Structure-based Selection of Tumor-antigens for T-cell Based Immunotherapy

Developing effective cancer treatments remains one of the most important challenges for healthcare, and T-cell based immunotherapy has provided some very positive recent advances in cancer treatment. Cytotoxic T lymphocytes (CTLs) can circulate through the body and are capable of identifying and eliminating tumorigenic cells. The recognition of tumor depends on the specific interaction between the T-cell receptor of CTLs and Human Leucocyte Antigen (HLA) class I molecules at the tumor cell surface, which binds and displays peptides derived from intracellular proteins. Peptide-HLA complexes are presented by all nucleated cells, constituting an efficient surveillance mechanism by which the immune system can recognize aberrant changes within cells of the body. Although CTL surveillance likely evolved to eliminate virally infected cells, this system also provides very promising opportunities for cancer treatment and specifically the development of immune-based therapies. However, such therapies must be highly personalized since most of these tumor-associated peptides are patient-specific. This is due mainly to the high level of HLA diversity within the human population, combined with the fact that each person’s tumor acquires unique genetic aberrations. A further challenge is the identification of tumor-specific peptides that are not also expressed by normal cells, which will likely ensure less off-target effects during therapy. Our long-term goal is to perform structure-guided selection of tumor-derived peptides with potential for immunotherapy, which will also allow structural analysis of different peptide-HLA complexes recognized by a given T-cell; this knowledge will help to prevent dangerous off-target toxicities. The objective of this project is to develop computational tools to enable docking-based modeling of peptide-HLA complexes, starting with HLA variants (allotypes) that are highly prevalent within human population and moving toward others that are less prevalent (for personalized treatment). Our Preliminary Data supports the need for a structural framework to improve the selection of targets for immunotherapy, since current methods have important limitations, particularly with regard to less prevalent HLAs. The central hypothesis is that structure-based analysis can be used to improve peptide target selection for individual HLA allotypes and thus facilitate the development of personalized immunotherapies for all cancer patients. Two specific aims were designed to test this hypothesis. In Specific Aim 1, a docking method will be specifically tailored to make binding predictions of tumor-derived peptides to two highly frequent and well- studied HLAs, HLA-A*2402 and HLA*A1101, collectively expressed by >55% of the world population. In Specific Aim 2, the HLA-A3 superfamily, collectively expressed by >40% of the human population, will be used as a model for extending the methods towards less well-studied HLAs. Innovative computational methods will be applied in this project and cutting-edge experimental resources will be used to train and validate computational methods. The underlying rationale is that developing a computational framework for these prevalent HLA allotypes will facilitate the development of personalized, antigen-specific immunotherapies, which would benefit a much larger number of cancer patients.

This work has been supported by grant NIH 1R21CA209941.

Related Publications

1. K. R. Jackson, D. A. Antunes, A. H. Talukder, A. R. Maleki, K. Amagai, A. Salmon, A. S. Katailiha, Y. Chiu, R. Fasoulis, M. M. Rigo, J. R. Abella, B. D. Melendez, F. Li, Y. Sun, H. M. Sonnemann, V. Belousov, F. Frenkel, S. Justesen, A. Makaju, Y. Liu, D. Horn, D. Lopez-Ferrer, A. F. Huhmer, P. Hwu, J. Roszik, D. Hawke, L. E. Kavraki, and G. Lizée, “Charge-based interactions through peptide position 4 drive diversity of antigen presentation by human leukocyte antigen class I molecules,” PNAS Nexus, vol. 1, no. 3, Aug. 2022.
   
