Affinity and specificity of protein U1A-RNA complex formation based on an additive component free energy model

An MM-GBSA computational protocol was used to investigate wild-type U1A-RNA and F56 U1A mutant experimental binding free energies. The trend in mutant binding free energies compared to wild-type is well-reproduced. Following application of a linear-response-like equation to scale the various energy components, the binding free energies agree quantitatively with observed experimental values. Conformational adaptation contributes to the binding free energy for both the protein and the RNA in these systems. Small differences in DeltaGs are the result of different and sometimes quite large relative contributions from various energetic components. Residual free energy decomposition indicates differences not only at the site of mutation, but throughout the entire protein. MM-GBSA and ab initio calculations performed on model systems suggest that stacking interactions may nearly, but not completely, account for observed differences in mutant binding affinities. This study indicates that there may be different underlying causes of ostensibly similar experimentally observed binding affinities of different mutants, and thus recommends caution in the interpretation of binding affinities and specificities purely by inspection.     

Diabetes mellitus caused by mutations in human insulin: analysis of impaired receptor binding of insulins Wakayama,Los Angeles and Chicago using pharmacoinformatics

Several naturally occuring mutations in the human insulin gene are associated with diabetes mellitus. The three known mutant molecules, Wakayama, Los Angeles and Chicago were evaluated using molecular docking and molecular dynamics (MD) to analyse mechanisms of deprived binding affinity for insulin receptor (IR). Insulin Wakayama, is a variant in which valine at position A3 is substituted by leucine, while in insulin Los Angeles and Chicago, phenylalanine at positions B24 and B25 is replaced by serine and leucine, respectively. These mutations show radical changes in binding affinity for IR. The ZDOCK server was used for molecular docking, while AMBER 14 was used for the MD study. The published crystal structure of IR bound to natural insulin was also used for MD. The binding interactions and MD trajectories clearly explained the critical factors for deprived binding to the IR. The surface area around position A3 was increased when valine was substituted by leucine, while at positions B24 and B25 aromatic amino acid phenylalanine replaced by non-aromatic serine and leucine might be responsible for fewer binding interactions at the binding site of IR that leads to instability of the complex. In the MD simulation, the normal mode analysis, rmsd trajectories and prediction of fluctuation indicated instability of complexes with mutant insulin in order of insulin native insulin < insulin Chicago < insulin Los Angeles < insulin Wakayama molecules which corresponds to the biological evidence of the differing affinities of the mutant insulins for the IR.     

Prediction of the binding mode between GSK3β and a peptide derived from GSKIP using molecular dynamics simulation

GSK3β plays an important role in many physiological functions; dysregulated GSK3β is involved in human diseases such as diabetes, cancer, and Alzheimer's disease. This study uses MD simulations to determine the interaction between GSK3β and a peptide derived from GSKIP, a novel GSK3β interacting protein. Results show that GSKIPtide is inlaid in a binding pocket consisting of an α-helix and an extended loop near the carboxy-terminal end. This binding pocket is hydrophobic, and is responsible for the protein-protein interaction of two other GSK3β interacting proteins: FRAT and Axin. The GSKIPtide binding mode is closer to that of AxinGID (in the Axin-GSK3-interacting domain). The single-point mutations of V267G and Y288F in GSK3β differentiate the binding modes between GSK3 and GSKIPtide, AxinGID, and FRATide. The V2677G mutation of GSK3β reduces the GSKIPtide binding affinity by 70% and abolishes the binding affinity with AxinGID, but has no effect on FRATide. However, GSK3β Y288F completely abolishes the FRATide binding without affecting GSKIPtide or AxinGID binding. An analysis of the GSK3β-GSKIPtide complex structure and the X-ray crystal structures of GSK3β-FRATide and GSK3β-AxinGID complexes suggests that the hydroxyl group of Y288 is crucial to maintaining a hydrogen bond network in GSK3β-FRATide. The hydrophobic side chain of V267 maintains the integrity of helix-helix ridge-groove hydrophobic interaction for GSK3β-GSKIPtide and GSK3β-AxinGID. This study simulates these two mutant systems to provide atomic-level evidence of the aforementioned experimental results and validate the wild-type complex structure prediction.     

