An Integrated Approach to Characterizing Conformational Changes of Large Proteins

One of the major hurdles in the understanding of physiological processes and the design of new pharmaceutical drugs has been the understanding of the three-dimensional structure and flexibility of molecules, which hold key insight into their function and interactions. In many biological pathways conformational flexibility is known to play an essential role in driving functional mechanisms and has been the topic of both computational and experimental investigations. Modern experimental methods such as Hydrogen/Deuterium Exchange coupled with Mass Spectroscopy (HDX-MS) can probe the flexibility of large proteins, but the findings are mapped on the available (static) crystal structures, thereby losing a considerable part of the dynamic information. This project will investigate the extent to which computational methods can help discover and visualize conformational changes of large proteins that are in agreement with experiment. Because of the specific proteins selected for study in the context of this project, the proposed work has a great societal impact: it helps advance the understanding of processes that are involved in a variety of immune, inflammatory, neurodegenerative, ischemic, and age-related diseases. This project will train undergraduate, graduate, and postdoctoral students at Rice and the University of Pennsylvania on interdisciplinary topics. It will also provide training opportunities to women students. All work will be disseminated not only through technical presentations at national meetings, publications in peer reviewed journals and book chapters, but also through visits and talks with researchers in academia and the biotechnology industry, as well as with open source development and distribution of the algorithms developed for widespread use. A central part of this project concerns the development of a computational framework that samples the conformational space of proteins. It relies on a new representation of the degrees of freedom of proteins and protein complexes and on the definition of sampling primitives at various resolutions. The framework draws from geometry-inspired sampling techniques that efficiently explore large search spaces while keeping track of the extent that the search space is covered. In parallel, HDX-MS experiments will be carried out and used as probes for conformational variability. The experimental results will be correlated with the results of the computational framework to select the conformations that most closely match experiments. A distinguishing feature of this work is the connection with experiment and the ways in which each can feed the other. This project lies at the intersection of algorithms, computational geometry, dimensionality reduction, data mining, and visualization methods and connects with modern experimental methods which are actively under development and hold great promise for the understanding of protein structure and function.

This work has been supported by grant NSF AF 1423304.

Related Publications

1. D. Devaurs, D. A. Antunes, and L. E. Kavraki, “Computational analysis of complement inhibitor compstatin using molecular dynamics,” Journal of Molecular Modeling, vol. 26, no. 231, Aug. 2020.
   
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   @article{devaurs2020-compstatin,
     title = {Computational analysis of complement inhibitor compstatin using molecular dynamics},
     author = {Devaurs, Didier and Antunes, Dinler A. and Kavraki, Lydia E.},
     journal = {Journal of Molecular Modeling},
     month = aug,
     year = {2020},
     volume = {26},
     number = {231},
     doi = {10.1007/s00894-020-04472-8},
     abstract = {The complement system plays a major role in human immunity, but its abnormal
         activation can have severe pathological impacts. By mimicking a natural mechanism of
         complement regulation, the small peptide compstatin has proven to be a very promising
         complement inhibitor. Over the years, several compstatin analogs have been created, with
         improved inhibitory potency. A recent analog is being developed as a candidate drug against
         several pathological conditions, including COVID-19. However, the reasons behind its higher
         potency and increased binding affinity to complement proteins are not fully clear. This
         computational study highlights the mechanistic properties of several compstatin analogs,
         thus complementing previous experimental studies. We perform molecular dynamics simulations
         involving six analogs alone in solution and two complexes with compstatin bound to
         complement component 3. These simulations reveal that all the analogs we consider, except
         the original compstatin, naturally adopt a pre-bound conformation in solution.
         Interestingly, this set of analogs adopting a pre-bound conformation includes analogs that
         were not known to benefit from this behavior. We also show that the most recent compstatin
         analog (among those we consider) forms a stronger hydrogen bond network with its complement
         receptor than an earlier analog.},
   }
   

