A multidomain flexible docking approach to deal with large conformational changes in the modeling of biomolecular complexes

Integrative Modeling in the Age of Machine Learning: A Summary of HADDOCK Strategies in CAPRI Rounds 47-55

The HADDOCK team participated in CAPRI rounds 47-55 as server, manual predictor, and scorers. Throughout these CAPRI rounds, we used a plethora of computational strategies to predict the structure of protein complexes. Of the 10 targets comprising 24 interfaces, we achieved acceptable or better models for 3 targets in the human category and 1 in the server category. Our performance in the scoring challenge was slightly better, with our simple scoring protocol being the only one capable of identifying an acceptable model for Target 234. This result highlights the robustness of the simple, fully physics-based HADDOCK scoring function, especially when applied to highly flexible antibody-antigen complexes. Inspired by the significant advances in machine learning for structural biology and the dramatic improvement in our success rates after the public release of Alphafold2, we identify the integration of classical approaches like HADDOCK with AI-driven structure prediction methods as a key strategy for improving the accuracy of model generation and scoring.

Rational Prediction of PROTAC-Compatible Protein-Protein Interfaces by Molecular Docking

Proteolysis targeting chimeras (PROTACs) are heterobifunctional ligands that mediate the interaction between a protein target and an E3 ligase, resulting in a ternary complex, whose interaction with the ubiquitination machinery leads to target degradation. This technology is emerging as an exciting new avenue for therapeutic development, with several PROTACs currently undergoing clinical trials targeting cancer. Here, we describe a general and computationally efficient methodology combining restraint-based docking, energy-based rescoring, and a filter based on the minimal solvent-accessible surface distance to produce PROTAC-compatible PPIs suitable for when there is no a priori known PROTAC ligand. In a benchmark employing a manually curated data set of 13 ternary complex crystals, we achieved an accuracy of 92% when starting from bound structures and 77% when starting from unbound structures, respectively. Our method only requires that the ligand-bound structures of the monomeric forms of the E3 ligase and target proteins be given to run, making it general, accurate, and highly efficient, with the ability to impact early-stage PROTAC-based drug design campaigns where no structural information about the ternary complex structure is available.

Domain-Based Protein Docking with Extremely Large Conformational Changes

Proteins are key components in many processes in living cells, and physical interactions with other proteins and nucleic acids often form key parts of their functions. In many cases, large flexibility of proteins as they interact is key to their function. To understand the mechanisms of these processes, it is necessary to consider the 3D structures of such protein complexes. When such structures are not yet experimentally determined, protein docking has long been present to computationally generate useful structure models. However, protein docking has long had the limitation that the consideration of flexibility is usually limited to very small movements or very small structures. Methods have been developed which handle minor flexibility via normal mode or other structure sampling, but new methods are required to model ordered proteins which undergo large-scale conformational changes to elucidate their function at the molecular level. Here, we present Flex-LZerD, a framework for docking such complexes. Via partial assembly multidomain docking and an iterative normal mode analysis admitting curvilinear motions, we demonstrate the ability to model the assembly of a variety of protein-protein and protein-nucleic acid complexes.

Molecular characterization of a complex of apoptosis-inducing factor 1 with cytochrome c oxidase of the mitochondrial respiratory chain

Combining mass spectrometry-based chemical cross-linking and complexome profiling, we analyzed the interactome of heart mitochondria. We focused on complexes of oxidative phosphorylation and found that dimeric apoptosis-inducing factor 1 (AIFM1) forms a defined complex with ∼10% of monomeric cytochrome c oxidase (COX) but hardly interacts with respiratory chain supercomplexes. Multiple AIFM1 intercross-links engaging six different COX subunits provided structural restraints to build a detailed atomic model of the COX-AIFM12 complex (PDBDEV_00000092). An application of two complementary proteomic approaches thus provided unexpected insight into the macromolecular organization of the mitochondrial complexome. Our structural model excludes direct electron transfer between AIFM1 and COX. Notably, however, the binding site of cytochrome c remains accessible, allowing formation of a ternary complex. The discovery of the previously overlooked COX-AIFM12 complex and clues provided by the structural model hint at potential roles of AIFM1 in oxidative phosphorylation biogenesis and in programmed cell death.

Native or Non-Native Protein-Protein Docking Models? Molecular Dynamics to the Rescue

Molecular docking excels at creating a plethora of potential models of protein-protein complexes. To correctly distinguish the favorable, native-like models from the remaining ones remains, however, a challenge. We assessed here if a protocol based on molecular dynamics (MD) simulations would allow distinguishing native from non-native models to complement scoring functions used in docking. To this end, the first models for 25 protein-protein complexes were generated using HADDOCK. Next, MD simulations complemented with machine learning were used to discriminate between native and non-native complexes based on a combination of metrics reporting on the stability of the initial models. Native models showed higher stability in almost all measured properties, including the key ones used for scoring in the Critical Assessment of PRedicted Interaction (CAPRI) competition, namely the positional root mean square deviations and fraction of native contacts from the initial docked model. A random forest classifier was trained, reaching a 0.85 accuracy in correctly distinguishing native from non-native complexes. Reasonably modest simulation lengths of the order of 50-100 ns are sufficient to reach this accuracy, which makes this approach applicable in practice.