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   @article{jackson2022-charge-interactions,
     author = {Jackson, Kyle R and Antunes, Dinler A and Talukder, Amjad H and Maleki, Ariana R and Amagai, Kano and Salmon, Avery and Katailiha, Arjun S and Chiu, Yulun and Fasoulis, Romanos and Rigo, Maurício Menegatti and Abella, Jayvee R and Melendez, Brenda D and Li, Fenge and Sun, Yimo and Sonnemann, Heather M and Belousov, Vladislav and Frenkel, Felix and Justesen, Sune and Makaju, Aman and Liu, Yang and Horn, David and Lopez-Ferrer, Daniel and Huhmer, Andreas F and Hwu, Patrick and Roszik, Jason and Hawke, David and Kavraki, Lydia E and Lizée, Gregory},
     title = {{Charge-based interactions through peptide position 4 drive diversity of antigen
         presentation by human leukocyte antigen class I molecules}},
     journal = {PNAS Nexus},
     volume = {1},
     number = {3},
     year = {2022},
     month = aug,
     abstract = {Human leukocyte antigen class I (HLA-I) molecules bind and present peptides at the
         cell surface to facilitate the induction of appropriate CD8+ T cell-mediated immune
         responses to pathogen- and self-derived proteins. The HLA-I peptide-binding cleft contains
         dominant anchor sites in the B and F pockets that interact primarily with amino acids at
         peptide position 2 and the C-terminus, respectively. Nonpocket peptide–HLA interactions
         also contribute to peptide binding and stability, but these secondary interactions are
         thought to be unique to individual HLA allotypes or to specific peptide antigens. Here, we
         show that two positively charged residues located near the top of peptide-binding cleft
         facilitate interactions with negatively charged residues at position 4 of presented
         peptides, which occur at elevated frequencies across most HLA-I allotypes. Loss of these
         interactions was shown to impair HLA-I/peptide binding and complex stability, as
         demonstrated by both in vitro and in silico experiments. Furthermore, mutation of these
         Arginine-65 (R65) and/or Lysine-66 (K66) residues in HLA-A*02:01 and A*24:02 significantly
         reduced HLA-I cell surface expression while also reducing the diversity of the presented
         peptide repertoire by up to 5-fold. The impact of the R65 mutation demonstrates that
         nonpocket HLA-I/peptide interactions can constitute anchor motifs that exert an unexpectedly
         broad influence on HLA-I-mediated antigen presentation. These findings provide fundamental
         insights into peptide antigen binding that could broadly inform epitope discovery in the
         context of viral vaccine development and cancer immunotherapy.},
     issn = {2752-6542},
     doi = {10.1093/pnasnexus/pgac124},
     url = {https://doi.org/10.1093/pnasnexus/pgac124}
   }
   

   Abstract

   ×

   Human leukocyte antigen class I (HLA-I) molecules bind and present peptides at the cell surface to facilitate the induction of appropriate CD8+ T cell-mediated immune responses to pathogen- and self-derived proteins. The HLA-I peptide-binding cleft contains dominant anchor sites in the B and F pockets that interact primarily with amino acids at peptide position 2 and the C-terminus, respectively. Nonpocket peptide–HLA interactions also contribute to peptide binding and stability, but these secondary interactions are thought to be unique to individual HLA allotypes or to specific peptide antigens. Here, we show that two positively charged residues located near the top of peptide-binding cleft facilitate interactions with negatively charged residues at position 4 of presented peptides, which occur at elevated frequencies across most HLA-I allotypes. Loss of these interactions was shown to impair HLA-I/peptide binding and complex stability, as demonstrated by both in vitro and in silico experiments. Furthermore, mutation of these Arginine-65 (R65) and/or Lysine-66 (K66) residues in HLA-A*02:01 and A*24:02 significantly reduced HLA-I cell surface expression while also reducing the diversity of the presented peptide repertoire by up to 5-fold. The impact of the R65 mutation demonstrates that nonpocket HLA-I/peptide interactions can constitute anchor motifs that exert an unexpectedly broad influence on HLA-I-mediated antigen presentation. These findings provide fundamental insights into peptide antigen binding that could broadly inform epitope discovery in the context of viral vaccine development and cancer immunotherapy.
   Details
2. D. A. Antunes, J. R. Abella, S. Hall-Swan, D. Devaurs, A. Conev, M. Moll, G. Lizée, and L. E. Kavraki, “HLA-Arena: a customizable environment for the structural modeling and analysis of peptide-HLA complexes for cancer immunotherapy,” JCO Clinical Cancer Informatics, vol. 4, pp. 623–636, Jul. 2020. PMID: 32667823, PMCID: 7397777
   
