, particularly for long read sequencing data. As the first high-quality T2T diploid reference genome for Indian, the largest worlds population, I002C contributes to the growing set of population-specific reference genomes and helps to overcome a significant gap in human genome diversity.

genomics↗

bioRxiv2025

Single-Cell Pharmacogenomic Landscapes of Epigenetic Therapy in Gastric Cancer

Gene expression signatures ("molecular phenotypes") are extensively utilized in cancer research. To study how gastric cancer (GC) molecular phenotypes are shaped by cell-intrinsic genetic alterations interacting with cell-extrinsic therapeutic pressures, we performed direct capture Perturb-seq (dcPerturb-seq) to interrogate >200 GC-related genes across 4 distinct epigenetic drug classes in multiple gastric lines. We captured 17.7 million pharmocogenomic expression interactions in 625,866 cells representing baseline and post-therapeutic molecular phenotypes. This single-cell pharmacogenomic compendium confirmed previously known gene-driven molecular phenotypes, elucidated poorly characterized genes, and uncovered novel gene dosage-molecular phenotype relationships. Molecular phenotypes in post-therapeutic surviving cells revealed diverse gene perturbation-associated pathways causing convergent drug resistance (EMT plasticity, cell cycle alterations, metabolic reprogramming), highlighting combinatorial strategies for restoring sensitivity. Mapping of in vitro molecular phenotypes to primary human GCs imparted prognostic information and insights into spatial heterogeneity. Comparative analysis of gene perturbations across therapies and lines revealed both conserved and context-specific molecular alterations. Our results illustrate how Perturb-seq approaches can systematically map diverse cancer-associated molecular phenotypes across multiple gene/drug/cell line interactions, yielding translational insights.

cancer biology↗

bioRxiv2025

A Computational Framework for Analysis of cfDNA Fragmentation Profiles

Circulating cell-free DNA (cfDNA) has emerged as a promising non-invasive medium for studying tumor molecular profiles. Non-random fragmentation patterns in plasma cfDNA, particularly around nucleosome-depleted regions (NDRs) near transcription start sites (TSS), have been shown to reflect epigenetic regulation and gene expression. In this study, coverage profiles of the NDR were utilized to derive an NDR score, which was subsequently used as a proxy for inferring gene expression. To reduce transcript-to-transcript variability and enhance the clarity of these expression-associated signals, we implement a method for GC-bias correction of cfDNA samples. A computational framework (NDRDiff) was then developed to enable comparative analyses of NDR score profiles across different sample groups. The GC-bias correction preserved the overall trend of the NDR signal while improving the separation of gene expression levels, as demonstrated by comparisons of healthy donor cfDNA samples with matched blood RNA-seq data. Validation on a simulated dataset showed that NDRDiff achieved an area under the precision-recall curve (AUPRC) of 0.916, outperforming a standard t-test (AUPRC of 0.777). When applied to a comparison of healthy donor cfDNA and metastatic colorectal cancer (mCRC) cfDNA, NDRDiff identified 531 differential NDR score (DNS) genes that facilitated clear separation between the two groups. These DNS genes were found to correlate with tumor fraction estimates (down-regulated DNS genes: Pearson R = 0.89, p < 0.05; up-regulated DNS genes: Pearson R = -0.88, p < 0.05) and included CLDN4, BIN2, and IRAG2, which exhibit strong associations with colorectal cancer or blood cell expression signatures. Gene set enrichment analysis further revealed enrichment of colon and other gastrointestinal tissue signatures. Collectively, these findings underscore the potential of NDR-based cfDNA analysis as a minimally invasive tool for monitoring tumor-related molecular features in cancer.

cancer biology↗

bioRxiv2025

Crystal structures of forty- and seventy-one-substitution variants of hydroxynitrile lyase from rubber tree

