Article
Biochemistry & Molecular Biology
Shi-Shi Yuan, Dong Gao, Xue-Qin Xie, Cai-Yi Ma, Wei Su, Zhao-Yue Zhang, Yan Zheng, Hui Ding
Summary: In this study, a computational method based on the random forest model was developed to quickly and efficiently identify ion binding proteins (IBPs). By extracting features from protein sequence information and physicochemical properties of residues, and using variance analysis for feature selection, accurate identification of IBPs was achieved.
COMPUTATIONAL AND STRUCTURAL BIOTECHNOLOGY JOURNAL
(2022)
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
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
Yan Zheng, Hao Wang, Yijie Ding, Fei Guo
Summary: The study emphasized the importance of identifying DNase I hypersensitive sites (DHSs) using computational techniques based on composition information and physicochemical properties. By enhancing the feature selection model CEPZ, the research achieved significant improvements in accuracy and Matthews correlation coefficient, indicating its potential as a valuable tool for future DHS research.
IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS
(2021)
Article
Biochemical Research Methods
Dan Zhang, Zhao-Chun Xu, Wei Su, Yu-He Yang, Hao Lv, Hui Yang, Hao Lin
Summary: In this study, a predictor called iCarPS was developed to identify carbonylation sites based on sequence information, utilizing a novel feature encoding scheme and feature selection technique. Experiments demonstrated the accuracy and robustness of iCarPS, showing its powerful performance in carbonylation sites identification compared to other published methods.
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
Xuan Xiao, Yu-Tao Shao, Zhen-Tao Luo, Wang-Ren Qiu
Summary: This paper aims to identify 5-methylcytosine sites in human promoters and constructs a predictor called m5C-HPromoter. The results demonstrate that m5C-HPromoter has good performance in terms of accuracy and sensitivity, and shows improvement compared to existing predictors.
CURRENT BIOINFORMATICS
(2022)
Article
Engineering, Chemical
Sofiane Benyahia
Summary: This study compares the results of two commonly used computational fluid dynamics methods for simulating fluidized beds with smooth type-A monodisperse particles. The results show that in some cases, the discrete particle method can yield faster and more accurate results compared to the continuum assumption method. However, for more complex cases involving particles with statical properties, the discrete particle method is a better choice for fluidized bed simulations.
Article
Biochemistry & Molecular Biology
Emma Silvester, Benjamin Vollmer, Vojtech Prazak, Daven Vasishtan, Emily A. Machala, Catheryne Whittle, Susan Black, Jonathan Bath, Andrew J. Turberfield, Kay Gruenewald, Lindsay A. Baker
Summary: Electron cryotomography (cryoET) has revolutionized our understanding of biological function by revealing molecular details of membranes, viruses, and cells. A new tagging strategy using DNA origami allows for precise identification of individual protein complexes in tomograms without relying on metal clusters, making it suitable for a wide range of biological surfaces in cryoET studies.
Article
Biology
Shulin Zhao, Yijie Ding, Xiaobin Liu, Xi Su
Summary: This study developed a hybrid kernel alignment maximization-based multiple kernel model for predicting DNA-binding proteins (DBPs) and demonstrated its superiority over other predictors in terms of efficiency and accuracy.
COMPUTERS IN BIOLOGY AND MEDICINE
(2022)
Review
Oncology
Zenichi Morise, Hidetoshi Katsuno, Kenji Kikuchi, Tomoyoshi Endo, Kazuhiro Matsuo, Yukio Asano, Akihiko Horiguchi
Summary: Repeat liver resection after intrahepatic cancer recurrence is often performed but comes with increased risk of complications. Laparoscopic liver resection for repeat resection has shown feasibility and short-term advantages. However, there are challenges with laparoscopic repeat liver resection, such as disorientation and difficulty in dissecting Glissonian pedicles. Emerging techniques like small anatomical resection, indocyanine green fluorescence-guided surgery, and robot-assisted surgery hold promise for the future development of laparoscopic repeat liver resection.
