Protein dynamics from X-ray crystallography: anisotropic, global motion in diffuse scattering patterns

Understanding X-ray crystallographic diffuse scattering is likely to improve our comprehension of equilibrium collective protein dynamics. Here, using molecular dynamics (MD) simulation, a detailed analysis is performed of the origins of diffuse scattering in crystalline Staphylococcal nuclease, for which the complete diffuse scattering pattern has been determined experimentally. The hydrogen-atom contribution and the scattering range over which the scattering can be considered to be a sum of solvent and protein scattering are determined. Two models of correlated protein motion are investigated by calculating the model-derived diffuse scattering and comparing with the scattering calculated directly from MD trajectories. In one model, previously used in diffuse scattering interpretation, the atomic displacement correlations decay isotropically with increasing separation. Model correlation lengths are obtained by refining the model scattering against the simulation-derived scattering pattern, and are found to be significantly different from those correlation lengths derived directly from the MD trajectories. Furthermore, the convergence between the model-derived and MD-derived scattering is poor. The second model, in which the displacement correlations are calculated from the principal components of the MD trajectories, is capable of fully reproducing the MD-derived diffuse scattering if the approximately 50% lowest-frequency modes are included. However, a small number ( approximately 10) of lowest-frequency and largest-amplitude modes dominates the diffuse scattering and thus the correlated protein motions. A detailed analysis of the principal components is performed. In particular, the effective free energy profile associated with each principle mode is analyzed and the eigenfrequency and damping coefficient computed using a model of Brownian dynamics. Those collective modes with effective frequencies below approximately 0.5 THz, including those that determine the diffuse scattering, are overdamped.     

A statistical analysis of N- and O-glycan linkage conformations from crystallographic data

We have generated a database of 639 glycosidic linkage structures by an exhaustive survey of the available crystallographic data for isolated oligosaccharides, glycoproteins, and glycan-binding proteins. For isolated oligosaccharides there is relatively little crystallographic data available. A much larger number of glycoprotein and glycan-binding protein structures have now been solved in which two or more linked monosaccharides can be resolved. In the majority of these cases, only a few residues can be seen. Using the 639 glycosidic linkage structures, we have identified one or more distinct conformers for all the linkages. The O5-C1-O-C(x)' torsion angles for all these distinct conformers appear to be determined chiefly by the exo-anomeric effect. The Manalpha1-6Man linkage appears to be less restrained than the others, showing a wide degree of dispersion outside the ranges of the defined conformers. The identification of distinct conformers for glyco-sidic linkages allows "average" glycan structures to be modeled and also allows the easy identification of distorted glycosidic linkages. Such an analysis shows that the interactions between IgG Fc and its own N-linked glycan result in severe distortion of the terminal Galbeta1-4GlcNAc linkage only, indicating the strong interactions that must be present between the Gal residue and the protein surface. The applicability of this crystallographic based analysis to glycan structures in solution is discussed. This database of linkagestructures should be a very useful reference tool in three-dimensional structure determinations.     

Protein networking: insights into global functional organization of proteomes

The formulation of network models from global protein studies is essential to understand the functioning of organisms. Network models of the proteome enable the application of Complex Network Analysis, a quantitative framework to investigate large complex networks using techniques from graph theory, statistical physics, dynamical systems and other fields. This approach has provided many insights into the functional organization of the proteome so far and will likely continue to do so. Currently, several network concepts have emerged in the field of proteomics. It is important to highlight the differences between these concepts, since different representations allow different insights into functional organization. One such concept is the protein interaction network, which contains proteins as nodes and undirected edges representing the occurrence of binding in large-scale protein-protein interaction studies. A second concept is the protein-signaling network, in which the nodes correspond to levels of post-translationally modified forms of proteins and directed edges to causal effects through post-translational modification, such as phosphorylation. Several other network concepts were introduced for proteomics. Although all formulated as networks, the concepts represent widely different physical systems. Therefore caution should be taken when applying relevant topological analysis. We review recent literature formulating and analyzing such networks.     

Protein interaction verification and functional annotation by integrated analysis of genome-scale data

Assays capable of determining the properties of thousands of genes in parallel present challenges with regard to accurate data processing and functional annotation. Collections of microarray expression data are applied here to assess the quality of different high-throughput protein interaction data sets. Significant differences are found. Confidence in 973 out of 5342 putative two-hybrid interactions from S. cerevisiae is increased. Besides verification, integration of expression and interaction data is employed to provide functional annotation for over 300 previously uncharacterized genes. The robustness of these approaches is demonstrated by experiments that test the in silico predictions made. This study shows how integration improves the utility of different types of functional genomic data and how well this contributes to functional annotation.     

