Solvated docking: introducing water into the modelling of biomolecular complexes

MOTIVATION: Interfacial water, which plays an important role in mediating biomolecular interactions, has been neglected in the modelling of biomolecular complexes. METHODS: We present a solvated docking approach that explicitly accounts for the presence of water in protein-protein complexes. Our solvated docking protocol is based on the concept of the first encounter complex in which a water layer is present in-between the molecules. It mimics the pathway from this initial complex towards the final assembly in which most waters have been expelled from the interface. Docking is performed from solvated biomolecules and waters are removed in a biased Monte Carlo procedure based on water-mediated contact propensities obtained from an analysis of high-resolution crystal structures. RESULTS: We demonstrate the feasibility of this approach for protein-protein complexes representing both 'wet' and 'dry' interfaces. Solvated docking leads to improvements both in quality and scoring. Water molecules are recovered that closely match the ones in the crystal structures. AVAILABILTY: Solvated docking will be made available in the future release of HADDOCK version 2.0 (http://www.nmr.chem.uu.nl/haddock).     

A multidomain flexible docking approach to deal with large conformational changes in the modeling of biomolecular complexes

Binding-induced backbone and large-scale conformational changes represent one of the major challenges in the modeling of biomolecular complexes by docking. To address this challenge, we have developed a flexible multidomain docking protocol that follows a "divide-and-conquer" approach to model both large-scale domain motions and small- to medium-scale interfacial rearrangements: the flexible binding partner is treated as an assembly of subparts/domains that are docked simultaneously making use of HADDOCK's multidomain docking ability. For this, the flexible molecules are cut at hinge regions predicted using an elastic network model. The performance of this approach is demonstrated on a benchmark covering an unprecedented range of conformational changes of 1.5 to 19.5 ?. We show from a statistical survey of known complexes that the cumulative sum of eigenvalues obtained from the elastic network has some predictive power to indicate the extent of the conformational change to be expected.     

Data-driven docking: HADDOCK's adventures in CAPRI

We have shown previously that given high-resolution structures of the unbound molecules, structure determination of protein complexes is possible by including biochemical and/or biophysical data as highly ambiguous distance restraints in a docking approach. We applied this method, implemented in the HADDOCK (High Ambiguity Driven DOCKing) package (Dominguez et al., J Am Chem Soc 2003;125:1731-1737), to the targets in the fourth and fifth rounds of CAPRI. Here we describe our results and analyze them in detail. Special attention is given to the role of flexibility in our docking method and the way in which this improves the docking results. We describe extensions to our approach that were developed as a direct result of our participation in CAPRI. In addition to experimental information, we also included interface residue predictions from PPISP (Protein-Protein Interaction Site Predictor; Zhou and Shan, Proteins 2001;44:336-343), a neural network method. Using HADDOCK we were able to generate acceptable structures for 6 of the 8 targets, and to submit at least 1 acceptable structure for 5 of them. Of these 5 submissions, 3 were of medium quality (Targets 10, 11, and 15) and 2 of high quality (Targets 13 and 14). In all cases, predictions were obtained containing at least 40% of the correct epitope at the interface for both ligand and receptor simultaneously.     

An introduction to biomolecular simulations and docking

The biomolecules in and around a living cell – proteins, nucleic acids, lipids and carbohydrates – continuously sample myriad conformational states that are thermally accessible at physiological temperatures. Simultaneously, a given biomolecule also samples (and is sampled by) a rapidly fluctuating local environment comprising other biopolymers, small molecules, water, ions, etc. that diffuse to within a few nanometres, leading to inter-molecular contacts that stitch together large supramolecular assemblies. Indeed, all biological systems can be viewed as dynamic networks of molecular interactions. As a complement to experimentation, molecular simulation offers a uniquely powerful approach to analyse biomolecular structure, mechanism and dynamics; this is possible because the molecular contacts that define a complicated biomolecular system are governed by the same physical principles (forces and energetics) that characterise individual small molecules, and these simpler systems are relatively well-understood. With modern algorithms and computing capabilities, simulations are now an indispensable tool for examining biomolecular assemblies in atomic detail, from the conformational motion in an individual protein to the diffusional dynamics and inter-molecular collisions in the early stages of formation of cellular-scale assemblies such as the ribosome. This text introduces the physicochemical foundations of molecular simulations and docking, largely from the perspective of biomolecular interactions.     

