301 research outputs found
Predicting Protein-Protein Interaction Sites From Amino Acid Sequence
No full textPredicting Protein-Protein Interaction Sites From Amino Acid Sequence Changhui Yan, Vasant Honavar and Drena Dobbs Artificial Intelligence Research Laboratory Bioinformatics and Computational Biology Graduate Program Iowa State University Ames, Iowa 50011 Corresponding author: Changhui Yan Email address of the corresponding author: [email protected] Abstract We describe an approach for computational prediction of protein-protein interaction sites using a support vector machine (SVM) classifier. Interface residues and other surface residues were extracted from 115 proteins derived from a set of 70 heterocomplexes in PDB. The SVM classifier was trained to predict whether or not a surface residue is located in the interface based on the identity of the target residue and its 10 sequence neighbors. The effectiveness of the approach was evaluated using 115 leave-one-out cross validation (jack-knife) experiments. In each experiment, an SVM classifier was trained using a set of 1250 randomly chosen interface residues and an equal number of non-interface residues from 114 of the 115 molecules. The resulting classifier was used to classify surface residues from the remaining molecule into interface and non-interface residues. The classifier in each experiment was evaluated in terms of several performance measures. In results averaged over 115 experiments, interface residues and non-interface residues were identified with relatively high specificity (71%) and sensitivity (67%), and with a correlation coefficient of 0.29 between predicted and actual class labels, indicating that the method performs substantially better than chance (zero correlation). We also investigated the classifier's performance in terms of overall interactions site recognition. In 80% of the proteins, the classifier recognized the interaction surface by identifying at least half of the interface residues, and in 98% of the proteins, at least 20% of the interface residues were correctly identified. The success of this approach was confirmed by examination of predicted interfaces in the context of the three-dimensional structures of representative complexes. This study demonstrates that an SVM classifier can be used to predict whether or not a surface residue is an interface residue using amino acid sequence information. Because surface residues can be identified based on their solvent accessible surface area (ASA), given recent progress in computational methods for predicting ASA from sequence, the approach described in this paper provides a basis for computational prediction of interaction sites in proteins for which only amino acid sequence information is available. Keywords: protein-protein interaction; interaction site prediction; interface residues; support vector machine.</p
Supplemental_Material – Supplemental material for Comparative Genome Analysis of 2 Mycobacterium Tuberculosis Strains from Pakistan: Insights Globally Into Drug Resistance, Virulence, and Niche Adaptation
No full textSupplemental material, Supplemental_Material for Comparative Genome Analysis of 2 Mycobacterium Tuberculosis Strains from Pakistan: Insights Globally Into Drug Resistance, Virulence, and Niche Adaptation by Asma Muhammad Yar, Ghanva Zaman, Annam Hussain, Yan Changhui, Azhar Rasul, Abrar Hussain, Zhu Bo, Habib Bokhari and Muhammad Ibrahim in Evolutionary Bioinformatics</p
A Critical Analysis of the Carbon Neutrality Assumption in Life Cycle Assessment of Forest Bioenergy Systems
No full textThis study presents a critical analysis regarding the assumption of carbon neutrality in life cycle assessment (LCA) models aimed at assessing climate change impacts of bioenergy usage. We identified a complex of problems in the carbon neutrality assumption, especially regarding bioenergy derived from forest residues. In this study, we summarized several issues related to carbon neutral assumptions, with particular emphasis on possible carbon accounting errors at the product level. We analyzed errors in estimating emissions in the supply chain, direct and indirect emissions due to forest residue extraction, biogenic CO2 emission from biomass combustion for energy, and other effects related to forest residue extraction. Various modeling approaches are discussed in detail. We concluded that there is a need to correct accounting errors when estimating climate change impacts and proposed possible remedies. To accurately assess climate change impacts of bioenergy use, greater efforts are required to improve forest carbon cycle modeling, especially to identify and correct pitfalls associated with LCA accounting, forest residue extraction effects on forest fire risk and biodiversity. Uncertainties in accounting carbon emissions in LCA are also highlighted, and associated risks are discussed.The accepted manuscript in pdf format is listed with the files at the bottom of this page. The presentation of the authors' names and (or) special characters in the title of the manuscript may differ slightly between what is listed on this page and what is listed in the pdf file of the accepted manuscript; that in the pdf file of the accepted manuscript is what was submitted by the author
A Hidden Markov Model Approach to Model Protein Sequence and Structural Information: Identification of Helix-Turn-Helix DNA-Binding Motif
No full textIdentification of interface residues involved in protein-protein and protein-DNA interactions from sequence using machine learning approaches
No full textIdentification of interface residues involved in protein-protein and protein-DNA interactions is critical for understanding the functions of biological systems. Because identifying interface residues using experimental methods cannot catch up with the pace at which protein sequences are determined, computational methods that can identify interface residues are urgently needed. In this study, we apply machine-learning methods to identify interface residues with the focus on the methods using amino acid sequence information alone. We have developed classifiers for identification of the residues involved in protein-protein and protein-DNA interactions using a window of primary sequence as input. The classifiers were evaluated using both representative datasets and specific cases of interest based on multiple measurements. The results have shown the feasibility of identifying interface residues from sequence. We have also explored information besides primary sequence to improve the performance of sequence-based classifiers. The results show that the performance of sequence-based classifiers can be improved by using solvent accessibility and sequence entropy of the target residue as additional inputs. We have developed a database of protein-protein interfaces that consists of all the protein-protein interfaces derived from the Protein Data Bank. This database, for the first time, makes possible the quick and flexible retrieval of interface sets and various interface features. We have systematically analyzed the characteristics of interfaces using the largest dataset available. In particular, we compared interfaces with the samples that had the same solvent accessibility as the interfaces. This strategy excludes the effect of solvent accessibility on the distributions of residues, secondary structure, and sequence entropy.</p
Identification of deleterious non-synonymous single nucleotide polymorphisms using sequence-derived information
No full textAbstract Background As the number of non-synonymous single nucleotide polymorphisms (nsSNPs), also known as single amino acid polymorphisms (SAPs), increases rapidly, computational methods that can distinguish disease-causing SAPs from neutral SAPs are needed. Many methods have been developed to distinguish disease-causing SAPs based on both structural and sequence features of the mutation point. One limitation of these methods is that they are not applicable to the cases where protein structures are not available. In this study, we explore the feasibility of classifying SAPs into disease-causing and neutral mutations using only information derived from protein sequence. Results We compiled a set of 686 features that were derived from protein sequence. For each feature, the distance between the wild-type residue and mutant-type residue was computed. Then a greedy approach was used to select the features that were useful for the classification of SAPs. 10 features were selected. Using the selected features, a decision tree method can achieve 82.6% overall accuracy with 0.607 Matthews Correlation Coefficient (MCC) in cross-validation. When tested on an independent set that was not seen by the method during the training and feature selection, the decision tree method achieves 82.6% overall accuracy with 0.604 MCC. We also evaluated the proposed method on all SAPs obtained from the Swiss-Prot, the method achieves 0.42 MCC with 73.2% overall accuracy. This method allows users to make reliable predictions when protein structures are not available. Different from previous studies, in which only a small set of features were arbitrarily chosen and considered, here we used an automated method to systematically discover useful features from a large set of features well-annotated in public databases. Conclusion The proposed method is a useful tool for the classification of SAPs, especially, when the structure of the protein is not available.</p
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