51
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Abstract
Protein-protein interfaces regulate many critical processes for cellular function. The ability to accurately control and regulate these molecular interactions is of major interest for biomedical and synthetic biology applications, as well as to address fundamental biological questions. In recent years, computational protein design has emerged as a tool for designing novel protein-protein interactions with functional relevance. Although attractive, these computational tools carry a steep learning curve. In order to make some of these methods more accessible, we present detailed descriptions and examples of ROSETTA computational protocols for the design of functional protein binders using seeded protein interface design. In these protocols, a motif of known structure that interacts with the target site is grafted into a scaffold protein, followed by design of the surrounding interaction surface.
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Affiliation(s)
| | - Bruno E Correia
- Institute of Bioengineering, Ecole polytechnique fédérale de Lausanne, Lausanne, Switzerland
| | - Erik Procko
- Department of Biochemistry, University of Illinois, 600 S. Matthews, Urbana, IL, 61801, USA.
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52
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Ka D, Lee H, Jung YD, Kim K, Seok C, Suh N, Bae E. Crystal Structure of Streptococcus pyogenes Cas1 and Its Interaction with Csn2 in the Type II CRISPR-Cas System. Structure 2015; 24:70-79. [PMID: 26671707 DOI: 10.1016/j.str.2015.10.019] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2015] [Revised: 09/25/2015] [Accepted: 10/20/2015] [Indexed: 11/27/2022]
Abstract
CRISPRs and Cas proteins constitute an RNA-guided microbial immune system against invading nucleic acids. Cas1 is a universal Cas protein found in all three types of CRISPR-Cas systems, and its role is implicated in new spacer acquisition during CRISPR-mediated adaptive immunity. Here, we report the crystal structure of Streptococcus pyogenes Cas1 (SpCas1) in a type II CRISPR-Cas system and characterize its interaction with S. pyogenes Csn2 (SpCsn2). The SpCas1 structure reveals a unique conformational state distinct from type I Cas1 structures, resulting in a more extensive dimerization interface, a more globular overall structure, and a disruption of potential metal-binding sites for catalysis. We demonstrate that SpCas1 directly interacts with SpCsn2, and identify the binding interface and key residues for Cas complex formation. These results provide structural information for a type II Cas1 protein, and lay a foundation for studying multiprotein Cas complexes functioning in type II CRISPR-Cas systems.
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Affiliation(s)
- Donghyun Ka
- Department of Agricultural Biotechnology, Seoul National University, Seoul 151-921, Korea
| | - Hasup Lee
- Department of Chemistry, Seoul National University, Seoul 151-747, Korea
| | - Yi-Deun Jung
- Stem Cell Center, Asan Institute for Life Sciences, Asan Medical Center, Seoul 138-736, Korea
| | - Kyunggon Kim
- Clinical Proteomics Core Lab, Asan Institute for Life Sciences, Asan Medical Center, Seoul 138-736, Korea; Department of Convergence Medicine, University of Ulsan College of Medicine, Seoul 138-736, Korea
| | - Chaok Seok
- Department of Chemistry, Seoul National University, Seoul 151-747, Korea
| | - Nayoung Suh
- Stem Cell Center, Asan Institute for Life Sciences, Asan Medical Center, Seoul 138-736, Korea; Department of Convergence Medicine, University of Ulsan College of Medicine, Seoul 138-736, Korea
| | - Euiyoung Bae
- Department of Agricultural Biotechnology, Seoul National University, Seoul 151-921, Korea; Center for Food and Bioconvergence, Seoul National University, Seoul 151-921, Korea; Research Institute of Agriculture and Life Sciences, Seoul National University, Seoul 151-921, Korea.
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53
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Abstract
We report the performance of our approaches for protein-protein docking and interface analysis in CAPRI rounds 20-26. At the core of our pipeline was the ZDOCK program for rigid-body protein-protein docking. We then reranked the ZDOCK predictions using the ZRANK or IRAD scoring functions, pruned and analyzed energy landscapes using clustering, and analyzed the docking results using our interface prediction approach RCF. When possible, we used biological information from the literature to apply constraints to the search space during or after the ZDOCK runs. For approximately half of the standard docking challenges we made at least one prediction that was acceptable or better. For the scoring challenges we made acceptable or better predictions for all but one target. This indicates that our scoring functions are generally able to select the correct binding mode.
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Affiliation(s)
- Thom Vreven
- Program in Bioinformatics and Integrative Biology, University of Massachusetts Medical School, Worcester, Massachusetts, 01605
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54
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Computational design and experimental verification of a symmetric protein homodimer. Proc Natl Acad Sci U S A 2015; 112:10714-9. [PMID: 26269568 DOI: 10.1073/pnas.1505072112] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
Homodimers are the most common type of protein assembly in nature and have distinct features compared with heterodimers and higher order oligomers. Understanding homodimer interactions at the atomic level is critical both for elucidating their biological mechanisms of action and for accurate modeling of complexes of unknown structure. Computation-based design of novel protein-protein interfaces can serve as a bottom-up method to further our understanding of protein interactions. Previous studies have demonstrated that the de novo design of homodimers can be achieved to atomic-level accuracy by β-strand assembly or through metal-mediated interactions. Here, we report the design and experimental characterization of a α-helix-mediated homodimer with C2 symmetry based on a monomeric Drosophila engrailed homeodomain scaffold. A solution NMR structure shows that the homodimer exhibits parallel helical packing similar to the design model. Because the mutations leading to dimer formation resulted in poor thermostability of the system, design success was facilitated by the introduction of independent thermostabilizing mutations into the scaffold. This two-step design approach, function and stabilization, is likely to be generally applicable, especially if the desired scaffold is of low thermostability.
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55
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Haliloglu T, Bahar I. Adaptability of protein structures to enable functional interactions and evolutionary implications. Curr Opin Struct Biol 2015; 35:17-23. [PMID: 26254902 DOI: 10.1016/j.sbi.2015.07.007] [Citation(s) in RCA: 75] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2015] [Revised: 07/15/2015] [Accepted: 07/20/2015] [Indexed: 12/21/2022]
Abstract
Several studies in recent years have drawn attention to the ability of proteins to adapt to intermolecular interactions by conformational changes along structure-encoded collective modes of motions. These so-called soft modes, primarily driven by entropic effects, facilitate, if not enable, functional interactions. They represent excursions on the conformational space along principal low-ascent directions/paths away from the original free energy minimum, and they are accessible to the protein even before protein-protein/ligand interactions. An emerging concept from these studies is the evolution of structures or modular domains to favor such modes of motion that will be recruited or integrated for enabling functional interactions. Structural dynamics, including the allosteric switches in conformation that are often stabilized upon formation of complexes and multimeric assemblies, emerge as key properties that are evolutionarily maintained to accomplish biological activities, consistent with the paradigm sequence→structure→dynamics→function where 'dynamics' bridges structure and function.
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Affiliation(s)
- Turkan Haliloglu
- Department of Chemical Engineering and Polymer Research Center, and Center for Life Sciences and Technologies, Bogazici University, 34342 Istanbul, Turkey; Department of Computational and Systems Biology, School of Medicine, University of Pittsburgh, Pittsburgh, PA 15260, USA
| | - Ivet Bahar
- Department of Computational and Systems Biology, School of Medicine, University of Pittsburgh, Pittsburgh, PA 15260, USA.
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56
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Lapidoth GD, Baran D, Pszolla GM, Norn C, Alon A, Tyka MD, Fleishman SJ. AbDesign: An algorithm for combinatorial backbone design guided by natural conformations and sequences. Proteins 2015; 83:1385-406. [PMID: 25670500 PMCID: PMC4881815 DOI: 10.1002/prot.24779] [Citation(s) in RCA: 65] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2014] [Revised: 01/13/2015] [Accepted: 01/26/2015] [Indexed: 12/20/2022]
Abstract
Computational design of protein function has made substantial progress, generating new enzymes, binders, inhibitors, and nanomaterials not previously seen in nature. However, the ability to design new protein backbones for function--essential to exert control over all polypeptide degrees of freedom--remains a critical challenge. Most previous attempts to design new backbones computed the mainchain from scratch. Here, instead, we describe a combinatorial backbone and sequence optimization algorithm called AbDesign, which leverages the large number of sequences and experimentally determined molecular structures of antibodies to construct new antibody models, dock them against target surfaces and optimize their sequence and backbone conformation for high stability and binding affinity. We used the algorithm to produce antibody designs that target the same molecular surfaces as nine natural, high-affinity antibodies; in five cases interface sequence identity is above 30%, and in four of those the backbone conformation at the core of the antibody binding surface is within 1 Å root-mean square deviation from the natural antibodies. Designs recapitulate polar interaction networks observed in natural complexes, and amino acid sidechain rigidity at the designed binding surface, which is likely important for affinity and specificity, is high compared to previous design studies. In designed anti-lysozyme antibodies, complementarity-determining regions (CDRs) at the periphery of the interface, such as L1 and H2, show greater backbone conformation diversity than the CDRs at the core of the interface, and increase the binding surface area compared to the natural antibody, potentially enhancing affinity and specificity.
