Characterizing and Predicting Protein Hinges for Mechanistic Insight

The functioning of proteins requires highly specific dynamics, which depend critically on the details of how amino acids are packed. Hinge motions are the most common type of large motion, typified by the opening and closing of enzymes around their substrates. The packing and geometries of residues are characterized here by graph theory. This characterization is sufficient to enable reliable hinge predictions from a single static structure, and notably, this can be from either the open or the closed form of a structure. This new method to identify hinges within protein structures is called PACKMAN. The predicted hinges are validated by using permutation tests on B-factors. Hinge prediction results are compared against lists of manually-curated hinge residues, and the results suggest that PACKMAN is robust enough to reproduce the known conformational changes and is able to predict hinge regions equally well from either the open or the closed forms of a protein. A group of 167 protein pairs with open and closed structures has been investigated Examples are shown for several additional proteins, including Zika virus non-structured (NS) proteins where there are 6 hinge regions in the NS5 protein, 5 hinge regions in the NS2B bound in the NS3 protease complex and 5 hinges in the NS3 helicase protein. Results obtained from this method can be important for generating conformational ensembles of protein targets for drug design. PACKMAN is freely accessible at (https://PACKMAN.bb.iastate.edu/)

Role of protein packing in protein dynamics

Proteins play a crucial role in all activities of living organisms and viruses. To perform their functions, they require specific structural characteristics that are optimal for functioning in their particular cellular environment. This restriction creates a condition that evolutionarily favors proteins with specific ‘folds’ or a combination of folds that offer significant stability, as well as geometries favoring specific functional mechanisms. The arrangement of amino acids with respect to one another in 3D space is protein packing. The protein packing regularities are reflected by low diversity in sequence space and even convergent evolution in the shapes of the proteins. This regularity presents the opportunity to incorporate features of packing into a variety of computations that can help to understand protein function. Various local protein features can be investigated, such as backbone torsion angles and sidechain conformations and how these relate to packing. Globular proteins are typically packed as densely as a sphere can pack. We see in this dissertation how protein packing can be investigated to yield a new understanding of function. We highlight and demonstrate how this property can be used to solve crucial challenges in structural bioinformatics. We use protein packing to predict the location of ‘hinges’ (hinge motion is the most common type of motion) that are the flexible hotspots on the protein structure. We further used the hinge information in a novel elastic network model that incorporates the protein packing into its core mathematics and also calculated perturbation responses using the concept of structural compliance that is often used in civil and mechanical engineering, and we also see the utility of packing for calculating the protein entropy, using only a single conformation of the protein without utilizing atomic Molecular Dynamics, therefore, making it tremendously faster and free of the butterfly effect and statistical problems associated with Molecular Dynamics. We also use Elastic Network Models to include the protein-solvent interactions in simple dynamics models and to identify the structural domains in the proteins. The software package ‘PACKMAN’ that we developed (https://github.com/Pranavkhade/PACKMAN) contains all the code to reproduce the research presented in this dissertation and the molecular toolbox to modify and customize the different features to carry out further research with ease. The PACKMAN has more tens of thousands of downloads on The Python Package Index (PyPI)

Role of protein packing in protein dynamics

Proteins play a crucial role in all activities of living organisms and viruses. To perform their functions, they require specific structural characteristics that are optimal for functioning in their particular cellular environment. This restriction creates a condition that evolutionarily favors proteins with specific ‘folds’ or a combination of folds that offer significant stability, as well as geometries favoring specific functional mechanisms. The arrangement of amino acids with respect to one another in 3D space is protein packing. The protein packing regularities are reflected by low diversity in sequence space and even convergent evolution in the shapes of the proteins. This regularity presents the opportunity to incorporate features of packing into a variety of computations that can help to understand protein function. Various local protein features can be investigated, such as backbone torsion angles and sidechain conformations and how these relate to packing. Globular proteins are typically packed as densely as a sphere can pack. We see in this dissertation how protein packing can be investigated to yield a new understanding of function. We highlight and demonstrate how this property can be used to solve crucial challenges in structural bioinformatics. We use protein packing to predict the location of ‘hinges’ (hinge motion is the most common type of motion) that are the flexible hotspots on the protein structure. We further used the hinge information in a novel elastic network model that incorporates the protein packing into its core mathematics and also calculated perturbation responses using the concept of structural compliance that is often used in civil and mechanical engineering, and we also see the utility of packing for calculating the protein entropy, using only a single conformation of the protein without utilizing atomic Molecular Dynamics, therefore, making it tremendously faster and free of the butterfly effect and statistical problems associated with Molecular Dynamics. We also use Elastic Network Models to include the protein-solvent interactions in simple dynamics models and to identify the structural domains in the proteins. The software package ‘PACKMAN’ that we developed (https://github.com/Pranavkhade/PACKMAN) contains all the code to reproduce the research presented in this dissertation and the molecular toolbox to modify and customize the different features to carry out further research with ease. The PACKMAN has more tens of thousands of downloads on The Python Package Index (PyPI)

