scholarly journals Local Protein Surface Patch Method for Protein-Ligand Binding Prediction

2011 ◽  
Vol 100 (3) ◽  
pp. 394a
Author(s):  
Lee Sael ◽  
Daisuke Kihara
Keyword(s):  
Ligand Binding ◽  
Surface Patch ◽  
Protein Surface ◽  
Binding Prediction ◽  
Local Protein

scholarly journals DEELIG: A Deep Learning Approach to Predict Protein-Ligand Binding Affinity

2021 ◽  
Vol 15 ◽  
pp. 117793222110303
Author(s):  
Asad Ahmed ◽  
Bhavika Mam ◽  
Ramanathan Sowdhamini
Keyword(s):  
Deep Learning ◽  
Ligand Binding ◽  
Binding Affinity ◽  
Chemical Space ◽  
Biological Significance ◽  
Protein Crystal ◽  
Ligand Docking ◽  
Complex Data ◽  
Binding Prediction ◽  
Data Set

Protein-ligand binding prediction has extensive biological significance. Binding affinity helps in understanding the degree of protein-ligand interactions and is a useful measure in drug design. Protein-ligand docking using virtual screening and molecular dynamic simulations are required to predict the binding affinity of a ligand to its cognate receptor. Performing such analyses to cover the entire chemical space of small molecules requires intense computational power. Recent developments using deep learning have enabled us to make sense of massive amounts of complex data sets where the ability of the model to “learn” intrinsic patterns in a complex plane of data is the strength of the approach. Here, we have incorporated convolutional neural networks to find spatial relationships among data to help us predict affinity of binding of proteins in whole superfamilies toward a diverse set of ligands without the need of a docked pose or complex as user input. The models were trained and validated using a stringent methodology for feature extraction. Our model performs better in comparison to some existing methods used widely and is suitable for predictions on high-resolution protein crystal (⩽2.5 Å) and nonpeptide ligand as individual inputs. Our approach to network construction and training on protein-ligand data set prepared in-house has yielded significant insights. We have also tested DEELIG on few COVID-19 main protease-inhibitor complexes relevant to the current public health scenario. DEELIG-based predictions can be incorporated in existing databases including RSCB PDB, PDBMoad, and PDBbind in filling missing binding affinity data for protein-ligand complexes.

Improving protein–ligand binding prediction by considering the bridging water molecules in Autodock

2019 ◽  
Vol 18 (05) ◽  
pp. 1950027 ◽  
Author(s):  
Qiangna Lu ◽  
Lian-Wen Qi ◽  
Jinfeng Liu
Keyword(s):  
Ligand Binding ◽  
Considerable Improvement ◽  
Ligand Docking ◽  
Water Molecules ◽  
Binding Modes ◽  
Binding Prediction ◽  
Docking Simulations ◽  
Protein Ligand Interactions ◽  
Ligand Interactions ◽  
Docking Program

Water plays a significant role in determining the protein–ligand binding modes, especially when water molecules are involved in mediating protein–ligand interactions, and these important water molecules are receiving more and more attention in recent years. Considering the effects of water molecules has gradually become a routine process for accurate description of the protein–ligand interactions. As a free docking program, Autodock has been most widely used in predicting the protein–ligand binding modes. However, whether the inclusion of water molecules in Autodock would improve its docking performance has not been systematically investigated. Here, we incorporate important bridging water molecules into Autodock program, and systematically investigate the effectiveness of these water molecules in protein–ligand docking. This approach was evaluated using 18 structurally diverse protein–ligand complexes, in which several water molecules bridge the protein–ligand interactions. Different treatment of water molecules were tested by using the fixed and rotatable water molecules, and a considerable improvement in successful docking simulations was found when including these water molecules. This study illustrates the necessity of inclusion of water molecules in Autodock docking, and emphasizes the importance of a proper treatment of water molecules in protein–ligand binding predictions.

scholarly journals Image-based effective feature generation for protein structural class and ligand binding prediction

2020 ◽  
Vol 6 ◽  
pp. e253
Author(s):  
Nafees Sadique ◽  
Al Amin Neaz Ahmed ◽  
Md Tajul Islam ◽  
Md. Nawshad Pervage ◽  
Swakkhar Shatabda
Keyword(s):  
Machine Learning ◽  
Ligand Binding ◽  
Tertiary Structure ◽  
Learning Algorithms ◽  
Three Dimensional ◽  
Machine Learning Algorithms ◽  
Binding Prediction ◽  
Structural Class ◽  
Protein Structural Class ◽  
Atom Bond

