A single protein crystal structure contains information about dynamic properties of the protein as well as providing a static view of one three-dimensional conformation. This additional information is to be found in the distribution of observed electron density about the mean position of each atom. It is general practice to account for this by refining a separate atomic displacement parameter (ADP) for each atomic center. However, these same displacements are often described well by simpler models based on TLS (translation/libration/screw) rigid-body motion of large groups of atoms, for example interdomain hinge motion. A procedure, TLSMD, has been developed that analyzes the distribution of ADPs in a previously refined protein crystal structure in order to generate optimal multi-group TLS descriptions of the constituent protein chains. TLSMD is applicable to crystal structures at any resolution. The models generated by TLSMD analysis can significantly improve the standard crystallographic residuals R and R(free) and can reveal intrinsic dynamic properties of the protein.
The TLSMD web server extracts information about dynamic properties of a protein based on information derived from a single‐crystal structure. It does so by analyzing the spatial distribution of individual atomic thermal parameters present in an input structural model. The server partitions the protein structure into multiple, contiguous chain segments, each segment corresponding to one group in a multi‐group description of the protein's overall dynamic motion. For each polypeptide chain of the input protein, the analysis generates the optimal partition into two segments, three segments, … up to 20 segments. Each such partition is optimal in the sense that it is the best approximation of the overall spatial distribution of input thermal parameters in terms of N chain segments, each acting as a rigid group undergoing TLS (translation/libration/screw) motion. This multi‐group TLS model may be used as a starting point for further crystallographic refinement, or as the basis for analyzing inter‐domain and other large‐scale motions implied by the crystal structure.
TLS (translation/libration/screw) models describe rigid-body vibrational motions of arbitrary objects. A single-group TLS model can be used to approximate the vibration of an entire protein molecule within a crystal lattice. More complex TLS models are broadly applicable to describing inter-domain and other internal vibrational modes of proteins. Such models can be derived and refined from crystallographic data, but they can also be used to describe the vibrational modes observed through other physical techniques or derived from molecular dynamics. The use of TLS models for protein motion has been relatively limited, partly because the physical meaning of the refined TLS parameters is not intuitive. Here, a molecular viewer, TLSView, is introduced using OpenGL and based on the mmLib library for describing and manipulating macromolecular structural models. This visualization tool allows an intuitive understanding of the physical significance of TLS models derived from crystallographic or other data and may be used as an interactive tool to display and interpret inter-domain or other motions in protein structural models. TLSView may also be used to prepare, analyze and validate TLS models for crystallographic refinement.
Protein motion is often the link between structure and function and a substantial fraction of proteins move through a domain hinge bending mechanism. Predicting the location of the hinge from a single structure is thus a logical first step towards predicting motion. Here, we describe ways to predict the hinge location by grouping residues with correlated normal-mode motions. We benchmarked our normal-mode based predictor against a gold standard set of carefully annotated hinge locations taken from the Database of Macromolecular Motions. We then compared it with three existing structure-based hinge predictors (TLSMD, StoneHinge, and FlexOracle), plus HingeSeq, a sequence-based hinge predictor. Each of these methods predicts hinges using very different sources of information-normal modes, experimental thermal factors, bond constraint networks, energetics, and sequence, respectively. Thus it is logical that using these algorithms together would improve predictions. We integrated all the methods into a combined predictor using a weighted voting scheme. Finally, we encapsulated all our results in a web tool which can be used to run all the predictors on submitted proteins and visualize the results.
The Python Macromolecular Library (mmLib) is a software toolkit and library of routines for the analysis and manipulation of macromolecular structural models, implemented in the Python programming language. It is accessed via a layered object‐oriented application programming interface, and provides a range of useful software components for parsing mmCIF, PDB and MTZ files, a library of atomic elements and monomers, an object‐oriented data structure describing biological macromolecules, and an OpenGL molecular viewer. The mmLib data model is designed to provide easy access to the various levels of detail needed to implement high‐level application programs for macromolecular crystallography, NMR, modeling and visualization. We describe here the establishment of mmLib as a collaborative open‐source code base, and the use of mmLib to implement several simple illustrative application programs.
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