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It has been shown that the pairwise residue potentials of the interface residues may be useful for improving the prediction of hot spots. These amino acids are termed as hot spots that appear to be clustered in tightly packed regions in the center of protein interfaces, and are observed to be crucial for preserving protein function and maintaining the stability of protein association [ 5 β€” 8 ]. Our results indicate that these features are more effective than the conventional evolutionary conservation, pairwise residue potentials and other traditional features considered previously, and that the combination of our and traditional features may support the creation of a discriminative feature set for efficient prediction of hot spot residues. A popular systematic experimental technique for identifying hot spots is through site-directed mutagenesis like alanine scanning [ 9 ], which aims to evaluate the change in the binding energy resulting from the mutations of protein side-chains to alanine within a protein interface. To build a predictor that can best distinguish hot spot residues from non-hot spots, we performed an extensive search so as to derive, optimize and evaluate features based on the sequence and structure characteristics of protein binding sites. Furthermore, some studies indicate that the hot spots are more conserved than non-hot spots [ 14 , 15 ]. We also removed protein chains for which we could not obtain the corresponding Consurf-DB files [ 30 ] from the original data set. In this work, 62 multifaceted features were generated as described before. Feature selection is an important step in designing classifiers. Bogan and Thorn reported that hot spots are enriched in Tyr, Trp and Arg due to their size and conformation. Although current feature-based methods achieve relative success for identifying hot spots in protein interfaces, they are still at the primary stage. We then performed feature selection to remove noisy and irrelevant features, and thus improved the performance of the classifier. In BID, the relative disruptive effect of the mutation is listed as either 'strong', 'intermediate', 'weak' or 'insignificant'. Ofran and Rost [ 23 ] used a neural network, based on local sequence environment and evolutionary profile of residues, to identify hot spots. A combination of these two models using a simple OR rule led to better prediction accuracy than computational alanine scanning. These residues are crucial for understanding the function of proteins and studying their interactions. Here, we obtained features derived from protein interface potentials according to their method. They also found that hot spots are surrounded by energetically less important residues that shape like an O-ring to occlude bulk water molecules from the hot spots [ 12 , 13 ]. Other feature-based methods include those from Guney et al. A major contribution of this study is to propose several new features based on the protrusion index of amino acid residues, which has been shown to significantly improve the prediction performance of hot spots. Initially, we extracted a wide variety of features from a combination of protein sequence and structure information. The interface residue with binding free energy less than 0. For more details about the implementation of their algorithm, please refer to the original paper [ 27 , 41 ]. Up to now, the biological properties that are responsible for hot spots have not been fully understood. Protein-protein interactions play a key role in cellular function and form the backbone of most biological processes [ 1 β€” 3 ]. In the present work, feature selection was performed using the F-score [ 33 ], which assesses the discriminatory power of each individual feature. We systematically investigate a wide variety of 62 features from a combination of protein sequence and structure information. Molecular dynamics simulations [ 21 , 22 ] can also be used to estimate the free energy of association. Generally speaking, these methods can be split into two groups: energy-based methods and feature-based methods. It was suggested that interface residues have lower temperature factors than the protein exterior, which generally reflects the lesser flexibility of the interfacial regions [ 42 ]. Temperature factor is a measure of atomic thermal motion and disorder. Previous works [ 31 β€” 34 ] suggest that these ten values correlate well with the interface properties of a protein. It is apparent that the models built based on these large sets of features would overfit the training data. Another study [ 17 ] illustrated that hot spots from different monomers prefer to interact. Moreover, we identify a compact and useful feature subset that has an important implication for identifying hot spot residues. These values were only related to the amino acid types and did not contain any structural information. In this paper, we present a new efficient feature-based method to identify hot spots in protein interfaces. Another database, i. Residue evolutionary rate is a conservation score to quantify the evolutionary information. The values of the ten physicochemical properties for each amino acid can be found in Additional file 3. Note that we used exactly the same dataset as the one used in Cho et al. Their method can directly predict hot spot residues from protein primary sequences and suggests that the commonalities of hot spots have been imprinted clearly onto amino acid sequences. The training data set used in this study was extracted from a set of 17 protein-protein complexes defined by Cho et al. Therefore, reliable and efficient computational methods for identifying hot spots are greatly desired and urgently required. Experimental hot spots detection methods such as alanine scanning mutagenesis are not applicable on a large scale since they are time consuming and expensive. Consequently, the features previously identified as being correlated with hot spots are still insufficient. Lise et al. The residue conservation score represents a natural indicator to compare the conservation level of any residue in a protein sequence. Other interface residues with binding free energy between 0. Darnell et al. The F-score was calculated as:. According to the above definitions, we obtained interface residues, of which 62 residues are hot spots and 92 residues are non-hot spots, as shown in Table 1 and Additional file 1. Finally, we