Article
Biochemical Research Methods
Lei Xu, Shanshan Jiang, Jin Wu, Quan Zou
Summary: Exploring the function of proteins in protein-nucleic acid interactions is important for understanding related biological events and predicting these interactions. Establishing databases by collecting and identifying protein sequence information helps in predicting protein function, leading to improved prediction accuracy.
BRIEFINGS IN BIOINFORMATICS
(2021)
Article
Biochemical Research Methods
Xiuquan Du, Jiajia Hu
Summary: In this study, a novel deep multi-label joint learning framework is proposed to leverage the relationship between multiple labels and binding proteins. A multi-label variant network is designed to explore multi-scale context hidden information, and a multi-label Long Short-Term Memory (multiLSTM) is used to mine the potential relationship between labels. Extensive experiments are carried out to compare the proposed method with other existing methods.
IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS
(2023)
Article
Biochemistry & Molecular Biology
Sam M. Ireland, Andrew C. R. Martin
Summary: Computational models for predicting zinc binding sites are faster and more accurate, achieving high MCC, recall, and precision scores for both structure and sequence prediction. Models focusing on binding sites with four liganding residues perform particularly well. The predictors outperform other zinc binding site predictors and are accessible online.
Article
Biology
Ozgur Can Arican, Ozgur Gumus
Summary: The classification of DNA-binding proteins (DBPs) and RNA-binding proteins (RBPs) is crucial for maintaining vital cellular activities. This study developed a multilayer perceptron (MLP) based predictor, PredDRBP-MLP, which outperforms existing CNN-BiLSTM based predictors in terms of lower processing power requirements and faster training time. PredDRBP-MLP achieved highly successful results in multi-class classification of DBPs, RBPs, and non-nucleic acid-binding proteins (NNABP), particularly in the NNABP class.
COMPUTERS IN BIOLOGY AND MEDICINE
(2023)
Article
Medicine, General & Internal
Oleg Tutanov, Tatiana Shtam, Alina Grigor'eva, Alexey Tupikin, Yuri Tsentalovich, Svetlana Tamkovich
Summary: The study shows that circulating DNA predominantly binds to the outer membrane of exosomes through association with DNA-binding proteins.
Article
Multidisciplinary Sciences
Nitesh Kumar Sharma, Sagar Gupta, Ashwani Kumar, Prakash Kumar, Upendra Kumar Pradhan, Ravi Shankar
Summary: By utilizing ultra-fast inexact k-mers search and Deep Feed-forward Neural Network modeling, RBPSpot software efficiently and accurately identifies RBP binding sites in RNA, outperforming other tools in various performance metrics.
Article
Biochemical Research Methods
Mengting Niu, Quan Zou, Chen Lin
Summary: In this study, a novel calculation model CRBPDL is proposed to accurately identify the binding sites of circular RNA-RBP. By integrating deep learning model, the prediction performance and reliability of the model are improved. Experimental results on multiple datasets demonstrate the universality, reliability, and robustness of CRBPDL.
PLOS COMPUTATIONAL BIOLOGY
(2022)
Article
Biochemistry & Molecular Biology
Elena Belova, Oksana Maksimenko, Pavel Georgiev, Artem Bonchuk
Summary: The presence of a conserved proline residue in Kaiso protein and its cis-conformation play crucial roles in efficient DNA binding.
INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES
(2022)
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
Biochemistry & Molecular Biology
Brittany Cain, Jordan Webb, Zhenyu Yuan, David Cheung, Hee-Woong Lim, Rhett A. Kovall, Matthew T. Weirauch, Brian Gebelein
Summary: Homeodomain proteins can form cooperative homodimer complexes on DNA sites with precise spacing requirements, with approximately one third of the paired-like homeodomain proteins binding to palindromic sequences spaced 3 bp apart. Other homeodomain proteins bind to sites with distinct orientation and spacing requirements. Computational analysis using structural models and computational mining of HT-SELEX data allowed the identification of key amino acid differences that differentiate between cooperative and non-cooperative factors. In vivo validation using available genomic data confirmed the predicted cooperative dimer sites for a subset of factors.
NUCLEIC ACIDS RESEARCH
(2023)
Article
Biology
Shohei Yamaguchi, Haruka Nakashima, Yoshitaka Moriwaki, Tohru Terada, Kentaro Shimizu
Summary: In this study, a system for predicting protein binding sites for mononucleotides was developed, integrating machine learning and template-based predictors. The system achieved high accuracy through data augmentation and structure modeling.
COMPUTATIONAL BIOLOGY AND CHEMISTRY
(2022)
Article
Biochemical Research Methods
Haodong Xu, Peilin Jia, Zhongming Zhao
Summary: This study reviewed the computational prediction of 4mC sites and developed Deep4mC, a deep learning-based model, which showed high accuracy and robust performance in predicting putative 4mC sites in genomes of various species. With feature optimization and reinforcement learning, Deep4mC achieved significant improvement in performance compared to previous tools.
BRIEFINGS IN BIOINFORMATICS
(2021)
Article
Automation & Control Systems
Muhammad Tahir, Hilal Tayara, Maqsood Hayat, Kil To Chong
Summary: RNA-binding proteins play a crucial role in gene regulation and provide essential information for patient care. An intelligent prediction model named kDeepBind is proposed using deep learning, showing better performance in identifying RBPs binding sites than comparative methods.
CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS
(2021)
Article
Automation & Control Systems
Farman Ali, Harish Kumar, Shruti Patil, Aftab Ahmed, Ameen Banjar, Ali Daud
Summary: In this study, a deep learning-based predictor (DBP-DeepCNN) is proposed to improve the prediction of DNA-binding proteins (DBPs). By using a novel feature extraction method and training with various models, the predictor achieved higher accuracies on both training and independent datasets, indicating its potential for large scale DBP prediction and promising therapeutic strategies for chronic diseases.
CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS
(2022)
Article
Oncology
Linan Cao, Pei Liu, Jialong Chen, Lei Deng
Summary: In this study, we developed an accurate and interpretable attention-based hybrid approach called DeepARC, which combines CNN and RNN to predict TFBS. DeepARC utilizes a positional embedding method to extract hidden embeddings from DNA sequences and uses a CNN-BiLSTM-Attention framework to search for motifs. Our results demonstrate that DeepARC achieves promising performances on multiple cell lines and provides interpretability through attention weight graphs.
FRONTIERS IN ONCOLOGY
(2022)
Article
Genetics & Heredity
Mikhail A. Moldovan, Svetlana A. Petrova, Mikhail S. Gelfand
FUNGAL GENETICS AND BIOLOGY
(2018)
Article
Multidisciplinary Sciences
Pavel V. Mazin, Elena Shagimardanova, Olga Kozlova, Alexander Cherkasov, Roman Sutormin, Vita V. Stepanova, Alexey Stupnikov, Maria Logacheva, Aleksey Penin, Yoichiro Sogame, Richard Cornette, Shoko Tokumoto, Yugo Miyata, Takahiro Kikawada, Mikhail S. Gelfand, Oleg Gusev
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
(2018)
Article
Biochemistry & Molecular Biology
Pavel V. Mazin, Xi Jiang, Ning Fu, Dingding Han, Meng Guo, Mikhail S. Gelfand, Philipp Khaitovich
Article
Multidisciplinary Sciences
Anna Kaznadzey, Pavel Shelyakin, Evgeniya Belousova, Aleksandra Eremina, Uliana Shvyreva, Darya Bykova, Vera Emelianenko, Anastasiya Korosteleva, Maria Tutukina, Mikhail S. Gelfand
SCIENTIFIC REPORTS
(2018)
Article
Microbiology
Sofya K. Garushyants, Alexandra Y. Beliavskaia, Dmitry B. Malko, Maria D. Logacheva, Maria S. Rautian, Mikhail S. Gelfand
FRONTIERS IN MICROBIOLOGY
(2018)
Article
Microbiology
Mikhail A. Moldovan, Mikhail S. Gelfand
FRONTIERS IN MICROBIOLOGY
(2018)
Article
Biochemical Research Methods
Evgeny E. Akkuratov, Mikhail S. Gelfand, Ekaterina E. Khrameeva
JOURNAL OF BIOINFORMATICS AND COMPUTATIONAL BIOLOGY
(2018)
Article
Multidisciplinary Sciences
Olga O. Bochkareva, Natalia O. Dranenko, Elena S. Ocheredko, German M. Kanevsky, Yaroslav N. Lozinsky, Vera A. Khalaycheva, Irena I. Artamonova, Mikhail S. Gelfand
Article
Multidisciplinary Sciences
Pavel V. Shelyakin, Sofya K. Garushyants, Mikhail A. Nikitin, Sofya V. Mudrova, Michael Berumen, Arjen G. C. L. Speksnijder, Bert W. Hoeksema, Diego Fontaneto, Mikhail S. Gelfand, Viatcheslav N. Ivanenko
SCIENTIFIC REPORTS
(2018)
Article
Biotechnology & Applied Microbiology
Olga O. Bochkareva, Elena V. Moroz, Iakov I. Davydov, Mikhail S. Gelfand
Review
Biochemical Research Methods
Aleksandra A. Galitsyna, Mikhail S. Gelfand
Summary: Genome-wide assays for chromatin interactions in single cells have advanced in the past decade, but specialized processing is still needed for sparse interactome data recovery. These methods have highlighted directions for future development in this rapidly moving field.
BRIEFINGS IN BIOINFORMATICS
(2021)
Article
Multidisciplinary Sciences
Alexandra M. Ozerova, Mikhail S. Gelfand
Summary: Holometabolous insects show significant changes in gene expression during metamorphosis, resembling the embryonic stage rather than the larval stage. The transition from pupa to imago shows similar gene expression patterns as the transition from embryo to larva. Genes associated with metabolism and development undergo changes in expression levels during the larval stage and revert to an embryonic-like state during metamorphosis.
SCIENTIFIC REPORTS
(2022)
Article
Multidisciplinary Sciences
Ivan N. Semenkov, Pavel Shelyakin, Daria D. Nikolaeva, Maria N. Tutukina, Anna Sharapova, Sergey A. Lednev, Yuliya Sarana, Mikhail S. Gelfand, Pavel P. Krechetov, Tatiana Koroleva
Summary: This article presents original data on the impact of jet-fuel spillage on topsoil properties. The data set includes information on kerosene concentration over time, as well as physicochemical and biological properties of the soil. Additionally, sequencing data on microbial communities are provided.
Article
Biochemistry & Molecular Biology
Almira Chervova, Bulat Fatykhov, Alexander Koblov, Evgeny Shvarov, Julia Preobrazhenskaya, Dmitry Vinogradov, Gennady V. Ponomarev, Mikhail S. Gelfand, Marat D. Kazanov
Summary: The mechanisms of APOBEC mutagenesis in human cancers remain poorly understood, with evidence suggesting the importance of replication and the underexplored role of transcription. Gene expression and whole genome sequencing data from five types of human cancers with significant APOBEC activity were analyzed to estimate the involvement of transcription, compare its impact with that of replication, and explore the relative effects on APOBEC mutagenesis. Active APOBEC mutagenesis was found to correlate with gene expression, with an increase in APOBEC-induced mutations in early-replicating regions, and a higher APOBEC mutation density positively correlated with gene expression levels.
Article
Biology
Anna Kaznadzey, Pavel Shelyakin, Mikhail S. Gelfand