Information-driven modeling of biomolecular complexes

Proteins play crucial roles in every cellular process by interacting with each other, nucleic acids, metabolites, and other molecules. The resulting assemblies can be very large and intricate and pose challenges to experimental methods. In the current era of integrative modeling, it is often only by a combination of various experimental techniques and computations that three-dimensional models of those molecular machines can be obtained. Among the various computational approaches available, molecular docking is often the method of choice when it comes to predicting three-dimensional structures of complexes. Docking can generate particularly accurate models when taking into account the available information on the complex of interest. We review here the use of experimental and bioinformatics data in protein-protein docking, describing recent software developments and highlighting applications for the modeling of antibody–antigen complexes and membrane protein complexes, and the use of evolutionary and shape information.     

Coarse-grained and atomic resolution biomolecular docking with the ATTRACT approach

The ATTRACT protein-protein docking program has been employed to predict protein-protein complex structures in CAPRI rounds 38-45. For 11 out of 16 targets acceptable or better quality solutions have been submitted (~70%). It includes also several cases of peptide-protein docking and the successful prediction of the geometry of carbohydrate-protein interactions. The option of combining rigid body minimization and simultaneous optimization in collective degrees of freedom based on elastic network modes was employed and systematically evaluated. Application to a large benchmark set indicates a modest improvement in docking performance compared to rigid docking. Possible further improvements of the docking approach in particular at the scoring and the flexible refinement steps are discussed.     

A method for biomolecular structural recognition and docking allowing conformational flexibility.

In this work, we present an algorithm developed to handle biomolecular structural recognition problems, as part of an interdisciplinary research endeavor of the Computer Vision and Molecular Biology fields. A key problem in rational drug design and in biomolecular structural recognition is the generation of binding modes between two molecules, also known as molecular docking. Geometrical fitness is a necessary condition for molecular interaction. Hence, docking a ligand (e.g., a drug molecule or a protein molecule), to a protein receptor (e.g., enzyme), involves recognition of molecular surfaces. Conformational transitions by "hinge-bending" involves rotational movements of relatively rigid parts with respect to each other. The generation of docked binding modes between two associating molecules depends on their three dimensional structures (3-D) and their conformational flexibility. In comparison to the particular case of rigid-body docking, the computational difficulty grows considerably when taking into account the additional degrees of freedom intrinsic to the flexible molecular docking problem. Previous docking techniques have enabled hinge movements only within small ligands. Partial flexibility in the receptor molecule is enabled by a few techniques. Hinge-bending motions of protein receptors domains are not addressed by these methods, although these types of transitions are significant, e.g., in enzymes activity. Our approach allows hinge induced motions to exist in either the receptor or the ligand molecules of diverse sizes. We allow domains/subdomains/group of atoms movements in either of the associating molecules. We achieve this by adapting a technique developed in Computer Vision and Robotics for the efficient recognition of partially occluded articulated objects. These types of objects consist of rigid parts which are connected by rotary joints (hinges). Our method is based on an extension and generalization of the Hough transform and the Geometric Hashing paradigms for rigid object recognition. We show experimental results obtained by the successful application of the algorithm to cases of bound and unbound molecular complexes, yielding fast matching times. While the "correct" molecular conformations of the known complexes are obtained with small RMS distances, additional, predictive good-fitting binding modes are generated as well. We conclude by discussing the algorithm's implications and extensions, as well as its application to investigations of protein structures in Molecular Biology and recognition problems in Computer Vision.     