employed an ensemble classifier approach, which further improved prediction accuracies of hot spots. The results on two benchmark datasets, ASEdb and BID, show that this proposed method yields significantly better prediction accuracy than those previously published in the literature. On the other hand, Leu, Ser, Thr and Val residues [ 5 , 6 ] are disfavored and essentially absent in hot spots despite their importance for protein structures. In this work, we introduce an efficient approach that uses support vector machine SVM to predict hot spot residues in protein interfaces. Feature selection, more precisely feature subset selection, aims at finding p features out of the original d ones according to a selection criterion. Some energy-based methods, such as computational alanine scanning [ 20 ], use a free energy function to calculate the effects of alanine mutations on the binding free energy of a protein-protein complex. Although the principles governing protein interactions are not fully understood, it is well known that most of the binding energy in an interaction is contributed by a small portion of the total number of amino acids [ 4 , 5 ]. Analysis of various complexes has also shown that Asn and Asp are more prevalent in hot spots than Gln and Glu [ 5 , 6 ], which might be due to the differences in side-chain conformational entropy. On the other hand, the feature-based methods try to discriminate hot spots from the rest of the interface residues by using sequence, structure or a combination of both structure and sequence information. The classification model for predicting hot spots was based on SVM [ 44 ], which is a class of effective supervised learning methods that demonstrate high prediction accuracy whilst efficiently avoiding the overfitting problem [ 45 ].{/INSERTKEYS}{/PARAGRAPH} In our experiment, the evolutionary rate for each residue was obtained using the Rate4Site algorithm [ 43 ], which is implemented in the ConSurf-DB server [ 30 ]. The structure information in both isolated monomer unbound and complex bound form was calculated by PSAIA [ 36 , 40 ]. Thus, the lower the value, the more conserved the corresponding residue of the protein. For DI and PI, we used four residue attributes: total mean mean value of all atom values , side-chain mean mean value of all side-chain atom values , maximum highest of all atom values and minimum lowest of all atom values. They also found that the hot spots are surrounded by residues that are moderately conserved. It is well known that most of the binding free energy of protein interaction is contributed by a few key hot spot residues. Therefore feature selection needs to be performed to generate robust and general prediction models. An independent test set was extracted from the BID database [ 11 ] to further assess the performance of our proposed method. As a result, it has been used to improve the prediction of protein-protein interaction sites. Empirical studies show that our method can yield significantly better prediction accuracy than those previously published in the literature. It has also been shown that hot spots are related to central interface resides, which are conserved in sequence alignments and are not exposed to the solvent in protein complex [ 16 ]. More details can be found in the Additional file 2. Note that it is different from feature extraction, where a d -dimensional feature vector is projected to a p -dimensional subspace e. Keskin et al. These features see Additional file 2 can be roughly divided into three groups: i Physicochemical features; ii Features based on protein tertiary structures; and iii Residue-residue pairing preferences at the interface, residue evolutionary conservation scores and temperature factors. Based on the selected features, nine individual-feature based predictors are developed to identify hot spots using SVMs. Furthermore, a new ensemble classifier, namely APIS A combined model based on Protrusion Index and Solvent accessibility , is developed to further improve the prediction accuracy. In addition, the relative changes in ASA, DI and PI between the complex and monomer state of the residues were also calculated as follows:. A hot spot residue is defined as an interface residue in the data set if its corresponding binding free energy is higher or equal to 2. For ASA and RASA, we obtained five residue attributes: total sum of all atom values , backbone sum of all backbone atom values , side-chain sum of all side-chain atom values , polar sum of all oxygen, nitrogen atom values and non-polar sum of all carbon atom values. After extensive feature selection, nine individual-feature based predictors were developed to identify hot spots using support vector machines SVMs. In other words, the F-score measures the separation of the means for two populations hot spots and non-hot spots in terms of their variances, and it is very closely related to the F-statistics, which is commonly used to evaluate the separation of the means for two random variables. As a result, we obtained 49 structural features. We have developed an accurate prediction model for hot spot residues, given the structure of a protein complex. For example, Tuncbag et al. Based on the studies on the characteristics of hot spots, a number of computational methods have been developed to predict and identify hot spot residues from interface residues. They found that pairwise potential is a major discriminative feature in hot spot prediction. In our study, hot spot residues are labeled as the ones with 'strong' mutations and others are regarded as non-hot spots. In a more recent work, Cho et al. Several works have disclosed that the amino acid compositions are different between hot spot and non-hot spot regions [ 6 ]. Ma et al. The correlation between these couplings and structural conservation was found to be remarkable [ 18 ]. With feature selection, we can readily remove redundant and irrelevant features to further improve the performance of a classifier. Then, to remove redundant and irrelevant features and improve the prediction performance, feature selection is employed using the F-score method. Although these methods give good predictive results, they are not applicable in large scale hot spot predictions due to the high computational cost and the difficulty in operation. {PARAGRAPH}{INSERTKEYS}Metrics details. Due to the crucial role played by hot spots, their characteristics have been extensively studied.