Structures Of Protein-protein Complexes Research Articles

Refinement of Docked Protein-Protein Complexes Using Repulsive Scaling Replica Exchange Simulations.

Accurate prediction and evaluation of protein-protein complex structures is of major importance to understand the cellular interactome. Predicted complex structures based on deep learning approaches or traditional docking methods require often structural refinement and rescoring for realistic evaluation. Standard molecular dynamics (MD) simulations are time-consuming and often do not structurally improve docking solutions. Better refinement can be achieved with our recently developed replica-exchange-based scheme employing different levels of repulsive biasing between proteins in each replica simulation (RS-REMD). The bias acts specifically on the intermolecular interactions based on an increase in effective pairwise van der Waals radii without changing interactions within each protein or with the solvent. It allows for an improvement of the predicted protein-protein complex structure and simultaneous realistic free energy scoring of protein-protein complexes. The setup of RS-REMD simulations is described in detail including the application on two examples (all necessary scripts and input files can be obtained from https://gitlab.com/TillCyrill/mmib ).

MpbPPI: a multi-task pre-training-based equivariant approach for the prediction of the effect of amino acid mutations on protein-protein interactions.

The accurate prediction of the effect of amino acid mutations for protein-protein interactions (PPI $\Delta \Delta G$) is a crucial task in protein engineering, as it provides insight into the relevant biological processes underpinning protein binding and provides a basis for further drug discovery. In this study, we propose MpbPPI, a novel multi-task pre-training-based geometric equivariance-preserving framework to predict PPI $\Delta \Delta G$. Pre-training on a strictly screened pre-training dataset is employed to address the scarcity of protein-protein complex structures annotated with PPI $\Delta \Delta G$ values. MpbPPI employs a multi-task pre-training technique, forcing the framework to learn comprehensive backbone and side chain geometric regulations of protein-protein complexes at different scales. After pre-training, MpbPPI can generate high-quality representations capturing the effective geometric characteristics of labeled protein-protein complexes for downstream $\Delta \Delta G$ predictions. MpbPPI serves as a scalable framework supporting different sources of mutant-type (MT) protein-protein complexes for flexible application. Experimental results on four benchmark datasets demonstrate that MpbPPI is a state-of-the-art framework for PPI $\Delta \Delta G$ predictions. The data and source code are available at https://github.com/arantir123/MpbPPI.

Improved prediction of protein-protein interactions by a modified strategy using three conventional docking software in combination

Proteins play a crucial role in many biological processes, where their interaction with other proteins are integral. Abnormal protein-protein interactions (PPIs) have been linked to various diseases including cancer, and thus targeting PPIs holds promise for drug development. However, experimental confirmation of the peculiarities of PPIs is challenging due to their dynamic and transient nature. As a complement to experimental technologies, multiple computational molecular docking (MD) methods have been developed to predict the structures of protein-protein complexes and their dynamics, still requiring further improvements in several issues. Here, we report an improved MD method, namely three-software docking (3SD), by employing three popular protein-peptide docking software (CABS-dock, HPEPDOCK, and HADDOCK) in combination to ensure constant quality for most targets. We validated our 3SD performance in known protein-peptide interactions (PpIs). We also enhanced MD performance in proteins having intrinsically disordered regions (IDRs) by applying the modified 3SD strategy, the three-software docking after removing random coiled IDR (3SD-RR), to the comparable crystal PpI structures. At the end, we applied 3SD-RR to the AlphaFold2-predicted receptors, yielding an efficient prediction of PpI pose with high relevance to the experimental data regardless of the presence of IDRs or the availability of receptor structures. Our study provides an improved solution to the challenges in studying PPIs through computational docking and has the potential to contribute to PPIs-targeted drug discovery. Significance statementProtein-protein interactions (PPIs) are integral to life, and abnormal PPIs are associated with diseases such as cancer. Studying protein-peptide interactions (PpIs) is challenging due to their dynamic and transient nature. Here we developed improved docking methods (3SD and 3SD-RR) to predict the PpI poses, ensuring constant quality in most targets and also addressing issues like intrinsically disordered regions (IDRs) and artificial intelligence-predicted structures. Our study provides an improved solution to the challenges in studying PpIs through computational docking and has the potential to contribute to PPIs-targeted drug discovery.

A dynamical view of protein-protein complexes: Studies by molecular dynamics simulations.

