HydrogelFinder: A Foundation Model for Efficient Self‐Assembling Peptide Discovery Guided by Non‐Peptidal Small Molecules

Advanced Science, Journal Year: 2024, Volume and Issue: 11(26)

Published: May 5, 2024

Abstract Self‐assembling peptides have numerous applications in medicine, food chemistry, and nanotechnology. However, their discovery has traditionally been serendipitous rather than driven by rational design. Here, HydrogelFinder, a foundation model is developed for the design of self‐assembling from scratch. This explores self‐assembly properties molecular structure, leveraging 1,377 non‐peptidal small molecules to navigate chemical space improve structural diversity. Utilizing 111 peptide candidates are generated synthesized 17 peptides, subsequently experimentally validating biophysical characteristics nine ranging 1–10 amino acids—all achieved within 19‐day workflow. Notably, two de novo‐designed demonstrated low cytotoxicity biocompatibility, as confirmed live/dead assays. work highlights capacity HydrogelFinder diversify through molecules, offering powerful toolkit paradigm future endeavors.

Language: Английский

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AI-driven 3D bioprinting for regenerative medicine: From bench to bedside

Bioactive Materials, Journal Year: 2024, Volume and Issue: 45, P. 201 - 230

Published: Nov. 23, 2024

Language: Английский

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Disulfide‐Rich Self‐Assembling Peptides Based on Aromatic Amino Acid

Small, Journal Year: 2024, Volume and Issue: unknown

Published: Nov. 3, 2024

Abstract Aromatic residues in assembling peptides play a crucial role driving peptide self‐assembly through π–π stacking and hydrophobic interactions. Although various aromatic capping groups have been extensively studied, systematic investigations into the effects of single amino acids remain limited. In this study, influence aromatic‐aromatic interactions on disulfide‐rich is systematically investigated by incorporating three different acids. Their folding propensity, self‐assembling properties, rheological behaviors are evaluated. These results indicate that significant effect abilities, as determined critical aggregation concentration (CAC) measurements. Furthermore, biocompatibility these hydrogels assessed, their potential for 3D cell culture explored. The injectable F1‐ox hydrogel demonstrate excellent SHED NIH3T3 cells exhibit porous structure facilitates nutrient cellular metabolic waste exchange. This work provides new insights molecular design peptide‐based biomaterials, with focus impact peptides.

Language: Английский

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Predicting the Mechanical Properties of Supramolecular Gels

Advanced Materials, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 9, 2025

Abstract The prediction of gelation is an important target, yet current models do not predict any post‐gel properties. Gels can be formed through the self‐assembly many molecules, but close analogs often form gels. There has been success using a number computational approaches to understand and from molecular structures. However, these focus on whether or gel will form, properties resulting Critically, it gels that are for specific application, simply formed. Supramolecular kinetically trapped, meaning predicting inherently difficult challenge. Here, first successful priori such self‐assembled, supramolecular systems reported.

Language: Английский

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DGCLCMI: a deep graph collaboration learning method to predict circRNA-miRNA interactions

BMC Biology, Journal Year: 2025, Volume and Issue: 23(1)

Published: April 23, 2025

Abstract Background Numerous studies have shown that circRNA can act as a miRNA sponge, competitively binding to miRNAs, thereby regulating gene expression and disease progression. Due the high cost time-consuming nature of traditional wet lab experiments, analyzing circRNA-miRNA associations is often inefficient labor-intensive. Although some computational models been developed identify these associations, they fail capture deep collaborative features between interactions do not guide training feature extraction networks based on high-order relationships, leading poor prediction performance. Results To address issues, we innovatively propose novel graph collaboration learning method for interaction, called DGCLCMI. First, it uses word2vec encode sequences into word embeddings. Next, present joint model combines an improved neural filtering with network optimization. Deep interaction information embedded informative within sequence representations prediction. Comprehensive experiments three well-established datasets across seven metrics demonstrate our algorithm significantly outperforms previous models, achieving average AUC 0.960. In addition, case study reveals 18 out 20 predicted unknown CMI data points are accurate. Conclusions The DGCLCMI improves representation by capturing information, superior performance compared prior methods. It facilitates discovery sheds light their roles in physiological processes.

