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Advanced Research in Prediction of Protein Structure and Function, 2nd Edition

A special issue of International Journal of Molecular Sciences (ISSN 1422-0067). This special issue belongs to the section "Molecular Informatics".

Deadline for manuscript submissions: closed (20 August 2024) | Viewed by 2335

Special Issue Editor


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Guest Editor
1. Department of Computer Science, City University of Hong Kong, Kowloon 999077, Hong Kong
2. Department of Biomedical Engineering, City University of Hong Kong, Kowloon 999077, Hong Kong
Interests: bioinformatics; machine learning; algorithms
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Special Issue Information

Dear Colleagues,

As the Holy Grail of biophysics, predicting the protein structure of primary peptide sequences has received widespread research attention. Deep learning approaches, especially Alpha Fold, have dramatically advanced this field of research. These approaches take the multiple sequence alignment (MSA) of amino acid sequences as input and detect the co-evolved residues, or infer contact maps, to predict 3D structures. Some recent approaches also claim that they can predict 3D structures without the MSA. Many downstream tasks, such as protein function prediction, protein complex prediction, and drug design, become possible once accurately predicted 3D structures are acquired.

This Special Issue focuses on the most recent advances in the prediction of protein structure and protein function, as well as developments in the downstream applications that rely on these predictions.

Dr. Shuai Cheng Li
Guest Editor

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Keywords

  • prediction
  • protein structure
  • protein function
  • protein complex
  • machine learning
  • deep learning
  • multiple sequence alignment

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Published Papers (2 papers)

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Research

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21 pages, 5850 KiB  
Article
Structural Characterization of Heat Shock Protein 90β and Molecular Interactions with Geldanamycin and Ritonavir: A Computational Study
by Carlyle Ribeiro Lima, Deborah Antunes, Ernesto Caffarena and Nicolas Carels
Int. J. Mol. Sci. 2024, 25(16), 8782; https://doi.org/10.3390/ijms25168782 - 12 Aug 2024
Viewed by 658
Abstract
Drug repositioning is an important therapeutic strategy for treating breast cancer. Hsp90β chaperone is an attractive target for inhibiting cell progression. Its structure has a disordered and flexible linker region between the N-terminal and central domains. Geldanamycin was the first Hsp90β inhibitor to [...] Read more.
Drug repositioning is an important therapeutic strategy for treating breast cancer. Hsp90β chaperone is an attractive target for inhibiting cell progression. Its structure has a disordered and flexible linker region between the N-terminal and central domains. Geldanamycin was the first Hsp90β inhibitor to interact specifically at the N-terminal site. Owing to the toxicity of geldanamycin, we investigated the repositioning of ritonavir as an Hsp90β inhibitor, taking advantage of its proven efficacy against cancer. In this study, we used molecular modeling techniques to analyze the contribution of the Hsp90β linker region to the flexibility and interaction between the ligands geldanamycin, ritonavir, and Hsp90β. Our findings indicate that the linker region is responsible for the fluctuation and overall protein motion without disturbing the interaction between the inhibitors and the N-terminus. We also found that ritonavir established similar interactions with the substrate ATP triphosphate, filling the same pharmacophore zone. Full article
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Review

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21 pages, 1549 KiB  
Review
AI-Driven Deep Learning Techniques in Protein Structure Prediction
by Lingtao Chen, Qiaomu Li, Kazi Fahim Ahmad Nasif, Ying Xie, Bobin Deng, Shuteng Niu, Seyedamin Pouriyeh, Zhiyu Dai, Jiawei Chen and Chloe Yixin Xie
Int. J. Mol. Sci. 2024, 25(15), 8426; https://doi.org/10.3390/ijms25158426 - 1 Aug 2024
Viewed by 1380
Abstract
Protein structure prediction is important for understanding their function and behavior. This review study presents a comprehensive review of the computational models used in predicting protein structure. It covers the progression from established protein modeling to state-of-the-art artificial intelligence (AI) frameworks. The paper [...] Read more.
Protein structure prediction is important for understanding their function and behavior. This review study presents a comprehensive review of the computational models used in predicting protein structure. It covers the progression from established protein modeling to state-of-the-art artificial intelligence (AI) frameworks. The paper will start with a brief introduction to protein structures, protein modeling, and AI. The section on established protein modeling will discuss homology modeling, ab initio modeling, and threading. The next section is deep learning-based models. It introduces some state-of-the-art AI models, such as AlphaFold (AlphaFold, AlphaFold2, AlphaFold3), RoseTTAFold, ProteinBERT, etc. This section also discusses how AI techniques have been integrated into established frameworks like Swiss-Model, Rosetta, and I-TASSER. The model performance is compared using the rankings of CASP14 (Critical Assessment of Structure Prediction) and CASP15. CASP16 is ongoing, and its results are not included in this review. Continuous Automated Model EvaluatiOn (CAMEO) complements the biennial CASP experiment. Template modeling score (TM-score), global distance test total score (GDT_TS), and Local Distance Difference Test (lDDT) score are discussed too. This paper then acknowledges the ongoing difficulties in predicting protein structure and emphasizes the necessity of additional searches like dynamic protein behavior, conformational changes, and protein–protein interactions. In the application section, this paper introduces some applications in various fields like drug design, industry, education, and novel protein development. In summary, this paper provides a comprehensive overview of the latest advancements in established protein modeling and deep learning-based models for protein structure predictions. It emphasizes the significant advancements achieved by AI and identifies potential areas for further investigation. Full article
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