Protein-protein interactions are at the basis of many protein functions, and the knowledge of 3D structures of protein-protein complexes provides structural, mechanical and dynamical pieces of information essential to understand these functions. Protein-protein interfaces can be seen as stable, organized regions where residues from different partners form non-covalent interactions that are responsible for interaction specificity and strength. They are commonly described as a peripheral region, whose role is to protect the core region that concentrates the most contributing interactions, from the solvent. To get insights into the dynamics of protein-protein complexes, we carried out all-atom molecular dynamics simulations in explicit solvent on eight different protein-protein complexes of different functional class and interface size by taking into account the bound and unbound forms. On the one hand, we characterized structural changes upon binding of the proteins, and on the other hand we extensively analyzed the interfaces and the structural waters involved in the binding. Based on our analysis, in 6 cases out of 8, the interfaces rearranged during the simulation time, in stable and long-lived substates with alternative residue-residue contacts. These rearrangements are not restricted to side-chain fluctuations in the periphery but also affect the core interface. Finally, the analysis of the waters at the interface and involved in the binding pointed out the importance to take into account their role in the estimation of the interaction strength.

Common Structural Features of Hydrophobic α-Helical Hot Spots: Insights for the Design of Novel α-Helix Mimetics.

The binding conformations of α-helical hydrophobic hot spots are convergent into two spatial areas in protein-protein complex structures. The physical basis for convergence was disclosed, which allows the development of pharmacophore models for i/i + 4/i + 7 or i/i + 3/i + 4 α-helical hot spots. Further investigation revealed that this convergence of binding conformations is common among all hydrophobic hot spots regardless of their α-helical positions. This observation led to a streamlined generation of pharmacophore models for hydrophobic hot spots at any positions along the α-helix. These successfully evaluated pharmacophore models may be useful for designing novel α-helical hot spot mimetics.

Inclusion of Desolvation Energy into Protein-Protein Docking through Atomic Contact Potentials.

Protein-protein interactions are crucial in many biological processes. Therefore, determining the structure of a protein-protein complex is valuable for understanding its molecular mechanisms and developing drugs. Molecular docking is a powerful computational tool in the prediction of protein-protein complex structures, in which a scoring function with good performance is very important. In this study, we have proposed a hybrid scoring function of atomic contact-based desolvation energies and distance-dependent interatomic potentials for protein-protein interactions, named HITScorePP, where the atomic contact desolvation energies were derived using an iterative method and the distance-dependent potentials were directly taken from our ITScorePP scoring function. Integrating the hybrid scoring function into our fast Fourier transform (FFT) based HDOCK docking scheme, the updated docking program, named HDOCK2.0, significantly improved the docking performance on the 55 newly added complexes in the protein docking benchmark 5.0 and a data set of 19 antibacterial protein complexes. HDOCK2.0 was also compared with four other state-of-the-art docking programs including Rosetta, ZDOCK3.0.2, FRODOCK3.0, ATTRACT, and PatchDock and obtained the overall best performance in binding mode predictions. These results demonstrated the accuracy of our hybrid scoring function and the necessity of included desolvation effects in protein-protein docking.

Match_Motif: A rapid computational tool to assist in protein-protein interaction design.

In order to generate protein assemblies with a desired function, the rational design of protein–protein binding interfaces is of significant interest. Approaches based on random mutagenesis or directed evolution may involve complex experimental selection procedures. Also, molecular modeling approaches to design entirely new proteins and interactions with partner molecules can involve large computational efforts and screening steps. In order to simplify at least the initial effort for designing a putative binding interface between two proteins the Match_Motif approach has been developed. It employs the large collection of known protein–protein complex structures to suggest interface modifications that may lead to improved binding for a desired input interaction geometry. The approach extracts interaction motifs based on the backbone structure of short (four residues) segments and the relative arrangement with respect to short segments on the partner protein. The interaction geometry is used to search through a database of such motifs in known stable bound complexes. All matches are rapidly identified (within a few seconds) and collected and can be used to guide changes in the interface that may lead to improved binding. In the output, an alternative interface structure is also proposed based on the frequency of occurrence of side chains at a given interface position in all matches and based on sterical considerations. Applications of the procedure to known complex structures and alternative arrangements are presented and discussed. The program, data files, and example applications can be downloaded from https://www.groups.ph.tum.de/t38/downloads/.

PTMdyna: exploring the influence of post-translation modifications on protein conformational dynamics.

