scholarly journals Learning the local landscape of protein structures with convolutional neural networks

2021 ◽  
Author(s):  
Anastasiya V. Kulikova ◽  
Daniel J. Diaz ◽  
James M. Loy ◽  
Andrew D. Ellington ◽  
Claus O. Wilke
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Protein Structures

scholarly journals Predicting changes in protein thermodynamic stability upon point mutation with deep 3D convolutional neural networks

2020 ◽  
Author(s):  
Bian Li ◽  
Yucheng T. Yang ◽  
John A. Capra ◽  
Mark B. Gerstein
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Thermodynamic Stability ◽  
Protein Structures ◽  
Biophysical Properties ◽  
3D Images ◽  
Processing Power ◽  
Protein Biophysics ◽  
Induced Changes ◽  
Protein Thermodynamic Stability

AbstractPredicting mutation-induced changes in protein thermodynamic stability (∆∆G) is of great interest in protein engineering, variant interpretation, and understanding protein biophysics. We introduce ThermoNet, a deep, 3D-convolutional neural network designed for structure-based prediction of ∆∆Gs upon point mutation. To leverage the image-processing power inherent in convolutional neural networks, we treat protein structures as if they were multi-channel 3D images. In particular, the inputs to ThermoNet are uniformly constructed as multi-channel voxel grids based on biophysical properties derived from raw atom coordinates. We train and evaluate ThermoNet with a curated data set that accounts for protein homology and is balanced with direct and reverse mutations; this provides a framework for addressing biases that have likely influenced many previous ∆∆G prediction methods. ThermoNet demonstrates performance comparable to the best available methods on the widely used Ssym test set. However, ThermoNet accurately predicts the effects of both stabilizing and destabilizing mutations, while most other methods exhibit a strong bias towards predicting destabilization. We further show that homology between Ssym and widely used training sets like S2648 and VariBench has likely led to overestimated performance in previous studies. Finally, we demonstrate the practical utility of ThermoNet in predicting the ∆∆Gs for two clinically relevant proteins, p53 and myoglobin, and for pathogenic and benign missense variants from ClinVar. Overall, our results suggest that 3D convolutional neural networks can model the complex, non-linear interactions perturbed by mutations, directly from biophysical properties of atoms.Author SummaryThe thermodynamic stability of a protein, usually represented as the Gibbs free energy for the biophysical process of protein folding (∆G), is a fundamental thermodynamic quantity. Predicting mutation-induced changes in protein thermodynamic stability (∆∆G) is of great interest in protein engineering, variant interpretation, and understanding protein biophysics. However, predicting ∆∆Gs in an accurate and unbiased manner has been a long-standing challenge in the field of computational biology. In this work, we introduce ThermoNet, a deep, 3D-convolutional neural network designed for structure-based ∆∆G prediction. To leverage the image-processing power inherent in convolutional neural networks, we treat protein structures as if they were multi-channel 3D images. ThermoNet demonstrates performance comparable to the best available methods. However, ThermoNet accurately predicts the effects of both stabilizing and destabilizing mutations, while most other methods exhibit a strong bias towards predicting destabilization. We also demonstrate that the presence of homologous proteins in commonly used training and testing sets for ∆∆G prediction methods has likely influenced previous performance estimates. Finally, we highlight the practical utility of ThermoNet by applying it to predicting the ∆∆Gs for two clinically relevant proteins, p53 and myoglobin, and for pathogenic and benign missense variants from ClinVar.

scholarly journals DeepPocket: Ligand Binding Site Detection and Segmentation using 3D Convolutional Neural Networks

2021 ◽  
Author(s):  
Rishal Aggarwal ◽  
Akash Gupta ◽  
Vineeth Chelur ◽  
C. V. Jawahar ◽  
U. Deva Priyakumar
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Ligand Binding ◽  
Binding Site ◽  
Convolutional Neural Networks ◽  
Binding Sites ◽  
Protein Structures ◽  
3D Structure ◽  
Ligand Binding Site ◽  
Structure Based Drug Design

<div> A structure-based drug design pipeline involves the development of potential drug molecules or ligands that form stable complexes with a given receptor at its binding site. A prerequisite to this is finding druggable and functionally relevant binding sites on the 3D structure of the protein. Although several methods for detecting binding sites have been developed beforehand, a majority of them surprisingly fail in the identification and ranking of binding sites accurately. The rapid adoption and success of deep learning algorithms in various sections of structural biology beckons the usage of such algorithms for accurate binding site detection. As a combination of geometry based software and deep learning, we report a novel framework, DeepPocket that utilises 3D convolutional neural networks for the rescoring of pockets identified by Fpocket and further segments these identified cavities on the protein surface. Apart from this, we also propose another dataset SC6K containing protein structures submitted in the Protein Data Bank (PDB) from January 2018 till February 2020 for ligand binding site (LBS) detection. DeepPocket's results on various binding site datasets and SC6K highlights its better performance over current state-of-the-art methods and good generalization ability over novel structures. </div><div><br></div>

scholarly journals Learning the local landscape of protein structures with convolutional neural networks