Article
Biochemical Research Methods
Jun Zhang, Qingcai Chen, Bin Liu
Summary: DBPs and RBPs are crucial proteins associated with various cell activities and diseases. DeepDRBP-2L, combining CNN and LSTM, is the first computational method able to identify DBPs, RBPs and DRBPs, overcoming existing methods' shortcomings with high prediction accuracy.
IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS
(2021)
Article
Biochemical Research Methods
Swarnagowri Vaidyanathan, Kumuditha M. Weerakoon-Ratnayake, Franklin Uba, Bo Hu, David Kaufman, Junseo Choi, Sunggook Park, Steven A. Soper
Summary: The study presents a novel method for single-molecule DNA damage detection using a nanofluidic chip, which accurately reads AP sites while stretching DNA. This approach tackles the challenge of obtaining large amounts of DNA from millions of cells and addresses the growing importance of liquid biopsies as a source of biomarkers in vitro diagnostics.
Article
Biochemistry & Molecular Biology
Hongliang Zou
Summary: In this study, a machine learning method based on support vector machine was proposed for the accurate and efficient identification of hormone-binding proteins (HBPs). The method achieved high classification accuracies by encoding protein sequences, capturing connection information, fusing features, and performing feature selection. The proposed method outperforms existing approaches, showing its potential as a useful tool for identifying HBPs.
JOURNAL OF BIOMOLECULAR STRUCTURE & DYNAMICS
(2023)
Article
Mathematical & Computational Biology
Dan Zhang, Hua-Dong Chen, Hasan Zulfiqar, Shi-Shi Yuan, Qin-Lai Huang, Zhao-Yue Zhang, Ke-Jun Deng
Summary: Bioluminescent proteins (BLPs) are a class of proteins with various mechanisms of light emission widely distributed in living organisms. This paper proposed a novel predicting framework for BLP identification based on XGBoost algorithm and sequence-derived features, leading to the construction of a robust predictor named iBLP. The experimental results demonstrated the effectiveness and superiority of the proposed method for BLP identification.
COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE
(2021)
Article
Pharmacology & Pharmacy
Mansi Budhiraja, Sobiya Zafar, Sohail Akhter, Majed Alrobaian, Md Abdur Rashid, Md. Abul Barkat, Sarwar Beg, Farhan J. Ahmad
Summary: This study developed a combinational drug delivery system by embedding mupirocin-loaded chitosan microspheres in a collagen scaffold containing Piper betle extract, aiming to improve wound healing. The results showed that this method effectively promoted wound healing and exhibited good antibacterial effects.
Article
Food Science & Technology
Sarushi Rastogi, Vinita Kumari, Vasudha Sharma, F. J. Ahmad
Summary: The use of gold nanoparticles (AuNP) in food safety is crucial due to their unique properties. Nanosensors are emerging as key technologies to detect various contaminants in food industries. Research is ongoing in the food industry to explore the applications of AuNP-based nanosensors and nanobiosensors.
FOOD ANALYTICAL METHODS
(2022)
Article
Immunology
Lauren M. Walker, Andrea R. Shiakolas, Rohit Venkat, Zhaojing Ariel Liu, Steven Wall, Nagarajan Raju, Kelsey A. Pilewski, Ian Setliff, Amyn A. Murji, Rebecca Gillespie, Nigel A. Makoah, Masaru Kanekiyo, Mark Connors, Lynn Morris, Ivelin S. Georgiev
Summary: The development of novel technologies for discovering human monoclonal antibodies has been extremely valuable in combating infectious diseases. LIBRA-seq with epitope mapping is a next-generation sequencing technology that can determine residue-level epitopes for thousands of single B cells simultaneously, making it an efficient tool for high-throughput identification of antibodies against specific antigen epitopes.