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   @article{antunes2020-hla-arena,
     title = {{HLA}-{A}rena: a customizable environment for the structural modeling and analysis of
         peptide-{HLA} complexes for cancer immunotherapy},
     author = {Antunes, Dinler A. and Abella, Jayvee R. and Hall-Swan, Sarah and Devaurs, Didier and Conev, Anja and Moll, Mark and Liz\'{e}e, Gregory and Kavraki, Lydia E.},
     journal = {JCO Clinical Cancer Informatics},
     month = jul,
     year = {2020},
     volume = {4},
     pages = {623--636},
     doi = {10.1200/CCI.19.00123},
     abstract = {PURPOSE: HLA protein receptors play a key role in cellular immunity. They bind
         intracellular peptides and display them for recognition by T-cell lymphocytes. Because
         T-cell activation is partially driven by structural features of these peptide-HLA complexes,
         their structural modeling and analysis are becoming central components of cancer
         immunotherapy projects. Unfortunately, this kind of analysis is limited by the small number
         of experimentally determined structures of peptide-HLA complexes. Overcoming this limitation
         requires developing novel computational methods to model and analyze peptide-HLA structures.
         
         METHODS: Here we describe a new platform for the structural modeling and analysis of
         peptide-HLA complexes, called HLA-Arena, which we have implemented using Jupyter Notebook
         and Docker. It is a customizable environment that facilitates the use of computational
         tools, such as APE-Gen and DINC, which we have previously applied to peptide-HLA complexes.
         By integrating other commonly used tools, such as MODELLER and MHCflurry, this environment
         includes support for diverse tasks in structural modeling, analysis, and visualization.
         
         RESULTS: To illustrate the capabilities of HLA-Arena, we describe 3 example workflows
         applied to peptide-HLA complexes. Leveraging the strengths of our tools, DINC and APE-Gen,
         the first 2 workflows show how to perform geometry prediction for peptide-HLA complexes and
         structure-based binding prediction, respectively. The third workflow presents an example of
         large-scale virtual screening of peptides for multiple HLA alleles.
         
         CONCLUSION: These workflows illustrate the potential benefits of HLA-Arena for the
         structural modeling and analysis of peptide-HLA complexes. Because HLA-Arena can easily be
         integrated within larger computational pipelines, we expect its potential impact to vastly
         increase. For instance, it could be used to conduct structural analyses for personalized
         cancer immunotherapy, neoantigen discovery, or vaccine development.},
     keyword = {fundamentals of protein modeling, proteins and drugs, other biomedical computing},
     note = {PMID: 32667823, PMCID: 7397777}
   }
   

   Abstract

   ×

   PURPOSE: HLA protein receptors play a key role in cellular immunity. They bind intracellular peptides and display them for recognition by T-cell lymphocytes. Because T-cell activation is partially driven by structural features of these peptide-HLA complexes, their structural modeling and analysis are becoming central components of cancer immunotherapy projects. Unfortunately, this kind of analysis is limited by the small number of experimentally determined structures of peptide-HLA complexes. Overcoming this limitation requires developing novel computational methods to model and analyze peptide-HLA structures. METHODS: Here we describe a new platform for the structural modeling and analysis of peptide-HLA complexes, called HLA-Arena, which we have implemented using Jupyter Notebook and Docker. It is a customizable environment that facilitates the use of computational tools, such as APE-Gen and DINC, which we have previously applied to peptide-HLA complexes. By integrating other commonly used tools, such as MODELLER and MHCflurry, this environment includes support for diverse tasks in structural modeling, analysis, and visualization. RESULTS: To illustrate the capabilities of HLA-Arena, we describe 3 example workflows applied to peptide-HLA complexes. Leveraging the strengths of our tools, DINC and APE-Gen, the first 2 workflows show how to perform geometry prediction for peptide-HLA complexes and structure-based binding prediction, respectively. The third workflow presents an example of large-scale virtual screening of peptides for multiple HLA alleles. CONCLUSION: These workflows illustrate the potential benefits of HLA-Arena for the structural modeling and analysis of peptide-HLA complexes. Because HLA-Arena can easily be integrated within larger computational pipelines, we expect its potential impact to vastly increase. For instance, it could be used to conduct structural analyses for personalized cancer immunotherapy, neoantigen discovery, or vaccine development.
   Details
3. D. Devaurs, D. A. Antunes, S. Hall-Swan, N. Mitchell, M. Moll, G. Lizée, and L. E. Kavraki, “Using parallelized incremental meta-docking can solve the conformational sampling issue when docking large ligands to proteins,” BMC Molecular and Cell Biology, vol. 20, no. 1, p. 42, Sep. 2019. PMID: 31488048, PMCID: PMC6729087
   