2021 ◽  
Author(s):  
Anastasiya V Kulikova ◽  
Daniel J Diaz ◽  
James M Loy ◽  
Andrew D Ellington ◽  
Claus O Wilke
Keyword(s):  
Neural Networks ◽  
Protein Structure ◽  
Amino Acid ◽  
Protein Engineering ◽  
Convolutional Neural Networks ◽  
Fitness Landscape ◽  
Fundamental Problem ◽  
Protein Structures ◽  
Wild Type ◽  
Multiple Sequence

The fundamental problem of protein biochemistry is to predict protein structure from amino acid sequence. The inverse problem, predicting either entire sequences or individual mutations that are consistent with a given protein structure, has received much less attention even though it has important applications in both protein engineering and evolutionary biology. Here, we ask whether 3D convolutional neural networks (3D CNNs) can learn the local fitness landscape of protein structure to reliably predict either the wild-type amino acid or the consensus in a multiple sequence alignment from the local structural context surrounding a site of interest. We find that the network can predict wild type with good accuracy, and that network confidence is a reliable measure of whether a given prediction is likely going to be correct or not. Predictions of consensus are less accurate, and are primarily driven by whether or not the consensus matches the wild type. Our work suggests that high-confidence mis-predictions of the wild type may identify sites that are primed for mutation and likely targets for protein engineering.

scholarly journals DeepPocket: Ligand Binding Site Detection and Segmentation using 3D Convolutional Neural Networks

2021 ◽  
Author(s):  
Rishal Aggarwal ◽  
Akash Gupta ◽  
Vineeth Chelur ◽  
C. V. Jawahar ◽  
U. Deva Priyakumar
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Ligand Binding ◽  
Binding Site ◽  
Convolutional Neural Networks ◽  
Binding Sites ◽  
Protein Structures ◽  
3D Structure ◽  
Ligand Binding Site ◽  
Structure Based Drug Design

<div> A structure-based drug design pipeline involves the development of potential drug molecules or ligands that form stable complexes with a given receptor at its binding site. A prerequisite to this is finding druggable and functionally relevant binding sites on the 3D structure of the protein. Although several methods for detecting binding sites have been developed beforehand, a majority of them surprisingly fail in the identification and ranking of binding sites accurately. The rapid adoption and success of deep learning algorithms in various sections of structural biology beckons the usage of such algorithms for accurate binding site detection. As a combination of geometry based software and deep learning, we report a novel framework, DeepPocket that utilises 3D convolutional neural networks for the rescoring of pockets identified by Fpocket and further segments these identified cavities on the protein surface. Apart from this, we also propose another dataset SC6K containing protein structures submitted in the Protein Data Bank (PDB) from January 2018 till February 2020 for ligand binding site (LBS) detection. DeepPocket's results on various binding site datasets and SC6K highlights its better performance over current state-of-the-art methods and good generalization ability over novel structures. </div><div><br></div>

Deep learning methods for protein prediction problem

2017 ◽  
Author(s):  
◽  
Son Phong Nguyen
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Networks ◽  
Structure Prediction ◽  
State Of The Art ◽  
Protein Structures ◽  
Distance Matrix ◽  
Loop Modeling ◽  
Deep Convolutional Neural Networks

