Article
Chemistry, Medicinal
Dmitrij Rappoport, Adrian Jinich
Summary: This study constructs and evaluates three-dimensional feature representations of protein structures based on space-filling curves (SFCs), with the aim of accurately predicting protein properties and functions. SFCs, such as the Hilbert curve and the Morton curve, enable the encoding of three-dimensional molecular structures in a system-independent manner using a few adjustable parameters. The performance of the SFC-based feature representations is assessed in predicting enzyme substrate properties, yielding high accuracies and AUC values for the classification tasks.
JOURNAL OF CHEMICAL INFORMATION AND MODELING
(2023)
Article
Biochemistry & Molecular Biology
Rhiju Das, Rachael C. Kretsch, Adam J. Simpkin, Thomas Mulvaney, Phillip Pham, Ramya Rangan, Fan Bu, Ronan M. Keegan, Maya Topf, Daniel J. Rigden, Zhichao Miao, Eric Westhof
Summary: This study reports the assessment of RNA structure predictions and highlights the performance of traditional methods compared to deep learning approaches. The evaluation, based on modeling and comparison with experimental data, shows that models generated by deep learning were worse than those generated by traditional methods. The study also demonstrates the potential utility of current RNA modeling approaches in RNA nanotechnology and structural biology.
PROTEINS-STRUCTURE FUNCTION AND BIOINFORMATICS
(2023)
Article
Biochemistry & Molecular Biology
Do Soon Kim, Andrew Watkins, Erik Bidstrup, Joongoo Lee, Ved Topkar, Camila Kofman, Kevin J. Schwarz, Yan Liu, Grigore Pintilie, Emily Roney, Rhiju Das, Michael C. Jewett
Summary: The study introduces a method called Evolink, which enables high-throughput evolution of sequence-distant regions in large macromolecular machines and guides library design through computational RNA modeling. This approach may enhance the engineering of macromolecular machines for new functions in synthetic biology.
NATURE CHEMICAL BIOLOGY
(2022)
Article
Chemistry, Physical
Wenjin Cao, Hanhui Zhang, Qinqin Yuan, Xiaoguo Zhou, Steven R. Kass, Xue-Bin Wang
Summary: This study used NIPES to probe specific binding sites of methylated glycine derivatives, revealing that increasing methylation leads to simplification of the iodide clusters due to fewer contributing structures. Low energy conformers and tautomers of each cluster were computationally identified and assigned based on excellent agreement between experimental NIPE spectra and theoretical simulations. Zwitterionic cluster structures were found to be less stable than canonical forms and did not contribute to the observed spectra.
JOURNAL OF PHYSICAL CHEMISTRY LETTERS
(2021)
Article
Multidisciplinary Sciences
Hua Deng, Chaofeng Lou, Zengrui Wu, Weihua Li, Guixia Liu, Yun Tang
Summary: In this study, a two-layer stacking ensemble model called AIPStack was proposed for accurate prediction of anti-inflammatory peptides (AIPs). By constructing a new dataset and using hybrid features representation, AIPStack achieved high accuracy and discriminative performance on an independent set, demonstrating its potential for aiding inflammation treatment.
Review
Biochemistry & Molecular Biology
Yuchao Liang, Siqi Yang, Lei Zheng, Hao Wang, Jian Zhou, Shenghui Huang, Lei Yang, Yongchun Zuo
Summary: This article systematically reviews and summarizes the strategies and methods used in reducing amino acid alphabets and their applications in protein sequence alignment, functional classification, and prediction of structural properties.
COMPUTATIONAL AND STRUCTURAL BIOTECHNOLOGY JOURNAL
(2022)
Article
Genetics & Heredity
Xu Zhang, Yiwei Liu, Yaming Wang, Liang Zhang, Lin Feng, Bo Jin, Hongzhe Zhang
Summary: Predicting protein secondary structure is crucial in the field of bioinformatics. Existing methods face performance limitations. This study proposes a framework called Multistage Combination Classifier Augmented Model (MCCM) that improves prediction performance through feature extraction, multistage combination classifiers, and sample difficulty discrimination. Experimental results demonstrate that this method outperforms most state-of-the-art models.
