Biology subjects

Ma, J.

Publications and source records attributed to Ma, J..

At least 163 records · Page 9Linked to original sources

bioRxiv2024

BMP4 is sufficient to induce a caudal organizer

The caudal part of a vertebrate embryo consists of somites, neural tube, lateral plate mesoderm derivatives and the tailbud. However, the minimal conditions and factors sufficient to induce the caudal region, particularly in humans, remain unresolved. Here, we show that BMP4 alone, when administered at appropriate dosage, is sufficient to induce the formation of an organizer for caudal induction. This organizer induces caudal cell fate specification and morphogenesis in zebrafish embryos. In 3D human pluripotent stem cells (hPSCs) aggregates, BMP4 can induce an elongated embryonic structure characterized by caudal fates. Importantly, hPSCs instructed by BMP4 are sufficient to induce a secondary caudal region when grafted into the animal pole of the zebrafish embryo. Our study thus uncovers BMP4 as the inducer in the formation of a caudal organizer in the vertebrate embryo. Significance statementWe demonstrate that BMP4 alone can induce the formation of a caudal organizer, a critical structure that guides the development of the caudal region in vertebrates. Using zebrafish embryos, embryonic explants, human pluripotent stem cell xenografts, and 3D human stem cell aggregates, we show that this organizer replicates morphogenesis and key differentiation pathways seen in natural development. Our findings uncover a novel role for BMP4 and define the minimal requirements for inducing this organizer, offering new insights into vertebrate development and potential applications in regenerative medicine.

developmental biology↗

bioRxiv2024

An adaptive autoregressive diffusion approach to design active humanized antibody and nanobody

Humanization is a critical process for designing efficiently specific antibodies and nanobodies prior to clinical trials. Developing widely recognized deep learning techniques or frameworks for humanizing conventional antibodies and nanobodies presents a valuable yet challenging task. Inspired by the effectiveness of diffusion models across various applications, we introduce HuDiff, an adaptive diffusion approach for humanizing antibodies and nanobodies from scratch, referred to as HuDiff-Ab and HuDiff-Nb, respectively. This approach begins the humanization process exclusively with complementarity-determining region (CDR) sequences, eliminating the need for humanized templates. On public benchmark datasets, the results of HuDiff-Abs humanized antibodies are more similar to experimentally humanized antibodies than to those of the Sapiens humanization model. Besides, humanized nanobodies produced by HuDiff-Nb exhibit a higher humanness score and greater nativeness than those generated by the Lammanade pipeline for humanization nanobody. We apply HuDiff to humanize a mouse antibody and an alpaca nanobody, both targeting the SARS-CoV-2 RBD, and validate the binding affinity of humanized sequences through Bio-Layer Interferometry (BLI) experiments. The results show the binding affinity of the best humanized antibody is nearly equal to that of the parental mouse antibody (0.15 nM vs. 0.12 nM). Remarkably, the top-performing humanized nanobody exhibits a significantly enhanced binding affinity compared to the parental alpaca nanobody (2.52 nM vs. 5.47 nM), representing a 54% increase. These findings indicate that our approach HuDiff is highly effective in enhancing the humanness of antibodies and nanobodies while maintaining or potentially improving the binding affinity of the designed humanized sequences. The code and checkpoints of HuDiff are available at https://github.com/TencentAI4S/HuDiff.

bioinformatics↗

bioRxiv2024

The E3 ligase NEDD4L prevents colorectal cancer liver metastasis via degradation of PRMT5 to inhibit the AKT/mTOR signaling pathway

Colorectal cancer is the second most common cause of cancer mortality worldwide, and liver metastasis is the major cause of death in patients with colorectal cancer. Dysfunctional E3 ligase activity has recently been shown to be associated with colorectal cancer. However, the key E3 ligase affecting colorectal cancer liver metastasis remains unknown. Therefore, we used an shRNA library targeting 156 E3 ubiquitin ligases to perform an in vivo loss-of-function screen of a human colorectal cancer cell line in a mouse model of colorectal cancer liver metastasis. The screen revealed that NEDD4L knockdown promoted colorectal cancer liver metastasis. Moreover, overexpression of NEDD4L prevented colorectal cancer liver metastasis. Mechanistic studies revealed that NEDD4L bound to the PPNAY motif in PRMT5 and ubiquitinated PRMT5 to promote its degradation. PRMT5 degradation attenuated the methylation of an arginine residue in AKT1 to inhibit the AKT/mTOR signaling pathway, consequently decreasing colorectal cancer cell proliferation. We are the first to show that PRMT5 is a substrate protein of the E3 ligase NEDD4L and reveals not only the metastasis-inhibiting function of NEDD4L but also a novel mechanism by which NEDD4L suppresses colorectal cancer liver metastasis. These findings may provide a new preventive strategy for liver metastasis.

