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

Docking Foundations: From Rigid to Flexible Docking

Despite the development of methods for the experimental determination of protein structures, the dissonance between the number of known sequences and their solved structures is still enormous. This is particularly evident in protein-protein complexes. To fill this gap, diverse technologies have been developed to study protein-protein interactions (PPIs) in a cellular context including a range of biological and computational methods. The latter derive from techniques originally published and applied almost half a century ago and are based on interdisciplinary knowledge from the nexus of the fields of biology, chemistry, and physics about protein sequences, structures, and their folding. Protein-protein docking, the main protagonist of this chapter, is routinely treated as an integral part of protein research. Herein, we describe the basic foundations of the whole process in general terms, but step by step from protein representations through docking methods and evaluation of complexes to their final validation.

Multi-domain and complex protein structure prediction using inter-domain interactions from deep learning

Accurately capturing domain-domain interactions is key to understanding protein function and designing structure-based drugs. Although AlphaFold2 has made a breakthrough on single domain, it should be noted that the structure modeling for multi-domain protein and complex remains a challenge. In this study, we developed a multi-domain and complex structure assembly protocol, named DeepAssembly, based on domain segmentation and single domain modeling algorithms. Firstly, DeepAssembly uses a population-based evolutionary algorithm to assemble multi-domain proteins by inter-domain interactions inferred from a developed deep learning network. Secondly, protein complexes are assembled by means of domains rather than chains using DeepAssembly. Experimental results show that on 219 multi-domain proteins, the average inter-domain distance precision by DeepAssembly is 22.7% higher than that of AlphaFold2. Moreover, DeepAssembly improves accuracy by 13.1% for 164 multi-domain structures with low confidence deposited in AlphaFold database. We apply DeepAssembly for the prediction of 247 heterodimers. We find that DeepAssembly successfully predicts the interface (DockQ ≥ 0.23) for 32.4% of the dimers, suggesting a lighter way to assemble complex structures by treating domains as assembly units and using inter-domain interactions learned from monomer structures.

Modeling protein-nucleic acid complexes with extremely large conformational changes using Flex-LZerD

Proteins and nucleic acids are key components in many processes in living cells, and interactions between proteins and nucleic acids are often crucial pathway components. In many cases, large flexibility of proteins as they interact with nucleic acids is key to their function. To understand the mechanisms of these processes, it is necessary to consider the 3D atomic structures of such protein-nucleic acid complexes. When such structures are not yet experimentally determined, protein docking can be used to computationally generate useful structure models. However, such docking has long had the limitation that the consideration of flexibility is usually limited to small movements or to small structures. We previously developed a method of flexible protein docking which could model ordered proteins which undergo large-scale conformational changes, which we also showed was compatible with nucleic acids. Here, we elaborate on the ability of that pipeline, Flex-LZerD, to model specifically interactions between proteins and nucleic acids, and demonstrate that Flex-LZerD can model more interactions and types of conformational change than previously shown.

Structural predictions of protein-DNA binding: MELD-DNA

Structural, regulatory and enzymatic proteins interact with DNA to maintain a healthy and functional genome. Yet, our structural understanding of how proteins interact with DNA is limited. We present MELD-DNA, a novel computational approach to predict the structures of protein-DNA complexes. The method combines molecular dynamics simulations with general knowledge or experimental information through Bayesian inference. The physical model is sensitive to sequence-dependent properties and conformational changes required for binding, while information accelerates sampling of bound conformations. MELD-DNA can: (i) sample multiple binding modes; (ii) identify the preferred binding mode from the ensembles; and (iii) provide qualitative binding preferences between DNA sequences. We first assess performance on a dataset of 15 protein-DNA complexes and compare it with state-of-the-art methodologies. Furthermore, for three selected complexes, we show sequence dependence effects of binding in MELD predictions. We expect that the results presented herein, together with the freely available software, will impact structural biology (by complementing DNA structural databases) and molecular recognition (by bringing new insights into aspects governing protein-DNA interactions).