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Affiliation(s)
- Gideon D. Lapidoth
- Department of Biological Chemistry, Weizmann Institute of Science, Rehovot 76100, Israel
| | - Dror Baran
- Department of Biological Chemistry, Weizmann Institute of Science, Rehovot 76100, Israel
| | - Gabriele M. Pszolla
- Department of Biological Chemistry, Weizmann Institute of Science, Rehovot 76100, Israel
| | - Christoffer Norn
- Department of Biological Chemistry, Weizmann Institute of Science, Rehovot 76100, Israel
| | - Assaf Alon
- Department of Biological Chemistry, Weizmann Institute of Science, Rehovot 76100, Israel
| | - Michael D. Tyka
- Google Inc., 1600 Amphitheatre Pkwy, Mountain View, CA 94043
| | - Sarel J. Fleishman
- Department of Biological Chemistry, Weizmann Institute of Science, Rehovot 76100, Israel
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57
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Vangone A, Bonvin AM. Contacts-based prediction of binding affinity in protein-protein complexes. eLife 2015. [PMID: 26193119 DOI: 10.7554/elife07454] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/12/2023] Open
Abstract
Almost all critical functions in cells rely on specific protein-protein interactions. Understanding these is therefore crucial in the investigation of biological systems. Despite all past efforts, we still lack a thorough understanding of the energetics of association of proteins. Here, we introduce a new and simple approach to predict binding affinity based on functional and structural features of the biological system, namely the network of interfacial contacts. We assess its performance against a protein-protein binding affinity benchmark and show that both experimental methods used for affinity measurements and conformational changes have a strong impact on prediction accuracy. Using a subset of complexes with reliable experimental binding affinities and combining our contacts and contact-types-based model with recent observations on the role of the non-interacting surface in protein-protein interactions, we reach a high prediction accuracy for such a diverse dataset outperforming all other tested methods.
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Affiliation(s)
- Anna Vangone
- Computational Structural Biology Group, Bijvoet Center for Biomolecular Research, Faculty of Science-Chemistry, Utrecht University, Utrecht, Netherlands
| | - Alexandre Mjj Bonvin
- Computational Structural Biology Group, Bijvoet Center for Biomolecular Research, Faculty of Science-Chemistry, Utrecht University, Utrecht, Netherlands
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58
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Vangone A, Bonvin AMJJ. Contacts-based prediction of binding affinity in protein-protein complexes. eLife 2015; 4:e07454. [PMID: 26193119 PMCID: PMC4523921 DOI: 10.7554/elife.07454] [Citation(s) in RCA: 313] [Impact Index Per Article: 34.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2015] [Accepted: 07/08/2015] [Indexed: 12/13/2022] Open
Abstract
Almost all critical functions in cells rely on specific protein-protein interactions. Understanding these is therefore crucial in the investigation of biological systems. Despite all past efforts, we still lack a thorough understanding of the energetics of association of proteins. Here, we introduce a new and simple approach to predict binding affinity based on functional and structural features of the biological system, namely the network of interfacial contacts. We assess its performance against a protein-protein binding affinity benchmark and show that both experimental methods used for affinity measurements and conformational changes have a strong impact on prediction accuracy. Using a subset of complexes with reliable experimental binding affinities and combining our contacts and contact-types-based model with recent observations on the role of the non-interacting surface in protein-protein interactions, we reach a high prediction accuracy for such a diverse dataset outperforming all other tested methods.
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Affiliation(s)
- Anna Vangone
- Computational Structural Biology Group, Bijvoet Center for Biomolecular Research, Faculty of Science—Chemistry, Utrecht University, Utrecht, Netherlands
| | - Alexandre MJJ Bonvin
- Computational Structural Biology Group, Bijvoet Center for Biomolecular Research, Faculty of Science—Chemistry, Utrecht University, Utrecht, Netherlands
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59
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Jiang H, Sheong FK, Zhu L, Gao X, Bernauer J, Huang X. Markov State Models Reveal a Two-Step Mechanism of miRNA Loading into the Human Argonaute Protein: Selective Binding followed by Structural Re-arrangement. PLoS Comput Biol 2015; 11:e1004404. [PMID: 26181723 PMCID: PMC4504477 DOI: 10.1371/journal.pcbi.1004404] [Citation(s) in RCA: 48] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2015] [Accepted: 06/16/2015] [Indexed: 01/17/2023] Open
Abstract
Argonaute (Ago) proteins and microRNAs (miRNAs) are central components in RNA interference, which is a key cellular mechanism for sequence-specific gene silencing. Despite intensive studies, molecular mechanisms of how Ago recognizes miRNA remain largely elusive. In this study, we propose a two-step mechanism for this molecular recognition: selective binding followed by structural re-arrangement. Our model is based on the results of a combination of Markov State Models (MSMs), large-scale protein-RNA docking, and molecular dynamics (MD) simulations. Using MSMs, we identify an open state of apo human Ago-2 in fast equilibrium with partially open and closed states. Conformations in this open state are distinguished by their largely exposed binding grooves that can geometrically accommodate miRNA as indicated in our protein-RNA docking studies. miRNA may then selectively bind to these open conformations. Upon the initial binding, the complex may perform further structural re-arrangement as shown in our MD simulations and eventually reach the stable binary complex structure. Our results provide novel insights in Ago-miRNA recognition mechanisms and our methodology holds great potential to be widely applied in the studies of other important molecular recognition systems. In RNA interference, Argonaute proteins and microRNAs together form the functional core that regulates the gene expression with high sequence specificity. Elucidating the detailed mechanism of molecular recognition between Argonaute proteins and microRNAs is thus important not only for the fundamental understanding of RNA interference, but also for the further development of microRNA-based therapeutic application. In this work, we propose a two-step model to understand the mechanism of microRNA loading into human Argonaute-2: selective binding followed by structural re-arrangement. Our model is based on the results from a combined approach of molecular dynamics simulations, Markov State Models and protein-RNA docking. In particular, we identify a metastable open state of apo hAgo2 in rapid equilibrium with other states. Some of conformations in this open state have largely exposed RNA binding groove that can accommodate microRNA. We further show that the initial Argonaute-microRNA binding complex undergoes structural re-arrangement to reach stable binary crystal structure. These results provide novel insights into the underlying mechanism of Argonaute-microRNA recognition. In addition, our method is readily applicable to the investigation of other complex molecular recognition events such as protein-protein interactions and protein-ligand binding.
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Affiliation(s)
- Hanlun Jiang
- Bioengineering Graduate Program, Division of Biomedical Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong
- The HKUST Shenzhen Research Institute, Shenzhen, China
| | - Fu Kit Sheong
- Department of Chemistry, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong
| | - Lizhe Zhu
- The HKUST Shenzhen Research Institute, Shenzhen, China
- Department of Chemistry, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong
- Center of Systems Biology and Human Health, School of Science and Institute for Advance Study, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong
| | - Xin Gao
- Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia
| | - Julie Bernauer
- Inria Saclay-Île de France, Bâtiment Alan Turing, Campus de l’École Polytechnique, Palaiseau, France
- Laboratoire d’Informatique de l’École Polytechnique (LIX), CNRS UMR 7161, École Polytechnique, Palaiseau, France
- * E-mail: (JB); (XH)
| | - Xuhui Huang
- Bioengineering Graduate Program, Division of Biomedical Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong
- The HKUST Shenzhen Research Institute, Shenzhen, China
- Department of Chemistry, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong
- Center of Systems Biology and Human Health, School of Science and Institute for Advance Study, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong
- * E-mail: (JB); (XH)
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60
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Sowmya G, Breen EJ, Ranganathan S. Linking structural features of protein complexes and biological function. Protein Sci 2015; 24:1486-94. [PMID: 26131659 DOI: 10.1002/pro.2736] [Citation(s) in RCA: 28] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2015] [Revised: 06/19/2015] [Accepted: 06/24/2015] [Indexed: 11/11/2022]
Abstract
Protein-protein interaction (PPI) establishes the central basis for complex cellular networks in a biological cell. Association of proteins with other proteins occurs at varying affinities, yet with a high degree of specificity. PPIs lead to diverse functionality such as catalysis, regulation, signaling, immunity, and inhibition, playing a crucial role in functional genomics. The molecular principle of such interactions is often elusive in nature. Therefore, a comprehensive analysis of known protein complexes from the Protein Data Bank (PDB) is essential for the characterization of structural interface features to determine structure-function relationship. Thus, we analyzed a nonredundant dataset of 278 heterodimer protein complexes, categorized into major functional classes, for distinguishing features. Interestingly, our analysis has identified five key features (interface area, interface polar residue abundance, hydrogen bonds, solvation free energy gain from interface formation, and binding energy) that are discriminatory among the functional classes using Kruskal-Wallis rank sum test. Significant correlations between these PPI interface features amongst functional categories are also documented. Salt bridges correlate with interface area in regulator-inhibitors (r = 0.75). These representative features have implications for the prediction of potential function of novel protein complexes. The results provide molecular insights for better understanding of PPIs and their relation to biological functions.
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Affiliation(s)
- Gopichandran Sowmya
- Department of Chemistry and Biomolecular Sciences, Macquarie University, Sydney, New South Wales 2109, Australia
| | - Edmond J Breen
- Australian Proteome Analysis Facility (APAF), Macquarie University, Sydney, New South Wales 2109, Australia
| | - Shoba Ranganathan
- Department of Chemistry and Biomolecular Sciences, Macquarie University, Sydney, New South Wales 2109, Australia
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61
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Surfing the Protein-Protein Interaction Surface Using Docking Methods: Application to the Design of PPI Inhibitors. Molecules 2015; 20:11569-603. [PMID: 26111183 PMCID: PMC6272567 DOI: 10.3390/molecules200611569] [Citation(s) in RCA: 44] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2015] [Revised: 06/02/2015] [Accepted: 06/15/2015] [Indexed: 02/06/2023] Open
Abstract
Blocking protein-protein interactions (PPI) using small molecules or peptides modulates biochemical pathways and has therapeutic significance. PPI inhibition for designing drug-like molecules is a new area that has been explored extensively during the last decade. Considering the number of available PPI inhibitor databases and the limited number of 3D structures available for proteins, docking and scoring methods play a major role in designing PPI inhibitors as well as stabilizers. Docking methods are used in the design of PPI inhibitors at several stages of finding a lead compound, including modeling the protein complex, screening for hot spots on the protein-protein interaction interface and screening small molecules or peptides that bind to the PPI interface. There are three major challenges to the use of docking on the relatively flat surfaces of PPI. In this review we will provide some examples of the use of docking in PPI inhibitor design as well as its limitations. The combination of experimental and docking methods with improved scoring function has thus far resulted in few success stories of PPI inhibitors for therapeutic purposes. Docking algorithms used for PPI are in the early stages, however, and as more data are available docking will become a highly promising area in the design of PPI inhibitors or stabilizers.
