Article
Biochemical Research Methods
Tzu-Hsien Yang
Summary: A novel method was proposed in this research to identify the functionally interpretable structure ensemble of a given RNA sequence and provide the meta-stable structure based on the ensemble. The method outperformed existing tools in predicting meta-stable structures and showed resistance to sequence length-dependent performance deterioration. The study demonstrated the importance of considering RNA structure ensembles for functional hypotheses.
IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS
(2022)
Review
Biochemistry & Molecular Biology
Xunxun Wang, Shixiong Yu, En Lou, Ya-Lan Tan, Zhi-Jie Tan
Summary: Ribonucleic acid (RNA) molecules have important biological functions such as catalysis and gene regulation, which are strongly dependent on their structure. In the past two decades, computational models have been developed to predict RNA three-dimensional (3D) structures. These models predict the structure ensemble, evaluate near-native RNAs, and refine the identified RNAs. This review provides a comprehensive overview of recent advances in RNA 3D structure modeling, including structure ensemble prediction, evaluation, and refinement, and highlights insights and perspectives in modeling RNA 3D structures.
Article
Biochemical Research Methods
Sumit Mukherjee, Matan Drory Retwitzer, Sara M. Hubbell, Michelle M. Meyer, Danny Barash
Summary: Riboswitches are conserved RNA sensors that mainly regulate genes/operons in bacteria. The challenge is to discover riboswitch classes in eukaryotes and understand the evolution of bacterial riboswitches. A novel approach based on inverse RNA folding was developed to identify potential structural candidates in fungi that could be distant homologs of bacterial riboswitches. This method transforms a structure-based search into a sequence-based search, considering the conservation of secondary structure shape and ligand-binding residues.
BRIEFINGS IN BIOINFORMATICS
(2023)
Article
Biochemical Research Methods
Kengo Sato, Yuki Kato
Summary: Pseudoknots are important RNA structural elements involved in various biological phenomena. Current methods for secondary structure prediction considering pseudoknots are not widely available. We propose an improved version of IPknot that enables linear time calculation and automatic selection of optimal threshold parameters.
BRIEFINGS IN BIOINFORMATICS
(2022)
Article
Biochemistry & Molecular Biology
Margherita A. G. Matarrese, Alessandro Loppini, Martina Nicoletti, Simonetta Filippi, Letizia Chiodo
Summary: The study of RNA structure is crucial in understanding RNA molecular functioning. With the flexibility of RNA, the large number of expressed RNAs, and the diverse functions they have, it is difficult to obtain structural information on the same scale as is available for proteins. In silico prediction of RNA 3D structures is particularly important to understand the relationship between structure and function, as the 3D structure plays a significant role in molecular interactions with DNA or protein complexes. The accuracy of RNA 3D structure prediction relies on a properly predicted or measured secondary structure. This paper comparatively evaluates computational tools for modeling RNA secondary structure, focusing on freely available web-server versions for more accessible use. The evaluation focuses on the performance for long sequences and aims to select the best methods for investigating long non-coding RNAs (lncRNAs), which are of special relevance due to their involvement in regulatory mechanisms.
JOURNAL OF BIOMOLECULAR STRUCTURE & DYNAMICS
(2023)
Review
Biochemical Research Methods
L. A. Bugnon, A. A. Edera, S. Prochetto, M. Gerard, J. Raad, E. Fenoy, M. Rubiolo, U. Chorostecki, T. Gabaldon, F. Ariel, L. E. Di Persia, D. H. Milone, G. Stegmayer
Summary: This study compares the performance of classical methods and recently proposed approaches for predicting RNA secondary structure, and introduces a new metric based on chemical probing data to assess their predictive performance. The results provide a comprehensive assessment and a benchmark resource for future approaches.
BRIEFINGS IN BIOINFORMATICS
(2022)
Article
Biochemistry & Molecular Biology
Gabriel Bianchin de Oliveira, Helio Pedrini, Zanoni Dias
Summary: Protein secondary structure prediction is vital in biological processes, with computational methods becoming the primary approach. By utilizing ensembles of different sub-classifiers, the accuracy of predictions can be improved.
INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES
(2021)
Article
Biochemical Research Methods
Mehdi Saman Booy, Alexander Ilin, Pekka Orponen
Summary: This paper presents a simple yet effective data-driven approach for predicting the secondary structure of RNA strands. By using a convolutional neural network and three-dimensional tensors representation, the method achieves significant accuracy improvements on experimental datasets for 10 RNA families and performs well across a wide range of sequence lengths.
BMC BIOINFORMATICS
(2022)
Review
Genetics & Heredity
Payal Gupta, Rushikesh M. Khadake, Shounok Panja, Krushna Shinde, Ambadas B. Rode
Summary: RNA molecules play crucial roles in cell physiology and disease, with their ability to adopt complex structures rivaling that of proteins. Understanding RNA structures is important for understanding genetic diseases, infectious diseases, and therapeutic applications.