Computational approaches for automatic structural analysis of large biomolecular complexes

We present computational solutions to two problemsof macromolecular structure interpretation from reconstructedthree-dimensional electron microscopy (3D-EM) maps of largebio-molecular complexes at intermediate resolution (5A-15A). Thetwo problems addressed are: (a) 3D structural alignment (matching)between identified and segmented 3D maps of structure units(e.g. trimeric configuration of proteins), and (b) the secondarystructure identification of a segmented protein 3D map (i.e.locations of a-helices, b -sheets). For problem (a), we presentan efficient algorithm to correlate spatially (and structurally)two 3D maps of structure units. Besides providing a similarityscore between structure units, the algorithm yields an effectivetechnique for resolution refinement of repeated structure units,by 3D alignment and averaging. For problem (b), we present anefficient algorithm to compute eigenvalues and link eigenvectorsof a Gaussian convoluted structure tensor derived from theprotein 3D Map, thereby identifying and locating secondarystructural motifs of proteins. The efficiency and performanceof our approach is demonstrated on several experimentallyreconstructed 3D maps of virus capsid shells from single-particlecryo-EM, as well as computationally simulated protein structuredensity 3D maps generated from protein model entries in theProtein Data Bank.     

A method for the analysis of domain movements in large biomolecular complexes

A new method for the analysis of domain movements in large, multichain, biomolecular complexes is presented. The method is applicable to any molecule for which two atomic structures are available that represent a conformational change indicating a possible domain movement. The method is blind to atomic bonding and atom type and can, therefore, be applied to biomolecular complexes containing different constituent molecules such as protein, RNA, or DNA. At the heart of the method is the use of blocks located at grid points spanning the whole molecule. The rotation vector for the rotation of atoms from each block between the two conformations is calculated. Treating components of these vectors as coordinates means that each block is associated with a point in a “rotation space” and that blocks with atoms that rotate together, perhaps as part of the same rigid domain, will have colocated points. Thus a domain can be identified from the clustering of points from blocks that span it. Domain pairs are accepted for analysis of their relative movements in terms of screw axes based upon a set of reasonable criteria. Here, we report on the application of the method to biomolecules covering a considerable size range: hemoglobin, liver alcohol dehydrogenase, S‐Adenosylhomocysteine hydrolase, aspartate transcarbamylase, and the 70S ribosome. The results provide a depiction of the conformational change within each molecule that is easily understood, giving a perspective that is expected to lead to new insights. Of particular interest is the allosteric mechanism in some of these molecules. Results indicate that common boundaries between subunits and domains are good regions to focus on as movement in one subunit can be transmitted to another subunit through such interfaces. Proteins 2009. © 2008 Wiley‐Liss, Inc.     

Clustering biomolecular complexes by residue contacts similarity

Inaccuracies in computational molecular modeling methods are often counterweighed by brute-force generation of a plethora of putative solutions. These are then typically sieved via structural clustering based on similarity measures such as the root mean square deviation (RMSD) of atomic positions. Albeit widely used, these measures suffer from several theoretical and technical limitations (e.g., choice of regions for fitting) that impair their application in multicomponent systems (N > 2), large-scale studies (e.g., interactomes), and other time-critical scenarios. We present here a simple similarity measure for structural clustering based on atomic contacts--the fraction of common contacts--and compare it with the most used similarity measure of the protein docking community--interface backbone RMSD. We show that this method produces very compact clusters in remarkably short time when applied to a collection of binary and multicomponent protein-protein and protein-DNA complexes. Furthermore, it allows easy clustering of similar conformations of multicomponent symmetrical assemblies in which chain permutations can occur. Simple contact-based metrics should be applicable to other structural biology clustering problems, in particular for time-critical or large-scale endeavors.     

Towards a calculus of biomolecular complexes at equilibrium

An overview is presented of the construction and use of algebraic partition functions to represent the equilibrium statistical mechanics of multimolecular complexes and their action within a larger regulatory network. Unlike many applications of equilibrium statistical mechanics, multimolecular complexes may operate with various subsets of their components present and connected to the others, the rest remaining in solution. Thus they are variable-structure systems. This aspect of their behavior may be accounted for by the use of 'fugacity' variables as a representation within the partition functions. Four principles are proposed by which the combinatorics of molecular complex construction can be reflected in the construction of their partition functions. The corresponding algebraic operations on partition functions are multiplication, addition, function composition and a less commonly used operation called contraction. Each has a natural interpretation in terms of probability distributions on multimolecular structures. Possible generalizations to nonequilibrium statistical mechanics are briefly discussed.     

