Conformational Transitions upon Ligand Binding: Holo-Structure Prediction from Apo Conformations

Biological function of proteins is frequently associated with the formation of complexes with small-molecule ligands. Experimental structure determination of such complexes at atomic resolution, however, can be time-consuming and costly. Computational methods for structure prediction of protein/ligand complexes, particularly docking, are as yet restricted by their limited consideration of receptor flexibility, rendering them not applicable for predicting protein/ligand complexes if large conformational changes of the receptor upon ligand binding are involved. Accurate receptor models in the ligand-bound state (holo structures), however, are a prerequisite for successful structure-based drug design. Hence, if only an unbound (apo) structure is available distinct from the ligand-bound conformation, structure-based drug design is severely limited. We present a method to predict the structure of protein/ligand complexes based solely on the apo structure, the ligand and the radius of gyration of the holo structure. The method is applied to ten cases in which proteins undergo structural rearrangements of up to 7.1 Å backbone RMSD upon ligand binding. In all cases, receptor models within 1.6 Å backbone RMSD to the target were predicted and close-to-native ligand binding poses were obtained for 8 of 10 cases in the top-ranked complex models. A protocol is presented that is expected to enable structure modeling of protein/ligand complexes and structure-based drug design for cases where crystal structures of ligand-bound conformations are not available.     

DynaDock: A new molecular dynamics‐based algorithm for protein–peptide docking including receptor flexibility

Molecular docking programs play an important role in drug development and many well‐established methods exist. However, there are two situations for which the performance of most approaches is still not satisfactory, namely inclusion of receptor flexibility and docking of large, flexible ligands like peptides. In this publication a new approach is presented for docking peptides into flexible receptors. For this purpose a two step procedure was developed: first, the protein–peptide conformational space is scanned and approximate ligand poses are identified and second, the identified ligand poses are refined by a new molecular dynamics‐based method, optimized potential molecular dynamics (OPMD). The OPMD approach uses soft‐core potentials for the protein–peptide interactions and applies a new optimization scheme to the soft‐core potential. Comparison with refinement results obtained by conventional molecular dynamics and a soft‐core scaling approach shows significant improvements in the sampling capability for the OPMD method. Thus, the number of starting poses needed for successful refinement is much lower than for the other methods. The algorithm was evaluated on 15 protein–peptide complexes with 2–16mer peptides. Docking poses with peptide RMSD values <2.10 Å from the equilibrated experimental structures were obtained in all cases. For four systems docking into the unbound receptor structures was performed, leading to peptide RMSD values <2.12 Å. Using a specifically fitted scoring function in 11 of 15 cases the best scoring poses featured a peptide RMSD ≤2.10 Å. Proteins 2010. © 2009 Wiley‐Liss, Inc.     

Features of Large Hinge-Bending Conformational Transitions. Prediction of Closed Structure from Open State

We performed a detailed analysis of conformational transition pathways for a set of 10 proteins, which undergo large hinge-bending-type motions with 4–12 Å RMSD (root mean-square distance) between open and closed crystal structures. Anisotropic network model-Monte Carlo (ANM-MC) algorithm generates a targeted pathway between two conformations, where the collective modes from the ANM are used for deformation at each iteration and the conformational energy of the deformed structure is minimized via an MC algorithm. The target structure was approached successfully with an RMSD of 0.9–4.1 Å when a relatively low cutoff radius of 10 Å was used in ANM. Even though one predominant mode (first or second) directed the open-to-closed conformational transition, changes in the dominant mode character were observed for most cases along the transition. By imposing radius of gyration constraint during mode selection, it was possible to predict the closed structure for eight out of 10 proteins (with initial 4.1–7.1 Å and final 1.7–2.9 Å RMSD to target). Deforming along a single mode leads to most successful predictions. Based on the previously reported free energy surface of adenylate kinase, deformations along the first mode produced an energetically favorable path, which was interestingly facilitated by a change in mode shape (resembling second and third modes) at key points. Pathway intermediates are provided in our database of conformational transitions (http://safir.prc.boun.edu.tr/anmmc/method/1).     