Identification of Relevant Protein Interactions with Partial Knowledge: A Complex Network and Deep Learning Approach

Protein-protein interactions (PPIs) are the basis for understanding most cellular events in biological systems. Several experimental methods, e.g., biochemical, molecular, and genetic methods, have been used to identify protein-protein associations. However, some of them, such as mass spectrometry, are time-consuming and expensive. Machine learning (ML) techniques have been widely used to characterize PPIs, increasing the number of proteins analyzed simultaneously and optimizing time and resources for identifying and predicting protein-protein functional linkages. Previous ML approaches have focused on well-known networks or specific targets but not on identifying relevant proteins with partial or null knowledge of the interaction networks. The proposed approach aims to generate a relevant protein sequence based on bidirectional Long-Short Term Memory (LSTM) with partial knowledge of interactions. The general framework comprises conducting a scale-free and fractal complex network analysis. The outcome of these analyses is then used to fine-tune the fractal method for the vital protein extraction of PPI networks. The results show that several PPI networks are self-similar or fractal, but that both features cannot coexist. The generated protein sequences (by the bidirectional LSTM) also contain an average of 39.5% of proteins in the original sequence. The average length of the generated sequences was 17% of the original one. Finally, 95% of the generated sequences were true.

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.

Structural aspects of SARS-CoV-2 mutations: Implications to plausible infectivity with ACE-2 using computational modeling approach

Some of the SARS-CoV-2 variants are said to be more infectious than the previous others and are causing panic around the globe. Cases related to Delta plus (δ+) and omicron (ο) variants are on the rise worldwide. This sudden surge warrants an investigation into the reasons for its binding with ACE-2. The present study attempts to find out the structural basis of binding interactions of SARS-CoV-2 mutants based on computational modeling and comparative analysis. In silico strategies including protein-protein docking, mutation analysis, molecular dynamics, and binding energy calculations were used to study the binding of the 'receptor binding domain' (RBD) of the seven 'variants of concern' which include Alpha (α), Beta (β), Gamma (γ), Kappa (κ), Delta (δ), Delta plus (δ+) and omicron (ο) with ACE-2 (human angiotensin-converting enzyme-2) and with antibodies. Among all the variants dealt with in this study, Delta plus and omicron were found to be binding more strongly to ACE-2 than others due to inherent mutations and the consequent change in the hydrophilic and hydrophobic environment of the binding site. Furthermore, molecular dynamic (MD) simulations and subsequent MM/PBSA calculations provided useful structural insights into key residues participating in the interaction. Infectivity of a virus could be dependent on the interplay of evading antibodies and simultaneously attaching strongly with the host receptor. A cross-correlation between mutant spike proteins' binding with ACE-2 and antibodies provides a holistic assessment of the binding nature of these mutants vis-à-vis native virus and offers opportunities for designing potential therapeutics against these new mutants.

Modeling the Dynamics of Protein–Protein Interfaces, How and Why?

Protein–protein assemblies act as a key component in numerous cellular processes. Their accurate modeling at the atomic level remains a challenge for structural biology. To address this challenge, several docking and a handful of deep learning methodologies focus on modeling protein–protein interfaces. Although the outcome of these methods has been assessed using static reference structures, more and more data point to the fact that the interaction stability and specificity is encoded in the dynamics of these interfaces. Therefore, this dynamics information must be taken into account when modeling and assessing protein interactions at the atomistic scale. Expanding on this, our review initially focuses on the recent computational strategies aiming at investigating protein–protein interfaces in a dynamic fashion using enhanced sampling, multi-scale modeling, and experimental data integration. Then, we discuss how interface dynamics report on the function of protein assemblies in globular complexes, in fuzzy complexes containing intrinsically disordered proteins, as well as in active complexes, where chemical reactions take place across the protein–protein interface.