FRONTIERS IN GENETICS
(2022)
Article
Pharmacology & Pharmacy
Muhammad Ali, Muhammad Aurongzeb, Yasmeen Rashid
Summary: The study investigated the three-dimensional structures of key pathogenic factors of Neisseria meningitidis and identified their roles in amino acid biosynthesis. Analysis of homology models and templates revealed structural and topological similarities, as well as conserved active site residues. These findings lay the foundation for future computer-aided drug design.
PAKISTAN JOURNAL OF PHARMACEUTICAL SCIENCES
(2021)
Article
Biochemical Research Methods
Tzu-Hsuan Wu, Peng-Chan Lin, Hsin-Hung Chou, Meng-Ru Shen, Sun-Yuan Hsieh
Summary: In this study, machine learning methods and Rosetta Energy Function 2015 were combined to predict the pathogenicity of single amino acid variants (SAVs). The accuracy level of the proposed model (0.76) was higher than that of six other prediction tools. Differential reference energies, attractive energies, and solvation of polar atoms between wildtype and mutant side-chains played essential roles in distinguishing benign from pathogenic variants. These findings suggest that energy scores calculated from protein structures are more appropriate and detailed representations of SAV pathogenicity.
IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS
(2023)
Article
Multidisciplinary Sciences
Diego del Alamo, Lillian DeSousa, Rahul M. Nair, Suhaila Rahman, Jens Meiler, Hassane S. Mchaourab
Summary: The APC transporter GadC plays a role in the survival of pathogenic bacteria under extreme acid stress. Through studying its conformational dynamics using DEER spectroscopy, researchers discovered acid-induced conformational changes in GadC, enabling isomerization between inward- and outward-facing states. The substrate glutamate modulates the dynamics of an extracellular gate. This study provides insights into the conformational cycle of GadC and highlights the divergence in dynamics among different families in the LeuT-fold.
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
(2022)
Article
Biochemistry & Molecular Biology
Yang Yang, Zhang Chong, Mauno Vihinen
Summary: Most proteins fold into unique three-dimensional structures and their folding rates can be influenced by variations in proteins. We developed a machine-learning-based method, PON-Fold, to predict the folding rate effects of amino acid substitutions in two-state folding proteins. PON-Fold outperformed existing tools in blind tests, showing higher specificity, sensitivity, and correlation coefficient. The tool was also tested for protein domain substitutions and showed varying predictions depending on protein conformations and structures.
INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES
(2023)
Article
Biochemistry & Molecular Biology
Yao Xiong, Jing-Bo Zhou, Ke An, Wei Han, Tao Wang, Zhi-Qiang Ye, Yun-Dong Wu
Summary: The study introduces a novel AAS3D-RF prediction model that incorporates both sequence and three-dimensional structure features, offering more accurate predictions and understanding of pathogenic AAS. The model demonstrates superior performance on two independent testing datasets, with higher accuracy and Matthews correlation coefficient compared to seven existing tools.
FRONTIERS IN BIOSCIENCE-LANDMARK
(2021)
Article
Biochemistry & Molecular Biology
Riley E. Perszyk, Anders S. Kristensen, Polina Lyuboslavsky, Stephen F. Traynelis
Summary: This study utilizes genetic information to investigate single-nucleotide variants in the human population and introduces a new method (3DMTR) for more accurately classifying the functional consequences of protein mutations.
Article
Biochemistry & Molecular Biology
Ge Wang, Yu-Jia Zhai, Zhen-Zhen Xue, Ying-Ying Xu
Summary: Protein subcellular locations are closely related to their functions; Deep neural networks can extract various features for classifying protein subcellular locations; Structural features have a certain effect on protein location classification and can help improve predictor performance.
Article
Biochemistry & Molecular Biology
Arthur M. Lesk, Arun S. Konagurthu
Summary: Alignment is the fundamental operation in protein evolution analysis, which involves determining residue-residue correspondences. Structural alignments, based on atomic positions in protein structures, are more accurate than sequence-based pairwise alignments, even for highly diverged proteins. AlphaFold2's success in predicting three-dimensional structures from amino acid sequences implies that reliable alignments can be achieved by applying AlphaFold2 to sequence data, even for highly diverged proteins without experimentally determined structures.
