This network structure fully integrates the advantages of identical mapping and residual mapping of ResNet with the dense connection of DenseNet, so that the network depth of our method is not too deep, it can effectively reduce gradient disappearance, enhance feature transmission, and to a certain extent, reduce the number of parameters. In this paper, we propose a novel deep neural network framework for contact prediction which combines ResNet and DenseNet. If we treat a protein contact map as an image, then protein contact prediction is kind of similar to (but not exactly same as) pixel-level image labeling, so some techniques effective for image labeling may also work for contact prediction. If two residues are in contact in the protein contact map [12] means that the Euclidean distance between the two Cβ atoms of the residues (glycine is a Cα atom) is less than 8 Å. In our model, the maximum likelihood function is used to train the model parameters, and the loss function is defined as a negative log-likelihood function, namely, the cross-entropy function. The residual neural network (ResNet) consists of a residual learning model (Figure 3) which can be defined as: We find the feature combination in the proposed method can obtain better accuracy than other two feature combinations. The accuracy of the long-range contact predictions on the PDB25 dataset is illustrated in Figure 6, and the detailed prediction accuracies of the long-range contact in () are shown in Table 3. In spite of continuous progress in developing contact map predictors, highly accurate prediction is still unresolved problem. Based on these indexes, we use the following evaluation criteria to predict the performance of our method and compare it to other methods. 2019 Dec;87(12):1069-1081. doi: 10.1002/prot.25810. Protein residue–residue contact prediction is the problem of predicting whether any two residues in a protein sequence are spatially close to each other in the folded 3D structure. LZ19A010002. 2014;30(21):3128–30. We are committed to sharing findings related to COVID-19 as quickly as possible. Ab initio folding using our predicted contacts as restraints but without any force fields can yield correct folds (i.e., TMscore>0.6) for 203 of the 579 test proteins, while that using MetaPSICOV- and CCMpred-predicted contacts can do so for only 79 and 62 of them, respectively. 2020 Nov 5;21(1):503. doi: 10.1186/s12859-020-03793-y. We find the prediction accuracy by our network structure is higher than that by other three network structures. Proteins. Long-, medium- and short-range contact results for Mems400. The rightmost column displays the TMscore of submitted models. We will be providing unlimited waivers of publication charges for accepted research articles as well as case reports and case series related to COVID-19. With the development of artificial neural networks, deep learning methods (including various forms of recurrent neural networks [9] and deep belief networks [10]) have become mainstream frameworks for biological prediction programs including Betacon [11], CMAPPro [12], DeepConPred [13], NNCon [14], and MetaPSICOV [15]. 10.1093/bioinformatics/btu500 It should be noted that amino acid sequences in the test set have no similarity with the training set (at the 25% identity level) to prevent any overestimation of our predictor’s performance, and that we used the same datasets for all four models. where means to connect the feature map from layer 0 to layer . Compared with the convolutional neural network and other deep learning methods, the residual neural network can, to a certain extent, solve the problems of gradient descent and disappearance. (B) Conformation change of…, Fig 19. And 4xmqB is a 254-residue long protein with all α-helix released by the CAMEO dataset. The list of top models submitted by CAMEO-participating servers for 5dcjA and their…, Fig 12. Jones DT, Singh T, Kosciolek T, Tetchner S. Bioinformatics. Get the latest research from NIH: Nature Reviews Genetics. For many proteins, there are no suitable templates available and it is therefore necessary to develop methods that can use only the amino acid sequence to predict protein structure. The list of top models submitted by CAMEO-participating servers for 5f5pH and their…, (A) Structure superimposition of Drosophila…, (A) Structure superimposition of Drosophila SD2 and Human SD2. Overlap between top L/2 predicted…, Fig 15. Top L/2 predicted contacts by each method are shown. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The one-dimensional feature of our method is then expressed by a two-dimensional matrix of . -, de Juan D, Pazos F, Valencia A. Predicted contacts of 5eo9B from the CAMEO dataset. 11671009 and 61762035 and the Zhejiang Provincial Natural Science Foundation of China under Grant No. -, Jones DT, Buchan DW, Cozzetto D, Pontil M. PSICOV: precise structural contact prediction using sparse inverse covariance estimation on large multiple sequence alignments. For a query sequence, TripletRes starts with the collection of deep multiple sequence alignments (MSAs) through whole-genome and metagenome sequence databases. The prediction accuracy of long-range contact on the 76 hard CAMEO test set is illustrated in Figure 7, and the detailed prediction results for different methods on the 76 hard CAMEO dataset are shown in Table 4. The first uses a series of intermediate convolutional neural networks to predict the contact map at five distances (6~10 Å), and the second combines these separate predictions into another convolutional neural networks to provide a final contact map at 8 Å. FilterDCA: Interpretable supervised contact prediction using inter-domain coevolution. PDB code and chain identification for CASP13 hard targets. 10.1093/bioinformatics/btr638 The first machine learning methods used support vector machines (SVM) [6] and other related methods such as SVMCon [7] and R2C [8], due to their capacity to construct classification models. Proteins perform a wide range of cellular functions and, in most instances, their function is related to their structure. NIH 2020 Oct 27;7(Pt 6):1168-1178. doi: 10.1107/S2052252520013494. RaptorX-Contact [16] is one of the state-of-the-art contact predictors. Besides, we separate the long, medium and short contact results on the hard and easy CASP13 targets which are shown in Table 10. For the CASP13 dataset, we divide the CASP13 targets into hard and easy targets which are shown in Tables 8 and 9. Superimposition between the predicted models (red) and the native structure (blue) for the…, Fig 14. -, Seemayer S, Gruber M, Söding J. CCMpred—fast and precise prediction of protein residue–residue contacts from correlated mutations. In this paper, we have presented a prediction method for constructing protein contact maps using an integrated framework with ResNet and DenseNet. 10.1038/nrg3414 The 3D models built from our contact prediction have TMscore>0.5 for 208 of the 398 membrane proteins, while those from homology modeling have TMscore>0.5 for only 10 of them. Illustration of our deep learning model for contact prediction where L is the…, Fig 2.