Combining NMR relaxation with chemical shift perturbation data to drive protein-protein docking

Computational Approaches to Predict Protein-Protein Interactions in Crowded Cellular Environments

Investigating protein-protein interactions is crucial for understanding cellular biological processes because proteins often function within molecular complexes rather than in isolation. While experimental and computational methods have provided valuable insights into these interactions, they often overlook a critical factor: the crowded cellular environment. This environment significantly impacts protein behavior, including structural stability, diffusion, and ultimately the nature of binding. In this review, we discuss theoretical and computational approaches that allow the modeling of biological systems to guide and complement experiments and can thus significantly advance the investigation, and possibly the predictions, of protein-protein interactions in the crowded environment of cell cytoplasm. We explore topics such as statistical mechanics for lattice simulations, hydrodynamic interactions, diffusion processes in high-viscosity environments, and several methods based on molecular dynamics simulations. By synergistically leveraging methods from biophysics and computational biology, we review the state of the art of computational methods to study the impact of molecular crowding on protein-protein interactions and discuss its potential revolutionizing effects on the characterization of the human interactome.

Comprehensive mapping of mutations in TDP-43 and α-Synuclein that affect stability and binding

Abnormal aggregation and amyloid inclusions of TAR DNA-binding protein 43 (TDP-43) and α-Synuclein (α-Syn) are frequently co-observed in amyotrophic lateral sclerosis, Parkinson's disease, and Alzheimer's disease. Several reports showed TDP-43 C-terminal domain (CTD) and α-Syn interact with each other and the aggregates of these two proteins colocalized together in different cellular and animal models. Molecular dynamics simulation was conducted to elucidate the stability of the TDP-43 and Syn complex structure. The interfacial mutations in protein complexes changes the stability and binding affinity of the protein that may cause diseases. Here, we have utilized the computational saturation mutagenesis approach including structure-based stability and binding energy calculations to compute the systemic effects of missense mutations of TDP-43 CTD and α-Syn on protein stability and binding affinity. Most of the interfacial mutations of CTD and α-Syn were found to destabilize the protein and reduced the protein binding affinity. The results thus shed light on the functional consequences of missense mutations observed in TDP-43 associated proteinopathies and may provide the mechanisms of co-morbidities involving these two proteins.Communicated by Ramaswamy H. Sarma.

Modeling and Structure Determination of Homo-Oligomeric Proteins: An Overview of Challenges and Current Approaches

Protein homo-oligomerization is a very common phenomenon, and approximately half of proteins form homo-oligomeric assemblies composed of identical subunits. The vast majority of such assemblies possess internal symmetry which can be either exploited to help or poses challenges during structure determination. Moreover, aspects of symmetry are critical in the modeling of protein homo-oligomers either by docking or by homology-based approaches. Here, we first provide a brief overview of the nature of protein homo-oligomerization. Next, we describe how the symmetry of homo-oligomers is addressed by crystallographic and non-crystallographic symmetry operations, and how biologically relevant intermolecular interactions can be deciphered from the ordered array of molecules within protein crystals. Additionally, we describe the most important aspects of protein homo-oligomerization in structure determination by NMR. Finally, we give an overview of approaches aimed at modeling homo-oligomers using computational methods that specifically address their internal symmetry and allow the incorporation of other experimental data as spatial restraints to achieve higher model reliability.

Exploring the Binding Interaction of Raf Kinase Inhibitory Protein With the N-Terminal of C-Raf Through Molecular Docking and Molecular Dynamics Simulation