Applying Physics-Based Scoring to Calculate Free Energies of Binding for Single Amino Acid Mutations in Protein-Protein Complexes

Predicting changes in protein binding affinity due to single amino acid mutations helps us better understand the driving forces underlying protein-protein interactions and design improved biotherapeutics. Here, we use the MM-GBSA approach with the OPLS2005 force field and the VSGB2.0 solvent model to calculate differences in binding free energy between wild type and mutant proteins. Crucially, we made no changes to the scoring model as part of this work on protein-protein binding affinity—the energy model has been developed for structure prediction and has previously been validated only for calculating the energetics of small molecule binding. Here, we compare predictions to experimental data for a set of 418 single residue mutations in 21 targets and find that the MM-GBSA model, on average, performs well at scoring these single protein residue mutations. Correlation between the predicted and experimental change in binding affinity is statistically significant and the model performs well at picking “hotspots,” or mutations that change binding affinity by more than 1 kcal/mol. The promising performance of this physics-based method with no tuned parameters for predicting binding energies suggests that it can be transferred to other protein engineering problems.     

Investigation into mechanism of orotidine 5′-monophosphate decarboxylase enzyme by MM-PBSA/MM-GBSA and molecular docking

In this work, we studied the binding affinity of orotidine 5′-monophosphate (OMP) and 6-hydroxy-UMP (BMP) for Saccharomyces cerevisiae orotidine 5′-monophosphate decarboxylase (OMPDC) enzyme by using Molecular Mechanics-Poisson–Boltzmann Surface Area (MM-PBSA) and the Molecular Mechanics-Generalised Born Surface Area (MM-GBSA) calculations. In all simulations, Asp91, which is an important residue in the enzyme active site, was considered in both anionic (present in the native form of the enzyme) and neutral states. A series of 10-ns molecular dynamics simulations were performed for the four OMPDC–ligand complexes, two ligand-free enzymes and two free ligands, followed by MM-PBSA/MM-GBSA calculations on the collected snapshots, and molecular docking calculations using the free enzymes and ligands. The results of MM-PBSA/MM-GBSA calculations indicate that all of the OMPDC–ligand complexes form favourable systems in water, which is in agreement with corresponding experimental data. The results of the MM-PBSA and molecular docking methods also showed that OMPDC–BMP complexes, transition state analogue and inhibitor of the OMPDC enzyme have the highest binding affinities. The fact that in the native anionic state BMP shows a higher binding affinity compared with the substrate suggests the contribution of a transition state stabilisation mechanism in the debatable catalytic mechanism of the OMPDC enzyme.     

Refined models of New Delhi metallo-beta-lactamase-1 with inhibitors: an QM/MM modeling study

New Delhi metallo-beta-lactamase 1 (NDM-1) has been identified as a potential target for the treatment of multi-drug resistance bacterial infections. We used molecular docking, normal MD, SIE, QM/MM MD simulations, QM/MM GBSA binding free energy, and QM/MM GBSA alanine-scanning mutagenesis techniques to investigate interactions of the NDM-1 with 11 inhibitors (Tigecycline, BAL30072, D-captopril, Penicillin G, Ampicillin, Carbenicillin, Cephalexin, Cefaclor, Nitrocefin, Meropenem, and Imipenem). From our normal MD and QM/MM simulations, the correlation coefficients between the predicted binding free energies and experimental values are .88 and .93, respectively. Then simulations, which combined QM/MM/GBSA and alanine-scanning mutagenesis techniques, were performed and our results show that two residues (Lys211 and His250) have the strongest impact on the binding affinities of the 11 NDM-1/inhibitors. Therefore, our approach theoretically suggests that the two residues (Lys211 and His250) are responsible for the selectivity of NDM-1 associated inhibitors.     

Structure-based rational design of a Toll-like receptor 4 (TLR4) decoy receptor with high binding affinity for a target protein

Repeat proteins are increasingly attracting much attention as alternative scaffolds to immunoglobulin antibodies due to their unique structural features. Nonetheless, engineering interaction interface and understanding molecular basis for affinity maturation of repeat proteins still remain a challenge. Here, we present a structure-based rational design of a repeat protein with high binding affinity for a target protein. As a model repeat protein, a Toll-like receptor4 (TLR4) decoy receptor composed of leucine-rich repeat (LRR) modules was used, and its interaction interface was rationally engineered to increase the binding affinity for myeloid differentiation protein 2 (MD2). Based on the complex crystal structure of the decoy receptor with MD2, we first designed single amino acid substitutions in the decoy receptor, and obtained three variants showing a binding affinity (K(D)) one-order of magnitude higher than the wild-type decoy receptor. The interacting modes and contributions of individual residues were elucidated by analyzing the crystal structures of the single variants. To further increase the binding affinity, single positive mutations were combined, and two double mutants were shown to have about 3000- and 565-fold higher binding affinities than the wild-type decoy receptor. Molecular dynamics simulations and energetic analysis indicate that an additive effect by two mutations occurring at nearby modules was the major contributor to the remarkable increase in the binding affinities.     