GUARDD: user-friendly MATLAB software for rigorous analysis of CPMG RD NMR data

Molecular dynamics are essential for life, and nuclear magnetic resonance (NMR) spectroscopy has been used extensively to characterize these phenomena since the 1950s. For the past 15 years, the Carr-Purcell Meiboom-Gill relaxation dispersion (CPMG RD) NMR experiment has afforded advanced NMR labs access to kinetic, thermodynamic, and structural details of protein and RNA dynamics in the crucial μs-ms time window. However, analysis of RD data is challenging because datasets are often large and require many non-linear fitting parameters, thereby confounding assessment of accuracy. Moreover, novice CPMG experimentalists face an additional barrier because current software options lack an intuitive user interface and extensive documentation. Hence, we present the open-source software package GUARDD (Graphical User-friendly Analysis of Relaxation Dispersion Data), which is designed to organize, automate, and enhance the analytical procedures which operate on CPMG RD data (). This MATLAB-based program includes a graphical user interface, permits global fitting to multi-field, multi-temperature, multi-coherence data, and implements χ ²-mapping procedures, via grid-search and Monte Carlo methods, to enhance and assess fitting accuracy. The presentation features allow users to seamlessly traverse the large amount of results, and the RD Simulator feature can help design future experiments as well as serve as a teaching tool for those unfamiliar with RD phenomena. Based on these innovative features, we expect that GUARDD will fill a well-defined gap in service of the RD NMR community.     

Functional richness, functional evenness and functional divergence: the primary components of functional diversity

Functional diversity is hypothesised as being beneficial for ecosystem functions, such as productivity and resistance to invasion. However, a precise definition of functional diversity, and hence a framework for its quantification, have proved elusive. We present a definition based on the analogy of the components of species diversity – richness, evenness and divergence. These concepts are applied to functional characters to give three components of functional diversity – functional richness, functional evenness and functional divergence. We demonstrate how each of these components may be calculated. It is hoped that our definition of functional diversity and its components will aid in elucidation of the mechanisms behind diversity/ecosystem-function relationships.     

Molecular dynamics analysis of chymosin conformations in solution and in crystalline environment

The method of molecular dynamics in explicit solvent was applied to test the hypothesis of the existence of a self-inhibited form of chymosin in solution. The paths and energies were calculated for chymosin in solution and in a crystalline environment. The modeling revealed that the intermolecular contacts of chymosin in crystal have negligible influence on the energy stabilization of its self-inhibited conformation. On the other hand, upon molecular dynamics simulation of the active and self-inhibited forms in solution their conformational energies proved to be quite close and the potential barrier between them relatively low. All this supports the possibility of chymosin to adopt spontaneously the self-inhibited conformation in solution, and indicates that it is one of the really existing enzyme forms rather than a crystal packing artifact. The results obtained open novel approaches to studying the specificity of chymosin as well as other aspartic proteinases.     

SQUID: a program for the analysis and display of data from crystallography and molecular dynamics

SQUID is a flexible computer program that allows the analysis and display of molecular coordinates from crystallography, NMR, and molecular dynamics. The program can also display two-dimensional and three-dimensional data using many graph types, as well as perform array processing of data with numerous intrinsic functions. Graphics are based on the use of “move” and “draw” instructions, allowing easy development of new device drivers, including vector plotters.     

Protein dynamics and enzyme catalysis: insights from simulations

The role of protein dynamics in enzyme catalysis is one of the most active and controversial areas in enzymology today. Some researchers claim that protein dynamics are at the heart of enzyme catalytic efficiency, while others state that dynamics make no significant contribution to catalysis. What is the biochemist - or student - to make of the ferocious arguments in this area? Protein dynamics are complex and fascinating, as molecular dynamics simulations and experiments have shown. The essential question is: do these complex motions have functional significance? In particular, how do they affect or relate to chemical reactions within enzymes, and how are chemical and conformational changes coupled together? Biomolecular simulations can analyse enzyme reactions and dynamics in atomic detail, beyond that achievable in experiments: accurate atomistic modelling has an essential part to play in clarifying these issues. This article is part of a Special Issue entitled: Protein Dynamics: Experimental and Computational Approaches.     

