Honors & Awards
Dean’s Postdoctoral Fellowship, Stanford School of Medicine (2017)
Niels Stensen Fellowship for Postdoctoral Studies, Niels Stensen Foundation (2016)
Google Summer of Code Fellowship, Google & Open Bioinformatics Foundation (2010)
Bachelor of Science, Universidade De Coimbra (2008)
Master of Science, Utrecht University (2010)
Doctor of Philosophy, Utrecht University (2014)
Michael Levitt, Postdoctoral Faculty Sponsor
Current Research and Scholarly Interests
I am interested in the structure of protein complexes and understanding how it relates to their biological function. Throughout my career, I have developed and applied computational methods to integrate crystallography, NMR, FRET, Cryo-EM, and mutagenesis data to build high-resolution (atomic) models of proteins and protein interactions. I am also interested in education and outreach, and in how computational tools can help the public understand science better.
Michael Levitt, Levitt Lab (1/1/2016)
Publisher Correction: Structural insights into binding specificity, efficacy and bias of a beta2AR partial agonist.
Nature chemical biology
In the version of this paper originally published, the structure for epinephrine shown in Figure 1a was redrawn with an extra carbon. The structure has been replaced in the HTML and PDF versions of the article. The original and corrected versions of the structure are shown below.
View details for DOI 10.1038/s41589-018-0182-5
View details for PubMedID 30504785
Structural insights into binding specificity, efficacy and bias of a beta2AR partial agonist.
Nature chemical biology
2018; 14 (11): 1059–66
Salmeterol is a partial agonist for the beta2 adrenergic receptor (beta2AR) and the first long-acting beta2AR agonist to be widely used clinically for the treatment of asthma and chronic obstructive pulmonary disease. Salmeterol's safety and mechanism of action have both been controversial. To understand its unusual pharmacological action and partial agonism, we obtained the crystal structure of salmeterol-bound beta2AR in complex with an active-state-stabilizing nanobody. The structure reveals the location of the salmeterol exosite, where sequence differences between beta1AR and beta2AR explain the high receptor-subtype selectivity. A structural comparison with the beta2AR bound to the full agonist epinephrine reveals differences in the hydrogen-bond network involving residues Ser2045.43 and Asn2936.55. Mutagenesis and biophysical studies suggested that these interactions lead to a distinct active-state conformation that is responsible for the partial efficacy of G-protein activation and the limited beta-arrestin recruitment for salmeterol.
View details for DOI 10.1038/s41589-018-0145-x
View details for PubMedID 30327561
Proteomic analysis of monolayer-integrated proteins on lipid droplets identifies amphipathic interfacial alpha-helical membrane anchors.
Proceedings of the National Academy of Sciences of the United States of America
Despite not spanning phospholipid bilayers, monotopic integral proteins (MIPs) play critical roles in organizing biochemical reactions on membrane surfaces. Defining the structural basis by which these proteins are anchored to membranes has been hampered by the paucity of unambiguously identified MIPs and a lack of computational tools that accurately distinguish monolayer-integrating motifs from bilayer-spanning transmembrane domains (TMDs). We used quantitative proteomics and statistical modeling to identify 87 high-confidence candidate MIPs in lipid droplets, including 21 proteins with predicted TMDs that cannot be accommodated in these monolayer-enveloped organelles. Systematic cysteine-scanning mutagenesis showed the predicted TMD of one candidate MIP, DHRS3, to be a partially buried amphipathic alpha-helix in both lipid droplet monolayers and the cytoplasmic leaflet of endoplasmic reticulum membrane bilayers. Coarse-grained molecular dynamics simulations support these observations, suggesting that this helix is most stable at the solvent-membrane interface. The simulations also predicted similar interfacial amphipathic helices when applied to seven additional MIPs from our dataset. Our findings suggest that interfacial helices may be a common motif by which MIPs are integrated into membranes, and provide high-throughput methods to identify and study MIPs.