cancer biology↗

bioRxiv2024

Rag2Mol: Structure-based drug design based on Retrieval Augmented Generation

Artificial intelligence (AI) has brought tremendous progress to drug discovery, yet identifying hit and lead compounds with optimal physicochemical and pharmacological properties remains a significant challenge. Structure-based drug design (SBDD) has emerged as a promising paradigm, but the inherent data biases and ignorance of synthetic accessibility render SBDD models disconnected from practical drug discovery. In this work, we explore two methodologies, Rag2Mol-G and Rag2Mol-R, both based on retrieval-augmented generation (RAG) to design small molecules to fit a 3D pocket. These two methods involve searching for similar small molecules that are purchasable in the database based on the generated ones, or creating new molecules from those in the database that can fit into a 3D pocket. Experimental results demonstrate that Rag2Mol methods consistently produce drug candidates with superior binding affinities and drug-likeness. We find that Rag2Mol-R provides a broader coverage of the chemical landscapes and more precise targeting capability than advanced virtual screening models. Notably, both workflows identified promising inhibitors for the challenging target PTPN2, which was considered undruggable because of structural characteristics of phosphatases. Our highly extensible framework can integrate diverse SBDD methods, marking a significant advancement in AI-driven SBDD. The codes are available at: https://github.com/CQ-zhang-2016/Rag2Mol.

bioinformatics↗

bioRxiv2024

Decipher Fundamental Atomic Interactions to Unify Generative Molecular Docking and Design

Understanding molecular interactions is essential to structural biology and drug discovery. Despite the progress of AI models in revealing and exploiting the interaction mechanisms for various applications, they are predominantly tailored to specific tasks without fully exploiting the underlying transferability across molecular data and tasks. Here, we present PocketXMol, an atom-level generative foundation model to decipher fundamental atomic interactions for general protein-pocket-interacting molecular tasks. It adopts a novel unified generative framework with an innovative task prompt mechanism and an exclusive atom-level representation, making it applicable to diverse tasks covering structure prediction and design of small molecules and peptides, without requiring fine-tuning. PocketXMol was compared to 55 baseline models across 13 typical tasks, achieving state-of-the-art performance in 11 tasks and remaining competitive in the others. We successfully utilized PocketXMol to design novel small molecules that inhibit caspase-9 with efficacy comparable to that of commercial pan-caspase inhibitors. Furthermore, we employed PocketXMol to design PD-L1-binding peptides, demonstrating a success rate substantially higher than random library screening. Three representative peptides underwent further experiments, which validated the cellular specificity and confirmed their potential for molecular probing and therapeutics. PocketXMol presents a powerful and versatile tool with promising prospects for future applications and will have a profound impact on AI-aided drug discovery.

bioinformatics↗

bioRxiv2024

Weekly inhaled salmeterol xinafoate, a selective β2 adrenergic receptor agonist drug, therapeutically inhibits cancer growth and prolongs survival in vivo

Humans have spent approximately 99.5% of the 2.5 million years of evolutionary history as hunter-gatherers, which helped to evolutionarily mold our minds and bodies to the hunting and gathering lifestyle. Physical activity associated with hunting animals, gathering plants, and evading predators is essential for human survival, evolution, and prosperity. Thus, physical activity (acute stress) is mostly beneficial to the body, whereas psycho-emotional stress (chronic stress) is usually detrimental at the psychological, physiological, and pathological levels. Although physical activity exerts anticancer effects and psycho-emotional stress exhibits cancer-promoting effects, both function through the same pathway involving activation of the sympathetic nervous system, release of catecholamines, and stimulation of {beta}2 adrenergic receptor ({beta}2AR) expressed on cancer cells. Here, salmeterol xinafoate was chosen to mimic physical activity. A once-weekly administration of salmeterol xinafoate by the intraperitoneal, inhalation, or transdermal route, dose-dependently extended latency, reduced cancer incidence and growth, and prolonged survival in vivo. Remarkably, the best complete response rates were 75%, 30%, and 71.43% for the therapeutic treatment of lung cancer, breast cancer, and melanoma, respectively. Notably, the salmeterol xinafoate doses used in our experiments are comparable to those used in patients. Moreover, in endogenous {beta}2AR knockout cancer cells, the exogenous {beta}2AR mutant with impaired salmeterol xinafoate binding or downstream signaling prevented the anticancer effects of salmeterol xinafoate in vivo, demonstrating the on-target action of salmeterol xinafoate on cancer cells and the requirement of the downstream signaling pathway. Finally, we showed that treatment with salmeterol xinafoate dose-dependently decreased the self-renewal capacity of lung cancer stem cells, breast cancer stem cells, and melanoma stem cells. From the perspective of human evolution into cancer biology, our study reveals that {beta}2AR represents an ideal anticancer drug target and that salmeterol xinafoate monotherapy has strong anticancer activity.

