Advances and Challenges in Scoring Functions for RNA–Protein Complex Structure Prediction DOI Creative Commons
Chengwei Zeng, Zhuo Chen,

Jiaming Gao

et al.

Biomolecules, Journal Year: 2024, Volume and Issue: 14(10), P. 1245 - 1245

Published: Oct. 1, 2024

RNA-protein complexes play a crucial role in cellular functions, providing insights into mechanisms and potential therapeutic targets. However, experimental determination of these complex structures is often time-consuming resource-intensive, it rarely yields high-resolution data. Many computational approaches have been developed to predict recent years. Despite advances, achieving accurate predictions remains formidable challenge, primarily due the limitations inherent current scoring functions. These functions are critical tools for evaluating interpreting interactions. This review comprehensively explores latest advancements docking, delving fundamental principles underlying various approaches, including coarse-grained knowledge-based, all-atom machine-learning-based methods. We critically evaluate strengths existing detailed performance assessment. Considering significant progress demonstrated by machine learning techniques, we discuss emerging trends propose future research directions enhance accuracy efficiency prediction. aim inspire development more sophisticated reliable this rapidly evolving field.

Language: Английский

Predicting Small Molecule Binding Nucleotides in RNA Structures Using RNA Surface Topography DOI

Jiaming Gao,

Haoquan Liu, Zhuo Chen

et al.

Journal of Chemical Information and Modeling, Journal Year: 2024, Volume and Issue: 64(18), P. 6979 - 6992

Published: Sept. 4, 2024

RNA small molecule interactions play a crucial role in drug discovery and inhibitor design. Identifying binding nucleotides is essential requires methods that exhibit high predictive ability to facilitate Existing can predict the of simple structures, but it hard complex structures with junctions. To address this limitation, we developed new deep learning model based on spatial correlation, ZHmolReSTasite, which accurately large We utilize surface topography consider characterizing from sequence tertiary learn high-level representation. Our method outperforms existing for benchmark test sets composed achieving precision values 72.9% TE18 76.7% RB9 sets. For challenging set junctions, our second best by 11.6% precision. Moreover, ZHmolReSTasite demonstrates robustness regarding predicted structures. In summary, successfully incorporates previous using topography, provide valuable insights into prediction accelerate

Language: Английский

Citations

4
AI-integrated network for RNA complex structure and dynamic prediction DOI

Haoquan Liu,

Zhuo Chen,

Jiaming Gao

et al.

Biophysics Reviews, Journal Year: 2024, Volume and Issue: 5(4)

Published: Nov. 5, 2024

RNA complexes are essential components in many cellular processes. The functions of these linked to their tertiary structures, which shaped by detailed interface information, such as binding sites, contact, and dynamic conformational changes. Network-based approaches have been widely used analyze complex structures. With roots the graph theory, methods a long history providing insight into static properties molecules. These effective identifying functional sites analyzing behavior complexes. Recently, advent artificial intelligence (AI) has brought transformative changes field. technologies increasingly applied studying new avenues for understanding interactions within By integrating AI with traditional network analysis methods, researchers can build more accurate models predict behaviors, even design RNA-based inhibitors. In this review, we introduce integration network-based methodologies techniques enhance We examine how advanced computational tools be model information behaviors Additionally, explore potential future directions AI-integrated networks aid modeling

Language: Английский

Citations

4
Advances and Mechanisms of RNA–Ligand Interaction Predictions DOI Creative Commons
Zhuo Chen, Chengwei Zeng,

Haoquan Liu

et al.

Life, Journal Year: 2025, Volume and Issue: 15(1), P. 104 - 104

Published: Jan. 15, 2025

The diversity and complexity of RNA include sequence, secondary structure, tertiary structure characteristics. These elements are crucial for RNA's specific recognition other molecules. With advancements in biotechnology, RNA-ligand structures allow researchers to utilize experimental data uncover the mechanisms complex interactions. However, determining these complexes experimentally can be technically challenging often results low-resolution data. Many machine learning computational approaches have recently emerged learn multiscale-level features predict Predicting interactions remains an unexplored area. Therefore, studying is essential understanding biological processes. In this review, we analyze interaction characteristics by examining structure. Our goal clarify how specifically recognizes ligands. Additionally, systematically discuss methods predicting guide future research directions. We aim inspire creation more reliable prediction tools.

