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Latest trends & strategies in ocular drug delivery

IF 4.2 3区生物学 Q1 BIOCHEMICAL RESEARCH METHODS

Methods

Pub Date : 2025-02-12 DOI: 10.1016/j.ymeth.2025.02.003

Nishant S. Kulkarni , Alexander Josowitz , Roshan James , Yang Liu , Bindhu Rayaprolu , Botir Sagdullaev , Amardeep S. Bhalla , Mohammed Shameem

Ocular drug delivery is one of the most challenging routes of administration, and this may be attributed to the complex interplay of ocular barriers and clearance mechanisms that restrict therapeutic payload residence. Most of the currently approved products that ameliorate ocular disease conditions are topical, i.e., delivering therapeutics to the outside anterior segment of the eye. This site of administration works well for certain conditions such as local infections but due to the presence of numerous ocular barriers, the permeation of therapeutics to the posterior segment of the eye is limited. Conditions such as age-related macular degeneration and diabetic retinopathy that contribute to an extreme deterioration of vision acuity require therapeutic interventions at the posterior segment of the eye. This necessitates development of intraocular delivery systems such as intravitreal injections, implants, and specialized devices that deliver therapeutics to the posterior segment of the eye. Frequent dosing regimens and high concentration formulations have been strategized and developed to achieve desired therapeutic outcomes by overcoming some of the challenges of drug clearance and efficacy. Correspondingly, development of suitable delivery platforms such as biodegradable and non-biodegradable implants, nano delivery systems, and implantable devices have been explored. This article provides an overview of the current trends in the development of suitable formulations & delivery systems for ocular drug delivery with an emphasis on late-stage clinical and approved product. Moreover, this work aims to summarize current challenges and highlights exciting pre-clinical developments, and future opportunities in cell and gene therapies that may be explored for effective ocular therapeutic outcomes.

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引用次数: 0

A high sensitivity assay of UBE3A ubiquitin ligase activity

IF 4.2 3区生物学 Q1 BIOCHEMICAL RESEARCH METHODS

Methods

Pub Date : 2025-02-09 DOI: 10.1016/j.ymeth.2025.02.002

Linna Han, Z. Begum Yagci, Albert J. Keung

UBE3A is an E3 ubiquitin ligase associated with several neurodevelopmental disorders. The development of several preclinical therapeutic approaches involving UBE3A, such as gene therapy, enzyme replacement therapy, and epigenetic reactivation, require the detection of its ubiquitin ligase activity. Prior commercial assays leveraged Western Blotting to detect shifts in substrate size due to ubiquitination, but these suffered from long assay times and have also been discontinued. Here we develop a new assay that quantifies UBE3A activity. It measures the fluorescence intensity of ubiquitinated p53 substrates with a microplate reader, eliminating the need for Western Blot antibodies and instruments, and enables detection in just 1 h. The assay is fast, cost-effective, low noise, and uses components with long shelf lives. Importantly, it is also highly sensitive, detecting UBE3A levels as low as 1 nM, similar to that observed in human and mouse cerebrospinal fluid. It also differentiates the activity of wild-type UBE3A and catalytic mutants. We also design a p53 substrate with a triple-epitope tag HIS-HA-CMYC on the N terminus, which allows for versatile detection of UBE3A activity from diverse natural and engineered sources. This new assay provides a timely solution for growing needs in preclinical validation, quality control, endpoint measurements for clinical trials, and downstream manufacturing testing and validation.

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引用次数: 0

HybProm: An attention-assisted hybrid CNN-BiLSTM model for the interpretable prediction of DNA promoter

IF 4.2 3区生物学 Q1 BIOCHEMICAL RESEARCH METHODS

Methods

Pub Date : 2025-02-08 DOI: 10.1016/j.ymeth.2025.02.001

Rentao Luo, Jiawei Liu, Lixin Guan, Mengshan Li

Promoter prediction is essential for analyzing gene structures, understanding regulatory networks, transcription mechanisms, and precisely controlling gene expression. Recently, computational and deep learning methods for promoter prediction have gained attention. However, there is still room to improve their accuracy. To address this, we propose the HybProm model, which uses DNA2Vec to transform DNA sequences into low-dimensional vectors, followed by a CNN-BiLSTM-Attention architecture to extract features and predict promoters across species, including E. coli, humans, mice, and plants. Experiments show that HybProm consistently achieves high accuracy (90%-99%) and offers good interpretability by identifying key sequence patterns and positions that drive predictions.

