Improved Protein Structure Prediction Using a New Multi‐Scale Network and Homologous Templates

Su, Hong; Wang, Wenkai; Du, Zongyang; Peng, Zhenling; Gao, Shanghua; Cheng, Ming–Ming; Yang, Jianyi

doi:10.1002/advs.202102592

Cited by 68 publications

(51 citation statements)

References 68 publications

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“…In the structure described by Gross et al, the α-helical domain extends beyond the MORN domains without interacting with them at all. However, Alphafold2 software is considered to be more accurate than most (if not all) of the currently existing structure-predicting software, especially for proteins for which no homologous structures exist [ 75 , 76 ], and the reciprocal arrangement of JPH2 MORN motifs and α-helical domain predicted by Alphafold2 agrees with data from Li and collaborators [ 32 ] based on the crystal structure of the protein MORN4. MORN4 contains a series of MORN motifs arranged in a half-pipe configuration followed by a brief α-helical region.…”

Section: New Insights From Deep Learning Protein Structure Predictionmentioning

confidence: 88%

The Builders of the Junction: Roles of Junctophilin1 and Junctophilin2 in the Assembly of the Sarcoplasmic Reticulum–Plasma Membrane Junctions in Striated Muscle

Perni

2022

Biomolecules

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Contraction of striated muscle is triggered by a massive release of calcium from the sarcoplasmic reticulum (SR) into the cytoplasm. This intracellular calcium release is initiated by membrane depolarization, which is sensed by voltage-gated calcium channels CaV1.1 (in skeletal muscle) and CaV1.2 (in cardiac muscle) in the plasma membrane (PM), which in turn activate the calcium-releasing channel ryanodine receptor (RyR) embedded in the SR membrane. This cross-communication between channels in the PM and in the SR happens at specialized regions, the SR-PM junctions, where these two compartments come in close proximity. Junctophilin1 and Junctophilin2 are responsible for the formation and stabilization of SR-PM junctions in striated muscle and actively participate in the recruitment of the two essential players in intracellular calcium release, CaV and RyR. This short review focuses on the roles of junctophilins1 and 2 in the formation and organization of SR-PM junctions in skeletal and cardiac muscle and on the functional consequences of the absence or malfunction of these proteins in striated muscle in light of recently published data and recent advancements in protein structure prediction.

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Section: New Insights From Deep Learning Protein Structure Predictionmentioning

confidence: 88%

The Builders of the Junction: Roles of Junctophilin1 and Junctophilin2 in the Assembly of the Sarcoplasmic Reticulum–Plasma Membrane Junctions in Striated Muscle

Perni

2022

Biomolecules

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“…Recent advances in protein structure prediction mean that structures are now available for increasing amounts of proteins (e.g. Su et al , 2021 ; Tunyasuvunakool et al , 2021 ), which opens up new types of features to be included in DL methods for interface prediction (e.g. Dai and Bailey-Kellogg, 2021 ; Xie and Xu, 2021 ).…”

Section: Discussionmentioning

confidence: 99%

“…Notwithstanding the enormous progress that has been made in the area of structure prediction, no reliable structural information is available (e.g. Su et al , 2021 ; Tunyasuvunakool et al , 2021 ) for many organisms, types of proteins and protein regions. Moreover, the usefulness of predicted structures for interface prediction may be limited (e.g.…”

Section: Introductionmentioning

confidence: 99%

PIPENN: protein interface prediction from sequence with an ensemble of neural nets

et al. 2022

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Motivation The interactions between proteins and other molecules are essential to many biological and cellular processes. Experimental identification of interface residues is a time-consuming, costly, and challenging task, while protein sequence data is ubiquitous. Consequently, many computational and machine learning approaches have been developed over the years to predict such interface residues from sequence. However, the effectiveness of different deep learning architectures and learning strategies for protein–protein, protein–nucleotide, and protein–small molecule interface prediction, has not yet been investigated in great detail. Therefore, we here explore the prediction of protein interface residues using six deep learning architectures and various learning strategies with sequence-derived input features. Results We constructed a large data set dubbed BioDL, comprising protein–protein interactions from the PDB, and DNA/RNA and small molecule interactions from the BioLip database. We also constructed six DL architectures, and evaluated them on the BioDL benchmarks. This shows that no single architecture performs best on all instances. An ensemble architecture, which combines all six architectures, does consistently achieve peak prediction accuracy. We confirmed these results on the published benchmark set by Zhang & Kurgan (ZK448), and on our own existing curated homo- and heteromeric protein interaction data set. Our PIPENN sequence-based ensemble predictor outperforms current state-of-the-art sequence-based protein interface predictors on ZK448 on all interaction types, achieving an AUC-ROC of 0.718 for protein–protein, 0.823 for protein–nucleotide and 0.842 for protein–small molecule. Availability Source code and data sets at https://github.com/ibivu/pipenn/

