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Recent Progress for Structure and Function Prediction of Protein and RNA

A special issue of Molecules (ISSN 1420-3049). This special issue belongs to the section "Computational and Theoretical Chemistry".

Deadline for manuscript submissions: 30 June 2024 | Viewed by 17999

Special Issue Editors

Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA
Interests: protein structure prediction; protein function prediction; RNA/DNA structure prediction; deep learning; structure bioinformatics

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Guest Editor
College of Life Sciences, Sichuan University, Chengdu 610000, China
Interests: protein–ligand docking; protein structure modeling

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Guest Editor
Department of Information Theory and Data Science, Nankai University, Tianjin 300071, China
Interests: protein structure prediction and analysis; machine learning application in bioinformatics
School of Statistics and Data Science, Nankai University, Tianjin 300071, China
Interests: structural bioinformatics; statistical genomics; transcriptomics; intrinsically disordered proteins; single cell omics
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Department of Computer Science and Engineering, Michigan State University, East Lansing, MI, USA
Interests: protein structure and function analysis; computational biology; genome bioinformatics

Special Issue Information

Dear Colleagues,

Deep learning techniques have significantly impacted protein/RNA structure prediction and function prediction. In particular, after DeepMind released the end-to-end deep learning protein structure prediction tool AlphaFold2, the computational biology field was largely changed. The occurrences of AlphaFold2 and follow-up deep-learning-based methods are not occasional; most of the protein folding technologies used in these deep-learning-based methods have been well-studied by the community for a long time. For instance, multiple sequence alignment generation, coevolutionary-based contact/distance prediction, template detection, domain partition and assembly, deep-learning-based spatial restraints prediction, protein folding by L-BFGS or Monte Carlo simulations, most cutting-edge attention and transformer mechanisms in deep learning, and so on. These well-studied topics are believed to be the foundation for the success of protein structure prediction. Furthermore, deep learning also starts to show a powerful impact on protein function prediction and RNA-related research. The main focus of this Special Issue is on articles describing novel computational algorithms, software, models, and tools, including statistical methods, machine learning, deep learning, and artificial intelligence, on large data across areas of computational structure biology, including protein structure prediction, protein function prediction, and the corresponding research on RNA/DNA.

Dr. Wei Zheng
Dr. Yang Cao
Dr. Jianzhao Gao
Dr. Gang Hu
Dr. Qiqige Wuyun
Guest Editors

Manuscript Submission Information

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • protein structure prediction
  • protein function prediction
  • RNA structure prediction
  • protein-ligand binding
  • end-to-end protein folding
  • distance–map prediction
  • model quality estimation
  • protein-protein complex

Published Papers (7 papers)