Characterization of domain-peptide interaction interface: a case study on the amphiphysin-1 SH3 domain

Many important protein-protein interactions are mediated by peptide recognition modular domains, such as the Src homology 3 (SH3), SH2, PDZ, and WW domains. Characterizing the interaction interface of domain-peptide complexes and predicting binding specificity for modular domains are critical for deciphering protein-protein interaction networks. Here, we propose the use of an energetic decomposition analysis to characterize domain-peptide interactions and the molecular interaction energy components (MIECs), including van der Waals, electrostatic, and desolvation energy between residue pairs on the binding interface. We show a proof-of-concept study on the amphiphysin-1 SH3 domain interacting with its peptide ligands. The structures of the human amphiphysin-1 SH3 domain complexed with 884 peptides were first modeled using virtual mutagenesis and optimized by molecular mechanics (MM) minimization. Next, the MIECs between domain and peptide residues were computed using the MM/generalized Born decomposition analysis. We conducted two types of statistical analyses on the MIECs to demonstrate their usefulness for predicting binding affinities of peptides and for classifying peptides into binder and non-binder categories. First, combining partial least squares analysis and genetic algorithm, we fitted linear regression models between the MIECs and the peptide binding affinities on the training data set. These models were then used to predict binding affinities for peptides in the test data set; the predicted values have a correlation coefficient of 0.81 and an unsigned mean error of 0.39 compared with the experimentally measured ones. The partial least squares-genetic algorithm analysis on the MIECs revealed the critical interactions for the binding specificity of the amphiphysin-1 SH3 domain. Next, a support vector machine (SVM) was employed to build classification models based on the MIECs of peptides in the training set. A rigorous training-validation procedure was used to assess the performances of different kernel functions in SVM and different combinations of the MIECs. The best SVM classifier gave satisfactory predictions for the test set, indicated by average prediction accuracy rates of 78% and 91% for the binding and non-binding peptides, respectively. We also showed that the performance of our approach on both binding affinity prediction and binder/non-binder classification was superior to the performances of the conventional MM/Poisson-Boltzmann solvent-accessible surface area and MM/generalized Born solvent-accessible surface area calculations. Our study demonstrates that the analysis of the MIECs between peptides and the SH3 domain can successfully characterize the binding interface, and it provides a framework to derive integrated prediction models for different domain-peptide systems.     

Molecular dynamics simulation of a human thiopurine S-methyltransferase complexed with 6-mercaptopurine model

Human thiopurine S-methyltransferase (TPMT) is an essential protein in 6-mercaptopurine (6MP) drug metabolism. To understand the pharmacogenetics of TPMT and 6MP, X-ray co-crystal structures of TPMT complexes with S-adenosyl-L-methionine (AdoMet) and 6MP are required. However, the co-crystal structure of this complex has not been reported because 6MP is poorly water soluble. We used molecular dynamics (MD) simulation to predict the structure of the complex of human TPMT-AdoHcy(CH₂)6MP, where the sulfur atoms of AdoHcy and 6MP were linked by a CH₂ group. After 1300 picoseconds of MD simulation, the trajectory showed that 6MP was stabilized in the TPMT active site by formation of non-bonded interactions between 6MP and Phe40, Pro196 and Arg226 side chains of TPMT. The intersulfur distance between AdoHcy and 6MP as well as the binding modes and the interactions of our TPMT-AdoHcy model are consistent with those observed in the X-ray crystal structure of murine TPMT-AdoHcy-6MP complex. The predicted binding modes of AdoHcy and 6MP in our model are consistent with those observed in murine TPMT X-ray crystal structures, which provides structural insights into the interactions of TPMT, AdoHcy, and 6MP at the atomic level and may be used as a starting point for further study of thiopurine drug pharmacogenetics.     

Directed mutagenesis identifies amino acid residues involved in elongation factor Tu binding to yeast Phe-tRNAPhe

The co-crystal structure of Thermus aquaticus elongation factor Tu.guanosine 5'- [beta,gamma-imido]triphosphate (EF-Tu.GDPNP) bound to yeast Phe-tRNA(Phe) reveals that EF-Tu interacts with the tRNA body primarily through contacts with the phosphodiester backbone. Twenty amino acids in the tRNA binding cleft of Thermus Thermophilus EF-Tu were each mutated to structurally conservative alternatives and the affinities of the mutant proteins to yeast Phe-tRNA(Phe) determined. Eleven of the 20 mutations reduced the binding affinity from fourfold to >100-fold, while the remaining ten had no effect. The thermodynamically important residues were spread over the entire tRNA binding interface, but were concentrated in the region which contacts the tRNA T-stem. Most of the data could be reconciled by considering the crystal structures of both free EF-Tu.GTP and the ternary complex and allowing for small (1.0 A) movements in the amino acid side-chains. Thus, despite the non-physiological crystallization conditions and crystal lattice interactions, the crystal structures reflect the biochemically relevant interaction in solution.     