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“…TrRosetta modeled the structures by presenting inter-residue orientations and efficient energy minimization-based structure realization of Rosetta. The TrRosetta deep neural network is on millions of known sequences and structures ( 51 ). Then the structure models of these servers were validated using ERRAT, QMean, Verify 3D, ProCheck, and QMeanBrane.…”

Section: Methodsmentioning

confidence: 99%

Interaction Analysis of Adenovirus L5 Protein With Pancreatic Cancer Cell Surface Receptor to Analyze Its Affinity for Oncolytic Virus Therapy

et al. 2022

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This study seeks to investigate the interaction profile of the L5 protein of oncolytic adenovirus with the overexpressed surface receptors of pancreatic cancer. This is an important area of research because pancreatic cancer is one of the most fatal malignancies with a very low patient survival rate. Multiple therapies to date to improve the survival rate are reported; however, they show a comparatively low success rate. Among them, oncolytic virus therapy is a type of immunotherapy that is currently under deliberation by researchers for multiple cancer types in various clinical trials. Talimogene laherparepvec (T-VEC) is the first oncolytic virus approved by the US Food and Drug Administration (FDA) for melanoma. The oncolytic virus not only kills cancer cells but also activates the anticancer immune response. Therefore, it is preferred over others to deal with aggressive pancreatic cancer. The efficacy of therapy primarily depends on how effectively the oncolytic virus enters and infects the cancer cell. Cell surface receptors and their interactions with virus coat proteins are a crucial step for oncolytic virus entry and a pivotal determinant. The L5 proteins of the virus coat are the first to interact with host cell surface receptors. Therefore, the objective of this study is to analyze the interaction profile of the L5 protein of oncolytic adenovirus with overexpressed surface receptors of pancreatic cancer. The L5 proteins of three adenovirus serotypes HAdV2, HAdV5, and HAdV3 were utilized in this study. Overexpressed pancreatic cancer receptors include SLC2A1, MET, IL1RAP, NPR3, GABRP, SLC6A6, and TMPRSS4. The protein structures of viral and cancer cell protein were docked using the High Ambiguity Driven protein–protein DOCKing (HADDOCK) server. The binding affinity and interaction profile of viral proteins against all the receptors were analyzed. Results suggest that the HAdV3 L5 protein shows better interaction as compared to HAdV2 and HAdV5 by elucidating high binding affinity with 4 receptors (NPR3, GABRP, SLC6A6, and TMPRSS4). The current study proposed that HAdV5 or HAdV2 virus pseudotyped with the L5 protein of HAdV3 can be able to effectively infect pancreatic cancer cells. Moreover, the current study surmises that the affinity maturation of HAdV3 L5 can enhance virus attachment with all the receptors of cancer cells.

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Improved Protein Structure Prediction Using a New Multi‐Scale Network and Homologous Templates

Cited by 68 publications

References 68 publications

The Builders of the Junction: Roles of Junctophilin1 and Junctophilin2 in the Assembly of the Sarcoplasmic Reticulum–Plasma Membrane Junctions in Striated Muscle

The Builders of the Junction: Roles of Junctophilin1 and Junctophilin2 in the Assembly of the Sarcoplasmic Reticulum–Plasma Membrane Junctions in Striated Muscle

PIPENN: protein interface prediction from sequence with an ensemble of neural nets

Interaction Analysis of Adenovirus L5 Protein With Pancreatic Cancer Cell Surface Receptor to Analyze Its Affinity for Oncolytic Virus Therapy

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