Computational Ways to Enhance Protein Inhibitor Design

Two new computational approaches are described to aid in the design of new peptide-based drugs by evaluating ensembles of protein structures from their dynamics and through the assessing of structures using empirical contact potential. These approaches build on the concept that conformational variability can aid in the binding process and, for disordered proteins, can even facilitate the binding of more diverse ligands. This latter consideration indicates that such a design process should be less restrictive so that multiple inhibitors might be effective. The example chosen here focuses on proteins/peptides that bind to hemagglutinin (HA) to block the large-scale conformational change for activation. Variability in the conformations is considered from sets of experimental structures, or as an alternative, from their simple computed dynamics; the set of designe peptides/small proteins from the David Baker lab designed to bind to hemagglutinin, is the large set considered and is assessed with the new empirical contact potentials.

Conformational Landscape of Cytochrome P450 Reductase Interactions

Oxidative reactions catalyzed by Cytochrome P450 enzymes (CYPs), which constitute the most relevant group of drug-metabolizing enzymes, are enabled by their redox partner Cytochrome P450 reductase (CPR). Both proteins are anchored to the membrane of the endoplasmic reticulum and the CPR undergoes a conformational change in order to interact with the respective CYP and transfer electrons. Here, we conducted over 22 microseconds of molecular dynamics (MD) simulations in combination with protein-protein docking to investigate the conformational changes necessary for the formation of the CPR-CYP complex. While some structural features of the CPR and the CPR-CYP2D6 complex that we highlighted confirmed previous observations, our simulations revealed additional mechanisms for the conformational transition of the CPR. Unbiased simulations exposed a movement of the whole protein relative to the  membrane, potentially to facilitate interactions with its diverse set of redox partners. Further, we present a structural mechanism for the susceptibility of the CPR to different redox states based on the flip of a glycine residue disrupting the local interaction network that maintains inter-domain proximity. Simulations of the CPR-CYP2D6 complex pointed toward an additional interaction surface of the FAD domain and the proximal side of CYP2D6. Altogether, this study provides novel structural insight into the mechanism of CPR-CYP interactions and underlying conformational changes, improving our understanding of this complex machinery Cytochrome P450 reductase; CPR; conformational; dynamicsrelevant for drug metabolism.

Selective cross-linking of coinciding protein assemblies by in-gel cross-linking mass spectrometry

Cross-linking mass spectrometry has developed into an important method to study protein structures and interactions. The in-solution cross-linking workflows involve time and sample consuming steps and do not provide sensible solutions for differentiating cross-links obtained from co-occurring protein oligomers, complexes, or conformers. Here we developed a cross-linking workflow combining blue native PAGE with in-gel cross-linking mass spectrometry (IGX-MS). This workflow circumvents steps, such as buffer exchange and cross-linker concentration optimization. Additionally, IGX-MS enables the parallel analysis of co-occurring protein complexes using only small amounts of sample. Another benefit of IGX-MS, demonstrated by experiments on GroEL and purified bovine heart mitochondria, is the substantial reduction of undesired over-length cross-links compared to in-solution cross-linking. We next used IGX-MS to investigate the complement components C5, C6, and their hetero-dimeric C5b6 complex. The obtained cross-links were used to generate a refined structural model of the complement component C6, resembling C6 in its inactivated state. This finding shows that IGX-MS can provide new insights into the initial stages of the terminal complement pathway.

Structural basis of DNA replication origin recognition by human Orc6 protein binding with DNA

The six-subunit origin recognition complex (ORC), a DNA replication initiator, defines the localization of the origins of replication in eukaryotes. The Orc6 subunit is the smallest and the least conserved among ORC subunits. It is required for DNA replication and essential for viability in all species. Orc6 in metazoans carries a structural homology with transcription factor TFIIB and can bind DNA on its own. Here, we report a solution structure of the full-length human Orc6 (HsOrc6) alone and in a complex with DNA. We further showed that human Orc6 is composed of three independent domains: N-terminal, middle and C-terminal (HsOrc6-N, HsOrc6-M and HsOrc6-C). We also identified a distinct DNA-binding domain of human Orc6, named as HsOrc6-DBD. The detailed analysis of the structure revealed novel amino acid clusters important for the interaction with DNA. Alterations of these amino acids abolish DNA-binding ability of Orc6 and result in reduced levels of DNA replication. We propose that Orc6 is a DNA-binding subunit of human/metazoan ORC and may play roles in targeting, positioning and assembling the functional ORC at the origins.

MAP Kinase-Mediated Activation of RSK1 and MK2 Substrate Kinases

Mitogen-activated protein kinases (MAPKs) control essential eukaryotic signaling pathways. While much has been learned about MAPK activation, much less is known about substrate recruitment and specificity. MAPK substrates may be other kinases that are crucial to promote a further diversification of the signaling outcomes. Here, we used a variety of molecular and cellular tools to investigate the recruitment of two substrate kinases, RSK1 and MK2, to three MAPKs (ERK2, p38α, and ERK5). Unexpectedly, we identified that kinase heterodimers form structurally and functionally distinct complexes depending on the activation state of the MAPK. These may be incompatible with downstream signaling, but naturally they may also form structures that are compatible with the phosphorylation of the downstream kinase at the activation loop, or alternatively at other allosteric sites. Furthermore, we show that small-molecule inhibitors may affect the quaternary arrangement of kinase heterodimers and thus influence downstream signaling in a specific manner.

