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Eukaryotic cells, when exposed to environmental or internal stress, activate the integrated stress response (ISR) to restore homeostasis and promote cell survival. Specific stress stimuli prompt dedicated stress kinases to phosphorylate eukaryotic initiation factor 2 (eIF2). Phosphorylated eIF2 (p-eIF2) in turn sequesters the eIF2-specific guanine exchange factor eIF2B to block eIF2 recycling, thereby halting translation initiation and reducing global protein synthesis. To circumvent stress-induced translational shutdown, viruses encode ISR antagonists. Those identified so far prevent or reverse eIF2 phosphorylation. We now describe two viral proteins-one from a coronavirus and the other from a picornavirus-that have independently acquired the ability to counteract the ISR at its very core by acting as a competitive inhibitor of p-eIF2-eIF2B interaction. This allows continued formation of the eIF2-GTP-Met-tRNAi ternary complex and unabated global translation at high p-eIF2 levels that would otherwise cause translational arrest. We conclude that eIF2 and p-eIF2 differ in their interaction with eIF2B to such effect that p-eIF2-eIF2B association can be selectively inhibited.

De novo macrocyclic peptides, derived using selection technologies such as phage and mRNA display, present unique and unexpected solutions to challenging biological problems. This is due in part to their unusual folds, which are able to present side chains in ways not available to canonical structures such as α-helices and β-sheets. Despite much recent interest in these molecules, their folding and binding behavior remains poorly characterized. In this work, we present cocrystallization, docking, and solution NMR structures of three de novo macrocyclic peptides that all bind as competitive inhibitors with single-digit nanomolar K i to the active site of human pancreatic α-amylase. We show that a short stably folded motif in one of these is nucleated by internal hydrophobic interactions in an otherwise dynamic conformation in solution. Comparison of the solution structures with a target-bound structure from docking indicates that stabilization of the bound conformation is provided through interactions with the target protein after binding. These three structures also reveal a surprising functional convergence to present a motif of a single arginine sandwiched between two aromatic residues in the interactions of the peptide with the key catalytic residues of the enzyme, despite little to no other structural homology. Our results suggest that intramolecular hydrophobic interactions are important for priming binding of small macrocyclic peptides to their target and that high rigidity is not necessary for high affinity.

Our information‐driven docking approach HADDOCK has demonstrated a sustained performance since the start of its participation to CAPRI. This is due, in part, to its ability to integrate data into the modeling process, and to the robustness of its scoring function. We participated in CAPRI both as server and manual predictors. In CAPRI rounds 38‐45, we have used various strategies depending on the available information. These ranged from imposing restraints to a few residues identified from literature as being important for the interaction, to binding pockets identified from homologous complexes or template‐based refinement/CA‐CA restraint‐guided docking from identified templates. When relevant, symmetry restraints were used to limit the conformational sampling. We also tested for a large decamer target a new implementation of the MARTINI coarse‐grained force field in HADDOCK. Overall, we obtained acceptable or better predictions for 13 and 11 server and manual submissions, respectively, out of the 22 interfaces. Our server performance (acceptable or higher‐quality models when considering the top 10) was better (59%) than the manual (50%) one, in which we typically experiment with various combinations of protocols and data sources. Again, our simple scoring function based on a linear combination of intermolecular van der Waals and electrostatic energies and an empirical desolvation term demonstrated a good performance in the scoring experiment with a 63% success rate across all 22 interfaces. An analysis of model quality indicates that, while we are consistently performing well in generating acceptable models, there is room for improvement for generating/identifying higher quality models

