Protein-protein Docking Methods Research Articles

Structural and molecular dynamics simulation studies of CBL-interacting protein kinase CIPK and its complexes related to plant salinity stress.

Calcium-dependent signaling in plants is responsible for several major cellular events, including the activation of the salinity-responsive pathways. Calcium binds to calcineurin B-like protein (CBL), and the resulting CBL-Ca2+ complex binds to CBL-interacting protein kinase (CIPK). The CBL-CIPK complex enhances the CIPK interaction with an upstream kinase. The upstream kinase phosphorylates CIPK that, in turn, phosphorylates membrane transporters. Phosphorylation influences transporter activity to kick-start many downstream functions, such as balancing the cytosolic Na+-to-K+ ratio. The CBL-CIPK interaction is pivotal for Ca2+-dependent salinity stress signaling. Computational methods are used to model the entire Arabidopsis thaliana CIPK24 protein structure in its autoinhibited and open-activated states. Arabidopsis thaliana CIPK24-CBL4 complex is predicted based on the protein-protein docking methods. The available structural and functional data support the CIPK24 and the CIPK24-CBL4 complex models. Models are energy-minimized and subjected to molecular dynamics (MD) simulations. MD simulations for 500ns and 300ns enabled us to predict the importance of conserved residues of the proteins. Finally, the work is extended to predict the CIPK24-CBL4 complex with the upstream kinase GRIK2. MD simulation for 300ns on the ternary complex structure enabled us to identify the critical CIPK24-GRIK2 interactions. Together, these data could be used to engineer the CBL-CIPK interaction network for developing salt tolerance in crops.

Review and Comparative Analysis of Methods and Advancements in Predicting Protein Complex Structure.

Protein complexes perform diverse biological functions, and obtaining their three-dimensional structure is critical to understanding and grasping their functions. In many cases, it's not just two proteins interacting to form a dimer; instead, multiple proteins interact to form a multimer. Experimentally resolving protein complex structures can be quite challenging. Recently, there have been efforts and methods that build upon prior predictions of dimer structures to attempt to predict multimer structures. However, in comparison to monomeric protein structure prediction, the accuracy of protein complex structure prediction remains relatively low. This paper provides an overview of recent advancements in efficient computational models for predicting protein complex structures. We introduce protein-protein docking methods in detail and summarize their main ideas, applicable modes, and related information. To enhance prediction accuracy, other critical protein-related information is also integrated, such as predicting interchain residue contact, utilizing experimental data like cryo-EM experiments, and considering protein interactions and non-interactions. In addition, we comprehensively reviewcomputational approaches for end-to-end prediction ofprotein complex structuresbased on artificial intelligence (AI) technologyand describe commonly used datasets and representative evaluation metrics in protein complexes. Finally, we analyze the formidable challenges faced in current protein complex structure prediction tasks, including the structure prediction of heteromeric complex, disordered regions in complex, antibody-antigen complex, and RNA-related complex, as well as the evaluation metrics for complex assessment. We hope that this work will provide comprehensive knowledge of complex structure predictions to contribute to future advanced predictions.

PEDF inhibits VEGF-induced vascular leakage through binding to VEGFR2 in acute myocardial infarction

Pigment epithelium-derived factor (PEDF) could bind to vascular endothelial growth factor receptor 2 (VEGFR2) and inhibit its activation induced by VEGF. But how PEDF affects VEGFR2 pathway is still poorly understood. In this study, we elucidated the precise mechanism underlying the interaction between PEDF and VEGFR2, and subsequently corroborated our findings using a rat AMI model. PEDF prevented endocytosis of VE-cadherin induced by hypoxia, thereby protecting the endothelium integrity. A three-dimensional model of the VEGFR2-PEDF complex was constructed by protein-protein docking method. The results showed that the VEGFR2-PEDF complex was stable during the simulation. Hydrogen bonds, binding energy and binding modes were analyzed during molecular dynamics simulations, which indicated that hydrogen bonds and hydrophobic interactions were important for the recognition of VEGFR2 with PEDF. In addition, the results from exudation of fibrinogen suggested that PEDF inhibits vascular leakage in acute myocardial infarction and confirmed the critical role of key amino acids in the regulation of endothelial cell permeability. This observation is also supported by echocardiography studies showing that the 34mer peptide sustained cardiac function during acute myocardial infarction. Besides, PEDF and 34mer could inhibit the aggregation of myofiber in the heart and promoted the formation of a dense cell layer in cardiomyocytes, which suggested that PEDF and 34mer peptide protect against AMI-induced cardiac dysfunction. These results suggest that PEDF inhibits the phosphorylation of downstream proteins, thereby preventing vascular leakage, which provides a new therapeutic direction for the treatment of acute myocardial infarction. Communicated by Ramaswamy H. Sarma

