Struct2Graph: A graph attention network for structure based predictions of protein-protein interactions

AbstractDevelopment of new methods for analysis of protein-protein interactions (PPIs) at molecular and nanometer scales gives insights into intracellular signaling pathways and will improve understanding of protein functions, as well as other nanoscale structures of biological and abiological origins. Recent advances in computational tools, particularly the ones involving modern deep learning algorithms, have been shown to complement experimental approaches for describing and rationalizing PPIs. However, most of the existing works on PPI predictions use protein-sequence information, and thus have difficulties in accounting for the three-dimensional organization of the protein chains. In this study, we address this problem and describe a PPI analysis method based on a graph attention network, named Struct2Graph, for identifying PPIs directly from the structural data of folded protein globules. Our method is capable of predicting the PPI with an accuracy of 98.89% on the balanced set consisting of an equal number of positive and negative pairs. On the unbalanced set with the ratio of 1:10 between positive and negative pairs, Struct2Graph achieves a five-fold cross validation average accuracy of 99.42%. Moreover, unsupervised prediction of the interaction sites by Struct2Graph for phenol-soluble modulins are found to be in concordance with the previously reported binding sites for this family.Author summaryPPIs are the central part of signal transduction, metabolic regulation, environmental sensing, and cellular organization. Despite their success, most strategies to decode PPIs use sequence based approaches do not generalize to broader classes of chemical compounds of similar scale as proteins that are equally capable of forming complexes with proteins that are not based on amino acids, and thus lack of an equivalent sequence-based representation. Here, we address the problem of prediction of PPIs using a first of its kind, 3D structure based graph attention network (available at https://github.com/baranwa2/Struct2Graph). Despite its excellent prediction performance, the novel mutual attention mechanism provides insights into likely interaction sites through its knowledge selection process in a completely unsupervised manner.

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Amalgamation of 3D structure and sequence information for protein–protein interaction prediction

Scientific Reports ◽

10.1038/s41598-020-75467-x ◽

2020 ◽

Vol 10 (1) ◽

Author(s):

Kanchan Jha ◽

Sriparna Saha

Keyword(s):

Amino Acids ◽

Deep Learning ◽

Protein Interactions ◽

3D Structure ◽

Protein Sequences ◽

Building Blocks ◽

New Drugs ◽

Sequence Information ◽

Protein Protein Interactions ◽

Structure Information

Abstract Protein is the primary building block of living organisms. It interacts with other proteins and is then involved in various biological processes. Protein–protein interactions (PPIs) help in predicting and hence help in understanding the functionality of the proteins, causes and growth of diseases, and designing new drugs. However, there is a vast gap between the available protein sequences and the identification of protein–protein interactions. To bridge this gap, researchers proposed several computational methods to reveal the interactions between proteins. These methods merely depend on sequence-based information of proteins. With the advancement of technology, different types of information related to proteins are available such as 3D structure information. Nowadays, deep learning techniques are adopted successfully in various domains, including bioinformatics. So, current work focuses on the utilization of different modalities, such as 3D structures and sequence-based information of proteins, and deep learning algorithms to predict PPIs. The proposed approach is divided into several phases. We first get several illustrations of proteins using their 3D coordinates information, and three attributes, such as hydropathy index, isoelectric point, and charge of amino acids. Amino acids are the building blocks of proteins. A pre-trained ResNet50 model, a subclass of a convolutional neural network, is utilized to extract features from these representations of proteins. Autocovariance and conjoint triad are two widely used sequence-based methods to encode proteins, which are used here as another modality of protein sequences. A stacked autoencoder is utilized to get the compact form of sequence-based information. Finally, the features obtained from different modalities are concatenated in pairs and fed into the classifier to predict labels for protein pairs. We have experimented on the human PPIs dataset and Saccharomyces cerevisiae PPIs dataset and compared our results with the state-of-the-art deep-learning-based classifiers. The results achieved by the proposed method are superior to those obtained by the existing methods. Extensive experimentations on different datasets indicate that our approach to learning and combining features from two different modalities is useful in PPI prediction.

