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"abstract": "Background: Proteins have the fundamental ability to selectively bind to other molecules and perform specific functions through such interactions, such as protein-ligand binding. Accurate prediction of protein residues that physically bind to ligands is important for drug design and protein docking studies. Most of the successful protein-ligand binding predictions were based on known structures. However, structural information is not largely available in practice due to the huge gap between the number of known protein sequences and that of experimentally solved structures. Results: This paper proposes a dynamic ensemble approach to identify protein-ligand binding residues by using sequence information only. To avoid problems resulting from highly imbalanced samples between the ligand-binding sites and non ligand-binding sites, we constructed several balanced data sets and we trained a random forest classifier for each of them. We dynamically selected a subset of classifiers according to the similarity between the target protein and the proteins in the training data set. The combination of the predictions of the classifier subset to each query protein target yielded the final predictions. The ensemble of these classifiers formed a sequence-based predictor to identify protein-ligand binding sites. Conclusions: Experimental results on two Critical Assessment of protein Structure Prediction datasets and the ccPDB dataset demonstrated that of our proposed method compared favorably with the state-of-the-art. Availability: http://www2.ahu.edu.cn/pchen/web/LigandDSES.htm",
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"content": "Background: Proteins have the fundamental ability to selectively bind to other molecules and perform specific functions through such interactions, such as protein-ligand binding. Accurate prediction of protein residues that physically bind to ligands is important for drug design and protein docking studies. Most of the successful protein-ligand binding predictions were based on known structures. However, structural information is not largely available in practice due to the huge gap between the number of known protein sequences and that of experimentally solved structures. Results: This paper proposes a dynamic ensemble approach to identify protein-ligand binding residues by using sequence information only. To avoid problems resulting from highly imbalanced samples between the ligand-binding sites and non ligand-binding sites, we constructed several balanced data sets and we trained a random forest classifier for each of them. We dynamically selected a subset of classifiers according to the similarity between the target protein and the proteins in the training data set. The combination of the predictions of the classifier subset to each query protein target yielded the final predictions. The ensemble of these classifiers formed a sequence-based predictor to identify protein-ligand binding sites. Conclusions: Experimental results on two Critical Assessment of protein Structure Prediction datasets and the ccPDB dataset demonstrated that of our proposed method compared favorably with the state-of-the-art. Availability: http://www2.ahu.edu.cn/pchen/web/LigandDSES.htm",
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"normalizedAbstract": "Background: Proteins have the fundamental ability to selectively bind to other molecules and perform specific functions through such interactions, such as protein-ligand binding. Accurate prediction of protein residues that physically bind to ligands is important for drug design and protein docking studies. Most of the successful protein-ligand binding predictions were based on known structures. However, structural information is not largely available in practice due to the huge gap between the number of known protein sequences and that of experimentally solved structures. Results: This paper proposes a dynamic ensemble approach to identify protein-ligand binding residues by using sequence information only. To avoid problems resulting from highly imbalanced samples between the ligand-binding sites and non ligand-binding sites, we constructed several balanced data sets and we trained a random forest classifier for each of them. We dynamically selected a subset of classifiers according to the similarity between the target protein and the proteins in the training data set. The combination of the predictions of the classifier subset to each query protein target yielded the final predictions. The ensemble of these classifiers formed a sequence-based predictor to identify protein-ligand binding sites. Conclusions: Experimental results on two Critical Assessment of protein Structure Prediction datasets and the ccPDB dataset demonstrated that of our proposed method compared favorably with the state-of-the-art. Availability: http://www2.ahu.edu.cn/pchen/web/LigandDSES.htm",
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"abstract": "Discovering sequence patterns with variations unveils significant functions of a protein family. Existing combinatorial methods of discovering patterns with variations are computationally expensive, and probabilistic methods require more elaborate probabilistic representation of the amino acid associations. To overcome these shortcomings, this paper presents a new computationally efficient method for representing patterns with variations in a compact representation called Aligned Pattern Cluster (AP Cluster). To tackle the runtime, our method discovers a shortened list of non-redundant statistically significant sequence associations based on our previous work. To address the representation of protein functional regions, our pattern alignment and clustering step, presented in this paper captures the conservations and variations of the aligned patterns. We further refine our solution to allow more coverage of sequences via extending the AP Clusters containing only statistically significant patterns to Weak and Conserved AP Clusters. When applied to the cytochrome c, the ubiquitin, and the triosephosphate isomerase protein families, our algorithm identifies the binding segments as well as the binding residues. When compared to other methods, ours discovers all binding sites in the AP Clusters with superior entropy and coverage. The identification of patterns with variations help biologists to avoid time-consuming simulations and experimentations. (Software available upon request).",
"abstracts": [
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"content": "Discovering sequence patterns with variations unveils significant functions of a protein family. Existing combinatorial methods of discovering patterns with variations are computationally expensive, and probabilistic methods require more elaborate probabilistic representation of the amino acid associations. To overcome these shortcomings, this paper presents a new computationally efficient method for representing patterns with variations in a compact representation called Aligned Pattern Cluster (AP Cluster). To tackle the runtime, our method discovers a shortened list of non-redundant statistically significant sequence associations based on our previous work. To address the representation of protein functional regions, our pattern alignment and clustering step, presented in this paper captures the conservations and variations of the aligned patterns. We further refine our solution to allow more coverage of sequences via extending the AP Clusters containing only statistically significant patterns to Weak and Conserved AP Clusters. When applied to the cytochrome c, the ubiquitin, and the triosephosphate isomerase protein families, our algorithm identifies the binding segments as well as the binding residues. When compared to other methods, ours discovers all binding sites in the AP Clusters with superior entropy and coverage. The identification of patterns with variations help biologists to avoid time-consuming simulations and experimentations. (Software available upon request).",
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