Protein post-translational modifications (PTM) play vital roles in cellular regulation, modulating functions by driving changes in protein structure and dynamics. Exploring comprehensively the influence of PTM on conformational dynamics can facilitate the understanding of the related biological function and molecular mechanism. Currently, a series of excellent computation tools have been designed to analyze the time-dependent structural properties of proteins. However, the protocol aimed to explore conformational dynamics of post-translational modified protein is still a blank. To fill this gap, we present PTMdyna to visually predict the conformational dynamics differences between unmodified and modified proteins, thus indicating the influence of specific PTM. PTMdyna exhibits an AUC of 0.884 tested on 220 protein-protein complex structures. The case of heterochromatin protein 1α complexed with lysine 9-methylated histone H3, which is critical for genomic stability and cell differentiation, was used to demonstrate its applicability. PTMdyna provides a reliable platform to predict the influence of PTM on protein dynamics, making it easier to interpret PTM functionality at the structure level. The web server is freely available at http://ccbportal.com/PTMdyna.

Structure and thermodynamics of transient protein-protein complexes by chemometric decomposition of SAXS datasets

Transient biomolecular interactions play crucial roles in many cellular signaling and regulation processes. However, deciphering the structure of these assemblies is challenging owing to the difficulties in isolating complexes from the individual partners. The additive nature of small-angle X-ray scattering (SAXS) data allows for probing the species present in these mixtures, but decomposition into structural and thermodynamic information is difficult. We present a chemometric approach enabling the decomposition of titration SAXS data into species-specific information. Using extensive synthetic SAXS data, we demonstrate that robust decomposition can be achieved for titrations with a maximum fraction of complex of 0.5 that can be extended to 0.3 when two orthogonal titrations are simultaneously analyzed. The effect of the structural features, titration points, relative concentrations, and noise are thoroughly analyzed. The validation of the strategy with experimental data highlights the power of the approach to provide unique insights into this family of biomolecular assemblies.

Efficient Refinement and Free Energy Scoring of Predicted Protein-Protein Complexes Using Replica Exchange with Repulsive Scaling.

Accurate prediction and evaluation of protein-protein complex structures are of major importance to understand the cellular interactome. Typically, putative complexes are predicted based on docking methods, and simple force field or knowledge-based scoring functions are applied to evaluate single complex structures. We have extended a replica-exchange-based scheme employing different levels of a repulsive biasing between partners in each replica simulation (RS-REMD) to simultaneously refine and score protein-protein complexes. The bias acts specifically on the intermolecular interactions based on an increase in effective pairwise van der Waals radii (repulsive scaling (RS)-REMD) without affecting interactions within each protein or with the solvent. The method provides a free energy score that correlates quite well with experimental binding free energies on a set of 36 complexes with correlation coefficients of 0.77 and 0.55 in explicit and implicit solvent simulations, respectively. For a large set of docked decoy complexes, significant improvement of docked complexes was found in many cases with the starting structure in the vicinity (within 20 Å) of the native complex. In the majority of cases (14 out of 20 in explicit solvent), near native docking solutions were identified as the best scoring complexes. The approach is computational demanding but may offer a route for refinement and realistic ranking of predicted protein-protein docking geometries.

Performance of human and server prediction in CAPRI rounds 38‐45

CAPRI challenges offer a variety of blind tests for protein-protein interaction prediction. In CAPRI Rounds 38-45, we generated a set of putative binding modes for each target with an FFT-based docking algorithm, and then scored and ranked these binding modes with a proprietary scoring function, ITScorePP. We have also developed a novel web server, Rebipp. The algorithm utilizes information retrieval to identify relevant biological information to significantly reduce the search space for a particular protein. In parallel, we have also constructed a GPU-based docking server, MDockPP, for protein-protein complex structure prediction. Here, the performance of our protocol in CAPRI rounds 38-45 is reported, which include 16 docking and scoring targets. Among them, three targets contain multiple interfaces: Targets 124, 125, and 136 have 2, 4, and 3 interfaces, respectively. In the predictor experiments, we predicted correct binding modes for nine targets, including one high-accuracy interface, six medium-accuracy binding modes, and six acceptable-accuracy binding modes. For the docking server prediction experiments, we predicted correct binding modes for eight targets, including one high-accuracy, three medium-accuracy, and five acceptable-accuracy binding modes.

How to choose templates for modeling of protein complexes: Insights from benchmarking template-based docking.

