AlphaFold2 and its applications in the fields of biology and medicine

Yang, Zhenyu; Zeng, Xiaoxi; Zhao, Yi; Chen, Runsheng

doi:10.1038/s41392-023-01381-z

Cited by 93 publications

(61 citation statements)

References 211 publications

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“…Until now, many protein structure prediction algorithms have been reported and can be found in several review papers , , and . In these reviews, protein structure prediction methods are divided into two categories: TBM and FM.…”

Section: Advances In Protein Structure Prediction Methodsmentioning

confidence: 99%

“…Structure-based drug discovery typically relies on structural information on the target protein, but the limited number of protein structures in the PDB limits drug discovery speed. 26 High-accuracy structural models of unknown proteins can significantly accelerate drug discovery projects, thereby meeting the high demand for drug discovery. Alex Zhavoronkov et al used AlphaFold2 predicted structures as inputs for the automated drug discovery artificial intelligence (AI) engines PandaOmics and Chemistry42, 27 enabling rapid identification of CDK20 hit molecules within 30 days.…”

Section: Introductionmentioning

confidence: 99%

“…Numerous other studies that combined AlphaFold2 and experimental methods to determine protein structures, such as references and , and so on. Structure-based drug discovery typically relies on structural information on the target protein, but the limited number of protein structures in the PDB limits drug discovery speed . High-accuracy structural models of unknown proteins can significantly accelerate drug discovery projects, thereby meeting the high demand for drug discovery.…”

Section: Introductionmentioning

confidence: 99%

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Recent Advances and Challenges in Protein Structure Prediction

Peng,

Liang,

Xia

et al. 2023

J. Chem. Inf. Model.

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Artificial intelligence has made significant advances in the field of protein structure prediction in recent years. In particular, DeepMind's end-to-end model, AlphaFold2, has demonstrated the capability to predict three-dimensional structures of numerous unknown proteins with accuracy levels comparable to those of experimental methods. This breakthrough has opened up new possibilities for understanding protein structure and function as well as accelerating drug discovery and other applications in the field of biology and medicine. Despite the remarkable achievements of artificial intelligence in the field, there are still some challenges and limitations. In this Review, we discuss the recent progress and some of the challenges in protein structure prediction. These challenges include predicting multidomain protein structures, protein complex structures, multiple conformational states of proteins, and protein folding pathways. Furthermore, we highlight directions in which further improvements can be conducted.

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Section: Advances In Protein Structure Prediction Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Recent Advances and Challenges in Protein Structure Prediction

Peng,

Liang,

Xia

et al. 2023

J. Chem. Inf. Model.

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“…OmegaFold performs true ab initio folding without the use of MSA, 31 the advantages of which include (1) that it does not rely on known protein structures, which means that it is able to predict protein structures without any prior structural knowledge; and (2) that it has the possibility of discovering new types of protein structures. 32 OmegaFold comes close to the performance of AlphaFold2 in general, sometimes surpassing it on orphan sequences, by using a language model trained on amino acid sequences instead of the direct use of MSA. 33 Predicting mutation-induced stability changes is crucial for protein design and precision medicine.…”

Section: ■ Introductionmentioning

confidence: 90%

OmeDDG: Improved Protein Mutation Stability Prediction Based on Predicted 3D Structures

Liu,

Jiang,

Yang

et al. 2023

J. Phys. Chem. B

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Determining changes in the protein's thermal stability following mutations is critical in protein engineering and understanding pathogenic missense mutations. Despite the development of various computational methods to predict the effects of single-point mutations, their accuracy remains limited. In this study, we propose a new computational method, OmeDDG, that more accurately predicts mutation-induced Gibbs free energy changes in protein folding (ΔΔG). OmeDDG takes the sequences of wild-type and mutant proteins as input, utilizes OmegaFold to obtain the 3D structure, employs a convolutional neural network to extract structural features, and combines them with protein mutation features and pretraining features to predict the stability of single-point mutations in proteins. We performed a comprehensive comparison between OmeDDG and other available prediction methods on four blind test datasets, confirming that OmeDDG can effectively enhance protein mutation prediction performance. Notably, on the antisymmetric dataset Ssym, OmeDDG achieves the best performance, demonstrating favorable antisymmetry with PCC = 0.79 and RMSE = 0.96 for forward mutations and PCC = 0.77 and RMSE = 0.97 for reverse mutant types.

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“…Latterly, the field of protein structure prediction has seen remarkable advancements, with the introduction of groundbreaking methods such as AlphaFold 21 and AlphaFold2. 22 AlphaFold, developed by DeepMind, has garnered significant attention for its unprecedented accuracy in predicting protein structures. This transformative development has reshaped the landscape of structural biology and holds great promise for various applications.…”

Section: ■ Introductionmentioning

confidence: 99%

PLM-GAN: A Large-Scale Protein Loop Modeling Using pix2pix GAN

Khalaf,

Soliman,

Mohamed

2023

ACS Omega

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Revealing the tertiary structure of proteins holds huge significance as it unveils their vital properties and functions. These intricate three-dimensional configurations comprise diverse interactions including ionic, hydrophobic, and disulfide forces. In certain instances, these structures exhibit missing regions, necessitating the reconstruction of specific segments, thereby resulting in challenges in protein design, which encompasses loop modeling, circular permutation, and interface prediction. To address this problem, we present two pioneering models: pix2pix generative adversarial network (GAN) and PLM-GAN. The pix2pix GAN model is adept at generating and inpainting distance matrices of protein structures, whereas the PLM-GAN model incorporates residual blocks into the U-Net network of the GAN, building upon the foundation of the pix2pix GAN model. To bolster the models' performance, we introduce a novel loss function named the "missing to real regions loss" (L MTR ) within the GAN framework. Additionally, we introduce a distinctive approach of pairing two different distance matrices: one representing the native protein structure and the other representing the same structure with a missing region that undergoes changes in each successive epoch. Moreover, we extend the reconstruction of missing regions, encompassing up to 30 amino acids and increase the protein length by 128 amino acids. The evaluation of our pix2pix GAN and PLM-GAN models on a random selection of natural proteins (4ZCB, 3FJB, and 2REZ) demonstrated promising experimental results. Our models constitute significant contributions to addressing intricate challenges in protein structure design. These contributions hold immense potential to propel advancements in protein−protein interactions, drug design, and further innovations in protein engineering. Data, code, trained models, examples, and measurements are available on https://github.com/mena01/PLM-

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AlphaFold2 and its applications in the fields of biology and medicine

Cited by 93 publications

References 211 publications

Recent Advances and Challenges in Protein Structure Prediction

Recent Advances and Challenges in Protein Structure Prediction

OmeDDG: Improved Protein Mutation Stability Prediction Based on Predicted 3D Structures

PLM-GAN: A Large-Scale Protein Loop Modeling Using pix2pix GAN

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