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   @article{devaurs2019using-parallelized-incremental-meta-docking,
     title = {Using parallelized incremental meta-docking can solve the conformational sampling issue
         when docking large ligands to proteins},
     author = {Devaurs, Didier and Antunes, Dinler A and Hall-Swan, Sarah and Mitchell, Nicole and Moll, Mark and Liz{\'e}e, Gregory and Kavraki, Lydia E},
     journal = {BMC Molecular and Cell Biology},
     month = sep,
     year = {2019},
     volume = {20},
     number = {1},
     pages = {42},
     doi = {10.1186/s12860-019-0218-z},
     abstract = {Background: Docking large ligands, and especially peptides, to protein receptors is
         still considered a challenge in computational structural biology. Besides the issue of
         accurately scoring the binding modes of a protein-ligand complex produced by a molecular
         docking tool, the conformational sampling of a large ligand is also often considered a
         challenge because of its underlying combinatorial complexity. In this study, we evaluate the
         impact of using parallelized and incremental paradigms on the accuracy and performance of
         conformational sampling when docking large ligands. We use five datasets of protein-ligand
         complexes involving ligands that could not be accurately docked by classical protein-ligand
         docking tools in previous similar studies.
         
         Results: Our computational evaluation shows that simply increasing the amount of
         conformational sampling performed by a protein-ligand docking tool, such as Vina, by running
         it for longer is rarely beneficial. Instead, it is more efficient and advantageous to run
         several short instances of this docking tool in parallel and group their results together,
         in a straightforward parallelized docking protocol. Even greater accuracy and efficiency are
         achieved by our parallelized incremental meta-docking tool, DINC, showing the additional
         benefits of its incremental paradigm. Using DINC, we could accurately reproduce the vast
         majority of the protein-ligand complexes we considered.
         
         Conclusions: Our study suggests that, even when trying to dock large ligands to proteins,
         the conformational sampling of the ligand should no longer be considered an issue, as simple
         docking protocols using existing tools can solve it. Therefore, scoring should currently be
         regarded as the biggest unmet challenge in molecular docking.
         
         Keywords: molecular docking; protein-ligand docking; protein-peptide docking; conformational
         sampling; scoring; parallelism; incremental protocol},
     keyword = {proteins and drugs},
     note = {PMID: 31488048, PMCID: PMC6729087}
   }
   

   Abstract

   ×

   Background: Docking large ligands, and especially peptides, to protein receptors is still considered a challenge in computational structural biology. Besides the issue of accurately scoring the binding modes of a protein-ligand complex produced by a molecular docking tool, the conformational sampling of a large ligand is also often considered a challenge because of its underlying combinatorial complexity. In this study, we evaluate the impact of using parallelized and incremental paradigms on the accuracy and performance of conformational sampling when docking large ligands. We use five datasets of protein-ligand complexes involving ligands that could not be accurately docked by classical protein-ligand docking tools in previous similar studies. Results: Our computational evaluation shows that simply increasing the amount of conformational sampling performed by a protein-ligand docking tool, such as Vina, by running it for longer is rarely beneficial. Instead, it is more efficient and advantageous to run several short instances of this docking tool in parallel and group their results together, in a straightforward parallelized docking protocol. Even greater accuracy and efficiency are achieved by our parallelized incremental meta-docking tool, DINC, showing the additional benefits of its incremental paradigm. Using DINC, we could accurately reproduce the vast majority of the protein-ligand complexes we considered. Conclusions: Our study suggests that, even when trying to dock large ligands to proteins, the conformational sampling of the ligand should no longer be considered an issue, as simple docking protocols using existing tools can solve it. Therefore, scoring should currently be regarded as the biggest unmet challenge in molecular docking. Keywords: molecular docking; protein-ligand docking; protein-peptide docking; conformational sampling; scoring; parallelism; incremental protocol
   Details
4. D. A. Antunes, J. R. Abella, D. Devaurs, M. M. Rigo, and L. E. Kavraki, “Structure-based methods for binding mode and binding affinity prediction for peptide-MHC complexes,” Current Topics in Medicinal Chemistry, vol. 19, no. 1, 2019. PMID: 30582480, PMCID: PMC6361695
   