Multi-LZerD: multiple protein docking for asymmetric complexes

The tertiary structures of protein complexes provide a crucial insight about the molecular mechanisms that regulate their functions and assembly. However, solving protein complex structures by experimental methods is often more difficult than single protein structures. Here, we have developed a novel computational multiple protein docking algorithm, Multi‐LZerD, that builds models of multimeric complexes by effectively reusing pairwise docking predictions of component proteins. A genetic algorithm is applied to explore the conformational space followed by a structure refinement procedure. Benchmark on eleven hetero‐multimeric complexes resulted in near‐native conformations for all but one of them (a root mean square deviation smaller than 2.5Å). We also show that our method copes with unbound docking cases well, outperforming the methodology that can be directly compared with our approach. Multi‐LZerD was able to predict near‐native structures for multimeric complexes of various topologies.Proteins 2012; © 2012 Wiley Periodicals, Inc.     

A soft docking algorithm for predicting the structure of antibody-antigen complexes

An efficient soft docking algorithm is described for predicting the mode of binding between an antibody and its antigen based on the three-dimensional structures of the molecules. The basic tools are the "simplified protein" model and the docking algorithm of Wodak and Janin. The side-chain flexibility of Arg, Lys, Asp, Glu, and Met residues on the protein surface is taken into account. A combined filtering technique is used to select candidate binding modes. After energy minimization, we calculate a scoring function, which includes electrostatic and desolvation energy terms. This procedure was applied to targets 04, 05, and 06 of CAPRI, which are complexes of three different camelid antibody VHH variable domains with pig alpha-amylase. For target 06, two native-like structures with a root-mean-square deviation < 4.0 A relative to the X-ray structure were found within the five top ranking structures. For targets 04 and 05, our procedure produced models where more than half of the antigen residues forming the epitope were correctly predicted, albeit with a wrong VHH domain orientation. Thus, our soft docking algorithm is a promising tool for predicting antibody-antigen recognition.     

Low-resolution docking: Prediction of complexes for underdetermined structures

One of the most fundamental questions concerning ligand-receptor interaction is whether such a process of intermolecular association is generally determined by local structural elements of the participating molecules, or whether there are also large-scale motifs in molecule structures that facilitate complex formation. From the point of view of practical docking computations, the elaborate character of local structural details in ligand-receptor interaction creates a large number of false-positive matches, which interfere with determination of the best fit. Another significant obstacle in protein docking is the problem of structural data inaccuracy (poor structure resolution, conformational changes upon complex formation, etc.). Our study [Vakser (1995) Protein Eng., 8, 371–377], based on ultralow (∼7 Å resolution) representation of molecular structures, allowes to average all high-resolution structural details, and still predict most of the structural features of the ligand-receptor complex. The approach dramatically improves the signal-to-noise ratio in determination of the best fit, and moves the structure inaccuracy tolerance to the range of the macrostructure. In the present paper, we describe a further validation of the main principles of this approach and a detailed analysis of the low-resolution docking results. This includes clustering of ligand positions around the receptor molecule and cross-validation of ligands and receptors from different complexes. We also discuss the important implications of the approach to the multiple-minima problem and a possible role of different structural elements in the recognition mechanism. © 1996 John Wiley & Sons, Inc.     

An automated computer vision and roboticsbased technique for 3-D flexible biomolecular docking and matching

The generation of binding modes between two molecules, alsoknown as molecular docking, is a key problem in rational drugdesign and biomolecular recognition. Docking a ligand, e.g.,a drug molecule or a protein molecule, to a protein receptor,involves recognition of molecular surfaces as molecules interactat their surface. Recent studies report that the activity ofmany molecules induces conformational transitions by ‘hinge-bending’,which involves movements of relatively rigid parts with respectto each other. In ligand–receptor binding, relative rotationalmovements of molecu–lar substructures about their commonhinges have been observed. For automatically predicting flexiblemolecular interactions, we adapt a new technique developed inComputer Vision and Robotics for the efficient recognition ofpartially occluded articulated objects. These type of objectsconsist of rigid parts which are connected by rotary joints(hinges). Our approach is based on an extension and generalizationof the Geometric Hashing and Generalized Hough Transform paradigmfor rigid object recognition. Unlike other techniques whichmatch each part individually, our approach exploits forcefullyand efficiently enough the fact that the different rigid partsdo belong to the same flexible molecule. We show experimentalresults obtained by an implementation of the algorithm for rigidand flexible docking. While the ‘correct’, crystal–boundcomplex is obtained with a small RMSD, additional, predictive‘high scoring’ binding modes are generated as well.The diverse applications and implications of this general, powerfultool are discussed     