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NMR spectroscopy in the conformational analysis of peptides: an overview

Current Medicinal Chemistry ◽

10.2174/0929867327666200702131032 ◽

2020 ◽

Vol 27 ◽

Author(s):

Marian Vincenzi ◽

Flavia Anna Mercurio ◽

Marilisa Leone

Keyword(s):

Nmr Spectroscopy ◽

Protein Interactions ◽

3D Structure ◽

Theoretical Background ◽

Triple Resonance ◽

Protein Protein Interactions ◽

Nmr Studies ◽

Conformational Preferences ◽

Dynamic Perspective ◽

Nmr Methods

Background: NMR spectroscopy is one of the most powerful tools to study the structure and interaction properties of peptides and proteins from a dynamic perspective. Knowing the bioactive conformations of peptides is crucial in the drug discovery field to design more efficient analogue ligands and inhibitors of protein-protein interactions targeting therapeutically relevant systems. Objective: This review provides a toolkit to investigate peptide conformational properties by NMR. Methods: Articles cited herein, related to NMR studies of peptides and proteins were mainly searched through Pubmed and the web. More recent and old books on NMR spectroscopy written by eminent scientists in the field were consulted as well. Results: The review is mainly focused on NMR tools to gain the 3D structure of small unlabeled peptides. It is more application-oriented as it is beyond its goal to deliver a profound theoretical background. However, the basic principles of 2D homonuclear and heteronuclear experiments are briefly described. Protocols to obtain isotopically labeled peptides and principal triple resonance experiments needed to study them, are discussed as well. Conclusion: NMR is a leading technique in the study of conformational preferences of small flexible peptides whose structure can be often only described by an ensemble of conformations. Although NMR studies of peptides can be easily and fast performed by canonical protocols established a few decades ago, more recently we have assisted to tremendous improvements of NMR spectroscopy to investigate instead large systems and overcome its molecular weight limit.

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Decoding Protein-protein Interactions: An Overview

Current Topics in Medicinal Chemistry ◽

10.2174/1568026620666200226105312 ◽

2020 ◽

Vol 20 (10) ◽

pp. 855-882

Author(s):

Olivia Slater ◽

Bethany Miller ◽

Maria Kontoyianni

Keyword(s):

Drug Discovery ◽

Protein Interactions ◽

Drug Repurposing ◽

Protein Docking ◽

Target Space ◽

Protein Protein Interactions ◽

X Ray Crystallography ◽

Protein Protein Interaction ◽

Interaction Sites ◽

Long Time

Drug discovery has focused on the paradigm “one drug, one target” for a long time. However, small molecules can act at multiple macromolecular targets, which serves as the basis for drug repurposing. In an effort to expand the target space, and given advances in X-ray crystallography, protein-protein interactions have become an emerging focus area of drug discovery enterprises. Proteins interact with other biomolecules and it is this intricate network of interactions that determines the behavior of the system and its biological processes. In this review, we briefly discuss networks in disease, followed by computational methods for protein-protein complex prediction. Computational methodologies and techniques employed towards objectives such as protein-protein docking, protein-protein interactions, and interface predictions are described extensively. Docking aims at producing a complex between proteins, while interface predictions identify a subset of residues on one protein that could interact with a partner, and protein-protein interaction sites address whether two proteins interact. In addition, approaches to predict hot spots and binding sites are presented along with a representative example of our internal project on the chemokine CXC receptor 3 B-isoform and predictive modeling with IP10 and PF4.

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Furan warheads for covalent trapping of weak protein-protein interactions: cross-linking of thymosin β4 to actin

Chemical Communications ◽

10.1039/d1cc01731d ◽

2021 ◽

Author(s):

Laia Miret Casals ◽

Willem Vannecke ◽

Kurt Hoogewijs ◽

Gianluca Arauz ◽

Marina Gay ◽

...

