scholarly journals Modeling of protein complexes in CASP14 with emphasis on the interaction interface prediction

2021 ◽  
Author(s):  
Justas Dapkunas ◽  
Kliment Olechnovič ◽  
Česlovas Venclovas
Keyword(s):  
Protein Structure ◽  
Structure Prediction ◽  
Protein Complexes ◽  
Assessment Method ◽  
Protein Assemblies ◽  
Structure Quality ◽  
Interaction Interface ◽  
Quality Assessment Method ◽  
Interface Prediction ◽  
Antigen Interaction

The goal of CASP experiments is to monitor the progress in the protein structure prediction field. During the 14th CASP edition we aimed to test our capabilities of predicting structures of protein complexes. Our protocol for modeling protein assemblies included both template-based modeling and free docking. Structural templates were identified using sensitive sequence-based searches. If sequence-based searches failed, we performed structure-based template searches using selected CASP server models. In the absence of reliable templates we applied free docking starting from monomers generated by CASP servers. We evaluated and ranked models of protein complexes using an improved version of protein structure quality assessment method, VoroMQA, taking into account both interaction interface and global structure scores. If reliable templates could be identified, generally accurate models of protein assemblies were generated with the exception of an antibody-antigen interaction. The success of free docking mainly depended on the accuracy of initial subunit models and on the scoring of docking solutions. To put our overall results in perspective, we analyzed our performance in the context of other CASP groups. Although the subunits in our assembly models often were not of the top quality, these models had, overall, the best predicted interfaces according to several protein-protein interface accuracy measures. Since we did not use co-evolution-based prediction of inter-chain contacts, we attribute our relative success in predicting interfaces primarily to the emphasis on the interaction interface when modeling and scoring.

scholarly journals A single-model quality assessment method for poor quality protein structure

2020 ◽  
Author(s):  
Jianquan Ouyang ◽  
Ningqiao Huang ◽  
Yunqi Jiang
Keyword(s):  
Protein Structure ◽  
Quality Assessment ◽  
Structure Prediction ◽  
Assessment Method ◽  
Poor Quality ◽  
Single Model ◽  
Model Quality ◽  
Model Quality Assessment ◽  
Quality Assessment Method

Abstract Quality assessment of protein tertiary structure prediction models, in which structures of the best quality are selected from decoys, is a major challenge in protein structure prediction, and is crucial to determine a model’s utility and potential applications. Estimating the quality of a single model predicts the model’s quality based on the single model itself. In general, the Pearson correlation value of the quality assessment method increases in tandem with an increase in the quality of the model pool. However, there is no consensus regarding the best method to select a few good models from the poor quality model pool. In this work, we introduce a novel single-model quality assessment method for poor quality models that uses simple linear combinations of six features. We perform weighted search and linear regression on a large dataset of models from the 12th Critical Assessment of Protein Structure Prediction (CASP12) and benchmark the results on CASP13 models. We demonstrate that our method achieves outstanding performance on poor quality models.

scholarly journals A single-model quality assessment method for poor quality protein structure

2020 ◽  
Author(s):  
Jianquan Ouyang ◽  
Ningqiao Huang ◽  
Yunqi Jiang
Keyword(s):  
Protein Structure ◽  
Quality Assessment ◽  
Structure Prediction ◽  
Assessment Method ◽  
Poor Quality ◽  
Single Model ◽  
Model Quality ◽  
Model Quality Assessment ◽  
Quality Assessment Method

Abstract Background: Quality assessment of protein tertiary structure prediction models, in which structures of the best quality are selected from decoys, is a major challenge in protein structure prediction, and is crucial to determine a model’s utility and potential applications. Estimating the quality of a single model predicts the model’s quality based on the single model itself. In general, the Pearson correlation value of the quality assessment method increases in tandem with an increase in the quality of the model pool. However, there is no consensus regarding the best method to select a few good models from the poor quality model pool.Results: We introduce a novel single-model quality assessment method for poor quality models that uses simple linear combinations of six features. We perform weighted search and linear regression on a large dataset of models from the 12th Critical Assessment of Protein Structure Prediction (CASP12) and benchmark the results on CASP13 models. We demonstrate that our method achieves outstanding performance on poor quality models.Conclusions: According to results of poor protein structure assessment based on six features, contact prediction and relying on fewer prediction features can improve selection accuracy.

scholarly journals LZerD Protein-Protein Docking Webserver Enhanced With de novo Structure Prediction