Assessment and Challenges of Ligand Docking into Comparative Models of G-Protein Coupled Receptors

The rapidly increasing number of high-resolution X-ray structures of G-protein coupled receptors (GPCRs) creates a unique opportunity to employ comparative modeling and docking to provide valuable insight into the function and ligand binding determinants of novel receptors, to assist in virtual screening and to design and optimize drug candidates. However, low sequence identity between receptors, conformational flexibility, and chemical diversity of ligands present an enormous challenge to molecular modeling approaches. It is our hypothesis that rapid Monte-Carlo sampling of protein backbone and side-chain conformational space with Rosetta can be leveraged to meet this challenge. This study performs unbiased comparative modeling and docking methodologies using 14 distinct high-resolution GPCRs and proposes knowledge-based filtering methods for improvement of sampling performance and identification of correct ligand-receptor interactions. On average, top ranked receptor models built on template structures over 50% sequence identity are within 2.9 Å of the experimental structure, with an average root mean square deviation (RMSD) of 2.2 Å for the transmembrane region and 5 Å for the second extracellular loop. Furthermore, these models are consistently correlated with low Rosetta energy score. To predict their binding modes, ligand conformers of the 14 ligands co-crystalized with the GPCRs were docked against the top ranked comparative models. In contrast to the comparative models themselves, however, it remains difficult to unambiguously identify correct binding modes by score alone. On average, sampling performance was improved by 10³ fold over random using knowledge-based and energy-based filters. In assessing the applicability of experimental constraints, we found that sampling performance is increased by one order of magnitude for every 10 residues known to contact the ligand. Additionally, in the case of DOR, knowledge of a single specific ligand-protein contact improved sampling efficiency 7 fold. These findings offer specific guidelines which may lead to increased success in determining receptor-ligand complexes.     

A Unified Conformational Selection and Induced Fit Approach to Protein-Peptide Docking

Protein-peptide interactions are vital for the cell. They mediate, inhibit or serve as structural components in nearly 40% of all macromolecular interactions, and are often associated with diseases, making them interesting leads for protein drug design. In recent years, large-scale technologies have enabled exhaustive studies on the peptide recognition preferences for a number of peptide-binding domain families. Yet, the paucity of data regarding their molecular binding mechanisms together with their inherent flexibility makes the structural prediction of protein-peptide interactions very challenging. This leaves flexible docking as one of the few amenable computational techniques to model these complexes. We present here an ensemble, flexible protein-peptide docking protocol that combines conformational selection and induced fit mechanisms. Starting from an ensemble of three peptide conformations (extended, a-helix, polyproline-II), flexible docking with HADDOCK generates 79.4% of high quality models for bound/unbound and 69.4% for unbound/unbound docking when tested against the largest protein-peptide complexes benchmark dataset available to date. Conformational selection at the rigid-body docking stage successfully recovers the most relevant conformation for a given protein-peptide complex and the subsequent flexible refinement further improves the interface by up to 4.5 Å interface RMSD. Cluster-based scoring of the models results in a selection of near-native solutions in the top three for ∼75% of the successfully predicted cases. This unified conformational selection and induced fit approach to protein-peptide docking should open the route to the modeling of challenging systems such as disorder-order transitions taking place upon binding, significantly expanding the applicability limit of biomolecular interaction modeling by docking.     