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   @article{antunes2019structure-based-methods-for-binding,
     title = {Structure-based methods for binding mode and binding affinity prediction for
         peptide-{MHC} complexes},
     author = {Antunes, Dinler A. and Abella, Jayvee R. and Devaurs, Didier and Rigo, Maur\'{i}cio M. and Kavraki, Lydia E.},
     journal = {Current Topics in Medicinal Chemistry},
     year = {2019},
     volume = {19},
     number = {1},
     doi = {10.2174/1568026619666181224101744},
     abstract = {Understanding the mechanisms involved in the activation of an immune response is
         essential to many fields in human health, including vaccine development and personalized
         cancer immunotherapy. A central step in the activation of the adaptive immune response is
         the recognition, by T-cell lymphocytes, of peptides displayed by a special type of receptor
         known as Major Histocompatibility Complex (MHC). Considering the key role of MHC receptors
         in T-cell activation, the computational prediction of peptide binding to MHC has been an
         important goal for many immunological applications. Sequence-based methods have become the
         gold standard for peptide-MHC binding affinity prediction, but structure-based methods are
         expected to provide more general predictions (i.e., predictions applicable to all types of
         MHC receptors). In addition, structural modeling of peptide-MHC complexes has the potential
         to uncover yet unknown drivers of T-cell activation, thus allowing for the development of
         better and safer therapies. In this review, we discuss the use of computational methods for
         the structural modeling of peptide-MHC complexes (i.e., binding mode prediction) and for the
         structure-based prediction of binding affinity.},
     keyword = {proteins and drugs, molecular docking, binding mode prediction, binding affinity
         prediction, peptide-MHC complexes, immunogenicity, T-cell activation},
     note = {PMID: 30582480, PMCID: PMC6361695}
   }
   

   Abstract

   ×

   Understanding the mechanisms involved in the activation of an immune response is essential to many fields in human health, including vaccine development and personalized cancer immunotherapy. A central step in the activation of the adaptive immune response is the recognition, by T-cell lymphocytes, of peptides displayed by a special type of receptor known as Major Histocompatibility Complex (MHC). Considering the key role of MHC receptors in T-cell activation, the computational prediction of peptide binding to MHC has been an important goal for many immunological applications. Sequence-based methods have become the gold standard for peptide-MHC binding affinity prediction, but structure-based methods are expected to provide more general predictions (i.e., predictions applicable to all types of MHC receptors). In addition, structural modeling of peptide-MHC complexes has the potential to uncover yet unknown drivers of T-cell activation, thus allowing for the development of better and safer therapies. In this review, we discuss the use of computational methods for the structural modeling of peptide-MHC complexes (i.e., binding mode prediction) and for the structure-based prediction of binding affinity.
   Details
5. D. Devaurs, M. Papanastasiou, D. A. Antunes, J. R. Abella, M. Moll, D. Ricklin, J. D. Lambris, and L. E. Kavraki, “Native state of complement protein C3d analysed via hydrogen exchange and conformational sampling,” International Journal of Computational Biology and Drug Design, vol. 11, no. 1/2, pp. 90–113, 2018. PMID: 30700993, PMCID: PMC6349257
   
