DNCON2_Inter: predicting interchain contacts for homodimeric and homomultimeric protein complexes using multiple sequence alignments of monomers and deep learning

Quadir, Farhan; Roy, Raj; Halfmann, Randal; Cheng, Jianlin

doi:10.1038/s41598-021-91827-7

Cited by 20 publications

(18 citation statements)

References 37 publications

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“…Two residues from the two chains in a homodimer are considered an interchain contact if the Euclidean distance between any two heavy atoms of the two residues is less than or equal to 6Å (Ovchinnikov et al, 2014;Quadir et al, 2021;Zhou et al, 2018). Multiple homodimer datasets with known quaternary structures and interchain contacts are used to develop DRCon.…”

Section: Datasetsmentioning

confidence: 99%

“…The DCA based methods require a large number of sequences in MSAs to generate accurate interchain contact predictions, which are not available for most protein complexes because there are not many known protein complexes available. The problem is alleviated for protein homodimers (a protein complex consisting of two identical chains) because the MSA of a monomer (a single chain) in a homodimer contains both intrachain and interchain residue-residue co-evolutionary signals (Quadir et al, 2021). The advantage of using the MSA of a monomer is that it is generally much deeper than the MSA of a protein complex.…”

Section: Introductionmentioning

confidence: 99%

“…The advantage of using the MSA of a monomer is that it is generally much deeper than the MSA of a protein complex. Recently several deep learning methods such as DNCON2_Inter (Quadir et al, 2021) and DeepHomo (Yan & Huang, 2021) use the MSA of a monomer in a homodimer to predict interchain contacts in homodimers.…”

Section: Introductionmentioning

confidence: 99%

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A deep dilated convolutional residual network for predicting interchain contacts of protein homodimers

Roy

Quadir

Soltanikazemi

et al. 2021

Preprint

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Deep learning has revolutionized protein tertiary structure prediction recently. The cutting-edge deep learning methods such as AlphaFold can predict high-accuracy tertiary structures for most individual protein chains. However, the accuracy of predicting quaternary structures of protein complexes consisting of multiple chains is still relatively low due to lack of advanced deep learning methods in the field. Because interchain residue-residue contacts can be used as distance restraints to guide quaternary structure modeling, here we develop a deep dilated convolutional residual network method (DRCon) to predict interchain residue-residue contacts in homodimers from residue-residue co-evolutionary signals derived from multiple sequence alignments of monomers, intrachain residue-residue contacts of monomers extracted from true/predicted tertiary structures or predicted by deep learning, and other sequence and structural features. Tested on three homodimer test datasets (Homo_std dataset, DeepHomo dataset, and CASP14-CAPRI dataset), the precision of DRCon for top L/5 interchain contact predictions (L: length of monomer in a homodimer) is 43.46%, 47.15%, and 24.81% respectively, which is substantially better than two existing deep learning interchain contact prediction methods. Moreover, our experiments demonstrate that using predicted tertiary structure or intrachain contacts of monomers in the unbound state as input, DRCon still performs reasonably well, even though its accuracy is lower than when true tertiary structures in the bound state are used as input. Finally, our case study shows that good interchain contact predictions can be used to build high-accuracy quaternary structure models of homodimers.

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Section: Datasetsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A deep dilated convolutional residual network for predicting interchain contacts of protein homodimers

Roy

Quadir

Soltanikazemi

et al. 2021

Preprint

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“…Proteins interact to form complexes to perform biological functions like gene regulation, signal transduction and enzymatic catalysis ( Szklarczyk et al, 2015 ; Quadir et al, 2021 ). High-throughput experimental approaches (e.g., yeast two-hybridization) can figure out whether two proteins form a permanent or transient complex; however, these techniques cannot accurately determine the 3D shape of the complex.…”

Section: Introductionmentioning

confidence: 99%

“…Template-based modelling approaches can predict the quaternary structure of some dimers accurately if good structural templates are available ( Biasini et al, 2014 ; Lensink et al, 2019 ). However, these methods can only be applied to a small portion of proteins, for which the experimental complex structures of interacting homologues (interlogs) are available ( Quadir et al, 2021 ).…”

Section: Introductionmentioning

confidence: 99%

DeepComplex: A Web Server of Predicting Protein Complex Structures by Deep Learning Inter-chain Contact Prediction and Distance-Based Modelling

Quadir

Roy

Soltanikazemi

et al. 2021

Front. Mol. Biosci.