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62
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Mou Y, Huang PS, Thomas LM, Mayo SL. Using Molecular Dynamics Simulations as an Aid in the Prediction of Domain Swapping of Computationally Designed Protein Variants. J Mol Biol 2015; 427:2697-706. [PMID: 26101839 DOI: 10.1016/j.jmb.2015.06.006] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2015] [Revised: 06/11/2015] [Accepted: 06/16/2015] [Indexed: 11/29/2022]
Abstract
In standard implementations of computational protein design, a positive-design approach is used to predict sequences that will be stable on a given backbone structure. Possible competing states are typically not considered, primarily because appropriate structural models are not available. One potential competing state, the domain-swapped dimer, is especially compelling because it is often nearly identical with its monomeric counterpart, differing by just a few mutations in a hinge region. Molecular dynamics (MD) simulations provide a computational method to sample different conformational states of a structure. Here, we tested whether MD simulations could be used as a post-design screening tool to identify sequence mutations leading to domain-swapped dimers. We hypothesized that a successful computationally designed sequence would have backbone structure and dynamics characteristics similar to that of the input structure and that, in contrast, domain-swapped dimers would exhibit increased backbone flexibility and/or altered structure in the hinge-loop region to accommodate the large conformational change required for domain swapping. While attempting to engineer a homodimer from a 51-amino-acid fragment of the monomeric protein engrailed homeodomain (ENH), we had instead generated a domain-swapped dimer (ENH_DsD). MD simulations on these proteins showed increased B-factors derived from MD simulation in the hinge loop of the ENH_DsD domain-swapped dimer relative to monomeric ENH. Two point mutants of ENH_DsD designed to recover the monomeric fold were then tested with an MD simulation protocol. The MD simulations suggested that one of these mutants would adopt the target monomeric structure, which was subsequently confirmed by X-ray crystallography.
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Affiliation(s)
- Yun Mou
- Division of Chemistry and Chemical Engineering, California Institute of Technology, Pasadena, CA 91125, USA
| | - Po-Ssu Huang
- Biochemistry and Molecular Biophysics Option, California Institute of Technology, Pasadena, CA 91125, USA
| | - Leonard M Thomas
- Department of Chemistry and Biochemistry, University of Oklahoma, Norman, OK 73019, USA
| | - Stephen L Mayo
- Division of Chemistry and Chemical Engineering, California Institute of Technology, Pasadena, CA 91125, USA; Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA 91125, USA.
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63
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Janin J, Wodak SJ, Lensink MF, Velankar S. Assessing Structural Predictions of Protein-Protein Recognition: The CAPRI Experiment. REVIEWS IN COMPUTATIONAL CHEMISTRY 2015. [DOI: 10.1002/9781118889886.ch4] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
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64
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Moal IH, Dapkūnas J, Fernández-Recio J. Inferring the microscopic surface energy of protein-protein interfaces from mutation data. Proteins 2015; 83:640-50. [PMID: 25586563 DOI: 10.1002/prot.24761] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2014] [Revised: 12/04/2014] [Accepted: 12/21/2014] [Indexed: 11/11/2022]
Abstract
Mutations at protein-protein recognition sites alter binding strength by altering the chemical nature of the interacting surfaces. We present a simple surface energy model, parameterized with empirical ΔΔG values, yielding mean energies of -48 cal mol(-1) Å(-2) for interactions between hydrophobic surfaces, -51 to -80 cal mol(-1) Å(-2) for surfaces of complementary charge, and 66-83 cal mol(-1) Å(-2) for electrostatically repelling surfaces, relative to the aqueous phase. This places the mean energy of hydrophobic surface burial at -24 cal mol(-1) Å(-2) . Despite neglecting configurational entropy and intramolecular changes, the model correlates with empirical binding free energies of a functionally diverse set of rigid-body interactions (r = 0.66). When used to rerank docking poses, it can place near-native solutions in the top 10 for 37% of the complexes evaluated, and 82% in the top 100. The method shows that hydrophobic burial is the driving force for protein association, accounting for 50-95% of the cohesive energy. The model is available open-source from http://life.bsc.es/pid/web/surface_energy/ and via the CCharpPPI web server http://life.bsc.es/pid/ccharppi/.
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Affiliation(s)
- Iain H Moal
- Joint BSC-IRB Research Program in Computational Biology, Life Science Department, Barcelona Supercomputing Center, Barcelona, 08034, Spain
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65
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Visscher KM, Kastritis PL, Bonvin AMJJ. Non-interacting surface solvation and dynamics in protein-protein interactions. Proteins 2015; 83:445-58. [DOI: 10.1002/prot.24741] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2014] [Revised: 11/10/2014] [Accepted: 11/26/2014] [Indexed: 12/14/2022]
Affiliation(s)
- Koen M. Visscher
- Bijvoet Center for Biomolecular Research; Faculty of Science-Chemistry, Utrecht University; 3584CH Utrecht The Netherlands
| | - Panagiotis L. Kastritis
- Bijvoet Center for Biomolecular Research; Faculty of Science-Chemistry, Utrecht University; 3584CH Utrecht The Netherlands
| | - Alexandre M. J. J. Bonvin
- Bijvoet Center for Biomolecular Research; Faculty of Science-Chemistry, Utrecht University; 3584CH Utrecht The Netherlands
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66
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Newberry RW, Bartlett GJ, VanVeller B, Woolfson DN, Raines RT. Signatures of n→π* interactions in proteins. Protein Sci 2014; 23:284-8. [PMID: 24375625 DOI: 10.1002/pro.2413] [Citation(s) in RCA: 75] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2013] [Revised: 12/21/2013] [Accepted: 12/23/2013] [Indexed: 11/10/2022]
Abstract
The folding of proteins is directed by a variety of interactions, including hydrogen bonding, electrostatics, van der Waals' interactions, and the hydrophobic effect. We have argued previously that an n→π* interaction between carbonyl groups be added to this list. In an n→π* interaction, the lone pair (n) of one carbonyl oxygen overlaps with the π* antibonding orbital of another carbonyl group. The tendency of backbone carbonyl groups in proteins to engage in this interaction has consequences for the structures of folded proteins that we unveil herein. First, we employ density functional theory to demonstrate that the n→π* interaction causes the carbonyl carbon to deviate from planarity. Then, we detect this signature of the n→π* interaction in high-resolution structures of proteins. Finally, we demonstrate through natural population analysis that the n→π* interaction causes polarization of the electron density in carbonyl groups and detect that polarization in the electron density map of cholesterol oxidase, further validating the existence of n→π* interactions. We conclude that the n→π* interaction is operative in folded proteins.
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Affiliation(s)
- Robert W Newberry
- Department of Chemistry, University of Wisconsin-Madison, Madison, Wisconsin
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67
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Sirin S, Pearlman DA, Sherman W. Physics-based enzyme design: predicting binding affinity and catalytic activity. Proteins 2014; 82:3397-409. [PMID: 25243583 DOI: 10.1002/prot.24694] [Citation(s) in RCA: 34] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2014] [Revised: 09/04/2014] [Accepted: 09/10/2014] [Indexed: 11/11/2022]
Abstract
Computational enzyme design is an emerging field that has yielded promising success stories, but where numerous challenges remain. Accurate methods to rapidly evaluate possible enzyme design variants could provide significant value when combined with experimental efforts by reducing the number of variants needed to be synthesized and speeding the time to reach the desired endpoint of the design. To that end, extending our computational methods to model the fundamental physical-chemical principles that regulate activity in a protocol that is automated and accessible to a broad population of enzyme design researchers is essential. Here, we apply a physics-based implicit solvent MM-GBSA scoring approach to enzyme design and benchmark the computational predictions against experimentally determined activities. Specifically, we evaluate the ability of MM-GBSA to predict changes in affinity for a steroid binder protein, catalytic turnover for a Kemp eliminase, and catalytic activity for α-Gliadin peptidase variants. Using the enzyme design framework developed here, we accurately rank the most experimentally active enzyme variants, suggesting that this approach could provide enrichment of active variants in real-world enzyme design applications.
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Affiliation(s)
- Sarah Sirin
- Department of Biology, Massachusetts Institute of Technology, Cambridge, Massachusetts, 02140
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68
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Erijman A, Rosenthal E, Shifman JM. How structure defines affinity in protein-protein interactions. PLoS One 2014; 9:e110085. [PMID: 25329579 PMCID: PMC4199723 DOI: 10.1371/journal.pone.0110085] [Citation(s) in RCA: 63] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2014] [Accepted: 09/14/2014] [Indexed: 01/29/2023] Open
Abstract
Protein-protein interactions (PPI) in nature are conveyed by a multitude of binding modes involving various surfaces, secondary structure elements and intermolecular interactions. This diversity results in PPI binding affinities that span more than nine orders of magnitude. Several early studies attempted to correlate PPI binding affinities to various structure-derived features with limited success. The growing number of high-resolution structures, the appearance of more precise methods for measuring binding affinities and the development of new computational algorithms enable more thorough investigations in this direction. Here, we use a large dataset of PPI structures with the documented binding affinities to calculate a number of structure-based features that could potentially define binding energetics. We explore how well each calculated biophysical feature alone correlates with binding affinity and determine the features that could be used to distinguish between high-, medium- and low- affinity PPIs. Furthermore, we test how various combinations of features could be applied to predict binding affinity and observe a slow improvement in correlation as more features are incorporated into the equation. In addition, we observe a considerable improvement in predictions if we exclude from our analysis low-resolution and NMR structures, revealing the importance of capturing exact intermolecular interactions in our calculations. Our analysis should facilitate prediction of new interactions on the genome scale, better characterization of signaling networks and design of novel binding partners for various target proteins.