引用次数: 0

A transferability-guided protein-ligand interaction prediction method

IF 4.2 3区生物学 Q1 BIOCHEMICAL RESEARCH METHODS

Methods

Pub Date : 2025-02-05 DOI: 10.1016/j.ymeth.2025.01.019

Weihong Zhang , Fan Hu , Peng Yin , Yunpeng Cai

Accurate prediction of protein–ligand interaction (PLI) is crucial for drug discovery and development. However, existing methods often struggle with effectively integrating heterogeneous protein and ligand data modalities and optimizing knowledge transfer from pretraining to the target task. This paper proposes a novel transferability-guided PLI prediction method that maximizes knowledge transfer by deeply integrating protein and ligand representations through a cross-attention mechanism and incorporating transferability metrics to guide fine-tuning. The cross-attention mechanism facilitates interactive information exchange between modalities, enabling the model to capture intricate interdependencies. Meanwhile, the transferability-guided strategy quantifies transferability from pretraining tasks and incorporates it into the training objective, ensuring the effective utilization of beneficial knowledge while mitigating negative transfer. Extensive experiments demonstrate significant and consistent improvements over traditional fine-tuning, validated by statistical tests. Ablation studies highlight the pivotal role of cross-attention, and quantitative analysis reveals the method’s ability to reduce harmful transfer. Our guided strategy provides a paradigm for more comprehensive utilization of pretraining knowledge, offering prospects for enhancing other PLI prediction approaches. This method advances PLI prediction via innovative modality fusion and guided knowledge transfer, paving the way for accelerated drug discovery pipelines. Code and data are freely available at https://github.com/brian-zZZ/Guided-PLI.

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引用次数: 0

ZFP-CanPred: Predicting the effect of mutations in zinc-finger proteins in cancers using protein language models

IF 4.2 3区生物学 Q1 BIOCHEMICAL RESEARCH METHODS

Methods

Pub Date : 2025-02-03 DOI: 10.1016/j.ymeth.2025.01.020

Amit Phogat , Sowmya Ramaswamy Krishnan , Medha Pandey , M. Michael Gromiha

Zinc-finger proteins (ZNFs) constitute the largest family of transcription factors and play crucial roles in various cellular processes. Missense mutations in ZNFs significantly alter protein-DNA interactions, potentially leading to the development of various types of cancers. This study presents ZFP-CanPred, a novel deep learning-based model for predicting cancer-associated driver mutations in ZNFs. The representations derived from protein language models (PLMs) from the structural neighbourhood of mutated sites were utilized to train ZFP-CanPred for differentiating between cancer-causing and neutral mutations. ZFP-CanPred, achieved a superior performance with an accuracy of 0.72, F1-score of 0.79, and area under the Receiver Operating Characteristics (ROC) Curve (AUC) of 0.74, on an independent test set. In a comparative analysis against 11 existing prediction tools using a curated dataset of 331 mutations, ZFP-CanPred demonstrated the highest AU-ROC of 0.74, outperforming both generic and cancer-specific methods. The model’s balanced performance across specificity and sensitivity addresses a significant limitation of current methodologies. The source code and other related files are available on GitHub at https://github.com/amitphogat/ZFP-CanPred.git. We envisage that the present study contributes to understand the oncogenic processes and developing targeted therapeutic strategies.

