Docking Molecular Structures into EM Lecture 2

Another Rigid Body Docking Example Rossman 2000, Fitting atomic models into electron microscopy maps, Acta Cryst D56, nice description of 3 examples - nice description of 3 examples

Rossman 2000 Human rhinovirus- about 90 of 100 known serotypes use the cell surface glycoprotein ICAM-1 or CD54 as a receptor structure of ICAM-1 has been determined crystallographically HRV-ICAM complex only determined via cryo-EM. May be impossible to determine crystal structure because binding of ICAM is a recognition event that initiates virus break-up and the release of the virus RNA into host cell cytoplasm ICAM-1 usually functions to promote intercellular adhesion and signalling in response to inflammation HRV14-Fab17-IA

Rossman 2000 glycosolated structures of ICAM-1 with various elbow angles were fitted into EM map manually models were refined as rigid bodies in reciprocal space with respect to difference maps obtained by subtraction of HRV density from complex density using program X-PLOR the refinement process was reproducible to better than 1.5 A between equivalent atoms determine atomic interaction between virus and receptor

Automated Rigid-Body Docking Originally, docking was done manually However, a variety of computational docking algorithms have been developed to perform reliable and reproducible fitting of rigid structures into low-resolution maps

Correlation Coefficients The most widely used method for fitting atomic structures into low-resolution EM maps is a systematic maximization of the density cross-correlation of atomic models with electron density maps. packages used for this: COAN, DOCKEM and EMFIT The best results are obtained when the surface edges of individual components in a complex are well defined, and where there are only small regions of densities that cannot be assigned uniquely to a single component. However, at low resolution, standard correlation coefficients lie within a small range – problem of false positives in the fitting, particularly where the map includes densities not accounted for by docked compounds

Correlation Coefficients: Density Filtering Several density filtering operations have been proposed to overcome these difficulties: One approach involves the use of a mask to block the overlapping region between the densities arising from the individual docked components and the target map in calculations of the correlation coefficient ( Roseman, 2000). One approach involves the use of a mask to block the overlapping region between the densities arising from the individual docked components and the target map in calculations of the correlation coefficient ( Roseman, 2000). Roseman, 2000 Roseman, 2000

Correlation Coefficients: Density Filtering Another approach involves altering the functional form of the compared densities by applying a filter that enhances detection of contours (or surface) in the maps being compared Another approach involves altering the functional form of the compared densities by applying a filter that enhances detection of contours (or surface) in the maps being compared Filtering maximizes both density and contour overlap and therefore enhances numerical scoring contrast between potential solutions Filtering maximizes both density and contour overlap and therefore enhances numerical scoring contrast between potential solutions

Density Filtering

Correlation Coefficients: Density Filtering A drawback of density filtering is that the significant levels of noise that are present in low-resolution maps derived from electron microscopy can be amplified by certain density filtering approaches, which may increase the likelihood of "false- positive" fits of density.

Vector Quantization Automated docking is sometimes combined with vector distributions by vector quantization of the atomic model vector quantization is a data compression technique used in image and speech processing applications Vector quantization offers a flexible way to develop a reduced representation of 3D biological data from a variety of biophysical sources Situs package can be used for this

Vector Quantization data is represented by a small number of “codebook” vectors to reduce the combinatorial complexity of the structural comparison an actin monomer (backbone trace + atoms) encoded by four codebook vectors :

Vector Quantization In rigid-body docking, vector quantization is used to discretize both high- and low-resolution data sets. Pairs of corresponding codebook vectors are then identified and superimposed by a least- squares fit. The codebook vector least-squares fit results in a superposition of the corresponding high- and low-resolution data sets:

Core Weighting "core-weighting" approach - the construction of a complex structure from many components is simplified to a series of single component fitting procedures. The "core" region of a structure is defined as the part whose density distribution is unlikely to be altered by the presence of adjacent components. The "surface" region is the part that is accessible or can interact with other components. The region enclosed by the accessible surface thus belongs to the core region.

Core Weighting The single component fitting is conducted using a grid-threading Monte Carlo (GTMC) method that identifies the global maximum state (best fit) among a series of local maximum states determined by short Monte Carlo searches originating at a variety of grid points.