Keyword(s):

Protein Interaction ◽

Protein Interactions ◽

3D Structure ◽

Cross Linking ◽

Thymosin Β4 ◽

Protein Protein Interactions ◽

Site Specific ◽

Protein Protein Interaction ◽

Covalent Trapping

We describe furan as a triggerable ‘warhead’ for site-specific cross-linking using the actin and thymosin β4 (Tβ4)-complex as model of a weak and dynamic protein-protein interaction with known 3D structure...

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Multimodal deep representation learning for protein interaction identification and protein family classification

BMC Bioinformatics ◽

10.1186/s12859-019-3084-y ◽

2019 ◽

Vol 20 (S16) ◽

Cited By ~ 4

Author(s):

Da Zhang ◽

Mansur Kabuka

Keyword(s):

Protein Interactions ◽

Protein Sequence ◽

Representation Learning ◽

Superior Performance ◽

Sequence Information ◽

Protein Protein Interactions ◽

Learning Framework ◽

Topological Features ◽

Ppi Networks ◽

Ppi Prediction

Abstract Background Protein-protein interactions(PPIs) engage in dynamic pathological and biological procedures constantly in our life. Thus, it is crucial to comprehend the PPIs thoroughly such that we are able to illuminate the disease occurrence, achieve the optimal drug-target therapeutic effect and describe the protein complex structures. However, compared to the protein sequences obtainable from various species and organisms, the number of revealed protein-protein interactions is relatively limited. To address this dilemma, lots of research endeavor have investigated in it to facilitate the discovery of novel PPIs. Among these methods, PPI prediction techniques that merely rely on protein sequence data are more widespread than other methods which require extensive biological domain knowledge. Results In this paper, we propose a multi-modal deep representation learning structure by incorporating protein physicochemical features with the graph topological features from the PPI networks. Specifically, our method not only bears in mind the protein sequence information but also discerns the topological representations for each protein node in the PPI networks. In our paper, we construct a stacked auto-encoder architecture together with a continuous bag-of-words (CBOW) model based on generated metapaths to study the PPI predictions. Following by that, we utilize the supervised deep neural networks to identify the PPIs and classify the protein families. The PPI prediction accuracy for eight species ranged from 96.76% to 99.77%, which signifies that our multi-modal deep representation learning framework achieves superior performance compared to other computational methods. Conclusion To the best of our knowledge, this is the first multi-modal deep representation learning framework for examining the PPI networks.

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LRRpredictor—A New LRR Motif Detection Method for Irregular Motifs of Plant NLR Proteins Using an Ensemble of Classifiers

Genes ◽

10.3390/genes11030286 ◽

2020 ◽

Vol 11 (3) ◽

pp. 286 ◽

Cited By ~ 3

Author(s):

Eliza C. Martin ◽

Octavina C. A. Sukarta ◽

Laurentiu Spiridon ◽

Laurentiu G. Grigore ◽

Vlad Constantinescu ◽

...

Keyword(s):

Protein Interactions ◽

Structural Data ◽

Innate Immune ◽

Protein Protein Interactions ◽

Ensemble Of Classifiers ◽

Immune Receptor ◽

Motif Detection ◽

Protein Architecture ◽

Recognition Specificity ◽

Leucine Rich Repeats

Leucine-rich-repeats (LRRs) belong to an archaic procaryal protein architecture that is widely involved in protein–protein interactions. In eukaryotes, LRR domains developed into key recognition modules in many innate immune receptor classes. Due to the high sequence variability imposed by recognition specificity, precise repeat delineation is often difficult especially in plant NOD-like Receptors (NLRs) notorious for showing far larger irregularities. To address this problem, we introduce here LRRpredictor, a method based on an ensemble of estimators designed to better identify LRR motifs in general but particularly adapted for handling more irregular LRR environments, thus allowing to compensate for the scarcity of structural data on NLR proteins. The extrapolation capacity tested on a set of annotated LRR domains from six immune receptor classes shows the ability of LRRpredictor to recover all previously defined specific motif consensuses and to extend the LRR motif coverage over annotated LRR domains. This analysis confirms the increased variability of LRR motifs in plant and vertebrate NLRs when compared to extracellular receptors, consistent with previous studies. Hence, LRRpredictor is able to provide novel insights into the diversification of LRR domains and a robust support for structure-informed analyses of LRRs in immune receptor functioning.