{"title":"ZFP-CanPred: Predicting the effect of mutations in zinc-finger proteins in cancers using protein language models","authors":"Amit Phogat , Sowmya Ramaswamy Krishnan , Medha Pandey , M. Michael Gromiha","doi":"10.1016/j.ymeth.2025.01.020","DOIUrl":"10.1016/j.ymeth.2025.01.020","url":null,"abstract":"<div><div>Zinc-finger proteins (ZNFs) constitute the largest family of transcription factors and play crucial roles in various cellular processes. Missense mutations in ZNFs significantly alter protein-DNA interactions, potentially leading to the development of various types of cancers. This study presents ZFP-CanPred, a novel deep learning-based model for predicting cancer-associated driver mutations in ZNFs. The representations derived from protein language models (PLMs) from the structural neighbourhood of mutated sites were utilized to train ZFP-CanPred for differentiating between cancer-causing and neutral mutations. ZFP-CanPred, achieved a superior performance with an accuracy of 0.72, F1-score of 0.79, and area under the Receiver Operating Characteristics (ROC) Curve (AUC) of 0.74, on an independent test set. In a comparative analysis against 11 existing prediction tools using a curated dataset of 331 mutations, ZFP-CanPred demonstrated the highest AU-ROC of 0.74, outperforming both generic and cancer-specific methods. The model’s balanced performance across specificity and sensitivity addresses a significant limitation of current methodologies. The source code and other related files are available on GitHub at <span><span>https://github.com/amitphogat/ZFP-CanPred.git</span><svg><path></path></svg></span>. We envisage that the present study contributes to understand the oncogenic processes and developing targeted therapeutic strategies.</div></div>","PeriodicalId":390,"journal":{"name":"Methods","volume":"235 ","pages":"Pages 55-63"},"PeriodicalIF":4.2,"publicationDate":"2025-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143254023","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Exploring drug-target interaction prediction on cold-start scenarios via meta-learning-based graph transformer 通过基于元学习的图转换器探索冷启动情景下的药物-目标相互作用预测。

IF 4.2 3区生物学 Q1 BIOCHEMICAL RESEARCH METHODS

Methods

Pub Date : 2025-02-01 DOI: 10.1016/j.ymeth.2024.11.010

Chengxin He , Zhenjiang Zhao , Xinye Wang , Huiru Zheng , Lei Duan , Jie Zuo

Predicting drug-target interaction (DTI) is of great importance for drug discovery and development. With the rapid development of biological and chemical technologies, computational methods for DTI prediction are becoming a promising approach. However, there are few solutions to the cold-start problem in DTI prediction scenarios, as these methods rely on existing interaction information to support their modeling. Consequently, they are unable to effectively predict DTIs for new drugs or targets with limited interaction data in the existing work. To this end, we propose a graph transformer method based on meta-learning named MGDTI (short for Meta-learning-based Graph Transformer for Drug-Target Interaction prediction) to fill this gap. Technically, we employ drug-drug similarity and target-target similarity as additional information to mitigate the scarcity of interactions. Besides, we trained MGDTI via meta-learning to be adaptive to cold-start tasks. Moreover, we employed graph transformer to prevent over-smoothing by capturing long-range dependencies. Extensive results on the benchmark dataset demonstrate that MGDTI is effective on DTI prediction under cold-start scenarios.

预测药物-靶点相互作用（DTI）对药物发现和开发具有重要意义。随着生物和化学技术的快速发展，用于 DTI 预测的计算方法正成为一种前景广阔的方法。然而，由于这些方法依赖于现有的相互作用信息来支持其建模，因此在 DTI 预测中很少有解决冷启动问题的方案。因此，在现有工作中，它们无法有效预测新药或相互作用数据有限的靶点的 DTI。为此，我们提出了一种基于元学习的图转换器方法，命名为 MGDTI（基于元学习的药物-靶点相互作用预测图转换器的简称），以填补这一空白。在技术上，我们采用了药物-药物相似性和目标-目标相似性作为额外信息，以减少相互作用的稀缺性。此外，我们还通过元学习训练 MGDTI，使其能够适应冷启动任务。此外，我们还采用了图转换器，通过捕捉长程依赖关系来防止过度平滑。在基准数据集上的大量结果表明，MGDTI 在冷启动场景下对 DTI 预测非常有效。

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引用次数: 0

Cleaving the way for heterologous peptide production: An overview of cleavage strategies 为异源肽的生产开辟道路：切割策略的概述。

