Structure fluctuations and conformational changes in protein binding

Structure fluctuations and conformational changes accompany all biological processes involving macromolecules. The paper presents a classification of protein residues based on the normalized equilibrium fluctuations of the residue centers of mass in proteins and a statistical analysis of conformation changes in the side-chains upon binding. Normal mode analysis and an elastic network model were applied to a set of protein complexes to calculate the residue fluctuations and develop the residue classification. Comparison with a classification based on normalized B-factors suggests that the B-factors may underestimate protein flexibility in solvent. Our classification shows that protein loops and disordered fragments are enriched with highly fluctuating residues and depleted with weakly fluctuating residues. To calculate the dihedral angles distribution functions, the configuration space was divided into cells by a cubic grid. The effect of protein association on the distribution functions depends on the amino acid type and a grid step in the dihedral angles space. The changes in the dihedral angles increase from the near-backbone dihedral angle to the most distant one, for most residues. On average, one fifth of the interface residues change the rotamer state upon binding, whereas the rest of the interface residues undergo local readjustments within the same rotamer.Comment: 13 pages, 6 figure

Structure Fluctuations and Conformational Changes in Protein Binding

Structure fluctuations and conformational changes accompany all biological processes involving macromolecules. The paper presents a classification of protein residues based on the normalized equilibrium fluctuations of the residue centers of mass in proteins and a statistical analysis of conformation changes in the side-chains upon binding. Normal mode analysis and an elastic network model were applied to a set of protein complexes to calculate the residue fluctuations and develop the residue classification. Comparison with a classification based on normalized B-factors suggests that the B-factors may underestimate protein flexibility in solvent. Our classification shows that protein loops and disordered fragments are enriched with highly fluctuating residues and depleted with weakly fluctuating residues. Strategies for engineering thermostable proteins are discussed. To calculate the dihedral angles distribution functions, the configuration space was divided into cells by a cubic grid. The effect of protein association on the distribution functions depends on the amino acid type and a grid step in the dihedral angles space. The changes in the dihedral angles increase from the near-backbone dihedral angle to the most distant one, for most residues. On average, one fifth of the interface residues change the rotamer state upon binding, whereas the rest of the interface residues undergo local readjustments within the same rotamer

Exploration of the Disambiguation of Amino Acid Types to Chi-1 Rotamer Types in Protein Structure Prediction and Design

A protein’s global fold provide insight into function; however, function specificity is often detailed in sidechain orientation. Thus, determining the rotamer conformations is often crucial in the contexts of protein structure/function prediction and design. For all non-glycine and non-alanine types, chi-1 rotamers occupy a small number of discrete number of states. Herein, we explore the possibility of describing evolution from the perspective of the sidechains’ structure versus the traditional twenty amino acid types. To validate our hypothesis that this perspective is more crucial to our understanding of evolutionary relationships, we investigate its uses as evolutionary, substitution matrices for sequence alignments for fold recognition purposes and computational protein design with specific focus in designing beta sheet environments, where previous studies have been done on amino acid-types alone. Throughout this study, we also propose the concept of the “chi-1 rotamer sequence” that describes the chi-1 rotamer composition of a protein. We also present attempts to predict these sequences and real-value torsion angles from amino acid sequence information. First, we describe our developments of log-odds scoring matrices for sequence alignments. Log-odds substitution matrices are widely used in sequence alignments for their ability to determine evolutionary relationship between proteins. Traditionally, databases of sequence information guide the construction of these matrices which illustrates its power in discovering distant or weak homologs. Weak homologs, typically those that share low sequence identity (< 30%), are often difficult to identify when only using basic amino acid sequence alignment. While protein threading approaches have addressed this issue, many of these approaches include sequenced-based information or profiles guided by amino acid-based substitution matrices, namely BLOSUM62. Here, we generated a structural-based substitution matrix born by TM-align structural alignments that captures both the sequence mutation rate within same protein family folds and the chi-1 rotamer that represents each amino acid. These rotamer substitution matrices (ROTSUMs) discover new homologs and improved alignments in the PDB that traditional substitution matrices, based solely on sequence information, cannot identify. Certain tools and algorithms to estimate rotamer torsions angles have been developed but typically require either knowledge of backbone coordinates and/or experimental data to help guide the prediction. Herein, we developed a fragment-based algorithm, Rot1Pred, to determine the chi-1 states in each position of a given amino acid sequence, yielding a chi-1 rotamer sequence. This approach employs fragment matching of the query sequence to sequence-structure fragment pairs in the PDB to predict the query’s sidechain structure information. Real-value torsion angles were also predicted and compared against SCWRL4. Results show that overall and for most amino-acid types, Rot1Pred can calculate chi-1 torsion angles significantly closer to native angles compared to SCWRL4 when evaluated on I-TASSER generated model backbones. Finally, we’ve developed and explored chi-1-rotamer-based statistical potentials and evolutionary profiles constructed for de novo computational protein design. Previous analyses which aim to energetically describe the preference of amino acid types in beta sheet environments (parallel vs antiparallel packing or n- and c-terminal beta strand capping) have been performed with amino acid types although no explicit rotamer representation is given in their scoring functions. In our study, we construct statistical functions which describes chi-1 rotamer preferences in these environments and illustrate their improvement over previous methods. These specialized knowledge-based energy functions have generated sequences whose I-TASSER predicted models are structurally-alike to their input structures yet consist of low sequence identity.PHDChemical BiologyUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/145951/1/jarrettj_1.pd