Proteins are the building blocks of all cells in both human and all living creatures of the world. Most of the work in the living organism is performed by proteins. Proteins are polymers of amino acid monomers which are biomolecules or macromolecules. The tertiary structure of protein represents the three-dimensional shape of a protein. The functions, classification and binding sites are governed by the protein’s tertiary structure. If two protein structures are alike, then the two proteins can be of the same kind implying similar structural class and ligand binding properties. In this paper, we have used the protein tertiary structure to generate effective features for applications in structural similarity to detect structural class and ligand binding. Firstly, we have analyzed the effectiveness of a group of image-based features to predict the structural class of a protein. These features are derived from the image generated by the distance matrix of the tertiary structure of a given protein. They include local binary pattern (LBP) histogram, Gabor filtered LBP histogram, separate row multiplication matrix with uniform LBP histogram, neighbor block subtraction matrix with uniform LBP histogram and atom bond. Separate row multiplication matrix and neighbor block subtraction matrix filters, as well as atom bond, are our novels. The experiments were done on a standard benchmark dataset. We have demonstrated the effectiveness of these features over a large variety of supervised machine learning algorithms. Experiments suggest support vector machines is the best performing classifier on the selected dataset using the set of features. We believe the excellent performance of Hybrid LBP in terms of accuracy would motivate the researchers and practitioners to use it to identify protein structural class. To facilitate that, a classification model using Hybrid LBP is readily available for use at http://brl.uiu.ac.bd/PL/. Protein-ligand binding is accountable for managing the tasks of biological receptors that help to cure diseases and many more. Therefore, binding prediction between protein and ligand is important for understanding a protein’s activity or to accelerate docking computations in virtual screening-based drug design. Protein-ligand binding prediction requires three-dimensional tertiary structure of the target protein to be searched for ligand binding. In this paper, we have proposed a supervised learning algorithm for predicting protein-ligand binding, which is a similarity-based clustering approach using the same set of features. Our algorithm works better than the most popular and widely used machine learning algorithms.

scholarly journals PickPocket : Pocket binding prediction for specific ligands family using neural networks.

2020 ◽  
Author(s):  
Benjamin Thomas VIART ◽  
Claudio Lorenzi ◽  
María Moriel-Carretero ◽  
Sofia Kossida
Keyword(s):  
Neural Networks ◽  
Fatty Acid ◽  
Ligand Binding ◽  
Binding Sites ◽  
Structural Data ◽  
Binding Pocket ◽  
Fatty Acid Binding Proteins ◽  
Binding Prediction ◽  
Fatty Acid Binding ◽  
Binding Pockets

Most of the protein biological functions occur through contacts with other proteins or ligands. The residues that constitute the contact surface of a ligand-binding pocket are usually located far away within its sequence. Therefore, the identification of such motifs is more challenging than the linear protein domains. To discover new binding sites, we developed a tool called PickPocket that focuses on a small set of user-defined ligands and uses neural networks to train a ligand-binding prediction model. We tested PickPocket on fatty acid-like ligands due to their structural similarities and their under-representation in the ligand-pocket binding literature. Our results show that for fatty acid-like molecules, pocket descriptors and secondary structures are enough to obtain predictions with accuracy >90% using a dataset of 1740 manually curated ligand-binding pockets. The trained model could also successfully predict the ligand-binding pockets using unseen structural data of two recently reported fatty acid-binding proteins. We think that the PickPocket tool can help to discover new protein functions by investigating the binding sites of specific ligand families. The source code and all datasets contained in this work are freely available at https://github.com/benjaminviart/PickPocket .

scholarly journals Ligand Binding Sites of Inducible Costimulator and High Avidity Mutants with Improved Function

2002 ◽  
Vol 195 (8) ◽  
pp. 1033-1041 ◽  
Author(s):  
Shengdian Wang ◽  
Gefeng Zhu ◽  
Koji Tamada ◽  
Lieping Chen ◽  
Jürgen Bajorath
Keyword(s):  
T Cell ◽  
Immune Responses ◽  
Ligand Binding ◽  
Binding Sites ◽  
T Cell Response ◽  
Surface Patch ◽  
High Avidity ◽  
Wild Type ◽  
Ligand Interaction ◽  
Inducible Costimulator

Interaction between inducible costimulator (ICOS) and its ligand is implicated in the induction of cell-mediated and humoral immune responses. However, the molecular details of this interaction are unknown. We report here a mutagenesis analysis of residues in ICOS that are critical for ligand binding. A three-dimensional model of the extracellular immunoglobulin-like domain of ICOS was used to map the residues conserved within the CD28 family. This analysis identified a surface patch containing the characteristic “PPP” sequence and is conserved in human and mouse ICOS. Mutations in this region of human ICOS reduce or abolish ligand binding. Our results suggest that the ligand binding site in ICOS maps to a region overlapping yet distinct from the CD80/CD86 binding sites in CD28 and cytotoxic T lymphocyte antigen (CTLA)-4. Thus, the analysis suggests that differences in ligand binding specificity between these related costimulatory molecules have evolved by utilization of overlapping regions with different patterns of conserved and nonconserved residues. Two site-specific mutants generated in the course of our studies bound ICOS ligand with higher avidity than wild-type ICOS. An S76E mutant protein of ICOS blocked T cell costimulatory function of ICOS ligand and inhibited T cell response to allogeneic antigens superior to wild-type ICOS. Our studies thus identified critical residues involving in ICOS receptor–ligand interaction and provide new modulators for immune responses.

scholarly journals Conformational epitope matching and prediction based on protein surface spiral features

