Article
Biochemistry & Molecular Biology
Balachandran Manavalan, Mahesh Chandra Patra
Summary: The article introduces MLCPP 2.0, a machine learning model for predicting cell-penetrating peptides and their uptake efficiency. By improving the benchmarking dataset, feature encoding algorithms, and machine learning classifiers, MLCPP 2.0 achieves outstanding performance on an independent test set and outperforms existing predictors.
JOURNAL OF MOLECULAR BIOLOGY
(2022)
Article
Plant Sciences
Yifan Chen, Zejun Li, Zhiyong Li
Summary: A new method called StackRPred is proposed in this study to predict plant R proteins, and it outperforms other methods in both five-fold cross-validation and independent test validation.
FRONTIERS IN PLANT SCIENCE
(2022)
Article
Engineering, Chemical
Peng Liu, Yijie Ding, Ying Rong, Dong Chen
Summary: Cell penetrating peptides (CPPs) are short peptides that can carry biomolecules across the cell membrane. This study proposes a novel CPP predictor that can predict CPPs and their uptake efficiency. The predictor uses feature selection and prediction algorithms to identify crucial features for CPP recognition. The results show that the predictor is comparable to state-of-the-art CPP predictors and can handle large-scale CPP data.
Article
Biology
P. W. C. M. Purijjala, P. V. G. M. Rathnayake, B. T. Kumara, B. C. M. Gunathunge, R. A. A. P. Ranasinghe, D. N. Karunaratne, R. J. K. U. Ranatunga
Summary: In this study, computational simulations were used to investigate the structure and behavior of C6 peptides in aqueous media. The results show that these peptides have stable secondary structures and have the ability to efficiently deliver siRNA.
COMPUTATIONAL BIOLOGY AND CHEMISTRY
(2022)
Review
Chemistry, Physical
Omid Mahian, Evangelos Bellos, Christos N. Markides, Robert A. Taylor, Avinash Alagumalai, Liu Yang, Caiyan Qin, Bong Jae Lee, Goodarz Ahmadi, Mohammad Reza Safaei, Somchai Wongwises
Summary: Nanofluids, mixtures of common liquids and solid nanoparticles with enhanced thermal and optical properties, have been studied for over two decades despite barriers to commercial adoption. Research has focused on the effects of adding nanoparticles on the thermal efficiency and emission reductions in renewable energy systems, particularly in solar systems.
Review
Pharmacology & Pharmacy
Andreea Gostaviceanu, Simona Gavrilas, Lucian Copolovici, Dana Maria Copolovici
Summary: Membrane-active peptides (MAPs) have unique properties that make them valuable tools for studying membrane structure and function and promising candidates for therapeutic applications. They can selectively interact with multiple membranes and disrupt lipid bilayers through different pathways. MAPs have shown antimicrobial activity, selective targeting of cancer cells, and drug delivery capabilities, making them a fascinating class of biomolecules with significant potential in basic research and clinical applications.
Article
Biochemical Research Methods
Ke Yan, Hongwu Lv, Yichen Guo, Yongyong Chen, Hao Wu, Bin Liu
Summary: In this study, an adaptive multi-view method is proposed for predicting different types of therapeutic peptides. Experimental results show that the proposed method performs well in predicting multiple types of therapeutic peptides.
Article
Biochemical Research Methods
Muhammad Arif, Muhammad Kabir, Saeed Ahmed, Abid Khan, Fang Ge, Adel Khelifi, Dong-Jun Yu
Summary: Cell-penetrating peptides (CPPs) are special peptides capable of carrying bioactive molecules into cells. This study developed a two-layer deep learning framework, DeepCPPred, to predict CPPs and their uptake efficiencies. The proposed method achieved high accuracy and outperformed existing sequence-based CPP approaches.
IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS
(2022)
Article
Biochemistry & Molecular Biology
Nedal Darif, Kathrin Vogelsang, Elena Vorgia, David Schneider, Elena Deligianni, Sven Geibel, John Vontas, Shane Denecke
Summary: Cell penetrating peptides (CPPs) are small peptides that can deliver molecular cargo into cells. This study characterized the entry of four fluorescently tagged CPPs into insect cells and dissected midgut tissues. The results showed that CPPs can penetrate the plasma membrane of ovarian and midgut-derived lepidopteran cells through endosomal uptake and that this process can be inhibited by specific endocytosis inhibitors. The study also found differences in the quantity and mode of penetration among different CPPs, with CPP-1838 showing the highest efficiency in penetrating membranes through passive diffusion.
PESTICIDE BIOCHEMISTRY AND PHYSIOLOGY
(2023)
Article
Chemistry, Medicinal
Gabrielle Lupeti De Cena, Bruna Vitoria Scavassa, Katia Conceicao
Summary: This study used in silico methods to predict anti-infective and cell-penetrating peptides derived from natterins from a venomous fish. The results showed that these peptides have potential as alternative therapies and drug delivery systems. The study also analyzed the physicochemical properties, membrane-binding potential, and cellular location of the peptides, and identified several peptides with promising biological activity.
Article
Chemistry, Multidisciplinary
Mo'ath Yousef, Ildiko Szabo, Beata Biri-Kovacs, Balint Szeder, Francoise Illien, Sandrine Sagan, Zoltan Banoczi
Summary: Modified tetrarginine derivatives were studied for their internalization mechanisms in breast cancer cells and ovarian cells, showing potential for delivering anti-tumor drugs effectively.
Article
Biochemistry & Molecular Biology
Ildiko Szabo, Francoise Illien, Levente E. Dokus, Mo'ath Yousef, Zsuzsa Baranyai, Szilvia Bosze, Shoko Ise, Kenichi Kawano, Sandrine Sagan, Shiroh Futaki, Ferenc Hudecz, Zoltan Banoczi
Summary: Studies have shown that adding a Dabcyl group to tetraarginine and hexaarginine can increase their cellular uptake efficiency, with hexaarginine showing more significant effects. The modified hexaarginine may enter cells more effectively than octaarginine, and has shown good distribution even at low concentrations.
Article
Biochemistry & Molecular Biology
Ona Illa, Jimena Ospina, Jose-Emilio Sanchez-Aparicio, Ximena Pulido, Maria Angeles Abengozar, Nerea Gaztelumendi, Daniel Carbajo, Carme Nogues, Luis Rivas, Jean-Didier Marechal, Miriam Royo, Rosa M. Ortuno
Summary: The newly synthesized hybrid beta,gamma-peptidomimetics show no toxicity on human tumoral cells but have limited cell uptake ability. They exhibit some antimicrobial activity against Leishmania parasites, but with modest intracellular accumulation. In contrast, the previously published gamma,gamma-peptidomimetics have higher antimicrobial activity and intracellular accumulation.
INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES
(2021)
Article
Chemistry, Multidisciplinary
Ramesh Singh, Pankaj Yadav, Hema Naveena, Dhiraj Bhatia
Summary: Self-assembled DNA nanocages have great potential in bioimaging and payload delivery. However, the cellular uptake of tetrahedral nanocages is hindered by electrostatic repulsion. In this study, we enhance the cellular uptake of DNA nanocages through the functionalization of a model cage with a cationic lipid. The results demonstrate the enhancement in cellular uptake and suggest multiple applications in gene transfection, drug delivery, and targeted bioimaging.
Article
Nanoscience & Nanotechnology
Mathilde Le Jeune, Emilie Secret, Michael Trichet, Aude Michel, Delphine Ravault, Francoise Illien, Jean-Michel Siaugue, Sandrine Sagan, Fabienne Burlina, Christine Menager
Summary: The endosomal entrapment of functional nanoparticles is a severe limitation to their use for biomedical applications. In this study, a conjugation strategy using cationic peptides was developed to improve the access of magnetic nanoparticles (MNPs) to the cytosol. It was found that peptides rich in histidine residues can promote endosomal escape and enhance the delivery of MNPs to the cytosol.
