scholarly journals Recognizing ion ligand binding sites by SMO algorithm

2019 ◽  
Vol 20 (S3) ◽  
Author(s):  
Shan Wang ◽  
Xiuzhen Hu ◽  
Zhenxing Feng ◽  
Xiaojin Zhang ◽  
Liu Liu ◽  
...  
Keyword(s):  
Ligand Binding ◽  
Binding Sites ◽  
Protein Function ◽  
Metal Ion ◽  
Cross Validation ◽  
Sequence Information ◽  
Sequential Minimal Optimization ◽  
Ligand Binding Sites ◽  
Smo Algorithm ◽  
Fold Cross Validation

Abstract Background In many important life activities, the execution of protein function depends on the interaction between proteins and ligands. As an important protein binding ligand, the identification of the binding site of the ion ligands plays an important role in the study of the protein function. Results In this study, four acid radical ion ligands (NO2−,CO32−,SO42−,PO43−) and ten metal ion ligands (Zn2+,Cu2+,Fe2+,Fe3+,Ca2+,Mg2+,Mn2+,Na+,K+,Co2+) are selected as the research object, and the Sequential minimal optimization (SMO) algorithm based on sequence information was proposed, better prediction results were obtained by 5-fold cross validation. Conclusions An efficient method for predicting ion ligand binding sites was presented.

Recognition of ion ligand binding sites based on amino acid features with the fusion of energy, physicochemical and structural features

2020 ◽  
Vol 26 ◽  
Author(s):  
Shan Wang ◽  
Xiuzhen Hu ◽  
Zhenxing Feng ◽  
Liu Liu ◽  
Kai Sun ◽  
...  
Keyword(s):  
Amino Acid ◽  
Ligand Binding ◽  
Binding Site ◽  
Binding Sites ◽  
Metal Ion ◽  
Structural Features ◽  
Support Vector ◽  
Sequence Information ◽  
Ligand Binding Site ◽  
Ligand Binding Sites

Background: Rational drug molecular design based on virtual screening requires the ligand binding site to be known. Recently, the recognition of ion ligand binding site has become an important research direction in pharmacology. Methods: In this work, we selected the binding residues of 4 acid radical ion ligands(NO2 - , CO3 2- , SO4 2- and PO4 3- ) and 10 metal ion ligands (Zn2+,Cu2+, Fe2+, Fe3+, Ca2+, Mg2+, Mn2+, Na+ , K+ and Co2+) as research objects. Based on the protein sequence information, we extracted amino acid features, energy, physicochemical and structure features. Then we incorporating the above features and input them into the MultilayerPerceptron (MLP) and support vector machine (SVM) algorithms. Results: In the independent test, the best accuracy was higher than 92.5%, which was better than the previous result on Conclusion: Finally, we set up a free web server for the prediction of protein-ion ligand binding sites (http://39.104.77.103:8081/lsb/HomePage/HomePage.html). This study is helpful for molecular drug design.

scholarly journals Aplikasi Diagnosis Penyakit Kanker Payudara Menggunakan Algoritma Sequential Minimal Optimization

2017 ◽  
Vol 5 (4) ◽  
pp. 153
Author(s):  
Agung Wibowo
Keyword(s):  
Breast Cancer ◽  
Cross Validation ◽  
Sequential Minimal Optimization ◽  
Diagnose Breast Cancer ◽  
Web Based ◽  
Test Dataset ◽  
Smo Algorithm ◽  
Increase In Accuracy ◽  
Model Formation ◽  
Fold Cross Validation

Various methods for the diagnosis of breast cancer exist, but not many have been implemented as an application. This study aims to develop an application using SMO algorithm assisted by Weka to diagnose breast cancer. The application was web-based application and developed using Javascript. Test dataset and model formation used original Breast Cancer Database (WBCD) data without missing value. Test mode used 10-fold cross-validation. This application can diagnose breast cancer with an accuracy of 97.3645% and has a significant increase in accuracy for the diagnosis of malignant cancer.Beragam metode untuk diagnosis kanker payudara, namun belum banyak yang diimplementasikan menjadi sebuah aplikasi. Penelitian ini bertujuan untuk mengembangkan aplikasi berdasarkan model hasil kalkulasi algoritma SMO berbantuan Weka untuk mendiagnosis penyakit kanker payudara. Aplikasi dikembangkan berbasis web menggunakan Javascript. Dataset pengujian dan pembentukan model menggunakan data Winconsin Breast Cancer Database original (WBCD) tanpa nilai hilang. Mode pengujian menggunakan 10-fold cross validation. Aplikasi ini dapat mendiagnosis kanker payudara dengan akurasi 97.3645% dan memiliki peningkatan akurasi yang signifikan untuk diagnosis kanker ganas.

scholarly journals SXGBsite: Prediction of Protein–Ligand Binding Sites Using Sequence Information and Extreme Gradient Boosting

Genes ◽  
2019 ◽  
Vol 10 (12) ◽  
pp. 965 ◽  
Author(s):  
Ziqi Zhao ◽  
Yonghong Xu ◽  
Yong Zhao
Keyword(s):  
Ligand Binding ◽  
Binding Site ◽  
Binding Sites ◽  
Gradient Boosting ◽  
Sequence Information ◽  
Ligand Binding Site ◽  
Extreme Gradient Boosting ◽  
Ligand Binding Sites ◽  
Independent Test ◽  
Test Sets

