A Review of Protein Inter-residue Distance Prediction

2021 ◽  
Vol 15 (8) ◽  
pp. 821-830
Author(s):  
He Huang ◽  
Xinqi Gong
Keyword(s):  
Structure Prediction ◽  
Tertiary Structure ◽  
Contact Map ◽  
Distance Information ◽  
Distance Map ◽  
Large Molecules ◽  
Residue Contact ◽  
Linear Sequence ◽  
Residue Contacts ◽  
Distance Prediction

Proteins are large molecules consisting of a linear sequence of amino acids. Protein performs biological functions with specific 3D structures. The main factors that drive proteins to form these structures are constraint between residues. These constraints usually lead to important inter-residue relationships, including short-range inter-residue contacts and long-range interresidue distances. Thus, a highly accurate prediction of inter-residue contact and distance information is of great significance for protein tertiary structure computations. Some methods have been proposed for inter-residue contact prediction, most of which focus on contact map prediction and some reviews have summarized the progresses. However, inter-residue distance prediction is found to provide better guidance for protein structure prediction than contact map prediction in recent years. The methods for inter-residue distance prediction can be roughly divided into two types according to the consideration of distance value: one is based on multi-classification with discrete value and the other is based on regression with continuous value. Here, we summarize these algorithms and show that they have obtained good results. Compared to contact map prediction, distance map prediction is in its infancy. There is a lot to do in the future including improving distance map prediction precision and incorporating them into residue-residue distanceguided ab initio protein folding.

scholarly journals ISSEC: inferring contacts among protein secondary structure elements using deep object detection

2020 ◽  
Vol 21 (1) ◽  
Author(s):  
Qi Zhang ◽  
Jianwei Zhu ◽  
Fusong Ju ◽  
Lupeng Kong ◽  
Shiwei Sun ◽  
...  
Keyword(s):  
Secondary Structure ◽  
Object Detection ◽  
Structure Prediction ◽  
Tertiary Structure ◽  
Secondary Structure Prediction ◽  
Protein Secondary Structure ◽  
Confidence Score ◽  
Contact Map ◽  
Residue Contact ◽  
Residue Contacts

Abstract Background The formation of contacts among protein secondary structure elements (SSEs) is an important step in protein folding as it determines topology of protein tertiary structure; hence, inferring inter-SSE contacts is crucial to protein structure prediction. One of the existing strategies infers inter-SSE contacts directly from the predicted possibilities of inter-residue contacts without any preprocessing, and thus suffers from the excessive noises existing in the predicted inter-residue contacts. Another strategy defines SSEs based on protein secondary structure prediction first, and then judges whether each candidate SSE pair could form contact or not. However, it is difficult to accurately determine boundary of SSEs due to the errors in secondary structure prediction. The incorrectly-deduced SSEs definitely hinder subsequent prediction of the contacts among them. Results We here report an accurate approach to infer the inter-SSE contacts (thus called as ISSEC) using the deep object detection technique. The design of ISSEC is based on the observation that, in the inter-residue contact map, the contacting SSEs usually form rectangle regions with characteristic patterns. Therefore, ISSEC infers inter-SSE contacts through detecting such rectangle regions. Unlike the existing approach directly using the predicted probabilities of inter-residue contact, ISSEC applies the deep convolution technique to extract high-level features from the inter-residue contacts. More importantly, ISSEC does not rely on the pre-defined SSEs. Instead, ISSEC enumerates multiple candidate rectangle regions in the predicted inter-residue contact map, and for each region, ISSEC calculates a confidence score to measure whether it has characteristic patterns or not. ISSEC employs greedy strategy to select non-overlapping regions with high confidence score, and finally infers inter-SSE contacts according to these regions. Conclusions Comprehensive experimental results suggested that ISSEC outperformed the state-of-the-art approaches in predicting inter-SSE contacts. We further demonstrated the successful applications of ISSEC to improve prediction of both inter-residue contacts and tertiary structure as well.

scholarly journals DeepDist: real-value inter-residue distance prediction with deep residual convolutional network

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Tianqi Wu ◽  
Zhiye Guo ◽  
Jie Hou ◽  
Jianlin Cheng
Keyword(s):  
Deep Learning ◽  
Structure Prediction ◽  
Tertiary Structure ◽  
Convolutional Network ◽  
Distance Map ◽  
Contact Distance ◽  
Real Value ◽  
Contact Predictions ◽  
Distance Prediction ◽  
Better Than