[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI AT AUTHOR'S REQUEST.] Computational protein structure prediction is very important for many applications in bioinformatics. Many prediction methods have been developed, including Modeller, HHpred, I-TASSER, Robetta, and MUFOLD. In the process of predicting protein structures, it is essential to accurately assess the quality of generated models. Consensus quality assessment (QA) methods, such as Pcons-net and MULTICOM-refine, which are based on structure similarity, performed well on QA tasks. The drawback of consensus QA methods is that they require a pool of diverse models to work well, which is not always available. More importantly, they cannot evaluate the quality of a single protein model, which is a very common task in protein predictions and other applications. Although many single-model quality assessment methods, such as ProQ2, MQAPmulti, OPUS-CA, DOPE, DFIRE, and RW, etc. have been developed to address that problem, their accuracy is not good enough for most real applications. In this dissertation, based on the idea of using C-[alpha] atoms distance matrix and deep learning methods, two methods have been proposed for assessing quality of protein structures. First, a novel algorithm based on deep learning techniques, called DL-Pro, is proposed. From training examples of distance matrices corresponding to good and bad models, DL-Pro learns a stacked autoencoder network as a classifier. In experiments on selected targets from the Critical Assessment of Structure Prediction (CASP) competition, DL-Pro obtained promising results, outperforming state-of-the-art energy/scoring functions, including OPUS-CA, DOPE, DFIRE, and RW. Second, a new method DeepCon-QA is developed to predict quality of single protein model. Based on the idea of using protein vector representation and distance matrix, DeepCon-QA was able to achieve comparable performance with the best state-of-the-art QA method in our experiments. It also takes advantage the strength of deep convolutional neural networks to “learn” and “understand” the input data to be able to predict output data precisely. On the other hand, this dissertation also proposes several new methods for solving loop modeling problem. Five new loop modeling methods based on machine learning techniques, called NearLooper, ConLooper, ResLooper, HyLooper1 and HyLooper2 are proposed. NearLooper is based on the nearest neighbor technique; ConLooper applies deep convolutional neural networks to predict Cα atoms distance matrix as an orientation-independent representation of protein structure; ResLooper uses residual neural networks instead of deep convolutional neural networks; HyLooper1 combines the results of NearLooper and ConLooper while HyLooper2 combines NearLooper and ResLooper. Three commonly used benchmarks for loop modeling are used to compare the performance between these methods and existing state-of-the-art methods. The experiment results show promising performance in which our best method improves existing state-of-the-art methods by 28% and 54% of average RMSD on two datasets while being comparable on the other one.

scholarly journals Structural Learning of Proteins Using Graph Convolutional Neural Networks

2019 ◽  
Author(s):  
Rafael Zamora-Resendiz ◽  
Silvia Crivelli
Keyword(s):  
Neural Networks ◽  
Protein Structure ◽  
Data Storage ◽  
Convolutional Neural Networks ◽  
Network Architecture ◽  
Structure Prediction ◽  
Large Scale ◽  
Protein Structures ◽  
Data Representation ◽  
Structural Proteomics

AbstractThe exponential growth of protein structure databases has motivated the development of efficient deep learning methods that perform structural analysis tasks at large scale, ranging from the classification of experimentally determined proteins to the quality assessment and ranking of computationally generated protein models in the context of protein structure prediction. Yet, the literature discussing these methods does not usually interpret what the models learned from the training or identify specific data attributes that contribute to the classification or regression task. While 3D and 2D CNNs have been widely used to deal with structural data, they have several limitations when applied to structural proteomics data. We pose that graph-based convolutional neural networks (GCNNs) are an efficient alternative while producing results that are interpretable. In this work, we demonstrate the applicability of GCNNs to protein structure classification problems. We define a novel spatial graph convolution network architecture which employs graph reduction methods to reduce the total number of trainable parameters and promote abstraction in interme-diate representations. We show that GCNNs are able to learn effectively from simplistic graph representations of protein structures while providing the ability to interpret what the network learns during the training and how it applies it to perform its task. GCNNs perform comparably to their 2D CNN counterparts in predictive performance and they are outperformed by them in training speeds. The graph-based data representation allows GCNNs to be a more efficient option over 3D CNNs when working with large-scale datasets as preprocessing costs and data storage requirements are negligible in comparison.

CNN-based Classification of Degraded Images

2020 ◽  
Vol 2020 (10) ◽  
pp. 28-1-28-7 ◽  
Author(s):  
Kazuki Endo ◽  
Masayuki Tanaka ◽  
Masatoshi Okutomi
Keyword(s):  
Neural Networks ◽  
Image Restoration ◽  
Image Classification ◽  
Convolutional Neural Networks ◽  
Deep Convolutional Neural Networks ◽  
Alternative Approach ◽  
Degraded Image ◽  
Degraded Images ◽  
Straightforward Approach

Classification of degraded images is very important in practice because images are usually degraded by compression, noise, blurring, etc. Nevertheless, most of the research in image classification only focuses on clean images without any degradation. Some papers have already proposed deep convolutional neural networks composed of an image restoration network and a classification network to classify degraded images. This paper proposes an alternative approach in which we use a degraded image and an additional degradation parameter for classification. The proposed classification network has two inputs which are the degraded image and the degradation parameter. The estimation network of degradation parameters is also incorporated if degradation parameters of degraded images are unknown. The experimental results showed that the proposed method outperforms a straightforward approach where the classification network is trained with degraded images only.

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