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   @article{devaurs_16_icibm,
     title = {Native state of complement protein {C3d} analysed via hydrogen exchange and
         conformational sampling},
     author = {Devaurs, Didier and Papanastasiou, Malvina and Antunes, Dinler A. and Abella, Jayvee R. and Moll, Mark and Ricklin, Daniel and Lambris, John D. and Kavraki, Lydia E.},
     journal = {International Journal of Computational Biology and Drug Design},
     year = {2018},
     volume = {11},
     number = {1/2},
     pages = {90--113},
     doi = {10.1504/IJCBDD.2018.10011903},
     abstract = {Hydrogen/deuterium exchange detected by mass spectrometry (HDX-MS) provides valuable
         information on protein structure and dynamics. Although HDX-MS data is often interpreted
         using crystal structures, it was suggested that conformational ensembles produced by
         molecular dynamics simulations yield more accurate interpretations. In this paper, we
         analyse the complement protein C3d through HDX-MS data and evaluate several interpretation
         methodologies, using an existing prediction model to derive HDX-MS data from protein
         structure. We perform an HDX-MS experiment on C3d and, then, to interpret and refine the
         obtained data, we look for a conformation (or conformational ensemble) of C3d that allows
         computationally replicating this data. First, we confirm that crystal structures are not a
         good choice. Second, we suggest that conformational ensembles produced by molecular dynamics
         simulations might not always be satisfactory either. Finally, we show that coarse-grained
         conformational sampling of C3d produces a conformation from which the HDX-MS data can be
         replicated and refined.},
     keyword = {fundamentals of protein modeling},
     note = {PMID: 30700993, PMCID: PMC6349257}
   }
   

   Abstract

   ×

   Hydrogen/deuterium exchange detected by mass spectrometry (HDX-MS) provides valuable information on protein structure and dynamics. Although HDX-MS data is often interpreted using crystal structures, it was suggested that conformational ensembles produced by molecular dynamics simulations yield more accurate interpretations. In this paper, we analyse the complement protein C3d through HDX-MS data and evaluate several interpretation methodologies, using an existing prediction model to derive HDX-MS data from protein structure. We perform an HDX-MS experiment on C3d and, then, to interpret and refine the obtained data, we look for a conformation (or conformational ensemble) of C3d that allows computationally replicating this data. First, we confirm that crystal structures are not a good choice. Second, we suggest that conformational ensembles produced by molecular dynamics simulations might not always be satisfactory either. Finally, we show that coarse-grained conformational sampling of C3d produces a conformation from which the HDX-MS data can be replicated and refined.
   Details
6. D. A. Antunes, D. Devaurs, M. Moll, G. Lizée, and L. E. Kavraki, “General prediction of peptide-MHC binding modes using incremental docking: A proof of concept,” Scientific Reports, vol. 8, p. 4327, 2018. PMCID: PMC5847594, PMID: 29531253
   
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   @article{antunes2018_dinc-hla-proof,
     title = {General prediction of peptide-{MHC} binding modes using incremental docking: A proof of
         concept},
     author = {Antunes, Dinler A and Devaurs, Didier and Moll, Mark and Liz\'{e}e, Gregory and Kavraki, Lydia E},
     journal = {Scientific Reports},
     year = {2018},
     volume = {8},
     pages = {4327},
     doi = {10.1038/s41598-018-22173-4},
     abstract = {The class I major histocompatibility complex (MHC) is capable of binding peptides
         derived from intracellular proteins and displaying them at the cell surface. The recognition
         of these peptide-MHC (pMHC) complexes by T-cells is the cornerstone of cellular immunity,
         enabling the elimination of infected or tumoral cells. T-cell-based immunotherapies against
         cancer, which leverage this mechanism, can greatly benefit from structural analyses of pMHC
         complexes. Several attempts have been made to use molecular docking for such analyses, but
         pMHC structure remains too challenging for even state-of-the-art docking tools. To overcome
         these limitations, we describe the use of an incremental meta-docking approach for
         structural prediction of pMHC complexes. Previous methods applied in this context used
         specific constraints to reduce the complexity of this prediction problem, at the expense of
         generality. Our strategy makes no assumption and can potentially be used to predict binding
         modes for any pMHC complex. Our method has been tested in a re-docking experiment,
         reproducing the binding modes of 25 pMHC complexes whose crystal structures are available.
         This study is a proof of concept that incremental docking strategies can lead to general
         geometry prediction of pMHC complexes, with potential applications for immunotherapy against
         cancer or infectious diseases.},
     keyword = {proteins and drugs, peptide-docking, pHLA structure, geometry prediction, DINC,
         cancer immunotherapy},
     note = {PMCID: PMC5847594, PMID: 29531253}
   }
   