cancer biology↗

bioRxiv2024

Synthesis and characterization of chloroquine-modified albumin-binding siRNA-lipid conjugates for improved intracellular delivery and gene silencing in cancer cells

siRNA therapeutics have considerable potential as molecularly-targeted therapeutics in malignant disease, but identification of effective delivery strategies that mediate rapid intracellular delivery while minimizing toxicity has been challenging. Our group recently developed and optimized an siRNA conjugate platform termed "siRNA-L2," which harnesses non-covalent association with endogenous circulating albumin to extend circulation half-life and achieve tumor-selective delivery without the use of traditional cationic lipids or polymers for transfection. To improve intracellular delivery and particularly the endosomal escape properties of siRNA-L2 towards more efficient gene silencing, we report synthesis of siRNA-CQ-L2 conjugates, in which chloroquine (CQ), an endosomolytic quinoline alkaloid, is covalently incorporated into the branching lipid tail structure. We accomplished this via synthesis of a novel CQ phosphoramidite, which can be incorporated into a modular siRNA-L2 backbone using on-column solid-phase synthesis through use of asymmetric branchers with levulinyl-protected hydroxide groups that allow covalent addition of pendant CQ repeats. We demonstrate that siRNA-CQ-L2 maintains the ability to non-covalently bind albumin, and with multiple copies of CQ, siRNA-CQ-L2 mediates higher endosomal disruption, cellular uptake/retention, and reporter gene knockdown in cancer cells. Further, in mice, the addition of CQ did not significantly affect circulation kinetics nor organ biodistribution and did not produce hematologic or organ-level toxicity. Thus, controlled, multivalent conjugation of albumin-binding siRNA-L2 to endosomolytic small molecule compounds holds promise in improving siRNA-L2 knockdown potency while maintaining albumin-binding properties and overall safety.

bioengineering↗

bioRxiv2024

The utility of single-cell RNA sequencing data in predicting plant metabolic pathway genes

It is an ever challenging task to make genome-wide predictions for plant metabolic pathway genes (MPGs) encoding enzymes that catalyze the biosynthesis of plant natural products. Here, starting from 1,129 benchmark MPGs that have experimental evidence in Arabidopsis thaliana, we investigate the utilities of single-cell RNA sequencing (scRNA-seq) data--a recently arisen omics data that has been used in several other fields--in predicting MPGs using five machine learning (ML) algorithms that support multi-label tasks. Compared with traditional bulk RNA-seq data, scRNA-seq data lead to different but comparable co-expression networks among MPGs within metabolic classes, and significantly higher prediction accuracy of MPGs into classes. Prediction accuracy for individual metabolic classes is not associated with the co-expression network tightness, but correlated with the number of MPGs within each class, indicating that including more benchmark genes in the future will improve the MPG prediction. Splitting the RNA-seq data into genetic background/condition or tissue-specific subsets can improve the gene co-expression network tightness and MPG prediction accuracy for some classes; scRNA-seq-based models still outperform bulk RNA-seq-based models for most classes when corresponding subsets are used. In addition, deep learning approaches outperform classical machine learning approaches; approaches implemented in an ensembled workflow AutoGluon tend to have severe overfitting issues potentially due to the relative scarcity of benchmark MPGs within classes. Our results demonstrate the superiority of scRNA-seq data over bulk RNA-seq data in predicting MPGs into metabolic classes, and propose that scRNA-seq data should be included in the future to advance the identification of plant MPGs.

plant biology↗

bioRxiv2024

Endogenous Huntingtin aggregates are a huge organized scaffold and mold for Golgi apparatus impaired by mutant Huntingtin protein