ACS APPLIED MATERIALS & INTERFACES
(2022)
Article
Biochemical Research Methods
Chao Wang, Ying Ju, Quan Zou, Chen Lin
Summary: A novel tool, DeepAc4C, was developed to identify ac4C using convolutional neural networks, achieving better and more balanced performance than existing predictors. By evaluating the impact of specific features on model predictions and their interaction effects, several interesting sequence motifs specific to ac4C were identified.
Article
Biochemical Research Methods
Xinyu Yu, Likun Jiang, Shuting Jin, Xiangxiang Zeng, Xiangrong Liu
Summary: The interaction between microRNA and long non-coding RNA plays a crucial role in biological processes. A new deep learning model, preMLI, based on rna2vec pre-training and deep feature mining mechanism, is proposed to predict the interaction. The model outperforms existing methods on benchmark datasets and demonstrates cross-species prediction capabilities.
BRIEFINGS IN BIOINFORMATICS
(2022)
Article
Biochemical Research Methods
Yajie Meng, Changcheng Lu, Min Jin, Junlin Xu, Xiangxiang Zeng, Jialiang Yang
Summary: In this study, a novel neural collaborative filtering approach is proposed for drug repositioning, which utilizes deep-learning approaches based on a heterogeneous network. The approach takes advantage of localized information in different networks and models the complex drug-disease associations effectively. The effectiveness of the approach is verified through benchmarking comparisons and validated against clinical trials and authoritative databases.
BRIEFINGS IN BIOINFORMATICS
(2022)
Article
Biochemical Research Methods
Bosheng Song, Xiaoyan Luo, Xiaoli Luo, Yuansheng Liu, Zhangming Niu, Xiangxiang Zeng
Summary: The spatial structures of proteins are important for their functions, but the limited quantity of known protein structures restricts their application in prediction methods. Utilizing predicted protein structure information can improve sequence-based prediction methods. TAGPPI is a novel framework that uses only protein sequences to predict protein-protein interactions and extracts spatial structure information from contact maps to improve prediction performance.
BRIEFINGS IN BIOINFORMATICS
(2022)
Review
Biochemical Research Methods
Yue Liu, Junfeng Zhang, Shulin Wang, Xiangxiang Zeng, Wei Zhang
Summary: The progress of single-cell sequencing technology has allowed researchers to study cell development and differentiation processes at a single-cell level. This paper focuses on the inference of dropout events in single-cell ATAC-seq data, which is currently lacking specific methods. The authors selected several state-of-the-art scRNA-seq imputation methods and systematically evaluated their performance through various downstream analyses. The results indicated that MAGIC performed consistently better than the other methods across different assessments.
BRIEFINGS IN BIOINFORMATICS
(2022)
Review
Biochemical Research Methods
Jingxin Dong, Mingyi Zhao, Yuansheng Liu, Yansen Su, Xiangxiang Zeng
Summary: This review comprehensively summarizes the development process of retrosynthesis in the context of deep learning, covering aspects such as datasets, models, and tools. Representative models from academia and available platforms in the industry are discussed. The review also addresses the limitations of existing models and provides potential future trends for beginners to understand and participate in retrosynthesis planning.
BRIEFINGS IN BIOINFORMATICS
(2022)
Article
Biochemistry & Molecular Biology
Xiangxiang Zeng, Xinqi Tu, Yuansheng Liu, Xiangzheng Fu, Yansen Su
Summary: In this review, knowledge graph-based works for drug repurposing and adverse drug reaction prediction in drug discovery are summarized. The graph provides both structured and unstructured relations, while knowledge representation learning is a common approach to explore knowledge graphs.