   Abstract

   ×

   The complement system plays a major role in human immunity, but its abnormal activation can have severe pathological impacts. By mimicking a natural mechanism of complement regulation, the small peptide compstatin has proven to be a very promising complement inhibitor. Over the years, several compstatin analogs have been created, with improved inhibitory potency. A recent analog is being developed as a candidate drug against several pathological conditions, including COVID-19. However, the reasons behind its higher potency and increased binding affinity to complement proteins are not fully clear. This computational study highlights the mechanistic properties of several compstatin analogs, thus complementing previous experimental studies. We perform molecular dynamics simulations involving six analogs alone in solution and two complexes with compstatin bound to complement component 3. These simulations reveal that all the analogs we consider, except the original compstatin, naturally adopt a pre-bound conformation in solution. Interestingly, this set of analogs adopting a pre-bound conformation includes analogs that were not known to benefit from this behavior. We also show that the most recent compstatin analog (among those we consider) forms a stronger hydrogen bond network with its complement receptor than an earlier analog.
   Details
2. E. Hruska, J. R. Abella, F. Nüske, L. E. Kavraki, and C. Clementi, “Quantitative comparison of adaptive sampling methods for protein dynamics,” The Journal of Chemical Physics, vol. 149, no. 24, p. 244119, Dec. 2018. PMID: 30599712
   
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   @article{hruska2018quantitative-comparison-of-adaptive-sampling,
     title = {Quantitative comparison of adaptive sampling methods for protein dynamics},
     author = {Hruska, Eugen and Abella, Jayvee R. and N{\"u}ske, Feliks and Kavraki, Lydia E. and Clementi, Cecilia},
     journal = {The Journal of Chemical Physics},
     month = dec,
     year = {2018},
     volume = {149},
     number = {24},
     pages = {244119},
     doi = {10.1063/1.5053582},
     abstract = {Adaptive sampling methods, often used in combination with Markov state models, are
         becoming increasingly popular for speeding up rare events in simulation such as molecular
         dynamics (MD) without biasing the system dynamics. Several adaptive sampling strategies have
         been proposed, but it is not clear which methods perform better for different physical
         systems. In this work, we present a systematic evaluation of selected adaptive sampling
         strategies on a wide selection of fast folding proteins. The adaptive sampling strategies
         were emulated using models constructed on already existing MD trajectories. We provide
         theoretical limits for the sampling speed-up and compare the performance of different
         strategies with and without using some a priori knowledge of the system. The results show
         that for different goals, different adaptive sampling strategies are optimal. In order to
         sample slow dynamical processes such as protein folding without a priori knowledge of the
         system, a strategy based on the identification of a set of metastable regions is
         consistently the most efficient, while a strategy based on the identification of microstates
         performs better if the goal is to explore newer regions of the conformational space.
         Interestingly, the maximum speed-up achievable for the adaptive sampling of slow processes
         increases for proteins with longer folding times, encouraging the application of these
         methods for the characterization of slower processes, beyond the fast-folding proteins
         considered here.},
     keyword = {fundamentals of protein modeling},
     note = {PMID: 30599712}
   }
   

   Abstract

   ×

   Adaptive sampling methods, often used in combination with Markov state models, are becoming increasingly popular for speeding up rare events in simulation such as molecular dynamics (MD) without biasing the system dynamics. Several adaptive sampling strategies have been proposed, but it is not clear which methods perform better for different physical systems. In this work, we present a systematic evaluation of selected adaptive sampling strategies on a wide selection of fast folding proteins. The adaptive sampling strategies were emulated using models constructed on already existing MD trajectories. We provide theoretical limits for the sampling speed-up and compare the performance of different strategies with and without using some a priori knowledge of the system. The results show that for different goals, different adaptive sampling strategies are optimal. In order to sample slow dynamical processes such as protein folding without a priori knowledge of the system, a strategy based on the identification of a set of metastable regions is consistently the most efficient, while a strategy based on the identification of microstates performs better if the goal is to explore newer regions of the conformational space. Interestingly, the maximum speed-up achievable for the adaptive sampling of slow processes increases for proteins with longer folding times, encouraging the application of these methods for the characterization of slower processes, beyond the fast-folding proteins considered here.
   Details
3. 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
4. D. Devaurs, D. A. Antunes, and L. E. Kavraki, “Revealing Unknown Protein Structures Using Computational Conformational Sampling Guided by Experimental Hydrogen-Exchange Data,” International Journal of Molecular Sciences, vol. 19, no. 11, p. 3406, 2018. PMID: 30384411, PMCID: PMC6280153
   