Structure elucidation and identification of novel lead molecules against Sulfur import protein cysA of Mycobacterium tuberculosis.

The present work considers the Sulphate import ABC transporter protein (cysA) as a potential drug target for the identification of inhibitors for the protein. The ABC (ATP binding cassette) transporters play a crucial role in the survival and virulence of Mycobacterium tuberculosis by the acquisition of micronutrients from host tissue. The 3D structural features of the cysA protein are built. Molecular scaffolds are identified by implementing active site identification, ADME properties, Virtual Screening, and a few other computational techniques. The theoretical model of cysA is predicted using homology modeling protocols, and the structure is validated by various validation methods. The prediction of partial dimer formation through protein-protein docking methods gave insight into the conformational changes taking place in the cysA protein. The natural substrate ATP is docked with cysA protein that confirms the ATP binding site. To find the drug-like compounds, virtual screening studies were carried out around the active site by several ligand databases. The findings demonstrate the significance of residues SER41, GLY42, ARG50, GLN85, HIS86, LYS91, ARG142, and ASP161 in drug-target interactions. The docking studies of existing TB drugs against cysA were also performed. The result analysis shows that none of the existing drugs inhibits the ATP active site, which confirms cysA as a promising drug target. Using in-silico methods, the ADME parameters of a few chosen ligand molecules are predicted and contrasted with the ADME characteristics of the available TB medications. The results revealed the values of ADME parameters of selected ligand molecules are more permissible than existing TB drugs, which emphasizes the drug-like activity of ligand molecules by inhibition of cysA proteins. The structural data, active site information, and selected ligand molecules help in the identification of new therapeutic scaffolds for Tuberculosis.

DETERMINING THE ROLE OF GALECTIN1/HOXA5 INTERACTION AND REGULATION IN GLIOBLASTOMA DEVELOPMENT

Abstract LGALS1 is directly transcribed by STAT3, which promotes glioblastoma tumorigenesis via its interaction with HOXA5. However, the underling molecular mechanism of this interaction remains uninvestigated. In this study, we predicted the interacting mode between galectin1 and HOXA5 using a protein-protein docking method. Our analysis suggests that the interaction between Galectin1 monomer and the homeobox domain of HOXA5 impedes the interaction between the homeobox domain of HOXA5 and DNA. In contrast, the interaction of Galectin1 homodimer and the N-terminal region of HOXA5 facilitates or stabilizes the homeobox domain of HOXA5 to bind DNA. Based on this predicted binding mode, we hypothesized that HOXA5 transcriptional activity is regulated by phosphorylated HOXA5, which may impact the interaction by attenuating the binding of HOXA5 to negatively charged DNA. To validate this model, we generated a mutant Galectin1 that cannot form dimers by substituting Cys at position 2 with Ser and Val at position 5 with Asp. We also generated functional domain deletion mutants of HOXA5 to confirm the interaction of the N- terminal region of HOXA5 with Galectin1, according to the prediction on functional domains in HOXA5 by Pfam. Using wild type (dimer) and mutant (monomer) recombinant galectin1 proteins and deletion-mutant proteins of HOXA5, we are conducting a binding assay to validate this model. Next, we employed knock-in lines to explore the role of HOXA5 phosphorylation in tumorigenesis using a glioblastoma mouse model. The proposed models will help uncover the underlying regulatory mechanisms of HOXA5/Galectin1 on BTSC self-renewal and glioblastoma tumorigenesis.

