Publications by authors named "Alexandre M J J Bonvin"

202 Publications

A click-flipped enzyme substrate boosts the performance of the diagnostic screening for Hunter syndrome.

Chem Sci 2020 Oct 23;11(47):12671-12676. Epub 2020 Oct 23.

Institute of Applied Synthetic Chemistry, TU Wien Getreidemarkt 9 1060 Vienna Austria

We report on the unexpected finding that click modification of iduronyl azides results in a conformational flip of the pyranose ring, which led to the development of a new strategy for the design of superior enzyme substrates for the diagnostic assaying of iduronate-2-sulfatase (I2S), a lysosomal enzyme related to Hunter syndrome. Synthetic substrates are essential in testing newborns for metabolic disorders to enable early initiation of therapy. Our click-flipped iduronyl triazole showed a remarkably better performance with I2S than commonly used -iduronates. We found that both - and triazole-linked substrates are accepted by the enzyme, irrespective of their different conformations, but only the -linked product inhibits the activity of I2S. Thus, in the long reaction times required for clinical assays, the triazole substrate substantially outperforms the -iduronate. Applying our click-flipped substrate to assay I2S in dried blood spots sampled from affected patients and random newborns significantly increased the confidence in discriminating between these groups, clearly indicating the potential of the click-flip strategy to control the biomolecular function of carbohydrates.
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http://dx.doi.org/10.1039/d0sc04696eDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8163285PMC
October 2020

50 years of PBD: a catalyst in structural biology.

Nat Methods 2021 May;18(5):448-449

Science for Life, Bijvoet Centre for Biomolecular Research, Faculty of Science, Department of Chemistry, Utrecht University, Utrecht, the Netherlands.

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http://dx.doi.org/10.1038/s41592-021-01138-yDOI Listing
May 2021

MENSAdb: a thorough structural analysis of membrane protein dimers.

Database (Oxford) 2021 Apr;2021

Department of Life Sciences, University of Coimbra, Coimbra, 3000-456, Portugal.

Membrane proteins (MPs) are key players in a variety of different cellular processes and constitute the target of around 60% of all Food and Drug Administration-approved drugs. Despite their importance, there is still a massive lack of relevant structural, biochemical and mechanistic information mainly due to their localization within the lipid bilayer. To help fulfil this gap, we developed the MEmbrane protein dimer Novel Structure Analyser database (MENSAdb). This interactive web application summarizes the evolutionary and physicochemical properties of dimeric MPs to expand the available knowledge on the fundamental principles underlying their formation. Currently, MENSAdb contains features of 167 unique MPs (63% homo- and 37% heterodimers) and brings insights into the conservation of residues, accessible solvent area descriptors, average B-factors, intermolecular contacts at 2.5 Å and 4.0 Å distance cut-offs, hydrophobic contacts, hydrogen bonds, salt bridges, π-π stacking, T-stacking and cation-π interactions. The regular update and organization of all these data into a unique platform will allow a broad community of researchers to collect and analyse a large number of features efficiently, thus facilitating their use in the development of prediction models associated with MPs. Database URL: http://www.moreiralab.com/resources/mensadb.
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http://dx.doi.org/10.1093/database/baab013DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8023553PMC
April 2021

Understanding Docking Complexes of Macromolecules Using HADDOCK: The Synergy between Experimental Data and Computations.

Bio Protoc 2020 Oct 20;10(20):e3793. Epub 2020 Oct 20.

Department of Chemistry, University of Florence, Florence, Italy.

This protocol illustrates the modelling of a protein-peptide complex using the synergic combination of analysis and experimental results. To this end, we use the integrative modelling software HADDOCK, which possesses the powerful ability to incorporate experimental data, such as NMR Chemical Shift Perturbations and biochemical protein-peptide interaction data, as restraints to guide the docking process. Based on the modelling results, a rational mutagenesis approach is used to validate the generated models. The experimental results allow to select a final structural model best representing the protein-peptide complex. The described protocol can also be applied to model protein-protein complexes. There is no size limit for the macromolecular complexes that can be characterized by HADDOCK as long as the 3D structures of the individual components are available.
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http://dx.doi.org/10.21769/BioProtoc.3793DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7842552PMC
October 2020

Integrating quantitative proteomics with accurate genome profiling of transcription factors by greenCUT&RUN.