Multivalent assembly of KRAS with the RAS-binding and cysteine-rich domains of CRAF on the membrane

Membrane anchoring of farnesylated KRAS is critical for activation of RAF kinases, yet our understanding of how these proteins interact on the membrane is limited to isolated domains. The RAS-binding domain (RBD) and cysteine-rich domain (CRD) of RAF engage KRAS and the plasma membrane, unleashing the kinase domain from autoinhibition. Due to experimental challenges, structural insight into this tripartite KRAS:RBD-CRD:membrane complex has relied on molecular dynamics simulations. Here, we report NMR studies of the KRAS:CRAF RBD-CRD complex. We found that the nucleotide-dependent KRAS-RBD interaction results in transient electrostatic interactions between KRAS and CRD, and we mapped the membrane interfaces of the CRD, RBD-CRD, and the KRAS:RBD-CRD complex. RBD-CRD exhibits dynamic interactions with the membrane through the canonical CRD lipid-binding site (CRD β7-8), as well as an alternative interface comprising β6 and the C terminus of CRD and β2 of RBD. Upon complex formation with KRAS, two distinct states were observed by NMR: State A was stabilized by membrane association of CRD β7-8 and KRAS α4-α5 while state B involved the C terminus of CRD, β3-5 of RBD, and part of KRAS α5. Notably, α4-α5, which has been proposed to mediate KRAS dimerization, is accessible only in state B. A cancer-associated mutation on the state B membrane interface of CRAF RBD (E125K) stabilized state B and enhanced kinase activity and cellular MAPK signaling. These studies revealed a dynamic picture of the assembly of the KRAS-CRAF complex via multivalent and dynamic interactions between KRAS, CRAF RBD-CRD, and the membrane.

A single amino acid switch converts the Sleeping Beauty transposase into an efficient unidirectional excisionase with utility in stem cell reprogramming

The Sleeping Beauty (SB) transposon is an advanced tool for genetic engineering and a useful model to investigate cut-and-paste DNA transposition in vertebrate cells. Here, we identify novel SB transposase mutants that display efficient and canonical excision but practically unmeasurable genomic re-integration. Based on phylogenetic analyses, we establish compensating amino acid replacements that fully rescue the integration defect of these mutants, suggesting epistasis between these amino acid residues. We further show that the transposons excised by the exc⁺/int⁻ transposase mutants form extrachromosomal circles that cannot undergo a further round of transposition, thereby representing dead-end products of the excision reaction. Finally, we demonstrate the utility of the exc⁺/int⁻ transposase in cassette removal for the generation of reprogramming factor-free induced pluripotent stem cells. Lack of genomic integration and formation of transposon circles following excision is reminiscent of signal sequence removal during V(D)J recombination, and implies that cut-and-paste DNA transposition can be converted to a unidirectional process by a single amino acid change.

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.

Pre- and post-docking sampling of conformational changes using ClustENM and HADDOCK for protein-protein and protein-DNA systems

Incorporating the dynamic nature of biomolecules in the modeling of their complexes is a challenge, especially when the extent and direction of the conformational changes taking place upon binding is unknown. Estimating whether the binding of a biomolecule to its partner(s) occurs in a conformational state accessible to its unbound form (“conformational selection”) and/or the binding process induces conformational changes (“induced‐fit”) is another challenge. We propose here a method combining conformational sampling using ClustENM—an elastic network‐based modeling procedure—with docking using HADDOCK, in a framework that incorporates conformational selection and induced‐fit effects upon binding. The extent of the applied deformation is estimated from its energetical costs, inspired from mechanical tensile testing on materials. We applied our pre‐ and post‐docking sampling of conformational changes to the flexible multidomain protein‐protein docking benchmark and a subset of the protein‐DNA docking benchmark. Our ClustENM‐HADDOCK approach produced acceptable to medium quality models in 7/11 and 5/6 cases for the protein‐protein and protein‐DNA complexes, respectively. The conformational selection (sampling prior to docking) has the highest impact on the quality of the docked models for the protein‐protein complexes. The induced‐fit stage of the pipeline (post‐sampling), however, improved the quality of the final models for the protein‐DNA complexes. Compared to previously described strategies to handle conformational changes, ClustENM‐HADDOCK performs better than two‐body docking in protein‐protein cases but worse than a flexible multidomain docking approach. However, it does show a better or similar performance compared to previous protein‐DNA docking approaches, which makes it a suitable alternative.

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.

The computational prediction of protein assemblies

The function of proteins in the cell is almost always mediated by their interaction with different partners, including other proteins, nucleic acids or small organic molecules. The ability of identifying all of them is an essential step in our quest for understanding life at the molecular level. The inference of the protein complex composition and of its molecular details can also provide relevant clues for the development and the design of drugs. In this short review, I will discuss the computational aspects of the analysis and prediction of protein-protein assemblies and discuss some of the most recent developments as seen in the last Critical Assessment of Techniques for Protein Structure Prediction (CASP) experiment.

Structural snapshots of Xer recombination reveal activation by synaptic complex remodeling and DNA bending

Bacterial Xer site-specific recombinases play an essential genome maintenance role by unlinking chromosome multimers, but their mechanism of action has remained structurally uncharacterized. Here, we present two high-resolution structures of Helicobacter pylori XerH with its recombination site DNA dif_H, representing pre-cleavage and post-cleavage synaptic intermediates in the recombination pathway. The structures reveal that activation of DNA strand cleavage and rejoining involves large conformational changes and DNA bending, suggesting how interaction with the cell division protein FtsK may license recombination at the septum. Together with biochemical and in vivo analysis, our structures also reveal how a small sequence asymmetry in dif_H defines protein conformation in the synaptic complex and orchestrates the order of DNA strand exchanges. Our results provide insights into the catalytic mechanism of Xer recombination and a model for regulation of recombination activity during cell division.

DOI:http://dx.doi.org/10.7554/eLife.19706.001

Similar to humans, bacteria store their genetic material in the form of DNA and arrange it into structures called chromosomes. In fact, most bacteria have a single circular chromosome. Bacteria multiply by simply dividing in two, and before that happens they must replicate their DNA so that each of the newly formed cells receives one copy of the chromosome.