Potentials of Mean Force for Protein Structure Prediction Vindicated, Formalized and Generalized

Understanding protein structure is of crucial importance in science, medicine and biotechnology. For about two decades, knowledge based potentials based on pairwise distances -- so-called "potentials of mean force" (PMFs) -- have been center stage in the prediction and design of protein structure and the simulation of protein folding. However, the validity, scope and limitations of these potentials are still vigorously debated and disputed, and the optimal choice of the reference state -- a necessary component of these potentials -- is an unsolved problem. PMFs are loosely justified by analogy to the reversible work theorem in statistical physics, or by a statistical argument based on a likelihood function. Both justifications are insightful but leave many questions unanswered. Here, we show for the first time that PMFs can be seen as approximations to quantities that do have a rigorous probabilistic justification: they naturally arise when probability distributions over different features of proteins need to be combined. We call these quantities reference ratio distributions deriving from the application of the reference ratio method. This new view is not only of theoretical relevance, but leads to many insights that are of direct practical use: the reference state is uniquely defined and does not require external physical insights; the approach can be generalized beyond pairwise distances to arbitrary features of protein structure; and it becomes clear for which purposes the use of these quantities is justified. We illustrate these insights with two applications, involving the radius of gyration and hydrogen bonding. In the latter case, we also show how the reference ratio method can be iteratively applied to sculpt an energy funnel. Our results considerably increase the understanding and scope of energy functions derived from known biomolecular structures

Just a Flexible Linker? the Structural and Dynamic Properties of CBP-ID4 Revealed by NMR Spectroscopy

Here, we present a structural and dynamic description of CBP-ID4 at atomic resolution. ID4 is the fourth intrinsically disordered linker of CREB-binding protein (CBP). In spite of the largely disordered nature of CBP-ID4, NMR chemical shifts and relaxation measurements show a significant degree of α-helix sampling in the protein regions encompassing residues 2-25 and 101-128 (1852-1875 and 1951-1978 in full-length CBP). Proline residues are uniformly distributed along the polypeptide, except for the two α-helical regions, indicating that they play an active role in modulating the structural features of this CBP fragment. The two helical regions are lacking known functional motifs, suggesting that they represent thus-far uncharacterized functional modules of CBP. This work provides insights into the functions of this protein linker that may exploit its plasticity to modulate the relative orientations of neighboring folded domains of CBP and fine-tune its interactions with a multitude of partners. © 2016 Biophysical Society

Quantification of Conformational Heterogeneity and its Role in Protein Aggregation and Unfolding