Abstract Background Driven by deep learning, inter-residue contact/distance prediction has been significantly improved and substantially enhanced ab initio protein structure prediction. Currently, most of the distance prediction methods classify inter-residue distances into multiple distance intervals instead of directly predicting real-value distances. The output of the former has to be converted into real-value distances to be used in tertiary structure prediction. Results To explore the potentials of predicting real-value inter-residue distances, we develop a multi-task deep learning distance predictor (DeepDist) based on new residual convolutional network architectures to simultaneously predict real-value inter-residue distances and classify them into multiple distance intervals. Tested on 43 CASP13 hard domains, DeepDist achieves comparable performance in real-value distance prediction and multi-class distance prediction. The average mean square error (MSE) of DeepDist’s real-value distance prediction is 0.896 Å2 when filtering out the predicted distance ≥ 16 Å, which is lower than 1.003 Å2 of DeepDist’s multi-class distance prediction. When distance predictions are converted into contact predictions at 8 Å threshold (the standard threshold in the field), the precision of top L/5 and L/2 contact predictions of DeepDist’s multi-class distance prediction is 79.3% and 66.1%, respectively, higher than 78.6% and 64.5% of its real-value distance prediction and the best results in the CASP13 experiment. Conclusions DeepDist can predict inter-residue distances well and improve binary contact prediction over the existing state-of-the-art methods. Moreover, the predicted real-value distances can be directly used to reconstruct protein tertiary structures better than multi-class distance predictions due to the lower MSE. Finally, we demonstrate that predicting the real-value distance map and multi-class distance map at the same time performs better than predicting real-value distances alone.

scholarly journals Identification of residue pairing in interacting β-strands from a predicted residue contact map

2017 ◽  
Author(s):  
Wenzhi Mao ◽  
Tong Wang ◽  
Wenxuan Zhang ◽  
Haipeng Gong
Keyword(s):  
Random Forest ◽  
Structure Prediction ◽  
Tertiary Structure ◽  
Residue Level ◽  
Contact Map ◽  
Tertiary Structure Prediction ◽  
Contact Prediction ◽  
Residue Contact ◽  
Ridge Detection ◽  
Multi Stage

AbstractDespite the rapid progress of protein residue contact prediction, predicted residue contact maps frequently contain many errors. However, information of residue pairing in β strands could be extracted from a noisy contact map, due to the presence of characteristic contact patterns in β-β interactions. This information may benefit the tertiary structure prediction of mainly β proteins. In this work, we introduce a novel ridge-detection-based β-β contact predictor, RDb2C, to identify residue pairing in β strands from any predicted residue contact map. The algorithm adopts ridge detection, a well-developed technique in computer image processing, to capture consecutive residue contacts, and then utilizes a novel multi-stage random forest framework to integrate the ridge information and additional features for prediction. Starting from the predicted contact map of CCMpred, RDb2C remarkably outperforms all state-of-the-art methods on two conventional test sets of β proteins (BetaSheet916 and BetaSheet1452), and achieves F1-scores of ~62% and ~76% at the residue level and strand level, respectively. Taking the prediction of the more advanced RaptorX-Contact as input, RDb2C achieves impressively higher performance, with F1-scores reaching ~76% and ~86% at the residue level and strand level, respectively. According to our tests on 61 mainly β proteins, improvement in the β-β contact prediction can further ameliorate the structural prediction.Availability: All source data and codes are available at http://166.111.152.91/Downloads.html or at the GitHub address of https://github.com/wzmao/RDb2C.Author summaryDue to the topological complexity, mainly β proteins are challenging targets in protein structure prediction. Knowledge of the pairing between β strands, especially the residue pairing pattern, can greatly facilitate the tertiary structure prediction of mainly β proteins. In this work, we developed a novel algorithm to identify the residue pairing in β strands from a predicted residue contact map. This method adopts the ridge detection technique to capture the characteristic pattern of β-β interactions from the map and then utilizes a multi-stage random forest framework to predict β-β contacts at the residue level. According to our tests, our method could effectively improve the prediction of β-β contacts even from a highly noisy contact map. Moreover, the refined β-β contact information could effectively improve the structural modeling of mainly β proteins.

scholarly journals Protein Contact Map Denoising Using Generative Adversarial Networks

2020 ◽  
Author(s):  
Sai Raghavendra Maddhuri Venkata Subramaniya ◽  
Genki Terashi ◽  
Aashish Jain ◽  
Yuki Kagaya ◽  
Daisuke Kihara
Keyword(s):  
Protein Structure ◽  
Structure Prediction ◽  
Tertiary Structure ◽  
Substantial Improvement ◽  
Generative Adversarial Networks ◽  
Sequence Information ◽  
Contact Map ◽  
Denoising Method ◽  
Contact Prediction ◽  
Adversarial Networks