Improved prediction of protein-protein interactions using AlphaFold2

Predicting the structure of interacting protein chains is a fundamental step towards understanding protein function. Unfortunately, no computational method can produce accurate structures of protein complexes. AlphaFold2, has shown unprecedented levels of accuracy in modelling single chain protein structures. Here, we apply AlphaFold2 for the prediction of heterodimeric protein complexes. We find that the AlphaFold2 protocol together with optimised multiple sequence alignments, generate models with acceptable quality (DockQ ≥ 0.23) for 63% of the dimers. From the predicted interfaces we create a simple function to predict the DockQ score which distinguishes acceptable from incorrect models as well as interacting from non-interacting proteins with state-of-art accuracy. We find that, using the predicted DockQ scores, we can identify 51% of all interacting pairs at 1% FPR.

Predicting the structure of protein complexes is extremely difficult. Here, authors apply AlphaFold2 with optimized multiple sequence alignments to model complexes of interacting proteins, enabling prediction of both if and how proteins interact with state-of-art accuracy.

Searching for Low Probability Opening Events in a DNA Sliding Clamp

The β subunit of E. coli DNA polymererase III is a DNA sliding clamp associated with increasing the processivity of DNA synthesis. In its free form, it is a circular homodimer structure that can accomodate double-stranded DNA in a nonspecific manner. An open state of the clamp must be accessible before loading the DNA. The opening mechanism is still a matter of debate, as is the effect of bound DNA on opening/closing kinetics. We use a combination of atomistic, coarse-grained, and enhanced sampling strategies in both explicit and implicit solvents to identify opening events in the sliding clamp. Such simulations of large nucleic acid and their complexes are becoming available and are being driven by improvements in force fields and the creation of faster computers. Different models support alternative opening mechanisms, either through an in-plane or out-of-plane opening event. We further note some of the current limitations, despite advances, in modeling these highly charged systems with implicit solvent.

Sampling of Protein Conformational Space Using Hybrid Simulations: A Critical Assessment of Recent Methods

Recent years have seen several hybrid simulation methods for exploring the conformational space of proteins and their complexes or assemblies. These methods often combine fast analytical approaches with computationally expensive full atomic molecular dynamics (MD) simulations with the goal of rapidly sampling large and cooperative conformational changes at full atomic resolution. We present here a systematic comparison of the utility and limits of four such hybrid methods that have been introduced in recent years: MD with excited normal modes (MDeNM), collective modes-driven MD (CoMD), and elastic network model (ENM)-based generation, clustering, and relaxation of conformations (ClustENM) as well as its updated version integrated with MD simulations (ClustENMD). We analyzed the predicted conformational spaces using each of these four hybrid methods, applied to four well-studied proteins, triosephosphate isomerase (TIM), 3-phosphoglycerate kinase (PGK), HIV-1 protease (PR) and HIV-1 reverse transcriptase (RT), which provide extensive ensembles of experimental structures for benchmarking and comparing the methods. We show that a rigorous multi-faceted comparison and multiple metrics are necessary to properly assess the differences between conformational ensembles and provide an optimal protocol for achieving good agreement with experimental data. While all four hybrid methods perform well in general, being especially useful as computationally efficient methods that retain atomic resolution, the systematic analysis of the same systems by these four hybrid methods highlights the strengths and limitations of the methods and provides guidance for parameters and protocols to be adopted in future studies.

