Highly accurate sequence-based prediction of half-sphere exposures of amino acid residues in proteins

2015 ◽  
Vol 32 (6) ◽  
pp. 843-849 ◽  
Author(s):  
Rhys Heffernan ◽  
Abdollah Dehzangi ◽  
James Lyons ◽  
Kuldip Paliwal ◽  
Alok Sharma ◽  
...  
Keyword(s):  
Protein Structure ◽  
Amino Acid ◽  
Structure Prediction ◽  
Protein Structures ◽  
Correlation Coefficients ◽  
Accessible Surface Area ◽  
Solvent Accessible Surface Area ◽  
Supplementary Information ◽  
Amino Acid Residues ◽  
Solvent Exposure

Abstract Motivation: Solvent exposure of amino acid residues of proteins plays an important role in understanding and predicting protein structure, function and interactions. Solvent exposure can be characterized by several measures including solvent accessible surface area (ASA), residue depth (RD) and contact numbers (CN). More recently, an orientation-dependent contact number called half-sphere exposure (HSE) was introduced by separating the contacts within upper and down half spheres defined according to the Cα-Cβ (HSEβ) vector or neighboring Cα-Cα vectors (HSEα). HSEα calculated from protein structures was found to better describe the solvent exposure over ASA, CN and RD in many applications. Thus, a sequence-based prediction is desirable, as most proteins do not have experimentally determined structures. To our best knowledge, there is no method to predict HSEα and only one method to predict HSEβ. Results: This study developed a novel method for predicting both HSEα and HSEβ (SPIDER-HSE) that achieved a consistent performance for 10-fold cross validation and two independent tests. The correlation coefficients between predicted and measured HSEβ (0.73 for upper sphere, 0.69 for down sphere and 0.76 for contact numbers) for the independent test set of 1199 proteins are significantly higher than existing methods. Moreover, predicted HSEα has a higher correlation coefficient (0.46) to the stability change by residue mutants than predicted HSEβ (0.37) and ASA (0.43). The results, together with its easy Cα-atom-based calculation, highlight the potential usefulness of predicted HSEα for protein structure prediction and refinement as well as function prediction. Availability and implementation: The method is available at http://sparks-lab.org. Contact: [email protected] or [email protected] Supplementary information: Supplementary data are available at Bioinformatics online.

USING RIGIDITY ANALYSIS TO PROBE MUTATION-INDUCED STRUCTURAL CHANGES IN PROTEINS

2012 ◽  
Vol 10 (03) ◽  
pp. 1242010 ◽  
Author(s):  
FILIP JAGODZINSKI ◽  
JEANNE HARDY ◽  
ILEANA STREINU
Keyword(s):  
Amino Acid ◽  
Amino Acid Substitution ◽  
Structural Changes ◽  
Protein Structures ◽  
Data Bank ◽  
Accessible Surface Area ◽  
Solvent Accessible Surface Area ◽  
Single Amino Acid ◽  
Fast Evaluation

Predicting the effect of a single amino acid substitution on the stability of a protein structure is a fundamental task in macromolecular modeling. It has relevance to drug design and understanding of disease-causing protein variants. We present KINARI-Mutagen, a web server for performing in silico mutation experiments on protein structures from the Protein Data Bank. Our rigidity-theoretical approach permits fast evaluation of the effects of mutations that may not be easy to perform in vitro, because it is not always possible to express a protein with a specific amino acid substitution. We use KINARI-Mutagen to identify critical residues, and we show that our predictions correlate with destabilizing mutations to glycine. In two in-depth case studies we show that the mutated residues identified by KINARI-Mutagen as critical correlate with experimental data, and would not have been identified by other methods such as Solvent Accessible Surface Area measurements or residue ranking by contributions to stabilizing interactions. We also generate 48 mutants for 14 proteins, and compare our rigidity-based results against experimental mutation stability data. KINARI-Mutagen is available at http://kinari.cs.umass.edu .

scholarly journals FASPR: an open-source tool for fast and accurate protein side-chain packing

2020 ◽  
Vol 36 (12) ◽  
pp. 3758-3765 ◽  
Author(s):  
Xiaoqiang Huang ◽  
Robin Pearce ◽  
Yang Zhang
Keyword(s):  
Protein Structure ◽  
Protein Design ◽  
Structure Prediction ◽  
Protein Structures ◽  
Scoring Function ◽  
Supplementary Information ◽  
Side Chain ◽  
Chain Packing ◽  
And Function ◽  
Side Chain Packing