View details for DOI 10.1073/pnas.1807981115
View details for PubMedID 30104359
- Defining distance restraints in HADDOCK. Nature protocols 2018
The solution structure of monomeric CCL5 in complex with a doubly sulfated N-terminal segment of CCR5
2018; 285 (11): 1988–2003
The inflammatory chemokine CCL5, which binds the chemokine receptor CCR5 in a two-step mechanism so as to activate signaling pathways in hematopoetic cells, plays an important role in immune surveillance, inflammation, and development as well as in several immune system pathologies. The recently published crystal structure of CCR5 bound to a high-affinity variant of CCL5 lacks the N-terminal segment of the receptor that is post-translationally sulfated and is known to be important for high-affinity binding. Here, we report the NMR solution structure of monomeric CCL5 bound to a synthetic doubly sulfated peptide corresponding to the missing first 27 residues of CCR5. Our structures show that two sulfated tyrosine residues, sY10 and sY14, as well as the unsulfated Y15 form a network of strong interactions with a groove on a surface of CCL5 that is formed from evolutionarily conserved basic and hydrophobic amino acids. We then use our NMR structures, in combination with available crystal data, to create an atomic model of full-length wild-type CCR5:CCL5. Our findings reveal the structural determinants involved in the recognition of CCL5 by the CCR5 N terminus. These findings, together with existing structural data, provide a complete structural framework with which to understand the specificity of receptor:chemokine interactions.Structural data are available in the PDB under the accession number 6FGP.
View details for DOI 10.1111/febs.14460
View details for Web of Science ID 000434234600005
View details for PubMedID 29619777
SILAC-based phosphoproteomics reveals new PP2A-Cdc55-regulated processes in budding
2018; 7 (5)
Protein phosphatase 2A (PP2A) is a family of conserved serine/threonine phosphatases involved in several essential aspects of cell growth and proliferation. PP2ACdc55 phosphatase has been extensively related to cell cycle events in budding yeast; however, few PP2ACdc55 substrates have been identified. Here, we performed a quantitative mass spectrometry approach to reveal new substrates of PP2ACdc55 phosphatase and new PP2A-related processes in mitotic arrested cells.We identified 62 statistically significant PP2ACdc55 substrates involved mainly in actin-cytoskeleton organization. In addition, we validated new PP2ACdc55 substrates such as Slk19 and Lte1, involved in early and late anaphase pathways, and Zeo1, a component of the cell wall integrity pathway. Finally, we constructed docking models of Cdc55 and its substrate Mob1. We found that the predominant interface on Cdc55 is mediated by a protruding loop consisting of residues 84-90, thus highlighting the relevance of these aminoacids for substrate interaction.We used phosphoproteomics of Cdc55-deficient cells to uncover new PP2ACdc55 substrates and functions in mitosis. As expected, several hyperphosphorylated proteins corresponded to Cdk1-dependent substrates, although other kinases' consensus motifs were also enriched in our dataset, suggesting that PP2ACdc55 counteracts and regulates other kinases distinct from Cdk1. Indeed, Pkc1 emerged as a novel node of PP2ACdc55 regulation, highlighting a major role of PP2ACdc55 in actin cytoskeleton and cytokinesis, gene ontology terms significantly enriched in the PP2ACdc55-dependent phosphoproteome.
View details for DOI 10.1093/gigascience/giy047
View details for Web of Science ID 000438568000001
View details for PubMedID 29688323
View details for PubMedCentralID PMC5967524
Performance of HADDOCK and a simple contact-based protein-ligand binding affinity predictor in the D3R Grand Challenge 2.
Journal of computer-aided molecular design
2018; 32 (1): 175–85
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.
View details for DOI 10.1007/s10822-017-0049-y
View details for PubMedID 28831657
View details for PubMedCentralID PMC5767195
pdb-tools: a swiss army knife for molecular structures.
2018; 7: 1961
The pdb-tools are a collection of Python scripts for working with molecular structure data in the Protein Data Bank (PDB) format. They allow users to edit, convert, and validate PDB files, from the command-line, in a simple but efficient manner. The pdb-tools are implemented in Python, without any external dependencies, and are freely available under the open-source Apache License at https://github.com/haddocking/pdb-tools/ and on PyPI.
View details for DOI 10.12688/f1000research.17456.1
View details for PubMedID 30705752
Template-based protein-protein docking exploiting pairwise interfacial residue restraints.