Flexible Docking Large macromolecular assemblies often undergo large functional rearrangements in some cases, the X-ray structure does not correspond to the EM structure This complicates the fitting, as not only orientation, but conformational rearrangements must be considered

Flexible Docking flexible fitting is often done by domain segmentation followed by fitting of each domain as a rigid body block (see earlier example with actin/myosin) However, large conformational changes often involve tightly coupled motions between domains

Flexible Docking: Vector Quantization in Situs Flexible docking combines vector quantization with molecular mechanics simulations where conformational changes require flexible fitting. Codebook vectors are used as constraints for the fitting. Flexible docking combines vector quantization with molecular mechanics simulations where conformational changes require flexible fitting. Codebook vectors are used as constraints for the fitting. The colored regions in the following diagram are the so-called Voronoi (nearest neighbor) cells whose centroids should coincide with the generating vectors:

Flexible Docking: Vector Quantization Fitting of the centroids to the low-resolution vector positions induces the desired conformational change. it is possible to constrain the distances between the vectors to reduce the effect of noise and experimental limitations on the vector positions:

Flexible Fitting: Normal Mode Analysis novel method for quantitative flexible docking of structures into EM maps in many cases, functional rearrangements of macromolecules can be described by a small number of low-frequency normal modes

Flexible Fitting: Normal Mode Analysis Normal mode refinement procedure relies on real space correlation between the calculated electron density map from atomic model and EM map The fitting is performed by deforming the structure along a set of low-frequency normal modes in order to maximize the correlation coefficient

Situs Situs is set of routines for quantitative docking of 3D data at variable resolution The software supports both rigid-body and flexible docking using a variety of fitting strategies. topology-representing neural networks are used to correlate the high- and low-resolution data sets – a form of vector quantization Situs is developed by the Laboratory for Structural Bioinformatics, biomachina.org. biomachina.org (other public software for map fitting - COAN ( Volkmann and Hanein, 1999), EMfit ( Rossmann et al., 2001), EMAN ( Jiang et al., 2001; Ludtke et al., 1999) (other public software for map fitting - COAN ( Volkmann and Hanein, 1999), EMfit ( Rossmann et al., 2001), EMAN ( Jiang et al., 2001; Ludtke et al., 1999) Volkmann and Hanein, 1999Rossmann et al., 2001Jiang et al., 2001Ludtke et al., 1999 Volkmann and Hanein, 1999Rossmann et al., 2001Jiang et al., 2001Ludtke et al., 1999

Situs Papers J Struc. Biol, 1999, 125: J Struc. Biol, 2001, 133:

Situs microtubules decorated with kinesin-related ncd proteins are used to demonstrate utility of packages Situs Situs step-by-step tutorial on this available Situs

Situs: Vector Quantization Given a certain number of vectors, need to make sure that the vector positions are statistically reliable and reproducible. Situs employs two quantization algorithms with different characteristics: the topology-representing neural network (TRN) performs a global stochastic search using random start vectors. Often a number of statistically independent TRN runs is repeated and the results are clustered and averaged. Useful if no prior information exists about the vector positions the topology-representing neural network (TRN) performs a global stochastic search using random start vectors. Often a number of statistically independent TRN runs is repeated and the results are clustered and averaged. Useful if no prior information exists about the vector positions Linde-Buzo-Gray (LBG, also called k -means) algorithms performs only a single run of local gradient descent search. It is useful if one wants to update existing vector positions, or if one wants to add distance constraints. Linde-Buzo-Gray (LBG, also called k -means) algorithms performs only a single run of local gradient descent search. It is useful if one wants to update existing vector positions, or if one wants to add distance constraints.

Situs: VIRTUAL REALITY developing a 3-dimensional graphics extension for Situs, termed SenSitus, that can support virtual-reality devices such as stereo glasses, 3- dimensional trackers, and force-feedback (haptic) devices. A force-feedback device measures a user's hand position and exerts a precisely controlled force on the hand. forces are calculated according to the correlation coefficient of density maps and crystallographic data.

Docking Issues/ Open Problems estimation of fitting quality validation of results estimation of fitting errors detection of ambiguities attempt at automated solutions

Possible Future Developments parallel computing can speedup process further development of methods to combine data from NMR, XRC and EM models derived by homology modelling are increasingly being used for docking studies - integration of docking methods with structural database searches

The “Other” docking: ligands and proteins Not this year …!