Protein-protein Docking Algorithms Research Articles

Flexible protein-protein docking with a multitrack iterative transformer.

Conventional protein-protein docking algorithms usually rely on heavy candidate sampling and reranking, but these steps are time-consuming and hinder applications that require high-throughput complex structure prediction, for example, 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 multitrack 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, 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 Database of Interacting Protein Structures (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 1 s 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.

Assessing Protein-Protein Docking Protocols: Case Studies of G-Protein-Coupled Receptor Interactions.

The interactions of G-protein-coupled receptors (GPCRs) with other proteins are critical in several cellular processes but resolving their structural dynamics remains challenging. An increasing number of GPCR complexes have been experimentally resolved but others including receptor variants are yet to be characterized, necessitating computational predictions of their interactions. Although integrative approaches with multi-scale simulations would provide rigorous estimates of their conformational dynamics, protein-protein docking remains a first tool of choice of many researchers due to the availability of open-source programs and easy to use web servers with reasonable predictive power. Protein-protein docking algorithms have limited ability to consider protein flexibility, environment effects, and entropy contributions and are usually a first step towards more integrative approaches. The two critical steps of docking: the sampling and scoring algorithms have improved considerably and their performance has been validated against experimental data. In this chapter, we provide an overview and generalized protocol of a few docking protocols using GPCRs as test cases. In particular, we demonstrate the interactions of GPCRs with extracellular protein ligands and an intracellular protein effectors (G-protein) predicted from docking approaches and test their limitations. The current chapter will help researchers critically assess docking protocols and predict experimentally testable structures of GPCR complexes.

Approaches to Backbone Flexibility in Protein-Protein Docking.

Proteins are the fundamental organic macromolecules in living systems that play a key role in a variety of biological functions including immunological detection, intracellular trafficking, and signal transduction. The docking of proteins has greatly advanced during recent decades and has become a crucial complement to experimental methods. Protein-protein docking is a helpful method for simulating protein complexes whose structures have not yet been solved experimentally. This chapter focuses on major search tactics along with various docking programs used in protein-protein docking algorithms, which include: direct search, exhaustive global search, local shape feature matching, randomized search, and broad category of post-docking approaches. As backbone flexibility predictions and interactions in high-resolution protein-protein docking remain important issues in the overall optimization context, we have put forward several methods and solutions used to handle backbone flexibility. In addition, various docking methods that are utilized for flexible backbone docking, including ATTRACT, FlexDock, FLIPDock, HADDOCK, RosettaDock, FiberDock, etc., along with their scoring functions, algorithms, advantages, and limitations are discussed. Moreover, what progress in search technology is expected, including not only the creation of new search algorithms but also the enhancement of existing ones, has been debated. As conformational flexibility is one of the most crucial factors affecting docking success, more work should be put into evaluating the conformational flexibility upon binding for a particular case in addition to developing new algorithms to replace the rigid body docking and scoring approach.

Approaches to Backbone Flexibility in Protein-Protein Docking.

Proteins are the fundamental organic macromolecules in living systems that play a key role in a variety of biological functions including immunological detection, intracellular trafficking, and signal transduction. The docking of proteins has greatly advanced during recent decades and has become a crucial complement to experimental methods. Protein-protein docking is a helpful method for simulating protein complexes whose structures have not yet been solved experimentally. This chapter focuses on major search tactics along with various docking programs used in protein-protein docking algorithms, which include: direct search, exhaustive global search, local shape feature matching, randomized search, and broad category of post-docking approaches. As backbone flexibility predictions and interactions in high-resolution protein-protein docking remain important issues in the overall optimization context, we have put forward several methods and solutions used to handle backbone flexibility. In addition, various docking methods that are utilized for flexible backbone docking, including ATTRACT, FlexDock, FLIPDock, HADDOCK, RosettaDock, FiberDock, etc., along with their scoring functions, algorithms, advantages, and limitations are discussed. Moreover, what progress in search technology is expected, including not only the creation of new search algorithms but also the enhancement of existing ones, has been debated. As conformational flexibility is one of the most crucial factors affecting docking success, more work should be put into evaluating the conformational flexibility upon binding for a particular case in addition to developing new algorithms to replace the rigid body docking and scoring approach.