   Abstract

   ×

   The class I major histocompatibility complex (MHC) is capable of binding peptides derived from intracellular proteins and displaying them at the cell surface. The recognition of these peptide-MHC (pMHC) complexes by T-cells is the cornerstone of cellular immunity, enabling the elimination of infected or tumoral cells. T-cell-based immunotherapies against cancer, which leverage this mechanism, can greatly benefit from structural analyses of pMHC complexes. Several attempts have been made to use molecular docking for such analyses, but pMHC structure remains too challenging for even state-of-the-art docking tools. To overcome these limitations, we describe the use of an incremental meta-docking approach for structural prediction of pMHC complexes. Previous methods applied in this context used specific constraints to reduce the complexity of this prediction problem, at the expense of generality. Our strategy makes no assumption and can potentially be used to predict binding modes for any pMHC complex. Our method has been tested in a re-docking experiment, reproducing the binding modes of 25 pMHC complexes whose crystal structures are available. This study is a proof of concept that incremental docking strategies can lead to general geometry prediction of pMHC complexes, with potential applications for immunotherapy against cancer or infectious diseases.
   Details
7. D. A. Antunes, M. Moll, D. Devaurs, K. R. Jackson, G. Lizée, and L. E. Kavraki, “DINC 2.0: a new protein-peptide docking webserver using an incremental approach,” Cancer Research, vol. 77, no. 21, pp. 55–57, Nov. 2017. PMCID: PMC5679007, PMID: 29092940
   
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   @article{antunes2017_dinc2,
     title = {{DINC} 2.0: a new protein-peptide docking webserver using an incremental approach},
     author = {Antunes, Dinler A and Moll, Mark and Devaurs, Didier and Jackson, KR and Liz\'{e}e, Gregory and Kavraki, Lydia E},
     journal = {Cancer Research},
     month = nov,
     year = {2017},
     volume = {77},
     number = {21},
     pages = {55--57},
     doi = {10.1158/0008-5472.CAN-17-0511},
     abstract = {Molecular docking is a standard computational approach to predict binding modes of
         protein-ligand complexes, by exploring alternative orientations and conformations of the
         ligand (i.e., by exploring ligand flexibility). Docking tools are largely used for virtual
         screening of small drug-like molecules, but their accuracy and efficiency greatly decays for
         ligands with more than 10 flexible bonds. This prevents a broader use of these tools to dock
         larger ligands such as peptides, which are molecules of growing interest in cancer research.
         To overcome this limitation, our group has previously proposed a meta-docking strategy,
         called DINC, to predict binding modes of large ligands. By incrementally docking overlapping
         fragments of a ligand, DINC allowed predicting binding modes of peptide-based inhibitors of
         transcription factors involved in cancer. Here we describe DINC 2.0, a revamped version of
         the DINC webserver with enhanced capabilities and a more user-friendly interface. DINC 2.0
         allows docking ligands that were previously too challenging for DINC, such as peptides with
         more than 25 flexible bonds. The webserver is freely accessible at
         \url{http://dinc.kavrakilab.org}, together with additional documentation and video
         tutorials. Our team will provide continuous support for this tool and is working on
         extending its applicability to other challenging fields, such as personalized immunotherapy
         against cancer.},
     keyword = {proteins and drugs, other biomedical computing},
     note = {PMCID: PMC5679007, PMID: 29092940}
   }
   