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Mechanism of the G-protein mimetic nanobody binding to a muscarinic G-protein-coupled receptor

Proceedings of the National Academy of Sciences ◽

10.1073/pnas.1800756115 ◽

2018 ◽

Vol 115 (12) ◽

pp. 3036-3041 ◽

Cited By ~ 43

Author(s):

Yinglong Miao ◽

J. Andrew McCammon

Keyword(s):

Protein Binding ◽

G Protein ◽

Protein Interactions ◽

Intracellular Signaling ◽

Md Simulations ◽

Binding Pocket ◽

Signaling Proteins ◽

Protein Protein Interactions ◽

Intracellular Loops ◽

G Protein Coupled

Protein–protein binding is key in cellular signaling processes. Molecular dynamics (MD) simulations of protein–protein binding, however, are challenging due to limited timescales. In particular, binding of the medically important G-protein-coupled receptors (GPCRs) with intracellular signaling proteins has not been simulated with MD to date. Here, we report a successful simulation of the binding of a G-protein mimetic nanobody to the M2 muscarinic GPCR using the robust Gaussian accelerated MD (GaMD) method. Through long-timescale GaMD simulations over 4,500 ns, the nanobody was observed to bind the receptor intracellular G-protein-coupling site, with a minimum rmsd of 2.48 Å in the nanobody core domain compared with the X-ray structure. Binding of the nanobody allosterically closed the orthosteric ligand-binding pocket, being consistent with the recent experimental finding. In the absence of nanobody binding, the receptor orthosteric pocket sampled open and fully open conformations. The GaMD simulations revealed two low-energy intermediate states during nanobody binding to the M2 receptor. The flexible receptor intracellular loops contribute remarkable electrostatic, polar, and hydrophobic residue interactions in recognition and binding of the nanobody. These simulations provided important insights into the mechanism of GPCR–nanobody binding and demonstrated the applicability of GaMD in modeling dynamic protein–protein interactions.

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Protein expression of prenyltransferase subunits in postmortem schizophrenia dorsolateral prefrontal cortex

Translational Psychiatry ◽

10.1038/s41398-019-0610-7 ◽

2020 ◽

Vol 10 (1) ◽

Cited By ~ 3

Author(s):

Anita L. Pinner ◽

Toni M. Mueller ◽

Khaled Alganem ◽

Robert McCullumsmith ◽

James H. Meador-Woodruff

Keyword(s):

Prefrontal Cortex ◽

Protein Expression ◽

Protein Interactions ◽

Intracellular Signaling ◽

Dorsolateral Prefrontal Cortex ◽

Signaling Proteins ◽

Protein Protein Interactions ◽

Protein Prenylation ◽

Lipid Modifications ◽

Dorsolateral Prefrontal

AbstractThe pathophysiology of schizophrenia includes altered neurotransmission, dysregulated intracellular signaling pathway activity, and abnormal dendritic morphology that contribute to deficits of synaptic plasticity in the disorder. These processes all require dynamic protein–protein interactions at cell membranes. Lipid modifications target proteins to membranes by increasing substrate hydrophobicity by the addition of a fatty acid or isoprenyl moiety, and recent evidence suggests that dysregulated posttranslational lipid modifications may play a role in multiple neuropsychiatric disorders, including schizophrenia. Consistent with these emerging findings, we have recently reported decreased protein S-palmitoylation in schizophrenia. Protein prenylation is a lipid modification that occurs upstream of S-palmitoylation on many protein substrates, facilitating membrane localization and activity of key intracellular signaling proteins. Accordingly, we hypothesized that, in addition to palmitoylation, protein prenylation may be abnormal in schizophrenia. To test this, we assayed protein expression of the five prenyltransferase subunits (FNTA, FNTB, PGGT1B, RABGGTA, and RABGGTB) in postmortem dorsolateral prefrontal cortex from patients with schizophrenia and paired comparison subjects (n = 13 pairs). We found decreased levels of FNTA (14%), PGGT1B (13%), and RABGGTB (8%) in schizophrenia. To determine whether upstream or downstream factors may be driving these changes, we also assayed protein expression of the isoprenoid synthases FDPS and GGPS1 and prenylation-dependent processing enzymes RCE and ICMT. We found these upstream and downstream enzymes to have normal protein expression. To rule out effects from chronic antipsychotic treatment, we assayed FNTA, PGGT1B, and RABGGTB in the cortex from rats treated long-term with haloperidol decanoate and found no change in the expression of these proteins. Given the role prenylation plays in localization of key signaling proteins found at the synapse, these data offer a potential mechanism underlying abnormal protein–protein interactions and protein localization in schizophrenia.