Abstract Motivation Protein structure and function are essentially determined by how the side-chain atoms interact with each other. Thus, accurate protein side-chain packing (PSCP) is a critical step toward protein structure prediction and protein design. Despite the importance of the problem, however, the accuracy and speed of current PSCP programs are still not satisfactory. Results We present FASPR for fast and accurate PSCP by using an optimized scoring function in combination with a deterministic searching algorithm. The performance of FASPR was compared with four state-of-the-art PSCP methods (CISRR, RASP, SCATD and SCWRL4) on both native and non-native protein backbones. For the assessment on native backbones, FASPR achieved a good performance by correctly predicting 69.1% of all the side-chain dihedral angles using a stringent tolerance criterion of 20°, compared favorably with SCWRL4, CISRR, RASP and SCATD which successfully predicted 68.8%, 68.6%, 67.8% and 61.7%, respectively. Additionally, FASPR achieved the highest speed for packing the 379 test protein structures in only 34.3 s, which was significantly faster than the control methods. For the assessment on non-native backbones, FASPR showed an equivalent or better performance on I-TASSER predicted backbones and the backbones perturbed from experimental structures. Detailed analyses showed that the major advantage of FASPR lies in the optimal combination of the dead-end elimination and tree decomposition with a well optimized scoring function, which makes FASPR of practical use for both protein structure modeling and protein design studies. Availability and implementation The web server, source code and datasets are freely available at https://zhanglab.ccmb.med.umich.edu/FASPR and https://github.com/tommyhuangthu/FASPR. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Improving prediction of protein secondary structure, backbone angles, solvent accessibility and contact numbers by using predicted contact maps and an ensemble of recurrent and residual convolutional neural networks

2018 ◽  
Vol 35 (14) ◽  
pp. 2403-2410 ◽  
Author(s):  
Jack Hanson ◽  
Kuldip Paliwal ◽  
Thomas Litfin ◽  
Yuedong Yang ◽  
Yaoqi Zhou
Keyword(s):  
Neural Networks ◽  
Protein Structure ◽  
Secondary Structure ◽  
Structure Prediction ◽  
Rapid Expansion ◽  
Solvent Accessible Surface Area ◽  
Supplementary Information ◽  
Test Set ◽  
Contact Maps ◽  
Unseen Data

Abstract Motivation Sequence-based prediction of one dimensional structural properties of proteins has been a long-standing subproblem of protein structure prediction. Recently, prediction accuracy has been significantly improved due to the rapid expansion of protein sequence and structure libraries and advances in deep learning techniques, such as residual convolutional networks (ResNets) and Long-Short-Term Memory Cells in Bidirectional Recurrent Neural Networks (LSTM-BRNNs). Here we leverage an ensemble of LSTM-BRNN and ResNet models, together with predicted residue-residue contact maps, to continue the push towards the attainable limit of prediction for 3- and 8-state secondary structure, backbone angles (θ, τ, ϕ and ψ), half-sphere exposure, contact numbers and solvent accessible surface area (ASA). Results The new method, named SPOT-1D, achieves similar, high performance on a large validation set and test set (≈1000 proteins in each set), suggesting robust performance for unseen data. For the large test set, it achieves 87% and 77% in 3- and 8-state secondary structure prediction and 0.82 and 0.86 in correlation coefficients between predicted and measured ASA and contact numbers, respectively. Comparison to current state-of-the-art techniques reveals substantial improvement in secondary structure and backbone angle prediction. In particular, 44% of 40-residue fragment structures constructed from predicted backbone Cα-based θ and τ angles are less than 6 Å root-mean-squared-distance from their native conformations, nearly 20% better than the next best. The method is expected to be useful for advancing protein structure and function prediction. Availability and implementation SPOT-1D and its data is available at: http://sparks-lab.org/. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Protein Structure Prediction Based on Improved Genetic Algorithm

2020 ◽  
Vol 11 (9) ◽  
pp. 450-454
Author(s):  
Jiaxi Liu ◽  
Keyword(s):  
Genetic Algorithm ◽  
Protein Structure ◽  
Amino Acid ◽  
Protein Structure Prediction ◽  
Structure Prediction ◽  
Protein Structures ◽  
Three Dimensional ◽  
Dimensional Structure ◽  
Improved Genetic Algorithm ◽  
Research Areas