ABSTRACTProtein residue-residue contact prediction from protein sequence information has undergone substantial improvement in the past few years, which has made it a critical driving force for building correct protein tertiary structure models. Improving accuracy of contact predictions has, therefore, become the forefront of protein structure prediction. Here, we show a novel contact map denoising method, ContactGAN, which uses Generative Adversarial Networks (GAN) to refine predicted protein contact maps. ContactGAN was able to make a consistent and significant improvement over predictions made by recent contact prediction methods when tested on two datasets including protein structure modeling targets in CASP13. ContactGAN will be a valuable addition in the structure prediction pipeline to achieve an extra gain in contact prediction accuracy.

scholarly journals Prediction of residue-residue contacts in CASP12 targets from its predicted tertiary structures

2017 ◽  
Author(s):  
Piyush Agrawal ◽  
Sandeep Singh ◽  
Gandharva Nagpal ◽  
Deepti Sethi ◽  
Gajendra P.S. Raghava
Keyword(s):  
Structural Biology ◽  
Tertiary Structure ◽  
Protein Structures ◽  
Simple Approach ◽  
Structural Proteomics ◽  
List Size ◽  
Tertiary Structures ◽  
Residue Contact ◽  
Residue Contacts ◽  
Better Than

AbstractOne of the challenges in the field of structural proteomics is to predict residue-residue contacts in a protein. It is an integral part of CASP competitions due to its importance in the field of structural biology. This manuscript describes RRCPred 2.0 a method participated in CASP12 and predicted residue-residue contact in targets with high precision. In this approach, firstly 150 predicted protein structures were obtained from CASP12 Stage 2 tarball and ranked using clustering-based quality assessment software. Secondly, residue-residue contacts were assigned in top 10 protein structures based on distance between residues. Finally, residue-residue contacts were predicted in target protein based on consensus/average in top 10 predicted structures. This simple approach performs better than most of CASP12 methods in the categories of TBM and TBM/FM. It ranked 1st in following categories; i) TBM domain on list size L/5, ii) TBM/FM domain on list size L/5 and iii) TBM/FM domain on Top 10. These observations indicate that predicted tertiary structure of a protein can be used for predicting residue-residue contacts in protein with high accuracy.

scholarly journals Predicting protein distance maps according to physicochemical properties

2011 ◽  
Vol 8 (3) ◽  
pp. 158-175
Author(s):  
Gualberto Asencio Cortés ◽  
Jesús A. Aguilar-Ruiz
Keyword(s):  
Amino Acids ◽  
Amino Acid ◽  
Physicochemical Properties ◽  
Structure Prediction ◽  
Tertiary Structure ◽  
Protein Structures ◽  
Contact Map ◽  
Acid Properties ◽  
Amino Acid Properties ◽  
3D Surfaces

SummaryThe prediction of protein structures is a current issue of great significance in structural bioinformatics. More specifically, the prediction of the tertiary structure of a protein con- sists in determining its three-dimensional conformation based solely on its amino acid sequence. This study proposes a method in which protein fragments are assembled according to their physicochemical similarities, using information extracted from known protein structures. Many approaches cited in the literature use the physicochemical properties of amino acids, generally hydrophobicity, polarity and charge, to predict structure. In our method, implemented with parallel multithreading, we used a set of 30 physicochemical amino acid properties selected from the AAindex database. Several protein tertiary structure prediction methods produce a contact map. Our proposed method produces a distance map, which provides more information about the structure of a protein than a contact map. We performed several preliminary analysis of the protein physicochemical data distributions using 3D surfaces. Three main pattern types were found in 3D surfaces, thus it is possible to extract rules in order to predict distances between amino acids according to their physicochemical properties. We performed an experimental validation of our method using five non-homologous protein sets and we showed the generality of this method and its prediction quality using the amino acid properties considered. Finally, we included a study of the algorithm efficiency according to the number of most similar fragments considered and we notably improved the precision with the studied proteins sets.

scholarly journals Residue proximity information and protein model discrimination using saturation-suppressor mutagenesis

eLife ◽  
2015 ◽  
Vol 4 ◽  
Author(s):  
Anusmita Sahoo ◽  
Shruti Khare ◽  
Sivasankar Devanarayanan ◽  
Pankaj C. Jain ◽  
Raghavan Varadarajan
Keyword(s):  
Structure Prediction ◽  
Integral Membrane Protein ◽  
Test Case ◽  
X Ray ◽  
X Ray Crystallography ◽  
Residue Contact ◽  
Protein Model ◽  
Residue Contacts ◽  
Macromolecular System