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   @article{devaurs2018revealing-unknown-protein0structures,
     title = {Revealing Unknown Protein Structures Using Computational Conformational Sampling Guided
         by Experimental Hydrogen-Exchange Data},
     author = {Devaurs, Didier and Antunes, Dinler A. and Kavraki, Lydia E.},
     journal = {International Journal of Molecular Sciences},
     year = {2018},
     volume = {19},
     number = {11},
     pages = {3406},
     doi = {10.3390/ijms19113406},
     abstract = {Both experimental and computational methods are available to gather information
         about a protein’s conformational space and interpret changes in protein structure.
         However, experimentally observing and computationally modeling large proteins remain
         critical challenges for structural biology. Our work aims at addressing these challenges by
         combining computational and experimental techniques relying on each other to overcome their
         respective limitations. Indeed, despite its advantages, an experimental technique such as
         hydrogen-exchange monitoring cannot produce structural models because of its low resolution.
         Additionally, the computational methods that can generate such models suffer from the curse
         of dimensionality when applied to large proteins. Adopting a common solution to this issue,
         we have recently proposed a framework in which our computational method for protein
         conformational sampling is biased by experimental hydrogen-exchange data. In this paper, we
         present our latest application of this computational framework: generating an
         atomic-resolution structural model for an unknown protein state. For that, starting from an
         available protein structure, we explore the conformational space of this protein, using
         hydrogen-exchange data on this unknown state as a guide. We have successfully used our
         computational framework to generate models for three proteins of increasing size, the
         biggest one undergoing large-scale conformational changes.},
     keyword = {fundamentals of protein modeling},
     note = {PMID: 30384411, PMCID: PMC6280153}
   }
   

   Abstract

   ×

   Both experimental and computational methods are available to gather information about a protein’s conformational space and interpret changes in protein structure. However, experimentally observing and computationally modeling large proteins remain critical challenges for structural biology. Our work aims at addressing these challenges by combining computational and experimental techniques relying on each other to overcome their respective limitations. Indeed, despite its advantages, an experimental technique such as hydrogen-exchange monitoring cannot produce structural models because of its low resolution. Additionally, the computational methods that can generate such models suffer from the curse of dimensionality when applied to large proteins. Adopting a common solution to this issue, we have recently proposed a framework in which our computational method for protein conformational sampling is biased by experimental hydrogen-exchange data. In this paper, we present our latest application of this computational framework: generating an atomic-resolution structural model for an unknown protein state. For that, starting from an available protein structure, we explore the conformational space of this protein, using hydrogen-exchange data on this unknown state as a guide. We have successfully used our computational framework to generate models for three proteins of increasing size, the biggest one undergoing large-scale conformational changes.
   Details
5. J. R. Abella, M. Moll, and L. E. Kavraki, “Maintaining and Enhancing Diversity of Sampled Protein Conformations in Robotics-Inspired Methods,” Journal of Computational Biology, vol. 25, no. 1, pp. 3–20, 2018. PMID: 29035572, PMCID: PMC5756939
   
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   @article{abella2018maintaining-and-enhancing-diversity-of-sampled,
     title = {Maintaining and Enhancing Diversity of Sampled Protein Conformations in
         Robotics-Inspired Methods},
     author = {Abella, Jayvee R. and Moll, Mark and Kavraki, Lydia E.},
     journal = {Journal of Computational Biology},
     year = {2018},
     volume = {25},
     number = {1},
     pages = {3--20},
     doi = {10.1089/cmb.2017.0164},
     abstract = {The ability to efficiently sample structurally diverse protein conformations allows
         one to gain a high-level view of a protein's energy landscape. Algorithms from robot motion
         planning have been used for conformational sampling and promote diversity by keeping track
         of ``coverage'' in conformational space based on the local sampling density. However, large
         proteins present special challenges. In particular, larger systems require running many
         concurrent instances of these algorithms, but these algorithms can quickly become memory
         intensive because they typically keep previously sampled conformations in memory to maintain
         coverage estimates. Additionally, many of these algorithms depend on defining useful
         perturbation strategies for exploring the conformational space, which is a very difficult
         task for large proteins because such systems are typically more constrained and exhibit
         complex motions. In this paper, we introduce two methodologies for maintaining and enhancing
         diversity in robotics-inspired conformational sampling. The first method leverages the use
         of a low-dimensional projection to define a global coverage grid that maintains coverage
         across concurrent runs of sampling. The second method is an automatic definition of a
         perturbation strategy through readily available flexibility information derived from
         B-factors, secondary structure, and rigidity analysis. Our results show a significant
         increase in the diversity of the conformations sampled for proteins consisting of up to 500
         residues. The methodologies presented in this paper may be vital components for the
         scalability of robotics-inspired approaches.},
     keyword = {fundamentals of protein modeling},
     note = {PMID: 29035572, PMCID: PMC5756939}
   }
   