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Affiliation(s)
- Ariel Erijman
- Department of Biological Chemistry, The Alexander Silberman Institute of Life Sciences, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Eran Rosenthal
- Department of Biological Chemistry, The Alexander Silberman Institute of Life Sciences, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Julia M. Shifman
- Department of Biological Chemistry, The Alexander Silberman Institute of Life Sciences, The Hebrew University of Jerusalem, Jerusalem, Israel
- * E-mail:
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69
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Guilhot-Gaudeffroy A, Froidevaux C, Azé J, Bernauer J. Protein-RNA complexes and efficient automatic docking: expanding RosettaDock possibilities. PLoS One 2014; 9:e108928. [PMID: 25268579 PMCID: PMC4182525 DOI: 10.1371/journal.pone.0108928] [Citation(s) in RCA: 26] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2014] [Accepted: 09/05/2014] [Indexed: 12/03/2022] Open
Abstract
Protein-RNA complexes provide a wide range of essential functions in the cell. Their atomic experimental structure solving, despite essential to the understanding of these functions, is often difficult and expensive. Docking approaches that have been developed for proteins are often challenging to adapt for RNA because of its inherent flexibility and the structural data available being relatively scarce. In this study we adapted the RosettaDock protocol for protein-RNA complexes both at the nucleotide and atomic levels. Using a genetic algorithm-based strategy, and a non-redundant protein-RNA dataset, we derived a RosettaDock scoring scheme able not only to discriminate but also score efficiently docking decoys. The approach proved to be both efficient and robust for generating and identifying suitable structures when applied to two protein-RNA docking benchmarks in both bound and unbound settings. It also compares well to existing strategies. This is the first approach that currently offers a multi-level optimized scoring approach integrated in a full docking suite, leading the way to adaptive fully flexible strategies.
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Affiliation(s)
- Adrien Guilhot-Gaudeffroy
- AMIB Project, Inria Saclay-Île de France, Palaiseau, France
- Laboratoire de Recherche en Informatique (LRI), CNRS UMR 8623, Université Paris-Sud, Orsay, France
- Laboratoire d'Informatique de l'École Polytechnique (LIX), CNRS UMR 7161, École Polytechnique, Palaiseau, France
| | - Christine Froidevaux
- AMIB Project, Inria Saclay-Île de France, Palaiseau, France
- Laboratoire de Recherche en Informatique (LRI), CNRS UMR 8623, Université Paris-Sud, Orsay, France
| | - Jérôme Azé
- AMIB Project, Inria Saclay-Île de France, Palaiseau, France
- Laboratoire de Recherche en Informatique (LRI), CNRS UMR 8623, Université Paris-Sud, Orsay, France
- Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier (LIRMM), CNRS UMR 5506, Université Montpellier 2, Montpellier, France
| | - Julie Bernauer
- AMIB Project, Inria Saclay-Île de France, Palaiseau, France
- Laboratoire d'Informatique de l'École Polytechnique (LIX), CNRS UMR 7161, École Polytechnique, Palaiseau, France
- * E-mail:
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70
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Lensink MF, Wodak SJ. Score_set: A CAPRI benchmark for scoring protein complexes. Proteins 2014; 82:3163-9. [DOI: 10.1002/prot.24678] [Citation(s) in RCA: 49] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2014] [Revised: 08/05/2014] [Accepted: 08/22/2014] [Indexed: 12/26/2022]
Affiliation(s)
- Marc F. Lensink
- CNRS USR3078; University Lille North of France, Parc de la Haute Borne; F-59658 Villeneuve d'Ascq France
| | - Shoshana J. Wodak
- Structural Biology Program; Hospital for Sick Children; Toronto Ontario M5G 1X8 Canada
- Department of Biochemistry; University of Toronto; Ontario Canada
- Department of Molecular Genetics; University of Toronto; Ontario Canada
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71
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Moal IH, Jiménez-García B, Fernández-Recio J. CCharPPI web server: computational characterization of protein-protein interactions from structure. Bioinformatics 2014; 31:123-5. [PMID: 25183488 DOI: 10.1093/bioinformatics/btu594] [Citation(s) in RCA: 48] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/03/2023] Open
Abstract
SUMMARY The atomic structures of protein-protein interactions are central to understanding their role in biological systems, and a wide variety of biophysical functions and potentials have been developed for their characterization and the construction of predictive models. These tools are scattered across a multitude of stand-alone programs, and are often available only as model parameters requiring reimplementation. This acts as a significant barrier to their widespread adoption. CCharPPI integrates many of these tools into a single web server. It calculates up to 108 parameters, including models of electrostatics, desolvation and hydrogen bonding, as well as interface packing and complementarity scores, empirical potentials at various resolutions, docking potentials and composite scoring functions. AVAILABILITY AND IMPLEMENTATION The server does not require registration by the user and is freely available for non-commercial academic use at http://life.bsc.es/pid/ccharppi.
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Affiliation(s)
- Iain H Moal
- Joint BSC-IRB Research Programme in Computational Biology, Department of Life Sciences, Barcelona Supercomputing Center, C/Jordi Girona 29, 08034 Barcelona, Spain
| | - Brian Jiménez-García
- Joint BSC-IRB Research Programme in Computational Biology, Department of Life Sciences, Barcelona Supercomputing Center, C/Jordi Girona 29, 08034 Barcelona, Spain
| | - Juan Fernández-Recio
- Joint BSC-IRB Research Programme in Computational Biology, Department of Life Sciences, Barcelona Supercomputing Center, C/Jordi Girona 29, 08034 Barcelona, Spain
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72
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Janin J. The targets of CAPRI rounds 20-27. Proteins 2014; 81:2075-81. [PMID: 23900782 DOI: 10.1002/prot.24375] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2013] [Revised: 07/12/2013] [Accepted: 07/16/2013] [Indexed: 01/26/2023]
Abstract
Eight CAPRI prediction rounds with a total of 15 targets were held in the years 2010-2012. Only five of the targets were protein assemblies comparable with those of earlier CAPRI rounds. In one target, the solvent positions at the interface had to be predicted; another was a protein-polysaccharide complex. The remainders were designed complexes issued from protein engineering experiments, and the prediction concerned either their structure or the binding affinity of the designed ligand. Affinity prediction was a new experiment in CAPRI, and a challenge for its participants. It pushed the community into developing novel procedures and score functions that will improve the performance of docking methods, help designing binders, and yield better structure-based estimates of the binding free energy of natural assemblies.
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Affiliation(s)
- Joël Janin
- IBBMC, CNRS UMR 8619, Bât. 430, Université Paris-Sud, Orsay, 91405, France
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73
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Andreani J, Guerois R. Evolution of protein interactions: From interactomes to interfaces. Arch Biochem Biophys 2014; 554:65-75. [DOI: 10.1016/j.abb.2014.05.010] [Citation(s) in RCA: 33] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2014] [Revised: 04/28/2014] [Accepted: 05/12/2014] [Indexed: 12/16/2022]
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74
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Proteins Feel More Than They See: Fine-Tuning of Binding Affinity by Properties of the Non-Interacting Surface. J Mol Biol 2014; 426:2632-52. [DOI: 10.1016/j.jmb.2014.04.017] [Citation(s) in RCA: 79] [Impact Index Per Article: 7.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2013] [Revised: 03/11/2014] [Accepted: 04/17/2014] [Indexed: 11/21/2022]
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75
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Esmaielbeiki R, Nebel JC. Scoring docking conformations using predicted protein interfaces. BMC Bioinformatics 2014; 15:171. [PMID: 24906633 PMCID: PMC4057934 DOI: 10.1186/1471-2105-15-171] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2012] [Accepted: 05/29/2014] [Indexed: 12/22/2022] Open
Abstract
Background Since proteins function by interacting with other molecules, analysis of protein-protein interactions is essential for comprehending biological processes. Whereas understanding of atomic interactions within a complex is especially useful for drug design, limitations of experimental techniques have restricted their practical use. Despite progress in docking predictions, there is still room for improvement. In this study, we contribute to this topic by proposing T-PioDock, a framework for detection of a native-like docked complex 3D structure. T-PioDock supports the identification of near-native conformations from 3D models that docking software produced by scoring those models using binding interfaces predicted by the interface predictor, Template based Protein Interface Prediction (T-PIP). Results First, exhaustive evaluation of interface predictors demonstrates that T-PIP, whose predictions are customised to target complexity, is a state-of-the-art method. Second, comparative study between T-PioDock and other state-of-the-art scoring methods establishes T-PioDock as the best performing approach. Moreover, there is good correlation between T-PioDock performance and quality of docking models, which suggests that progress in docking will lead to even better results at recognising near-native conformations. Conclusion Accurate identification of near-native conformations remains a challenging task. Although availability of 3D complexes will benefit from template-based methods such as T-PioDock, we have identified specific limitations which need to be addressed. First, docking software are still not able to produce native like models for every target. Second, current interface predictors do not explicitly consider pairwise residue interactions between proteins and their interacting partners which leaves ambiguity when assessing quality of complex conformations.
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Affiliation(s)
- Reyhaneh Esmaielbeiki
- Department of Statistics, University of Oxford, 1 South Parks Road, Oxford OX1 3TG, UK.