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Research

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15 pages, 6689 KiB  
Article
Quantitative Predictive Studies of Multiple Biological Activities of TRPV1 Modulators
by Xinmiao Wei, Tengxin Huang, Zhijiang Yang, Li Pan, Liangliang Wang and Junjie Ding
Molecules 2024, 29(2), 295; https://doi.org/10.3390/molecules29020295 - 5 Jan 2024
Viewed by 713
Abstract
TRPV1 channel agonists and antagonists, which have powerful analgesic effects without the addictive qualities associated with traditional analgesics, have become a focus area for the development of novel analgesics. In this study, quantitative structure–activity relationship (QSAR) models for three bioactive endpoints (Ki [...] Read more.
TRPV1 channel agonists and antagonists, which have powerful analgesic effects without the addictive qualities associated with traditional analgesics, have become a focus area for the development of novel analgesics. In this study, quantitative structure–activity relationship (QSAR) models for three bioactive endpoints (Ki, IC50, and EC50) were successfully constructed using four machine learning algorithms: SVM, Bagging, GBDT, and XGBoost. These models were based on 2922 TRPV1 modulators and incorporated four types of molecular descriptors: Daylight, E-state, ECFP4, and MACCS. After the rigorous five-fold cross-validation and external test set validation, the optimal models for the three endpoints were obtained. For the Ki endpoint, the Bagging-ECFP4 model had a Q2 value of 0.778 and an R2 value of 0.780. For the IC50 endpoint, the XGBoost-ECFP4 model had a Q2 value of 0.806 and an R2 value of 0.784. For the EC50 endpoint, the SVM-Daylight model had a Q2 value of 0.784 and an R2 value of 0.809. These results demonstrate that the constructed models exhibit good predictive performance. In addition, based on the model feature importance analysis, the influence between substructure and biological activity was also explored, which can provide important theoretical guidance for the efficient virtual screening and structural optimization of novel TRPV1 analgesics. And subsequent studies on novel TRPV1 modulators will be based on the feature substructures of the three endpoints. Full article
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19 pages, 3670 KiB  
Article
Naive Prediction of Protein Backbone Phi and Psi Dihedral Angles Using Deep Learning
by Matic Broz, Marko Jukič and Urban Bren
Molecules 2023, 28(20), 7046; https://doi.org/10.3390/molecules28207046 - 12 Oct 2023
Cited by 1 | Viewed by 1123
Abstract
Protein structure prediction represents a significant challenge in the field of bioinformatics, with the prediction of protein structures using backbone dihedral angles recently achieving significant progress due to the rise of deep neural network research. However, there is a trend in protein structure [...] Read more.
Protein structure prediction represents a significant challenge in the field of bioinformatics, with the prediction of protein structures using backbone dihedral angles recently achieving significant progress due to the rise of deep neural network research. However, there is a trend in protein structure prediction research to employ increasingly complex neural networks and contributions from multiple models. This study, on the other hand, explores how a single model transparently behaves using sequence data only and what can be expected from the predicted angles. To this end, the current paper presents data acquisition, deep learning model definition, and training toward the final protein backbone angle prediction. The method applies a simple fully connected neural network (FCNN) model that takes only the primary structure of the protein with a sliding window of size 21 as input to predict protein backbone ϕ and ψ dihedral angles. Despite its simplicity, the model shows surprising accuracy for the ϕ angle prediction and somewhat lower accuracy for the ψ angle prediction. Moreover, this study demonstrates that protein secondary structure prediction is also possible with simple neural networks that take in only the protein amino-acid residue sequence, but more complex models are required for higher accuracies. Full article
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18 pages, 3194 KiB  
Article
Two-Component System Sensor Kinases from Asgardian Archaea May Be Witnesses to Eukaryotic Cell Evolution
by Felipe Padilla-Vaca, Javier de la Mora, Rodolfo García-Contreras, Jorge Humberto Ramírez-Prado, Nayeli Alva-Murillo, Sofia Fonseca-Yepez, Isaac Serna-Gutiérrez, Carolina Lisette Moreno-Galván, José Manolo Montufar-Rodríguez, Marcos Vicente-Gómez, Ángeles Rangel-Serrano, Naurú Idalia Vargas-Maya and Bernardo Franco
Molecules 2023, 28(13), 5042; https://doi.org/10.3390/molecules28135042 - 28 Jun 2023
Cited by 1 | Viewed by 1246
Abstract
The signal transduction paradigm in bacteria involves two-component systems (TCSs). Asgardarchaeota are archaea that may have originated the current eukaryotic lifeforms. Most research on these archaea has focused on eukaryotic-like features, such as genes involved in phagocytosis, cytoskeleton structure, and vesicle trafficking. However, [...] Read more.
The signal transduction paradigm in bacteria involves two-component systems (TCSs). Asgardarchaeota are archaea that may have originated the current eukaryotic lifeforms. Most research on these archaea has focused on eukaryotic-like features, such as genes involved in phagocytosis, cytoskeleton structure, and vesicle trafficking. However, little attention has been given to specific prokaryotic features. Here, the sequence and predicted structural features of TCS sensor kinases analyzed from two metagenome assemblies and a genomic assembly from cultured Asgardian archaea are presented. The homology of the sensor kinases suggests the grouping of Lokiarchaeum closer to bacterial homologs. In contrast, one group from a Lokiarchaeum and a meta-genome assembly from Candidatus Heimdallarchaeum suggest the presence of a set of kinases separated from the typical bacterial TCS sensor kinases. AtoS and ArcB homologs were found in meta-genome assemblies along with defined domains for other well-characterized sensor kinases, suggesting the close link between these organisms and bacteria that may have resulted in the metabolic link to the establishment of symbiosis. Several kinases are predicted to be cytoplasmic; some contain several PAS domains. The data shown here suggest that TCS kinases in Asgardian bacteria are witnesses to the transition from bacteria to eukaryotic organisms. Full article
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Review