The prediction of protein three-dimensional structure from amino acid sequence has been a challenge problem in bioinformatics, owing to the many potential applications for robust protein structure prediction methods. Protein structure prediction is essential to bioscience, and its research results are important for other research areas. Methods for the prediction an才d design of protein structures have advanced dramatically. The prediction of protein structure based on average hydrophobic values is discussed and an improved genetic algorithm is proposed to solve the optimization problem of hydrophobic protein structure prediction. An adjustment operator is designed with the average hydrophobic value to prevent the overlapping of amino acid positions. Finally, some numerical experiments are conducted to verify the feasibility and effectiveness of the proposed algorithm by comparing with the traditional HNN algorithm.

scholarly journals Amino acid torsion angles enable prediction of protein fold classification

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Kun Tian ◽  
Xin Zhao ◽  
Xiaogeng Wan ◽  
Stephen S.-T. Yau
Keyword(s):  
Protein Structure ◽  
Amino Acid ◽  
Protein Structure Prediction ◽  
Structure Prediction ◽  
High Throughput Sequencing ◽  
Protein Structures ◽  
Amino Acid Sequences ◽  
Single Amino Acid ◽  
New Approach ◽  
Protein Functions

AbstractProtein structure can provide insights that help biologists to predict and understand protein functions and interactions. However, the number of known protein structures has not kept pace with the number of protein sequences determined by high-throughput sequencing. Current techniques used to determine the structure of proteins are complex and require a lot of time to analyze the experimental results, especially for large protein molecules. The limitations of these methods have motivated us to create a new approach for protein structure prediction. Here we describe a new approach to predict of protein structures and structure classes from amino acid sequences. Our prediction model performs well in comparison with previous methods when applied to the structural classification of two CATH datasets with more than 5000 protein domains. The average accuracy is 92.5% for structure classification, which is higher than that of previous research. We also used our model to predict four known protein structures with a single amino acid sequence, while many other existing methods could only obtain one possible structure for a given sequence. The results show that our method provides a new effective and reliable tool for protein structure prediction research.

How many water molecules are detected in X-ray protein crystal structures?

2017 ◽  
Vol 50 (1) ◽  
pp. 96-101 ◽  
Author(s):  
Marco Gnesi ◽  
Oliviero Carugo
Keyword(s):  
Amino Acid ◽  
Crystal Structures ◽  
External Validation ◽  
Secondary Analysis ◽  
Accessible Surface Area ◽  
Protein Crystal ◽  
Solvent Accessible Surface Area ◽  
Water Molecules ◽  
Amino Acid Residues ◽  
Expected Number

The positions of several water molecules can be determined in protein crystallography, either buried in internal cavities or at the protein surface. It is important to be able to estimate the expected number of these water molecules to facilitate crystal structure determination. Here, a multiple Poisson regression model implemented on nearly 10 000 protein crystal structures shows that the number of detectable water molecules depends on eight variables: crystallographic resolution, R factor, percentage of solvent in the crystal, average B factor of the protein atoms, percentage of amino acid residues in loops, average solvent-accessible surface area of the amino acid residues, grand average of hydropathy of the protein(s) in the asymmetric unit and normalized number of heteroatoms that are not water molecules. Furthermore, a secondary analysis tested the effect of different software packages. Given the values of these eight variables, it is possible to compute the expected number of water molecules detectable in electron-density maps with reasonable accuracy (as suggested by an external validation of the model).

scholarly journals NEPRE: a Scoring Function for Protein Structures based on Neighbourhood Preference

2018 ◽  
Author(s):  
Siyuan Liu ◽  
Xilun Xiang ◽  
Haiguang Liu
Keyword(s):  
Protein Structure ◽  
Amino Acid ◽  
Energy Function ◽  
Structure Prediction ◽  
Protein Structures ◽  
Scoring Function ◽  
Data Bank ◽  
Native Structure ◽  
Scoring Algorithm ◽  
Model Ranking

ABSTRACTProtein structure prediction relies on two major components, a method to generate good models that are close to the native structure and a scoring function that can select the good models. Based on the statistics from known structures in the protein data bank, a statistical energy function is derived to reflect the amino acid neighbourhood preferences. The neighbourhood of one amino acid is defined by its contacting residues, and the energy function is determined by the neighbhoring residue types and relative positions. A scoring algorithm, Nepre, has been implemented and its performance was tested with several decoy sets. The results show that the Nepre program can be applied in model ranking to improve the success rate in structure predictions.

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