Occasionally, mistakes during the DNA replication process can cause the two chromosomes to become tangled with each other; this prevents them from separating into the newly formed cells. For instance, the chromosomes can become physically connected like links in a chain, or merge into one long string. This kind of tangling can result in cell death, so bacteria encode enzymes called Xer recombinases that can untangle chromosomes. These enzymes separate the chromosomes by cutting and rejoining the DNA strands in a process known as Xer recombination.

Although Xer recombinases have been studied in quite some detail, many questions remain unanswered about how they work. How do Xer recombinases interact with DNA? How do they ensure they only work on tangled chromosomes? And how does a protein called FtsK ensure that Xer recombination takes place at the correct time and place?

Bebel et al. have now studied the Xer recombinase from a bacterium called Helicobacter pylori, which causes stomach ulcers, using a technique called X-ray crystallography. This enabled the three-dimensional structure of the Xer recombinase to be visualized as it interacted with DNA to form a Xer-DNA complex. Structures of the enzyme before and after it cut the DNA show that Xer-DNA complexes first assemble in an inactive state and are then activated by large conformational changes that make the DNA bend. Bebel et al. propose that the FtsK protein might trigger these changes and help to bend the DNA as it activates Xer recombination.

Further work showed that the structures could be used to model and understand Xer recombinases from other species of bacteria. The next step is to analyze how FtsK activates Xer recombinases and to see if this process is universal amongst bacteria. Understanding how this process can be interrupted could help to develop new drugs that can kill harmful bacteria.

DOI:http://dx.doi.org/10.7554/eLife.19706.002

ClustENM: ENM-Based Sampling of Essential Conformational Space at Full Atomic Resolution

Accurate sampling of conformational space and, in particular, the transitions between functional substates has been a challenge in molecular dynamic (MD) simulations of large biomolecular systems. We developed an Elastic Network Model (ENM)-based computational method, ClustENM, for sampling large conformational changes of biomolecules with various sizes and oligomerization states. ClustENM is an iterative method that combines ENM with energy minimization and clustering steps. It is an unbiased technique, which requires only an initial structure as input, and no information about the target conformation. To test the performance of ClustENM, we applied it to six biomolecular systems: adenylate kinase (AK), calmodulin, p38 MAP kinase, HIV-1 reverse transcriptase (RT), triosephosphate isomerase (TIM), and the 70S ribosomal complex. The generated ensembles of conformers determined at atomic resolution show good agreement with experimental data (979 structures resolved by X-ray and/or NMR) and encompass the subspaces covered in independent MD simulations for TIM, p38, and RT. ClustENM emerges as a computationally efficient tool for characterizing the conformational space of large systems at atomic detail, in addition to generating a representative ensemble of conformers that can be advantageously used in simulating substrate/ligand-binding events.

Challenges in structural approaches to cell modeling

Computational modeling is essential for structural characterization of biomolecular mechanisms across the broad spectrum of scales. Adequate understanding of biomolecular mechanisms inherently involves our ability to model them. Structural modeling of individual biomolecules and their interactions has been rapidly progressing. However, in terms of the broader picture, the focus is shifting toward larger systems, up to the level of a cell. Such modeling involves a more dynamic and realistic representation of the interactomes in vivo, in a crowded cellular environment, as well as membranes and membrane proteins, and other cellular components. Structural modeling of a cell complements computational approaches to cellular mechanisms based on differential equations, graph models, and other techniques to model biological networks, imaging data, etc. Structural modeling along with other computational and experimental approaches will provide a fundamental understanding of life at the molecular level and lead to important applications to biology and medicine. A cross section of diverse approaches presented in this review illustrates the developing shift from the structural modeling of individual molecules to that of cell biology. Studies in several related areas are covered: biological networks; automated construction of three-dimensional cell models using experimental data; modeling of protein complexes; prediction of non-specific and transient protein interactions; thermodynamic and kinetic effects of crowding; cellular membrane modeling; and modeling of chromosomes. The review presents an expert opinion on the current state-of-the-art in these various aspects of structural modeling in cellular biology, and the prospects of future developments in this emerging field.

Optimization of protein-protein docking for predicting Fc-protein interactions

The antibody crystallizable fragment (Fc) is recognized by effector proteins as part of the immune system. Pathogens produce proteins that bind Fc in order to subvert or evade the immune response. The structural characterization of the determinants of Fc-protein association is essential to improve our understanding of the immune system at the molecular level and to develop new therapeutic agents. Furthermore, Fc-binding peptides and proteins are frequently used to purify therapeutic antibodies. Although several structures of Fc-protein complexes are available, numerous others have not yet been determined. Protein-protein docking could be used to investigate Fc-protein complexes; however, improved approaches are necessary to efficiently model such cases. In this study, a docking-based structural bioinformatics approach is developed for predicting the structures of Fc-protein complexes. Based on the available set of X-ray structures of Fc-protein complexes, three regions of the Fc, loosely corresponding to three turns within the structure, were defined as containing the essential features for protein recognition and used as restraints to filter the initial docking search. Rescoring the filtered poses with an optimal scoring strategy provided a success rate of approximately 80% of the test cases examined within the top ranked 20 poses, compared to approximately 20% by the initial unrestrained docking. The developed docking protocol provides a significant improvement over the initial unrestrained docking and will be valuable for predicting the structures of currently undetermined Fc-protein complexes, as well as in the design of peptides and proteins that target Fc.