Nucleic Acids Res 2021 05;49(9):e49

Department of Urology, Medical Center-University of Freiburg, 79016 Freiburg, Germany.

Genome-wide localization of chromatin and transcription regulators can be detected by a variety of techniques. Here, we describe a novel method 'greenCUT&RUN' for genome-wide profiling of transcription regulators, which has a very high sensitivity, resolution, accuracy and reproducibility, whilst assuring specificity. Our strategy begins with tagging of the protein of interest with GFP and utilizes a GFP-specific nanobody fused to MNase to profile genome-wide binding events. By using a GFP-nanobody the greenCUT&RUN approach eliminates antibody dependency and variability. Robust genomic profiles were obtained with greenCUT&RUN, which are accurate and unbiased towards open chromatin. By integrating greenCUT&RUN with nanobody-based affinity purification mass spectrometry, 'piggy-back' DNA binding events can be identified on a genomic scale. The unique design of greenCUT&RUN grants target protein flexibility and yields high resolution footprints. In addition, greenCUT&RUN allows rapid profiling of mutants of chromatin and transcription proteins. In conclusion, greenCUT&RUN is a widely applicable and versatile genome-mapping technique.
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http://dx.doi.org/10.1093/nar/gkab038DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8136828PMC
May 2021

Integrative modeling of membrane-associated protein assemblies.

Nat Commun 2020 12 4;11(1):6210. Epub 2020 Dec 4.

Bijvoet Centre for Biomolecular Research, Faculty of Science-Chemistry, Utrecht University, Utrecht, The Netherlands.

Membrane proteins are among the most challenging systems to study with experimental structural biology techniques. The increased number of deposited structures of membrane proteins has opened the route to modeling their complexes by methods such as docking. Here, we present an integrative computational protocol for the modeling of membrane-associated protein assemblies. The information encoded by the membrane is represented by artificial beads, which allow targeting of the docking toward the binding-competent regions. It combines efficient, artificial intelligence-based rigid-body docking by LightDock with a flexible final refinement with HADDOCK to remove potential clashes at the interface. We demonstrate the performance of this protocol on eighteen membrane-associated complexes, whose interface lies between the membrane and either the cytosolic or periplasmic regions. In addition, we provide a comparison to another state-of-the-art docking software, ZDOCK. This protocol should shed light on the still dark fraction of the interactome consisting of membrane proteins.
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http://dx.doi.org/10.1038/s41467-020-20076-5DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7718903PMC
December 2020

PDB-tools web: A user-friendly interface for the manipulation of PDB files.

Proteins 2021 Mar 7;89(3):330-335. Epub 2020 Nov 7.

Bijvoet Centre for Biomolecular Research, Utrecht University, Utrecht, The Netherlands.

The Protein Data Bank (PDB) file format remains a popular format used and supported by many software to represent coordinates of macromolecular structures. It however suffers from drawbacks such as error-prone manual editing. Because of that, various software toolkits have been developed to facilitate its editing and manipulation, but, to date, there is no online tool available for this purpose. Here we present PDB-Tools Web, a flexible online service for manipulating PDB files. It offers a rich and user-friendly graphical user interface that allows users to mix-and-match more than 40 individual tools from the pdb-tools suite. Those can be combined in a few clicks to perform complex pipelines, which can be saved and uploaded. The resulting processed PDB files can be visualized online and downloaded. The web server is freely available at https://wenmr.science.uu.nl/pdbtools.
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http://dx.doi.org/10.1002/prot.26018DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7855443PMC
March 2021

Toward Increased Reliability, Transparency, and Accessibility in Cross-linking Mass Spectrometry.

Structure 2020 11 15;28(11):1259-1268. Epub 2020 Oct 15.

University of Konstanz, Department of Biology, Universitätsstrasse 10, 78457 Konstanz, Germany; Konstanz Research School Chemical Biology, University of Konstanz, Universitätsstrasse 10, 78457 Konstanz, Germany.

Cross-linking mass spectrometry (MS) has substantially matured as a method over the past 2 decades through parallel development in multiple labs, demonstrating its applicability to protein structure determination, conformation analysis, and mapping protein interactions in complex mixtures. Cross-linking MS has become a much-appreciated and routinely applied tool, especially in structural biology. Therefore, it is timely that the community commits to the development of methodological and reporting standards. This white paper builds on an open process comprising a number of events at community conferences since 2015 and identifies aspects of Cross-linking MS for which guidelines should be developed as part of a Cross-linking MS standards initiative.
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http://dx.doi.org/10.1016/j.str.2020.09.011DOI Listing
November 2020

Inhibition of the integrated stress response by viral proteins that block p-eIF2-eIF2B association.