Biologically enhanced sampling geometric docking and backbone flexibility treatment with multiconformational superposition

An efficient biologically enhanced sampling geometric docking method is presented based on the FTDock algorithm to predict the protein-protein binding modes. The active site data from different sources, such as biochemical and biophysical experiments or theoretical analyses of sequence data, can be incorporated in the rotation-translation scan. When discretizing a protein onto a 3-dimensional (3D) grid, a zero value is given to grid points outside a sphere centered on the geometric center of specified residues. In this way, docking solutions are biased toward modes where the interface region is inside the sphere. We also adopt a multiconformational superposition scheme to represent backbone flexibility in the proteins. When these procedures were applied to the targets of CAPRI, a larger number of hits and smaller ligand root-mean-square deviations (RMSDs) were obtained at the conformational search stage in all cases, and especially Target 19. With Target 18, only 1 near-native structure was retained by the biologically enhanced sampling geometric docking method, but this number increased to 53 and the least ligand RMSD decreased from 8.1 A to 2.9 A after performing multiconformational superposition. These results were obtained after the CAPRI prediction deadlines.     

Conformational selection in silico: loop latching motions and ligand binding in enzymes

Ligand binding frequently induces significant conformational changes in a protein receptor. Understanding and predicting such conformational changes represent an important challenge for computational biology, including applications to structure-based drug design. We describe an approach to this problem based on the assumption that the holo state is at least transiently populated in the absence of a ligand; this hypothesis has been referred to as "conformational selection." Here, we apply a method that tests this hypothesis on a challenging class of ligand-induced conformational changes, which we refer to as loop latching: the closing of a loop around an active site that sequesters the ligand from solvent. The method uses a combination of replica exchange molecular dynamics and a loop prediction algorithm to generate low-energy loop structures, and docking to select the conformation appropriate for binding a particular ligand. On a test set of six proteins, it yields loop structures including hololike conformations, generally below 2 A RMSD from the liganded structure, for loops that span up to 15 residues. Docking serves as a stringent test of the predictions. In five of the six cases, the predicted loop conformations improve the ranks of cognate ligands relative to using the apo structure, although the results remain, in most cases, significantly worse than using a holo structure. The poses of the cognate ligands are correct in four of the six test cases, while they are correct for five of the six using a holo structure.     

LigandRNA: computational predictor of RNA–ligand interactions

RNA molecules have recently become attractive as potential drug targets due to the increased awareness of their importance in key biological processes. The increase of the number of experimentally determined RNA 3D structures enabled structure-based searches for small molecules that can specifically bind to defined sites in RNA molecules, thereby blocking or otherwise modulating their function. However, as of yet, computational methods for structure-based docking of small molecule ligands to RNA molecules are not as well established as analogous methods for protein-ligand docking. This motivated us to create LigandRNA, a scoring function for the prediction of RNA–small molecule interactions. Our method employs a grid-based algorithm and a knowledge-based potential derived from ligand-binding sites in the experimentally solved RNA–ligand complexes. As an input, LigandRNA takes an RNA receptor file and a file with ligand poses. As an output, it returns a ranking of the poses according to their score. The predictive power of LigandRNA favorably compares to five other publicly available methods. We found that the combination of LigandRNA and Dock6 into a “meta-predictor” leads to further improvement in the identification of near-native ligand poses. The LigandRNA program is available free of charge as a web server at http://ligandrna.genesilico.pl.     

Ligand Clouds around Protein Clouds: A Scenario of Ligand Binding with Intrinsically Disordered Proteins

Intrinsically disordered proteins (IDPs) were found to be widely associated with human diseases and may serve as potential drug design targets. However, drug design targeting IDPs is still in the very early stages. Progress in drug design is usually achieved using experimental screening; however, the structural disorder of IDPs makes it difficult to characterize their interaction with ligands using experiments alone. To better understand the structure of IDPs and their interactions with small molecule ligands, we performed extensive simulations on the c-Myc_370–409 peptide and its binding to a reported small molecule inhibitor, ligand 10074-A4. We found that the conformational space of the apo c-Myc_370–409 peptide was rather dispersed and that the conformations of the peptide were stabilized mainly by charge interactions and hydrogen bonds. Under the binding of the ligand, c-Myc_370–409 remained disordered. The ligand was found to bind to c-Myc_370–409 at different sites along the chain and behaved like a ‘ligand cloud’. In contrast to ligand binding to more rigid target proteins that usually results in a dominant bound structure, ligand binding to IDPs may better be described as ligand clouds around protein clouds. Nevertheless, the binding of the ligand and a non-ligand to the c-Myc_370–409 target could be clearly distinguished. The present study provides insights that will help improve rational drug design that targets IDPs.     