Conformational selection of vasopressin upon V1a receptor binding

The neuropeptide vasopressin (VP) and its three G protein-coupled receptors (V_1aR, V_1bR and V₂R) are of high interest in a wide array of drug discovery programs. V_1aR is of particular importance due to its cardiovascular functions and diverse roles in the central nervous system. The structure–activity relationships underpinning ligand-receptor interactions remain however largely unclear, hindering rational drug design. This is not least due to the high structural flexibility of VP in its free as well as receptor-bound states. In this work, we developed a novel approach to reveal features of conformational selectivity upon VP-V_1aR complex formation. We employed virtual screening strategies to probe VP’s conformational space for transiently adopted structures that favor binding to V_1aR. To this end, we dissected the VP conformational space into three sub-ensembles, each containing distinct structural sets for VP’s three-residue C-terminal tail. We validated the computational results with experimental nuclear magnetic resonance (NMR) data and docked each sub-ensemble to V_1aR. We observed that the conformation of VP’s three-residue tail significantly modulated the complex dissociation constants. Solvent-exposed and proline trans-configured VP tail conformations bound to the receptor with three-fold enhanced affinities compared to compacted or cis-configured conformations. The solvent-exposed and more flexible structures facilitated unique interaction patterns between VP and V_1aR transmembrane helices 3, 4, and 6 which led to high binding energies. The presented “virtual conformational space screening” approach, integrated with NMR spectroscopy, thus enabled identification and characterization of a conformational selection-type complex formation mechanism that confers novel perspectives on targeting the VP-V_1aR interactions at the level of the encounter complex – an aspect that opens novel research avenues for understanding the functionality of the evolutionary selected conformational properties of VP, as well as guidance for ligand design strategies to provide more potent and selective VP analogues.

Using yeast two-hybrid system and molecular dynamics simulation to detect venom protein-protein interactions

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The venom protein-protein interactions in snake venom remain largely unknown.

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Y2H coupled with MD simulations was used to detect venom protein interactions.

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Venom PLA₂s interact with themselves and Lys49 PLA₂ interacts with venom CRISP.

Proteins and peptides are major components of snake venom. Venom protein transcriptomes and proteomes of many snake species have been reported; however, snake venom complexity (i.e., the venom protein-protein interactions, PPIs) remains largely unknown. To detect the venom protein interactions, we used the most common snake venom component, phospholipase A₂s (PLA₂s) as a “bait” to identify the interactions between PLA₂s and 14 of the most common proteins in Western diamondback rattlesnake (Crotalus atrox) venom by using yeast two-hybrid (Y2H) analysis, a technique used to detect PPIs. As a result, we identified PLA₂s interacting with themselves, and lysing-49 PLA₂ (Lys49 PLA₂) interacting with venom cysteine-rich secretory protein (CRISP). To reveal the complex structure of Lys49 PLA₂-CRISP interaction at the structural level, we first built the three-dimensional (3D) structures of Lys49 PLA₂ and CRISP by a widely used computational program-MODELLER. The binding modes of Lys49 PLA₂-CRISP interaction were then predicted through three different docking programs including ClusPro, ZDOCK and HADDOCK. Furthermore, the most likely complex structure of Lys49 PLA₂-CRISP was inferred by molecular dynamic (MD) simulations with GROMACS software. The techniques used and results obtained from this study strengthen the understanding of snake venom protein interactions and pave the way for the study of animal venom complexity.

Protein-Protein Docking with Large-Scale Backbone Flexibility Using Coarse-Grained Monte-Carlo Simulations

Most of the protein–protein docking methods treat proteins as almost rigid objects. Only the side-chains flexibility is usually taken into account. The few approaches enabling docking with a flexible backbone typically work in two steps, in which the search for protein–protein orientations and structure flexibility are simulated separately. In this work, we propose a new straightforward approach for docking sampling. It consists of a single simulation step during which a protein undergoes large-scale backbone rearrangements, rotations, and translations. Simultaneously, the other protein exhibits small backbone fluctuations. Such extensive sampling was possible using the CABS coarse-grained protein model and Replica Exchange Monte Carlo dynamics at a reasonable computational cost. In our proof-of-concept simulations of 62 protein–protein complexes, we obtained acceptable quality models for a significant number of cases.