Proteins can exhibit significant conformational heterogeneity either under denaturing conditions or in aqueous solutions. The latter is true for a class of proteins whose sequences predispose them to form heterogeneous ensembles of conformations. Characterization of conformational heterogeneity in a protein ensemble requires the quantification of the amplitudes of spontaneous fluctuations in conjunction with information regarding coarse grain measures that report on the average sizes, shapes, and densities. This often demands multiplexed experimental approaches whose readouts are interpreted or annotated using ensembles drawn from atomistic or coarse grain computational simulations. Efforts to characterize conformational heterogeneity contribute directly to our understanding of disorder-to-order transitions in protein folding and self-assembly. These efforts are also crucial to our understanding of the heterotypic interactions involving intrinsically disordered proteins and non-native states of well-folded proteins. These heterotypic interactions are important in signal transduction and the regulation of protein homeostasis. The onset and progression of several systemic and neurodegenerative conformational diseases are linked to the nature and degree of conformational heterogeneity in specific proteins or proteolytic products of proteins. This thesis work focuses on the quantitative characterization of conformational heterogeneity in simulated ensembles of inducibly unfolded and intrinsically disordered proteins. Advances in nuclear magnetic resonance spectroscopy afford the possibility of detailed measurements of inter-residue distances and modulations to the relaxation dynamics of paramagnetic spins that are inserted as probes into a protein. These state-of-the-art measurements show interesting features within denatured state ensembles that cannot be explained using canonical random coil models. Here, we use computer simulations to generate plausible facsimiles of denatured state ensembles that reproduce experimental data and demonstrate that the ensembles that are consistent with the data are characterized by the presence of low-likelihood, long-range intra-chain contacts between hydrophobic groups. When placed in the context of sequence conservation information, it appears that these contacts act as gatekeepers that protect proteins from the deleterious consequences of protein aggregation by sequestering hydrophobic groups in an assortment of intra-chain long-range contacts. We also characterize the nature and degree of conformational heterogeneity in glutamine- and asparagine-rich containing systems. These efforts lead to insights regarding the role of conformational heterogeneity in mediating intermolecular associations that are implicated in aggregation and self-assembly of these systems. Analysis of results from atomistic simulations leads to a phenomenological model for the modulation of conformational heterogeneity and degeneracies of intermolecular interactions by naturally occurring sequences that flank polyglutamine domains. Finally, we develop a formal order parameter to quantify the conformational heterogeneity in simulated ensembles of proteins. When combined with measures of density and fluctuations thereof, it can be used to provide a complete description of the degree and nature of conformational heterogeneity in different ensembles, thus affording the ability to compare different ensembles to each other while also providing a way to categorize conformational transitions

In-silico discovery and experimental verification of excipients for biologics

Protein-based pharmaceuticals such as monoclonal antibodies are the fastest growing class of therapeutic agent. As with all protein therapeutics, antibody aggregation must be avoided during production, storage and use. With recent advances in computing power, it is becoming feasible to simulate protein-protein interactions in-silico. Combining computational and experimental studies may offer a platform solution to design specific in-process stabilisers and excipients to accelerate the development of aggregation-resistant formulations. An antibody Fv fragment was first evaluated to understand the early stages of aggregate formation by identifying aggregation-prone regions. Three-dimensional structural information and protein-protein docking were used to identify exposed hydrophobic patches. Virtual screening was used to identify compounds that bind to the exposed hydrophobic patches as a means to prevent Fv-Fv interactions that could result in aggregation. An excipient with the highest calculated binding affinity was found to prevent Fv-Fv interactions as determined with the diffusion interaction parameter (kD) using DLS. Excipient performance was then evaluated using coarse-grained molecular dynamics (MD) simulations with MARTINI force field to provide a more in-depth view on Fv fragment dimer complex formation. Simulation results were further evaluated with free-energy calculations but these free-energy calculations were found to produce highly variable and therefore unreliable results. This coarse-grained MD approach was also used to virtually screen a library of dipeptides to identify peptide excipients. The results revealed a positive correlation between the calculated mean interaction energies and the diffusion interaction parameter measured with DLS. Use of the MD approach was further extended to accommodate challenging an antibody without published structural data through homology modelling and to suggest possible excipients to prevent high-affinity antibody-antibody interactions. Therefore this MD approach could potentially be used as a first step for the selection of excipients for antibodies

Neighbor-Dependent Ramachandran Probability Distributions of Amino Acids Developed from a Hierarchical Dirichlet Process Model

Distributions of the backbone dihedral angles of proteins have been studied for over 40 years. While many statistical analyses have been presented, only a handful of probability densities are publicly available for use in structure validation and structure prediction methods. The available distributions differ in a number of important ways, which determine their usefulness for various purposes. These include: 1) input data size and criteria for structure inclusion (resolution, R-factor, etc.); 2) filtering of suspect conformations and outliers using B-factors or other features; 3) secondary structure of input data (e.g., whether helix and sheet are included; whether beta turns are included); 4) the method used for determining probability densities ranging from simple histograms to modern nonparametric density estimation; and 5) whether they include nearest neighbor effects on the distribution of conformations in different regions of the Ramachandran map. In this work, Ramachandran probability distributions are presented for residues in protein loops from a high-resolution data set with filtering based on calculated electron densities. Distributions for all 20 amino acids (with cis and trans proline treated separately) have been determined, as well as 420 left-neighbor and 420 right-neighbor dependent distributions. The neighbor-independent and neighbor-dependent probability densities have been accurately estimated using Bayesian nonparametric statistical analysis based on the Dirichlet process. In particular, we used hierarchical Dirichlet process priors, which allow sharing of information between densities for a particular residue type and different neighbor residue types. The resulting distributions are tested in a loop modeling benchmark with the program Rosetta, and are shown to improve protein loop conformation prediction significantly. The distributions are available at http://dunbrack.fccc.edu/hdp