The MULTICOM Protein Structure Prediction Server Empowered by Deep Learning and Contact Distance Prediction

Hou, Jie; Wu, Tai Tsun; Guo, Zhiye; Quadir, Farhan; Cheng, Jianlin

doi:10.1007/978-1-0716-0708-4_2

Cited by 20 publications

(18 citation statements)

References 25 publications

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“…Since Protein Data Bank (PDB) files tend to contain chain breaks and more information than necessary, we cleaned them using MULTICOM Toolbox (Cheng et al, 2012; Jie Hou et al, 2020) as shown in Figure 1. It applies DSSP (Kabsch & Sander, 1983) to generate secondary structure and solvent accessibility information for each PDB file.…”

Section: Methodsmentioning

confidence: 99%

“…The coordinates of chain ‘A’ (the first chain) were used to calculate the intrachain residue-residue distance. An intrachain contact between two residues i and j is said to exist if the Euclidean distance between the respective C_ (C_ for glycine) atoms of residues i and j is less than or equal to 8.0 Å according to the definition used in DNCON2 (Adhikari et al, 2018; Jie Hou et al, 2020). If the C_ was not found in the PDB file, we chose C_ instead.…”

Section: Methodsmentioning

confidence: 99%

“…However, most of the recent focus has been on the development of computational tools for predicting intrachain (within the same chain) residue-residue contacts and distances to guide tertiary structure modeling (Hopf et al, 2014; Zhou et al, 2018). Some of these methods have performed well in the 12 th and 13 th Critical Assessment of Techniques for Protein Structure Prediction (CASP) competitions (Adhikari et al, 2018; Alquraishi & Valencia, 2019; Cheng et al, 2019; J. Hou et al, 2020; Schaarschmidt et al, 2018; Senior et al, 2020; Shrestha, Fajardo, Gil, Fidelis, Kryshtafovych, Bohdan Monastyrskyy, et al, 2019; Shrestha, Fajardo, Gil, Fidelis, Kryshtafovych, Monastyrskyy, et al, 2019; Wang et al, 2017, 2018; Xu & Wang, 2019).…”

Section: Introductionmentioning

confidence: 99%

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

Quadir

Roy

Halfmann

et al. 2020

Preprint

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Deep learning methods that achieved great success in predicting intrachain residue-residue contacts have been applied to predict interchain contacts between proteins. However, these methods require multiple sequence alignments (MSAs) of a pair of interacting proteins (dimers) as input, which are often difficult to obtain because there are not many known protein complexes available to generate MSAs of sufficient depth for a pair of proteins. In recognizing that multiple sequence alignments of a monomer that forms homomultimers contain the co-evolutionary signals of both intrachain and interchain residue pairs in contact, we applied DNCON2 (a deep learning-based protein intrachain residue-residue contact predictor) to predict both intrachain and interchain contacts for homomultimers using multiple sequence alignment (MSA) and other co-evolutionary features of a single monomer followed by discrimination of interchain and intrachain contacts according to the tertiary structure of the monomer. Allowing true-positive predictions within two residue shifts, the best average precision was obtained for the Top-L/10 predictions of DNCON2: 22.9% for homodimers, and 17.0% for higher order homomultimers. In some instances, especially where interchain contact densities are high, the approach predicted interchain contacts with 100% precision. We show that the predicted contacts can be used to accurately construct the structure of some complexes. Our experiment demonstrates that monomeric multiple sequence alignments can be used with deep learning to predict interchain contacts of homomeric proteins.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Quadir

Roy

Halfmann

et al. 2020

Preprint

Self Cite

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“…The Critical Assessment of Structure Prediction (CASP), which assesses prediction methods and models [ 64 ], recently noted substantial progress in structure modeling by deep learning, in particular, template free modeling (FM), that is, modeling structure without an existing template, as opposed to homology modeling. Numerous deep learning methods now require fewer proteins in the input MSA and have demonstrated increasing success in FM modelling [ 65 , 66 , 67 , 68 , 69 , 70 , 71 ], primarily due to more precise prediction of contact maps and inter-residue distances [ 64 ]. Some methods are narrower in scope and focus on contact prediction [ 72 , 73 , 74 , 75 ].…”

Section: Introductionmentioning

confidence: 99%

Incorporating Machine Learning into Established Bioinformatics Frameworks

Auslander

Gussow

Koonin

2021

IJMS

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The exponential growth of biomedical data in recent years has urged the application of numerous machine learning techniques to address emerging problems in biology and clinical research. By enabling the automatic feature extraction, selection, and generation of predictive models, these methods can be used to efficiently study complex biological systems. Machine learning techniques are frequently integrated with bioinformatic methods, as well as curated databases and biological networks, to enhance training and validation, identify the best interpretable features, and enable feature and model investigation. Here, we review recently developed methods that incorporate machine learning within the same framework with techniques from molecular evolution, protein structure analysis, systems biology, and disease genomics. We outline the challenges posed for machine learning, and, in particular, deep learning in biomedicine, and suggest unique opportunities for machine learning techniques integrated with established bioinformatics approaches to overcome some of these challenges.

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“…Deep learning has become a fascinating area of study when it comes to protein model quality assessment problems. DeepQA [15] and other deep learning methods [22,4,14] like ProQ3D [23], DeepTracer [24], 3DCNN [25] have a tendency to outperform other traditional machine learning methods (e.g., support vector machines, neural networks, etc.) in the Bioinformatics field, making deep learning as a QA strategy an important avenue of study to pursue.…”

Section: Introductionmentioning

confidence: 99%

Synthqa - Hierarchical Machine Learning-Based Protein Quality Assessment

Korovnik

Hippe

Hou

et al. 2021

Preprint

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MotivationIt has been a challenge for biologists to determine 3D shapes of proteins from a linear chain of amino acids and understand how proteins carry out life’s tasks. Experimental techniques, such as X-ray crystallography or Nuclear Magnetic Resonance, are time-consuming. This highlights the importance of computational methods for protein structure predictions. In the field of protein structure prediction, ranking the predicted protein decoys and selecting the one closest to the native structure is known as protein model quality assessment (QA), or accuracy estimation problem. Traditional QA methods don’t consider different types of features from the protein decoy, lack various features for training machine learning models, and don’t consider the relationship between features. In this research, we used multi-scale features from energy score to topology of the protein structure, and proposed a hierarchical architecture for training machine learning models to tackle the QA problem.ResultsWe introduce a new single-model QA method that incorporates multi-scale features from protein structures, utilizes the hierarchical architecture of training machine learning models, and predicts the quality of any protein decoy. Based on our experiment, the new hierarchical architecture is more accurate compared to traditional machine learning-based methods. It also considers the relationship between features and generates additional features so machine learning models can be trained more accurately. We trained our new tool, SynthQA, on the CASP dataset (CASP10 to CASP12), and validated our method on 33 targets from the latest CASP 14 dataset. The result shows that our method is comparable to other state-of-the-art single-model QA methods, and consistently outperforms each of the 14 used features.Availabilityhttps://github.com/Cao-Labs/SynthQA.gitContactcaora@plu.edu

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The MULTICOM Protein Structure Prediction Server Empowered by Deep Learning and Contact Distance Prediction

Cited by 20 publications

References 25 publications

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

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

Incorporating Machine Learning into Established Bioinformatics Frameworks

Synthqa - Hierarchical Machine Learning-Based Protein Quality Assessment

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