Briefings in bioinformatics
2017; 18 (3): 458-466
Although many advanced and sophisticated ab initio approaches for modeling protein-protein complexes have been proposed in past decades, template-based modeling (TBM) remains the most accurate and widely used approach, given a reliable template is available. However, there are many different ways to exploit template information in the modeling process. Here, we systematically evaluate and benchmark a TBM method that uses conserved interfacial residue pairs as docking distance restraints [referred to as alpha carbon-alpha carbon (CA-CA)-guided docking]. We compare it with two other template-based protein-protein modeling approaches, including a conserved non-pairwise interfacial residue restrained docking approach [referred to as the ambiguous interaction restraint (AIR)-guided docking] and a simple superposition-based modeling approach. Our results show that, for most cases, the CA-CA-guided docking method outperforms both superposition with refinement and the AIR-guided docking method. We emphasize the superiority of the CA-CA-guided docking on cases with medium to large conformational changes, and interactions mediated through loops, tails or disordered regions. Our results also underscore the importance of a proper refinement of superimposition models to reduce steric clashes. In summary, we provide a benchmarked TBM protocol that uses conserved pairwise interface distance as restraints in generating realistic 3D protein-protein interaction models, when reliable templates are available. The described CA-CA-guided docking protocol is based on the HADDOCK platform, which allows users to incorporate additional prior knowledge of the target system to further improve the quality of the resulting models.
View details for DOI 10.1093/bib/bbw027
View details for PubMedID 27013645
View details for PubMedCentralID PMC5428999
Structural and Functional Analysis of a beta(2)-Adrenergic Receptor Complex with GRK5
2017; 169 (3): 407-421 e16
The phosphorylation of agonist-occupied G-protein-coupled receptors (GPCRs) by GPCR kinases (GRKs) functions to turn off G-protein signaling and turn on arrestin-mediated signaling. While a structural understanding of GPCR/G-protein and GPCR/arrestin complexes has emerged in recent years, the molecular architecture of a GPCR/GRK complex remains poorly defined. We used a comprehensive integrated approach of cross-linking, hydrogen-deuterium exchange mass spectrometry (MS), electron microscopy, mutagenesis, molecular dynamics simulations, and computational docking to analyze GRK5 interaction with the β2-adrenergic receptor (β2AR). These studies revealed a dynamic mechanism of complex formation that involves large conformational changes in the GRK5 RH/catalytic domain interface upon receptor binding. These changes facilitate contacts between intracellular loops 2 and 3 and the C terminus of the β2AR with the GRK5 RH bundle subdomain, membrane-binding surface, and kinase catalytic cleft, respectively. These studies significantly contribute to our understanding of the mechanism by which GRKs regulate the function of activated GPCRs. PAPERCLIP.
View details for DOI 10.1016/j.cell.2017.03.047
View details for PubMedID 28431242
- Sense and Simplicity in HADDOCK Scoring: Lessons from CASP-CAPRI (page 418). Proteins 2017; 85 (8): 1589–90
M3: an integrative framework for structure determination of molecular machines.
2017; 14 (9): 897–902
We present a broadly applicable, user-friendly protocol that incorporates sparse and hybrid experimental data to calculate quasi-atomic-resolution structures of molecular machines. The protocol uses the HADDOCK framework, accounts for extensive structural rearrangements both at the domain and atomic levels and accepts input from all structural and biochemical experiments whose data can be translated into interatomic distances and/or molecular shapes.
View details for DOI 10.1038/nmeth.4392
View details for PubMedID 28805795
Supramolecular Organization and Functional Implications of K+ Channel Clusters in Membranes.
Angewandte Chemie (International ed. in English)
2017; 56 (43): 13222–27
The segregation of cellular surfaces in heterogeneous patches is considered to be a common motif in bacteria and eukaryotes that is underpinned by the observation of clustering and cooperative gating of signaling membrane proteins such as receptors or channels. Such processes could represent an important cellular strategy to shape signaling activity. Hence, structural knowledge of the arrangement of channels or receptors in supramolecular assemblies represents a crucial step towards a better understanding of signaling across membranes. We herein report on the supramolecular organization of clusters of the K+channel KcsA in bacterial membranes, which was analyzed by a combination of DNP-enhanced solid-state NMR experiments and MD simulations. We used solid-state NMR spectroscopy to determine the channel-channel interface and to demonstrate the strong correlation between channel function and clustering, which suggests a yet unknown mechanism of communication between K+channels.
View details for DOI 10.1002/anie.201705723
View details for PubMedID 28685953
View details for PubMedCentralID PMC5655921
Augmenting Research, Education, and Outreach with Client-Side Web Programming.