Protein-Protein Docking Approach to GPCR Oligomerization.

G-protein-coupled receptors (GPCRs), the largest family of human membrane proteins, play a crucial role in cellular control and are the target of approximately one-third of all drugs on the market. Targeting these complexes with selectivity or formulating small molecules capable of modulating receptor-receptor interactions could potentially offer novel avenues for drug discovery, fostering the development of more refined and safer pharmacotherapies. Due to the lack of experimentally derived X-ray crystallography spectra of GPCR oligomers, there is growing evidence supporting the development of new in silico approaches for predicting GPCR self-assembling structures. The significance of GPCR oligomerization, the challenges in modeling these structures, and the potential of protein-protein docking algorithms to address these challenges are discussed. The study also underscores the use of various software solutions for modeling GPCR oligomeric structures and presents practical cases where these techniques have been successfully applied.

Protein-Protein Docking Approach to GPCR Oligomerization.

G-protein-coupled receptors (GPCRs), the largest family of human membrane proteins, play a crucial role in cellular control and are the target of approximately one-third of all drugs on the market. Targeting these complexes with selectivity or formulating small molecules capable of modulating receptor-receptor interactions could potentially offer novel avenues for drug discovery, fostering the development of more refined and safer pharmacotherapies. Due to the lack of experimentally derived X-ray crystallography spectra of GPCR oligomers, there is growing evidence supporting the development of new in silico approaches for predicting GPCR self-assembling structures. The significance of GPCR oligomerization, the challenges in modeling these structures, and the potential of protein-protein docking algorithms to address these challenges are discussed. The study also underscores the use of various software solutions for modeling GPCR oligomeric structures and presents practical cases where these techniques have been successfully applied.

Heat shock protein 27 regulates myogenic and self-renewal potential of bovine satellite cells under heat stress.

While satellite cells play a key role in the hypertrophy, repair, and regeneration of skeletal muscles, their response to heat exposure remains poorly understood, particularly in beef cattle. This study aimed to investigate the changes in the transcriptome, proteome, and proliferation capability of bovine satellite cells in response to different levels of heat stress (HS) and exposure times. Satellite cells were isolated from 3-mo-old Holstein bulls (body weight: 77.10 ± 2.02 kg) and subjected to incubation under various temperature conditions: 1) control (38 °C; CON), 2) moderate (39.5 °C; MHS), and extreme (41 °C; EHS) for different durations ranging from 0 to 48 h. Following 3 h of exposure to extreme heat (EHS), satellite cells exhibited significantly increased gene expression and protein abundance of heat shock proteins (HSPs; HSP70, HSP90, HSP20) and paired box gene 7 (Pax7; P < 0.05). HSP27 expression peaked at 3 h of EHS and remained elevated until 24 h of exposure (P < 0.05). In contrast, the expression of myogenic factor 5 (Myf5) and paired box gene 3 (Pax3) was decreased by EHS compared to the control at 3 h of exposure (P < 0.05). Notably, the introduction of HSP27 small interference RNA (siRNA) transfection restored Myf5 expression to control levels, suggesting an association between HSP27 and Myf5 in regulating the self-renewal properties of satellite cells upon heat exposure. Immunoprecipitation experiments further confirmed the direct binding of HSP27 to Myf5, supporting its role as a molecular chaperone for Myf5. Protein-protein docking algorithms predicted a high probability of HSP27-Myf5 interaction as well. These findings indicate that extreme heat exposure intrinsically promotes the accumulation of HSPs and modulates the early myogenic regulatory factors in satellite cells. Moreover, HSP27 acts as a molecular chaperone by binding to Myf5, thereby regulating the division or differentiation of satellite cells in response to HS. The results of this study provide a better understanding of muscle physiology in heat-stressed cells, while unraveling the intricate molecular mechanisms that underlie the HS response in satellite cells.