The prediction of protein–ligand binding sites is important in drug discovery and drug design. Protein–ligand binding site prediction computational methods are inexpensive and fast compared with experimental methods. This paper proposes a new computational method, SXGBsite, which includes the synthetic minority over-sampling technique (SMOTE) and the Extreme Gradient Boosting (XGBoost). SXGBsite uses the position-specific scoring matrix discrete cosine transform (PSSM-DCT) and predicted solvent accessibility (PSA) to extract features containing sequence information. A new balanced dataset was generated by SMOTE to improve classifier performance, and a prediction model was constructed using XGBoost. The parallel computing and regularization techniques enabled high-quality and fast predictions and mitigated overfitting caused by SMOTE. An evaluation using 12 different types of ligand binding site independent test sets showed that SXGBsite performs similarly to the existing methods on eight of the independent test sets with a faster computation time. SXGBsite may be applied as a complement to biological experiments.

scholarly journals GRaSP: a graph-based residue neighborhood strategy to predict binding sites

2020 ◽  
Vol 36 (Supplement_2) ◽  
pp. i726-i734
Author(s):  
Charles A Santana ◽  
Sabrina de A Silveira ◽  
João P A Moraes ◽  
Sandro C Izidoro ◽  
Raquel C de Melo-Minardi ◽  
...  
Keyword(s):  
Ligand Binding ◽  
Binding Site ◽  
Binding Sites ◽  
Protein Function ◽  
Learning Strategy ◽  
State Of The Art ◽  
Supplementary Information ◽  
Major Step ◽  
Ligand Binding Sites ◽  
Residue Neighborhood

Abstract Motivation The discovery of protein–ligand-binding sites is a major step for elucidating protein function and for investigating new functional roles. Detecting protein–ligand-binding sites experimentally is time-consuming and expensive. Thus, a variety of in silico methods to detect and predict binding sites was proposed as they can be scalable, fast and present low cost. Results We proposed Graph-based Residue neighborhood Strategy to Predict binding sites (GRaSP), a novel residue centric and scalable method to predict ligand-binding site residues. It is based on a supervised learning strategy that models the residue environment as a graph at the atomic level. Results show that GRaSP made compatible or superior predictions when compared with methods described in the literature. GRaSP outperformed six other residue-centric methods, including the one considered as state-of-the-art. Also, our method achieved better results than the method from CAMEO independent assessment. GRaSP ranked second when compared with five state-of-the-art pocket-centric methods, which we consider a significant result, as it was not devised to predict pockets. Finally, our method proved scalable as it took 10–20 s on average to predict the binding site for a protein complex whereas the state-of-the-art residue-centric method takes 2–5 h on average. Availability and implementation The source code and datasets are available at https://github.com/charles-abreu/GRaSP. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Development of a Machine Learning Method to Predict Membrane Protein-Ligand Binding Residues Using Basic Sequence Information

2015 ◽  
Vol 2015 ◽  
pp. 1-7 ◽  
Author(s):  
M. Xavier Suresh ◽  
M. Michael Gromiha ◽  
Makiko Suwa
Keyword(s):  
Membrane Protein ◽  
Ligand Binding ◽  
Binding Sites ◽  
Transmembrane Domain ◽  
Three Dimensional ◽  
Sequence Information ◽  
Bayes Classifier ◽  
Binding Residue ◽  
Ligand Binding Sites ◽  
Binding Residues

Locating ligand binding sites and finding the functionally important residues from protein sequences as well as structures became one of the challenges in understanding their function. Hence a Naïve Bayes classifier has been trained to predict whether a given amino acid residue in membrane protein sequence is a ligand binding residue or not using only sequence based information. The input to the classifier consists of the features of the target residue and two sequence neighbors on each side of the target residue. The classifier is trained and evaluated on a nonredundant set of 42 sequences (chains with at least one transmembrane domain) from 31 alpha-helical membrane proteins. The classifier achieves an overall accuracy of 70.7% with 72.5% specificity and 61.1% sensitivity in identifying ligand binding residues from sequence. The classifier performs better when the sequence is encoded by psi-blast generated PSSM profiles. Assessment of the predictions in the context of three-dimensional structures of proteins reveals the effectiveness of this method in identifying ligand binding sites from sequence information. In 83.3% (35 out of 42) of the proteins, the classifier identifies the ligand binding sites by correctly recognizing more than half of the binding residues. This will be useful to protein engineers in exploiting potential residues for functional assessment.

scholarly journals Diversity in the structures and ligand-binding sites of nematode fatty acid and retinol-binding proteins revealed by Na-FAR-1 from Necator americanus

2015 ◽  
Vol 471 (3) ◽  
pp. 403-414 ◽  
Author(s):  
M. Florencia Rey-Burusco ◽  
Marina Ibáñez-Shimabukuro ◽  
Mads Gabrielsen ◽  
Gisela R. Franchini ◽  
Andrew J. Roe ◽  
...  
Keyword(s):  
Fatty Acid ◽  
Ligand Binding ◽  
Tissue Distribution ◽  
Binding Sites ◽  
Binding Proteins ◽  
Retinol Binding Protein ◽  
Internal Cavity ◽  
Binding Characteristics ◽  
Necator Americanus ◽  
Ligand Binding Sites

Necator americanus fatty acid and retinol-binding protein-1 (Na-FAR-1) is an abundantly expressed FAR from a parasitic hookworm. The present work describes its tissue distribution, structure and ligand-binding characteristics and shows that Na-FAR-1 expands to transport multiple FA molecules in its internal cavity.

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