Nat Microbiol 2020 11 20;5(11):1361-1373. Epub 2020 Jul 20.

Virology Division, Department of Infectious Diseases and Immunology, Faculty of Veterinary Medicine, Utrecht University, Utrecht, the Netherlands.

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.
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http://dx.doi.org/10.1038/s41564-020-0759-0DOI Listing
November 2020

proABC-2: PRediction of AntiBody contacts v2 and its application to information-driven docking.

Bioinformatics 2020 12;36(20):5107-5108

Department of Chemistry, Faculty of Science, Computational Structural Biology Group, Bijvoet Centre for Biomolecular Research, Utrecht University, Utrecht 3584CH, The Netherlands.

Motivation: Monoclonal antibodies are essential tools in the contemporary therapeutic armory. Understanding how these recognize their antigen is a fundamental step in their rational design and engineering. The rising amount of publicly available data is catalyzing the development of computational approaches able to offer valuable, faster and cheaper alternatives to classical experimental methodologies used for the study of antibody-antigen complexes.

Results: Here, we present proABC-2, an update of the original random-forest antibody paratope predictor, based on a convolutional neural network algorithm. We also demonstrate how the predictions can be fruitfully used to drive the docking in HADDOCK.

Availability And Implementation: The proABC-2 server is freely available at: https://wenmr.science.uu.nl/proabc2/.

Supplementary Information: Supplementary data are available at Bioinformatics online.
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http://dx.doi.org/10.1093/bioinformatics/btaa644DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7755408PMC
December 2020

A community proposal to integrate structural bioinformatics activities in ELIXIR (3D-Bioinfo Community).

F1000Res 2020 22;9. Epub 2020 Apr 22.

Institute of Biotechnology of the Czech Academy of Sciences, Vestec, CZ-25250, Czech Republic.

Structural bioinformatics provides the scientific methods and tools to analyse, archive, validate, and present the biomolecular structure data generated by the structural biology community. It also provides an important link with the genomics community, as structural bioinformaticians also use the extensive sequence data to predict protein structures and their functional sites. A very broad and active community of structural bioinformaticians exists across Europe, and 3D-Bioinfo will establish formal platforms to address their needs and better integrate their activities and initiatives. Our mission will be to strengthen the ties with the structural biology research communities in Europe covering life sciences, as well as chemistry and physics and to bridge the gap between these researchers in order to fully realize the potential of structural bioinformatics. Our Community will also undertake dedicated educational, training and outreach efforts to facilitate this, bringing new insights and thus facilitating the development of much needed innovative applications e.g. for human health, drug and protein design. Our combined efforts will be of critical importance to keep the European research efforts competitive in this respect. Here we highlight the major European contributions to the field of structural bioinformatics, the most pressing challenges remaining and how Europe-wide interactions, enabled by ELIXIR and its platforms, will help in addressing these challenges and in coordinating structural bioinformatics resources across Europe. In particular, we present recent activities and future plans to consolidate an ELIXIR 3D-Bioinfo Community in structural bioinformatics and propose means to develop better links across the community. These include building new consortia, organising workshops to establish data standards and seeking community agreement on benchmark data sets and strategies. We also highlight existing and planned collaborations with other ELIXIR Communities and other European infrastructures, such as the structural biology community supported by Instruct-ERIC, with whom we have synergies and overlapping common interests.
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http://dx.doi.org/10.12688/f1000research.20559.1DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7284151PMC
July 2020

Coarse-grained (hybrid) integrative modeling of biomolecular interactions.

Comput Struct Biotechnol J 2020 15;18:1182-1190. Epub 2020 May 15.

Bijvoet Centre for Biomolecular Research, Faculty of Science - Chemistry, Utrecht University, Utrecht 3584CH, the Netherlands.