Ligand Docking to Intermediate and Close-To-Bound Conformers Generated by an Elastic Network Model Based Algorithm for Highly Flexible Proteins

Incorporating receptor flexibility in small ligand-protein docking still poses a challenge for proteins undergoing large conformational changes. In the absence of bound structures, sampling conformers that are accessible by apo state may facilitate docking and drug design studies. For this aim, we developed an unbiased conformational search algorithm, by integrating global modes from elastic network model, clustering and energy minimization with implicit solvation. Our dataset consists of five diverse proteins with apo to complex RMSDs 4.7-15 Å. Applying this iterative algorithm on apo structures, conformers close to the bound-state (RMSD 1.4-3.8 Å), as well as the intermediate states were generated. Dockings to a sequence of conformers consisting of a closed structure and its "parents" up to the apo were performed to compare binding poses on different states of the receptor. For two periplasmic binding proteins and biotin carboxylase that exhibit hinge-type closure of two dynamics domains, the best pose was obtained for the conformer closest to the bound structure (ligand RMSDs 1.5-2 Å). In contrast, the best pose for adenylate kinase corresponded to an intermediate state with partially closed LID domain and open NMP domain, in line with recent studies (ligand RMSD 2.9 Å). The docking of a helical peptide to calmodulin was the most challenging case due to the complexity of its 15 Å transition, for which a two-stage procedure was necessary. The technique was first applied on the extended calmodulin to generate intermediate conformers; then peptide docking and a second generation stage on the complex were performed, which in turn yielded a final peptide RMSD of 2.9 Å. Our algorithm is effective in producing conformational states based on the apo state. This study underlines the importance of such intermediate states for ligand docking to proteins undergoing large transitions.

Cryo-EM Data Are Superior to Contact and Interface Information in Integrative Modeling

Protein-protein interactions carry out a large variety of essential cellular processes. Cryo-electron microscopy (cryo-EM) is a powerful technique for the modeling of protein-protein interactions at a wide range of resolutions, and recent developments have caused a revolution in the field. At low resolution, cryo-EM maps can drive integrative modeling of the interaction, assembling existing structures into the map. Other experimental techniques can provide information on the interface or on the contacts between the monomers in the complex. This inevitably raises the question regarding which type of data is best suited to drive integrative modeling approaches. Systematic comparison of the prediction accuracy and specificity of the different integrative modeling paradigms is unavailable to date. Here, we compare EM-driven, interface-driven, and contact-driven integrative modeling paradigms. Models were generated for the protein docking benchmark using the ATTRACT docking engine and evaluated using the CAPRI two-star criterion. At 20 Å resolution, EM-driven modeling achieved a success rate of 100%, outperforming the other paradigms even with perfect interface and contact information. Therefore, even very low resolution cryo-EM data is superior in predicting heterodimeric and heterotrimeric protein assemblies. Our study demonstrates that a force field is not necessary, cryo-EM data alone is sufficient to accurately guide the monomers into place. The resulting rigid models successfully identify regions of conformational change, opening up perspectives for targeted flexible remodeling.

The solution structure of the kallikrein-related peptidases inhibitor SPINK6

Kallikrein-related peptidases (KLKs) are crucial for epidermal barrier function and are involved in the proteolytic regulation of the desquamation process. Elevated KLK levels were reported in atopic dermatitis. In skin, the proteolytic activity of KLKs is regulated by specific inhibitors of the serine protease inhibitor of Kazal-type (SPINK) family. SPINK6 was shown to be expressed in human stratum corneum and is able to inhibit several KLKs such as KLK4, -5, -12, -13 and -14. In order to understand the structural traits of the specific inhibition we solved the structure of SPINK6 in solution by NMR-spectroscopy and studied its interaction with KLKs. Thereby, beside the conserved binding mode, we identified an alternate binding mode which has so far not been observed for SPINK inhibitors.

Computational prediction of protein interfaces: A review of data driven methods

Reliably pinpointing which specific amino acid residues form the interface(s) between a protein and its binding partner(s) is critical for understanding the structural and physicochemical determinants of protein recognition and binding affinity, and has wide applications in modeling and validating protein interactions predicted by high-throughput methods, in engineering proteins, and in prioritizing drug targets. Here, we review the basic concepts, principles and recent advances in computational approaches to the analysis and prediction of protein-protein interfaces. We point out caveats for objectively evaluating interface predictors, and discuss various applications of data-driven interface predictors for improving energy model-driven protein-protein docking. Finally, we stress the importance of exploiting binding partner information in reliably predicting interfaces and highlight recent advances in this emerging direction.

High-resolution protein-protein docking by global optimization: recent advances and future challenges

A computational protein-protein docking method that predicts atomic details of protein-protein interactions from protein monomer structures is an invaluable tool for understanding the molecular mechanisms of protein interactions and for designing molecules that control such interactions. Compared to low-resolution docking, high-resolution docking explores the conformational space in atomic resolution to provide predictions with atomic details. This allows for applications to more challenging docking problems that involve conformational changes induced by binding. Recently, high-resolution methods have become more promising as additional information such as global shapes or residue contacts are now available from experiments or sequence/structure data. In this review article, we highlight developments in high-resolution docking made during the last decade, specifically regarding global optimization methods employed by the docking methods. We also discuss two major challenges in high-resolution docking: prediction of backbone flexibility and water-mediated interactions.