BioExcel targets a broad and heterogeneous group of users from the biomolecular research community. Different kinds of consultancy and support will work best with different communities. In this project we have explored potential consultancy models through pilot use cases, contributed to a number of proposals for further related activities, and built solid foundations for future professional consultancy by building trust with potential customers. The project ran five Pilot Use Cases (UCs) which had an important role in bringing together work from across the project. The UC Alchemical Free Energy Calculations in Biomolecules covered the field of Free Energy Calculations, in which there is currently strong interest in the wider community, including drug discovery by pharmaceutical companies. It linked GROMACS with pmx, both codes being supported by BioExcel. Molecular Recognition, introduced a test bed where two BioExcel codes (HADDOCK and GROMACS) were used within the same automated workflow using the platform MDstudio, which will help to scale up and harvest of HPC/HTC resources in a simplified way. Virtual Screening was a collaboration with Nostrum Biodiscovery to study an important drug discovery process. Multi-scale modeling focused on validating a new highly parallel QM/MM interface of CPMD by applying the code to a state-of-the-art problem and comparing main results with the simulations performed with the original CPMD QM/MM interface. Rapid turnaround cancer analysis pipeline for high throughput sequencing data brought together existing processes into a new automated workflow and evaluated its suitability for an HPC implementation. The process of engaging with communities (and potential future customers) has been a gradual one. After an initial community landscaping exercise, several activities were begun to engage with communities of interest. Webinars proved a successful mechanism to begin engagement from the outset of the project; support forums provided a way for us to support existing users of the project’s codes from the start. As the project developed, there were opportunities to engage with communities face- to-face in smaller workshop meetings and the Community Forum, which was designed to bring members of different interest groups together. As well as supporting the wider community, considerable groundwork has been put in place to build a capability to offer professional consultancy, building trust with potential future customers. This groundwork included site visits and customised training for pharmaceutical companies such as Janssen and UCB. In addition, the project’s pilot use cases have provided insight into possible consultancy models and services such as webinars, forums and events which simultaneously allowed us to support best practice, share information about the centre, and learn more about the needs of the communities. The project has also provided opportunities for partners to engage in a number of new proposals, such as the consortium for Integrative Modelling of Antibodies (IMAbs) which includes UCB, AstraZeneca and MedImmune, some of which have led into further use-case work in BioExcel-2 which includes topics of specific interest to potential future industry clients.

Future software and hardware development will have a significant impact on all areas of scientific computing including the Life Sciences. Upcoming extreme-scale compute platforms will offer great opportunities for tackling important, large- scale scientific questions. In this document we update our previous analysis of the pre-exascale landscape from the perspective of biomolecular simulation software, including the pilot codes of BioExcel. These are largely unchanged from our previous deliverable 1.3 in this area, and so this report takes the form of an update to that report. Our findings are generally unchanged, and already well publicized among the European HPC stakeholders via several working groups which are involved in the development of EuroHPC, the newly updated PRACE Scientific Case, the ETP4HPC Strategic Research Agenda, and the EXDCI (http://exdci.eu) project in which BioExcel is leading the Life Science working group. Bio-molecular simulation scientists in industry and academe require effective and usable simulation software that runs well on the hardware resources they can access now. This software must be portable to emerging platforms, because we cannot afford to replace it to run well at the exascale. When we achieve this, we will be able to support the design of new drugs on scales impossible today, obtain better understanding of biochemical pathways, and open new doors for further innovation. This deliverable gives an overview of what we currently see as potential directions and then implementation plans for each of the pilot codes that will suit those directions.

Presentation slides from the first workshop on "Sharing Data from Molecular Simulations" taken place in Stockholm between 25-27 November, 2019. Recordings of the talks can be found on the BioExcel YouTube Channel

This document is the final report about the activities of the Work Package 4 (WP4), aiming at provisioning a consistent e-infrastructure gradually integrating the existing isolated software solutions in the structural biology field into a single computing and data processing environment, based on the state of the art grid and cloud open source software tools and frameworks. This report follows the documents D4.3, MS14, D4.5 and MS15, respectively delivered at project month 15, 24, 26, 34, so that mostly the progress achieved until project month 36 not already described in the previous D4.5 ten months ago will be reported here, with references to MS15 when possible. The document starts with an updated description of the resources potentially available for the project from the EGI e-infrastructure, on top of which we built the consolidated West-Life platform. It then presents a detailed view of resource usage and their geographical distribution in the third and last year of the project, as obtained from the EGI Accounting Portal. The remaining of the document reports in details the final achievements about the three main aspects of the platform: the consolidated job management mechanism, the programmatic access to datasets and the unified security and accounting model.

This document describes the contents of the final project release of software developed for pilot applications in BioExcel. The efforts of Work Package 1 are spread over the entire project timeframe, and some collaborations, previous deployments and benchmarks have also used preliminary new code. However, to provide reference versions of all codes used in the project (e.g. to integrate them in BioExcel workflows and facilitate use by other BioExcel teams), the Work Package also makes these formal code releases. This also comes with records describing the current status of the software that results from the planning, development, documentation, and testing processes described in Deliverable 1.1 (10.5281/zenodo.263908) and the BioExcel white paper on scientific software engineering (10.5281/zenodo.1194634). The release itself takes the form of a package of Open Source-licensed source code, containing the newly developed task-parallel and throughput-oriented modules, and can be downloaded from the BioExcel webpage at http://bioexcel.eu/software/code-repositories, or from the permanent repository at (10.5281/zenodo.1473685). For most of the codes this is a BioExcel- specific repository to sync the releases with other project needs, while general users are usually recommended to follow the documentation, download and installation instructions available on the main application web pages. This release updates the previous project release reported in Deliverable 1.2 (10.5281/zenodo.574459).