   Abstract

   ×

   The ability to efficiently sample structurally diverse protein conformations allows one to gain a high-level view of a protein’s energy landscape. Algorithms from robot motion planning have been used for conformational sampling and promote diversity by keeping track of “coverage” in conformational space based on the local sampling density. However, large proteins present special challenges. In particular, larger systems require running many concurrent instances of these algorithms, but these algorithms can quickly become memory intensive because they typically keep previously sampled conformations in memory to maintain coverage estimates. Additionally, many of these algorithms depend on defining useful perturbation strategies for exploring the conformational space, which is a very difficult task for large proteins because such systems are typically more constrained and exhibit complex motions. In this paper, we introduce two methodologies for maintaining and enhancing diversity in robotics-inspired conformational sampling. The first method leverages the use of a low-dimensional projection to define a global coverage grid that maintains coverage across concurrent runs of sampling. The second method is an automatic definition of a perturbation strategy through readily available flexibility information derived from B-factors, secondary structure, and rigidity analysis. Our results show a significant increase in the diversity of the conformations sampled for proteins consisting of up to 500 residues. The methodologies presented in this paper may be vital components for the scalability of robotics-inspired approaches.
   Details
6. A. Novinskaya, D. Devaurs, M. Moll, and L. E. Kavraki, “Defining low-dimensional projections to guide protein conformational sampling,” Journal of Computational Biology, vol. 24, no. 1, pp. 79–89, 2017. PMID: 27892695
   
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   @article{novinskaya2016_defining_proj,
     title = {Defining low-dimensional projections to guide protein conformational sampling},
     author = {Novinskaya, Anastasia and Devaurs, Didier and Moll, Mark and Kavraki, Lydia E.},
     journal = {Journal of Computational Biology},
     year = {2017},
     volume = {24},
     number = {1},
     pages = {79--89},
     doi = {10.1089/cmb.2016.0144},
     abstract = {Exploring the conformational space of proteins is critical to characterizing their
         functions. Numerous methods have been proposed to sample a protein's conformational space,
         including techniques developed in the field of robotics and known as sampling-based
         motion-planning algorithms (or sampling-based planners). However, these algorithms suffer
         from the curse of dimensionality when applied to large pro- teins. Many sampling-based
         planners attempt to mitigate this issue by keeping track of sampling density to guide
         conformational sampling toward unexplored regions of the conformational space. This is often
         done using low-dimensional projections as an indirect way to reduce the dimensionality of
         the exploration problem. However, how to choose an appropriate projection and how much it
         influences the planner's performance are still poorly understood problems. In this paper, we
         introduce two methodologies defining low-dimensional projections that can be used by
         sampling-based planners for protein conformational sampling. The first method leverages
         information about a protein's flexibility to construct projections that can efficiently
         guide conformational sampling, when expert knowledge is available. The second method builds
         similar projections automatically, without expert intervention. We evaluate the projections
         produced by both methodologies on two conformational-search problems involving three
         middle-size proteins. Our experiments demonstrate that (i) defining projections based on
         expert knowledge can benefit conformational sampling, and (ii) automatically constructing
         such projections is a reasonable alternative.},
     keyword = {fundamentals of protein modeling},
     note = {PMID: 27892695}
   }
   

   Abstract

   ×

   Exploring the conformational space of proteins is critical to characterizing their functions. Numerous methods have been proposed to sample a protein’s conformational space, including techniques developed in the field of robotics and known as sampling-based motion-planning algorithms (or sampling-based planners). However, these algorithms suffer from the curse of dimensionality when applied to large pro- teins. Many sampling-based planners attempt to mitigate this issue by keeping track of sampling density to guide conformational sampling toward unexplored regions of the conformational space. This is often done using low-dimensional projections as an indirect way to reduce the dimensionality of the exploration problem. However, how to choose an appropriate projection and how much it influences the planner’s performance are still poorly understood problems. In this paper, we introduce two methodologies defining low-dimensional projections that can be used by sampling-based planners for protein conformational sampling. The first method leverages information about a protein’s flexibility to construct projections that can efficiently guide conformational sampling, when expert knowledge is available. The second method builds similar projections automatically, without expert intervention. We evaluate the projections produced by both methodologies on two conformational-search problems involving three middle-size proteins. Our experiments demonstrate that (i) defining projections based on expert knowledge can benefit conformational sampling, and (ii) automatically constructing such projections is a reasonable alternative.
   Details
7. 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