Protein-protein interactions are indispensable physiological processes regulating several biological functions. Despite the availability of structural information on protein-protein complexes, deciphering their complex topology remains an outstanding challenge. Raf kinase inhibitory protein (RKIP) has gained substantial attention as a favorable molecular target for numerous pathologies including cancer and Alzheimer’s disease. RKIP interferes with the RAF/MEK/ERK signaling cascade by endogenously binding with C-Raf (Raf-1 kinase) and preventing its activation. In the current investigation, the binding of RKIP with C-Raf was explored by knowledge-based protein-protein docking web-servers including HADDOCK and ZDOCK and a consensus binding mode of C-Raf/RKIP structural complex was obtained. Molecular dynamics (MD) simulations were further performed in an explicit solvent to sample the conformations for when RKIP binds to C-Raf. Some of the conserved interface residues were mutated to alanine, phenylalanine and leucine and the impact of mutations was estimated by additional MD simulations and MM/PBSA analysis for the wild-type (WT) and constructed mutant complexes. Substantial decrease in binding free energy was observed for the mutant complexes as compared to the binding free energy of WT C-Raf/RKIP structural complex. Furthermore, a considerable increase in average backbone root mean square deviation and fluctuation was perceived for the mutant complexes. Moreover, per-residue energy contribution analysis of the equilibrated simulation trajectory by HawkDock and ANCHOR web-servers was conducted to characterize the key residues for the complex formation. One residue each from C-Raf (Arg398) and RKIP (Lys80) were identified as the druggable “hot spots” constituting the core of the binding interface and corroborated by additional long-time scale (300 ns) MD simulation of Arg398Ala mutant complex. A notable conformational change in Arg398Ala mutant occurred near the mutation site as compared to the equilibrated C-Raf/RKIP native state conformation and an essential hydrogen bonding interaction was lost. The thirteen binding sites assimilated from the overall analysis were mapped onto the complex as surface and divided into active and allosteric binding sites, depending on their location at the interface. The acquired information on the predicted 3D structural complex and the detected sites aid as promising targets in designing novel inhibitors to block the C-Raf/RKIP interaction.

Structural properties of [2Fe-2S] ISCA2-IBA57: a complex of the mitochondrial iron-sulfur cluster assembly machinery

In mitochondria, a complex protein machinery is devoted to the maturation of iron-sulfur cluster proteins. Structural information on the last steps of the machinery, which involve ISCA1, ISCA2 and IBA57 proteins, needs to be acquired in order to define how these proteins cooperate each other. We report here the use of an integrative approach, utilizing information from small-angle X-ray scattering (SAXS) and bioinformatics-driven docking prediction, to determine a low-resolution structural model of the human mitochondrial [2Fe-2S]²⁺ ISCA2-IBA57 complex. In the applied experimental conditions, all the data converge to a structural organization of dimer of dimers for the [2Fe-2S]²⁺ ISCA2-IBA57 complex with ISCA2 providing the homodimerization core interface. The [2Fe-2S] cluster is out of the ISCA2 core while being shared with IBA57 in the dimer. The specific interaction pattern identified from the dimeric [2Fe-2S]²⁺ ISCA2-IBA57 structural model allowed us to define the molecular grounds of the pathogenic Arg146Trp mutation of IBA57. This finding suggests that the dimeric [2Fe-2S] ISCA2-IBA57 hetero-complex is a physiologically relevant species playing a role in mitochondrial [4Fe-4S] protein biogenesis.

Lysozyme as the anti-proliferative agent to block the interaction between S100A6 and the RAGE V domain

In this report, using NMR and molecular modeling, we have studied the structure of lysozyme-S100A6 complex and the influence of tranilast [N-(3, 4-dimethoxycinnamoyl) anthranilic acid], an antiallergic drug which binds to lysozyme, on lysozyme-S100A6 and S100A6-RAGE complex formation and, finally, on cell proliferation. We have found that tranilast may block the S100A6-lysozyme interaction and enhance binding of S100A6 to RAGE. Using WST1 assay, we have found that lysozyme, most probably by blocking the interaction between S100A6 and RAGE, inhibits cell proliferation while tranilast may reverse this effect by binding to lysozyme. In conclusion, studies presented in this work, describing the protein-protein/-drug interactions, are of great importance for designing new therapies to treat diseases associated with cell proliferation such as cancers.

S100B as an antagonist to block the interaction between S100A1 and the RAGE V domain

Ca2+-binding human S100A1 protein is a type of S100 protein. S100A1 is a significant mediator during inflammation when Ca2+ binds to its EF-hand motifs. Receptors for advanced glycation end products (RAGE) correspond to 5 domains: the cytoplasmic, transmembrane, C2, C1, and V domains. The V domain of RAGE is one of the most important target proteins for S100A1. It binds to the hydrophobic surface and triggers signaling transduction cascades that induce cell growth, cell proliferation, and tumorigenesis. We used nuclear magnetic resonance (NMR) spectroscopy to characterize the interaction between S100A1 and the RAGE V domain. We found that S100B could interact with S100A1 via NMR 1H-15N HSQC titrations. We used the HADDOCK program to generate the following two binary complexes based on the NMR titration results: S100A1-RAGE V domain and S100A1-S100B. After overlapping these two complex structures, we found that S100B plays a crucial role in blocking the interaction site between RAGE V domain and S100A1. A cell proliferation assay WST-1 also supported our results. This report could potentially be useful for new protein development for cancer treatment.