   Abstract

   ×

   Molecular docking is a standard computational approach to predict binding modes of protein-ligand complexes, by exploring alternative orientations and conformations of the ligand (i.e., by exploring ligand flexibility). Docking tools are largely used for virtual screening of small drug-like molecules, but their accuracy and efficiency greatly decays for ligands with more than 10 flexible bonds. This prevents a broader use of these tools to dock larger ligands such as peptides, which are molecules of growing interest in cancer research. To overcome this limitation, our group has previously proposed a meta-docking strategy, called DINC, to predict binding modes of large ligands. By incrementally docking overlapping fragments of a ligand, DINC allowed predicting binding modes of peptide-based inhibitors of transcription factors involved in cancer. Here we describe DINC 2.0, a revamped version of the DINC webserver with enhanced capabilities and a more user-friendly interface. DINC 2.0 allows docking ligands that were previously too challenging for DINC, such as peptides with more than 25 flexible bonds. The webserver is freely accessible at http://dinc.kavrakilab.org, together with additional documentation and video tutorials. Our team will provide continuous support for this tool and is working on extending its applicability to other challenging fields, such as personalized immunotherapy against cancer.
   Details
8. D. Devaurs, D. A. Antunes, M. Papanastasiou, M. Moll, D. Ricklin, J. D. Lambris, and L. E. Kavraki, “Coarse-grained conformational sampling of protein structure improves the fit to experimental hydrogen-exchange data,” Frontiers in Molecular Biosciences, vol. 4, no. 13, 2017. PMCID: PMC5344923, PMID: 28344973
   
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   @article{devaurs-17-fmb,
     title = {Coarse-grained conformational sampling of protein structure improves the fit to
         experimental hydrogen-exchange data},
     author = {Devaurs, Didier and Antunes, Dinler A. and Papanastasiou, Malvina and Moll, Mark and Ricklin, Daniel and Lambris, John D. and Kavraki, Lydia E.},
     journal = {Frontiers in Molecular Biosciences},
     year = {2017},
     volume = {4},
     number = {13},
     doi = {10.3389/fmolb.2017.00013},
     abstract = {Monitoring hydrogen/deuterium exchange (HDX) undergone by a protein in solution
         produces experimental data that translates into valuable information about the protein’s
         structure. Data produced by HDX experiments is often interpreted using a crystal structure
         of the protein, when available. However, it has been shown that the correspondence between
         experimental HDX data and crystal structures is often not satisfactory. This creates
         difficulties when trying to perform a structural analysis of the HDX data. In this paper, we
         evaluate several strategies to obtain a conformation providing a good fit to the
         experimental HDX data, which is a premise of structural analysis. We show that performing
         molecular dynamics simulations can be inadequate to obtain such conformations, and we
         propose a novel methodology involving a coarse-grained conformational sampling approach
         instead. By extensively exploring the intrinsic flexibility of a protein with this approach,
         we produce a conformational ensemble from which we extract a single conformation providing a
         good fit to the experimental HDX data. We successfully demonstrate the applicability of our
         method to four small and medium-sized proteins.},
     keyword = {fundamentals of protein modeling},
     note = {PMCID: PMC5344923, PMID: 28344973}
   }
   

   Abstract

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   Monitoring hydrogen/deuterium exchange (HDX) undergone by a protein in solution produces experimental data that translates into valuable information about the protein’s structure. Data produced by HDX experiments is often interpreted using a crystal structure of the protein, when available. However, it has been shown that the correspondence between experimental HDX data and crystal structures is often not satisfactory. This creates difficulties when trying to perform a structural analysis of the HDX data. In this paper, we evaluate several strategies to obtain a conformation providing a good fit to the experimental HDX data, which is a premise of structural analysis. We show that performing molecular dynamics simulations can be inadequate to obtain such conformations, and we propose a novel methodology involving a coarse-grained conformational sampling approach instead. By extensively exploring the intrinsic flexibility of a protein with this approach, we produce a conformational ensemble from which we extract a single conformation providing a good fit to the experimental HDX data. We successfully demonstrate the applicability of our method to four small and medium-sized proteins.
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