Language: Английский

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Research on Bitter Peptides in the Field of Bioinformatics: A Comprehensive Review

International Journal of Molecular Sciences, Journal Year: 2024, Volume and Issue: 25(18), P. 9844 - 9844

Published: Sept. 12, 2024

Bitter peptides are small molecular produced by the hydrolysis of proteins under acidic, alkaline, or enzymatic conditions. These can enhance food flavor and offer various health benefits, with attributes such as antihypertensive, antidiabetic, antioxidant, antibacterial, immune-regulating properties. They show significant potential in development functional foods prevention treatment diseases. This review introduces diverse sources bitter discusses mechanisms bitterness generation their physiological functions taste system. Additionally, it emphasizes application bioinformatics peptide research, including establishment improvement databases, use quantitative structure–activity relationship (QSAR) models to predict thresholds, latest advancements classification prediction built using machine learning deep algorithms for identification. Future research directions include enhancing diversifying models, applying generative advance towards deepening discovering more practical applications.

Language: Английский

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Self-Assembling Peptides for Vaccine Adjuvant Discovery

Immuno, Journal Year: 2024, Volume and Issue: 4(4), P. 325 - 343

Published: Oct. 1, 2024

Vaccination is credited as a significant medical achievement contributing to the decline in morbidity and mortality of infectious diseases. Traditional vaccines composed inactivated live-attenuated whole pathogens confer induction potent long-term immune responses; however, traditional pose high risk eliciting autoimmune allergic responses well inflammations. New modern vaccines, such subunit employ minimum pathogenic components (such carbohydrates, proteins, or peptides), overcome drawbacks stimulate effective immunity against infections. However, low immunogenicity requires stimulants (adjuvants), which are an indispensable factor vaccine development. Although there several approved adjuvants human challenges matching designing appropriate for specific along with managing side effects toxicity existing humans, driving development new adjuvants. Self-assembling peptides promising biomaterial rapidly emerging fields biomedicine, vaccination material science. Here, self-assemble into ordered supramolecular structures, forming different building blocks nanoparticle size, including fibrils, tapes, nanotubes, micelles, hydrogels nanocages, great biostability, biocompatibility, effectiveness at controlled release. immunostimulatory agents used enhance prolong responses. This review describes predominant structures self-assembling summarises their recent applications Challenges future perspectives on self-assembled also highlighted.

Language: Английский

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TargetCLP: clathrin proteins prediction combining transformed and evolutionary scale modeling-based multi-view features via weighted feature integration approach

Briefings in Bioinformatics, Journal Year: 2024, Volume and Issue: 26(1)

Published: Nov. 22, 2024

Abstract Clathrin proteins, key elements of the vesicle coat, play a crucial role in various cellular processes, including neural function, signal transduction, and endocytosis. Disruptions clathrin protein functions have been associated with wide range diseases, such as Alzheimer’s, neurodegeneration, viral infection, cancer. Therefore, correctly identifying is critical to unravel mechanism these fatal diseases designing drug targets. This paper presents novel computational method, named TargetCLP, precisely identify proteins. TargetCLP leverages four single-view feature representation methods, two transformed sets (PSSM-CLBP RECM-CLBP), one qualitative characteristics feature, deep-learned-based embedding using ESM. The features are integrated based on their weights differential evolution, BTG selection algorithm utilized generate more optimal reduced subset. model trained classifiers, among which proposed SnBiLSTM achieved remarkable performance. Experimental comparative results both training independent datasets show that offers significant improvements terms prediction accuracy generalization unseen data, furthering advancements research field.

Language: Английский

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Injectable self-assembling peptide hydrogel as a promising vitreous substitute

Journal of Controlled Release, Journal Year: 2024, Volume and Issue: 376, P. 402 - 412

Published: Oct. 21, 2024

Language: Английский

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Voting-ac4C:Pre-trained large RNA language model enhances RNA N4-acetylcytidine site prediction

International Journal of Biological Macromolecules, Journal Year: 2024, Volume and Issue: 282, P. 136940 - 136940

Published: Oct. 30, 2024

Language: Английский

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