The computational modeling field has vastly evolved over the past decades. The early developments of simplified protein systems represented a stepping stone towards establishing more efficient approaches to sample intricated conformational landscapes. Downscaling the level of resolution of biomolecules to coarser representations allows for studying protein structure, dynamics and interactions that are not accessible by classical atomistic approaches. The combination of different resolutions, namely hybrid modeling, has also been proved as an alternative when mixed levels of details are required. In this review, we provide an overview of coarse-grained/hybrid models focusing on their applicability in the modeling of biomolecular interactions. We give a detailed list of ready-to-use modeling software for studying biomolecular interactions allowing various levels of coarse-graining and provide examples of complexes determined by integrative coarse-grained/hybrid approaches in combination with experimental information.
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http://dx.doi.org/10.1016/j.csbj.2020.05.002DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7264466PMC
May 2020

Mode of action of teixobactins in cellular membranes.

Nat Commun 2020 06 5;11(1):2848. Epub 2020 Jun 5.

NMR Spectroscopy, Bijvoet Centre for Biomolecular Research, Department of Chemistry, Faculty of Science, Utrecht University, Padualaan 8, 3584 CH, Utrecht, The Netherlands.

The natural antibiotic teixobactin kills pathogenic bacteria without detectable resistance. The difficult synthesis and unfavourable solubility of teixobactin require modifications, yet insufficient knowledge on its binding mode impedes the hunt for superior analogues. Thus far, teixobactins are assumed to kill bacteria by binding to cognate cell wall precursors (Lipid II and III). Here we present the binding mode of teixobactins in cellular membranes using solid-state NMR, microscopy, and affinity assays. We solve the structure of the complex formed by an improved teixobactin-analogue and Lipid II and reveal how teixobactins recognize a broad spectrum of targets. Unexpectedly, we find that teixobactins only weakly bind to Lipid II in cellular membranes, implying the direct interaction with cell wall precursors is not the sole killing mechanism. Our data suggest an additional mechanism affords the excellent activity of teixobactins, which can block the cell wall biosynthesis by capturing precursors in massive clusters on membranes.
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http://dx.doi.org/10.1038/s41467-020-16600-2DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7275090PMC
June 2020

Protein-Protein Modeling Using Cryo-EM Restraints.

Methods Mol Biol 2020 ;2112:145-162

Computational Structural Biology Group, Bijvoet Centre for Biomolecular Research, Faculty of Science-Chemistry, Utrecht University, Utrecht, The Netherlands.

Recent improvements in cryo-electron microscopy (cryo-EM) in the past few years are now allowing to observe molecular complexes at atomic resolution. As a consequence, numerous structures derived from cryo-EM are now available in the Protein Data Bank. However, if for some complexes atomic resolution is reached, this is not true for all. This is also the case in cryo-electron tomography where the achievable resolution is still limited. Furthermore the resolution in a cryo-EM map is not a constant, with often outer regions being of lower resolution, possibly linked to conformational variability. Although those low- to medium-resolution EM maps (or regions thereof) cannot directly provide atomic structure of large molecular complexes, they provide valuable information to model the individual components and their assembly into them. Most approaches for this kind of modeling are performing rigid fitting of the individual components into the EM density map. While this would appear an obvious option, they ignore key aspects of molecular recognition, the energetics and flexibility of the interfaces. Moreover, this often restricts the modeling to a unique source of data, the EM density map.In this chapter, we describe a protocol where an EM map is used as restraint in HADDOCK to guide the modeling process. In the first step, rigid-body fitting is performed with PowerFit in order to identify the most likely locations of the molecules into the map. These are then used as centroids to which distance restraints are defined from the center of mass of the components of the complex for the initial rigid-body docking. The EM density is then directly used as an additional restraint energy term, which can be combined with all the other types of data supported by HADDOCK. This protocol relies on the new version 2.4 of both the HADDOCK webserver and software. Preparation steps consisting of cropping the EM map and rigid-body fitting of the atomic structure are explained. Then, the EM-driven docking protocol using HADDOCK is illustrated.
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http://dx.doi.org/10.1007/978-1-0716-0270-6_11DOI Listing
January 2021

An overview of data-driven HADDOCK strategies in CAPRI rounds 38-45.

Proteins 2020 08 7;88(8):1029-1036. Epub 2020 Jan 7.

Faculty of Science, Department of Chemistry, Bijvoet Center for Biomolecular Research, Computational Structural Biology Group, Utrecht University, Utrecht, The Netherlands.