   ×

   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.
   Details
8. M. Moll, P. W. Finn, and L. E. Kavraki, “Structure-Guided Selection of Specificity Determining Positions in the Human Kinome,” BMC Genomics, vol. 17 (Suppl. 4), p. 431, 2016. PMID: 27556159, PMCID: PMC5001202
   
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   @article{moll2016structure-guided-selection-of-specificity-determining,
     title = {Structure-Guided Selection of Specificity Determining Positions in the Human Kinome},
     author = {Moll, Mark and Finn, Paul W. and Kavraki, Lydia E.},
     journal = {BMC Genomics},
     year = {2016},
     volume = {17 (Suppl.\ 4)},
     pages = {431},
     doi = {10.1186/s12864-016-2790-3},
     abstract = {Background: The human kinome contains many important drug targets. It is well-known
         that inhibitors of protein kinases bind with very different selectivity profiles. This is
         also the case for inhibitors of many other protein families. The increased availability of
         protein 3D structures has provided much information on the structural variation within a
         given protein family. However, the relationship between structural variations and binding
         specificity is complex and incompletely understood. We have developed a structural
         bioinformatics approach which provides an analysis of key determinants of binding
         selectivity as a tool to enhance the rational design of drugs with a specific selectivity
         profile.
         
         Results: We propose a greedy algorithm that computes a subset of residue positions in a
         multiple sequence alignment such that structural and chemical variation in those positions
         helps explain known binding affinities. By providing this information, the main purpose of
         the algorithm is to provide experimentalists with possible insights into how the selectivity
         profile of certain inhibitors is achieved, which is useful for lead optimization. In
         addition, the algorithm can also be used to predict binding affinities for structures whose
         affinity for a given inhibitor is unknown. The algorithm's performance is demonstrated using
         an extensive dataset for the human kinome.
         
         Conclusion: We show that the binding affinity of 38 different kinase inhibitors can be
         explained with consistently high precision and accuracy using the variation of at most six
         residue positions in the kinome binding site. We show for several inhibitors that we are
         able to identify residues that are known to be functionally important.},
     keyword = {functional annotation},
     note = {PMID: 27556159, PMCID: PMC5001202}
   }
   

   Abstract

   ×

   Background: The human kinome contains many important drug targets. It is well-known that inhibitors of protein kinases bind with very different selectivity profiles. This is also the case for inhibitors of many other protein families. The increased availability of protein 3D structures has provided much information on the structural variation within a given protein family. However, the relationship between structural variations and binding specificity is complex and incompletely understood. We have developed a structural bioinformatics approach which provides an analysis of key determinants of binding selectivity as a tool to enhance the rational design of drugs with a specific selectivity profile. Results: We propose a greedy algorithm that computes a subset of residue positions in a multiple sequence alignment such that structural and chemical variation in those positions helps explain known binding affinities. By providing this information, the main purpose of the algorithm is to provide experimentalists with possible insights into how the selectivity profile of certain inhibitors is achieved, which is useful for lead optimization. In addition, the algorithm can also be used to predict binding affinities for structures whose affinity for a given inhibitor is unknown. The algorithm’s performance is demonstrated using an extensive dataset for the human kinome. Conclusion: We show that the binding affinity of 38 different kinase inhibitors can be explained with consistently high precision and accuracy using the variation of at most six residue positions in the kinome binding site. We show for several inhibitors that we are able to identify residues that are known to be functionally important.
   Details
9. A. Novinskaya, D. Devaurs, M. Moll, and L. E. Kavraki, “Improving protein conformational sampling by using guiding projections,” in Proceedings of the IEEE International Conference on Bioinformatics and Biomedicine Workshops (BIBMW), 2015, pp. 1272–1279.
   