The Joint Research Activity of West-Life is aimed at exploring new ways to use existing or close to existing services so that broader user communities will be reached. Based on the capability of HADDOCK software to handle EM maps as input to drive the docking, and in the objective of interconnecting services that manipulate EM data, deliverable 7.5 is presenting a new version of the HADDOCK webserver. This new version, beyond introducing the processing of EM maps as input, together with EM-related parameters, has also been built on new technologies to improve user experience and administration by UU. The complete rewrite of the web portal framework started from the observation that adding new features with the former framework was time-consuming and with limited possibilities to improve user experience on the website. Moreover, protocols and technologies used within the former framework started to be outdated and prevented the deployment of webserver instances on new machines with up-to-date systems. The new web server is based on python Flask framework, a well-known framework (~22.000 questions on StackOverflow) that allows for website fast-prototyping. It is up and running is its development version on a local machine accessible at https://nestor.science.uu.nl/haddock. Main basic together with EM-related features are available through this URL but new features are added on a daily basis. We detail in this deliverable the workflow of the new webserver, from the technical details of the implementation to the end-product feature as seen by the users.

The BioExcel consortium develops and maintains several software packages, including GROMACS, HADDOCK, PMX and interfaces for QM/MM using CPMD or CP2K. These codes address different problems, are written in different languages by different sub-teams, delivered to users in different ways, and all have unique challenges in identifying good processes and seeking ways to improve them. They are real-world examples of major complex scientific software packages that have adopted more or less advanced formal software development processes. This white paper presents the experience of the developers and recommendations for development of high-quality software engineering processes.

Summary Recently we published PROtein binDIng enerGY (PRODIGY), a web-server for the prediction of binding affinity in protein–protein complexes. By using a combination of simple structural properties, such as the residue-contacts made at the interface, PRODIGY has demonstrated a top performance compared with other state-of-the-art predictors in the literature. Here we present an extension of it, named PRODIGY-LIG, aimed at the prediction of affinity in protein-small ligand complexes. The predictive method, properly readapted for small ligand by making use of atomic instead of residue contacts, has been successfully applied for the blind prediction of 102 protein–ligand complexes during the D3R Grand Challenge 2. PRODIGY-LIG has the advantage of being simple, generic and applicable to any kind of protein-ligand complex. It provides an automatic, fast and user-friendly tool ensuring broad accessibility. Availability and implementation PRODIGY-LIG is freely available without registration requirements at http://milou.science.uu.nl/services/PRODIGY-LIG.

The team from Utrecht University, as a partner in BioExcel, recently published two web portals, featuring two molecular modelling related programs: DisVis and PowerFit (dx.doi.org/10.1016/j.jmb.2016.11.032). DisVis allows you to visualise and quantify the information content of distance restraints between macromolecular complexes. Based on a systematic search of the 6D conformational space, DisVis highlights false positives and putative incoherent restraints within your set of restraints (dx.doi.org/doi:10.1093/bioinformatics/btv333). PowerFit automatically fits high-resolution atomic structures into cryo-EM density maps. To do so, it performs a full-exhaustive 6D cross-correlation search between the atomic structure and the density (dx.doi.org/doi:10.3934/biophy.2015.2.73). Output positions and rotations of the atomic structure, together with their high correlation values, are ranked and presented in a dynamic and interactive result page. The two new web portals offer very friendly interfaces, circumventing the installation process of the two programs while retaining the capability of customising the submission. Beyond the simple interfaces, the web portals have also been designed to submit jobs to EGI HPC/HTC resources, making specifically use of the recent GPGPU clusters available. They are both in production and accessible at: http://milou.science.uu.nl/cgi/services/DISVIS/disvis/ and http://milou.science.uu.nl/cgi/services/POWERFIT/powerfit/ In the coming webinar, the algorithms behind each program will be described by their developer, Gydo van Zundert, and the portal implementation will be presented by Mikael Trellet and Jörg Schaarschmidt. This presentation will include a live demo of the two portals. This workis supported by the West-Life (http://about.west-life.eu/), INDIGO-Datacloud (https://indigoprojects.eu/) and EGI-engage (http://www.egi.eu) European H2020 projects.