A memetic algorithm enables efficient local and global all-atom protein-protein docking with backbone and side-chain flexibility

Protein complex formation is encoded by specific interactions at the atomic scale, but the computational cost of modeling proteins at this level often requires use of simplified energy models and limited conformational flexibility. In particular, use of all-atom energy functions and backbone and side-chain flexibility results in rugged energy landscapes that are difficult to explore. In this study, we develop a protein-protein docking algorithm, EvoDOCK, that combines the strength of a differential evolution algorithm for efficient exploration of the global search space with the benefits of a local optimization method to refine detailed atomic interactions. EvoDOCK enabled accurate and fast local and global protein-protein docking using an all-atom energy function with side-chain flexibility. Comparison with a standard method built on Monte Carlo optimization demonstrated improved accuracy and increases in computational speed of up to 35 times. The evolutionary algorithm also enabled efficient atomistic docking with backbone flexibility.

MPPI: a database extension to visualize structural interactome in a one-to-many manner.

Protein-protein interaction (PPI) databases with structural information are useful to investigate biological functions at both systematic and atomic levels. However, most existing PPI databases only curate binary interactome. From the perspective of the display and function of PPI, as well as the structural binding interface, the related database and resources are summarized. We developed a database extension, named mPPI, for PPI structural visualization. Comparing with the existing structural interactomes that curate resolved PPI conformation in pairs, mPPI can visualize target protein and its multiple interactors simultaneously, which facilitates multi-target drug discovery and structure prediction of protein macro-complexes. By employing a protein-protein docking algorithm, mPPI largely extends the coverage of structural interactome from experimentally resolved complexes. mPPI is designed to be a customizable and convenient plugin for PPI databases. It possesses wide potential applications for various PPI databases, and it has been used for a neurodegenerative disease-related PPI database as demonstration. Scripts and implementation guidelines of mPPI are documented at the database tool website. Database URL http://bis.zju.edu.cn/mppi/.

GroScore: Accurate Scoring of Protein-Protein Binding Poses Using Explicit-Solvent Free-Energy Calculations.

Protein-protein docking algorithms promise a potential relief for the mismatch between the number of experimentally determined complex structures and the number of relevant protein interactions in an organism. To distinguish correctly from wrongly generated poses, it is necessary to score complexes according to their structural similarity to the real complex, which is usually done by computing interaction energies of some sort. Here, we explore the potential of free-energy calculations with statistical-mechanical foundation in the context of molecular dynamics (MD) simulations with explicit solvent to score a large number of complex poses. We introduce an adaptive sampling scheme which ensures that most sampling time is spent on the most promising poses. Our approach is illustrated by scoring of all targets in the CAPRI Score_set, a scoring benchmark set, and three additional CAPRI targets, together consisting of more than 22 000 poses. Our scoring scheme shows a performance that is competitive with the most successful approaches that were previously reported. All necessary scripts to run the automated scoring pipeline are available in the Supporting Information for this paper.

Pushing the accuracy limit of shape complementarity for protein-protein docking

BackgroundProtein-protein docking is a valuable computational approach for investigating protein-protein interactions. Shape complementarity is the most basic component of a scoring function and plays an important role in protein-protein docking. Despite significant progresses, shape representation remains an open question in the development of protein-protein docking algorithms, especially for grid-based docking approaches.ResultsWe have proposed a new pairwise shape-based scoring function (LSC) for protein-protein docking which adopts an exponential form to take into account long-range interactions between protein atoms. The LSC scoring function was incorporated into our FFT-based docking program and evaluated for both bound and unbound docking on the protein docking benchmark 4.0. It was shown that our LSC achieved a significantly better performance than four other similar docking methods, ZDOCK 2.1, MolFit/G, GRAMM, and FTDock/G, in both success rate and number of hits. When considering the top 10 predictions, LSC obtained a success rate of 51.71% and 6.82% for bound and unbound docking, respectively, compared to 42.61% and 4.55% for the second-best program ZDOCK 2.1. LSC also yielded an average of 8.38 and 3.94 hits per complex in the top 1000 predictions for bound and unbound docking, respectively, followed by 6.38 and 2.96 hits for the second-best ZDOCK 2.1.ConclusionsThe present LSC method will not only provide an initial-stage docking approach for post-docking processes but also have a general implementation for accurate representation of other energy terms on grids in protein-protein docking. The software has been implemented in our HDOCK web server at http://hdock.phys.hust.edu.cn/.