Comparative docking is based on experimentally determined structures of protein-protein complexes (templates), following the paradigm that proteins with similar sequences and/or structures form similar complexes. Modeling utilizing structure similarity of target monomers to template complexes significantly expands structural coverage of the interactome. Template-based docking by structure alignment can be performed for the entire structures or by aligning targets to the bound interfaces of the experimentally determined complexes. Systematic benchmarking of docking protocols based on full and interface structure alignment showed that both protocols perform similarly, with top 1 docking success rate 26%. However, in terms of the models' quality, the interface-based docking performed marginally better. The interface-based docking is preferable when one would suspect a significant conformational change in the full protein structure upon binding, for example, a rearrangement of the domains in multidomain proteins. Importantly, if the same structure is selected as the top template by both full and interface alignment, the docking success rate increases 2-fold for both top 1 and top 10 predictions. Matching structural annotations of the target and template proteins for template detection, as a computationally less expensive alternative to structural alignment, did not improve the docking performance. Sophisticated remote sequence homology detection added templates to the pool of those identified by structure-based alignment, suggesting that for practical docking, the combination of the structure alignment protocols and the remote sequence homology detection may be useful in order to avoid potential flaws in generation of the structural templates library.

Modeling of Protein Complexes and Molecular Assemblies with pyDock.

The study of the 3D structural details of protein interactions is essential to understand biomolecular functions at the molecular level. In this context, the limited availability of experimental structures of protein-protein complexes at atomic resolution is propelling the development of computational docking methods that aim to complement the current structural coverage of protein interactions. One of these docking approaches is pyDock, which uses van der Waals, electrostatics, and desolvation energy to score docking poses generated by a variety of sampling methods, typically FTDock or ZDOCK. The method has shown a consistently good prediction performance in community-wide assessment experiments like CAPRI or CASP, and has provided biological insights and insightful interpretation of experiments by modeling many biomolecular interactions of biomedical and biotechnological interest. Here, we describe in detail how to perform structural modeling of protein assemblies with pyDock, and the application of its modules to different biomolecular recognition phenomena, such as modeling of binding mode, interface, and hot-spot prediction, use of restraints based on experimental data, inclusion of low-resolution structural data, binding affinity estimation, or modeling of homo- and hetero-oligomeric assemblies.

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.

Coarse-grained and atomic resolution biomolecular docking with the ATTRACT approach.

The ATTRACT protein-protein docking program has been employed to predict protein-protein complex structures in CAPRI rounds 38-45. For 11 out of 16 targets acceptable or better quality solutions have been submitted (~70%). It includes also several cases of peptide-protein docking and the successful prediction of the geometry of carbohydrate-protein interactions. The option of combining rigid body minimization and simultaneous optimization in collective degrees of freedom based on elastic network modes was employed and systematically evaluated. Application to a large benchmark set indicates a modest improvement in docking performance compared to rigid docking. Possible further improvements of the docking approach in particular at the scoring and the flexible refinement steps are discussed.

EvERdock BAI: Machine-learning-guided selection of protein-protein complex structure.

Computational techniques for accurate and efficient prediction of protein-protein complex structures are widely used for elucidating protein-protein interactions, which play important roles in biological systems. Recently, it has been reported that selecting a structure similar to the native structure among generated structure candidates (decoys) is possible by calculating binding free energies of the decoys based on all-atom molecular dynamics (MD) simulations with explicit solvent and the solution theory in the energy representation, which is called evERdock. A recent version of evERdock achieves a higher-accuracy decoy selection by introducing MD relaxation and multiple MD simulations/energy calculations; however, huge computational cost is required. In this paper, we propose an efficient decoy selection method using evERdock and the best arm identification (BAI) framework, which is one of the techniques of reinforcement learning. The BAI framework realizes an efficient selection by suppressing calculations for nonpromising decoys and preferentially calculating for the promising ones. We evaluate the performance of the proposed method for decoy selection problems of three protein-protein complex systems. Their results show that computational costs are successfully reduced by a factor of 4.05 (in the best case) compared to a standard decoy selection approach without sacrificing accuracy.

Probing Specificity of Protein-Protein Interactions with Chiral Plasmonic Nanostructures.

Protein-protein interactions (PPIs) play a pivotal role in many biological processes. Discriminating functionally important well-defined protein-protein complexes formed by specific interactions from random aggregates produced by nonspecific interactions is therefore a critical capability. While there are many techniques which enable rapid screening of binding affinities in PPIs, there is no generic spectroscopic phenomenon which provides rapid characterization of the structure of protein-protein complexes. In this study we show that chiral plasmonic fields probe the structural order and hence the level of PPI specificity in a model antibody-antigen system. Using surface-immobilized Fab' fragments of polyclonal rabbit IgG antibodies with high specificity for bovine serum albumin (BSA), we show that chiral plasmonic fields can discriminate between a structurally anisotropic ensemble of BSA-Fab' complexes and random ovalbumin (OVA)-Fab' aggregates, demonstrating their potential as the basis of a useful proteomic technology for the initial rapid high-throughput screening of PPIs.