2021 ◽  
Vol 8 ◽  
Author(s):  
Charles Christoffer ◽  
Vijay Bharadwaj ◽  
Ryan Luu ◽  
Daisuke Kihara
Keyword(s):  
Protein Structure ◽  
Protein Structure Prediction ◽  
Structure Prediction ◽  
De Novo ◽  
Protein Complexes ◽  
Protein Sequences ◽  
Data Bank ◽  
Protein Docking ◽  
Functional Mechanisms ◽  
Established Technique

Protein-protein docking is a useful tool for modeling the structures of protein complexes that have yet to be experimentally determined. Understanding the structures of protein complexes is a key component for formulating hypotheses in biophysics regarding the functional mechanisms of complexes. Protein-protein docking is an established technique for cases where the structures of the subunits have been determined. While the number of known structures deposited in the Protein Data Bank is increasing, there are still many cases where the structures of individual proteins that users want to dock are not determined yet. Here, we have integrated the AttentiveDist method for protein structure prediction into our LZerD webserver for protein-protein docking, which enables users to simply submit protein sequences and obtain full-complex atomic models, without having to supply any structure themselves. We have further extended the LZerD docking interface with a symmetrical homodimer mode. The LZerD server is available at https://lzerd.kiharalab.org/.

scholarly journals A Review of Protein Structure Prediction using Deep Learning

2021 ◽  
Vol 41 ◽  
pp. 04003
Author(s):  
Meredita Susanty ◽  
Tati Erawati Rajab ◽  
Rukman Hertadi
Keyword(s):  
Deep Learning ◽  
Protein Structure ◽  
Protein Interactions ◽  
Structure Prediction ◽  
Protein Complexes ◽  
Dimensional Structure ◽  
Evolutionary Information ◽  
Learning Approaches ◽  
Predict Protein Structure ◽  
Specific Order

Proteins are macromolecules composed of 20 types of amino acids in a specific order. Understanding how proteins fold is vital because its 3-dimensional structure determines the function of a protein. Prediction of protein structure based on amino acid strands and evolutionary information becomes the basis for other studies such as predicting the function, property or behaviour of a protein and modifying or designing new proteins to perform certain desired functions. Machine learning advances, particularly deep learning, are igniting a paradigm shift in scientific study. In this review, we summarize recent work in applying deep learning techniques to tackle problems in protein structural prediction. We discuss various deep learning approaches used to predict protein structure and future achievements and challenges. This review is expected to help provide perspectives on problems in biochemistry that can take advantage of the deep learning approach. Some of the unanswered challenges with current computational approaches are predicting the location and precision orientation of protein side chains, predicting protein interactions with DNA, RNA and other small molecules and predicting the structure of protein complexes.

Sequence Specific Dihedral Angle Distribution: Application in Protein Structure Prediction and Evaluation

1970 ◽  
Vol 19 (2) ◽  
pp. 217-226
Author(s):  
S. M. Minhaz Ud-Dean ◽  
Mahdi Muhammad Moosa
Keyword(s):  
Protein Structure ◽  
Dihedral Angle ◽  
Protein Structure Prediction ◽  
Structure Prediction ◽  
Protein Structures ◽  
Angle Distribution ◽  
Ramachandran Plot ◽  
Specific Data ◽  
Specific Distribution ◽  
Structure Evaluation

Protein structure prediction and evaluation is one of the major fields of computational biology. Estimation of dihedral angle can provide information about the acceptability of both theoretically predicted and experimentally determined structures. Here we report on the sequence specific dihedral angle distribution of high resolution protein structures available in PDB and have developed Sasichandran, a tool for sequence specific dihedral angle prediction and structure evaluation. This tool will allow evaluation of a protein structure in pdb format from the sequence specific distribution of Ramachandran angles. Additionally, it will allow retrieval of the most probable Ramachandran angles for a given sequence along with the sequence specific data. Key words: Torsion angle, φ-ψ distribution, sequence specific ramachandran plot, Ramasekharan, protein structure appraisal D.O.I. 10.3329/ptcb.v19i2.5439 Plant Tissue Cult. & Biotech. 19(2): 217-226, 2009 (December)

Protein structure prediction of human connexin 30 and its mutations in hearing system

2014 ◽  
Vol 3 (5) ◽  
Author(s):  
S. Reiisi ◽  
M. Hashemzade-chaleshtori ◽  
S. Reisi ◽  
H. Shahi ◽  
S. Parchami ◽  
...  
Keyword(s):  
Protein Structure ◽  
Protein Structure Prediction ◽  
Structure Prediction ◽  
Connexin 30 ◽  
Hearing System
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