We present the results for CAPRI Round 46, the third joint CASP‐CAPRI protein assembly prediction challenge. The Round comprised a total of 20 targets including 14 homo‐oligomers and 6 heterocomplexes. Eight of the homo‐oligomer targets and one heterodimer comprised proteins that could be readily modeled using templates from the Protein Data Bank, often available for the full assembly. The remaining 11 targets comprised 5 homodimers, 3 heterodimers, and two higher‐order assemblies. These were more difficult to model, as their prediction mainly involved “ab‐initio” docking of subunit models derived from distantly related templates. A total of ~30 CAPRI groups, including 9 automatic servers, submitted on average ~2000 models per target. About 17 groups participated in the CAPRI scoring rounds, offered for most targets, submitting ~170 models per target. The prediction performance, measured by the fraction of models of acceptable quality or higher submitted across all predictors groups, was very good to excellent for the nine easy targets. Poorer performance was achieved by predictors for the 11 difficult targets, with medium and high quality models submitted for only 3 of these targets. A similar performance “gap” was displayed by scorer groups, highlighting yet again the unmet challenge of modeling the conformational changes of the protein components that occur upon binding or that must be accounted for in template‐based modeling. Our analysis also indicates that residues in binding interfaces were less well predicted in this set of targets than in previous Rounds, providing useful insights for directions of future improvements., Agence Nationale de la Recherche, Grant/Award Number: ANR‐15‐CE11‐0029‐03; Cancer Research UK, Grant/Award Number: FC001003; H2020 European Institute of Innovation and Technology, Grant/Award Numbers: 675728, 777536, 823830; Lietuvos Mokslo Taryba, Grant/Award Number: S‐MIP‐17‐60; Medical Research Council, Grant/Award Number: FC001003; National Institutes of Health, Grant/Award Numbers: R01GM074255, R01GM123055, R35GM124952; National Natural Science Foundation of China, Grant/Award Number: 31670724; National Research Foundation of Korea, Grant/Award Number: 2016M3C4A7952630; National Science Foundation, Grant/Award Number: DBI1565107; Nederlandse Organisatie voor Wetenschappelijk Onderzoek, Grant/Award Number: 718.015.001; Spanish >Programma Estatal I+D+i>, Grant/Award Number: BIO2016‐79930‐R; University Parthenope, Grant/Award Number: Finanziamento per il Sostegno alla Ricerca Individ; Wellcome Trust, Grant/Award Number: FC001003