Review
Biochemical Research Methods
Kengo Sato, Michiaki Hamada
Summary: Computational analysis of RNA sequences plays a crucial role in RNA biology. In recent years, the incorporation of artificial intelligence and machine learning techniques into RNA sequence analysis has gained significant traction. Machine learning-based approaches have shown remarkable advancements, enhancing the precision of sequence analysis related to RNA secondary structures. Furthermore, artificial intelligence and machine learning innovations are also applied in the analysis of RNA-small molecule interactions, RNA drug discovery, and the design of RNA aptamers.
BRIEFINGS IN BIOINFORMATICS
(2023)
Article
Biochemical Research Methods
Mengyi Tang, Kumbit Hwang, Sung Ha Kang
Summary: This study proposes a new deterministic methodology for predicting the secondary structure of RNA sequences, which uses a simple algorithm to predict the structure of short RNA and tRNA sequences and provides a deterministic answer.
IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS
(2023)
Article
Biochemical Research Methods
Chun-Chi Chen, Yi-Ming Chan
Summary: In this paper, a deep learning-based method called REDfold is proposed for RNA secondary structure prediction. The method utilizes a CNN-based encoder-decoder network to learn the dependencies between RNA sequences and employs symmetric skip connections to efficiently propagate activation information. Additionally, the network output is post-processed with constrained optimization for accurate predictions, even for RNAs with pseudoknots. Experimental results demonstrate that REDfold outperforms contemporary state-of-the-art methods in terms of efficiency and accuracy.
BMC BIOINFORMATICS
(2023)
Article
Biochemistry & Molecular Biology
Kangkun Mao, Jun Wang, Yi Xiao
Summary: Deep learning methods have shown better performance than traditional methods in RNA secondary structure prediction, but there is still room for improvement. This is because the length and secondary structures of RNAs vary significantly. Existing deep learning models can't learn very different secondary structures since they are length-independent. In this study, we propose a length-dependent model that further trains the length-independent model using transfer learning for different length ranges of RNAs. The benchmarking results demonstrate that the length-dependent model outperforms the usual length-independent model.
Article
Biochemistry & Molecular Biology
Minmin Zhang, Guangfeng Liu, Yunlong Zhang, Ting Chen, Shanshan Feng, Rujie Cai, Changrui Lu
Summary: Riboswitches are noncoding RNAs that regulate gene expressions through structural changes in response to ligand binding. This study focuses on the tetrahydrofolic acid-responsive second class of tetrahydrofolate (THF-II) riboswitches and reveals that they undergo local conformation changes in response to ligand binding, suggesting a unique regulatory mechanism. These findings have implications for RNA sensors and drug design.
INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES
(2022)
Article
Biochemical Research Methods
Mandy Ibene, Audrey Legendre, Guillaume Postic, Eric Angel, Fariza Tahi
Summary: RNAs can interact with other molecules to form complexes and predicting the structure of these complexes is important but challenging. This study focuses on RNA complexes composed of multiple interacting RNAs and shows how existing knowledge and probing data can help predict their secondary structure. The researchers developed an interactive tool called C-RCPRed, based on a multi-objective optimization algorithm, and demonstrated its efficiency and the positive impact of considering user knowledge and probing data through extensive benchmarking. C-RCPRed is freely available as an open-source program and web server on the EvryRNA website.
BRIEFINGS IN BIOINFORMATICS
(2023)
Article
Cell Biology
Yuxin Wang, Qiaoling Song, Wei Huang, Yuxi Lin, Xin Wang, Chenyao Wang, Belinda Willard, Chenyang Zhao, Jing Nan, Elise Holvey-Bates, Zhuoya Wang, Derek Taylor, Jinbo Yang, George R. Stark
Summary: Type I interferons (IFN-I) play a crucial role in protecting against viral infections, with STAT2 being a key component in driving gene expression in response to IFN-I. The phosphorylation of STAT2 at T404 acts as a critical conformational switch, disrupting an inactive STAT1-STAT2 dimer and enhancing the antiviral defense response to IFN-I. Additionally, virus infection-induced activation of IKK-epsilon can directly phosphorylate T404, further facilitating the antiviral defense mechanism.