In silico insights into the dimer structure and deiodinase activity of type III iodothyronine deiodinase from bioinformatics, molecular dynamics simulations, and QM/MM calculations

The homodimeric family of iodothyronine deiodinases (Dios) regioselectively remove iodine from thyroid hormones. Currently, structural data has only been reported for the monomer of the mus type III thioredoxin (Trx) fold catalytic domain (Dio3Trx), but the mode of dimerization has not yet been determined. Various groups have proposed dimer structures that are similar to the A-type and B-type dimerization modes of peroxiredoxins. Computational methods are used to compare the sequence of Dio3Trx to related proteins known to form A-type and B-type dimers. Sequence analysis and in silico protein-protein docking methods suggest that Dio3Trx is more consistent with proteins that adopt B-type dimerization. Molecular dynamics (MD) simulations of the refined Dio3Trx dimer constructed using the SymmDock and GalaxyRefineComplex databases indicate stable dimer formation along the β4α3 interface consistent with other Trx fold B-type dimers. Free energy calculations show that the dimer is stabilized by interdimer interactions between the β-sheets and α-helices. A comparison of MD simulations of the apo and thyroxine-bound dimers suggests that the active site binding pocket is not affected by dimerization. Determination of the transition state for deiodination of thyroxine from the monomer structure using QM/MM methods provides an activation barrier consistent with previous small model DFT studies. Communicated by Ramaswamy H. Sarma

Structural Design and Analysis of the RHOA-ARHGEF1 Binding Mode: Challenges and Applications for Protein-Protein Interface Prediction.

The interaction between two proteins may involve local movements, such as small side-chains re-positioning or more global allosteric movements, such as domain rearrangement. We studied how one can build a precise and detailed protein-protein interface using existing protein-protein docking methods, and how it can be possible to enhance the initial structures using molecular dynamics simulations and data-driven human inspection. We present how this strategy was applied to the modeling of RHOA-ARHGEF1 interaction using similar complexes of RHOA bound to other members of the Rho guanine nucleotide exchange factor family for comparative assessment. In parallel, a more crude approach based on structural superimposition and molecular replacement was also assessed. Both models were then successfully refined using molecular dynamics simulations leading to protein structures where the major data from scientific literature could be recovered. We expect that the detailed strategy used in this work will prove useful for other protein-protein interface design. The RHOA-ARHGEF1 interface modeled here will be extremely useful for the design of inhibitors targeting this protein-protein interaction (PPI).

Exploring effectiveness of ab-initio protein-protein docking methods on a novel antibacterial protein complex dataset.

Diseases caused by bacterial infections become a critical problem in public heath. Antibiotic, the traditional treatment, gradually loses their effectiveness due to the resistance. Meanwhile, antibacterial proteins attract more attention because of broad spectrum and little harm to host cells. Therefore, exploring new effective antibacterial proteins is urgent and necessary. In this paper, we are committed to evaluating the effectiveness of ab-initio docking methods in antibacterial protein-protein docking. For this purpose, we constructed a three-dimensional (3D) structure dataset of antibacterial protein complex, called APCset, which contained $19$ protein complexes whose receptors or ligands are homologous to antibacterial peptides from Antimicrobial Peptide Database. Then we selected five representative ab-initio protein-protein docking tools including ZDOCK3.0.2, FRODOCK3.0, ATTRACT, PatchDock and Rosetta to identify these complexes' structure, whose performance differences were obtained by analyzing from five aspects, including top/best pose, first hit, success rate, average hit count and running time. Finally, according to different requirements, we assessed and recommended relatively efficient protein-protein docking tools. In terms of computational efficiency and performance, ZDOCK was more suitable as preferred computational tool, with average running time of $6.144$ minutes, average Fnat of best pose of $0.953$ and average rank of best pose of $4.158$. Meanwhile, ZDOCK still yielded better performance on Benchmark 5.0, which proved ZDOCK was effective in performing docking on large-scale dataset. Our survey can offer insights into the research on the treatment of bacterial infections by utilizing the appropriate docking methods.