BMC Genomics ◽  
2021 ◽  
Vol 22 (S2) ◽  
Author(s):  
Ying-Tsang Lo ◽  
Tao-Chuan Shih ◽  
Tun-Wen Pai ◽  
Li-Ping Ho ◽  
Jen-Leih Wu ◽  
...  
Keyword(s):  
Amino Acid ◽  
Large Scale ◽  
Epitope Prediction ◽  
Conformational Epitope ◽  
Physicochemical Characteristics ◽  
Disease Diagnosis ◽  
Complete Sequence ◽  
Surface Patch ◽  
Protein Surface ◽  
Amino Acid Contents

Abstract Background A conformational epitope (CE) is composed of neighboring amino acid residues located on an antigenic protein surface structure. CEs bind their complementary paratopes in B-cell receptors and/or antibodies. An effective and efficient prediction tool for CE analysis is critical for the development of immunology-related applications, such as vaccine design and disease diagnosis. Results We propose a novel method consisting of two sequential modules: matching and prediction. The matching module includes two main approaches. The first approach is a complete sequence search (CSS) that applies BLAST to align the sequence with all known antigen sequences. Fragments with high epitope sequence identities are identified and the predicted residues are annotated on the query structure. The second approach is a spiral vector search (SVS) that adopts a novel surface spiral feature vector for large-scale surface patch detection when queried against a comprehensive epitope database. The prediction module also contains two proposed subsystems. The first system is based on knowledge-based energy and geometrical neighboring residue contents, and the second system adopts combinatorial features, including amino acid contents and physicochemical characteristics, to formulate corresponding geometric spiral vectors and compare them with all spiral vectors from known CEs. An integrated testing dataset was generated for method evaluation, and our two searching methods effectively identified all epitope regions. The prediction results show that our proposed method outperforms previously published systems in terms of sensitivity, specificity, positive predictive value, and accuracy. Conclusions The proposed method significantly improves the performance of traditional epitope prediction. Matching followed by prediction is an efficient and effective approach compared to predicting directly on specific surfaces containing antigenic characteristics.

scholarly journals Protein Flexibility Reduces Solvent-Mediated Friction Barriers of Ligand Binding to a Hydrophobic Surface Patch

2021 ◽  
Author(s):  
Christopher Päslack ◽  
Lars V Schäfer ◽  
Matthias Heyden
Keyword(s):  
Ligand Binding ◽  
Protein Flexibility ◽  
Hydrophobic Surface ◽  
Model Systems ◽  
Surface Patch ◽  
Protein Motions ◽  
Internal Protein

Solvent fluctuations have been explored in detail for idealized and rigid hydrophobic model systems, but so far it has remained unclear how internal protein motions and their coupling to the...

scholarly journals Image based effective feature generation for protein structural class and ligand binding prediction

2019 ◽  
Author(s):  
Nafees Sadique ◽  
Al Amin Neaz Ahmed ◽  
Md Tajul Islam ◽  
Md. Nawshad Pervage ◽  
Swakkhar Shatabda
Keyword(s):  
Ligand Binding ◽  
Local Binary Pattern ◽  
Tertiary Structure ◽  
Three Dimensional ◽  
Machine Learning Algorithms ◽  
Binding Prediction ◽  
Structural Class ◽  
Protein Structural Class ◽  
Uniform Local Binary Pattern ◽  
Pattern Histogram

Proteins are the building blocks of all cells in both human and all our living creatures of the world. Most of the work in the living organism is performed by Proteins. Proteins are polymers of amino acid monomers which are biomolecules or macromolecules. The tertiary structure of protein represents the three-dimensional shape of a protein. The functions, classification and binding sites are governed by protein’s tertiary structure. If two protein structures are alike then the two proteins can be of the same kind implying similar structural class and ligand binding properties. In this paper, we have used protein structure to generate effective features for applications in structural similarity to detect structural class and ligand binding. Firstly, we analyze the effectiveness of a group of image based features to predict the structural class of a protein. These features are derived from the image generated by the distance matrix of the tertiary structure of a given protein. They include local binary pattern histogram, Gabor filtered local binary pattern histogram, separate row multiplication matrix with uniform local binary pattern histogram, neighbour block subtraction matrix with uniform local binary pattern histogram and atom bond. The experiments were done on a standard benchmark dataset. We have demonstrated the effectiveness of these features over a large variety of supervised machine learning algorithms. Experiments suggest Random Forest is the best performing classifier on the selected dataset using the set of features. We believe the excellent performance of Hybrid LBP in terms of accuracy would motivate the researchers and practitioners to use it to identify protein structural class. To facilitate that, a classification model using Hybrid LBP is readily available for use at http://brl.uiu.ac.bd/PL/. Protein-Ligand binding is accountable for managing the tasks of biological receptors that helps to cure diseases and many more. So, binding prediction between protein and ligand is important for understanding a protein’s activity or to accelerate docking computations in virtual screening-based drug design. Protein-Ligand Binding Prediction requires three-dimensional tertiary structure of the target protein to be searched for ligand binding. In this paper, we’ve proposed a supervised learning algorithm for predicting Protein-Ligand Binding which is a Similarity-Based Clustering approach using the same set of features. Our algorithm works better than most popular and widely used machine learning algorithms

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