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76
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Téletchéa S, Stresing V, Hervouet S, Baud'huin M, Heymann MF, Bertho G, Charrier C, Ando K, Heymann D. Novel RANK antagonists for the treatment of bone-resorptive disease: theoretical predictions and experimental validation. J Bone Miner Res 2014; 29:1466-77. [PMID: 24390798 DOI: 10.1002/jbmr.2170] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/26/2013] [Revised: 12/17/2013] [Accepted: 01/01/2013] [Indexed: 12/15/2022]
Abstract
Receptor activator of nuclear factor-κB (RANK) and RANK ligand (RANKL) play a pivotal role in bone metabolism, and selective targeting of RANK signaling has become a promising therapeutic strategy in the management of resorptive bone diseases. Existing antibody-based therapies and novel inhibitors currently in development were designed to target the ligand, rather than the membrane receptor expressed on osteoclast precursors. We describe here an alternative approach to designing small peptides able to specifically bind to the hinge region of membrane RANK responsible for the conformational change upon RANKL association. A nonapeptide generated by this method was validated for its biological activity in vitro and in vivo and served as a lead compound for the generation of a series of peptide RANK antagonists derived from the original sequence. Our study presents a structure- and knowledge-based strategy for the design of novel effective and affordable small peptide inhibitors specifically targeting the receptor RANK and opens a new therapeutic opportunity for the treatment of resorptive bone disease.
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Affiliation(s)
- Stéphane Téletchéa
- INSERM, UMR 957, Equipe labellisée LIGUE 2012, Université de Nantes, Laboratory of the Physiopathology of Bone Resorption and Therapy of Primary Bone Tumors (LPRO), Nantes, France
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77
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Nick Pace C, Scholtz JM, Grimsley GR. Forces stabilizing proteins. FEBS Lett 2014; 588:2177-84. [PMID: 24846139 DOI: 10.1016/j.febslet.2014.05.006] [Citation(s) in RCA: 234] [Impact Index Per Article: 23.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2014] [Revised: 04/30/2014] [Accepted: 05/05/2014] [Indexed: 11/30/2022]
Abstract
The goal of this article is to summarize what has been learned about the major forces stabilizing proteins since the late 1980s when site-directed mutagenesis became possible. The following conclusions are derived from experimental studies of hydrophobic and hydrogen bonding variants. (1) Based on studies of 138 hydrophobic interaction variants in 11 proteins, burying a -CH2- group on folding contributes 1.1±0.5 kcal/mol to protein stability. (2) The burial of non-polar side chains contributes to protein stability in two ways: first, a term that depends on the removal of the side chains from water and, more importantly, the enhanced London dispersion forces that result from the tight packing in the protein interior. (3) Based on studies of 151 hydrogen bonding variants in 15 proteins, forming a hydrogen bond on folding contributes 1.1±0.8 kcal/mol to protein stability. (4) The contribution of hydrogen bonds to protein stability is strongly context dependent. (5) Hydrogen bonds by side chains and peptide groups make similar contributions to protein stability. (6) Polar group burial can make a favorable contribution to protein stability even if the polar group is not hydrogen bonded. (7) Hydrophobic interactions and hydrogen bonds both make large contributions to protein stability.
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Affiliation(s)
- C Nick Pace
- Department of Biochemistry and Biophysics, Texas A&M University, College Station, TX 77843, United States; Department of Molecular and Cellular Medicine, Texas A&M University Health Science Center, College Station, TX 77843, United States.
| | - J Martin Scholtz
- Department of Biochemistry and Biophysics, Texas A&M University, College Station, TX 77843, United States; Department of Molecular and Cellular Medicine, Texas A&M University Health Science Center, College Station, TX 77843, United States
| | - Gerald R Grimsley
- Department of Molecular and Cellular Medicine, Texas A&M University Health Science Center, College Station, TX 77843, United States
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78
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Sudarshan S, Kodathala SB, Mahadik AC, Mehta I, Beck BW. Protein-protein interface detection using the energy centrality relationship (ECR) characteristic of proteins. PLoS One 2014; 9:e97115. [PMID: 24830938 PMCID: PMC4022497 DOI: 10.1371/journal.pone.0097115] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2014] [Accepted: 04/14/2014] [Indexed: 01/17/2023] Open
Abstract
Specific protein interactions are responsible for most biological functions. Distinguishing Functionally Linked Interfaces of Proteins (FLIPs), from Functionally uncorrelated Contacts (FunCs), is therefore important to characterizing these interactions. To achieve this goal, we have created a database of protein structures called FLIPdb, containing proteins belonging to various functional sub-categories. Here, we use geometric features coupled with Kortemme and Baker's computational alanine scanning method to calculate the energetic sensitivity of each amino acid at the interface to substitution, identify hotspots, and identify other factors that may contribute towards an interface being FLIP or FunC. Using Principal Component Analysis and K-means clustering on a training set of 160 interfaces, we could distinguish FLIPs from FunCs with an accuracy of 76%. When these methods were applied to two test sets of 18 and 170 interfaces, we achieved similar accuracies of 78% and 80%. We have identified that FLIP interfaces have a stronger central organizing tendency than FunCs, due, we suggest, to greater specificity. We also observe that certain functional sub-categories, such as enzymes, antibody-heavy-light, antibody-antigen, and enzyme-inhibitors form distinct sub-clusters. The antibody-antigen and enzyme-inhibitors interfaces have patterns of physical characteristics similar to those of FunCs, which is in agreement with the fact that the selection pressures of these interfaces is differently evolutionarily driven. As such, our ECR model also successfully describes the impact of evolution and natural selection on protein-protein interfaces. Finally, we indicate how our ECR method may be of use in reducing the false positive rate of docking calculations.
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Affiliation(s)
- Sanjana Sudarshan
- Department of Biology, Texas Woman's University, Denton, Texas, United States of America
| | - Sasi B. Kodathala
- Department of Biology, Texas Woman's University, Denton, Texas, United States of America
| | - Amruta C. Mahadik
- Department of Biology, Texas Woman's University, Denton, Texas, United States of America
| | - Isha Mehta
- Department of Biology, Texas Woman's University, Denton, Texas, United States of America
| | - Brian W. Beck
- Department of Biology, Texas Woman's University, Denton, Texas, United States of America
- Department of Mathematics and Computer Science, Texas Woman's University, Denton, Texas, United States of America
- Department of Chemistry and Biochemistry, Texas Woman's University, Denton, Texas, United States of America
- * E-mail:
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79
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Lensink MF, Moal IH, Bates PA, Kastritis PL, Melquiond ASJ, Karaca E, Schmitz C, van Dijk M, Bonvin AMJJ, Eisenstein M, Jiménez-García B, Grosdidier S, Solernou A, Pérez-Cano L, Pallara C, Fernández-Recio J, Xu J, Muthu P, Praneeth Kilambi K, Gray JJ, Grudinin S, Derevyanko G, Mitchell JC, Wieting J, Kanamori E, Tsuchiya Y, Murakami Y, Sarmiento J, Standley DM, Shirota M, Kinoshita K, Nakamura H, Chavent M, Ritchie DW, Park H, Ko J, Lee H, Seok C, Shen Y, Kozakov D, Vajda S, Kundrotas PJ, Vakser IA, Pierce BG, Hwang H, Vreven T, Weng Z, Buch I, Farkash E, Wolfson HJ, Zacharias M, Qin S, Zhou HX, Huang SY, Zou X, Wojdyla JA, Kleanthous C, Wodak SJ. Blind prediction of interfacial water positions in CAPRI. Proteins 2014; 82:620-32. [PMID: 24155158 PMCID: PMC4582081 DOI: 10.1002/prot.24439] [Citation(s) in RCA: 44] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2013] [Revised: 09/16/2013] [Accepted: 09/26/2013] [Indexed: 12/30/2022]
Abstract
We report the first assessment of blind predictions of water positions at protein-protein interfaces, performed as part of the critical assessment of predicted interactions (CAPRI) community-wide experiment. Groups submitting docking predictions for the complex of the DNase domain of colicin E2 and Im2 immunity protein (CAPRI Target 47), were invited to predict the positions of interfacial water molecules using the method of their choice. The predictions-20 groups submitted a total of 195 models-were assessed by measuring the recall fraction of water-mediated protein contacts. Of the 176 high- or medium-quality docking models-a very good docking performance per se-only 44% had a recall fraction above 0.3, and a mere 6% above 0.5. The actual water positions were in general predicted to an accuracy level no better than 1.5 Å, and even in good models about half of the contacts represented false positives. This notwithstanding, three hotspot interface water positions were quite well predicted, and so was one of the water positions that is believed to stabilize the loop that confers specificity in these complexes. Overall the best interface water predictions was achieved by groups that also produced high-quality docking models, indicating that accurate modelling of the protein portion is a determinant factor. The use of established molecular mechanics force fields, coupled to sampling and optimization procedures also seemed to confer an advantage. Insights gained from this analysis should help improve the prediction of protein-water interactions and their role in stabilizing protein complexes.
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Affiliation(s)
- Marc F Lensink
- Interdisciplinary Research Institute USR3078 CNRS, University Lille North of France, Villeneuve d'Ascq, France
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80
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Jha RK, Gaiotto T, Bradbury ARM, Strauss CEM. An improved Protein G with higher affinity for human/rabbit IgG Fc domains exploiting a computationally designed polar network. Protein Eng Des Sel 2014; 27:127-34. [PMID: 24632761 DOI: 10.1093/protein/gzu005] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Protein G is an IgG binding protein that has been widely exploited for biotechnological purposes. Rosetta protein modeling identified a set of favorable polar mutations in Protein G, at its binding interface with the Fc domain of Immunoglobulin G, that were predicted to increase the stability and tighten the binding relative to native Protein G, with only a minor perturbation of the binding mode seen in the crystal structure. This triple mutant was synthesized and evaluated experimentally. Relative to the native protein G, the mutant showed a 3.5-fold enhancement in display level on the surface of yeast and a 5-fold tighter molar affinity for rabbit and human IgG. We attribute the improved affinity to a network of hydrogen bonds exploiting specific polar groups on human and rabbit Fc. The relative specificity increased as well since there was little affinity enhancement for goat and mouse Fc, while the affinity for rat Fc was poorer by half. This designed Protein G will be useful in biotechnological applications as a recombinant protein, where its improved affinity, display and specificity will increase antibody capture sensitivity and capacity. Furthermore, the display of this protein on the surface of yeast introduces the concept of the use of yeast as an affinity matrix.