FRONTIERS IN IMMUNOLOGY
(2022)
Article
Biochemical Research Methods
Puneet Rawat, Divya Sharma, R. Prabakaran, Fathima Ridha, Mugdha Mohkhedkar, Vani Janakiraman, M. Michael Gromiha
Summary: Ab-CoV is a database containing manually curated experimental interaction profiles of 1780 coronavirus-related neutralizing antibodies. It provides comprehensive data including IC50, EC50, and K-D, as well as predicted changes in stability and affinity of point mutations of interface residues.
Article
Cell Biology
Kelsey A. Pilewski, Steven Wall, Simone I. Richardson, Nelia P. Manamela, Kaitlyn Clark, Tandile Hermanus, Elad Binshtein, Rohit Venkat, Giuseppe A. Sautto, Kevin J. Kramer, Andrea R. Shiakolas, Ian Setliff, Jordan Salas, Rutendo E. Mapengo, Naveen Suryadevara, John R. Brannon, Connor J. Beebout, Rob Parks, Nagarajan Raju, Nicole Frumento, Lauren M. Walker, Emilee Friedman Fechter, Juliana S. Qin, Amyn A. Murji, Katarzyna Janowska, Bhishem Thakur, Jared Lindenberger, Aaron J. May, Xiao Huang, Salam Sammour, Priyamvada Acharya, Robert H. Carnahan, Ted M. Ross, Barton F. Haynes, Maria Hadjifrangiskou, James E. Crowe Jr, Justin R. Bailey, Spyros Kalams, Lynn Morris, Ivelin S. Georgiev
Summary: In a study of a chronically HIV-1/HCV co-infected individual, researchers identified five cross-reactive antibodies that show exceptional neutralization breadth and effector functions against both HIV-1 and HCV. One antibody also cross-reacts with influenza and coronaviruses, including SARS-CoV-2. The development of these antibodies is closely related to somatic hypermutation, providing potential directions for therapeutic and vaccine development against current and emerging infectious diseases. Chronic co-infection represents a complex immunological challenge that can provide insights into the fundamental rules of antibody-antigen specificity.
Article
Instruments & Instrumentation
Pavitra Solanki, Mohd Danish Ansari, Mohd Iqbal Alam, Mohd Aqil, Farhan J. Ahmad, Yasmin Sultana
Summary: This study developed an orally effective nanoformulation of disodium pamidronate for the treatment of osteoporosis. Through rational design and optimization, a commercially potential self nano-emulsifying drug delivery system (SNEDDS) was developed, which showed improved oral bioavailability and enhanced anti-osteoporotic activity. The study provided significant achievements in the treatment of postmenopausal osteoporosis and may lead to the use of nanotherapeutic-driven emerging biodegradable carriers-based drug delivery.
DRUG DELIVERY AND TRANSLATIONAL RESEARCH
(2023)
Letter
Biotechnology & Applied Microbiology
M. Michael Gromiha, Kannan Harini
Summary: Mei and colleagues introduced PNATDB, a thermodynamic database for protein-nucleic acid interactions with 12,635 experimentally determined parameters. They claimed that extracting data from existing databases is challenging. However, they did not discuss ProNAB, which contains over 20,000 experimental data points for binding affinities of protein-nucleic acid complexes and other information.
TRENDS IN BIOTECHNOLOGY
(2023)
Article
Genetics & Heredity
Sankaran Venkatachalam, Nisha Murlidharan, Sowmya R. Krishnan, C. Ramakrishnan, Mpho Setshedi, Ramesh Pandian, Debmalya Barh, Sandeep Tiwari, Vasco Azevedo, Yasien Sayed, M. Michael Gromiha
Summary: AIDS is a challenging infectious disease with a need for understanding drug resistance mechanisms. A new double-insertion mutation (L38HL) in HIV subtype C protease was investigated for its potential in inducing drug resistance towards the protease inhibitor Saquinavir (SQV). Computational techniques revealed that the L38HL mutation increased flexibility in certain regions and decreased binding affinity of SQV compared to wild-type. The mutation also resulted in a wide opening at the binding site and altered flap dynamics, leading to decreased interactions with the binding site and a potential drug resistance phenotype.