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.
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http://dx.doi.org/10.1002/prot.25869DOI Listing
August 2020

Modeling Antibody-Antigen Complexes by Information-Driven Docking.

Structure 2020 01 11;28(1):119-129.e2. Epub 2019 Nov 11.

Faculty of Science - Chemistry, Computational Structural Biology Group, Bijvoet Center for Biomolecular Research, Utrecht University, Padualaan 8, 3584 CH Utrecht, The Netherlands. Electronic address:

Antibodies are Y-shaped proteins essential for immune response. Their capability to recognize antigens with high specificity makes them excellent therapeutic targets. Understanding the structural basis of antibody-antigen interactions is therefore crucial for improving our ability to design efficient biological drugs. Computational approaches such as molecular docking are providing a valuable and fast alternative to experimental structural characterization for these complexes. We investigate here how information about complementarity-determining regions and binding epitopes can be used to drive the modeling process, and present a comparative study of four different docking software suites (ClusPro, LightDock, ZDOCK, and HADDOCK) providing specific options for antibody-antigen modeling. Their performance on a dataset of 16 complexes is reported. HADDOCK, which includes information to drive the docking, is shown to perform best in terms of both success rate and quality of the generated models in both the presence and absence of information about the epitope on the antigen.
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http://dx.doi.org/10.1016/j.str.2019.10.011DOI Listing
January 2020

Coupling enhanced sampling of the apo-receptor with template-based ligand conformers selection: performance in pose prediction in the D3R Grand Challenge 4.

J Comput Aided Mol Des 2020 02 13;34(2):149-162. Epub 2019 Nov 13.

Dipartimento Di Fisica, Università Di Cagliari, Cittadella Universitaria, S.P. 8 km 0.700, 09042, Monserrato, Italy.

We report the performance of our newly introduced Ensemble Docking with Enhanced sampling of pocket Shape (EDES) protocol coupled to a template-based algorithm to generate near-native ligand conformations in the 2019 iteration of the Grand Challenge (GC4) organized by the D3R consortium. Using either AutoDock4.2 or HADDOCK2.2 docking programs (each software in two variants of the protocol) our method generated native-like poses among the top 5 submitted for evaluation for most of the 20 targets with similar performances. The protein selected for GC4 was the human beta-site amyloid precursor protein cleaving enzyme 1 (BACE-1), a transmembrane aspartic-acid protease. We identified at least one pose whose heavy-atoms RMSD was less than 2.5 Å from the native conformation for 16 (80%) and 17 (85%) of the 20 targets using AutoDock and HADDOCK, respectively. Dissecting the possible sources of errors revealed that: (i) our EDES protocol (with minor modifications) was able to sample sub-ångstrom conformations for all 20 protein targets, reproducing the correct conformation of the binding site within ~ 1 Å RMSD; (ii) as already shown by some of us in GC3, even in the presence of near-native protein structures, a proper selection of ligand conformers is crucial for the success of ensemble-docking calculations. Importantly, our approach performed best among the protocols exploiting only structural information of the apo protein to generate conformations of the receptor for ensemble-docking calculations.
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http://dx.doi.org/10.1007/s10822-019-00244-6DOI Listing
February 2020

MARTINI-Based Protein-DNA Coarse-Grained HADDOCKing.

Front Mol Biosci 2019 1;6:102. Epub 2019 Oct 1.

Faculty of Science-Chemistry, Bijvoet Center for Biomolecular Research, Utrecht University, Utrecht, Netherlands.

Modeling biomolecular assemblies is an important field in computational structural biology. The inherent complexity of their energy landscape and the computational cost associated with modeling large and complex assemblies are major drawbacks for integrative modeling approaches. The so-called coarse-graining approaches, which reduce the degrees of freedom of the system by grouping several atoms into larger "pseudo-atoms," have been shown to alleviate some of those limitations, facilitating the identification of the global energy minima assumed to correspond to the native state of the complex, while making the calculations more efficient. Here, we describe and assess the implementation of the MARTINI force field for DNA into HADDOCK, our integrative modeling platform. We combine it with our previous implementation for protein-protein coarse-grained docking, enabling coarse-grained modeling of protein-nucleic acid complexes. The system is modeled using MARTINI topologies and interaction parameters during the rigid body docking and semi-flexible refinement stages of HADDOCK, and the resulting models are then converted back to atomistic resolution by an atom-to-bead distance restraints-guided protocol. We first demonstrate the performance of this protocol using 44 complexes from the protein-DNA docking benchmark, which shows an overall ~6-fold speed increase and maintains similar accuracy as compared to standard atomistic calculations. As a proof of concept, we then model the interaction between the PRC1 and the nucleosome (a former CAPRI target in round 31), using the same information available at the time the target was offered, and compare all-atom and coarse-grained models.
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http://dx.doi.org/10.3389/fmolb.2019.00102DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6779769PMC
October 2019

Computational approaches to therapeutic antibody design: established methods and emerging trends.