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Affiliation(s)
- Ramesh K Jha
- Bioscience Division, MS M888, Los Alamos National Laboratory, Los Alamos, NM 87545, USA
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81
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Mapping of the binding landscape for a picomolar protein-protein complex through computation and experiment. Structure 2014; 22:636-45. [PMID: 24613488 DOI: 10.1016/j.str.2014.01.012] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2013] [Revised: 01/17/2014] [Accepted: 01/21/2014] [Indexed: 11/22/2022]
Abstract
Our understanding of protein evolution would greatly benefit from mapping of binding landscapes, i.e., changes in protein-protein binding affinity due to all single mutations. However, experimental generation of such landscapes is a tedious task due to a large number of possible mutations. Here, we use a simple computational protocol to map the binding landscape for two homologous high-affinity complexes, involving a snake toxin fasciculin and acetylcholinesterase from two different species. To verify our computational predictions, we experimentally measure binding between 25 Fas mutants and the 2 enzymes. Both computational and experimental results demonstrate that the Fas sequence is close to the optimum when interacting with its targets, yet a few mutations could further improve Kd, kon, and koff. Our computational predictions agree well with experimental results and generate distributions similar to those observed in other high-affinity PPIs, demonstrating the potential of simple computational protocols in capturing realistic binding landscapes.
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82
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An accurate binding interaction model in de novo computational protein design of interactions: If you build it, they will bind. J Struct Biol 2014; 185:136-46. [DOI: 10.1016/j.jsb.2013.03.012] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2012] [Revised: 03/15/2013] [Accepted: 03/21/2013] [Indexed: 01/07/2023]
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83
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Kilambi KP, Pacella MS, Xu J, Labonte JW, Porter JR, Muthu P, Drew K, Kuroda D, Schueler-Furman O, Bonneau R, Gray JJ. Extending RosettaDock with water, sugar, and pH for prediction of complex structures and affinities for CAPRI rounds 20-27. Proteins 2013; 81:2201-9. [PMID: 24123494 PMCID: PMC4037910 DOI: 10.1002/prot.24425] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2013] [Revised: 09/12/2013] [Accepted: 09/13/2013] [Indexed: 11/09/2022]
Abstract
Rounds 20-27 of the Critical Assessment of PRotein Interactions (CAPRI) provided a testing platform for computational methods designed to address a wide range of challenges. The diverse targets drove the creation of and new combinations of computational tools. In this study, RosettaDock and other novel Rosetta protocols were used to successfully predict four of the 10 blind targets. For example, for DNase domain of Colicin E2-Im2 immunity protein, RosettaDock and RosettaLigand were used to predict the positions of water molecules at the interface, recovering 46% of the native water-mediated contacts. For α-repeat Rep4-Rep2 and g-type lysozyme-PliG inhibitor complexes, homology models were built and standard and pH-sensitive docking algorithms were used to generate structures with interface RMSD values of 3.3 Å and 2.0 Å, respectively. A novel flexible sugar-protein docking protocol was also developed and used for structure prediction of the BT4661-heparin-like saccharide complex, recovering 71% of the native contacts. Challenges remain in the generation of accurate homology models for protein mutants and sampling during global docking. On proteins designed to bind influenza hemagglutinin, only about half of the mutations were identified that affect binding (T55: 54%; T56: 48%). The prediction of the structure of the xylanase complex involving homology modeling and multidomain docking pushed the limits of global conformational sampling and did not result in any successful prediction. The diversity of problems at hand requires computational algorithms to be versatile; the recent additions to the Rosetta suite expand the capabilities to encompass more biologically realistic docking problems.
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Affiliation(s)
- Krishna Praneeth Kilambi
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, Maryland
| | - Michael S. Pacella
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland
| | - Jianqing Xu
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, Maryland
| | - Jason W. Labonte
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, Maryland
| | - Justin R. Porter
- Thomas C. Jenkins Department of Biophysics, Johns Hopkins University, Baltimore, Maryland
| | - Pravin Muthu
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, Maryland
| | - Kevin Drew
- Department of Biology, Center for Genomics and Systems Biology, New York University, New York, New York
| | - Daisuke Kuroda
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, Maryland
| | - Ora Schueler-Furman
- Department of Microbiology and Molecular Genetics, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Richard Bonneau
- Department of Biology, Center for Genomics and Systems Biology, New York University, New York, New York
| | - Jeffrey J. Gray
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, Maryland
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84
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Qin S, Zhou HX. Using the concept of transient complex for affinity predictions in CAPRI rounds 20-27 and beyond. Proteins 2013; 81:2229-36. [PMID: 23873496 PMCID: PMC3842397 DOI: 10.1002/prot.24366] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2013] [Revised: 06/26/2013] [Accepted: 06/29/2013] [Indexed: 11/10/2022]
Abstract
Predictions of protein-protein binders and binding affinities have traditionally focused on features pertaining to the native complexes. In developing a computational method for predicting protein-protein association rate constants, we introduced the concept of transient complex after mapping the interaction energy surface. The transient complex is located at the outer boundary of the bound-state energy well, having near-native separation and relative orientation between the subunits but not yet formed most of the short-range native interactions. We found that the width of the binding funnel and the electrostatic interaction energy of the transient complex are among the features predictive of binders and binding affinities. These ideas were very promising for the five affinity-related targets (T43-45, 55, and 56) of CAPRI rounds 20-27. For T43, we ranked the single crystallographic complex as number 1 and were one of only two groups that clearly identified that complex as a true binder; for T44, we ranked the only design with measurable binding affinity as number 4. For the nine docking targets, continuing on our success in previous CAPRI rounds, we produced 10 medium-quality models for T47 and acceptable models for T48 and T49. We conclude that the interaction energy landscape and the transient complex in particular will complement existing features in leading to better prediction of binding affinities.
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Affiliation(s)
- Sanbo Qin
- Department of Physics and Institute of Molecular Biophysics, Florida State University, Tallahassee, FL 32306, USA
| | - Huan-Xiang Zhou
- Department of Physics and Institute of Molecular Biophysics, Florida State University, Tallahassee, FL 32306, USA
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85
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Computational design of protein–protein interactions. Curr Opin Struct Biol 2013; 23:903-10. [DOI: 10.1016/j.sbi.2013.08.003] [Citation(s) in RCA: 47] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2013] [Revised: 08/06/2013] [Accepted: 08/08/2013] [Indexed: 11/23/2022]
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86
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Protein engineering and the use of molecular modeling and simulation: the case of heterodimeric Fc engineering. Methods 2013; 65:77-94. [PMID: 24211748 DOI: 10.1016/j.ymeth.2013.10.016] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2013] [Revised: 10/12/2013] [Accepted: 10/25/2013] [Indexed: 11/23/2022] Open
Abstract
Computational and structure guided methods can make significant contributions to the development of solutions for difficult protein engineering problems, including the optimization of next generation of engineered antibodies. In this paper, we describe a contemporary industrial antibody engineering program, based on hypothesis-driven in silico protein optimization method. The foundational concepts and methods of computational protein engineering are discussed, and an example of a computational modeling and structure-guided protein engineering workflow is provided for the design of best-in-class heterodimeric Fc with high purity and favorable biophysical properties. We present the engineering rationale as well as structural and functional characterization data on these engineered designs.
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87
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Schwans JP, Sunden F, Gonzalez A, Tsai Y, Herschlag D. Uncovering the determinants of a highly perturbed tyrosine pKa in the active site of ketosteroid isomerase. Biochemistry 2013; 52:7840-55. [PMID: 24151972 PMCID: PMC3890242 DOI: 10.1021/bi401083b] [Citation(s) in RCA: 35] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
Abstract
Within the idiosyncratic enzyme active-site environment, side chain and ligand pKa values can be profoundly perturbed relative to their values in aqueous solution. Whereas structural inspection of systems has often attributed perturbed pKa values to dominant contributions from placement near charged groups or within hydrophobic pockets, Tyr57 of a Pseudomonas putida ketosteroid isomerase (KSI) mutant, suggested to have a pKa perturbed by nearly 4 units to 6.3, is situated within a solvent-exposed active site devoid of cationic side chains, metal ions, or cofactors. Extensive comparisons among 45 variants with mutations in and around the KSI active site, along with protein semisynthesis, (13)C NMR spectroscopy, absorbance spectroscopy, and X-ray crystallography, was used to unravel the basis for this perturbed Tyr pKa. The results suggest that the origin of large energetic perturbations are more complex than suggested by visual inspection. For example, the introduction of positively charged residues near Tyr57 raises its pKa rather than lowers it; this effect, and part of the increase in the Tyr pKa from the introduction of nearby anionic groups, arises from accompanying active-site structural rearrangements. Other mutations with large effects also cause structural perturbations or appear to displace a structured water molecule that is part of a stabilizing hydrogen-bond network. Our results lead to a model in which three hydrogen bonds are donated to the stabilized ionized Tyr, with these hydrogen-bond donors, two Tyr side chains, and a water molecule positioned by other side chains and by a water-mediated hydrogen-bond network. These results support the notion that large energetic effects are often the consequence of multiple stabilizing interactions rather than a single dominant interaction. Most generally, this work provides a case study for how extensive and comprehensive comparisons via site-directed mutagenesis in a tight feedback loop with structural analysis can greatly facilitate our understanding of enzyme active-site energetics. The extensive data set provided may also be a valuable resource for those wishing to extensively test computational approaches for determining enzymatic pKa values and energetic effects.