Trends in biotechnology
View details for DOI 10.1016/j.tibtech.2017.11.009
View details for PubMedID 29254737
Information-Driven, Ensemble Flexible Peptide Docking Using HADDOCK.
Methods in molecular biology (Clifton, N.J.)
2017; 1561: 109-138
Modeling protein-peptide interactions remains a significant challenge for docking programs due to the inherent highly flexible nature of peptides, which often adopt different conformations whether in their free or bound forms. We present here a protocol consisting of a hybrid approach, combining the most frequently found peptide conformations in complexes with representative conformations taken from molecular dynamics simulations of the free peptide. This approach intends to broaden the range of conformations sampled during docking. The resulting ensemble of conformations is used as a starting point for information-driven flexible docking with HADDOCK. We demonstrate the performance of this protocol on six cases of increasing difficulty, taken from a protein-peptide benchmark set. In each case, we use knowledge of the binding site on the receptor to drive the docking process. In the majority of cases where MD conformations are added to the starting ensemble for docking, we observe an improvement in the quality of the resulting models.
View details for DOI 10.1007/978-1-4939-6798-8_8
View details for PubMedID 28236236
PRODIGY: a web server for predicting the binding affinity of protein-protein complexes
2016; 32 (23): 3676-3678
Gaining insights into the structural determinants of protein-protein interactions holds the key for a deeper understanding of biological functions, diseases and development of therapeutics. An important aspect of this is the ability to accurately predict the binding strength for a given protein-protein complex. Here we present PROtein binDIng enerGY prediction (PRODIGY), a web server to predict the binding affinity of protein-protein complexes from their 3D structure. The PRODIGY server implements our simple but highly effective predictive model based on intermolecular contacts and properties derived from non-interface surface.PRODIGY is freely available at: http://milou.science.uu.nl/services/PRODIGY CONTACT: firstname.lastname@example.org, email@example.com.
View details for DOI 10.1093/bioinformatics/btw514
View details for Web of Science ID 000392749500021
View details for PubMedID 27503228
Prediction of homoprotein and heteroprotein complexes by protein docking and template-based modeling: A CASP-CAPRI experiment
PROTEINS-STRUCTURE FUNCTION AND BIOINFORMATICS
2016; 84: 323-348
We present the results for CAPRI Round 30, the first joint CASP-CAPRI experiment, which brought together experts from the protein structure prediction and protein-protein docking communities. The Round comprised 25 targets from amongst those submitted for the CASP11 prediction experiment of 2014. The targets included mostly homodimers, a few homotetramers, and two heterodimers, and comprised protein chains that could readily be modeled using templates from the Protein Data Bank. On average 24 CAPRI groups and 7 CASP groups submitted docking predictions for each target, and 12 CAPRI groups per target participated in the CAPRI scoring experiment. In total more than 9500 models were assessed against the 3D structures of the corresponding target complexes. Results show that the prediction of homodimer assemblies by homology modeling techniques and docking calculations is quite successful for targets featuring large enough subunit interfaces to represent stable associations. Targets with ambiguous or inaccurate oligomeric state assignments, often featuring crystal contact-sized interfaces, represented a confounding factor. For those, a much poorer prediction performance was achieved, while nonetheless often providing helpful clues on the correct oligomeric state of the protein. The prediction performance was very poor for genuine tetrameric targets, where the inaccuracy of the homology-built subunit models and the smaller pair-wise interfaces severely limited the ability to derive the correct assembly mode. Our analysis also shows that docking procedures tend to perform better than standard homology modeling techniques and that highly accurate models of the protein components are not always required to identify their association modes with acceptable accuracy. Proteins 2016; 84(Suppl 1):323-348. © 2016 Wiley Periodicals, Inc.