A comparative study of trypsin specificity based on QM/MM molecular dynamics simulation and QM/MM GBSA calculation

Hydrogen bonding and polar interactions play a key role in identification of protein-inhibitor binding specificity. Quantum mechanics/molecular mechanics molecular dynamics (QM/MM MD) simulations combined with DFT and semi-empirical Hamiltonian (AM1d, RM1, PM3, and PM6) methods were performed to study the hydrogen bonding and polar interactions of two inhibitors BEN and BEN1 with trypsin. The results show that the accuracy of treating the hydrogen bonding and polar interactions using QM/MM MD simulation of PM6 can reach the one obtained by the DFT QM/MM MD simulation. Quantum mechanics/molecular mechanics generalized Born surface area (QM/MM-GBSA) method was applied to calculate binding affinities of inhibitors to trypsin and the results suggest that the accuracy of binding affinity prediction can be significantly affected by the accurate treatment of the hydrogen bonding and polar interactions. In addition, the calculated results also reveal the binding specificity of trypsin: (1) the amidinium groups of two inhibitors generate favorable salt bridge interaction with Asp189 and form hydrogen bonding interactions with Ser190 and Gly214, (2) the phenyl of inhibitors can produce favorable van der Waals interactions with the residues His58, Cys191, Gln192, Trp211, Gly212, and Cys215. This systematic and comparative study can provide guidance for the choice of QM/MM MD methods and the designs of new potent inhibitors targeting trypsin.     

A fluid salt-bridging cluster and the stabilization of p53

p53 is a homotetrameric tumor suppressor protein that is found to be mutated in most human cancers. Some of these mutations, particularly mutations to R337, fall in the tetramerization domain and cause defects in tetramer formation leading to loss of function. Mutation to His at this site has been found to destabilize the tetramer in a pH-dependent fashion. In structures of the tetramerization domain determined by crystallography, R337 from one monomer makes a salt bridge with D352 from another monomer, apparently helping to stabilize the tetramer. Here we present molecular dynamics simulations of wild-type p53 and the R337His mutant at several different pH and salt conditions. We find that the 337-352 salt bridge is joined by two other charged side chains, R333 and E349. These four residues do not settle into a fixed pattern of salt bridging, but continue to exchange salt-bridging partners on the nanosecond time scale throughout the simulation. This unusual system of fluid salt bridging may explain the previous finding from alanine scanning experiments that R333 contributes significantly to protein stability, even though in the crystal structure it is extended outward into solvent. This extended conformation of R333 appears to be the result of a specific crystal contact and, this contact being absent in the simulation, R333 turns inward to join its interaction partners. When R337 is mutated to His but remains positively charged, it maintains the original interaction with D352, but the newly observed interaction with E349 is weakened, accounting for the reduced stability of R337H even under mildly acidic conditions. When this His is deprotonated, the interaction with D352 is also lost, accounting for the further destabilization observed under mildly alkaline conditions. Simulations were carried out using both explicit and implicit solvent models, and both displayed similar behavior of the fluid salt-bridging cluster, suggesting that implicit solvent models can capture at least the qualitative features of this phenomenon as well as explicit solvent. Simulations under strongly acidic conditions in implicit solvent displayed the beginnings of the unfolding process, a destabilization of the hydrophobic dimer-dimer interface. Computational alanine scanning using the molecular mechanics Poisson-Boltzmann surface area method showed significant correlation to experimental unfolding data for charged and polar residues, but much weaker correlation for hydrophobic residues.     

Conformer selection and induced fit in flexible backbone protein-protein docking using computational and NMR ensembles