Survivin - caspase protein-protein interaction: Experimental evidence and computational investigations to decipher the hotspot residues for drug targeting

Survivin is an Inhibitor of Apoptosis (IAP) family protein that is involved in various protein-protein interactions (PPIs) and thereby regulates cell division, apoptosis, and autophagy. Besides, survivin is overexpressed in most of the human solid tumors, but not in differentiated normal cells. Hence, identification of survivin PPI hotspots could pave way for effective structure-based drug design. For this, we used both in vitro (proximity ligation assay) and in silico (protein-protein docking and molecular dynamics simulation) methods to understand survivin PPI interaction with caspase-3, caspase-7, and caspase-9 in the absence/presence of XIAP BIR domains. Computational results reveal that survivin interacts with the catalytic site and/or at the dimerization site of caspase-3, caspase-7, and caspase-9. This PPI could inhibit the catalytic activity of caspases and hinder apoptosis in cancer. Moreover, MM-PBSA binding energy calculation disclosed that survivin strongly interacts with all three caspases in the presence of XIAP BIR domains. Additionally, per-residue energy decomposition results revealed that survivin BIR domain residues Asp53, Glu65, Gly66, Glu68, Asp70, Asp71, and Glu76, and C-α helix residues Glu125, Lys129, Arg132, and Arg133 significantly contributed to binding with the caspases. Targeting these hotspot residues with small molecules could result in the disruption of survivin-caspase PPI, leading to the induction of apoptosis in cancer.

Contact Area-Based Structural Analysis of Proteins and Their Complexes Using CAD-Score.

Quantifying discrepancies between computationally derived and native (reference) structures is an essential step in the development and comparison of protein modeling and protein-protein docking methods. Measuring conformational differences of proteins or protein complexes is also important in other areas of structural biology such as molecular dynamics and crystallography. There are multiple scores to do that. However, nearly all of them, whether superposition-based (e.g., RMSD) or superposition-free, use distances to measure similarity. CAD-score is conceptually different as it uses physical contacts represented as contact areas. Such representation makes it possible to quantify differences of both structures and surfaces (e.g., protein-protein interfaces and binding sites) using the same framework. A number of studies have found CAD-score to be among the most robust scores. The method is implemented both as a web server and as standalone software available at http://bioinformatics.lt/software/cad-score . Here, we describe how to use the standalone CAD-score software for comparison and analysis of protein structures, interfaces, and binding sites.

A Guide for Protein-Protein Docking Using SwarmDock.

Many of the biological functions of the cell are driven by protein-protein interactions. However, determining which proteins interact and exactly how they do so to enable their functions, remain major research questions. Functional interactions are dependent on a number of complicated factors; therefore, modeling the three-dimensional structure of protein-protein complexes is still considered a complex endeavor. Nevertheless, the rewards for modeling protein interactions to atomic level detail are substantial, and there are numerous examples of how models can provide useful information for drug design, protein engineering, systems biology, and understanding of the immune system. Here, we provide practical guidelines for docking proteins using the web-server, SwarmDock, a flexible protein-protein docking method. Moreover, we provide an overview of the factors that need to be considered when deciding whether docking is likely to be successful.

GalaxyTongDock: Symmetric and asymmetric ab initio protein-protein docking web server with improved energy parameters.

Protein-protein docking methods are spotlighted for their roles in providing insights into protein-protein interactions in the absence of full structural information by experiment. GalaxyTongDock is an ab initio protein-protein docking web server that performs rigid-body docking just like ZDOCK but with improved energy parameters. The energy parameters were trained by iterative docking and parameter search so that more native-like structures are selected as top rankers. GalaxyTongDock performs asymmetric docking of two different proteins (GalaxyTongDock_A) and symmetric docking of homo-oligomeric proteins with Cn and Dn symmetries (GalaxyTongDock_C and GalaxyTongDock_D). Performance tests on an unbound docking benchmark set for asymmetric docking and a model docking benchmark set for symmetric docking showed that GalaxyTongDock is better or comparable to other state-of-the-art methods. Experimental and/or evolutionary information on binding interfaces can be easily incorporated by using block and interface options. GalaxyTongDock web server is freely available at http://galaxy.seoklab.org/tongdock. © 2019 Wiley Periodicals, Inc.