View details for DOI 10.1002/prot.25007
View details for Web of Science ID 000399417500025
View details for PubMedID 27122118
View details for PubMedCentralID PMC5030136
New Insight into the Catalytic Mechanism of Bacterial MraY from Enzyme Kinetics and Docking Studies
JOURNAL OF BIOLOGICAL CHEMISTRY
2016; 291 (29): 15057-15068
Phospho-MurNAc-pentapeptide translocase (MraY) catalyzes the synthesis of Lipid I, a bacterial peptidoglycan precursor. As such, MraY is essential for bacterial survival and therefore is an ideal target for developing novel antibiotics. However, the understanding of its catalytic mechanism, despite the recently determined crystal structure, remains limited. In the present study, the kinetic properties of Bacillus subtilis MraY (BsMraY) were investigated by fluorescence enhancement using dansylated UDP-MurNAc-pentapeptide and heptaprenyl phosphate (C35-P, short-chain homolog of undecaprenyl phosphate, the endogenous substrate of MraY) as second substrate. Varying the concentrations of both of these substrates and fitting the kinetics data to two-substrate models showed that the concomitant binding of both UDP-MurNAc-pentapeptide-DNS and C35-P to the enzyme is required before the release of the two products, Lipid I and UMP. We built a model of BsMraY and performed docking studies with the substrate C35-P to further deepen our understanding of how MraY accommodates this lipid substrate. Based on these modeling studies, a novel catalytic role was put forward for a fully conserved histidine residue in MraY (His-289 in BsMraY), which has been experimentally confirmed to be essential for MraY activity. Using the current model of BsMraY, we propose that a small conformational change is necessary to relocate the His-289 residue, such that the translocase reaction can proceed via a nucleophilic attack of the phosphate moiety of C35-P on bound UDP-MurNAc-pentapeptide.
View details for DOI 10.1074/jbc.M116.717884
View details for Web of Science ID 000380583200017
View details for PubMedID 27226570
View details for PubMedCentralID PMC4946923
Molecular dynamics characterization of the conformational landscape of small peptides: A series of hands-on collaborative practical sessions for undergraduate students
BIOCHEMISTRY AND MOLECULAR BIOLOGY EDUCATION
2016; 44 (2): 160-167
Molecular modelling and simulations are nowadays an integral part of research in areas ranging from physics to chemistry to structural biology, as well as pharmaceutical drug design. This popularity is due to the development of high-performance hardware and of accurate and efficient molecular mechanics algorithms by the scientific community. These improvements are also benefitting scientific education. Molecular simulations, their underlying theory, and their applications are particularly difficult to grasp for undergraduate students. Having hands-on experience with the methods contributes to a better understanding and solidification of the concepts taught during the lectures. To this end, we have created a computer practical class, which has been running for the past five years, composed of several sessions where students characterize the conformational landscape of small peptides using molecular dynamics simulations in order to gain insights on their binding to protein receptors. In this report, we detail the ingredients and recipe necessary to establish and carry out this practical, as well as some of the questions posed to the students and their expected results. Further, we cite some examples of the students' written reports, provide statistics, and share their feedbacks on the structure and execution of the sessions. These sessions were implemented alongside a theoretical molecular modelling course but have also been used successfully as a standalone tutorial during specialized workshops. The availability of the material on our web page also facilitates this integration and dissemination and lends strength to the thesis of open-source science and education.
View details for DOI 10.1002/bmb.20941
View details for Web of Science ID 000373008100006
View details for PubMedID 26751257
The HADDOCK2.2 Web Server: User-Friendly Integrative Modeling of Biomolecular Complexes
JOURNAL OF MOLECULAR BIOLOGY
2016; 428 (4): 720-725
The prediction of the quaternary structure of biomolecular macromolecules is of paramount importance for fundamental understanding of cellular processes and drug design. In the era of integrative structural biology, one way of increasing the accuracy of modeling methods used to predict the structure of biomolecular complexes is to include as much experimental or predictive information as possible in the process. This has been at the core of our information-driven docking approach HADDOCK. We present here the updated version 2.2 of the HADDOCK portal, which offers new features such as support for mixed molecule types, additional experimental restraints and improved protocols, all of this in a user-friendly interface. With well over 6000 registered users and 108,000 jobs served, an increasing fraction of which on grid resources, we hope that this timely upgrade will help the community to solve important biological questions and further advance the field. The HADDOCK2.2 Web server is freely accessible to non-profit users at http://haddock.science.uu.nl/services/HADDOCK2.2.