Abstract Huntingtin (HTT) is a naturally aggregating protein that causes Huntington's disease (HD) when its polyglutamine (polyQ) tract exceeds 38 repeats. Despite its importance, the biology of HTT aggregates remains poorly defined. Here, using high resolution imaging of cells derived from HD families, we redefine polyQ assemblies, traditionally regarded as pathogenic aggregates, as dynamic and structurally organized intracellular compartments resembling knitted-fabric patches. These assemblies closely associate with and encircle the Golgi apparatus, integrating Golgi ribbons and stacks to form a previously unrecognized polyQ assembly-Golgi complex. Mechanistically, fragmentation of polyQ assemblies is dynamically coupled with mitotic Golgi fragmentation, whereas inhibition of ADP-ribosylation factor (ARF) function by Brefeldin A disrupts and fragments the complex, revealing an intimate structural and functional coupling between polyQ assemblies and Golgi architecture. The presence of mutant HTT (mHTT) destabilizes these assemblies and complexes, altering their response to nutrient deprivation and autophagy enhancers but not to antisense oligonucleotide (ASO) therapy. Functionally, polyQ assemblies in HD cells compromise the scaffolding capacity of the Golgi apparatus, clathrin-coated vesicles, and ARF1, impair Golgi glycosylation, and promote aberrant neuronal firing activity. Collectively, these findings establish polyQ assemblies as dynamic structural regulators of Golgi organization and function and demonstrate that mHTT disrupts the homeostatic dynamics of the polyQ assembly-Golgi complex, leading to Golgipathy.

pathology↗

bioRxiv2024

Simultaneous observation of Pol II and potato spindle tuber viroid RNA in the nucleus by combining immunofluorescence and RNA in situ hybridization

RNA is key component of a versatile group of complexes that play diverse roles in a wide range of biological processes, including plant acclimation to ever-changing environments. From synthesis to degradation, RNA molecules interact with cognate proteins that assist in processes such as transcription, splicing, modification, trafficking, and the execution of their functions. While numerous valuable techniques exist to study RNA-protein interactions, visualizing RNAs and their associated proteins simultaneously within cells remains challenging, despite its potential to provide deeper insights into the biology of RNA-protein interactions. In this study, we adapted a modified immunofluorescence (IF) assay in combination with RNA fluorescence in situ hybridization (FISH) to successfully visualize the colocalization of endogenous as well as infectious RNAs with their cognate binding proteins in plant nucleus. This new method that combines IF and FISH will facilitate future studies on RNA and protein colocalization in various plant systems.

plant biology↗

bioRxiv2024

OPUS-DSD2: Disentangling Dynamics and Compositional Heterogeneity for Cryo-EM Single Particle Analysis

Cryo-electron microscopy and tomography (cryo-EM/ET) capture structural heterogeneities in macromolecules, ranging from dynamic motions to compositional changes-- key to understanding biological mechanisms. While the deep learning framework, OPUS-DSD, advanced heterogeneity analysis for cryo-EM, it conflates different types of heterogeneities, and remains incompatible with cryo-ET which can elucidate macromolecular functions in their native cellular environments. Here, we present OPUS-DSD2, a unified framework for disentangling structural heterogeneity in both cryo-EM and cryo-ET data. OPUS-DSD2 augments a 3D convolutional neural network with a multi-layer perceptron based rigid-body dynamics model, advocating the separation of subunit level rigid-body dynamics from other heterogeneities. Tests on real datasets demonstrate that OPUS-DSD2 effectively captures large-scale subunit motions while isolating spatially localized structural variations into distinct principal components of the composition latent space. Critically, OPUS-DSD2 enables direct analysis of noisy cryo-ET template-matching results, bypassing labor-intensive subtomogram classification and averaging and unlocking high-throughput visual proteomics. Backward-compatible with OPUS-DSD, OPUS-DSD2 is available at https://github.com/alncat/opusDSD.

biophysics↗

bioRxiv2024

YTHDF1 Facilitates Lung Adenocarcinoma Progression via Promotion of EEF1G Translation in a m6A-Dependent Manner