Unraveling the molecular architecture of a G protein-coupled receptor/β-arrestin/Erk module complex

β-arrestins serve as signaling scaffolds downstream of G protein-coupled receptors, and thus play a crucial role in a plethora of cellular processes. Although it is largely accepted that the ability of β-arrestins to interact simultaneously with many protein partners is key in G protein-independent signaling of GPCRs, only the precise knowledge of these multimeric arrangements will allow a full understanding of the dynamics of these interactions and their functional consequences. However, current experimental procedures for the determination of the three-dimensional structures of protein-protein complexes are not well adapted to analyze these short-lived, multi-component assemblies. We propose a model of the receptor/β-arrestin/Erk1 signaling module, which is consistent with most of the available experimental data. Moreover, for the β-arrestin/Raf1 and the β-arrestin/ERK interactions, we have used the model to design interfering peptides and shown that they compete with both partners, hereby demonstrating the validity of the predicted interaction regions.

Multidomain Assembler (MDA) Generates Models of Large Multidomain Proteins

Homology modeling predicts protein structures using known structures of related proteins as templates. We developed MULTIDOMAIN ASSEMBLER (MDA) to address the special problems that arise when modeling proteins with large numbers of domains, such as fibronectin with 30 domains, as well as cases with hundreds of templates. These problems include how to spatially arrange nonoverlapping template structures, and how to get the best template coverage when some sequence regions have hundreds of available structures while other regions have a few distant homologs. MDA automates the tasks of template searching, visualization, and selection followed by multidomain model generation, and is part of the widely used molecular graphics package UCSF CHIMERA (University of California, San Francisco). We demonstrate applications and discuss MDA's benefits and limitations.

The importance of dynamics in integrative modeling of supramolecular assemblies

Revealing the atomistic architecture of supramolecular complexes is a fundamental step toward a deeper understanding of cellular functioning. To date, this formidable task is facilitated by an emerging array of integrative modeling approaches that combine experimental data from different sources. One major challenge these methods have to face is the treatment of the dynamic rearrangements of the individual subunits upon assembly. While this flexibility can be sampled at different levels, integrating native dynamic determinants with available experimental inputs can provide an effective way to reveal the molecular recognition mechanisms at the basis of supramolecular assembly.

The interaction of RNA helicase DDX3 with HIV-1 Rev-CRM1-RanGTP complex during the HIV replication cycle

Molecular traffic between the nucleus and the cytoplasm is regulated by the nuclear pore complex (NPC), which acts as a highly selective channel perforating the nuclear envelope in eukaryotic cells. The human immunodeficiency virus (HIV) exploits the nucleocytoplasmic pathway to export its RNA transcripts across the NPC to the cytoplasm. Despite extensive study on the HIV life cycle and the many drugs developed to target this cycle, no current drugs have been successful in targeting the critical process of viral nuclear export, even though HIV's reliance on a single host protein, CRM1, to export its unspliced and partially spliced RNA transcripts makes it a tempting target. Due to recent findings implicating a DEAD-box helicase, DDX3, in HIV replication and a member of the export complex, it has become an appealing target for anti-HIV drug inhibition. In the present research, we have applied a hybrid computational protocol to analyze protein-protein interactions in the HIV mRNA export cycle. This method is based on molecular docking followed by molecular dynamics simulation and accompanied by approximate free energy calculation (MM/GBSA), computational alanine scanning, clustering, and evolutionary analysis. We highlight here some of the most likely binding modes and interfacial residues between DDX3 and CRM1 both in the absence and presence of RanGTP. This work shows that although DDX3 can bind to free CRM1, addition of RanGTP leads to more concentrated distribution of binding modes and stronger binding between CRM1 and RanGTP.

Information-driven structural modelling of protein-protein interactions

Protein-protein docking aims at predicting the three-dimensional structure of a protein complex starting from the free forms of the individual partners. As assessed in the CAPRI community-wide experiment, the most successful docking algorithms combine pure laws of physics with information derived from various experimental or bioinformatics sources. Of these so-called "information-driven" approaches, HADDOCK stands out as one of the most successful representatives. In this chapter, we briefly summarize which experimental information can be used to drive the docking prediction in HADDOCK, and then focus on the docking protocol itself. We discuss and illustrate with a tutorial example a "classical" protein-protein docking prediction, as well as more recent developments for modelling multi-body systems and large conformational changes.

MEGADOCK 4.0: an ultra-high-performance protein-protein docking software for heterogeneous supercomputers

Summary: The application of protein–protein docking in large-scale interactome analysis is a major challenge in structural bioinformatics and requires huge computing resources. In this work, we present MEGADOCK 4.0, an FFT-based docking software that makes extensive use of recent heterogeneous supercomputers and shows powerful, scalable performance of >97% strong scaling.

Availability and Implementation: MEGADOCK 4.0 is written in C++ with OpenMPI and NVIDIA CUDA 5.0 (or later) and is freely available to all academic and non-profit users at: http://www.bi.cs.titech.ac.jp/megadock.

Contact:akiyama@cs.titech.ac.jp

Supplementary information:Supplementary data are available at Bioinformatics online

An overview of recent advances in structural bioinformatics of protein-protein interactions and a guide to their principles

Rich data bearing on the structural and evolutionary principles of protein-protein interactions are paving the way to a better understanding of the regulation of function in the cell. This is particularly the case when these interactions are considered in the framework of key pathways. Knowledge of the interactions may provide insights into the mechanisms of crucial 'driver' mutations in oncogenesis. They also provide the foundation toward the design of protein-protein interfaces and inhibitors that can abrogate their formation or enhance them. The main features to learn from known 3-D structures of protein-protein complexes and the extensive literature which analyzes them computationally and experimentally include the interaction details which permit undertaking structure-based drug discovery, the evolution of complexes and their interactions, the consequences of alterations such as post-translational modifications, ligand binding, disease causing mutations, host pathogen interactions, oligomerization, aggregation and the roles of disorder, dynamics, allostery and more to the protein and the cell. This review highlights some of the recent advances in these areas, including design, inhibition and prediction of protein-protein complexes. The field is broad, and much work has been carried out in these areas, making it challenging to cover it in its entirety. Much of this is due to the fast increase in the number of molecules whose structures have been determined experimentally and the vast increase in computational power. Here we provide a concise overview.