View details for DOI 10.1016/j.jmb.2015.09.014
View details for Web of Science ID 000372561800008
View details for PubMedID 26410586
The Supramolecular Organization of a Peptide-Based Nanocarrier at High Molecular Detail
JOURNAL OF THE AMERICAN CHEMICAL SOCIETY
2015; 137 (24): 7775-7784
Nanovesicles self-assembled from amphiphilic peptides are promising candidates for applications in drug delivery. However, complete high-resolution data on the local and supramolecular organization of such materials has been elusive thus far, which is a substantial obstacle to their rational design. In the absence of precise information, nanovesicles built of amphiphilic "lipid-like" peptides are generally assumed to resemble liposomes that are organized from bilayers of peptides with a tail-to-tail ordering. Using the nanocarrier formed by the amphiphilic self-assembling peptide 2 (SA2 peptide) as an example, we derive the local and global organization of a multimega-Dalton peptide-based nanocarrier at high molecular detail and at close-to physiological conditions. By integrating a multitude of experimental techniques (solid-state NMR, AFM, SLS, DLS, FT-IR, CD) with large- and multiscale MD simulations, we show that SA2 nanocarriers are built of interdigitated antiparallel β-sheets, which bear little resemblance to phospholipid liposomes. Our atomic level study allows analyzing the vesicle surface structure and dynamics as well as the intermolecular forces between peptides, providing a number of potential leads to improve and tune the biophysical properties of the nanocarrier. The herein presented approach may be of general utility to investigate peptide-based nanomaterials at high-resolution and at physiological conditions.
View details for DOI 10.1021/jacs.5b02919
View details for Web of Science ID 000357062000045
View details for PubMedID 26022089
Information-driven structural modelling of protein-protein interactions.
Methods in molecular biology (Clifton, N.J.)
2015; 1215: 399-424
Protein-protein docking aims at predicting the three-dimensional structure of a protein complex starting from the free forms of the individual partners. As assessed in the CAPRI community-wide experiment, the most successful docking algorithms combine pure laws of physics with information derived from various experimental or bioinformatics sources. Of these so-called "information-driven" approaches, HADDOCK stands out as one of the most successful representatives. In this chapter, we briefly summarize which experimental information can be used to drive the docking prediction in HADDOCK, and then focus on the docking protocol itself. We discuss and illustrate with a tutorial example a "classical" protein-protein docking prediction, as well as more recent developments for modelling multi-body systems and large conformational changes.
View details for DOI 10.1007/978-1-4939-1465-4_18
View details for PubMedID 25330973
Binding Hotspots of BAZ2B Bromodomain: Histone Interaction Revealed by Solution NMR Driven Docking
2014; 53 (42): 6706-6716
Bromodomains are epigenetic reader domains, which have come under increasing scrutiny both from academic and pharmaceutical research groups. Effective targeting of the BAZ2B bromodomain by small molecule inhibitors has been recently reported, but no structural information is yet available on the interaction with its natural binding partner, acetylated histone H3K14ac. We have assigned the BAZ2B bromodomain and studied its interaction with H3K14ac acetylated peptides by NMR spectroscopy using both chemical shift perturbation (CSP) data and clean chemical exchange (CLEANEX-PM) NMR experiments. The latter was used to characterize water molecules known to play an important role in mediating interactions. Besides the anticipated Kac binding site, we consistently found the bromodomain BC loop as hotspots for the interaction. This information was used to create a data-driven model for the complex using HADDOCK. Our findings provide both structure and dynamics characterization that will be useful in the quest for potent and selective inhibitors to probe the function of the BAZ2B bromodomain.
View details for DOI 10.1021/bi500909d
View details for Web of Science ID 000343949800009
View details for PubMedID 25266743
View details for PubMedCentralID PMC4458377
Sequence co-evolution gives 3D contacts and structures of protein complexes
Protein-protein interactions are fundamental to many biological processes. Experimental screens have identified tens of thousands of interactions, and structural biology has provided detailed functional insight for select 3D protein complexes. An alternative rich source of information about protein interactions is the evolutionary sequence record. Building on earlier work, we show that analysis of correlated evolutionary sequence changes across proteins identifies residues that are close in space with sufficient accuracy to determine the three-dimensional structure of the protein complexes. We evaluate prediction performance in blinded tests on 76 complexes of known 3D structure, predict protein-protein contacts in 32 complexes of unknown structure, and demonstrate how evolutionary couplings can be used to distinguish between interacting and non-interacting protein pairs in a large complex. With the current growth of sequences, we expect that the method can be generalized to genome-wide elucidation of protein-protein interaction networks and used for interaction predictions at residue resolution.