Lung adenocarcinoma (LUAD) is a malignant tumor with high morbidity and mortality worldwide, and overall survival rates for LUAD patients remain unimproved. RNA modification is a key process in post-transcriptional gene regulation in epigenetics, with N6-methyladenosine (m6A) being a common RNA modification. The molecular mechanisms of LUAD are unclear, but evidence suggests that m6A RNA methylation plays a significant role. This study aimed to clarify the role of YTHDF1 in LUAD development and pathogenesis. These findings confirmed that YTHDF1, a m6A reader protein, is highly expressed in LUAD tissues and is correlated with tumor differentiation and TNM stage. The results of functional loss experiments in LUAD cell lines revealed that downregulating YTHDF1 inhibits proliferation, migration, and invasion and induces apoptosis, with opposite effects observed upon YTHDF1 upregulation. In vivo, YTHDF1 knockout suppressed LUAD xenograft growth. RNA-seq, MeRIP-seq, RIP-seq, and bioinformatics analyses identified EEF1G as a downstream target of YTHDF1 in LUAD, and high expression of EEF1G was confirmed. The interaction between YTHDF1 and EEF1G was validated through RIP-qPCR, Co-IP and Co-IF assays. The overexpression of EEF1G in LUAD cells partially counteracts the tumor suppression induced by YTHDF1 silencing, and the knockdown of EEF1G has the opposite effect, further confirming the regulatory relationship. In summary, this study describes a novel YTHDF1/EEF1G regulatory pathway in which YTHDF1 promotes LUAD progression by recognizing and binding to the m6A-modified mRNA of EEF1G, accelerating its translation, suggesting that YTHDF1 may be a potential biomarker and therapeutic target.

cancer biology↗

bioRxiv2024

Escaping the drug-bias trap: using debiasing design to improve interpretability and generalization of drug-target interaction prediction

Considering the high cost associated with determining reaction affinities through in-vitro experiments, virtual screening of potential drugs bound with specific protein pockets from vast compounds is critical in AI-assisted drug discovery. Deep-leaning approaches have been proposed for Drug-Target Interaction (DTI) prediction. However, they have shown overestimated accuracy because of the drug-bias trap, a challenge that results from excessive reliance on the drug branch in the traditional drug-protein dual-branch network approach. This casts doubt on the interpretability and generalizability of existing Drug-Target Interaction (DTI) models. Therefore, we introduce UdanDTI, an innovative deep-learning architecture designed specifically for predicting drug-protein interactions. UdanDTI applies an unbalanced dual-branch system and an attentive aggregation module to enhance interpretability from a biological perspective. Across various public datasets, UdanDTI demonstrates outstanding performance, outperforming state-of-the-art models under in-domain, cross-domain, and structural interpretability settings. Notably, it demonstrates exceptional accuracy in predicting drug responses of two crucial subgroups of Epidermal Growth Factor Receptor (EGFR) mutations associated with non-small cell lung cancer, consistent with experimental results. Meanwhile, UdanDTI could complement the advanced molecular docking software DiffDock. The codes and datasets of UdanDTI are available at https://github.com/CQ-zhang-2016/UdanDTI.

bioinformatics↗

bioRxiv2024

ProtRNA: A Protein-derived RNA Language Model by Cross-Modality Transfer Learning

Protein language models (PLM), such as the highly successful ESM-2, have proven particularly effective. However, language models designed for RNA continue to face challenges. A key question is: can the information derived from PLMs be harnessed and transferred to RNA? To investigate this, a model termed ProtRNA has been developed by cross-modality transfer learning strategy for addressing the challenges posed by RNAs limited and less conserved sequences. By leveraging the evolutionary and physicochemical information encoded in protein sequences, the ESM-2 model is adapted to processing "low-resource" RNA sequence data. The results show comparable or superior performance in various RNA downstream tasks, with only 1/8 the trainable parameters and 1/6 the training data employed by the primary reference baseline RNA language model. This approach highlights the potential of cross-modality transfer learning in biological language models.

bioinformatics↗

bioRxiv2024

Biomolecular condensation of ERC1 recruits ATG8 and NBR1 to drive autophagosome formation for plant heat tolerance

Macroautophagy (hereafter autophagy) is essential for cells to respond to nutrient stress by delivering cytosolic contents to vacuoles for degradation via the formation of a multi-layer vesicle named autophagosome. A set of autophagy-related (ATG) regulators are recruited to the phagophore assembly site for the initiation of phagophore, as well as its expansion and closure and subsequent delivery into the vacuole. However, it remains elusive that how the phagophore assembly is regulated under different stress conditions. Here, we described an unknown Arabidopsis (Arabidopsis thaliana) cytosolic ATG8-interaction protein family (ERC1/2), that binds ATG8 and NBR1 to promote autophagy. ERC1 proteins translocate to the phagophore membrane and develop into classical ring-like autophagosomes upon autophagic induction. However, ERC1 proteins form large droplets together with ATG8e proteins when in the absence of ATG8 lipidation activity. We described the property of these structures as phase-separated membraneless condensates by solving the in vivo organization with spatial and temporal resolution. Moreover, ERC1 condensates elicits a strong recruitment of the autophagic receptor NBR1. Loss of ERC1 suppressed NBR1 turnover and attenuated plant tolerance to heat stress condition. This work provides novel insights into the mechanical principle of phagophore initiation via an unreported ERC1-mediated biomolecular condensation for heat tolerance in Arabidopsis.

