HIT web server: A hybrid method to improve electrostatic calculations for biomolecules

Sun, Shengjie; López, Juan Antonio; Xie, Yixin; Guo, Wenhan; Liu, Dongfang; Li, Lin

doi:10.1016/j.csbj.2022.03.022

Cited by 6 publications

(3 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Additionally, a light gradient boosting machine (LGBM) is also widely used with outstanding performance [ 48 , 49 ] due to the advantages of fast training speed and high efficiency, accuracy, and capability to handle large-scale data [ 49 , 50 ]. Recently, deep learning (DL) algorithms (e.g., convolutional neural network (CNN)) have demonstrated superior prediction performance in the field of bioinformatics, such as in the prediction of modification sites on DNA, RNA, and proteins [ 26 , 27 , 31 , 51 , 52 , 53 ] and many domains of social relevance [ 54 , 55 , 56 , 57 , 58 , 59 , 60 , 61 , 62 ]…”

Section: Resultsmentioning

confidence: 99%

ResSUMO: A Deep Learning Architecture Based on Residual Structure for Prediction of Lysine SUMOylation Sites

Zhu

Liu²,

Chen

et al. 2022

Cells

View full text Add to dashboard Cite

Lysine SUMOylation plays an essential role in various biological functions. Several approaches integrating various algorithms have been developed for predicting SUMOylation sites based on a limited dataset. Recently, the number of identified SUMOylation sites has significantly increased due to investigation at the proteomics scale. We collected modification data and found the reported approaches had poor performance using our collected data. Therefore, it is essential to explore the characteristics of this modification and construct prediction models with improved performance based on an enlarged dataset. In this study, we constructed and compared 16 classifiers by integrating four different algorithms and four encoding features selected from 11 sequence-based or physicochemical features. We found that the convolution neural network (CNN) model integrated with residue structure, dubbed ResSUMO, performed favorably when compared with the traditional machine learning and CNN models in both cross-validation and independent tests. The area under the receiver operating characteristic (ROC) curve for ResSUMO was around 0.80, superior to that of the reported predictors. We also found that increasing the depth of neural networks in the CNN models did not improve prediction performance due to the degradation problem, but the residual structure could be included to optimize the neural networks and improve performance. This indicates that residual neural networks have the potential to be broadly applied in the prediction of other types of modification sites with great effectiveness and robustness. Furthermore, the online ResSUMO service is freely accessible.

show abstract

Section: Resultsmentioning

confidence: 99%

ResSUMO: A Deep Learning Architecture Based on Residual Structure for Prediction of Lysine SUMOylation Sites

Zhu

Liu²,

Chen

et al. 2022

Cells

View full text Add to dashboard Cite

show abstract

“…To overcome such limitations, a hybrid method involving DelPhi [ 48 , 49 , 50 , 51 ] (a popular Poisson–Boltzmann equation solver) and steered MD was developed. The method, termed DelPhi–Force MD (DFMD) [ 10 , 52 ], was successfully applied to dock substrates to an enzyme [ 2 ] and a biomolecule onto another biomolecule (such as barstar onto barnase) [ 10 ].…”

Section: Electrostatics Of Wild-type Biological Macromoleculesmentioning

confidence: 99%

Electrostatics in Computational Biophysics and Its Implications for Disease Effects

Sun

Poudel

Alexov

et al. 2022

IJMS

Self Cite

View full text Add to dashboard Cite

This review outlines the role of electrostatics in computational molecular biophysics and its implication in altering wild-type characteristics of biological macromolecules, and thus the contribution of electrostatics to disease mechanisms. The work is not intended to review existing computational approaches or to propose further developments. Instead, it summarizes the outcomes of relevant studies and provides a generalized classification of major mechanisms that involve electrostatic effects in both wild-type and mutant biological macromolecules. It emphasizes the complex role of electrostatics in molecular biophysics, such that the long range of electrostatic interactions causes them to dominate all other forces at distances larger than several Angstroms, while at the same time, the alteration of short-range wild-type electrostatic pairwise interactions can have pronounced effects as well. Because of this dual nature of electrostatic interactions, being dominant at long-range and being very specific at short-range, their implications for wild-type structure and function are quite pronounced. Therefore, any disruption of the complex electrostatic network of interactions may abolish wild-type functionality and could be the dominant factor contributing to pathogenicity. However, we also outline that due to the plasticity of biological macromolecules, the effect of amino acid mutation may be reduced, and thus a charge deletion or insertion may not necessarily be deleterious.

show abstract

“…To solve the above difficulties when treating highly charged biomolecules, we proposed a Hybridizing Ions Treatment (HIT) method [12] , [13] for representing ions in implicit solvation calculations. HIT method adds explicitly bound ions into the implicit solvent model for the electrostatic calculation.…”

Section: Introductionmentioning

confidence: 99%

HIT-2: Implementing machine learning algorithms to treat bound ions in biomolecules

Sun

Xie

et al. 2023

Computational and Structural Biotechnology Journal

Self Cite

View full text Add to dashboard Cite

HIT web server: A hybrid method to improve electrostatic calculations for biomolecules

Cited by 6 publications

References 27 publications

ResSUMO: A Deep Learning Architecture Based on Residual Structure for Prediction of Lysine SUMOylation Sites

ResSUMO: A Deep Learning Architecture Based on Residual Structure for Prediction of Lysine SUMOylation Sites

Electrostatics in Computational Biophysics and Its Implications for Disease Effects

HIT-2: Implementing machine learning algorithms to treat bound ions in biomolecules

Contact Info

Product

Resources

About