DOCK 6: Combining techniques to model RNA–small molecule complexes

With an increasing interest in RNA therapeutics and for targeting RNA to treat disease, there is a need for the tools used in protein-based drug design, particularly DOCKing algorithms, to be extended or adapted for nucleic acids. Here, we have compiled a test set of RNA–ligand complexes to validate the ability of the DOCK suite of programs to successfully recreate experimentally determined binding poses. With the optimized parameters and a minimal scoring function, 70% of the test set with less than seven rotatable ligand bonds and 26% of the test set with less than 13 rotatable bonds can be successfully recreated within 2 Å heavy-atom RMSD. When DOCKed conformations are rescored with the implicit solvent models AMBER generalized Born with solvent-accessible surface area (GB/SA) and Poisson–Boltzmann with solvent-accessible surface area (PB/SA) in combination with explicit water molecules and sodium counterions, the success rate increases to 80% with PB/SA for less than seven rotatable bonds and 58% with AMBER GB/SA and 47% with PB/SA for less than 13 rotatable bonds. These results indicate that DOCK can indeed be useful for structure-based drug design aimed at RNA. Our studies also suggest that RNA-directed ligands often differ from typical protein–ligand complexes in their electrostatic properties, but these differences can be accommodated through the choice of potential function. In addition, in the course of the study, we explore a variety of newly added DOCK functions, demonstrating the ease with which new functions can be added to address new scientific questions.     

Fragment-based modelling of single stranded RNA bound to RNA recognition motif containing proteins

Protein-RNA complexes are important for many biological processes. However, structural modeling of such complexes is hampered by the high flexibility of RNA. Particularly challenging is the docking of single-stranded RNA (ssRNA). We have developed a fragment-based approach to model the structure of ssRNA bound to a protein, based on only the protein structure, the RNA sequence and conserved contacts. The conformational diversity of each RNA fragment is sampled by an exhaustive library of trinucleotides extracted from all known experimental protein–RNA complexes. The method was applied to ssRNA with up to 12 nucleotides which bind to dimers of the RNA recognition motifs (RRMs), a highly abundant eukaryotic RNA-binding domain. The fragment based docking allows a precise de novo atomic modeling of protein-bound ssRNA chains. On a benchmark of seven experimental ssRNA–RRM complexes, near-native models (with a mean heavy-atom deviation of <3 Å from experiment) were generated for six out of seven bound RNA chains, and even more precise models (deviation < 2 Å) were obtained for five out of seven cases, a significant improvement compared to the state of the art. The method is not restricted to RRMs but was also successfully applied to Pumilio RNA binding proteins.     

Pushing the limits of what is achievable in protein–DNA docking: benchmarking HADDOCK’s performance

The intrinsic flexibility of DNA and the difficulty of identifying its interaction surface have long been challenges that prevented the development of efficient protein–DNA docking methods. We have demonstrated the ability our flexible data-driven docking method HADDOCK to deal with these before, by using custom-built DNA structural models. Here we put our method to the test on a set of 47 complexes from the protein–DNA docking benchmark. We show that HADDOCK is able to predict many of the specific DNA conformational changes required to assemble the interface(s). Our DNA analysis and modelling procedure captures the bend and twist motions occurring upon complex formation and uses these to generate custom-built DNA structural models, more closely resembling the bound form, for use in a second docking round. We achieve throughout the benchmark an overall success rate of 94% of one-star solutions or higher (interface root mean square deviation ≤4 Å and fraction of native contacts >10%) according to CAPRI criteria. Our improved protocol successfully predicts even the challenging protein–DNA complexes in the benchmark. Finally, our method is the first to readily dock multiple molecules (N > 2) simultaneously, pushing the limits of what is currently achievable in the field of protein–DNA docking.     