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   @inproceedings{novinskaya2015_guid_proj,
     title = {Improving protein conformational sampling by using guiding projections},
     author = {Novinskaya, Anastasia and Devaurs, Didier and Moll, Mark and Kavraki, Lydia E.},
     booktitle = {Proceedings of the IEEE International Conference on Bioinformatics and Biomedicine
         Workshops (BIBMW)},
     month = nov,
     year = {2015},
     pages = {1272--1279},
     doi = {10.1109/BIBM.2015.7359863},
     abstract = {Sampling-based motion planning algorithms from the field of robotics have been very
         successful in exploring the conformational space of proteins. However, studying the
         flexibility of large proteins with hundreds or thousands of Degrees of Freedom (DoFs)
         remains a big challenge. Large proteins are also highly-constrained systems, which makes
         them more challenging for standard robotic approaches. So-called ``expansive'' motion
         planning algorithms were specifically developed to address highly-dimensional and highly-
         constrained problems. Many such planners employ a low- dimensional projection to estimate
         exploration coverage and direct their search based on this information. We believe that such
         a projection plays an essential role in the success of these planners. This paper shows how
         the low-dimensional projection used by expansive planners can be tailored with respect to a
         given molecular system to enhance the process of conformational sampling. We introduce a
         methodology to generate an expert projection using any available information about a given
         protein. We evaluate this methodology on several conformational search problems involving
         proteins with hundreds of DoFs. Our experiments demonstrate that incorporating expert
         knowledge into the projection can significantly benefit the exploration process.},
     keyword = {fundamentals of protein modeling}
   }
   

   Abstract

   ×

   Sampling-based motion planning algorithms from the field of robotics have been very successful in exploring the conformational space of proteins. However, studying the flexibility of large proteins with hundreds or thousands of Degrees of Freedom (DoFs) remains a big challenge. Large proteins are also highly-constrained systems, which makes them more challenging for standard robotic approaches. So-called “expansive” motion planning algorithms were specifically developed to address highly-dimensional and highly- constrained problems. Many such planners employ a low- dimensional projection to estimate exploration coverage and direct their search based on this information. We believe that such a projection plays an essential role in the success of these planners. This paper shows how the low-dimensional projection used by expansive planners can be tailored with respect to a given molecular system to enhance the process of conformational sampling. We introduce a methodology to generate an expert projection using any available information about a given protein. We evaluate this methodology on several conformational search problems involving proteins with hundreds of DoFs. Our experiments demonstrate that incorporating expert knowledge into the projection can significantly benefit the exploration process.
   Details
10. M. Moll, P. W. Finn, and L. E. Kavraki, “Structure-Guided Selection of Specificity Determining Positions in the Human Kinome,” in Proceedings of the IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 2015, pp. 21–28.
    
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    BibTeX

    ×

    @inproceedings{moll2015structure-guided-selection-of-specificity-determining,
      title = {Structure-Guided Selection of Specificity Determining Positions in the Human Kinome},
      author = {Moll, Mark and Finn, Paul W. and Kavraki, Lydia E.},
      booktitle = {Proceedings of the IEEE International Conference on Bioinformatics and Biomedicine
          (BIBM)},
      month = nov,
      year = {2015},
      pages = {21--28},
      doi = {10.1109/BIBM.2015.7359650},
      abstract = {It is well-known that inhibitors of protein kinases bind with very different
          selectivity profiles. This is also the case for inhibitors of many other protein families. A
          better understanding of binding selectivity would enhance the design of drugs that target
          only a subfamily, thereby minimizing possible side-effects. The increased availability of
          protein 3D structures has made it possible to study the structural variation within a given
          protein family. However, not every structural variation is related to binding specificity.
          We propose a greedy algorithm that computes a subset of residue positions in a multiple
          sequence alignment such that structural and chemical variation in those positions helps
          explain known binding affinities. By providing this information, the main purpose of the
          algorithm is to provide experimentalists with possible insights into how the selectivity
          profile of certain inhibitors is achieved, which is useful for lead optimization. In
          addition, the algorithm can also be used to predict binding affinities for structures whose
          affinity for a given inhibitor is unknown. The algorithm's performance is demonstrated using
          an extensive dataset for the human kinome, which includes a large and important set of drug
          targets. We show that the binding affinity of 38 different kinase inhibitors can be
          explained with consistently high precision and accuracy using the variation of at most six
          residue positions in the kinome binding site.},
      keyword = {functional annotation}
    }
    