Performance and enhancement of the LZerD protein assembly pipeline in CAPRI 38-46.

We report the performance of the protein docking prediction pipeline of our group and the results for Critical Assessment of Prediction of Interactions (CAPRI) rounds 38-46. The pipeline integrates programs developed in our group as well as other existing scoring functions. The core of the pipeline is the LZerD protein-protein docking algorithm. If templates of the target complex are not found in PDB, the first step of our docking prediction pipeline is to run LZerD for a query protein pair. Meanwhile, in the case of human group prediction, we survey the literature to find information that can guide the modeling, such as protein-protein interface information. In addition to any literature information and binding residue prediction, generated docking decoys were selected by a rank aggregation of statistical scoring functions. The top 10 decoys were relaxed by a short molecular dynamics simulation before submission to remove atom clashes and improve side-chain conformations. In these CAPRI rounds, our group, particularly the LZerD server, showed robust performance. On the other hand, there are failed cases where some other groups were successful. To understand weaknesses of our pipeline, we analyzed sources of errors for failed targets. Since we noted that structure refinement is a step that needs improvement, we newly performed a comparative study of several refinement approaches. Finally, we show several examples that illustrate successful and unsuccessful cases by our group.

Ranking protein-protein docking results using steered molecular dynamics and potential of mean force calculations.

Crystallization of protein-protein complexes can often be problematic and therefore computational structural models are often relied on. Such models are often generated using protein-protein docking algorithms, where one of the main challenges is selecting which of several thousand potential predictions represents the most near-native complex. We have developed a novel technique that involves the use of steered molecular dynamics (sMD) and umbrella sampling to identify near-native complexes among protein-protein docking predictions. Using this technique, we have found a strong correlation between our predictions and the interface RMSD (iRMSD) in ten diverse test systems. On two of the systems, we investigated if the prediction results could be further improved using potential of mean force calculations. We demonstrated that a near-native (<2.0 Å iRMSD) structure could be identified in the top-1 ranked position for both systems. © 2016 Wiley Periodicals, Inc.

Memdock: an α-helical membrane protein docking algorithm.

A wide range of fundamental biological processes are mediated by membrane proteins. Despite their large number and importance, less than 1% of all 3D protein structures deposited in the Protein Data Bank are of membrane proteins. This is mainly due to the challenges of crystallizing such proteins or performing NMR spectroscopy analyses. All the more so, there is only a small number of membrane protein-protein complexes with known structure. Therefore, developing computational tools for docking membrane proteins is crucial. Numerous methods for docking globular proteins exist, however few have been developed especially for membrane proteins and designed to address docking within the lipid bilayer environment. We present a novel algorithm, Memdock, for docking α-helical membrane proteins which takes into consideration the lipid bilayer environment for docking as well as for refining and ranking the docking candidates. We show that our algorithm improves both the docking accuracy and the candidates ranking compared to a standard protein-protein docking algorithm. http://bioinfo3d.cs.tau.ac.il/Memdock/ namih@tau.ac.il or wolfson@tau.ac.il Supplementary data are available at Bioinformatics online.