Article
Chemistry, Medicinal
Sowmya Ramaswamy Krishnan, Ruben R. G. Soares, Narayanan Madaboosi, M. Michael Gromiha
Summary: The emergence of new zoonotic infections among humans has increased the burden on global healthcare systems to control their spread. To address this, a novel and integrated PLP design pipeline called AutoPLP has been developed, which can automate the probe design process for a diverse pathogen panel of interest.
ACS INFECTIOUS DISEASES
(2023)
Article
Biochemical Research Methods
Jianfeng Sun, Arulsamy Kulandaisamy, Jinlong Ru, M. Michael Gromiha, Adam P. Cribbs
Summary: TMKit is an open-source Python programming interface specifically designed for processing transmembrane protein data. It includes tools for database wrangling, feature engineering, and protein-protein interaction visualization. Additionally, it offers the high-performance computing library seqNetRR for fast construction of residue connections and allocation of correlation matrix-based features. TMKit serves as a useful tool for researchers studying transmembrane protein sequences and structures.
BRIEFINGS IN BIOINFORMATICS
(2023)
Article
Biochemistry & Molecular Biology
Anuja Jain, Tina Begum, Shandar Ahmad
Summary: Identifying the molecular features of host Toll-like receptors (TLRs), which are responsible for sensing pathogen nucleic acids, is important for understanding host defense mechanisms. We found that these features directly correlate with the strand specificity of the pathogen nucleic acids, but cannot fully explain the selectivity of pathogenic molecular patterns. Using machine learning, we developed a model that accurately predicts the strand specificity of TLRs based on protein-derived features.
JOURNAL OF MOLECULAR BIOLOGY
(2023)
Article
Genetics & Heredity
Nela Pragathi Sneha, S. Akila Parvathy Dharshini, Y. -H Taguchi, M. Michael Gromiha
Summary: In this study, the relationship between genetic variants and differentially expressed genes/transcripts in the BA4 region of Huntington's disease patients was investigated. The study identified variants that regulate gene expression and highlighted variants affecting miRNA and its targets. Co-expression network analysis revealed the role of novel genes, while function interaction network analysis showed the importance of genes involved in vesicle-mediated transport. The study also emphasized the crucial role of genes expressed in immune cells in reducing neuron death in Huntington's disease.
Proceedings Paper
Computer Science, Artificial Intelligence
Neha Vinayak, Shandar Ahmad
Summary: High quality and numerous data are essential for machine learning models. This study examines the issue using low-dimensional data sets and random forest as the classification model. The authors provide an initial estimate for the optimal data size requirement and observe that ML models can still perform well even with some class label errors.
ADVANCED NETWORK TECHNOLOGIES AND INTELLIGENT COMPUTING, ANTIC 2022, PT II
(2023)
Article
Computer Science, Information Systems
Hanif Amal Robbani, Alhadi Bustamam, Risman Adnan, Shandar Ahmad
Summary: This paper proposes an EKFAC preconditioned SGLD algorithm (EKSGLD), which improves the optimization process and combines the advantages of second-order optimization and the approximate Bayesian method. Experimental results show that EKSGLD outperforms existing preconditioning methods in terms of predictive accuracy and calibration.
Article
Biochemistry & Molecular Biology
Fathima Ridha, M. Michael Gromiha
Summary: Membrane protein-protein interactions are crucial for cellular functions. This study collected experimental data of membrane protein-protein complexes and derived features to understand the factors influencing binding affinity. A machine learning method, MPA-Pred, was developed to predict the binding affinity and showed high accuracy in the prediction.
PROTEINS-STRUCTURE FUNCTION AND BIOINFORMATICS
(2023)