The /{beta}-hydrolase fold family contains mostly esterases but includes other enzymes such as hydroxynitrile lyase from Hevea brasiliensis (rubber tree, HbHNL). HbHNL shares 44% sequence identity and a Ser-His-Asp catalytic triad with esterase SABP2 (salicylic acid binding protein 2 from Nicotiana tabacum (tobacco)). To identify how large a region within HbHNL influences the positions of the catalytic residues, we created variants where increasingly large regions surrounding the substrate-binding site had identical amino acid sequences to those in SABP2. Variant HNL40 contains 40 mutations (two inserted amino acid residues, 38 substitutions), shares 59% sequence identity with SABP2, and is identical in sequence to SABP2 within 10 [A] of the substrate-binding site. Variant HNL71 contains 31 additional substitutions for a total of 71 changes (two insertions, 69 substitutions) and shares 71% sequence identity with SABP2. The sequences within 14 [A] of the substrate-binding site are identical in SABP2 and HNL71. The crystal structures of HNL40 and HNL71 show that the positions of main chain C[a] atoms move from their positions in HbHNL to more closely match those in SABP2 (RMSD = 0.51 [A] over 235 C[a] atoms for HNL40, 0.41 [A] over 219 C[a] atoms for HNL71) and even more closely in the region within 10 [A] of the substrate-binding site (RMSD = 0.38 [A] over 58 C[a] atoms for HNL40, 0.28 [A] over 53 C[a] atoms for HNL71). The pattern of tunnels in HNL40 and HNL71 are similar to each other and intermediate between the pattern in HbHNL and SABP2. SynopsisVariants HNL40 and HNL71 of hydroxynitrile lyase from Hevea brasiliensis contain 40 and 71 mutations, respectively, to make regions surrounding the substrate-binding site identical in sequence to esterase SABP2. X-ray structures reveal increasing similarities to SABP2 in HNL40 and HNL71. PDB reference: hydroxynitrile lyase from Hevea brasiliensis with forty mutations, 8SNI, hydroxynitrile lyase from Hevea brasiliensis with seventy-one mutations, 9CLR

biochemistry↗

bioRxiv2025

National Scale Genomic Engine for Precision Medicine: Singapore PRECISE-SG100K Experience

The whole-of-nation National Precision Medicine (NPM) programme in Singapore aims to integrate a genomics backbone into standard healthcare. Phase II recently completed the sequencing of 102,202 genomes from the diverse Asian population of Singapore, generating the PRECISE-SG100K dataset. The scaling from the pilot SG10K_Health phase to national production scale demanded a central shift: from earlier generation workflows to an integrated, industrial grade "Genomic Engine". The initiative was co-developed under a public-private partnership model. Our Genomic Engine was designed with an automation-first approach, as a modular platform linking a purpose-built, centralized biospecimen processing and DNA extraction facility (EXTRACT Lab), a high-throughput sequencing operation (Illumina Data Generation Infrastructure, IDGI), with the cloud-native analytics (GRIDS) running on a fully integrated PRECISE Sample Tracking System (STS). Here we describe the key design features of our Genomic Engine. Highlighting the Stress Test phase where the automated DNA extraction yields failed to meet expectations. We share the operational adjustments which were implemented to stabilize production and resulted in a three-fold reduction in sample triage due to improved DNA recovery. Without establishing the operational guardrails during the Stress Test, the Engine would not have reached a steady-state production evidenced by >3,000 genomes per month, median turnaround time 11 days, 99.6% operational success, within in six months. This framework offers a practical blueprint for other population-scale initiatives seeking to bridge the gap between scientific piloting and industrial delivery.

genomics↗

bioRxiv2025

Unveiling Protein-DNA Interdependency: Harnessing Unified Multimodal Sequence Modeling, Understanding and Generation

The interdependent orchestration of proteins, coding DNA sequences (CDS), and adjacent regulatory DNA elements underpins biological functionality, where evolutionary constraints shape codon usage, transcriptional regulation, and structural stability. We present ProDMM, a multimodal framework that deciphers these systemic relationships through unified sequence modeling. Integrating a context-aware encoder with conditional generation, our architecture captures codependent patterns between protein motifs and cis-regulatory syntax. ProDMMs encoder demonstrates superior predictive capabilities in forecasting pathway-level phenotypes and protein expression profiles, outperforming conventional single-modality approaches. Its decoder generates synthetically optimized regulatory elements by resolving spatial dependencies between coding sequences and adjacent non-coding regions, enabling transcriptional fine-tuning unavailable to sequence-agnostic methods. By resolving how localized CDS-NCDS-protein interdependencies propagate to system-level phenotypes, ProDMM establishes a paradigm for holistic biological sequence engineering. This approach enables coordinated optimization of translation efficiency, enzymatic activity, and pathway flux, offering transformative potential for designing tailored biosynthesis systems and advancing sustainable bioproduction strategies.