Interface residues of transient protein-protein complexes have extensive intra-protein interactions apart from inter-protein interactions

BackgroundProtein-protein interactions are crucial for normal biological processes and to regulate cellular reactions that affect gene expression and function. Several previous studies have emphasized the roles of residues at the interface of protein-protein complexes in conferring stability and specificity to the complex. Interface residues in a protein are well known for their interactions with sidechain and main chain atoms with the interacting protein. However, the extent of intra-protein interactions involving interface residues in a protein-protein complex and their relative contribution in comparison to inter-protein interactions are not clearly understood. This paper probes this feature using a dataset of protein-protein complexes of known 3-D structure.ResultsWe have analysed a dataset of 45 transient protein-protein complex structures with at least one of the interacting proteins with a known structure available also in the unbound form. We observe that a large proportion of interface residues (1608 out of 2137 interface residues, 75%) are involved in intra and inter-protein interactions simultaneously. The amino acid propensities of such interfacial residues involved in bifurcated interactions are found to be highly similar to the general propensities to occur at protein-protein interfaces. Finally, we observe that a majority (83%) of intra-protein interactions of interface residues with bifurcated interactions, are also observed in the protein uncomplexed form.ConclusionsWe have shown, to the best of our knowledge for the first time, that a vast majority of the protein-protein interface residues are involved in extensive intra-protein interactions apart from inter-protein interactions. For a majority of such interface residues the microenvironment in the tertiary structure is pre-formed and retained upon complex formation with its cognate partner during transient interactions.ReviewersThis article was reviewed by Arumay Pal and Mallur Madhusudhan.

Refining evERdock: Improved selection of good protein-protein complex models achieved by MD optimization and use of multiple conformations.

A method for evaluating binding free energy differences of protein-protein complex structures generated by protein docking was recently developed by some of us. The method, termed evERdock, combined short (2 ns) molecular dynamics (MD) simulations in explicit water and solution theory in the energy representation (ER) and succeeded in selecting the near-native complex structures from a set of decoys. In the current work, we performed longer (up to 100 ns) MD simulations before employing ER analysis in order to further refine the structures of the decoy set with improved binding free energies. Moreover, we estimated the binding free energies for each complex structure based on an average value from five individual MD snapshots. After MD simulations, all decoys exhibit a decrease in binding free energy, suggesting that proper equilibration in explicit solvent resulted in more favourably bound complexes. During the MD simulations, non-native structures tend to become unstable and in some cases dissociate, while near-native structures maintain a stable interface. The energies after the MD simulations show an improved correlation between similarity criteria (such as interface root-mean-square distance) to the native (crystal) structure and the binding free energy. In addition, calculated binding free energies show sensitivity to the number of contacts, which was demonstrated to reflect the relative stability of structures at earlier stages of the MD simulation. We therefore conclude that the additional equilibration step along with the use of multiple conformations can make the evERdock scheme more versatile under low computational cost.

Comparison of Full and Interface Structure Alignment in Template-Based Protein Docking

Template-based protein docking, based on sequence/structure similarity to co-crystalyzed complexes complements ab initio docking in predicting the structure of protein-protein complexes. When highly homologous templates for the entire target complex are not available, docking by interface or partial structure alignment (PSA) may provide an adequate solution. Earlier studies indicated that PSA docking can also be used to further refine docking models generated by the full structure alignment (FSA) of the target and the template. We present a thorough comparative analysis of FSA and PSA docking methodologies based on the docking predictions for bound and unbound proteins from the Dockground benchmark set 4 (http://dockground.compbio.ku.edu). Using the unbound structures in docking (i.e. accounting for conformational changes upon binding) decreases the docking performance only slightly for both methods, compared to the docking of the bound structures (by ∼0.3% in top 10 predictions). FSA selects better templates for proteins with different function, whereas PSA provides better templates for multi-domain proteins in which only one domain participates in the interaction (e.g., enzyme-inhibitor). Template structures that have matching CATH annotations with the target yield better docking performance (the success rate 65% and 67% in top 100, for FSA and PSA, respectively, as opposed to 44% and 46% without the CATH annotation). Significant improvement in the unbound docking success rate (from ∼30 to 60% in top 10) is observed when the same template is selected by both PSA and FSA. By adding templates identified by sophisticated sequence/evolutionary based methods, we were able to build near-native models for 90% of complexes in our target set.