PROTEINS-STRUCTURE FUNCTION AND BIOINFORMATICS
(2022)
Article
Computer Science, Information Systems
Bruno Iochins Grisci, Mathias J. Krause, Marcio Dorn
Summary: The study introduces a relevance aggregation algorithm that combines the relevance computed from multiple samples by a neural network to generate scores for each input feature. Two visualization methods for learned patterns were presented to enhance model comprehension. The method accurately identifies the most important features for network predictions.
INFORMATION SCIENCES
(2021)
Article
Chemistry, Multidisciplinary
Ederson Sales Moreira Pinto, Bruno Cesar Feltes, Conrado Pedebos, Marcio Dorn
Summary: Bioremediation is highlighted as a cost-effective and eco-friendly alternative to reverse the damage caused by oil pollution, but more efforts are needed to design enzymes for the process.
JOURNAL OF COMPUTATIONAL CHEMISTRY
(2021)
Article
Biology
Manuel Villalobos-Cid, Cesar Rivera, Eduardo I. Kessi-Perez, Mario Inostroza-Ponta
Summary: Analyzing evolutionary data provides important information about the adaptation and evolution of organisms. Optimization methods, particularly multiobjective optimization, are effective approaches for addressing this problem. In this paper, a multi-modal metaheuristic approach is used to tackle the multiobjective phylogenetic inference problem. A novel metric based on topological tree distance is introduced. Comparative analysis with state of the art algorithms and a case study on yeast dataset demonstrate the improved diversity and quality of solutions achieved by the proposed method.
Article
Computer Science, Interdisciplinary Applications
Itamar Jose Guimaraes Nunes, Bruno Cesar Feltes, Murilo Zanini David, Marcio Dorn
Summary: This study presents a new R package called GEVA, which can analyze multiple gene expression datasets and identify genes with consistent differential expression across experiments. GEVA takes multiple differential expression analysis results as input and performs weighted summarization, partitioning, and clustering to find genes that vary less across experiments. The package also allows grouping of experimental conditions into factors, and performs additional statistical tests to identify genes specifically or dependently expressed in response to these factors. Results from the package were validated using knockout studies and were found to be consistent with published experimental studies. GEVA provides a robust alternative for multiple comparison analyses.
JOURNAL OF BIOMEDICAL INFORMATICS
(2022)
Article
Genetics & Heredity
Gabriela Flores Goncalves, Joice de Faria Poloni, Marcio Dorn
Summary: This study investigated the association between lncRNAs and host response to C. albicans infection. By analyzing a public RNA-Seq dataset, the researchers identified lncRNAs related to biological processes involved in immune response and reactive species production. These findings provide new insights into the functions of lncRNAs in the immune response.
Review
Genetics & Heredity
Regis Antonioli Junior, Joice de Faria Poloni, Ederson Sales Moreira Pinto, Marcio Dorn
Summary: Biosurfactants are amphipathic molecules that can lower interfacial and superficial tensions. They are biodegradable and have a wide diversity of categories, with lipopeptides being the most abundant and well-known. Protein-containing biosurfactants are less studied but could be a valuable alternative. Understanding the harsh conditions that target organisms can sustain is crucial for successful implementation. This article explores the biotechnological applications of lipopeptide and protein-containing biosurfactants, as well as their natural role and the potential research possibilities using meta-omics and machine learning.
Article
Computer Science, Artificial Intelligence
Manuel Villalobos-Cid, Marcio Dorn, Angela Contreras, Mario Inostroza-Ponta
Summary: Phylogenetic networks are used to capture evolutionary phenomena that cannot be described by phylogenetic trees, and different inference criteria may result in conflicting network topologies. Multi-objective optimization can handle conflicting objectives and reduce dependency on specific criteria. In this study, a multi-objective evolutionary algorithm called MO-PhyNet is proposed to infer phylogenetic networks by minimizing three criteria: hardwired parsimony, softwired parsimony, and the number of reticulations. The formalization of phylogenetic inference as a multi-objective optimization problem allows for the identification of different reticulated topologies representing distinct evolutionary hypotheses.