Exploring Energy Profiles of Protein-Protein Interactions (PPIs) Using DFT Method

Background: Large-scale energy landscape characterization of protein-protein interactions (PPIs) is important to understand the interaction mechanism and protein-protein docking methods. The experimental methods for detecting energy landscapes are tedious and the existing computational methods require longer simulation time. Objective: The objective of the present work is to ascertain the energy profiles at the interface regions in a rapid manner to analyze the energy landscape of protein-protein interactions. Methods: The atomic coordinates obtained from the X-ray and NMR spectroscopy data are considered as inputs to compute cumulative energy profiles for experimentally validated protein-protein complexes. The energies computed by the program were comparable to the standard molecular dynamics simulations. Results: The PPI Profiler not only enables rapid generation of energy profiles but also facilitates the detection of hot spot residue atoms involved therein. Conclusion: The hotspot residues and their computed energies matched with the experimentally determined hot spot residues and their energies which correlated well by employing the MM/GBSA method. The proposed method can be employed to scan entire proteomes across species at an atomic level to study the key PPI interactions.

Protein-Protein Docking Using Evolutionary Information.

The structural modeling of protein complexes by docking simulations has been attracting increasing interest with the rise of proteomics and of the number of experimentally identified binary interactions. Structures of unbound partners, either modeled or experimentally determined, can be used as input to sample as extensively as possible all putative binding modes and single out the most plausible ones. At the scoring step, evolutionary information contained in the joint multiple sequence alignments of both partners can provide key insights to recognize correct interfaces. Here, we describe a computational protocol based on the InterEvDock web server to exploit coevolution constraints in protein-protein docking methods. We provide methodology guidelines to prepare the input protein structures and generate improved alignments. We also explain how to extract and use the information returned by the server through the analysis of two representative examples.

Flexible Protein-Protein Docking with SwarmDock.

The atomic structures of protein complexes can provide useful information for drug design, protein engineering, systems biology, and understanding pathology. Obtaining this information experimentally can be challenging. However, if the structures of the subunits are known, then it is often possible to model the complex computationally. This chapter provide practical guidelines for docking proteins using the SwarmDock flexible protein-protein docking method, providing an overview of the factors that need to be considered when deciding whether docking is likely to be successful, the preparation of structural input, generation of docked poses, analysis and ranking of docked poses, and the validation of models using external data.

Modeling disordered protein interactions from biophysical principles.

Disordered protein-protein interactions (PPIs), those involving a folded protein and an intrinsically disordered protein (IDP), are prevalent in the cell, including important signaling and regulatory pathways. IDPs do not adopt a single dominant structure in isolation but often become ordered upon binding. To aid understanding of the molecular mechanisms of disordered PPIs, it is crucial to obtain the tertiary structure of the PPIs. However, experimental methods have difficulty in solving disordered PPIs and existing protein-protein and protein-peptide docking methods are not able to model them. Here we present a novel computational method, IDP-LZerD, which models the conformation of a disordered PPI by considering the biophysical binding mechanism of an IDP to a structured protein, whereby a local segment of the IDP initiates the interaction and subsequently the remaining IDP regions explore and coalesce around the initial binding site. On a dataset of 22 disordered PPIs with IDPs up to 69 amino acids, successful predictions were made for 21 bound and 18 unbound receptors. The successful modeling provides additional support for biophysical principles. Moreover, the new technique significantly expands the capability of protein structure modeling and provides crucial insights into the molecular mechanisms of disordered PPIs.

Performance of MDockPP in CAPRI rounds 28-29 and 31-35 including the prediction of water-mediated interactions.

Protein-protein interactions are either through direct contacts between two binding partners or mediated by structural waters. Both direct contacts and water-mediated interactions are crucial to the formation of a protein-protein complex. During the recent CAPRI rounds, a novel parallel searching strategy for predicting water-mediated interactions is introduced into our protein-protein docking method, MDockPP. Briefly, a FFT-based docking algorithm is employed in generating putative binding modes, and an iteratively derived statistical potential-based scoring function, ITScorePP, in conjunction with biological information is used to assess and rank the binding modes. Up to 10 binding modes are selected as the initial protein-protein complex structures for MD simulations in explicit solvent. Water molecules near the interface are clustered based on the snapshots extracted from independent equilibrated trajectories. Then, protein-ligand docking is employed for a parallel search for water molecules near the protein-protein interface. The water molecules generated by ligand docking and the clustered water molecules generated by MD simulations are merged, referred to as the predicted structural water molecules. Here, we report the performance of this protocol for CAPRI rounds 28-29 and 31-35 containing 20 valid docking targets and 11 scoring targets. In the docking experiments, we predicted correct binding modes for nine targets, including one high-accuracy, two medium-accuracy, and six acceptable predictions. Regarding the two targets for the prediction of water-mediated interactions, we achieved models ranked as "excellent" in accordance with the CAPRI evaluation criteria; one of these two targets is considered as a difficult target for structural water prediction. Proteins 2017; 85:424-434. © 2016 Wiley Periodicals, Inc.