Defective Chylomicron Synthesis as a Cause of Delayed Particle Clearance in Diabetes?

Chylomicron metabolism is abnormal in diabetes and the chylomicron particle may play a very important role in atherosclerosis. The aim of this study was to examine the effect of diabetes on the metabolism of chylomicrons in cholesterol-fed alloxan diabetic and nondiabetic rabbits. Five diabetic rabbits and 5 control rabbits were given [¹⁴C]linoleic acid and [³H]cholesterol by gavage. Lymph was collected following cannulation of the lymph duct and radiolabelled chylomicrons were isolated by ultracentrifugation. The chylomicrons from each animal were injected into paired control and diabetic recipients. Lymph apolipoprotein (apo) B48, apo B100, and apo E were measured using sodium dodecyl sulfate–polyacrylamide gradient gel electrophoresis. Mean blood sugar of the diabetic donors and diabetic recipients were 19.7 ± 2.3 and 17.2 ± 3.2 mmol/L. Diabetic rabbits had significantly raised plasma triglyceride (10.8 ± 13.9 versus 0.8 ± 0.5 mmol/L, P < 0.02). There was a large increase in apo B48 in lymph chylomicrons in the diabetic donor animals (0.19 ± 0.10 versus 0.04 ± 0.02 mg/h, P < 0.01) and apo B100 (0.22 ± 0.15 versus 0.07 ± 0.07 mg/h, P < 0.05) and a reduction in apo E on the lymph chylomicron particle (0.27 ± 0.01 versus 0.62 ± 0.07 mg/mg apo B, P < 0.001). Diabetic recipients cleared both control and diabetic chylomicron triglyceride significantly more slowly than control recipients (P < 0.05). Clearance of control chylomicron cholesterol was delayed when injected into diabetic recipients compared to when these chylomicrons were injected into control recipients (P < 0.005). Clearance of diabetic chylomicron cholesterol was significantly slower when injected into control animals compared to control chylomicron injected into control animals (P < 0.02). In this animal model of atherosclerosis, we have demonstrated that diabetes leads to the production of an increased number of lipid and apo E–deficient chylomicron particles. Chylomicron particles from the control animals were cleared more slowly by the diabetic recipient (both triglyceride and cholesterol). The chylomicron particles obtained from the diabetic animals were cleared even more slowly when injected into the diabetic recipient. Although there was an initial delay in clearance of chylomicron triglyceride from the diabetic particle when injected into the control animals, the clearance over the first 15 minutes was not significantly different when compared to the control chylomicron injected into the control animal. On the other hand, the cholesterol clearance was significantly delayed. Thus, diabetes resulted in the production of an increased number of lipid- and apo E–deficient chylomicron particles. These alterations account, in part, for the delay in clearance of these particles.     

Cytotoxic T-lymphocyte elicited therapeutic vaccine candidate targeting cancer against MAGE-A11 carcinogenic protein

Immunotherapy is a breakthrough approach for cancer treatment and prevention. By exploiting the fact that cancer cells have overexpression of tumor antigens responsible for its growth and progression, which can be identified and removed by boosting the immune system. In silico techniques have provided efficient ways for developing preventive measures to ward off cancer. Herein, we have designed a potent cytotoxic T-lymphocyte epitope to elicit a desirable immune response against carcinogenic melanoma-associated antigen-A11. Potent epitope was predicted using reliable algorithms and characterized by advanced computational avenue CABS molecular dynamics simulation, for full flexible binding with HLA-A*0201 and androgen receptor to large-scale rearrangements of the complex system. Results showed the potent immunogenic construct (KIIDLVHLL), from top epitopes using five algorithms. Molecular docking analyses showed the strong binding of epitope with HLA-A*0201 and androgen receptor with docking score of −780.6 and −641.06 kcal/mol, respectively. Molecular dynamics simulation analysis revealed strong binding of lead epitope with androgen receptor by involvement of 127 elements through atomic-model study. Full flexibility study showed stable binding of epitope with an average root mean square deviation (RMSD) 2.21 Å and maximum RMSD value of 6.48 Å in optimal cluster density area. The epitope also showed remarkable results with radius of gyration 23.0777 Å, world population coverage of 39.08% by immune epitope database, and transporter associated with antigen processing (TAP) affinity IC₅₀ value of 2039.65 nm. Moreover, in silico cloning approach confirmed the expression and translation capacity of the construct within a suitable expression vector. The present study paves way for a potential immunogenic construct for prevention of cancer.     