Brief Bioinform 2020 09;21(5):1549-1567

NaturalAntibody, Germany.

Antibodies are proteins that recognize the molecular surfaces of potentially noxious molecules to mount an adaptive immune response or, in the case of autoimmune diseases, molecules that are part of healthy cells and tissues. Due to their binding versatility, antibodies are currently the largest class of biotherapeutics, with five monoclonal antibodies ranked in the top 10 blockbuster drugs. Computational advances in protein modelling and design can have a tangible impact on antibody-based therapeutic development. Antibody-specific computational protocols currently benefit from an increasing volume of data provided by next generation sequencing and application to related drug modalities based on traditional antibodies, such as nanobodies. Here we present a structured overview of available databases, methods and emerging trends in computational antibody analysis and contextualize them towards the engineering of candidate antibody therapeutics.
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http://dx.doi.org/10.1093/bib/bbz095DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7947987PMC
September 2020

Blind prediction of homo- and hetero-protein complexes: The CASP13-CAPRI experiment.

Proteins 2019 12 25;87(12):1200-1221. Epub 2019 Oct 25.

School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei, China.

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.
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http://dx.doi.org/10.1002/prot.25838DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7274794PMC
December 2019

Less Is More: Coarse-Grained Integrative Modeling of Large Biomolecular Assemblies with HADDOCK.

J Chem Theory Comput 2019 Nov 10;15(11):6358-6367. Epub 2019 Oct 10.

Bijvoet Center for Biomolecular Research, Faculty of Science - Chemistry , Utrecht University , Utrecht 3584CH , The Netherlands.

Predicting the 3D structure of protein interactions remains a challenge in the field of computational structural biology. This is in part due to difficulties in sampling the complex energy landscape of multiple interacting flexible polypeptide chains. Coarse-graining approaches, which reduce the number of degrees of freedom of the system, help address this limitation by smoothing the energy landscape, allowing an easier identification of the global energy minimum. They also accelerate the calculations, allowing for modeling larger assemblies. Here, we present the implementation of the MARTINI coarse-grained force field for proteins into HADDOCK, our integrative modeling platform. Docking and refinement are performed at the coarse-grained level, and the resulting models are then converted back to atomistic resolution through a distance restraints-guided morphing procedure. Our protocol, tested on the largest complexes of the protein docking benchmark 5, shows an overall ∼7-fold speed increase compared to standard all-atom calculations, while maintaining a similar accuracy and yielding substantially more near-native solutions. To showcase the potential of our method, we performed simultaneous 7 body docking to model the 1:6 KaiC-KaiB complex, integrating mutagenesis and hydrogen/deuterium exchange data from mass spectrometry with symmetry restraints, and validated the resulting models against a recently published cryo-EM structure.
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http://dx.doi.org/10.1021/acs.jctc.9b00310DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6854652PMC
November 2019

Sharing Data from Molecular Simulations.

J Chem Inf Model 2019 10 11;59(10):4093-4099. Epub 2019 Oct 11.

Science for Life Laboratory, Department of Applied Physics , KTH Royal Institute of Technology , Box 1031, SE-171 21 Solna , Sweden.