    Abstract

    ×

    It is well-known that inhibitors of protein kinases bind with very different selectivity profiles. This is also the case for inhibitors of many other protein families. A better understanding of binding selectivity would enhance the design of drugs that target only a subfamily, thereby minimizing possible side-effects. The increased availability of protein 3D structures has made it possible to study the structural variation within a given protein family. However, not every structural variation is related to binding specificity. We propose a greedy algorithm that computes a subset of residue positions in a multiple sequence alignment such that structural and chemical variation in those positions helps explain known binding affinities. By providing this information, the main purpose of the algorithm is to provide experimentalists with possible insights into how the selectivity profile of certain inhibitors is achieved, which is useful for lead optimization. In addition, the algorithm can also be used to predict binding affinities for structures whose affinity for a given inhibitor is unknown. The algorithm’s performance is demonstrated using an extensive dataset for the human kinome, which includes a large and important set of drug targets. We show that the binding affinity of 38 different kinase inhibitors can be explained with consistently high precision and accuracy using the variation of at most six residue positions in the kinome binding site.
    Details
11. D. A. Antunes, D. Devaurs, and L. E. Kavraki, “Understanding the challenges of protein flexibility in drug design,” Expert Opinion on Drug Discovery, vol. 10, no. 12, pp. 1301–1313, 2015. PMID: 26414598
    
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    BibTeX

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    @article{antunes-15-eodd,
      title = {Understanding the challenges of protein flexibility in drug design},
      author = {Antunes, Dinler A. and Devaurs, Didier and Kavraki, Lydia E.},
      journal = {Expert Opinion on Drug Discovery},
      year = {2015},
      volume = {10},
      number = {12},
      pages = {1301--1313},
      doi = {10.1517/17460441.2015.1094458},
      abstract = {Protein-ligand interactions play key roles in various metabolic pathways, and the
          proteins involved in these interactions represent major targets for drug discovery.
          Molecular docking is widely used to predict the structure of protein-ligand complexes, and
          protein flexibility stands out as one of the most important and challenging issues for
          binding mode prediction. Various docking methods accounting for protein flexibility have
          been proposed, tackling problems of ever-increasing dimensionality. This paper presents an
          overview of conformational sampling methods treating target flexibility during molecular
          docking. Special attention is given to approaches considering full protein flexibility.
          Contrary to what is frequently done, this review does not rely on classical biomolecular
          recognition models to classify existing docking methods. Instead, it applies algorithmic
          considerations, focusing on the level of flexibility accounted for. This review also
          discusses the diversity of docking applications, from virtual screening of small drug-like
          compounds to geometry prediction of protein-peptide complexes. Considering the diversity of
          docking methods presented here, deciding which one is the best at treating protein
          flexibility depends on the system under study and the research application. In virtual
          screening experiments, ensemble docking can be used to implicitly account for large-scale
          conformational changes, and selective docking can additionally consider local binding-site
          rearrangements. In other cases, on-the-fly exploration of the whole protein-ligand complex
          might be needed for accurate geometry prediction of the binding mode. Among other things,
          future methods are expected to provide alternative binding modes, which will better reflect
          the dynamic nature of protein-ligand interactions.},
      keyword = {proteins and drugs},
      note = {PMID: 26414598}
    }
    

    Abstract

    ×

    Protein-ligand interactions play key roles in various metabolic pathways, and the proteins involved in these interactions represent major targets for drug discovery. Molecular docking is widely used to predict the structure of protein-ligand complexes, and protein flexibility stands out as one of the most important and challenging issues for binding mode prediction. Various docking methods accounting for protein flexibility have been proposed, tackling problems of ever-increasing dimensionality. This paper presents an overview of conformational sampling methods treating target flexibility during molecular docking. Special attention is given to approaches considering full protein flexibility. Contrary to what is frequently done, this review does not rely on classical biomolecular recognition models to classify existing docking methods. Instead, it applies algorithmic considerations, focusing on the level of flexibility accounted for. This review also discusses the diversity of docking applications, from virtual screening of small drug-like compounds to geometry prediction of protein-peptide complexes. Considering the diversity of docking methods presented here, deciding which one is the best at treating protein flexibility depends on the system under study and the research application. In virtual screening experiments, ensemble docking can be used to implicitly account for large-scale conformational changes, and selective docking can additionally consider local binding-site rearrangements. In other cases, on-the-fly exploration of the whole protein-ligand complex might be needed for accurate geometry prediction of the binding mode. Among other things, future methods are expected to provide alternative binding modes, which will better reflect the dynamic nature of protein-ligand interactions.
    Details