Two Distinct Calmodulin Binding Sites in the Third Intracellular Loop and Carboxyl Tail of Angiotensin II (AT1A) Receptor

In this study, we present data that support the presence of two distinct calmodulin binding sites within the angiotensin II receptor (AT_1A), at juxtamembrane regions of the N-terminus of the third intracellular loop (i3, amino acids 214–231) and carboxyl tail of the receptor (ct, 302–317). We used bioluminescence resonance energy transfer assays to document interactions of calmodulin with the AT_1A holo-receptor and GST-fusion protein pull-downs to demonstrate that i3 and ct interact with calmodulin in a Ca²⁺-dependent fashion. The former is a 1–12 motif and the latter belongs to 1-5-10 calmodulin binding motif. The apparent Kd of calmodulin for i3 is 177.0±9.1 nM, and for ct is 79.4±7.9 nM as assessed by dansyl-calmodulin fluorescence. Replacement of the tryptophan (W219) for alanine in i3, and phenylalanine (F309 or F313) for alanine in ct reduced their binding affinities for calmodulin, as predicted by computer docking simulations. Exogenously applied calmodulin attenuated interactions between G protein βγ subunits and i3 and ct, somewhat more so for ct than i3. Mutations W219A, F309A, and F313A did not alter Gβγ binding, but reduced the ability of calmodulin to compete with Gβγ, suggesting that calmodulin and Gβγ have overlapping, but not identical, binding requirements for i3 and ct. Calmodulin interference with the Gβγ binding to i3 and ct regions of the AT_1A receptor strongly suggests that calmodulin plays critical roles in regulating Gβγ-dependent signaling of the receptor.     

Structural prediction of peptides bound to MHC class I

An ab initio structure prediction approach adapted to the peptide-major histocompatibility complex (MHC) class I system is presented. Based on structure comparisons of a large set of peptide-MHC class I complexes, a molecular dynamics protocol is proposed using simulated annealing (SA) cycles to sample the conformational space of the peptide in its fixed MHC environment. A set of 14 peptide-human leukocyte antigen (HLA) A0201 and 27 peptide-non-HLA A0201 complexes for which X-ray structures are available is used to test the accuracy of the prediction method. For each complex, 1000 peptide conformers are obtained from the SA sampling. A graph theory clustering algorithm based on heavy atom root-mean-square deviation (RMSD) values is applied to the sampled conformers. The clusters are ranked using cluster size, mean effective or conformational free energies, with solvation free energies computed using Generalized Born MV 2 (GB-MV2) and Poisson-Boltzmann (PB) continuum models. The final conformation is chosen as the center of the best-ranked cluster. With conformational free energies, the overall prediction success is 83% using a 1.00 Angstroms crystal RMSD criterion for main-chain atoms, and 76% using a 1.50 Angstroms RMSD criterion for heavy atoms. The prediction success is even higher for the set of 14 peptide-HLA A0201 complexes: 100% of the peptides have main-chain RMSD values < or =1.00 Angstroms and 93% of the peptides have heavy atom RMSD values < or =1.50 Angstroms. This structure prediction method can be applied to complexes of natural or modified antigenic peptides in their MHC environment with the aim to perform rational structure-based optimizations of tumor vaccines.     