Information-driven modeling of large macromolecular assemblies using NMR data

Availability of high-resolution atomic structures is one of the prerequisites for a mechanistic understanding of biomolecular function. This atomic information can, however, be difficult to acquire for interesting systems such as high molecular weight and multi-subunit complexes. For these, low-resolution and/or sparse data from a variety of sources including NMR are often available to define the interaction between the subunits. To make best use of all the available information and shed light on these challenging systems, integrative computational tools are required that can judiciously combine and accurately translate the sparse experimental data into structural information. In this Perspective we discuss NMR techniques and data sources available for the modeling of large and multi-subunit complexes. Recent developments are illustrated by particularly challenging application examples taken from the literature. Within this context, we also position our data-driven docking approach, HADDOCK, which can integrate a variety of information sources to drive the modeling of biomolecular complexes. It is the synergy between experimentation and computational modeling that will provides us with detailed views on the machinery of life and lead to a mechanistic understanding of biomolecular function.

The scoring of poses in protein-protein docking: current capabilities and future directions

BACKGROUND

Protein-protein docking, which aims to predict the structure of a protein-protein complex from its unbound components, remains an unresolved challenge in structural bioinformatics. An important step is the ranking of docked poses using a scoring function, for which many methods have been developed. There is a need to explore the differences and commonalities of these methods with each other, as well as with functions developed in the fields of molecular dynamics and homology modelling.

RESULTS

We present an evaluation of 115 scoring functions on an unbound docking decoy benchmark covering 118 complexes for which a near-native solution can be found, yielding top 10 success rates of up to 58%. Hierarchical clustering is performed, so as to group together functions which identify near-natives in similar subsets of complexes. Three set theoretic approaches are used to identify pairs of scoring functions capable of correctly scoring different complexes. This shows that functions in different clusters capture different aspects of binding and are likely to work together synergistically.

CONCLUSIONS

All functions designed specifically for docking perform well, indicating that functions are transferable between sampling methods. We also identify promising methods from the field of homology modelling. Further, differential success rates by docking difficulty and solution quality suggest a need for flexibility-dependent scoring. Investigating pairs of scoring functions, the set theoretic measures identify known scoring strategies as well as a number of novel approaches, indicating promising augmentations of traditional scoring methods. Such augmentation and parameter combination strategies are discussed in the context of the learning-to-rank paradigm.

Molecular origins of binding affinity: seeking the Archimedean point

Connecting three dimensional structure and affinity is analogous to seeking the 'Archimedean point', a vantage point from where any observer can quantitatively perceive the subject of inquiry. Here we review current knowledge and challenges that lie ahead of us in the quest for this Archimedean point. We argue that current models are limited in reproducing measured data because molecular description of binding affinity must expand beyond the interfacial contribution and also incorporate effects stemming from conformational changes/dynamics and long-range interactions. Fortunately, explicit modeling of various kinetic schemes underlying biomolecular recognition and confined systems that reflect in vivo interactions are coming within reach. This quest will hopefully lead to an accurate biophysical interpretation of binding affinity that would allow unprecedented understanding of the molecular basis of life through unraveling the why's of interaction networks.

Nucleosomal DNA binding drives the recognition of H3K36-methylated nucleosomes by the PSIP1-PWWP domain

Background

Recognition of histone modifications by specialized protein domains is a key step in the regulation of DNA-mediated processes like gene transcription. The structural basis of these interactions is usually studied using histone peptide models, neglecting the nucleosomal context. Here, we provide the structural and thermodynamic basis for the recognition of H3K36-methylated (H3K36me) nucleosomes by the PSIP1-PWWP domain, based on extensive mutational analysis, advanced nuclear magnetic resonance (NMR), and computational approaches.

Results

The PSIP1-PWWP domain binds H3K36me3 peptide and DNA with low affinity, through distinct, adjacent binding surfaces. PWWP binding to H3K36me nucleosomes is enhanced approximately 10,000-fold compared to a methylated peptide. Based on mutational analyses and NMR data, we derive a structure of the complex showing that the PWWP domain is bound to H3K36me nucleosomes through simultaneous interactions with both methylated histone tail and nucleosomal DNA.

Conclusion

Concerted binding to the methylated histone tail and nucleosomal DNA underlies the high- affinity, specific recognition of H3K36me nucleosomes by the PSIP1-PWWP domain. We propose that this bipartite binding mechanism is a distinctive and general property in the recognition of histone modifications close to the nucleosome core.

The proteomics and interactomics of human erythrocytes

In this minireview, we focus on advances in our knowledge of the human erythrocyte proteome and interactome that have occurred since our seminal review on the topic published in 2007. As will be explained, the number of unique proteins has grown from 751 in 2007 to 2289 as of today. We describe how proteomics and interactomics tools have been used to probe critical protein changes in disorders impacting the blood. The primary example used is the work done on sickle cell disease where biomarkers of severity have been identified, protein changes in the erythrocyte membranes identified, pharmacoproteomic impact of hydroxyurea studied and interactomics used to identify erythrocyte protein changes that are predicted to have the greatest impact on protein interaction networks.