CURRENT OPINION IN STRUCTURAL BIOLOGY
(2022)
Article
Computer Science, Theory & Methods
Bosheng Song, Kenli Li, Xiangxiang Zeng
Summary: This article introduces the monodirectional evolutional symport tissue P systems with promoters (MESTP P systems) and the monodirectional evolutional symport tissue P systems with promoters and cell division (MESTPD P systems), and investigates their computational theory and applications. The results show that even with the imposition of the monodirectionality control mechanism, MESTP(D) P systems still have computational power, indicating the theoretical possibility and potential exploitation of membrane algorithms for these systems.
IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS
(2022)
Article
Biochemical Research Methods
Yansu Wang, Lei Xu, Quan Zou, Chen Lin
Summary: The study introduces a new computational approach called prPred-DRLF, which accurately predicts plant R proteins using deep representation learning models. The results show that prPred-DRLF outperforms traditional methods in plant R protein prediction tasks.
Article
Chemistry, Multidisciplinary
Xiaoqin Pan, Xuan Lin, Dongsheng Cao, Xiangxiang Zeng, Philip S. Yu, Lifang He, Ruth Nussinov, Feixiong Cheng
Summary: This review introduces guidelines on utilizing deep learning methodologies and tools for drug repurposing, which is of great importance in drug development. The article summarizes the commonly used bioinformatics and pharmacogenomics databases for drug repurposing and discusses the recently developed sequence-based and graph-based representation approaches as well as state-of-the-art deep learning-based methods. The applications of drug repurposing in fighting the COVID-19 pandemic are presented, along with an outline of future challenges.
WILEY INTERDISCIPLINARY REVIEWS-COMPUTATIONAL MOLECULAR SCIENCE
(2022)
Article
Computer Science, Artificial Intelligence
Chunyan Li, Junfeng Yao, Wei Wei, Zhangming Niu, Xiangxiang Zeng, Jin Li, Jianmin Wang
Summary: The study proposes a constrained variational autoencoder based on geometric representation for molecular generation with specific properties, which has the potential to contribute to drug development.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2022)
Article
Biochemical Research Methods
Yanyan Li, Bosheng Song, Xiangxiang Zeng
Summary: The article introduces neural-like P systems with plasmids (NP P systems) which are inspired by bacteria's DNA processing. It also presents NPMC P systems, which use multiple channels for communication between bacteria and explores their computation power.
IEEE TRANSACTIONS ON NANOBIOSCIENCE
(2023)
Article
Biochemical Research Methods
Xixi Yang, Zhangming Niu, Yuansheng Liu, Bosheng Song, Weiqiang Lu, Li Zeng, Xiangxiang Zeng
Summary: Prediction of drug-target affinity is crucial in drug discovery. Existing deep learning methods focus on single modality inputs, while our proposed Modality-DTA leverages the multimodality of drugs and targets for better prediction performance. Experimental results demonstrate the superiority of Modality-DTA over existing methods in all metrics.
IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS
(2023)
Article
Biochemical Research Methods
Xuan Lin, Zhe Quan, Zhi-Jie Wang, Yan Guo, Xiangxiang Zeng, Philip S. S. Yu
Summary: Effectively identifying compound-protein interactions (CPIs) is crucial for new drug design. We propose a deep learning framework named GraphCPI, which captures the structural information of compounds and leverages the chemical context of protein sequences. Our method shows competitiveness and feasibility in extensive experiments based on widely-used CPI datasets.
IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS
(2023)
Article
Automation & Control Systems
Ying Xu, Chong Xu, Huan Zhang, Lei Huang, Yiping Liu, Yusuke Nojima, Xiangxiang Zeng
Summary: This article proposes a new metric to calculate the contribution of each decision variable to the optimization objectives, and based on this, a multiobjective evolutionary algorithm called DVCOEA is introduced. The experimental results show that DVCOEA is a competitive approach for solving large-scale multi/many-objective problems.
IEEE TRANSACTIONS ON CYBERNETICS
(2023)