Machine Learning Approaches for Predicting Protein Complex Similarity.

Discriminating native-like structures from false positives with high accuracy is one of the biggest challenges in protein-protein docking. While there is an agreement on the existence of a relationship between various favorable intermolecular interactions (e.g., Van der Waals, electrostatic, and desolvation forces) and the similarity of a conformation to its native structure, the precise nature of this relationship is not known. Existing protein-protein docking methods typically formulate this relationship as a weighted sum of selected terms and calibrate their weights by using a training set to evaluate and rank candidate complexes. Despite improvements in the predictive power of recent docking methods, producing a large number of false positives by even state-of-the-art methods often leads to failure in predicting the correct binding of many complexes. With the aid of machine learning methods, we tested several approaches that not only rank candidate structures relative to each other but also predict how similar each candidate is to the native conformation. We trained a two-layer neural network, a multilayer neural network, and a network of Restricted Boltzmann Machines against extensive data sets of unbound complexes generated by RosettaDock and PyDock. We validated these methods with a set of refinement candidate structures. We were able to predict the root mean squared deviations (RMSDs) of protein complexes with a very small, often less than 1.5 Å, error margin when trained with structures that have RMSD values of up to 7 Å. In our most recent experiments with the protein samples having RMSD values up to 27 Å, the average prediction error was still relatively small, attesting to the potential of our approach in predicting the correct binding of protein-protein complexes.

GalaxyRefineComplex: Refinement of protein-protein complex model structures driven by interface repacking.

Protein-protein docking methods have been widely used to gain an atomic-level understanding of protein interactions. However, docking methods that employ low-resolution energy functions are popular because of computational efficiency. Low-resolution docking tends to generate protein complex structures that are not fully optimized. GalaxyRefineComplex takes such low-resolution docking structures and refines them to improve model accuracy in terms of both interface contact and inter-protein orientation. This refinement method allows flexibility at the protein interface and in the overall docking structure to capture conformational changes that occur upon binding. Symmetric refinement is also provided for symmetric homo-complexes. This method was validated by refining models produced by available docking programs, including ZDOCK and M-ZDOCK, and was successfully applied to CAPRI targets in a blind fashion. An example of using the refinement method with an existing docking method for ligand binding mode prediction of a drug target is also presented. A web server that implements the method is freely available at http://galaxy.seoklab.org/refinecomplex.

Early Events in the Amyloid Formation of the A546T Mutant of Transforming Growth Factor β-Induced Protein in Corneal Dystrophies Compared to the Nonfibrillating R555W and R555Q Mutants.

The human transforming growth factor β-induced protein (TGFBIp) is involved in several types of corneal dystrophies where protein aggregation and amyloid fibril formation severely impair vision. Most disease-causing mutations are located in the last of four homologous fasciclin-1 (FAS1) domains of the protein, and it has been shown that when isolated, the fourth FAS1 domain (FAS1-4) mimics the behavior of full-length TGFBIp. In this study, we use molecular dynamics simulations and principal component analysis to study the wild-type FAS1-4 domain along with three disease-causing mutations (R555W, R555Q, and A546T) to decipher any internal difference in dynamical properties of the domains that may explain their varied stabilities and aggregation properties. In addition, we use a protein-protein docking method in combination with chemical cross-linking experiments and mass spectrometry of the cross-linked species to obtain information about interaction faces between identical FAS1-4 domains. The results show that the pathogenic mutations A546T and R555W affect the packing in the hydrophobic core of FAS1-4 in different directions. We further show that the FAS1-4 monomers associate using their β-rich regions, consistent with peptides observed to be part of the amyloid fibril core in lattice corneal dystrophy patients.