APPLIED SOFT COMPUTING
(2023)
Article
Computer Science, Software Engineering
Adrian Kummerlaender, Marcio Dorn, Martin Frank, Mathias J. Krause
Summary: This article revisits and extends the work on implicit propagation on directly addressed grids by considering them as transformations of the underlying space filling curve. A new periodic shift (PS) pattern is proposed that provides consistent performance across a range of targets. Benchmark results for PS and shift-swap-streaming (SSS) on SIMD CPUs and Nvidia GPUs are provided, and the application of PS as the propagation pattern of the open source LBM framework OpenLB is summarized.
CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE
(2023)
Article
Chemistry, Multidisciplinary
Bruno Cesar Feltes, Ederson Sales Moreira Pinto, Arthur Tonietto Mangini, Marcio Dorn
Summary: The NIAS server is a tool that analyzes the conformational preferences of amino acids in proteins. It is based on the Angle Probability List and integrates structural information from the Protein Data Bank. The updated NIAS server includes data from various experimental techniques and provides examples of its application in structural biology research, as well as discussions on its limitations.
JOURNAL OF COMPUTATIONAL CHEMISTRY
(2023)
Article
Biochemistry & Molecular Biology
Regis Antonioli Junior, Joice de Faria Poloni, Manuel Riveros A. Escalona, Marcio Dorn
Summary: Crude oil contamination poses a significant threat to the environment and biodiversity. However, certain microorganisms in contaminated areas can utilize hydrocarbons as a source of nutrients, highlighting the importance of understanding local community dynamics in these environments. Through the analysis of genetic and functional data, we identified the prevalence of hydrocarbon-degrading functionality in contaminated sediments and potential targets for bioremediation.
Article
Biochemistry & Molecular Biology
Manuel Adrian Riveros Escalona, Joice de Faria Poloni, Mathias J. Krause, Marcio Dorn
Summary: Colorectal cancer is commonly associated with changes in the gut microbiota, and recent studies have provided a better understanding of the specific species related to its development. However, the importance of certain species and their interactions across different datasets still needs further exploration.
Review
Computer Science, Artificial Intelligence
Bruno I. Grisci, Bruno Cesar Feltes, Joice de Faria Poloni, Pedro H. Narloch, Marcio Dorn
Summary: A review of over 1200 publications on feature selection and gene expression between 2010 and 2020 found that 57% of the publications used outdated datasets, 23% used only outdated data, and 32% did not cite data sources. Problems such as referencing unavailable databases, slow adoption of RNA-seq datasets, and bias towards human cancer data were also identified. These issues can result in inaccurate and misleading biological results.
WILEY INTERDISCIPLINARY REVIEWS-DATA MINING AND KNOWLEDGE DISCOVERY
(2023)
Proceedings Paper
Computer Science, Artificial Intelligence
Mateus Boiani, Rafael Stubs Parpinelli, Marcio Dorn
Summary: Population diversity plays a crucial role in determining the quality of solutions in Evolutionary Algorithms. This paper proposes a new migration policy for the BRKGA and compares its performance with traditional strategies in continuous search spaces. The results show that the proposal can improve the optimization capability of the BRKGA.
INTELLIGENT SYSTEMS, PT I
(2022)
Proceedings Paper
Computer Science, Artificial Intelligence
Felipe Colombelli, Vitor Kehl Matter, Bruno Iochins Grisci, Leomar Lima, Karine Heinen, Marcio Borges, Sandro Jose Rigo, Jorge Luis Victoria Barbosa, Rodrigo Da Rosa Righi, Cristiano Andre da Costa, Gabriel De Oliveira Ramos
Summary: Securing information in data centers is a major challenge nowadays. Prioritizing vulnerabilities is crucial to prevent leaks and other security issues. However, there is limited research on intelligent methods for vulnerability prioritization. This study proposes a multi-objective method that ranks vulnerabilities based on user-chosen assessment metrics. Experimental results show that the proposed method can significantly reduce the number of vulnerabilities needed to achieve an organization's security targets compared to baseline analysis by a security team.