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Affiliation(s)
- Jason P. Schwans
- Department of Biochemistry, Stanford University, Stanford, California 94305
| | - Fanny Sunden
- Department of Biochemistry, Stanford University, Stanford, California 94305
| | - Ana Gonzalez
- Stanford Synchrotron Radiation Lightsource, SLAC National Accelerator Laboratory, Menlo Park, California 94025
| | - Yingssu Tsai
- Department of Chemistry, Stanford University, Stanford, California 94305
- Stanford Synchrotron Radiation Lightsource, SLAC National Accelerator Laboratory, Menlo Park, California 94025
| | - Daniel Herschlag
- Department of Biochemistry, Stanford University, Stanford, California 94305
- Department of Chemistry, Stanford University, Stanford, California 94305
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88
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Zhu X, Ericksen SS, Demerdash ONA, Mitchell JC. Data-driven models for protein interaction and design. Proteins 2013; 81:2221-8. [PMID: 24038640 DOI: 10.1002/prot.24405] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2013] [Revised: 08/12/2013] [Accepted: 08/21/2013] [Indexed: 12/13/2022]
Abstract
We describe methods and results for four new types of challenge in the Critical Assessment of PRedicted Interactions (CAPRI). Two new challenges asked predictors to create models related to protein interface design. The first of these was to distinguish binding interfaces from designed nonbinding interfaces. The second was to predict the effects of all single-point mutations on hemagglutinin binding to two small designed proteins. Two additional challenges asked predictors to submit high-resolution structures for interface-bound crystallographic waters and for binding heparin to a putative glycosylase.
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89
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Pallara C, Jiménez-García B, Pérez-Cano L, Romero-Durana M, Solernou A, Grosdidier S, Pons C, Moal IH, Fernandez-Recio J. Expanding the frontiers of protein-protein modeling: from docking and scoring to binding affinity predictions and other challenges. Proteins 2013; 81:2192-200. [PMID: 23934865 DOI: 10.1002/prot.24387] [Citation(s) in RCA: 20] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2013] [Revised: 07/31/2013] [Accepted: 08/01/2013] [Indexed: 11/06/2022]
Abstract
In addition to protein-protein docking, this CAPRI edition included new challenges, like protein-water and protein-sugar interactions, or the prediction of binding affinities and ΔΔG changes upon mutation. Regarding the standard protein-protein docking cases, our approach, mostly based on the pyDock scheme, submitted correct models as predictors and as scorers for 67% and 57% of the evaluated targets, respectively. In this edition, available information on known interface residues hardly made any difference for our predictions. In one of the targets, the inclusion of available experimental small-angle X-ray scattering (SAXS) data using our pyDockSAXS approach slightly improved the predictions. In addition to the standard protein-protein docking assessment, new challenges were proposed. One of the new problems was predicting the position of the interface water molecules, for which we submitted models with 20% and 43% of the water-mediated native contacts predicted as predictors and scorers, respectively. Another new problem was the prediction of protein-carbohydrate binding, where our submitted model was very close to being acceptable. A set of targets were related to the prediction of binding affinities, in which our pyDock scheme was able to discriminate between natural and designed complexes with area under the curve = 83%. It was also proposed to estimate the effect of point mutations on binding affinity. Our approach, based on machine learning methods, showed high rates of correctly classified mutations for all cases. The overall results were highly rewarding, and show that the field is ready to move forward and face new interesting challenges in interactomics.
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Affiliation(s)
- Chiara Pallara
- Joint BSC-IRB Research Programme in Computational Biology, Barcelona Supercomputing Center, Barcelona, Spain
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90
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Rodrigues JPGLM, Melquiond ASJ, Karaca E, Trellet M, van Dijk M, van Zundert GCP, Schmitz C, de Vries SJ, Bordogna A, Bonati L, Kastritis PL, Bonvin AMJJ. Defining the limits of homology modeling in information-driven protein docking. Proteins 2013; 81:2119-28. [PMID: 23913867 DOI: 10.1002/prot.24382] [Citation(s) in RCA: 54] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2013] [Revised: 07/16/2013] [Accepted: 07/25/2013] [Indexed: 12/28/2022]
Abstract
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.
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Affiliation(s)
- J P G L M Rodrigues
- Faculty of Science/Chemistry, Bijvoet Center for Biomolecular Research, Utrecht University, Utrecht, 3584CH, The Netherlands
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91
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Lensink MF, Wodak SJ. Docking, scoring, and affinity prediction in CAPRI. Proteins 2013; 81:2082-95. [DOI: 10.1002/prot.24428] [Citation(s) in RCA: 199] [Impact Index Per Article: 18.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2013] [Revised: 09/12/2013] [Accepted: 09/13/2013] [Indexed: 01/28/2023]
Affiliation(s)
- Marc F. Lensink
- Interdisciplinary Research Institute, USR3078 CNRS; University Lille North of France, Parc de la Haute Borne; 50 avenue de Halley F-59658 Villeneuve d'Ascq cedex France
| | - Shoshana J. Wodak
- Structure and Function Program; Hospital for Sick Children; Toronto Ontario M5G 1X8 Canada
- Department of Biochemistry; University of Toronto; Toronto Ontario Canada
- Department of Molecular Genetics; University of Toronto; Toronto Ontario Canada
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92
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Mikulecký P, Černý J, Biedermannová L, Petroková H, Kuchař M, Vondrášek J, Malý P, Šebo P, Schneider B. Increasing affinity of interferon-γ receptor 1 to interferon-γ by computer-aided design. BIOMED RESEARCH INTERNATIONAL 2013; 2013:752514. [PMID: 24199198 PMCID: PMC3807708 DOI: 10.1155/2013/752514] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/29/2013] [Revised: 08/06/2013] [Accepted: 08/13/2013] [Indexed: 12/12/2022]
Abstract
We describe a computer-based protocol to design protein mutations increasing binding affinity between ligand and its receptor. The method was applied to mutate interferon-γ receptor 1 (IFN-γ-Rx) to increase its affinity to natural ligand IFN-γ, protein important for innate immunity. We analyzed all four available crystal structures of the IFN-γ-Rx/IFN-γ complex to identify 40 receptor residues forming the interface with IFN-γ. For these 40 residues, we performed computational mutation analysis by substituting each of the interface receptor residues by the remaining standard amino acids. The corresponding changes of the free energy were calculated by a protocol consisting of FoldX and molecular dynamics calculations. Based on the computed changes of the free energy and on sequence conservation criteria obtained by the analysis of 32 receptor sequences from 19 different species, we selected 14 receptor variants predicted to increase the receptor affinity to IFN-γ. These variants were expressed as recombinant proteins in Escherichia coli, and their affinities to IFN-γ were determined experimentally by surface plasmon resonance (SPR). The SPR measurements showed that the simple computational protocol succeeded in finding two receptor variants with affinity to IFN-γ increased about fivefold compared to the wild-type receptor.
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Affiliation(s)
- Pavel Mikulecký
- Institute of Biotechnology AS CR, v. v. i., Vídeňská 1083, 142 20 Prague, Czech Republic
| | - Jiří Černý
- Institute of Biotechnology AS CR, v. v. i., Vídeňská 1083, 142 20 Prague, Czech Republic
| | - Lada Biedermannová
- Institute of Biotechnology AS CR, v. v. i., Vídeňská 1083, 142 20 Prague, Czech Republic
| | - Hana Petroková
- Institute of Biotechnology AS CR, v. v. i., Vídeňská 1083, 142 20 Prague, Czech Republic
| | - Milan Kuchař
- Institute of Biotechnology AS CR, v. v. i., Vídeňská 1083, 142 20 Prague, Czech Republic
| | - Jiří Vondrášek
- Institute of Biotechnology AS CR, v. v. i., Vídeňská 1083, 142 20 Prague, Czech Republic
| | - Petr Malý
- Institute of Biotechnology AS CR, v. v. i., Vídeňská 1083, 142 20 Prague, Czech Republic
| | - Peter Šebo
- Institute of Biotechnology AS CR, v. v. i., Vídeňská 1083, 142 20 Prague, Czech Republic
| | - Bohdan Schneider
- Institute of Biotechnology AS CR, v. v. i., Vídeňská 1083, 142 20 Prague, Czech Republic
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93
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Moal IH, Torchala M, Bates PA, Fernández-Recio J. The scoring of poses in protein-protein docking: current capabilities and future directions. BMC Bioinformatics 2013; 14:286. [PMID: 24079540 PMCID: PMC3850738 DOI: 10.1186/1471-2105-14-286] [Citation(s) in RCA: 76] [Impact Index Per Article: 6.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2013] [Accepted: 09/25/2013] [Indexed: 12/16/2022] Open
Abstract
BACKGROUND Protein-protein docking, which aims to predict the structure of a protein-protein complex from its unbound components, remains an unresolved challenge in structural bioinformatics. An important step is the ranking of docked poses using a scoring function, for which many methods have been developed. There is a need to explore the differences and commonalities of these methods with each other, as well as with functions developed in the fields of molecular dynamics and homology modelling. RESULTS We present an evaluation of 115 scoring functions on an unbound docking decoy benchmark covering 118 complexes for which a near-native solution can be found, yielding top 10 success rates of up to 58%. Hierarchical clustering is performed, so as to group together functions which identify near-natives in similar subsets of complexes. Three set theoretic approaches are used to identify pairs of scoring functions capable of correctly scoring different complexes. This shows that functions in different clusters capture different aspects of binding and are likely to work together synergistically. CONCLUSIONS All functions designed specifically for docking perform well, indicating that functions are transferable between sampling methods. We also identify promising methods from the field of homology modelling. Further, differential success rates by docking difficulty and solution quality suggest a need for flexibility-dependent scoring. Investigating pairs of scoring functions, the set theoretic measures identify known scoring strategies as well as a number of novel approaches, indicating promising augmentations of traditional scoring methods. Such augmentation and parameter combination strategies are discussed in the context of the learning-to-rank paradigm.