Surface energetics and protein-protein interactions: analysis and mechanistic implications

Understanding protein-protein interactions (PPI) at the molecular level is a fundamental task in the design of new drugs, the prediction of protein function and the clarification of the mechanisms of (dis)regulation of biochemical pathways. In this study, we use a novel computational approach to investigate the energetics of aminoacid networks located on the surface of proteins, isolated and in complex with their respective partners. Interestingly, the analysis of individual proteins identifies patches of surface residues that, when mapped on the structure of their respective complexes, reveal regions of residue-pair couplings that extend across the binding interfaces, forming continuous motifs. An enhanced effect is visible across the proteins of the dataset forming larger quaternary assemblies. The method indicates the presence of energetic signatures in the isolated proteins that are retained in the bound form, which we hypothesize to determine binding orientation upon complex formation. We propose our method, BLUEPRINT, as a complement to different approaches ranging from the ab-initio characterization of PPIs, to protein-protein docking algorithms, for the physico-chemical and functional investigation of protein-protein interactions.

Sperm Lysozyme-Like Protein 1 (SLLP1), an intra-acrosomal oolemmal-binding sperm protein, reveals filamentous organization in protein crystal form.

Sperm lysozyme-like protein 1 (SLLP1) is one of the lysozyme-like proteins predominantly expressed in mammalian testes that lacks bacteriolytic activity, localizes in the sperm acrosome, and exhibits high affinity for an oolemmal receptor, SAS1B. The crystal structure of mouse SLLP1 (mSLLP1) was determined at 2.15Å resolution. mSLLP1 monomer adopts a structural fold similar to that of chicken/mouse lysozymes retaining all four canonical disulfide bonds. mSLLP1 is distinct from c-lysozyme by substituting two essential catalytic residues (E35T/D52N), exhibiting different surface charge distribution, and by forming helical filaments approximately 75Å in diameter with a 25Å central pore comprised of six monomers per helix turn repeating every 33Å. Cross-species alignment of all reported SLLP1 sequences revealed a set of invariant surface regions comprising a characteristic fingerprint uniquely identifying SLLP1 from other c-lysozyme family members. The fingerprint surface regions reside around the lips of the putative glycan-binding groove including three polar residues (Y33/E46/H113). A flexible salt bridge (E46-R61) was observed covering the glycan-binding groove. The conservation of these regions may be linked to their involvement in oolemmal protein binding. Interaction between SLLP1 monomer and its oolemmal receptor SAS1B was modeled using protein-protein docking algorithms, utilizing the SLLP1 fingerprint regions along with the SAS1B conserved surface regions. This computational model revealed complementarity between the conserved SLLP1/SAS1B interacting surfaces supporting the experimentally observed SLLP1/SAS1B interaction involved in fertilization.

Intuitive, but not simple: Including explicit water molecules in protein–protein docking simulations improves model quality

Characterizing the nature of interaction between proteins that have not been experimentally cocrystallized requires a computational docking approach that can successfully predict the spatial conformation adopted in the complex. In this work, the Hydropathic INTeractions (HINT) force field model was used for scoring docked models in a data set of 30 high-resolution crystallographically characterized "dry" protein-protein complexes and was shown to reliably identify native-like models. However, most current protein-protein docking algorithms fail to explicitly account for water molecules involved in bridging interactions that mediate and stabilize the association of the protein partners, so we used HINT to illuminate the physical and chemical properties of bridging waters and account for their energetic stabilizing contributions. The HINT water Relevance metric identified the "truly" bridging waters at the 30 protein-protein interfaces and we utilized them in "solvated" docking by manually inserting them into the input files for the rigid body ZDOCK program. By accounting for these interfacial waters, a statistically significant improvement of ∼24% in the average hit-count within the top-10 predictions the protein-protein dataset was seen, compared to standard "dry" docking. The results also show scoring improvement, with medium and high accuracy models ranking much better than incorrect ones. These improvements can be attributed to the physical presence of water molecules that alter surface properties and better represent native shape and hydropathic complementarity between interacting partners, with concomitantly more accurate native-like structure predictions.