Article
Chemistry, Medicinal
Austin Clyde, Stephanie Galanie, Daniel W. Kneller, Heng Ma, Yadu Babuji, Ben Blaiszik, Alexander Brace, Thomas Brettin, Kyle Chard, Ryan Chard, Leighton Coates, Ian Foster, Darin Hauner, Vilmos Kertesz, Neeraj Kumar, Hyungro Lee, Zhuozhao Li, Andre Merzky, Jurgen G. Schmidt, Li Tan, Mikhail Titov, Anda Trifan, Matteo Turilli, Hubertus Van Dam, Srinivas C. Chennubhotla, Shantenu Jha, Andrey Kovalevsky, Arvind Ramanathan, Martha S. Head, Rick Stevens
Summary: This paper describes the discovery of a novel noncovalent small-molecule inhibitor targeting the main protease of SARS-CoV-2, utilizing high-throughput virtual screening and a compound library of over 6.5 million molecules. Downstream biochemical assays validate the effectiveness and binding mechanism of the inhibitor, providing a reference for future therapeutic design.
JOURNAL OF CHEMICAL INFORMATION AND MODELING
(2022)
Article
Multidisciplinary Sciences
Weiming Hu, Guido Cervone, Andre Merzky, Matteo Turilli, Shantenu Jha
Summary: This dataset is an ensemble of solar photovoltaic energy production simulations over the continental US. It provides data for a high spatio-temporal analysis of power production under different weather and engineering scenarios.
Article
Computer Science, Theory & Methods
Andre Merzky, Matteo Turilli, Mikhail Titov, Aymen Al-Saadi, Shantenu Jha
Summary: The article introduces RADICAL-Pilot as a portable, modular, and extensible pilot-enabled runtime system that can scalably execute large workloads on supercomputers.
IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS
(2022)
Article
Biochemistry & Molecular Biology
Eden M. Gallegos, Tanner D. Reed, Forge A. Mathes, Nelson V. Guevara, David B. Neau, Wei Huang, Marcia E. Newcomer, Nathaniel C. Gilbert
Summary: This study reveals two distinct conformations of 5-lipoxygenase (5-LOX) in its catalytic reaction through structural and dynamic experiments. By strategic mutations, researchers successfully unlocked a more open conformation and improved the enzyme's activity. These findings are of great importance for understanding the synthesis mechanism of leukotrienes.
JOURNAL OF BIOLOGICAL CHEMISTRY
(2022)
Article
Computer Science, Hardware & Architecture
Abigail Dommer, Lorenzo Casalino, Fiona Kearns, Mia Rosenfeld, Nicholas Wauer, Surl-Hee Ahn, John Russo, Sofia Oliveira, Clare Morris, Anthony Bogetti, Anda Trifan, Alexander Brace, Terra Sztain, Austin Clyde, Heng Ma, Chakra Chennubhotla, Hyungro Lee, Matteo Turilli, Syma Khalid, Teresa Tamayo-Mendoza, Matthew Welborn, Anders Christensen, Daniel Ga Smith, Zhuoran Qiao, Sai K. Sirumalla, Michael O'Connor, Frederick Manby, Anima Anandkumar, David Hardy, James Phillips, Abraham Stern, Josh Romero, David Clark, Mitchell Dorrell, Tom Maiden, Lei Huang, John McCalpin, Christopher Woods, Alan Gray, Matt Williams, Bryan Barker, Harinda Rajapaksha, Richard Pitts, Tom Gibbs, John Stone, Daniel M. Zuckerman, Adrian J. Mulholland, Thomas Miller, Shantenu Jha, Arvind Ramanathan, Lillian Chong, Rommie E. Amaro
Summary: This study aims to completely revise the current models of airborne transmission of SARS-CoV-2 virus by providing atomic-level views of the virus within respiratory aerosols. It extends the capabilities of multiscale computational microscopy to address the limitations of current experimental methods in interrogating aerosols at the atomic/molecular level. The study presents initial scientific discoveries for the SARS-CoV-2 Delta variant, highlighting the potential scientific impact of the work.
INTERNATIONAL JOURNAL OF HIGH PERFORMANCE COMPUTING APPLICATIONS
(2023)
Article
Multidisciplinary Sciences
Jiaqiang Zhu, Wei Huang, Jing Zhao, Loc Huynh, Derek J. Taylor, Michael E. Harris
Summary: This study reveals the conformational changes during the recognition process of bacterial RNase P using high-throughput enzymology and cryoEM. The specific pairing of precursor tRNA inhibits the formation of the catalytic conformation. Comparisons of different precursor tRNA structures show that RNase P primarily uses stacking interactions and shape complementarity to accommodate alternative sequences. The study uncovers the active site interactions and conformational changes of RNase P, laying the foundation for understanding the link between binding interactions and catalysis.