Given the need for modern researchers to produce open, reproducible scientific output, the lack of standards and best practices for sharing data and workflows used to produce and analyze molecular dynamics (MD) simulations has become an important issue in the field. There are now multiple well-established packages to perform molecular dynamics simulations, often highly tuned for exploiting specific classes of hardware, each with strong communities surrounding them, but with very limited interoperability/transferability options. Thus, the choice of the software package often dictates the workflow for both simulation production and analysis. The level of detail in documenting the workflows and analysis code varies greatly in published work, hindering reproducibility of the reported results and the ability for other researchers to build on these studies. An increasing number of researchers are motivated to make their data available, but many challenges remain in order to effectively share and reuse simulation data. To discuss these and other issues related to best practices in the field in general, we organized a workshop in November 2018 ( https://bioexcel.eu/events/workshop-on-sharing-data-from-molecular-simulations/ ). Here, we present a brief overview of this workshop and topics discussed. We hope this effort will spark further conversation in the MD community to pave the way toward more open, interoperable, and reproducible outputs coming from research studies using MD simulations.
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http://dx.doi.org/10.1021/acs.jcim.9b00665DOI Listing
October 2019

The structural details of the interaction of single-stranded DNA binding protein hSSB2 (NABP1/OBFC2A) with UV-damaged DNA.

Proteins 2020 02 30;88(2):319-326. Epub 2019 Aug 30.

School of Science and Health, Western Sydney University, Penrith, New South Wales, Australia.

Single-stranded DNA-binding proteins (SSBs) are required for all known DNA metabolic events such as DNA replication, recombination and repair. While a wealth of structural and functional data is available on the essential human SSB, hSSB1 (NABP2/OBFC2B), the close homolog hSSB2 (NABP1/OBFC2A) remains relatively uncharacterized. Both SSBs possess a well-structured OB (oligonucleotide/oligosaccharide-binding) domain that is able to recognize single-stranded DNA (ssDNA) followed by a flexible carboxyl-tail implicated in the interaction with other proteins. Despite the high sequence similarity of the OB domain, several recent studies have revealed distinct functional differences between hSSB1 and hSSB2. In this study, we show that hSSB2 is able to recognize cyclobutane pyrimidine dimers (CPD) that form in cellular DNA as a consequence of UV damage. Using a combination of biolayer interferometry and NMR, we determine the molecular details of the binding of the OB domain of hSSB2 to CPD-containing ssDNA, confirming the role of four key aromatic residues in hSSB2 (W59, Y78, W82, and Y89) that are also conserved in hSSB1. Our structural data thus demonstrate that ssDNA recognition by the OB fold of hSSB2 is highly similar to hSSB1, indicating that one SSB may be able to replace the other in any initial ssDNA binding event. However, any subsequent recruitment of other repair proteins most likely depends on the divergent carboxyl-tail and as such is likely to be different between hSSB1 and hSSB2.
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http://dx.doi.org/10.1002/prot.25806DOI Listing
February 2020

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

Proteins 2020 02 3;88(2):292-306. Epub 2019 Sep 3.

Bijvoet Center for Biomolecular Research, Faculty of Science - Chemistry, Utrecht University, Utrecht, the Netherlands.

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.
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http://dx.doi.org/10.1002/prot.25802DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6973081PMC
February 2020

LightDock goes information-driven.

Bioinformatics 2020 02;36(3):950-952

Bijvoet Center for Biomolecular Research, Faculty of Science, Department of Chemistry, Utrecht University, Utrecht, The Netherlands.

Motivation: The use of experimental information has been demonstrated to increase the success rate of computational macromolecular docking. Many methods use information to post-filter the simulation output while others drive the simulation based on experimental restraints, which can become problematic for more complex scenarios such as multiple binding interfaces.

Results: We present a novel method for including interface information into protein docking simulations within the LightDock framework. Prior to the simulation, irrelevant regions from the receptor are excluded for sampling (filter of initial swarms) and initial ligand poses are pre-oriented based on ligand input information. We demonstrate the applicability of this approach on the new 55 cases of the Protein-Protein Docking Benchmark 5, using different amounts of information. Even with incomplete or incorrect information, a significant improvement in performance is obtained compared to blind ab initio docking.

Availability And Implementation: The software is supported and freely available from https://github.com/brianjimenez/lightdock and analysis data from https://github.com/brianjimenez/lightdock_bm5.

Supplementary Information: Supplementary data are available at Bioinformatics online.
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http://dx.doi.org/10.1093/bioinformatics/btz642DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7005597PMC
February 2020

Folding Then Binding vs Folding Through Binding in Macrocyclic Peptide Inhibitors of Human Pancreatic α-Amylase.

ACS Chem Biol 2019 08 19;14(8):1751-1759. Epub 2019 Jul 19.