View details for DOI 10.7554/eLife.03430
View details for Web of Science ID 000342126700002
View details for PubMedID 25255213
View details for PubMedCentralID PMC4360534
Proteins Feel More Than They See: Fine-Tuning of Binding Affinity by Properties of the Non-Interacting Surface
JOURNAL OF MOLECULAR BIOLOGY
2014; 426 (14): 2632-2652
Protein-protein complexes orchestrate most cellular processes such as transcription, signal transduction and apoptosis. The factors governing their affinity remain elusive however, especially when it comes to describing dissociation rates (koff). Here we demonstrate that, next to direct contributions from the interface, the non-interacting surface (NIS) also plays an important role in binding affinity, especially polar and charged residues. Their percentage on the NIS is conserved over orthologous complexes indicating an evolutionary selection pressure. Their effect on binding affinity can be explained by long-range electrostatic contributions and surface-solvent interactions that are known to determine the local frustration of the protein complex surface. Including these in a simple model significantly improves the affinity prediction of protein complexes from structural models. The impact of mutations outside the interacting surface on binding affinity is supported by experimental alanine scanning mutagenesis data. These results enable the development of more sophisticated and integrated biophysical models of binding affinity and open new directions in experimental control and modulation of biomolecular interactions.
View details for DOI 10.1016/j.jmb.2014.04.017
View details for Web of Science ID 000339037700009
View details for PubMedID 24768922
Integrative computational modeling of protein interactions
2014; 281 (8): 1988-2003
Protein interactions define the homeostatic state of the cell. Our ability to understand these interactions and their role in both health and disease is tied to our knowledge of the 3D atomic structure of the interacting partners and their complexes. Despite advances in experimental method of structure determination, the majority of known protein interactions are still missing an atomic structure. High-resolution methods such as X-ray crystallography and NMR spectroscopy struggle with the high-throughput demand, while low-resolution techniques such as cryo-electron microscopy or small-angle X-ray scattering provide data that are too coarse. Computational structure prediction of protein complexes, or docking, was first developed to complement experimental research and has since blossomed into an independent and lively field of research. Its most successful products are hybrid approaches that combine powerful algorithms with experimental data from various sources to generate high-resolution models of protein complexes. This minireview introduces the concept of docking and docking with the help of experimental data, compares and contrasts the available integrative docking methods, and provides a guide for the experimental researcher for what types of data and which particular software can be used to model a protein complex.
View details for DOI 10.1111/febs.12771
View details for Web of Science ID 000334603500006
View details for PubMedID 24588898
HADDOCK(2P2I): A Biophysical Model for Predicting the Binding Affinity of Protein-Protein Interaction Inhibitors
JOURNAL OF CHEMICAL INFORMATION AND MODELING
2014; 54 (3): 826-836
The HADDOCK score, a scoring function for both protein-protein and protein-nucleic acid modeling, has been successful in selecting near-native docking poses in a variety of cases, including those of the CAPRI blind prediction experiment. However, it has yet to be optimized for small molecules, and in particular inhibitors of protein-protein interactions, that constitute an "unmined gold reserve" for drug design ventures. We describe here HADDOCK(2P2I), a biophysical model capable of predicting the binding affinity of protein-protein complex inhibitors close to experimental error (~2-fold larger). The algorithm was trained and 4-fold cross-validated against experimental data for 27 inhibitors targeting 7 protein-protein complexes of various functions and tested on an independent set of 24 different inhibitors for which K(d)/IC50 data are available. In addition, two popular ligand topology generation and parametrization methods (ACPYPE and PRODRG) were assessed. The resulting HADDOCK(2P2I) model, derived from the original HADDOCK score, provides insights into inhibition determinants: while the role of electrostatics and desolvation energies is case-dependent, the interface area plays a more critical role compared to protein-protein interactions.
View details for DOI 10.1021/ci4005332
View details for Web of Science ID 000333478800014
View details for PubMedID 24521147
View details for PubMedCentralID PMC3966529
Defining the limits of homology modeling in information-driven protein docking
PROTEINS-STRUCTURE FUNCTION AND BIOINFORMATICS
2013; 81 (12): 2119-2128
Information-driven docking is currently one of the most successful approaches to obtain structural models of protein interactions as demonstrated in the latest round of CAPRI. While various experimental and computational techniques can be used to retrieve information about the binding mode, the availability of three-dimensional structures of the interacting partners remains a limiting factor. Fortunately, the wealth of structural information gathered by large-scale initiatives allows for homology-based modeling of a significant fraction of the protein universe. Defining the limits of information-driven docking based on such homology models is therefore highly relevant. Here we show, using previous CAPRI targets, that out of a variety of measures, the global sequence identity between template and target is a simple but reliable predictor of the achievable quality of the docking models. This indicates that a well-defined overall fold is critical for the interaction. Furthermore, the quality of the data at our disposal to characterize the interaction plays a determinant role in the success of the docking. Given reliable interface information we can obtain acceptable predictions even at low global sequence identity. These results, which define the boundaries between trustworthy and unreliable predictions, should guide both experts and nonexperts in defining the limits of what is achievable by docking. This is highly relevant considering that the fraction of the interactome amenable for docking is only bound to grow as the number of experimentally solved structures increases.