Role of protein packing in protein dynamics

Proteins play a crucial role in all activities of living organisms and viruses. To perform their functions, they require specific structural characteristics that are optimal for functioning in their particular cellular environment. This restriction creates a condition that evolutionarily favors proteins with specific ‘folds’ or a combination of folds that offer significant stability, as well as geometries favoring specific functional mechanisms. The arrangement of amino acids with respect to one another in 3D space is protein packing. The protein packing regularities are reflected by low diversity in sequence space and even convergent evolution in the shapes of the proteins. This regularity presents the opportunity to incorporate features of packing into a variety of computations that can help to understand protein function. Various local protein features can be investigated, such as backbone torsion angles and sidechain conformations and how these relate to packing. Globular proteins are typically packed as densely as a sphere can pack. We see in this dissertation how protein packing can be investigated to yield a new understanding of function. We highlight and demonstrate how this property can be used to solve crucial challenges in structural bioinformatics. We use protein packing to predict the location of ‘hinges’ (hinge motion is the most common type of motion) that are the flexible hotspots on the protein structure. We further used the hinge information in a novel elastic network model that incorporates the protein packing into its core mathematics and also calculated perturbation responses using the concept of structural compliance that is often used in civil and mechanical engineering, and we also see the utility of packing for calculating the protein entropy, using only a single conformation of the protein without utilizing atomic Molecular Dynamics, therefore, making it tremendously faster and free of the butterfly effect and statistical problems associated with Molecular Dynamics. We also use Elastic Network Models to include the protein-solvent interactions in simple dynamics models and to identify the structural domains in the proteins. The software package ‘PACKMAN’ that we developed (https://github.com/Pranavkhade/PACKMAN) contains all the code to reproduce the research presented in this dissertation and the molecular toolbox to modify and customize the different features to carry out further research with ease. The PACKMAN has more tens of thousands of downloads on The Python Package Index (PyPI)

Role of protein packing in protein dynamics

Proteins play a crucial role in all activities of living organisms and viruses. To perform their functions, they require specific structural characteristics that are optimal for functioning in their particular cellular environment. This restriction creates a condition that evolutionarily favors proteins with specific ‘folds’ or a combination of folds that offer significant stability, as well as geometries favoring specific functional mechanisms. The arrangement of amino acids with respect to one another in 3D space is protein packing. The protein packing regularities are reflected by low diversity in sequence space and even convergent evolution in the shapes of the proteins. This regularity presents the opportunity to incorporate features of packing into a variety of computations that can help to understand protein function. Various local protein features can be investigated, such as backbone torsion angles and sidechain conformations and how these relate to packing. Globular proteins are typically packed as densely as a sphere can pack. We see in this dissertation how protein packing can be investigated to yield a new understanding of function. We highlight and demonstrate how this property can be used to solve crucial challenges in structural bioinformatics. We use protein packing to predict the location of ‘hinges’ (hinge motion is the most common type of motion) that are the flexible hotspots on the protein structure. We further used the hinge information in a novel elastic network model that incorporates the protein packing into its core mathematics and also calculated perturbation responses using the concept of structural compliance that is often used in civil and mechanical engineering, and we also see the utility of packing for calculating the protein entropy, using only a single conformation of the protein without utilizing atomic Molecular Dynamics, therefore, making it tremendously faster and free of the butterfly effect and statistical problems associated with Molecular Dynamics. We also use Elastic Network Models to include the protein-solvent interactions in simple dynamics models and to identify the structural domains in the proteins. The software package ‘PACKMAN’ that we developed (https://github.com/Pranavkhade/PACKMAN) contains all the code to reproduce the research presented in this dissertation and the molecular toolbox to modify and customize the different features to carry out further research with ease. The PACKMAN has more tens of thousands of downloads on The Python Package Index (PyPI)