UHM-ULM interactions in the RBM39-U2AF65 splicing-factor complex

RNA-binding protein 39 (RBM39) is a splicing factor and a transcriptional co-activator of estrogen receptors and Jun/AP-1, and its function has been associated with malignant progression in a number of cancers. The C-terminal RRM domain of RBM39 belongs to the U2AF homology motif family (UHM), which mediate protein-protein interactions through a short tryptophan-containing peptide known as the UHM-ligand motif (ULM). Here, crystal and solution NMR structures of the RBM39-UHM domain, and the crystal structure of its complex with U2AF65-ULM, are reported. The RBM39-U2AF65 interaction was confirmed by co-immunoprecipitation from human cell extracts, by isothermal titration calorimetry and by NMR chemical shift perturbation experiments with the purified proteins. When compared with related complexes, such as U2AF35-U2AF65 and RBM39-SF3b155, the RBM39-UHM-U2AF65-ULM complex reveals both common and discriminating recognition elements in the UHM-ULM binding interface, providing a rationale for the known specificity of UHM-ULM interactions. This study therefore establishes a structural basis for specific UHM-ULM interactions by splicing factors such as U2AF35, U2AF65, RBM39 and SF3b155, and a platform for continued studies of intermolecular interactions governing disease-related alternative splicing in eukaryotic cells.

The HADDOCK2.2 Web Server: User-Friendly Integrative Modeling of Biomolecular Complexes

The prediction of the quaternary structure of biomolecular macromolecules is of paramount importance for fundamental understanding of cellular processes and drug design. In the era of integrative structural biology, one way of increasing the accuracy of modeling methods used to predict the structure of biomolecular complexes is to include as much experimental or predictive information as possible in the process. This has been at the core of our information-driven docking approach HADDOCK. We present here the updated version 2.2 of the HADDOCK portal, which offers new features such as support for mixed molecule types, additional experimental restraints and improved protocols, all of this in a user-friendly interface. With well over 6000 registered users and 108,000 jobs served, an increasing fraction of which on grid resources, we hope that this timely upgrade will help the community to solve important biological questions and further advance the field. The HADDOCK2.2 Web server is freely accessible to non-profit users at http://haddock.science.uu.nl/services/HADDOCK2.2.

NMR approaches for structural analysis of multidomain proteins and complexes in solution

NMR spectroscopy is a key method for studying the structure and dynamics of (large) multidomain proteins and complexes in solution. It plays a unique role in integrated structural biology approaches as especially information about conformational dynamics can be readily obtained at residue resolution. Here, we review NMR techniques for such studies focusing on state-of-the-art tools and practical aspects. An efficient approach for determining the quaternary structure of multidomain complexes starts from the structures of individual domains or subunits. The arrangement of the domains/subunits within the complex is then defined based on NMR measurements that provide information about the domain interfaces combined with (long-range) distance and orientational restraints. Aspects discussed include sample preparation, specific isotope labeling and spin labeling; determination of binding interfaces and domain/subunit arrangements from chemical shift perturbations (CSP), nuclear Overhauser effects (NOEs), isotope editing/filtering, cross-saturation, and differential line broadening; and based on paramagnetic relaxation enhancements (PRE) using covalent and soluble spin labels. Finally, the utility of complementary methods such as small-angle X-ray or neutron scattering (SAXS, SANS), electron paramagnetic resonance (EPR) or fluorescence spectroscopy techniques is discussed. The applications of NMR techniques are illustrated with studies of challenging (high molecular weight) protein complexes.

Modeling protein-protein complexes using the HADDOCK webserver "modeling protein complexes with HADDOCK"

Protein-protein interactions lie at the heart of most cellular processes. Determining their high-resolution structures by experimental methods is a nontrivial task, which is why complementary computational approaches have been developed over the years. To gain structural and dynamical insight on an atomic scale in these interactions, computational modeling must often be complemented by low-resolution experimental information. For this purpose, we developed the user-friendly HADDOCK webserver, the interface to our biomolecular docking program, which can make use of a variety of low-resolution data to drive the docking process. In this chapter, we explain the use of the HADDOCK webserver based on the real-life Lys48-linked di-ubiquitin case, which led to the 2BGF PDB model. We demonstrate the use of chemical shift perturbation data in combination with residual dipolar couplings and further highlight a few other cases where our software was successfully used. The HADDOCK webserver is available to the science community for free at haddock.science.uu.nl/services/HADDOCK.