View details for DOI 10.1002/prot.24382
View details for Web of Science ID 000327344300006
View details for PubMedID 23913867
Community-wide evaluation of methods for predicting the effect of mutations on protein-protein interactions
PROTEINS-STRUCTURE FUNCTION AND BIOINFORMATICS
2013; 81 (11): 1980-1987
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.
View details for DOI 10.1002/prot.24356
View details for Web of Science ID 000325980300011
View details for PubMedID 23843247
View details for PubMedCentralID PMC4143140
- Unveiling the Interaction of Vanadium Compounds with Human Serum Albumin by Using H-1 STD NMR and Computational Docking Studies EUROPEAN JOURNAL OF INORGANIC CHEMISTRY 2013; 2013 (26): 4619-4627
- KoBaMIN: a knowledge-based minimization web server for protein structure refinement NUCLEIC ACIDS RESEARCH 2012; 40 (W1): W323-W328
Clustering biomolecular complexes by residue contacts similarity
PROTEINS-STRUCTURE FUNCTION AND BIOINFORMATICS
2012; 80 (7): 1810-1817
Inaccuracies in computational molecular modeling methods are often counterweighed by brute-force generation of a plethora of putative solutions. These are then typically sieved via structural clustering based on similarity measures such as the root mean square deviation (RMSD) of atomic positions. Albeit widely used, these measures suffer from several theoretical and technical limitations (e.g., choice of regions for fitting) that impair their application in multicomponent systems (N > 2), large-scale studies (e.g., interactomes), and other time-critical scenarios. We present here a simple similarity measure for structural clustering based on atomic contacts--the fraction of common contacts--and compare it with the most used similarity measure of the protein docking community--interface backbone RMSD. We show that this method produces very compact clusters in remarkably short time when applied to a collection of binary and multicomponent protein-protein and protein-DNA complexes. Furthermore, it allows easy clustering of similar conformations of multicomponent symmetrical assemblies in which chain permutations can occur. Simple contact-based metrics should be applicable to other structural biology clustering problems, in particular for time-critical or large-scale endeavors.
View details for DOI 10.1002/prot.24078
View details for Web of Science ID 000304866000009
View details for PubMedID 22489062
Strengths and weaknesses of data-driven docking in critical assessment of prediction of interactions
PROTEINS-STRUCTURE FUNCTION AND BIOINFORMATICS
2010; 78 (15): 3242-3249
The recent CAPRI rounds have introduced new docking challenges in the form of protein-RNA complexes, multiple alternative interfaces, and an unprecedented number of targets for which homology modeling was required. We present here the performance of HADDOCK and its web server in the CAPRI experiment and discuss the strengths and weaknesses of data-driven docking. HADDOCK was successful for 6 out of 9 complexes (6 out of 11 targets) and accurately predicted the individual interfaces for two more complexes. The HADDOCK server, which is the first allowing the simultaneous docking of generic multi-body complexes, was successful in 4 out of 7 complexes for which it participated. In the scoring experiment, we predicted the highest number of targets of any group. The main weakness of data-driven docking revealed from these last CAPRI results is its vulnerability for incorrect experimental data related to the interface or the stoichiometry of the complex. At the same time, the use of experimental and/or predicted information is also the strength of our approach as evidenced for those targets for which accurate experimental information was available (e.g., the 10 three-stars predictions for T40!). Even when the models show a wrong orientation, the individual interfaces are generally well predicted with an average coverage of 60% ± 26% over all targets. This makes data-driven docking particularly valuable in a biological context to guide experimental studies like, for example, targeted mutagenesis.
View details for DOI 10.1002/prot.22814
View details for Web of Science ID 000283565000024
View details for PubMedID 20718048