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Affiliation(s)
- Iain H Moal
- Joint BSC-IRB Research Program in Computational Biology, Life Science Department, Barcelona Super computing Center, Barcelona 08034, Spain
| | - Mieczyslaw Torchala
- Biomolecular Modelling Laboratory, Cancer Research UK London Research Institute, London WC2A 3LY, UK
| | - Paul A Bates
- Biomolecular Modelling Laboratory, Cancer Research UK London Research Institute, London WC2A 3LY, UK
| | - Juan Fernández-Recio
- Joint BSC-IRB Research Program in Computational Biology, Life Science Department, Barcelona Super computing Center, Barcelona 08034, Spain
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94
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Kim MS, Lee H, Heo L, Lim A, Seok C, Shin DH. New molecular interaction of IIANtr
and HPr from Burkholderia pseudomallei
identified by X-ray crystallography and docking studies. Proteins 2013; 81:1499-508. [DOI: 10.1002/prot.24275] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2012] [Revised: 01/24/2013] [Accepted: 02/18/2013] [Indexed: 11/11/2022]
Affiliation(s)
- Mi-Sun Kim
- Division of Life & Pharmaceutical Sciences; The Center for Cell Signaling & Drug Discovery Research; College of Pharmacy, Ewha Womans University; Seoul 120-750 Republic of Korea
| | - Hasup Lee
- Department of Chemistry; College of Natural Sciences; Seoul National University; Seoul 151-747 Republic of Korea
| | - Lim Heo
- Department of Chemistry; College of Natural Sciences; Seoul National University; Seoul 151-747 Republic of Korea
| | - Areum Lim
- Division of Life & Pharmaceutical Sciences; The Center for Cell Signaling & Drug Discovery Research; College of Pharmacy, Ewha Womans University; Seoul 120-750 Republic of Korea
| | - Chaok Seok
- Department of Chemistry; College of Natural Sciences; Seoul National University; Seoul 151-747 Republic of Korea
| | - Dong Hae Shin
- Division of Life & Pharmaceutical Sciences; The Center for Cell Signaling & Drug Discovery Research; College of Pharmacy, Ewha Womans University; Seoul 120-750 Republic of Korea
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95
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Demerdash ONA, Mitchell JC. Using physical potentials and learned models to distinguish native binding interfaces from de novo designed interfaces that do not bind. Proteins 2013; 81:1919-30. [DOI: 10.1002/prot.24337] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2012] [Revised: 04/04/2013] [Accepted: 05/23/2013] [Indexed: 11/05/2022]
Affiliation(s)
- Omar N. A. Demerdash
- Medical Scientist Training Program; University of Wisconsin-Madison; Madison Wisconsin
- Biophysics Program; University of Wisconsin-Madison; Madison Wisconsin
| | - Julie C. Mitchell
- Department of Biochemistry; University of Wisconsin-Madison; Madison Wisconsin
- Department of Mathematics; University of Wisconsin-Madison; Madison Wisconsin
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96
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Der BS, Kuhlman B. Strategies to control the binding mode of de novo designed protein interactions. Curr Opin Struct Biol 2013; 23:639-46. [PMID: 23731800 PMCID: PMC3737258 DOI: 10.1016/j.sbi.2013.04.010] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2013] [Accepted: 04/19/2013] [Indexed: 12/14/2022]
Abstract
There has been significant recent progress in the computational design of protein interactions including the creation of novel heterodimers, homodimers, nanohedra, fibril caps and a protein crystal. Essential to these successes has been the use of innovative strategies for finding binding modes that are achievable, that is, identifying binding partners and docked conformations that can be successfully stabilized via sequence optimization and backbone refinement. In many cases this has involved the use of structural motifs commonly found at naturally occurring interfaces including alpha helices inserted into hydrophobic grooves, beta-strand pairing, metal binding, established helix packing motifs, and the use of symmetry to form cooperative interactions. Future challenges include the creation of hydrogen bond networks and antibody-like interactions based on the redesign of protein surface loops.
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Affiliation(s)
- Bryan S. Der
- Department of Biochemistry and Biophysics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599
| | - Brian Kuhlman
- Department of Biochemistry and Biophysics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599
- Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599
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97
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Kastritis PL, Bonvin AMJJ. Molecular origins of binding affinity: seeking the Archimedean point. Curr Opin Struct Biol 2013; 23:868-77. [PMID: 23876790 DOI: 10.1016/j.sbi.2013.07.001] [Citation(s) in RCA: 32] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2013] [Revised: 07/01/2013] [Accepted: 07/02/2013] [Indexed: 11/29/2022]
Abstract
Connecting three dimensional structure and affinity is analogous to seeking the 'Archimedean point', a vantage point from where any observer can quantitatively perceive the subject of inquiry. Here we review current knowledge and challenges that lie ahead of us in the quest for this Archimedean point. We argue that current models are limited in reproducing measured data because molecular description of binding affinity must expand beyond the interfacial contribution and also incorporate effects stemming from conformational changes/dynamics and long-range interactions. Fortunately, explicit modeling of various kinetic schemes underlying biomolecular recognition and confined systems that reflect in vivo interactions are coming within reach. This quest will hopefully lead to an accurate biophysical interpretation of binding affinity that would allow unprecedented understanding of the molecular basis of life through unraveling the why's of interaction networks.
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Affiliation(s)
- Panagiotis L Kastritis
- Bijvoet Center for Biomolecular Research, Science Faculty - Chemistry, Utrecht University, 3584CH Utrecht, The Netherlands
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98
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Moal IH, Moretti R, Baker D, Fernández-Recio J. Scoring functions for protein-protein interactions. Curr Opin Struct Biol 2013; 23:862-7. [PMID: 23871100 DOI: 10.1016/j.sbi.2013.06.017] [Citation(s) in RCA: 52] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2013] [Revised: 06/26/2013] [Accepted: 06/29/2013] [Indexed: 12/24/2022]
Abstract
The computational evaluation of protein-protein interactions will play an important role in organising the wealth of data being generated by high-throughput initiatives. Here we discuss future applications, report recent developments and identify areas requiring further investigation. Many functions have been developed to quantify the structural and energetic properties of interacting proteins, finding use in interrelated challenges revolving around the relationship between sequence, structure and binding free energy. These include loop modelling, side-chain refinement, docking, multimer assembly, affinity prediction, affinity change upon mutation, hotspots location and interface design. Information derived from models optimised for one of these challenges can be used to benefit the others, and can be unified within the theoretical frameworks of multi-task learning and Pareto-optimal multi-objective learning.
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Affiliation(s)
- Iain H Moal
- Joint BSC-IRB Research Program in Computational Biology, Life Science Department, Barcelona Supercomputing Center, C/ Jordi Girona 29, 08034 Barcelona, Spain
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99
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Moal IH, Fernandez-Recio J. Intermolecular Contact Potentials for Protein-Protein Interactions Extracted from Binding Free Energy Changes upon Mutation. J Chem Theory Comput 2013; 9:3715-27. [PMID: 26584123 DOI: 10.1021/ct400295z] [Citation(s) in RCA: 34] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/31/2023]
Abstract
Understanding and predicting the energetics of protein-protein interactions is fundamental to the structural modeling of protein complexes. Binding free energy can be approximated as a sum of pairwise atomic or residue contact energies, which are commonly inferred from contact frequencies observed in experimental protein structures. However, such statistically inferred potentials require certain assumptions and approximation. Here, we explore the possibility of deriving atomic and residue contact potentials directly from experimental binding free energy changes following mutation and present a number of such potentials. The first set of potentials is obtained by unweighted least-squares fitting and bootsrap aggregating. The second set is calculated using a weighting scheme optimized against absolute binding affinity data, so as to account for the over-representation of certain complexes, residues, and families of interactions. The congruence of the potentials with known physical chemistry is investigated. The potentials are further validated by ranking and clustering protein-protein docking poses.
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Affiliation(s)
- Iain H Moal
- Joint BSC-IRB Research Program in Computational Biology, Life Science Department, Barcelona Supercomputing Center , C/Jordi Girona 29, 08034 Barcelona, Spain
| | - Juan Fernandez-Recio
- Joint BSC-IRB Research Program in Computational Biology, Life Science Department, Barcelona Supercomputing Center , C/Jordi Girona 29, 08034 Barcelona, Spain
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100
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Procko E, Hedman R, Hamilton K, Seetharaman J, Fleishman SJ, Su M, Aramini J, Kornhaber G, Hunt JF, Tong L, Montelione GT, Baker D. Computational design of a protein-based enzyme inhibitor. J Mol Biol 2013; 425:3563-75. [PMID: 23827138 DOI: 10.1016/j.jmb.2013.06.035] [Citation(s) in RCA: 75] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2013] [Revised: 06/15/2013] [Accepted: 06/25/2013] [Indexed: 01/30/2023]
Abstract
While there has been considerable progress in designing protein-protein interactions, the design of proteins that bind polar surfaces is an unmet challenge. We describe the computational design of a protein that binds the acidic active site of hen egg lysozyme and inhibits the enzyme. The design process starts with two polar amino acids that fit deep into the enzyme active site, identifies a protein scaffold that supports these residues and is complementary in shape to the lysozyme active-site region, and finally optimizes the surrounding contact surface for high-affinity binding. Following affinity maturation, a protein designed using this method bound lysozyme with low nanomolar affinity, and a combination of NMR studies, crystallography, and knockout mutagenesis confirmed the designed binding surface and orientation. Saturation mutagenesis with selection and deep sequencing demonstrated that specific designed interactions extending well beyond the centrally grafted polar residues are critical for high-affinity binding.
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Affiliation(s)
- Erik Procko
- Department of Biochemistry and Howard Hughes Medical Institute, University of Washington, Seattle, WA 98195, USA.
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