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Proteins interact to form complexes. Predicting the quaternary structure of protein complexes is useful for protein function analysis, protein engineering, and drug design. However, few user-friendly tools leveraging the latest deep learning technology for inter-chain contact prediction and the distance-based modelling to predict protein quaternary structures are available. To address this gap, we develop DeepComplex, a web server for predicting structures of dimeric protein complexes. It uses deep learning to predict inter-chain contacts in a homodimer or heterodimer. The predicted contacts are then used to construct a quaternary structure of the dimer by the distance-based modelling, which can be interactively viewed and analysed. The web server is freely accessible and requires no registration. It can be easily used by providing a job name and an email address along with the tertiary structure for one chain of a homodimer or two chains of a heterodimer. The output webpage provides the multiple sequence alignment, predicted inter-chain residue-residue contact map, and predicted quaternary structure of the dimer. DeepComplex web server is freely available at http://tulip.rnet.missouri.edu/deepcomplex/web_index.html

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Flexible protein–protein docking with a multitrack iterative transformer

Chu,

Ruffolo,

Harmalkar

et al. 2024

Protein Science

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Conventional protein‐protein docking algorithms usually rely on heavy candidate sampling and re‐ranking, but these steps are time‐consuming and hinder applications that require high‐throughput complex structure prediction, e.g., structure‐based virtual screening. Existing deep learning methods for protein‐protein docking, despite being much faster, suffer from low docking success rates. In addition, they simplify the problem to assume no conformational changes within any protein upon binding (rigid docking). This assumption precludes applications when binding‐induced conformational changes play a role, such as allosteric inhibition or docking from uncertain unbound model structures. To address these limitations, we present GeoDock, a multi‐track iterative transformer network to predict a docked structure from separate docking partners. Unlike deep learning models for protein structure prediction that input multiple sequence alignments (MSAs), GeoDock inputs just the sequences and structures of the docking partners, which suits the tasks when the individual structures are given. GeoDock is flexible at the protein residue level, allowing the prediction of conformational changes upon binding. On the DIPS test set, GeoDock achieves a 43% top‐1 success rate, outperforming all other tested methods. However, in the standard DIPS train/test splits, we discovered contamination of close homologs in the training set. After decontaminating the training set, the success rate is 31%. On the DB5.5 test set and a benchmark dataset of antibody‐antigen complexes, GeoDock outperforms the deep learning models trained using the same dataset but falls behind most of the conventional methods and AlphaFold‐Multimer. GeoDock attains an average inference speed of under one second on a single GPU, enabling its application in large‐scale structure screening. Although binding‐induced conformational changes are still a challenge owing to limited training and evaluation data, our architecture sets up the foundation to capture this backbone flexibility. Code and a demonstration Jupyter notebook are available at https://github.com/Graylab/GeoDock.This article is protected by copyright. All rights reserved.

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DNCON2_Inter: predicting interchain contacts for homodimeric and homomultimeric protein complexes using multiple sequence alignments of monomers and deep learning

Cited by 20 publications

References 37 publications

A deep dilated convolutional residual network for predicting interchain contacts of protein homodimers

A deep dilated convolutional residual network for predicting interchain contacts of protein homodimers

DeepComplex: A Web Server of Predicting Protein Complex Structures by Deep Learning Inter-chain Contact Prediction and Distance-Based Modelling

Flexible protein–protein docking with a multitrack iterative transformer

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