Language: Английский

Citations

0
Molecular Dynamics of Apolipoprotein Genotypes APOE4 and SNARE Family Proteins and Their Impact on Alzheimer’s Disease DOI Creative Commons
Yuqing Wang, Xuefeng Liu,

Pengtao Zheng

et al.

Life, Journal Year: 2025, Volume and Issue: 15(2), P. 223 - 223

Published: Feb. 2, 2025

Alzheimer's disease is a chronic neurodegenerative disorder characterized by progressive memory loss and significant impact on quality of life. The APOE ε4 allele major genetic contributor to AD pathogenesis, with synaptic dysfunction being central hallmark in its pathophysiology. While the role APOE4 reducing SNARE protein levels has been established, underlying molecular mechanisms this interaction remain obscure. Our research employs dynamics simulations analyze interactions between APOE3 isoforms proteins VAMP2, SNAP25, SYNTAXIN1, which play crucial roles presynaptic membrane. findings reveal that significantly destabilizes complex, suppresses structural dynamics, reduces hydrogen bonding, consequently partially hindering neurotransmitter release-a very likely discovery for elucidating disease. We identified exhibits diminished affinity complex comparison APOE3. This observation suggests may modulating stability potentially impacting progression occurrence through free energy analysis. work highlights perturbations function mediated APOE4, offer novel insights into underpinnings AD. By interplay our study not only enhances comprehension AD's pathology but also paves way devising innovative therapeutic interventions, such as targeting APOE4-SNARE or restore release.

Language: Английский

Citations

0
A Machine Learning Method for RNA–Small Molecule Binding Preference Prediction DOI
Zhuo Chen,

Jiaming Gao,

Anbang Li

et al.

Journal of Chemical Information and Modeling, Journal Year: 2024, Volume and Issue: unknown

Published: Sept. 12, 2024

The interaction between RNA and small molecules is crucial in various biological functions. Identifying targeting essential for the inhibitor design RNA-related studies. However, traditional methods focus on learning sequence secondary structure features neglect molecule characteristics, resulting poor performance unknown testing. To overcome this limitation, we developed a double-layer stacking-based machine model called ZHMol-RLinter. This approach more effectively predicts RNA-small binding preferences by to capture their information. ZHMol-RLinter also combines structural with geometric physicochemical environment information specificity of spatial conformations recognizing molecules. Our results demonstrate that has success rate 90.8% published RL98 testing set, representing significant improvement over existing methods. Additionally, achieved 77.1% UNK96 showing substantial evaluation predicted structures confirms reliable accurate predicting preferences, even challenging Predicting can help understanding interactions promote drugs medical applications.

Language: Английский

Citations

3
Advances and Challenges in Scoring Functions for RNA–Protein Complex Structure Prediction DOI Creative Commons
Chengwei Zeng, Zhuo Chen,

Jiaming Gao

et al.

Biomolecules, Journal Year: 2024, Volume and Issue: 14(10), P. 1245 - 1245

Published: Oct. 1, 2024

RNA-protein complexes play a crucial role in cellular functions, providing insights into mechanisms and potential therapeutic targets. However, experimental determination of these complex structures is often time-consuming resource-intensive, it rarely yields high-resolution data. Many computational approaches have been developed to predict recent years. Despite advances, achieving accurate predictions remains formidable challenge, primarily due the limitations inherent current scoring functions. These functions are critical tools for evaluating interpreting interactions. This review comprehensively explores latest advancements docking, delving fundamental principles underlying various approaches, including coarse-grained knowledge-based, all-atom machine-learning-based methods. We critically evaluate strengths existing detailed performance assessment. Considering significant progress demonstrated by machine learning techniques, we discuss emerging trends propose future research directions enhance accuracy efficiency prediction. aim inspire development more sophisticated reliable this rapidly evolving field.

Language: Английский

Citations

2