IF 4.2 3区生物学 Q1 BIOCHEMICAL RESEARCH METHODS

Methods

Pub Date : 2025-02-01 DOI: 10.1016/j.ymeth.2024.12.002

Karen Ofuji Osiro , Harry Morales Duque , Kamila Botelho Sampaio de Oliveira , Nadielle Tamires Moreira Melo , Letícia Ferreira Lima , Hugo Costa Paes , Octavio Luiz Franco

One of the main bottlenecks for recombinant peptide production is choosing the proper cleavage method to remove fusion protein tags from target peptides. While these tags are crucial for inhibiting the activity of the target peptide during heterologous expression, incorporating a cleavage site is essential for their later removal, ensuring the pure sequencing of the peptide. This review evaluates different cleavage methods, including protease-mediated, self-cleavable protein, and chemical-mediated sites, regarding their advantages and limitations. For instance, intein, N^pro EDDIE, enterokinase, factor Xa, SUMO, and CNBr are options for residue-free cleavage. Although protease-mediated cleavage is widely used, it can be expensive, due to its own cost added to the whole process. As an alternative, self-cleavable sites eliminate the requirement for proteinases. Another crucial step in defining the proper cleavage method is cost consideration, which relates to the purpose of peptide production. Here, we explore a range of cleavage approaches, meeting the needs of both cost-constrained applications and a more flexible budget. Overall, selecting the most suitable cleavage method should be based on careful consideration of toxicity, cost, accuracy, and specific application requirements to ensure a state-of-the-art approach.

选择合适的切割方法去除融合蛋白标签是重组肽生产的主要瓶颈之一。虽然这些标签在异源表达过程中对抑制目标肽的活性至关重要，但结合切割位点对于随后去除它们是必不可少的，以确保肽的纯测序。本文综述了不同的切割方法，包括蛋白酶介导、自切割蛋白和化学介导位点，以及它们的优点和局限性。例如，干扰素，nproeddie，肠激酶，因子Xa， SUMO和CNBr都是无残基切割的选择。虽然蛋白酶介导的裂解被广泛使用，但由于其本身的成本增加到整个过程中，它可能是昂贵的。作为一种选择，自切割位点消除了对蛋白酶的需求。在确定合适的切割方法的另一个关键步骤是成本考虑，这涉及到肽生产的目的。在这里，我们探索了一系列切割方法，以满足成本限制应用程序和更灵活的预算的需求。总的来说，选择最合适的解理方法应该基于仔细考虑毒性，成本，准确性和特定的应用要求，以确保最先进的方法。

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引用次数: 0

Trans-m5C: A transformer-based model for predicting 5-methylcytosine (m5C) sites Trans-m5C：一个基于变压器的预测5-甲基胞嘧啶（m5C）位点的模型。

IF 4.2 3区生物学 Q1 BIOCHEMICAL RESEARCH METHODS

Methods

Pub Date : 2025-02-01 DOI: 10.1016/j.ymeth.2024.12.010

Haitao Fu , Zewen Ding , Wen Wang

5-Methylcytosine (m5C) plays a pivotal role in various RNA metabolic processes, including RNA localization, stability, and translation. Current high-throughput sequencing technologies for m5C site identification are resource-intensive in terms of cost, labor, and time. As such, there is a pressing need for efficient computational approaches. Many existing computational methods rely on intricate hand-crafted features, requiring unavailable features, often leading to suboptimal prediction accuracy. Addressing these challenges, we introduce a novel deep-learning method, Trans-m5C. We first categorize m5C sites into NSUN2-dependent and NSUN6-dependent types for independent feature extraction. Subsequently, meticulously crafted transformer neural networks are employed to distill global features. The prediction of m5C sites is then accomplished using a discriminator built from a multi-layer perceptron. A rigorous evaluation for the performance of Trans-m5C on experimentally validated m5C data from human and mouse species reveals that our method offers a competitive edge over both baseline and existing methodologies.