ATP transport through VDAC and the VDAC-tubulin complex probed by equilibrium and nonequilibrium MD simulations.

Voltage-dependent anion channel (VDAC), the major channel of the mitochondrial outer membrane, serves as a principal pathway for ATP, ADP, and other respiratory substrates across this membrane. Using umbrella-sampling simulations, we established the thermodynamic and kinetic components governing ATP transport across the VDAC1 channel. We found that there are several low-affinity binding sites for ATP along the translocation pathway and that the main barrier for ATP transport is located around the center of the channel and is formed predominantly by residues in the N-terminus. The binding affinity of ATP to an open channel was found to be in the millimolar to micromolar range. However, we show that this weak binding increases the ATP translocation probability by about 10-fold compared with the VDAC pore in which attractive interactions were artificially removed. Recently, it was found that free dimeric tubulin induces a highly efficient, reversible blockage of VDAC reconstituted into planar lipid membranes. It was proposed that by blocking VDAC permeability for ATP/ADP and other mitochondrial respiratory substrates tubulin controls mitochondrial respiration. Using the Rosetta protein-protein docking algorithm, we established a tentative structure of the VDAC-tubulin complex. An extensive set of equilibrium and nonequilibrium (under applied electric field) molecular dynamics (MD) simulations was used to establish the conductance of the open and blocked channel. It was found that the presence of the unstructured C-terminal tail of tubulin in the VDAC pore decreases its conductance by more than 40% and switches its selectivity from anionic to cationic. The subsequent 1D potential of mean force (PMF) computations for the VDAC-tubulin complex show that the state renders ATP transport virtually impossible. A number of residues pivotal for tubulin binding to the channel were identified that help to clarify the molecular details of VDAC-tubulin interaction and to provide new insight into the mechanism of the control of mitochondria respiration by VDAC.

How to Use Not-Always-Reliable Binding Site Information in Protein-Protein Docking Prediction

In many protein-protein docking algorithms, binding site information is used to help predicting the protein complex structures. Using correct and accurate binding site information can increase protein-protein docking success rate significantly. On the other hand, using wrong binding sites information should lead to a failed prediction, or, at least decrease the success rate. Recently, various successful theoretical methods have been proposed to predict the binding sites of proteins. However, the predicted binding site information is not always reliable, sometimes wrong binding site information could be given. Hence there is a high risk to use the predicted binding site information in current docking algorithms. In this paper, a softly restricting method (SRM) is developed to solve this problem. By utilizing predicted binding site information in a proper way, the SRM algorithm is sensitive to the correct binding site information but insensitive to wrong information, which decreases the risk of using predicted binding site information. This SRM is tested on benchmark 3.0 using purely predicted binding site information. The result shows that when the predicted information is correct, SRM increases the success rate significantly; however, even if the predicted information is completely wrong, SRM only decreases success rate slightly, which indicates that the SRM is suitable for utilizing predicted binding site information.

Binding interface prediction by combining protein-protein docking results

We developed a method called residue contact frequency (RCF), which uses the complex structures generated by the protein-protein docking algorithm ZDOCK to predict interface residues. Unlike interface prediction algorithms that are based on monomers alone, RCF is binding partner specific. We evaluated the performance of RCF using the area under the precision-recall (PR) curve (AUC) on a large protein docking Benchmark. RCF (AUC = 0.44) performed as well as meta-PPISP (AUC = 0.43), which is one of the best monomer-based interface prediction methods. In addition, we test a support vector machine (SVM) to combine RCF with meta-PPISP and another monomer-based interface prediction algorithm Evolutionary Trace to further improve the performance. We found that the SVM that combined RCF and meta-PPISP achieved the best performance (AUC = 0.47). We used RCF to predict the binding interfaces of proteins that can bind to multiple partners and RCF was able to correctly predict interface residues that are unique for the respective binding partners. Furthermore, we found that residues that contributed greatly to binding affinity (hotspot residues) had significantly higher RCF than other residues.