bioinformatics↗

bioRxiv2024

Harnessing Protein Language Model for Structure-Based Discovery of Highly Efficient and Robust PET Hydrolases

Plastic waste, particularly polyethylene terephthalate (PET), presents significant environmental challenges, prompting extensive research into enzymatic biodegradation. Existing PET hydrolases are limited to a narrow sequence space and demonstrate insufficient performance for biodegradation. This study introduces a novel discovery pipeline that combines protein language models (PLMs) with a structural representation tree to identify enzymes based on structural similarity. Using the crystal structure of IsPETase as a template, we employed PLMs and a representation tree to efficiently search and cluster target proteins. Screening of candidate proteins was further refined using PLM-based assessments of solubility and thermal stability. Biochemical experiments showed that 14 of 34 candidates exhibited PET degradation activity across a temperature range of 30-60 {degrees}C. Notably, we identified a PET hydrolase -KbPETase, which possesses a melting temperature 32 {degrees}C higher than that of IsPETase and exhibits the highest PET degradation activity within 30-50 {degrees}C compared to other wild-type PETases. KbPETase also shows higher catalytic efficiency than FastPETase. X-ray crystallography and molecular dynamics simulations further revealed that KbPETase has a conserved catalytic domain and enhanced intramolecular interactions. This work develops a novel deep learning approach to discover natural PETases with enhanced functions.

molecular biology↗

bioRxiv2024

Reinforced identity confers cells with cancer hallmarks

Genetic alterations trigger tumorigenesis in a cell type-specific manner, but the biological basis for this, remains unclear. Here we define reinforced original cell identity established by interaction between genetic alterations and original cell type is a pivotal determinant of tumor development. Using AML-M5MLL-AF9 as a model system, we demonstrated that MLL-AF9 enhanced key transcriptional factors (TFs) expression of promonocyte and cooperated with them to amplify their downstream programs for AML hallmarks. Interactions between MLL-AF9 and promonocyte endowed cancer cells with a reinforced promonocyte identity. Reinforced intrinsic cell identity also sustained cancer hallmarks across cancer types. Furthermore, we identified narciclasine, as an effective disruptor of reinforced promonocyte identity, effectively treating AML-M5MLL-AF9. This study not only identified reinforced cell identity as a fundamental tumor driver beyond genetic mutations but also offers a proof-of-concept for reinforced identity- targeted cancer therapy.

cancer biology↗

bioRxiv2024

AI-enabled Alkaline-resistant Evolution of Protein to Apply in Mass Production

Artificial intelligence (AI) models have been used to study the compositional regularities of proteins in nature, enabling it to assist in protein design to improve the efficiency of protein engineering and reduce manufacturing cost. However, in industrial settings, proteins are often required to work in extreme environments where they are relatively scarce or even non-existent in nature. Since such proteins are almost absent in the training datasets, it is uncertain whether AI model possesses the capability of evolving the protein to adapt extreme conditions. Antibodies are crucial components of affinity chromatography, and they are hoped to remain active at the extreme environments where most proteins cannot tolerate. In this study, we applied an advanced large language model (LLM), the Pro-PRIME model, to improve the alkali resistance of a representative antibody, a VHH antibody capable of binding to growth hormone. Through two rounds of design, we ensured that the selected mutant has enhanced functionality, including higher thermal stability, extreme pH resistance and stronger affinity, thereby validating the generalized capability of the LLM in meeting specific demands. To the best of our knowledge, this is the first LLM-designed protein product, which is successfully applied in mass production.

bioinformatics↗