In structural biology, the theoretical study of molecular structures has four main activities organized in the following scenario: collection of experimental and theoretical data, visualization of 3D structures, molecular simulation, analysis and interpretation of results. This pipeline allows the expert to develop new hypotheses, to verify them experimentally and to produce new data as a starting point for a new scenario.The explosion in the amount of data to handle in this loop has two problems. Firstly, the resources and time dedicated to the tasks of transfer and conversion of data between each of these four activities increases significantly. Secondly, the complexity of molecular data generated by new experimental methodologies greatly increases the difficulty to properly collect, visualize and analyze the data.Immersive environments are often proposed to address the quantity and the increasing complexity of the modeled phenomena, especially during the viewing activity. Indeed, virtual reality offers a high quality stereoscopic perception, useful for a better understanding of inherently three-dimensional molecular data. It also displays a large amount of information thanks to the large display surfaces, but also to complete the immersive feeling with other sensorimotor channels (3D audio, haptic feedbacks,...).However, two major factors hindering the use of virtual reality in the field of structural biology. On one hand, although there are literature on navigation and environmental realistic virtual scenes, navigating abstract science is still very little studied. The understanding of complex 3D phenomena is however particularly conditioned by the subject’s ability to identify themselves in a complex 3D phenomenon. The first objective of this thesis work is then to propose 3D navigation paradigms adapted to the molecular structures of increasing complexity. On the other hand, the interactive context of immersive environments encourages direct interaction with the objects of interest. But the activities of: results collection, simulation and analysis, assume a working environment based on command-line inputs or through specific scripts associated to the tools. Usually, the use of virtual reality is therefore restricted to molecular structures exploration and visualization. The second thesis objective is then to bring all these activities, previously carried out in independent and interactive application contexts, within a homogeneous and unique interactive context. In addition to minimizing the time spent in data management between different work contexts, the aim is also to present, in a joint and simultaneous way, molecular structures and analyses, and allow their manipulation through direct interaction.Our contribution meets these objectives by building on an approach guided by both the content and the task. More precisely, navigation paradigms have been designed taking into account the molecular content, especially geometric properties, and tasks of the expert, to facilitate spatial referencing in molecular complexes and make the exploration of these structures more efficient. In addition, formalizing the nature of molecular data, their analysis and their visual representations, allows to interactively propose analyzes adapted to the nature of the data and create links between the molecular components and associated analyzes. These features go through the construction of a unified and powerful semantic representation making possible the integration of these activities in a unique interactive context., En biologie structurale, l’étude théorique de structures moléculaires comporte quatre activités principales organisées selon le processus séquentiel suivant : la collecte de données expérimentales/théoriques, la visualisation des structures 3d, la simulation moléculaire, l’analyse et l’interprétation des résultats. Cet enchaînement permet à l’expert d’élaborer de nouvelles hypothèses, de les vérifier de manière expérimentale et de produire de nouvelles données comme point de départ d’un nouveau processus.L’explosion de la quantité de données à manipuler au sein de cette boucle pose désormais deux problèmes. Premièrement, les ressources et le temps relatifs aux tâches de transfert et de conversion de données entre chacune de ces activités augmentent considérablement. Deuxièmement, la complexité des données moléculaires générées par les nouvelles méthodologies expérimentales accroît fortement la difficulté pour correctement percevoir, visualiser et analyser ces données.Les environnements immersifs sont souvent proposés pour aborder le problème de la quantité et de la complexité croissante des phénomènes modélisés, en particulier durant l’activité de visualisation. En effet, la Réalité Virtuelle offre entre autre une perception stéréoscopique de haute qualité utile à une meilleure compréhension de données moléculaires intrinsèquement tridimensionnelles. Elle permet également d’afficher une quantité d’information importante grâce aux grandes surfaces d’affichage, mais aussi de compléter la sensation d’immersion par d’autres canaux sensorimoteurs.Cependant, deux facteurs majeurs freinent l’usage de la Réalité Virtuelle dans le domaine de la biologie structurale. D’une part, même s’il existe une littérature fournie sur la navigation dans les scènes virtuelles réalistes et écologiques, celle-ci est très peu étudiée sur la navigation sur des données scientifiques abstraites. La compréhension de phénomènes 3d complexes est pourtant particulièrement conditionnée par la capacité du sujet à se repérer dans l’espace. Le premier objectif de ce travail de doctorat a donc été de proposer des paradigmes navigation 3d adaptés aux structures moléculaires complexes. D’autre part, le contexte interactif des environnements immersif favorise l’interaction directe avec les objets d’intérêt. Or les activités de collecte et d’analyse des résultats supposent un contexte de travail en "ligne de commande" ou basé sur des scripts spécifiques aux outils d’analyse. Il en résulte que l’usage de la Réalité Virtuelle se limite souvent à l’activité d’exploration et de visualisation des structures moléculaires. C’est pourquoi le second objectif de thèse est de rapprocher ces différentes activités, jusqu’alors réalisées dans des contextes interactifs et applicatifs indépendants, au sein d’un contexte interactif homogène et unique. Outre le fait de minimiser le temps passé dans la gestion des données entre les différents contextes de travail, il s’agit également de présenter de manière conjointe et simultanée les structures moléculaires et leurs analyses et de permettre leur manipulation par des interactions directes.Notre contribution répond à ces objectifs en s’appuyant sur une approche guidée à la fois par le contenu et la tâche. Des paradigmes de navigation ont été conçus en tenant compte du contenu moléculaire, en particulier des propriétés géométriques, et des tâches de l’expert, afin de faciliter le repérage spatial et de rendre plus performante l’activité d’exploration. Par ailleurs, formaliser la nature des données moléculaires, leurs analyses et leurs représentations visuelles, permettent notamment de proposer à la demande et interactivement des analyses adaptées à la nature des données et de créer des liens entre les composants moléculaires et les analyses associées. Ces fonctionnalités passent par la construction d’une représentation sémantique unifiée et performante rendant possible l’intégration de ces activités dans un contexte interactif unique.