Department of Chemical Biology and Drug Discovery , Utrecht Institute of Pharmaceutical Sciences, Utrecht University , 3584 CG Utrecht , The Netherlands.

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 macrocyclic peptides that all bind as competitive inhibitors with single-digit nanomolar 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.
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http://dx.doi.org/10.1021/acschembio.9b00290DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6700688PMC
August 2019

iScore: a novel graph kernel-based function for scoring protein-protein docking models.

Bioinformatics 2020 01;36(1):112-121

Bijvoet Center for Biomolecular Research, Faculty of Science - Chemistry, Utrecht University, Utrecht 3584 CH, The Netherlands.

Motivation: Protein complexes play critical roles in many aspects of biological functions. Three-dimensional (3D) structures of protein complexes are critical for gaining insights into structural bases of interactions and their roles in the biomolecular pathways that orchestrate key cellular processes. Because of the expense and effort associated with experimental determinations of 3D protein complex structures, computational docking has evolved as a valuable tool to predict 3D structures of biomolecular complexes. Despite recent progress, reliably distinguishing near-native docking conformations from a large number of candidate conformations, the so-called scoring problem, remains a major challenge.

Results: Here we present iScore, a novel approach to scoring docked conformations that combines HADDOCK energy terms with a score obtained using a graph representation of the protein-protein interfaces and a measure of evolutionary conservation. It achieves a scoring performance competitive with, or superior to, that of state-of-the-art scoring functions on two independent datasets: (i) Docking software-specific models and (ii) the CAPRI score set generated by a wide variety of docking approaches (i.e. docking software-non-specific). iScore ranks among the top scoring approaches on the CAPRI score set (13 targets) when compared with the 37 scoring groups in CAPRI. The results demonstrate the utility of combining evolutionary, topological and energetic information for scoring docked conformations. This work represents the first successful demonstration of graph kernels to protein interfaces for effective discrimination of near-native and non-native conformations of protein complexes.

Availability And Implementation: The iScore code is freely available from Github: https://github.com/DeepRank/iScore (DOI: 10.5281/zenodo.2630567). And the docking models used are available from SBGrid: https://data.sbgrid.org/dataset/684).

Supplementary Information: Supplementary data are available at Bioinformatics online.
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http://dx.doi.org/10.1093/bioinformatics/btz496DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6956772PMC
January 2020

Large-scale prediction of binding affinity in protein-small ligand complexes: the PRODIGY-LIG web server.

Bioinformatics 2019 05;35(9):1585-1587

Department of Chemistry, Faculty of Science, Computational Structural Biology Group, Bijvoet Center for Biomolecular Research, Utrecht University, Padualaan 8, Utrecht, The Netherlands.

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.
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http://dx.doi.org/10.1093/bioinformatics/bty816DOI Listing
May 2019

Holo-like and Druggable Protein Conformations from Enhanced Sampling of Binding Pocket Volume and Shape.

J Chem Inf Model 2019 04 28;59(4):1515-1528. Epub 2019 Mar 28.

Dipartimento di Fisica , Università di Cagliari, Cittadella Universitaria , I- 09042 Monserrato (CA) , Italy.

Understanding molecular recognition of small molecules by proteins in atomistic detail is key for drug design. Molecular docking is a widely used computational method to mimic ligand-protein association in silico. However, predicting conformational changes occurring in proteins upon ligand binding is still a major challenge. Ensemble docking approaches address this issue by considering a set of different conformations of the protein obtained either experimentally or from computer simulations, e.g., molecular dynamics. However, holo structures prone to host (the correct) ligands are generally poorly sampled by standard molecular dynamics simulations of the apo protein. In order to address this limitation, we introduce a computational approach based on metadynamics simulations called ensemble docking with enhanced sampling of pocket shape (EDES) that allows holo-like conformations of proteins to be generated by exploiting only their apo structures. This is achieved by defining a set of collective variables that effectively sample different shapes of the binding site, ultimately mimicking the steric effect due to the ligand. We assessed the method on three challenging proteins undergoing different extents of conformational changes upon ligand binding. In all cases our protocol generates a significant fraction of structures featuring a low RMSD from the experimental holo geometry. Moreover, ensemble docking calculations using those conformations yielded in all cases native-like poses among the top-ranked ones.
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http://dx.doi.org/10.1021/acs.jcim.8b00730DOI Listing
April 2019