A critical assessment of information-guided protein-protein docking predictions

The structures of protein complexes are increasingly predicted via protein-protein docking (PPD) using ambiguous interaction data to help guide the docking. These data often are incomplete and contain errors and therefore could lead to incorrect docking predictions. In this study, we performed a series of PPD simulations to examine the effects of incompletely and incorrectly assigned interface residues on the success rate of PPD predictions. The results for a widely used PPD benchmark dataset obtained using a new interface information-driven PPD (IPPD) method developed in this work showed that the success rate for an acceptable top-ranked model varied, depending on the information content used, from as high as 95% when contact relationships (though not contact distances) were known for all residues to 78% when only the interface/non-interface state of the residues was known. However, the success rates decreased rapidly to ∼40% when the interface/non-interface state of 20% of the residues was assigned incorrectly, and to less than 5% for a 40% incorrect assignment. Comparisons with results obtained by re-ranking a global search and with those reported for other data-guided PPD methods showed that, in general, IPPD performed better than re-ranking when the information used was more complete and more accurate, but worse when it was not, and that when using bioinformatics-predicted information on interface residues, IPPD and other data-guided PPD methods performed poorly, at a level similar to simulations with a 40% incorrect assignment. These results provide guidelines for using information about interface residues to improve PPD predictions and reveal a bottleneck for such improvement imposed by the low accuracy of current bioinformatic interface residue predictions.

Virtual interactomics of proteins from biochemical standpoint

Virtual interactomics represents a rapidly developing scientific area on the boundary line of bioinformatics and interactomics. Protein-related virtual interactomics then comprises instrumental tools for prediction, simulation, and networking of the majority of interactions important for structural and individual reproduction, differentiation, recognition, signaling, regulation, and metabolic pathways of cells and organisms. Here, we describe the main areas of virtual protein interactomics, that is, structurally based comparative analysis and prediction of functionally important interacting sites, mimotope-assisted and combined epitope prediction, molecular (protein) docking studies, and investigation of protein interaction networks. Detailed information about some interesting methodological approaches and online accessible programs or databases is displayed in our tables. Considerable part of the text deals with the searches for common conserved or functionally convergent protein regions and subgraphs of conserved interaction networks, new outstanding trends and clinically interesting results. In agreement with the presented data and relationships, virtual interactomic tools improve our scientific knowledge, help us to formulate working hypotheses, and they frequently also mediate variously important in silico simulations.

Integrative structural modeling with small angle X-ray scattering profiles

Recent technological advances enabled high-throughput collection of Small Angle X-ray Scattering (SAXS) profiles of biological macromolecules. Thus, computational methods for integrating SAXS profiles into structural modeling are needed more than ever. Here, we review specifically the use of SAXS profiles for the structural modeling of proteins, nucleic acids, and their complexes. First, the approaches for computing theoretical SAXS profiles from structures are presented. Second, computational methods for predicting protein structures, dynamics of proteins in solution, and assembly structures are covered. Third, we discuss the use of SAXS profiles in integrative structure modeling approaches that depend simultaneously on several data types.

Synergistic applications of MD and NMR for the study of biological systems

Modern biological sciences are becoming more and more multidisciplinary. At the same time, theoretical and computational approaches gain in reliability and their field of application widens. In this short paper, we discuss recent advances in the areas of solution nuclear magnetic resonance (NMR) spectroscopy and molecular dynamics (MD) simulations that were made possible by the combination of both methods, that is, through their synergistic use. We present the main NMR observables and parameters that can be computed from simulations, and how they are used in a variety of complementary applications, including dynamics studies, model-free analysis, force field validation, and structural studies.