Binding without folding – the biomolecular function of disordered polyelectrolyte complexes

1. Download : Download high-res image (171KB)
2. Download : Download full-size image

     

A new electrometric approach to studying specific biomolecular complexes

In the article the results of the influence of some types of biological molecules and their specific immune complexes on the volt-amperic characteristics of surface-barrier contact structures with the super thick metal film are presented. Moreover, the possibility to develop on this basis a simple instrumental method for separate registration of initial components and products of their interaction is discussed. It was shown that the volt-amperic characteristics of surface-barrier structures of Ni-Si changed at the deposition of myoglobin and its specific monoclonal antibodies. These structures more essentially reacted to the formed presence of specific immune complex and, in particular, to the direct formation of this complex on the surface at the subsequent use of the initial immune components in comparison with their separate presence. For all investigated types of biological molecules and their specific complexes we found optimal thickness of metal film when changes of volt-amperic characteristics of system achieved maximum level. It was concluded that this electrometrical method was suitable for the express and separation free registration of specific immune complex.     

Studying biomolecular complexes with pulsed electron-electron double resonance spectroscopy

The function of biomolecules is intrinsically linked to their structure and the complexes they form during function. Techniques for the determination of structures and dynamics of these nanometre assemblies are therefore important for an understanding on the molecular level. PELDOR (pulsed electron-electron double resonance) is a pulsed EPR method that can be used to reliably and precisely measure distances in the range 1.5-8?nm, to unravel orientations and to determine the number of monomers in complexes. In conjunction with site-directed spin labelling, it can be applied to biomolecules of all sizes in aqueous solutions or membranes. PELDOR is therefore complementary to the methods of X-ray crystallography, NMR and FRET (fluorescence resonance energy transfer) and is becoming a powerful method for structural determination of biomolecules. In the present review, the methods of PELDOR are discussed and examples where PELDOR has been used to obtain structural information on biomolecules are summarized.     

Nucleotide docking: prediction of reactant state complexes for ribonuclease enzymes

Ribonuclease enzymes (RNases) play key roles in the maturation and metabolism of all RNA molecules. Computational simulations of the processes involved can help to elucidate the underlying enzymatic mechanism and is often employed in a synergistic approach together with biochemical experiments. Theoretical calculations require atomistic details regarding the starting geometries of the molecules involved, which, in the absence of crystallographic data, can only be achieved from computational docking studies. Fortunately, docking algorithms have improved tremendously in recent years, so that reliable structures of enzyme–ligand complexes can now be successfully obtained from computation. However, most docking programs are not particularly optimized for nucleotide docking. In order to assist our studies on the cleavage of RNA by the two most important ribonuclease enzymes, RNase A and RNase H, we evaluated four docking tools—MOE2009, Glide 5.5, QXP-Flo+0802, and Autodock 4.0—for their ability to simulate complexes between these enzymes and RNA oligomers. To validate our results, we analyzed the docking results with respect to the known key interactions between the protein and the nucleotide. In addition, we compared the predicted complexes with X-ray structures of the mutated enzyme as well as with structures obtained from previous calculations. In this manner, we were able to prepare the desired reaction state complex so that it could be used as the starting structure for further DFT/B3LYP QM/MM reaction mechanism studies.     

A protein-protein docking algorithm dependent on the type of complexes

An efficient 'soft docking' algorithm is described to assist the prediction of protein-protein association using three-dimensional structures of molecules. The basic tools are the 'simplified protein' model and the docking algorithm of Wodak and Janin. The side chain flexibility of Arg, Lys, Asp, Glu and Met residues at the protein surface is taken into account. The complex type-dependent filtering technique on the basis of the geometric matching, hydrophobicity and electrostatic complementarity is used to select candidate binding modes. Subsequently, we calculate a scoring function which includes electrostatic and desolvation energy terms. In the 44 complexes tested including enzyme-inhibitor, antibody-antigen and other complexes, native-like structures were all found, of which 30 were ranked in the top 20. Thus, our soft docking algorithm has the potential to predict protein-protein recognition.