NATURE COMMUNICATIONS
(2022)
Article
Multidisciplinary Sciences
Austin Clyde, Xuefeng Liu, Thomas Brettin, Hyunseung Yoo, Alexander Partin, Yadu Babuji, Ben Blaiszik, Jamaludin Mohd-Yusof, Andre Merzky, Matteo Turilli, Shantenu Jha, Arvind Ramanathan, Rick Stevens
Summary: Protein-ligand docking is a computational method used to identify potential drug compounds, and it has been widely used in drug discovery. This study demonstrates the power of high-speed ML models by scoring 1 billion molecules in less than a day. By utilizing surrogate AI-based models as a pre-filter, the workflow is 10 times faster than the standard technique in screening compound libraries.
SCIENTIFIC REPORTS
(2023)
Article
Multidisciplinary Sciences
Thilini Abeywansha, Wei Huang, Xuan Ye, Allison Nawrocki, Xin Lan, Eckhard Jankowsky, Derek J. J. Taylor, Yi Zhang
Summary: Arginyl-tRNA-protein transferase 1 (ATE1) is a key regulator involved in protein homeostasis, stress response, cytoskeleton maintenance, and cell migration. The mechanism of how ATE1 hijacks tRNA and catalyzes the arginylation reaction from the ribosomal protein synthesis pathways remains unknown.
NATURE COMMUNICATIONS
(2023)
Article
Multidisciplinary Sciences
Wei Huang, Hongyun Li, Janna Kiselar, Stephen P. P. Fink, Sagar Regmi, Alexander Day, Yiyuan Yuan, Mark Chance, Joseph M. M. Ready, Sanford D. D. Markowitz, Derek J. J. Taylor
Summary: Inhibition of 15-prostaglandin dehydrogenase (15-PGDH) is a promising therapeutic target for regenerative medicine. We report the structure of 15-PGDH in complex with two different inhibitors. Unexpectedly, access to the binding pocket is regulated by a dynamic lid of the enzyme.
NATURE COMMUNICATIONS
(2023)
Article
Multidisciplinary Sciences
Sophie M. Travis, Brian P. Mahon, Wei Huang, Meisheng Ma, Michael J. Rale, Jodi Kraus, Derek J. Taylor, Rui Zhang, Sabine Petry
Summary: This study reveals the role of the augmin complex in microtubule branching through a structural model. By using cryo-electron microscopy and negative stain electron microscopy, the location and microtubule binding site of the augmin complex were identified. Evolutionary analysis shows that the structure and function of augmin are highly conserved across eukaryotes. These findings provide new insights into the mechanism of microtubule branching.
NATURE COMMUNICATIONS
(2023)
Proceedings Paper
Computer Science, Theory & Methods
Mikhail Titov, Matteo Turilli, Andre Merzky, Thomas Naughton, Wael Elwasif, Shantenu Jha
Summary: This paper provides a performance evaluation of the Process Management Interface for Exascale (PMIx) and its reference implementation PRRTE on the leadership-class HPC platform Summit. It explores the impact of resource partitioning and task execution allocation on workflow execution efficiency. The experimental results show that partitioning resources across multiple PRRTE Distributed Virtual Machine (DVM) environments and using the PMIx interface to launch tasks can improve workload execution performance and resource utilization.
JOB SCHEDULING STRATEGIES FOR PARALLEL PROCESSING, JSSPP 2022
(2023)
Proceedings Paper
Computer Science, Information Systems
Aymen Alsaadi, Logan Ward, Andre Merzky, Kyle Chard, Ian Foster, Shantenu Jha, Matteo Turilli
Summary: Workflow applications are crucial for scientific discovery, but the presence of numerous workflow management systems has resulted in a fragmented software ecosystem. Integrating existing workflow tools can enhance development efficiency and ensure the sustainability of scientific workflow software.
2022 IEEE/ACM WORKSHOP ON WORKFLOWS IN SUPPORT OF LARGE-SCALE SCIENCE, WORKS
(2022)
Proceedings Paper
Computer Science, Interdisciplinary Applications
Srinivasan Ramesh, Mikhail Titov, Matteo Turilli, Shantenu Jha, Allen Malony
Summary: This paper investigates the performance fluctuations in ensemble computing, particularly in the context of GROMACS tasks. It discusses the unsuccessful attempts to identify the causes of these fluctuations and raises important questions about the reproducibility of performance and the need for rethinking how we define and measure application performance.
2022 IEEE 18TH INTERNATIONAL CONFERENCE ON E-SCIENCE (ESCIENCE 2022)
(2022)
Proceedings Paper
Computer Science, Hardware & Architecture
Andre Merzky, Matteo Turilli, Shantenu Jha
Summary: RAPTOR is a tool for executing tasks on high-performance computing platforms, supporting various types of tasks and achieving high throughput and resource utilization. It has shown significant application potential in finding therapeutic solutions for COVID-19.
2022 22ND IEEE/ACM INTERNATIONAL SYMPOSIUM ON CLUSTER, CLOUD AND INTERNET COMPUTING (CCGRID 2022)
(2022)