5-甲基胞嘧啶（m5C）在RNA定位、稳定和翻译等多种RNA代谢过程中起着关键作用。目前用于m5C位点鉴定的高通量测序技术在成本、人工和时间方面都是资源密集型的。因此，迫切需要高效的计算方法。许多现有的计算方法依赖于复杂的手工特征，需要不可用的特征，往往导致次优的预测精度。为了应对这些挑战，我们引入了一种新颖的深度学习方法Trans-m5C。我们首先将m5C站点分为依赖于nsun2和依赖于nsun6的类型，进行独立的特征提取。随后，精心制作的变压器神经网络用于提取全局特征。然后使用多层感知器构建的鉴别器来完成m5C位置的预测。对Trans-m5C在人类和小鼠实验验证的m5C数据上的性能进行了严格的评估，表明我们的方法比基线和现有方法都具有竞争优势。

引用次数: 0

Novel approaches to biomarker discover 生物标志物发现的新方法

IF 4.2 3区生物学 Q1 BIOCHEMICAL RESEARCH METHODS

Methods

Pub Date : 2025-02-01 DOI: 10.1016/j.ymeth.2025.01.005

Brian K. McFarlin PhD (Special Issue Editor)

引用次数: 0

Validation of plasmonic-based biosensors for rapid and in depth characterization of monoclonal antibodies directed against rabbit haemorrhagic and foot-and-mouth disease viruses in biological samples 验证基于等离子体的生物传感器在生物样品中快速和深入地表征针对兔出血性和口蹄疫病毒的单克隆抗体。

IF 4.2 3区生物学 Q1 BIOCHEMICAL RESEARCH METHODS

Methods

Pub Date : 2025-02-01 DOI: 10.1016/j.ymeth.2024.12.003

Chiara Urbinati , Giulia Pezzoni , Patrizia Cavadini , Vittoria Di Giovanni , Lorenzo Capucci , Marco Rusnati

ELISA and RT-PCR represent the standard tools for the sensitive identification of viruses in biological samples, but they lack the capacity to finely characterize the binding of viruses or viral antigens to monoclonal antibodies (MAbs). Biosensing technologies are gaining increasing importance as powerful MAb characterization tools in the field of virology. Surface plasmon resonance (SPR) is an optical biosensing technology already used for the in depth characterization of MAbs of diagnostic and therapeutic value. Rabbit haemorrhagic disease virus (RHDV) and foot-and-mouth disease virus (FMDV) are top veterinary issues for which the development of novel methods aimed at the characterization of antiviral MAbs represents a priority with important livestock healthcare and economic implications. With these premises in mind, here we prepared a series of SPR biosensors by immobilizing RHDV2 or its 6S subunit by different strategies that were then used to characterize the binding capacity of a panel of anti-RHDV2 MAbs. From the comparison of the results obtained, the biosensor composed of intact RHDV2 captured with catcher-MAb covalently immobilized to the surface showed the best analytical performances. To evaluate the versatility of the biosensor, the same strategy was then adopted using FMVD in cell extracts. The results obtained are discussed in view of the exploitation of SPR in the rapid and resilient fine characterization of antiviral MAbs for diagnostic or therapeutic purposes in the field of animal virology.

ELISA和RT-PCR是生物样品中病毒敏感鉴定的标准工具，但它们缺乏精细表征病毒或病毒抗原与单克隆抗体（mab）结合的能力。生物传感技术作为一种强大的单克隆抗体鉴定工具，在病毒学领域正变得越来越重要。表面等离子体共振（SPR）是一种光学生物传感技术，已被用于深入表征具有诊断和治疗价值的单克隆抗体。兔出血性疾病病毒（RHDV）和口蹄疫病毒（FMDV）是兽医面临的首要问题，因此开发针对抗病毒单克隆抗体特征的新方法是具有重要牲畜保健和经济意义的优先事项。考虑到这些前提，在这里，我们通过不同的策略固定RHDV2或其6S亚基制备了一系列SPR生物传感器，然后用于表征抗RHDV2单克隆抗体的结合能力。结果表明，以捕集剂-单抗共价固定在表面捕获的完整RHDV2组成的生物传感器的分析性能最好。为了评估生物传感器的通用性，然后在细胞提取物中使用FMVD采用相同的策略。本文讨论了SPR在动物病毒学诊断和治疗领域中对抗病毒单克隆抗体的快速和弹性精细鉴定中的应用。

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