A unified conformational selection and induced fit approach to protein-peptide docking

Protein-peptide interactions are vital for the cell. They mediate, inhibit or serve as structural components in nearly 40% of all macromolecular interactions, and are often associated with diseases, making them interesting leads for protein drug design. In recent years, large-scale technologies have enabled exhaustive studies on the peptide recognition preferences for a number of peptide-binding domain families. Yet, the paucity of data regarding their molecular binding mechanisms together with their inherent flexibility makes the structural prediction of protein-peptide interactions very challenging. This leaves flexible docking as one of the few amenable computational techniques to model these complexes. We present here an ensemble, flexible protein-peptide docking protocol that combines conformational selection and induced fit mechanisms. Starting from an ensemble of three peptide conformations (extended, a-helix, polyproline-II), flexible docking with HADDOCK generates 79.4% of high quality models for bound/unbound and 69.4% for unbound/unbound docking when tested against the largest protein-peptide complexes benchmark dataset available to date. Conformational selection at the rigid-body docking stage successfully recovers the most relevant conformation for a given protein-peptide complex and the subsequent flexible refinement further improves the interface by up to 4.5 Å interface RMSD. Cluster-based scoring of the models results in a selection of near-native solutions in the top three for ∼75% of the successfully predicted cases. This unified conformational selection and induced fit approach to protein-peptide docking should open the route to the modeling of challenging systems such as disorder-order transitions taking place upon binding, significantly expanding the applicability limit of biomolecular interaction modeling by docking.

Low-resolution structural modeling of protein interactome

Structural characterization of protein-protein interactions across the broad spectrum of scales is key to our understanding of life at the molecular level. Low-resolution approach to protein interactions is needed for modeling large interaction networks, given the significant level of uncertainties in large biomolecular systems and the high-throughput nature of the task. Since only a fraction of protein structures in interactome are determined experimentally, protein docking approaches are increasingly focusing on modeled proteins. Current rapid advancement of template-based modeling of protein-protein complexes is following a long standing trend in structure prediction of individual proteins. Protein-protein templates are already available for almost all interactions of structurally characterized proteins, and about one third of such templates are likely correct.

Cell-Dock: high-performance protein-protein docking

SUMMARY

The application of docking to large-scale experiments or the explicit treatment of protein flexibility are part of the new challenges in structural bioinformatics that will require large computer resources and more efficient algorithms. Highly optimized fast Fourier transform (FFT) approaches are broadly used in docking programs but their optimal code implementation leaves hardware acceleration as the only option to significantly reduce the computational cost of these tools. In this work we present Cell-Dock, an FFT-based docking algorithm adapted to the Cell BE processor. We show that Cell-Dock runs faster than FTDock with maximum speedups of above 200×, while achieving results of similar quality.

AVAILABILITY AND IMPLEMENTATION

The source code is released under GNU General Public License version 2 and can be downloaded from http://mmb.pcb.ub.es/~cpons/Cell-Dock.

CONTACT

djimenez@ac.upc.edu or juanf@bsc.es

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Flexible protein docking refinement using pose-dependent normal mode analysis

Modeling conformational changes in protein docking calculations is challenging. To make the calculations tractable, most current docking algorithms typically treat proteins as rigid bodies and use soft scoring functions that implicitly accommodate some degree of flexibility. Alternatively, ensembles of structures generated from molecular dynamics (MD) may be cross-docked. However, such combinatorial approaches can produce many thousands or even millions of docking poses, and require fast and sensitive scoring functions to distinguish them. Here, we present a novel approach called "EigenHex," which is based on normal mode analyses (NMAs) of a simple elastic network model of protein flexibility. We initially assume that the proteins to be docked are rigid, and we begin by performing conventional soft docking using the Hex polar Fourier correlation algorithm. We then apply a pose-dependent NMA to each of the top 1000 rigid body docking solutions, and we sample and re-score multiple perturbed docking conformations generated from linear combinations of up to 20 eigenvectors using a multi-threaded particle swarm optimization algorithm. When applied to the 63 "rigid body" targets of the Protein Docking Benchmark version 2.0, our results show that sampling and re-scoring from just one to three eigenvectors gives a modest but consistent improvement for these targets. Thus, pose-dependent NMA avoids the need to sample multiple eigenvectors and it offers a promising alternative to combinatorial cross-docking.

Accelerating protein docking in ZDOCK using an advanced 3D convolution library

Computational prediction of the 3D structures of molecular interactions is a challenging area, often requiring significant computational resources to produce structural predictions with atomic-level accuracy. This can be particularly burdensome when modeling large sets of interactions, macromolecular assemblies, or interactions between flexible proteins. We previously developed a protein docking program, ZDOCK, which uses a fast Fourier transform to perform a 3D search of the spatial degrees of freedom between two molecules. By utilizing a pairwise statistical potential in the ZDOCK scoring function, there were notable gains in docking accuracy over previous versions, but this improvement in accuracy came at a substantial computational cost. In this study, we incorporated a recently developed 3D convolution library into ZDOCK, and additionally modified ZDOCK to dynamically orient the input proteins for more efficient convolution. These modifications resulted in an average of over 8.5-fold improvement in running time when tested on 176 cases in a newly released protein docking benchmark, as well as substantially less memory usage, with no loss in docking accuracy. We also applied these improvements to a previous version of ZDOCK that uses a simpler non-pairwise atomic potential, yielding an average speed improvement of over 5-fold on the docking benchmark, while maintaining predictive success. This permits the utilization of ZDOCK for more intensive tasks such as docking flexible molecules and modeling of interactomes, and can be run more readily by those with limited computational resources.