NMR reveals a different mode of binding of the Stam2 VHS domain to ubiquitin and diubiquitin

The VHS domain of the Stam2 protein is a ubiquitin binding domain involved in the recognition of ubiquitinated proteins committed to lysosomal degradation. Among all VHS domains, the VHS domain of Stam proteins is the strongest binder to monoubiqiuitin and exhibits preferences for K63-linked chains. In the present paper, we report the solution NMR structure of the Stam2-VHS domain in complex with monoubiquitin by means of chemical shift perturbations, spin relaxation, and paramagnetic relaxation enhancements. We also characterize the interaction of Stam2-VHS with K48- and K63-linked diubiquitin chains and report the first evidence that VHS binds differently to these two chains. Our data reveal that VHS enters the hydrophobic pocket of K48-linked diubiquitin and binds the two ubiquitin subunits with different affinities. In contrast, VHS interacts with K63-linked diubiquitin in a mode similar to its interaction with monoubiquitin. We also suggest possible structural models for both K48- and K63-linked diubiquitin in interaction with VHS. Our results, which demonstrate a different mode of binding of VHS for K48- and K63-linked diubiquitin, may explain the preference of VHS for K63- over K48-linked diubiquitin chains and monoubiquitin.

NMR and small-angle scattering-based structural analysis of protein complexes in solution

Structural analysis of multi-domain protein complexes is a key challenge in current biology and a prerequisite for understanding the molecular basis of essential cellular processes. The use of solution techniques is important for characterizing the quaternary arrangements and dynamics of domains and subunits of these complexes. In this respect solution NMR is the only technique that allows atomic- or residue-resolution structure determination and investigation of dynamic properties of multi-domain proteins and their complexes. As experimental NMR data for large protein complexes are sparse, it is advantageous to combine these data with additional information from other solution techniques. Here, the utility and computational approaches of combining solution state NMR with small-angle X-ray and Neutron scattering (SAXS/SANS) experiments for structural analysis of large protein complexes is reviewed. Recent progress in experimental and computational approaches of combining NMR and SAS are discussed and illustrated with recent examples from the literature. The complementary aspects of combining NMR and SAS data for studying multi-domain proteins, i.e. where weakly interacting domains are connected by flexible linkers, are illustrated with the structural analysis of the tandem RNA recognition motif (RRM) domains (RRM1-RRM2) of the human splicing factor U2AF65 bound to a nine-uridine (U9) RNA oligonucleotide.

Building macromolecular assemblies by information-driven docking: introducing the HADDOCK multibody docking server

Over the last years, large scale proteomics studies have generated a wealth of information of biomolecular complexes. Adding the structural dimension to the resulting interactomes represents a major challenge that classical structural experimental methods alone will have difficulties to confront. To meet this challenge, complementary modeling techniques such as docking are thus needed. Among the current docking methods, HADDOCK (High Ambiguity-Driven DOCKing) distinguishes itself from others by the use of experimental and/or bioinformatics data to drive the modeling process and has shown a strong performance in the critical assessment of prediction of interactions (CAPRI), a blind experiment for the prediction of interactions. Although most docking programs are limited to binary complexes, HADDOCK can deal with multiple molecules (up to six), a capability that will be required to build large macromolecular assemblies. We present here a novel web interface of HADDOCK that allows the user to dock up to six biomolecules simultaneously. This interface allows the inclusion of a large variety of both experimental and/or bioinformatics data and supports several types of cyclic and dihedral symmetries in the docking of multibody assemblies. The server was tested on a benchmark of six cases, containing five symmetric homo-oligomeric protein complexes and one symmetric protein-DNA complex. Our results reveal that, in the presence of either bioinformatics and/or experimental data, HADDOCK shows an excellent performance: in all cases, HADDOCK was able to generate good to high quality solutions and ranked them at the top, demonstrating its ability to model symmetric multicomponent assemblies. Docking methods can thus play an important role in adding the structural dimension to interactomes. However, although the current docking methodologies were successful for a vast range of cases, considering the variety and complexity of macromolecular assemblies, inclusion of some kind of experimental information (e.g. from mass spectrometry, nuclear magnetic resonance, cryoelectron microscopy, etc.) will remain highly desirable to obtain reliable results.