Protein Fluctuations in Response to Random External Forces

Elastic network models (ENMs) have been widely used in the last decades to investigate protein motions and dynamics. There the intrinsic fluctuations based on the isolated structures are obtained from the normal modes of these elastic networks, and they generally show good agreement with the B-factors extracted from X-ray crystallographic experiments, which are commonly considered to be indicators of protein flexibility. In this paper, we propose a new approach to analyze protein fluctuations and flexibility, which has a more appropriate physical basis. It is based on the application of random forces to the protein ENM to simulate the effects of collisions of solvent on a protein structure. For this purpose, we consider both the Cα-atom coarse-grained anisotropic network model (ANM) and an elastic network augmented with points included for the crystallized waters. We apply random forces to these protein networks everywhere, as well as only on the protein surface alone. Despite the randomness of the directions of the applied perturbations, the computed average displacements of the protein network show a remarkably good agreement with the experimental B-factors. In particular, for our set of 919 protein structures, we find that the highest correlation with the B-factors is obtained when applying forces to the external surface of the water-augmented ANM (an overall gain of 3% in the Pearson’s coefficient for the entire dataset, with improvements up to 30% for individual proteins), rather than when evaluating the fluctuations obtained from the normal modes of a standard Cα-atom coarse-grained ANM. It follows that protein fluctuations should be considered not just as the intrinsic fluctuations of the internal dynamics, but also equally well as responses to external solvent forces, or as a combination of both

Characterizing and Predicting Protein Hinges for Mechanistic Insight

The functioning of proteins requires highly specific dynamics, which depend critically on the details of how amino acids are packed. Hinge motions are the most common type of large motion, typified by the opening and closing of enzymes around their substrates. The packing and geometries of residues are characterized here by graph theory. This characterization is sufficient to enable reliable hinge predictions from a single static structure, and notably, this can be from either the open or the closed form of a structure. This new method to identify hinges within protein structures is called PACKMAN. The predicted hinges are validated by using permutation tests on B-factors. Hinge prediction results are compared against lists of manually-curated hinge residues, and the results suggest that PACKMAN is robust enough to reproduce the known conformational changes and is able to predict hinge regions equally well from either the open or the closed forms of a protein. A group of 167 protein pairs with open and closed structures has been investigated Examples are shown for several additional proteins, including Zika virus non-structured (NS) proteins where there are 6 hinge regions in the NS5 protein, 5 hinge regions in the NS2B bound in the NS3 protease complex and 5 hinges in the NS3 helicase protein. Results obtained from this method can be important for generating conformational ensembles of protein targets for drug design. PACKMAN is freely accessible at (https://PACKMAN.bb.iastate.edu/).This is a manuscript of an article published as Khade, Pranav M., Ambuj Kumar, and Robert L. Jernigan. "Characterizing and Predicting Protein Hinges for Mechanistic Insight." Journal of Molecular Biology (2019). doi: 10.1016/j.jmb.2019.11.018</p

Protein Fluctuations in Response to Random External Forces

Elastic network models (ENMs) have been widely used in the last decades to investigate protein motions and dynamics. There the intrinsic fluctuations based on the isolated structures are obtained from the normal modes of these elastic networks, and they generally show good agreement with the B-factors extracted from X-ray crystallographic experiments, which are commonly considered to be indicators of protein flexibility. In this paper, we propose a new approach to analyze protein fluctuations and flexibility, which has a more appropriate physical basis. It is based on the application of random forces to the protein ENM to simulate the effects of collisions of solvent on a protein structure. For this purpose, we consider both the Cα-atom coarse-grained anisotropic network model (ANM) and an elastic network augmented with points included for the crystallized waters. We apply random forces to these protein networks everywhere, as well as only on the protein surface alone. Despite the randomness of the directions of the applied perturbations, the computed average displacements of the protein network show a remarkably good agreement with the experimental B-factors. In particular, for our set of 919 protein structures, we find that the highest correlation with the B-factors is obtained when applying forces to the external surface of the water-augmented ANM (an overall gain of 3% in the Pearson’s coefficient for the entire dataset, with improvements up to 30% for individual proteins), rather than when evaluating the fluctuations obtained from the normal modes of a standard Cα-atom coarse-grained ANM. It follows that protein fluctuations should be considered not just as the intrinsic fluctuations of the internal dynamics, but also equally well as responses to external solvent forces, or as a combination of both

2021 Clinical Reporting of MultiOmics Data! Hackathon

Official repository for the Clinical Reporting of MultiOmics Data! Hackathon hosted virtually in June 2021 by DNAnexus, the OpenCravat Group at Johns Hopkins University, and Carnegie Mellon University Libraries