plant biology↗

bioRxiv2024

Effect of fumarate and live yeast on methane emissions, rumen fermentation, blood metabolites, and lactation performance in dairy goats

The objective of this study was to evaluate the effects and combined effects of fumarate (32 g/d) and active dry yeast (ADY) (1.5 g/d) on methane emissions, rumen fermentation, microbial community and function, apparent total tract digestibility, blood metabolites, and lactation performance in 28 lactating goats using a randomized complete block design with a 2 x 2 factorial arrangement. Fumarate supplementation inhibited methane emission rates for 3 h post-feeding, while ADY increased the methane emission rates for 2 h pre-feeding, without combined effects between them. Fumarate increased the rumen pH and reduced total VFA concentration without changing the VFA profiles before feeding. Based on two custom dataset of 4,674 metagenome-assembled genomes and 7,758,615 nonredundant genes, neither fumarate nor live yeast influenced the rumen microbial community and function. The addition of fumarate reduced the concentrations of glucose, serum malondialdehyde (MDA), and ammonia in the serum, as well as the activity of insulin, while increasing the activity of serum malate dehydrogenase (MDH) and the levels of urea in both serum and milk. The addition of live yeast reduced serum glucose levels and increased serum MDA concentrations and MUN levels. There was a negative interaction between fumaric acid and live yeast on the levels of urea nitrogen in serum and milk. ConclusionsThe methane inhibition effect of fumaric acid persists for 3 h post-feeding, reflecting its retention or metabolism time in the rumen. Fumaric acid enhances the TCA cycle and the urea cycle in dairy goats, thereby improving energy utilization efficiency and reducing the risk of ammonia toxicity.

microbiology↗

bioRxiv2024

Estrus-Tracking Cortical Neurons Integrate Social cues and Reproductive states to Adaptively Control Sexually Dimorphic Sociosexual Behaviors

Female sociosexual behaviors, essential for survival and reproduction, are adaptively modulated by ovarian hormones. However, the neural mechanisms integrating internal hormonal states with external social cues to guide these behaviors remain poorly understood. Here we identified primary estrous-sensitive Cacna1h-expressing medial prefrontal (mPFCCacna1h+) neurons that orchestrate adaptive sociosexual behaviors. Bidirectional manipulation of mPFCCacna1h+ neurons drives opposite-sex-directed behavioral shifts between estrus and diestrus females. In males, these neurons serve opposite functions compared to estrus females, mediating sexually dimorphic effects via anterior hypothalamic outputs. Miniscope imaging reveals mixed-representation of self-estrous states and social target sex in distinct mPFCCacna1h+ subpopulations, with biased-encoding of opposite-sex social cues in estrus females and males. Mechanistically, ovarian hormone-driven upregulation of Cacna1h-encoded T-type calcium channels underlies estrus-specific activity changes and sexual-dimorphic function of mPFCCacna1h+ neurons. These findings uncover a prefrontal circuit that integrates internal hormonal states and target-sex information to exert sexually bivalent top-down control over adaptive social behaviors.

neuroscience↗

bioRxiv2024

OPUS-Design: Designing Protein Sequence from Backbone Structure with 3DCNN and Protein Language Model

Protein sequence design, also known as protein inverse folding, is a crucial task in protein engineering and design. Despite the recent advancements in this field, which have facilitated the identification of amino acid sequences based on backbone structures, achieving higher levels of accuracy in sequence recovery rates remains challenging. It this study, we introduce a two-stage protein sequence design method named OPUS-Design. Our evaluation on recently released targets from CAMEO and CASP15 shows that OPUS-Design significantly surpasses several other leading methods on both monomer and oligomer targets in terms of sequence recovery rate. Furthermore, by utilizing its finetune version OPUS-Design-ft and our previous work OPUS-Mut, we have successfully designed a thermal-tolerant double-point mutant of T4 lysozyme that demonstrates a residual enzyme activity exceeding that of the wild-type T4 by more than twofold when both are subjected to extreme heat treatment at 70{degrees}C. Importantly, this accomplishment is achieved through the experimental verification of less than 10 mutant candidates, thus significantly alleviating the burden of experimental verification process.

bioengineering↗