ClustENMD: Efficient sampling of biomolecular conformational space at atomic resolution

Summary

Efficient sampling of conformational space is essential for elucidating functional/allosteric mechanisms of proteins and generating ensembles of conformers for docking applications. However, unbiased sampling is still a challenge especially for highly flexible and/or large systems. To address this challenge, we describe a new implementation of our computationally efficient algorithm ClustENMD that is integrated with ProDy and OpenMM softwares. This hybrid method performs iterative cycles of conformer generation using elastic network model for deformations along global modes, followed by clustering and short molecular dynamics simulations. ProDy framework enables full automation and analysis of generated conformers and visualization of their distributions in the essential subspace.

Availability and implementation

ClustENMD is open-source and freely available under MIT License from https://github.com/prody/ProDy.

Supplementary information

Supplementary data are available at Bioinformatics online.

Modeling of protein complexes in CASP14 with emphasis on the interaction interface prediction

The goal of CASP experiments is to monitor the progress in the protein structure prediction field. During the 14th CASP edition we aimed to test our capabilities of predicting structures of protein complexes. Our protocol for modeling protein assemblies included both template‐based modeling and free docking. Structural templates were identified using sensitive sequence‐based searches. If sequence‐based searches failed, we performed structure‐based template searches using selected CASP server models. In the absence of reliable templates we applied free docking starting from monomers generated by CASP servers. We evaluated and ranked models of protein complexes using an improved version of our protein structure quality assessment method, VoroMQA, taking into account both interaction interface and global structure scores. If reliable templates could be identified, generally accurate models of protein assemblies were generated with the exception of an antibody‐antigen interaction. The success of free docking mainly depended on the accuracy of initial subunit models and on the scoring of docking solutions. To put our overall results in perspective, we analyzed our performance in the context of other CASP groups. Although the subunits in our assembly models often were not of the top quality, these models had, overall, the best‐predicted intersubunit interfaces according to several accuracy measures. We attribute our relative success primarily to the emphasis on the interaction interface when modeling and scoring.

Moving pictures: Reassessing docking experiments with a dynamic view of protein interfaces

The modeling of protein assemblies at the atomic level remains a central issue in structural biology, as protein interactions play a key role in numerous cellular processes. This problem is traditionally addressed using docking tools, where the quality of the models is based on their similarity to a single reference experimental structure. However, using a static reference does not take into account the dynamic quality of the protein interface. Here, we used all-atom classical Molecular Dynamics simulations to investigate the stability of the reference interface for three complexes that previously served as targets in the CAPRI competition. For each one of these targets, we also ran MD simulations for ten models that are distributed over the High, Medium and Acceptable accuracy categories. To assess the quality of these models from a dynamic perspective, we set up new criteria which take into account the stability of the reference experimental protein interface. We show that, when the protein interfaces are allowed to evolve along time, the original ranking based on the static CAPRI criteria no longer holds as over 50% of the docking models undergo a category change (which can be either toward a better or a lower accuracy group) when reassessing their quality using dynamic information.

HOPMA: Boosting Protein Functional Dynamics with Colored Contact Maps

In light of the recent very rapid progress in protein structure prediction, accessing the multitude of functional protein states is becoming more central than ever before. Indeed, proteins are flexible macromolecules, and they often perform their function by switching between different conformations. However, high-resolution experimental techniques such as X-ray crystallography and cryogenic electron microscopy can catch relatively few protein functional states. Many others are only accessible under physiological conditions in solution. Therefore, there is a pressing need to fill this gap with computational approaches. We present HOPMA, a novel method to predict protein functional states and transitions by using a modified elastic network model. The method exploits patterns in a protein contact map, taking its 3D structure as input, and excludes some disconnected patches from the elastic network. Combined with nonlinear normal mode analysis, this strategy boosts the protein conformational space exploration, especially when the input structure is highly constrained, as we demonstrate on a set of more than 400 transitions. Our results let us envision the discovery of new functional conformations, which were unreachable previously, starting from the experimentally known protein structures. The method is computationally efficient and available at https://github.com/elolaine/HOPMA and https://team.inria.fr/nano-d/software/nolb-normal-modes.