HADDOCK versus HADDOCK: new features and performance of HADDOCK2.0 on the CAPRI targets

Here we present version 2.0 of HADDOCK, which incorporates considerable improvements and new features. HADDOCK is now able to model not only protein-protein complexes but also other kinds of biomolecular complexes and multi-component (N > 2) systems. In the absence of any experimental and/or predicted information to drive the docking, HADDOCK now offers two additional ab initio docking modes based on either random patch definition or center-of-mass restraints. The docking protocol has been considerably improved, supporting among other solvated docking, automatic definition of semi-flexible regions, and inclusion of a desolvation energy term in the scoring scheme. The performance of HADDOCK2.0 is evaluated on the targets of rounds 4-11, run in a semi-automated mode using the original information we used in our CAPRI submissions. This enables a direct assessment of the progress made since the previous versions. Although HADDOCK performed very well in CAPRI (65% and 71% success rates, overall and for unbound targets only, respectively), a substantial improvement was achieved with HADDOCK2.0.

Energy minimization in low‐frequency normal modes to efficiently allow for global flexibility during systematic protein–protein docking

Protein-protein association can frequently involve significant backbone conformational changes of the protein partners. A computationally rapid method has been developed that allows to approximately account for global conformational changes during systematic protein-protein docking starting from many thousands of start configurations. The approach employs precalculated collective degrees of freedom as additional variables during protein-protein docking minimization. The global collective degrees of freedom are obtained from normal mode analysis using a Gaussian network model for the protein. Systematic docking searches were performed on 10 test systems that differed in the degree of conformational change associated with complex formation and in the degree of overlap between observed conformational changes and precalculated flexible degrees of freedom. The results indicate that in case of docking searches that minimize the influence of local side chain conformational changes inclusion of global flexibility can significantly improve the agreement of the near-native docking solutions with the corresponding experimental structures. For docking of unbound protein partners in several cases an improved ranking of near native docking solutions was observed. This was achieved at a very modest ( approximately 2-fold) increase of computational demands compared to rigid docking. For several test cases the number of docking solutions close to experiment was also significantly enhanced upon inclusion of soft collective degrees of freedom. This result indicates that inclusion of global flexibility can facilitate in silico protein-protein association such that a greater number of different start configurations results in favorable complex formation.

Structural assembly of multidomain proteins and protein complexes guided by the overall rotational diffusion tensor

We present a simple and robust approach that uses the overall rotational diffusion tensor as a structural constraint for domain positioning in multidomain proteins and protein-protein complexes. This method offers the possibility to use NMR relaxation data for detailed structure characterization of such systems provided the structures of individual domains are available. The proposed approach extends the concept of using long-range information contained in the overall rotational diffusion tensor. In contrast to the existing approaches, we use both the principal axes and principal values of protein's rotational diffusion tensor to determine not only the orientation but also the relative positioning of the individual domains in a protein. This is achieved by finding the domain arrangement in a molecule that provides the best possible agreement with all components of the overall rotational diffusion tensor derived from experimental data. The accuracy of the proposed approach is demonstrated for two protein systems with known domain arrangement and parameters of the overall tumbling: the HIV-1 protease homodimer and Maltose Binding Protein. The accuracy of the method and its sensitivity to domain positioning are also tested using computer-generated data for three protein complexes, for which the experimental diffusion tensors are not available. In addition, the proposed method is applied here to determine, for the first time, the structure of both open and closed conformations of a Lys48-linked diubiquitin chain, where domain motions render impossible accurate structure determination by other methods. The proposed method opens new avenues for improving structure characterization of proteins in solution.

A protein-specifically adapted scoring function for the reranking of docking solutions

In this work, we developed a protein-specifically adapted scoring function and applied it to the reranking of protein-protein docking solutions generated with a conventional docking program. The approach was validated using experimentally determined structures of the bacterial HPr-protein in complex with four structurally nonhomologous binding partners as an example. A sufficiently large data basis for the generation of protein-specifically adapted pair potentials was generated by modeling all orthologous complexes for each type of interaction resulting in a total of 224 complexes. The parameters for potential generation were systematically varied and resulted in a total of 66,132 different scoring functions that were tested for their ability of successful reranking of 1000 docking solutions generated from modeled structures of the unbound binding partners. Parameters that proved critical for the generation of good scoring functions were the distance cutoff used for the generation of the pair potential, and an additional cutoff that allows a proper weighting of conserved and nonconserved contacts in the interface. Compared to the original scoring function, application of this novel type of scoring functions resulted in a significant accumulation of acceptable docking solutions within the first 10 ranks. Depending on the type of complex investigated one to five acceptable complex geometries are found among the 10 highest-ranked solutions and for three of the four systems tested, an acceptable solution was placed on the first rank.