Protein-Protein Docking with Dynamic Residue Protonation States

Protein-protein interactions depend on a host of environmental factors. Local pH conditions influence the interactions through the protonation states of the ionizable residues that can change upon binding. In this work, we present a pH-sensitive docking approach, pHDock, that can sample side-chain protonation states of five ionizable residues (Asp, Glu, His, Tyr, Lys) on-the-fly during the docking simulation. pHDock produces successful local docking funnels in approximately half (79/161) the protein complexes, including 19 cases where standard RosettaDock fails. pHDock also performs better than the two control cases comprising docking at pH 7.0 or using fixed, predetermined protonation states. On average, the top-ranked pHDock structures have lower interface RMSDs and recover more native interface residue-residue contacts and hydrogen bonds compared to RosettaDock. Addition of backbone flexibility using a computationally-generated conformational ensemble further improves native contact and hydrogen bond recovery in the top-ranked structures. Although pHDock is designed to improve docking, it also successfully predicts a large pH-dependent binding affinity change in the Fc–FcRn complex, suggesting that it can be exploited to improve affinity predictions. The approaches in the study contribute to the goal of structural simulations of whole-cell protein-protein interactions including all the environmental factors, and they can be further expanded for pH-sensitive protein design.     

Pretransition state and apo structures of the filament-forming enzyme SgrAI elucidate mechanisms of activation and substrate specificity

Enzyme filamentation is a widespread phenomenon that mediates enzyme regulation and function. For the filament-forming sequence-specific DNA endonuclease SgrAI, the process of filamentation both accelerates its DNA cleavage activity and expands its DNA sequence specificity, thus allowing for many additional DNA sequences to be rapidly cleaved. Both outcomes—the acceleration of DNA cleavage and the expansion of sequence specificity—are proposed to regulate critical processes in bacterial innate immunity. However, the mechanistic bases underlying these events remain unclear. Herein, we describe two new structures of the SgrAI enzyme that shed light on its catalytic function. First, we present the cryo-EM structure of filamentous SgrAI bound to intact primary site DNA and Ca²⁺ resolved to ∼2.5 Å within the catalytic center, which represents the trapped enzyme–DNA complex prior to the DNA cleavage reaction. This structure reveals important conformational changes that contribute to the catalytic mechanism and the binding of a second divalent cation in the enzyme active site, which is expected to contribute to increased DNA cleavage activity of SgrAI in the filamentous state. Second, we present an X-ray crystal structure of DNA-free (apo) SgrAI resolved to 2.0 Å resolution, which reveals a disordered loop involved in DNA recognition. Collectively, these multiple new observations clarify the mechanism of expansion of DNA sequence specificity of SgrAI, including the indirect readout of sequence-dependent DNA structure, changes in protein–DNA interactions, and the disorder-to-order transition of a crucial DNA recognition element.     

Protein-protein docking with a reduced protein model accounting for side-chain flexibility

A protein-protein docking approach has been developed based on a reduced protein representation with up to three pseudo atoms per amino acid residue. Docking is performed by energy minimization in rotational and translational degrees of freedom. The reduced protein representation allows an efficient search for docking minima on the protein surfaces within. During docking, an effective energy function between pseudo atoms has been used based on amino acid size and physico-chemical character. Energy minimization of protein test complexes in the reduced representation results in geometries close to experiment with backbone root mean square deviations (RMSDs) of approximately 1 to 3 A for the mobile protein partner from the experimental geometry. For most test cases, the energy-minimized experimental structure scores among the top five energy minima in systematic docking studies when using both partners in their bound conformations. To account for side-chain conformational changes in case of using unbound protein conformations, a multicopy approach has been used to select the most favorable side-chain conformation during the docking process. The multicopy approach significantly improves the docking performance, using unbound (apo) binding partners without a significant increase in computer time. For most docking test systems using unbound partners, and without accounting for any information about the known binding geometry, a solution within approximately 2 to 3.5 A RMSD of the full mobile partner from the experimental geometry was found among the 40 top-scoring complexes. The approach could be extended to include protein loop flexibility, and might also be useful for docking of modeled protein structures.