PRosettaC: Rosetta Based Modeling of PROTAC Mediated Ternary Complexes

Proteolysis-targeting chimeras (PROTACs), which induce degradation by recruitment of an E3 ligase to a target protein, are gaining much interest as a new pharmacological modality. However, designing PROTACs is challenging. Formation of a ternary complex between the protein target, the PROTAC, and the recruited E3 ligase is considered paramount for successful degradation. A structural model of this ternary complex could in principle inform rational PROTAC design. Unfortunately, only a handful of structures are available for such complexes, necessitating tools for their modeling. We developed a combined protocol for the modeling of a ternary complex induced by a given PROTAC. Our protocol alternates between sampling of the protein-protein interaction space and the PROTAC molecule conformational space. Application of this protocol-PRosettaC-to a benchmark of known PROTAC ternary complexes results in near-native predictions, with often atomic accuracy prediction of the protein chains, as well as the PROTAC binding moieties. It allowed the modeling of a CRBN/BTK complex that recapitulated experimental results for a series of PROTACs. PRosettaC generated models may be used to design PROTACs for new targets, as well as improve PROTACs for existing targets, potentially cutting down time and synthesis efforts. To enable wide access to this protocol, we have made it available through a web server (https://prosettac.weizmann.ac.il/).

Computational structure modeling for diverse categories of macromolecular interactions

Computational protein-protein docking is one of the most intensively studied topics in structural bioinformatics. The field has made substantial progress through over three decades of development. The development began with methods for rigid-body docking of two proteins, which have now been extended in different directions to cover the various macromolecular interactions observed in a cell. Here, we overview the recent developments of the variations of docking methods, including multiple protein docking, peptide-protein docking, and disordered protein docking methods.

Challenges in protein docking

Current developments in protein docking aim at improvement of applicability, accuracy and utility of modeling macromolecular complexes. The challenges include the need for greater emphasis on protein docking to molecules of different types, proper accounting for conformational flexibility upon binding, new promising methodologies based on residue co-evolution and deep learning, affinity prediction, and further development of fully automated docking servers. Importantly, new developments increasingly focus on realistic modeling of protein interactions in vivo, including crowded environment inside a cell, which involves multiple transient encounters, and propagating the system in time. This opinion paper offers the author's perspective on these challenges in structural modeling of protein interactions and the future of protein docking.

Towards gaining sight of multiscale events: utilizing network models and normal modes in hybrid methods

With the explosion of normal mode analyses (NMAs) based on elastic network models (ENMs) in the last decade, and the proven precision of MD simulations for visualizing interactions at atomic scale, many hybrid methods have been proposed in recent years. These aim at exploiting the best of both worlds: the atomic precision of MD that often fall short of exploring time and length scales of biological interest, and the capability of ENM-NMA to predict the cooperative and often functional rearrangements of large structures and assemblies, albeit at low resolution. We present an overview of recent progress in the field with examples of successful applications highlighting the utility of such hybrid methods and pointing to emerging future directions guided by advances in experimental characterization of biomolecular systems structure and dynamics.

Intrinsic dynamics is evolutionarily optimized to enable allosteric behavior

Allosteric behavior is central to the function of many proteins, enabling molecular machinery, metabolism, signaling and regulation. Recent years have shown that the intrinsic dynamics of allosteric proteins defined by their 3-dimensional architecture or by the topology of inter-residue contacts favors cooperative motions that bear close similarity to structural changes they undergo during their allosteric actions. These conformational motions are usually driven by energetically favorable or soft modes at the low frequency end of the mode spectrum, and they are evolutionarily conserved among orthologs. These observations brought into light evolutionary adaptation mechanisms that help maintain, optimize or regulate allosteric behavior as the evolution from bacterial to higher organisms introduces sequential heterogeneities and structural complexities.

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.