Molecules

Research

Jump to: Review

13 pages, 3402 KiB

Open AccessArticle

Insights into Actin Isoform-Specific Interactions with Myosin via Computational Analysis

by Chan Jong Yu, Yoon Ho Park, Mi Young An, Bumhan Ryu and Hyun Suk Jung

Molecules 2024, 29(13), 2992; https://doi.org/10.3390/molecules29132992 - 23 Jun 2024

Viewed by 1595

Abstract

Actin, which plays a crucial role in cellular structure and function, interacts with various binding proteins, notably myosin. In mammals, actin is composed of six isoforms that exhibit high levels of sequence conservation and structural similarity overall. As a result, the selection of [...] Read more.

Actin, which plays a crucial role in cellular structure and function, interacts with various binding proteins, notably myosin. In mammals, actin is composed of six isoforms that exhibit high levels of sequence conservation and structural similarity overall. As a result, the selection of actin isoforms was considered unimportant in structural studies of their binding with myosin. However, recent high-resolution structural research discovered subtle structural differences in the N-terminus of actin isoforms, suggesting the possibility that each actin isoform may engage in specific interactions with myosin isoforms. In this study, we aimed to explore this possibility, particularly by understanding the influence of different actin isoforms on the interaction with myosin 7A. First, we compared the reported actomyosin structures utilizing the same type of actin isoforms as the high-resolution filamentous skeletal α-actin (3.5 Å) structure elucidated using cryo-EM. Through this comparison, we confirmed that the diversity of myosin isoforms leads to differences in interaction with the actin N-terminus, and that loop 2 of the myosin actin-binding sites directly interacts with the actin N-terminus. Subsequently, with the aid of multiple sequence alignment, we observed significant variations in the length of loop 2 across different myosin isoforms. We predicted that these length differences in loop 2 would likely result in structural variations that would affect the interaction with the actin N-terminus. For myosin 7A, loop 2 was found to be very short, and protein complex predictions using skeletal α-actin confirmed an interaction between loop 2 and the actin N-terminus. The prediction indicated that the positively charged residues present in loop 2 electrostatically interact with the acidic patch residues D24 and D25 of actin subdomain 1, whereas interaction with the actin N-terminus beyond this was not observed. Additionally, analyses of the actomyosin-7A prediction models generated using various actin isoforms consistently yielded the same results regardless of the type of actin isoform employed. The results of this study suggest that the subtle structural differences in the N-terminus of actin isoforms are unlikely to influence the binding structure with short loop 2 myosin 7A. Our findings are expected to provide a deeper understanding for future high-resolution structural binding studies of actin and myosin. Full article

(This article belongs to the Special Issue Recent Progress for Structure and Function Prediction of Protein and RNA)

► Show Figures

Figure 1

15 pages, 6689 KiB

Open AccessArticle

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

Cited by 1 | Viewed by 1122

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 (K_i [...] 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 (K_i, 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 K_i 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

(This article belongs to the Special Issue Recent Progress for Structure and Function Prediction of Protein and RNA)

► Show Figures

Graphical abstract

19 pages, 3670 KiB

Open AccessArticle

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 1812

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

(This article belongs to the Special Issue Recent Progress for Structure and Function Prediction of Protein and RNA)

► Show Figures

Graphical abstract

18 pages, 3194 KiB

Open AccessArticle

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 1646

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

(This article belongs to the Special Issue Recent Progress for Structure and Function Prediction of Protein and RNA)

► Show Figures

Figure 1

Review

Jump to: Research

28 pages, 7922 KiB

Open AccessReview

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 6 | Viewed by 5738

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

(This article belongs to the Special Issue Recent Progress for Structure and Function Prediction of Protein and RNA)

► Show Figures

Figure 1

25 pages, 2015 KiB

Open AccessReview

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 8 | Viewed by 5058

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

(This article belongs to the Special Issue Recent Progress for Structure and Function Prediction of Protein and RNA)

► Show Figures

Figure 1

35 pages, 512 KiB

Open AccessReview

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 22 | Viewed by 7215

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

(This article belongs to the Special Issue Recent Progress for Structure and Function Prediction of Protein and RNA)

► Show Figures

Figure 1

21 pages, 7980 KiB

Open AccessReview

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 5 | Viewed by 4477

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

(This article belongs to the Special Issue Recent Progress for Structure and Function Prediction of Protein and RNA)

► Show Figures

Figure 1

Journal Menu

Journal Browser

Recent Progress for Structure and Function Prediction of Protein and RNA

Share This Special Issue

Special Issue Editors

Special Issue Information

Keywords

Benefits of Publishing in a Special Issue

Related Special Issue

Published Papers (8 papers)

Research

Review

Further Information

Guidelines

MDPI Initiatives

Follow MDPI