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Robust and accurate prediction of protein–protein interactions by exploiting evolutionary information

Scientific Reports ◽

10.1038/s41598-021-96265-z ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Yang Li ◽

Zheng Wang ◽

Li-Ping Li ◽

Zhu-Hong You ◽

Wen-Zhun Huang ◽

...

Keyword(s):

Protein Interactions ◽

Protein Sequence ◽

Large Scale ◽

False Positive Rate ◽

Computational Method ◽

Evolutionary Information ◽

Local Alignment ◽

Protein Interaction Data ◽

Sequence Information ◽

Protein Protein Interactions

AbstractVarious biochemical functions of organisms are performed by protein–protein interactions (PPIs). Therefore, recognition of protein–protein interactions is very important for understanding most life activities, such as DNA replication and transcription, protein synthesis and secretion, signal transduction and metabolism. Although high-throughput technology makes it possible to generate large-scale PPIs data, it requires expensive cost of both time and labor, and leave a risk of high false positive rate. In order to formulate a more ingenious solution, biology community is looking for computational methods to quickly and efficiently discover massive protein interaction data. In this paper, we propose a computational method for predicting PPIs based on a fresh idea of combining orthogonal locality preserving projections (OLPP) and rotation forest (RoF) models, using protein sequence information. Specifically, the protein sequence is first converted into position-specific scoring matrices (PSSMs) containing protein evolutionary information by using the Position-Specific Iterated Basic Local Alignment Search Tool (PSI-BLAST). Then we characterize a protein as a fixed length feature vector by applying OLPP to PSSMs. Finally, we train an RoF classifier for the purpose of identifying non-interacting and interacting protein pairs. The proposed method yielded a significantly better results than existing methods, with 90.07% and 96.09% prediction accuracy on Yeast and Human datasets. Our experiment show the proposed method can serve as a useful tool to accelerate the process of solving key problems in proteomics.

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Computational Prediction of Protein-Protein Interactions of Human Tyrosinase

Enzyme Research ◽

10.1155/2012/192867 ◽

2012 ◽

Vol 2012 ◽

pp. 1-8 ◽

Cited By ~ 7

Author(s):

Su-Fang Wang ◽

Sangho Oh ◽

Yue-Xiu Si ◽

Zhi-Jiang Wang ◽

Hong-Yan Han ◽

...

Keyword(s):

Protein Interactions ◽

Rna Binding ◽

3D Structure ◽

Computational Prediction ◽

Binding Motif ◽

Protein Protein Interactions ◽

Lim Domains ◽

Binding Partners ◽

Computational Predictions ◽

Human Tyrosinase

The various studies on tyrosinase have recently gained the attention of researchers due to their potential application values and the biological functions. In this study, we predicted the 3D structure of human tyrosinase and simulated the protein-protein interactions between tyrosinase and three binding partners, four and half LIM domains 2 (FHL2), cytochrome b-245 alpha polypeptide (CYBA), and RNA-binding motif protein 9 (RBM9). Our interaction simulations showed significant binding energy scores of −595.3 kcal/mol for FHL2, −859.1 kcal/mol for CYBA, and −821.3 kcal/mol for RBM9. We also investigated the residues of each protein facing toward the predicted site of interaction with tyrosinase. Our computational predictions will be useful for elucidating the protein-protein interactions of tyrosinase and studying its binding mechanisms.

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