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28 pages, 7922 KiB  
Review
Recent Progress of Protein Tertiary Structure Prediction
by Qiqige Wuyun, Yihan Chen, Yifeng Shen, Yang Cao, Gang Hu, Wei Cui, Jianzhao Gao and Wei Zheng
Molecules 2024, 29(4), 832; https://doi.org/10.3390/molecules29040832 - 13 Feb 2024
Cited by 1 | Viewed by 2433
Abstract
The prediction of three-dimensional (3D) protein structure from amino acid sequences has stood as a significant challenge in computational and structural bioinformatics for decades. Recently, the widespread integration of artificial intelligence (AI) algorithms has substantially expedited advancements in protein structure prediction, yielding numerous [...] Read more.
The prediction of three-dimensional (3D) protein structure from amino acid sequences has stood as a significant challenge in computational and structural bioinformatics for decades. Recently, the widespread integration of artificial intelligence (AI) algorithms has substantially expedited advancements in protein structure prediction, yielding numerous significant milestones. In particular, the end-to-end deep learning method AlphaFold2 has facilitated the rise of structure prediction performance to new heights, regularly competitive with experimental structures in the 14th Critical Assessment of Protein Structure Prediction (CASP14). To provide a comprehensive understanding and guide future research in the field of protein structure prediction for researchers, this review describes various methodologies, assessments, and databases in protein structure prediction, including traditionally used protein structure prediction methods, such as template-based modeling (TBM) and template-free modeling (FM) approaches; recently developed deep learning-based methods, such as contact/distance-guided methods, end-to-end folding methods, and protein language model (PLM)-based methods; multi-domain protein structure prediction methods; the CASP experiments and related assessments; and the recently released AlphaFold Protein Structure Database (AlphaFold DB). We discuss their advantages, disadvantages, and application scopes, aiming to provide researchers with insights through which to understand the limitations, contexts, and effective selections of protein structure prediction methods in protein-related fields. Full article
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25 pages, 2015 KiB  
Review
RNA 3D Structure Prediction: Progress and Perspective
by Xunxun Wang, Shixiong Yu, En Lou, Ya-Lan Tan and Zhi-Jie Tan
Molecules 2023, 28(14), 5532; https://doi.org/10.3390/molecules28145532 - 20 Jul 2023
Cited by 3 | Viewed by 3649
Abstract
Ribonucleic acid (RNA) molecules play vital roles in numerous important biological functions such as catalysis and gene regulation. The functions of RNAs are strongly coupled to their structures or proper structure changes, and RNA structure prediction has been paid much attention in the [...] Read more.
Ribonucleic acid (RNA) molecules play vital roles in numerous important biological functions such as catalysis and gene regulation. The functions of RNAs are strongly coupled to their structures or proper structure changes, and RNA structure prediction has been paid much attention in the last two decades. Some computational models have been developed to predict RNA three-dimensional (3D) structures in silico, and these models are generally composed of predicting RNA 3D structure ensemble, evaluating near-native RNAs from the structure ensemble, and refining the identified RNAs. In this review, we will make a comprehensive overview of the recent advances in RNA 3D structure modeling, including structure ensemble prediction, evaluation, and refinement. Finally, we will emphasize some insights and perspectives in modeling RNA 3D structures. Full article
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35 pages, 512 KiB  
Review
Recent Advances in Deep Learning for Protein-Protein Interaction Analysis: A Comprehensive Review
by Minhyeok Lee
Molecules 2023, 28(13), 5169; https://doi.org/10.3390/molecules28135169 - 2 Jul 2023
Cited by 7 | Viewed by 4332
Abstract
Deep learning, a potent branch of artificial intelligence, is steadily leaving its transformative imprint across multiple disciplines. Within computational biology, it is expediting progress in the understanding of Protein–Protein Interactions (PPIs), key components governing a wide array of biological functionalities. Hence, an in-depth [...] Read more.
Deep learning, a potent branch of artificial intelligence, is steadily leaving its transformative imprint across multiple disciplines. Within computational biology, it is expediting progress in the understanding of Protein–Protein Interactions (PPIs), key components governing a wide array of biological functionalities. Hence, an in-depth exploration of PPIs is crucial for decoding the intricate biological system dynamics and unveiling potential avenues for therapeutic interventions. As the deployment of deep learning techniques in PPI analysis proliferates at an accelerated pace, there exists an immediate demand for an exhaustive review that encapsulates and critically assesses these novel developments. Addressing this requirement, this review offers a detailed analysis of the literature from 2021 to 2023, highlighting the cutting-edge deep learning methodologies harnessed for PPI analysis. Thus, this review stands as a crucial reference for researchers in the discipline, presenting an overview of the recent studies in the field. This consolidation helps elucidate the dynamic paradigm of PPI analysis, the evolution of deep learning techniques, and their interdependent dynamics. This scrutiny is expected to serve as a vital aid for researchers, both well-established and newcomers, assisting them in maneuvering the rapidly shifting terrain of deep learning applications in PPI analysis. Full article
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21 pages, 7980 KiB  
Review
Computational Modeling of DNA 3D Structures: From Dynamics and Mechanics to Folding
by Zi-Chun Mu, Ya-Lan Tan, Jie Liu, Ben-Gong Zhang and Ya-Zhou Shi
Molecules 2023, 28(12), 4833; https://doi.org/10.3390/molecules28124833 - 17 Jun 2023
Cited by 3 | Viewed by 3095
Abstract
DNA carries the genetic information required for the synthesis of RNA and proteins and plays an important role in many processes of biological development. Understanding the three-dimensional (3D) structures and dynamics of DNA is crucial for understanding their biological functions and guiding the [...] Read more.
DNA carries the genetic information required for the synthesis of RNA and proteins and plays an important role in many processes of biological development. Understanding the three-dimensional (3D) structures and dynamics of DNA is crucial for understanding their biological functions and guiding the development of novel materials. In this review, we discuss the recent advancements in computer methods for studying DNA 3D structures. This includes molecular dynamics simulations to analyze DNA dynamics, flexibility, and ion binding. We also explore various coarse-grained models used for DNA structure prediction or folding, along with fragment assembly methods for constructing DNA 3D structures. Furthermore, we also discuss the advantages and disadvantages of these methods and highlight their differences. Full article
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