2022 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION (CEC)
(2022)
Article
Biology
Kunal Bhattacharya, Shikha Mahato, Satyendra Deka, Nongmaithem Randhoni Chanu, Amit Kumar Shrivastava, Pukar Khanal
Summary: Chemoresistance, a major challenge in cancer treatment, is associated with the cellular glutathione-related detoxification system. A study has identified GSTP1 enzyme as critical in the inactivation of anticancer drugs and suggests the need for GSTP1 inhibitors to combat chemoresistance. Through molecular docking and simulations, the study found that quercetin 7-O-beta-D-glucoside showed promise as a potential candidate for addressing chemoresistance in cancer patients.
COMPUTATIONAL BIOLOGY AND CHEMISTRY
(2024)
Article
Biology
Manwi Shankar, Majji Sai Sudha Rani, Priyanka Gopi, P. Arsha, Prateek Pandya
Summary: This study investigates the interaction between the food dye BBY and the serum protein BSA. The results show that BBY binds to a specific site on BSA through hydrophobic interactions, affecting the structural stability of the protein. These findings enhance our understanding of the molecular-level interactions between BBY and BSA.
COMPUTATIONAL BIOLOGY AND CHEMISTRY
(2024)
Article
Biology
Chi Zhang, Qian Gao, Ming Li, Tianfei Yu
Summary: In this study, we propose a graph neural network-based autoencoder model, AGraphSAGE, that effectively predicts protein-protein interactions across diverse biological species by integrating gene ontology.
COMPUTATIONAL BIOLOGY AND CHEMISTRY
(2024)
Article
Biology
Kangjie Wu, Liqian Xu, Xinxiang Li, Youhua Zhang, Zhenyu Yue, Yujia Gao, Yiqiong Chen
Summary: Named Entity Recognition (NER) is a crucial task in natural language processing (NLP) and big data analysis, with wide application range. This paper proposes an improved neural network method for NER of rice genes and phenotypes, which can learn semantic information in the context without feature engineering. Experimental results show that the proposed model outperforms other models.
COMPUTATIONAL BIOLOGY AND CHEMISTRY
(2024)
Article
Biology
Suman Hait, Sudip Kundu
Summary: Interactions between amino acids in proteins are crucial for stability and structural integrity. Thermophiles have more and more stable interactions to survive in extreme environments. Different types of interactions are enriched in different structural regions.
COMPUTATIONAL BIOLOGY AND CHEMISTRY
(2024)
Article
Biology
Kountay Dwivedi, Ankit Rajpal, Sheetal Rajpal, Virendra Kumar, Manoj Agarwal, Naveen Kumar
Summary: This study aims to identify biomarkers for non-small cell lung cancer (NSCLC) using copy number variation (CNV) data. A novel deep learning architecture, XL1R-Net, is proposed to improve the classification accuracy for NSCLC subtyping. Twenty NSCLC-relevant biomarkers are uncovered using explainable AI (XAI)-based feature identification. The results show that the identified biomarkers have high classification performance and clinical relevance. Additionally, twelve of the biomarkers are potentially druggable and eighteen of them have a high probability of predicting NSCLC patients' survival likelihood according to the Drug-Gene Interaction Database and the K-M Plotter tool, respectively. This research suggests that investigating these seven novel biomarkers can contribute to NSCLC therapy, and the integration of multiomics data and other sources will help better understand NSCLC heterogeneity.
COMPUTATIONAL BIOLOGY AND CHEMISTRY
(2024)
Article
Biology
Pengli Lu, Wenqi Zhang, Jinkai Wu
Summary: Researchers have developed a computational method, AMPCDA, to predict circRNA-disease associations using predefined metapaths, achieving high predictive accuracy. This method effectively combines node embeddings with higher-order neighborhood representations and provides valuable guidance for revealing new disease mechanisms in biological research.
COMPUTATIONAL BIOLOGY AND CHEMISTRY
(2024)