Protein dynamics from solution NMR: theory and applications

Solution nuclear magnetic resonance (NMR) spectroscopy is unique in its ability to elucidate the details of atomic-level structural and dynamical properties of biological macromolecules under native-like conditions. Recent advances in NMR techniques and protein sample preparation now allow comprehensive investigation of protein dynamics over timescales ranging 14 orders of magnitude at nearly every atomic site. Thus, solution NMR is poised to reveal aspects of the physico-chemical properties that govern the ensemble distribution of protein conformers and the dynamics of their interconversion. We review these advances as well as their recent application to the study of proteins.     

In-silico analysis of Sirt2 from Schistosoma mansoni: structures,conformations, and interactions with inhibitors

Sirtuins are NAD+-dependent lysine deacetylases member of the class III HDAC family. These are demonstrated to be therapeutic targets in parasitic diseases like schistosomiasis. Observations suggested that sirtuin enzyme is necessary for the functionality of fe/male reproductive system, due to which SmSirt2 is treated as a potential therapeutic target. There are no structural and molecular features of SmSirt2 have been reported yet. In this study, homology modeling has been used to determine the three-dimensional features of the SmSITRT2. Further, structure validation has been performed by energy minimization and Ramachandran plot. Validated structures are further subjected to molecular docking and virtual screening to find the best lead molecules for downstream analysis. Ten lead molecules were selected while comparing virtual screening of hSirt2 and SmSirt2 both. These leads are further compared with AKG2 which is known inhibitor of hSirt2 (?8.8 kcal/mol). Out of selected 10 leads, four of them (ZINC23995485 (?9.5 kcal/mol), ZINC53298162 (?9.4 kcal/mol), ZINC70927268 (?10.0 kcal/mol), ZINC89878705 (?11.2 kcal/mol)) have shown better interaction with SmSirt2, in which ZINC89878705 (?11.2 kcal/mol) shows a more compact packing as compared to AKG2 and rest of ligands. These molecules could be further subject to in vitro study and model of SmSirt2 has been proposed for further structure-based drug design projects concerning sirtuins from Schistosoma mansoni.     

Nonlinear dynamics of immunogenic tumors: Parameter estimation and global bifurcation analysis

We present a mathematical model of the cytotoxic T lymphocyte response to the growth of an immunogenic tumor. The model exhibits a number of phenomena that are seenin vivo, including immunostimulation of tumor growth, “sneaking through” of the tumor, and formation of a tumor “dormant state”. The model is used to describe the kinetics of growth and regression of the B-lymphoma BCL₁ in the spleen of mice. By comparing the model with experimental data, numerical estimates of parameters describing processes that cannot be measuredin vivo are derived. Local and global bifurcations are calculated for realistic values of the parameters. For a large set of parameters we predict that the course of tumor growth and its clinical manifestation have a recurrent profile with a 3- to 4-month cycle, similar to patterns seen in certain leukemias.     

Predicting antibody hypervariable loop conformations. II: Minimization and molecular dynamics studies of MCPC603 from many randomly generated loop conformations

We describe a method for predicting the conformations of loops in proteins and its application to four of the complementarity determining regions [CDRs] in the crystallographically determined structure of MCPC603. The method is based on the generation of a large number of randomly generated conformations for the backbone of the loop being studied, followed by either minimization or molecular dynamics followed by minimization starting from these random structures. The details of the algorithm for the generation of the loops are presented in the first paper in this series (Shenkin et al. [submitted]). The results of minimization and molecular dynamics applied to these loops is presented here. For the two shortest CDRs studied (H1 and L2, which are five and seven amino acids long), minimizations and dynamics simulations which ignore interactions of the loop amino acids beyond the carbon beta replicate the conformation of the crystal structure closely. This suggests that these loops fold independently of sequence variation. For the third CDR (L3, which is nine amino acids), those portions of the CDR near its base which are hydrogen bonded to framework are well replicated by our procedures, but the top of the loop shows significant conformational variability. This variability persists when side chain interactions for the MCPC603 sequence are included. For a fourth CDR (H3, which is 11 amino acids long), new low-energy backbone conformations are found; however, only those which are close to the crystal are compatible with the sequence when side chain interactions are taken into account. Results from minimization and dynamics on single CDRs with all other CDRs removed are presented. These allow us to explore the extent to which individual CDR conformations are determined by interactions with framework only.