Despite their biological importance in many regulatory processes, protein-peptide recognition mechanisms are diffi cult to study experimentally at the structural level because of the inherent fl exibility of peptides and the often transient interactions on which they rely. Complementary methods like biomolecular docking are therefore required. The prediction of the three-dimensional structure of protein-peptide complexes raises unique challenges for computational algorithms, as exemplifi ed by the recent introduction of proteinpeptide targets in the blind international experiment CAPRI (Critical Assessment of PRedicted Interactions). Conventional protein-protein docking approaches are often struggling with the high fl exibility of peptides whose short sizes impede protocols and scoring functions developed for larger interfaces. On the other side, protein-small ligand docking methods are unable to cope with the larger number of degrees of freedom in peptides compared to small molecules and the typically reduced available information to defi ne the binding site. In this chapter, we describe a protocol to model protein-peptide complexes using the HADDOCK web server, working through a test case to illustrate every steps. The fl exibility challenge that peptides represent is dealt with by combining elements of conformational selection and induced fi t molecular recognition theories. Key words Biomolecular interactions, Information-driven docking, Conformational changes, Flexibility, HADDOCK, Molecular modeling.

International audience; Community-wide blind prediction experiments such as CAPRI and CASP provide an objective measure of the current state of predictive methodology. Here we describe a community-wide assessment of methods to predict the effects of mutations on protein-protein interactions. Twenty-two groups predicted the effects of comprehensive saturation mutagenesis for two designed influenza hemagglutinin binders and the results were compared with experimental yeast display enrichment data obtained using deep sequencing. The most successful methods explicitly considered the effects of mutation on monomer stability in addition to binding affinity, carried out explicit side-chain sampling and backbone relaxation, evaluated packing, electrostatic, and solvation effects, and correctly identified around a third of the beneficial mutations. Much room for improvement remains for even the best techniques, and large-scale fitness landscapes should continue to provide an excellent test bed for continued evaluation of both existing and new prediction methodologies.

We present the performance of HADDOCK, our information-driven docking software, in the second edition of the D3R Grand Challenge. In this blind experiment, participants were requested to predict the structures and binding affinities of complexes between the Farnesoid X nuclear receptor and 102 different ligands. The models obtained in Stage1 with HADDOCK and ligand-specific protocol show an average ligand RMSD of 5.1 Å from the crystal structure. Only 6/35 targets were within 2.5 Å RMSD from the reference, which prompted us to investigate the limiting factors and revise our protocol for Stage2. The choice of the receptor conformation appeared to have the strongest influence on the results. Our Stage2 models were of higher quality (13 out of 35 were within 2.5 Å), with an average RMSD of 4.1 Å. The docking protocol was applied to all 102 ligands to generate poses for binding affinity prediction. We developed a modified version of our contact-based binding affinity predictor PRODIGY, using the number of interatomic contacts classified by their type and the intermolecular electrostatic energy. This simple structure-based binding affinity predictor shows a Kendall’s Tau correlation of 0.37 in ranking the ligands (7th best out of 77 methods, 5th/25 groups). Those results were obtained from the average prediction over the top10 poses, irrespective of their similarity/correctness, underscoring the robustness of our simple predictor. This results in an enrichment factor of 2.5 compared to a random predictor for ranking ligands within the top 25%, making it a promising approach to identify lead compounds in virtual screening. Electronic supplementary material The online version of this article (doi:10.1007/s10822-017-0049-y) contains supplementary material, which is available to authorized users.

Our information-driven docking approach HADDOCK has demonstrated a sustained performance since the start of its participation to CAPRI. This is due, in part, to its ability to integrate data into the modeling process, and to the robustness of its scoring function. We participated in CAPRI both as server and manual predictors. In CAPRI rounds 38-45, we have used various strategies depending on the available information. These ranged from imposing restraints to a few residues identified from literature as being important for the interaction, to binding pockets identified from homologous complexes or template-based refinement/CA-CA restraint-guided docking from identified templates. When relevant, symmetry restraints were used to limit the conformational sampling. We also tested for a large decamer target a new implementation of the MARTINI coarse-grained force field in HADDOCK. Overall, we obtained acceptable or better predictions for 13 and 11 server and manual submissions, respectively, out of the 22 interfaces. Our server performance (acceptable or higher-quality models when considering the top 10) was better (59%) than the manual (50%) one, in which we typically experiment with various combinations of protocols and data sources. Again, our simple scoring function based on a linear combination of intermolecular van der Waals and electrostatic energies and an empirical desolvation term demonstrated a good performance in the scoring experiment with a 63% success rate across all 22 interfaces. An analysis of model quality indicates that, while we are consistently performing well in generating acceptable models, there is room for improvement for generating/identifying higher quality models.