Identification of residue-residue contacts from primary sequence can be used to guide protein structure prediction. Using Escherichia coli CcdB as the test case, we describe an experimental method termed saturation-suppressor mutagenesis to acquire residue contact information. In this methodology, for each of five inactive CcdB mutants, exhaustive screens for suppressors were performed. Proximal suppressors were accurately discriminated from distal suppressors based on their phenotypes when present as single mutants. Experimentally identified putative proximal pairs formed spatial constraints to recover >98% of native-like models of CcdB from a decoy dataset. Suppressor methodology was also applied to the integral membrane protein, diacylglycerol kinase A where the structures determined by X-ray crystallography and NMR were significantly different. Suppressor as well as sequence co-variation data clearly point to the X-ray structure being the functional one adopted in vivo. The methodology is applicable to any macromolecular system for which a convenient phenotypic assay exists.

scholarly journals Residue contacts predicted by evolutionary covariance extend the application ofab initiomolecular replacement to larger and more challenging protein folds

IUCrJ ◽  
2016 ◽  
Vol 3 (4) ◽  
pp. 259-270 ◽  
Author(s):  
Felix Simkovic ◽  
Jens M. H. Thomas ◽  
Ronan M. Keegan ◽  
Martyn D. Winn ◽  
Olga Mayans ◽  
...  
Keyword(s):  
Ab Initio ◽  
Structure Prediction ◽  
Sequence Information ◽  
Protein Targets ◽  
Residue Contact ◽  
Residue Contacts ◽  
Structure Solution ◽  
Improved Performance ◽  
Model Ensembles ◽  
Contact Predictions

For many protein families, the deluge of new sequence information together with new statistical protocols now allow the accurate prediction of contacting residues from sequence information alone. This offers the possibility of more accurateab initio(non-homology-based) structure prediction. Such models can be used in structure solution by molecular replacement (MR) where the target fold is novel or is only distantly related to known structures. Here,AMPLE, an MR pipeline that assembles search-model ensembles fromab initiostructure predictions (`decoys'), is employed to assess the value of contact-assistedab initiomodels to the crystallographer. It is demonstrated that evolutionary covariance-derived residue–residue contact predictions improve the quality ofab initiomodels and, consequently, the success rate of MR using search models derived from them. For targets containing β-structure, decoy quality and MR performance were further improved by the use of a β-strand contact-filtering protocol. Such contact-guided decoys achieved 14 structure solutions from 21 attempted protein targets, compared with nine for simpleRosettadecoys. Previously encountered limitations were superseded in two key respects. Firstly, much larger targets of up to 221 residues in length were solved, which is far larger than the previously benchmarked threshold of 120 residues. Secondly, contact-guided decoys significantly improved success with β-sheet-rich proteins. Overall, the improved performance of contact-guided decoys suggests that MR is now applicable to a significantly wider range of protein targets than were previously tractable, and points to a direct benefit to structural biology from the recent remarkable advances in sequencing.

scholarly journals Improving protein tertiary structure prediction by deep learning and distance prediction in CASP14

2021 ◽  
Author(s):  
Jian Liu ◽  
Tianqi Wu ◽  
Zhiye Guo ◽  
Jie Hou ◽  
Jianlin Cheng
Keyword(s):  
Deep Learning ◽  
Protein Structure ◽  
Structure Prediction ◽  
Tertiary Structure ◽  
Model Quality ◽  
Tertiary Structure Prediction ◽  
Model Quality Assessment ◽  
Template Free ◽  
Distance Prediction ◽  
Protein Tertiary Structure Prediction

Substantial progresses in protein structure prediction have been made by utilizing deep-learning and residue-residue distance prediction since CASP13. Inspired by the advances, we improve our CASP14 MULTICOM protein structure prediction system in the three main aspects: (1) a new deep-learning based protein inter-residue distance predictor (DeepDist) to improve template-free (ab initio) tertiary structure prediction, (2) an enhanced template-based tertiary structure prediction method, and (3) distance-based model quality assessment methods empowered by deep learning. In the 2020 CASP14 experiment, MULTICOM predictor was ranked 7th out of 146 predictors in protein tertiary structure prediction and ranked 3rd out of 136 predictors in inter-domain structure prediction. The results of MULTICOM demonstrate that the template-free modeling based on deep learning and residue-residue distance prediction can predict the correct topology for almost all template-based modeling targets and a majority of hard targets (template-free targets or targets whose templates cannot be recognized), which is a significant improvement over the CASP13 MULTICOM predictor. The performance of template-free tertiary structure prediction largely depends on the accuracy of distance predictions that is closely related to the quality of multiple sequence alignments. The structural model quality assessment works reasonably well on targets for which a sufficient number of good models can be predicted, but may perform poorly when only a few good models are predicted for a hard target and the distribution of model quality scores is highly skewed.

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