Accommodating backbone flexibility continues to be the most difficult challenge in computational docking of protein-protein complexes. Towards that end, we simulate four distinct biophysical models of protein binding in RosettaDock, a multiscale Monte-Carlo-based algorithm that uses a quasi-kinetic search process to emulate the diffusional encounter of two proteins and to identify low-energy complexes. The four binding models are as follows: (1) key-lock (KL) model, using rigid-backbone docking; (2) conformer selection (CS) model, using a novel ensemble docking algorithm; (3) induced fit (IF) model, using energy-gradient-based backbone minimization; and (4) combined conformer selection/induced fit (CS/IF) model. Backbone flexibility was limited to the smaller partner of the complex, structural ensembles were generated using Rosetta refinement methods, and docking consisted of local perturbations around the complexed conformation using unbound component crystal structures for a set of 21 target complexes. The lowest-energy structure contained > 30% of the native residue-residue contacts for 9, 13, 13, and 14 targets for KL, CS, IF, and CS/IF docking, respectively. When applied to 15 targets using nuclear magnetic resonance ensembles of the smaller protein, the lowest-energy structure recovered at least 30% native residue contacts in 3, 8, 4, and 8 targets for KL, CS, IF, and CS/IF docking, respectively. CS/IF docking of the nuclear magnetic resonance ensemble performed equally well or better than KL docking with the unbound crystal structure in 10 of 15 cases. The marked success of CS and CS/IF docking shows that ensemble docking can be a versatile and effective method for accommodating conformational plasticity in docking and serves as a demonstration for the CS theory—that binding-competent conformers exist in the unbound ensemble and can be selected based on their favorable binding energies.     

Probing the “fingers” domain binding pocket of Hepatitis C virus NS5B RdRp and D559G resistance mutation via molecular docking,molecular dynamics simulation and binding free energy calculations

The NS5B RdRp polymerase is a prominent enzyme for the replication of Hepatitis C virus (HCV). During the HCV replication, the template RNA binding takes place in the “fingers” sub-domain of NS5B. The “fingers” domain is a new emerging allosteric site for the HCV drug development. The inhibitors of the “fingers” sub-domain adopt a new antiviral mechanism called RNA intervention. The details of essential amino acid residues, binding mode of the ligand, and the active site intermolecular interactions of RNA intervention reflect that this mechanism is ambiguous in the experimental study. To elucidate these details, we performed molecular docking analysis of the fingers domain inhibitor quercetagetin (QGN) with NS5B polymerase. The detailed analysis of QGN-NS5B intermolecular interactions was carried out and found that QGN interacts with the binding pocket amino acid residues Ala97, Ala140, Ile160, Phe162, Gly283, Gly557, and Asp559; and also forms π?π stacking interaction with Phe162 and hydrogen bonding interaction with Gly283. These are found to be the essential interactions for the RNA intervention mechanism. Among the strong hydrogen bonding interactions, the QGN?Ala140 is a newly identified important hydrogen bonding interaction by the present work and this interaction was not resolved by the previously reported crystal structure. Since D559G mutation at the fingers domain was reported for reducing the inhibition percentage of QGN to sevenfold, we carried out molecular dynamics (MD) simulation for wild and D559G mutated complexes to study the stability of protein conformation and intermolecular interactions. At the end of 50?ns MD simulation, the π?π stacking interaction of Phe162 with QGN found in the wild-type complex is altered into T-shaped π stacking interaction, which reduces the inhibition strength. The origin of the D559G resistance mutation was studied using combined MD simulation, binding free energy calculations and principal component analysis. The results were compared with the wild-type complex. The mutation D559G reduces the binding affinity of the QGN molecule to the fingers domain. The free energy decomposition analysis of each residue of wild-type and mutated complexes revealed that the loss of non-polar energy contribution is the origin of the resistance.

Communicated by Ramaswamy H. Sarma     

Affinity enhancement of an in vivo matured therapeutic antibody using structure-based computational design

Improving the affinity of a high-affinity protein-protein interaction is a challenging problem that has practical applications in the development of therapeutic biomolecules. We used a combination of structure-based computational methods to optimize the binding affinity of an antibody fragment to the I-domain of the integrin VLA1. Despite the already high affinity of the antibody (Kd approximately 7 nM) and the moderate resolution (2.8 A) of the starting crystal structure, the affinity was increased by an order of magnitude primarily through a decrease in the dissociation rate. We determined the crystal structure of a high-affinity quadruple mutant complex at 2.2 A. The structure shows that the design makes the predicted contacts. Structural evidence and mutagenesis experiments that probe a hydrogen bond network illustrate the importance of satisfying hydrogen bonding requirements while seeking higher-affinity mutations. The large and diverse set of interface mutations allowed refinement of the mutant binding affinity prediction protocol and improvement of the single-mutant success rate. Our results indicate that structure-based computational design can be successfully applied to further improve the binding of high-affinity antibodies.     

Molecular mechanism of the association and dissociation of Deltarasin from the heterodimeric KRas4B-PDEδ complex

The formation of the KRas4B-PDEδ complex activates different signaling pathways required for the development and maintenance of cancer. Previous experimental and theoretical studies have allowed researchers to design an inhibitor of the KRas4B-PDEδ complex, “Deltarasin.” This inhibitor binds to the prenyl-binding pocket of PDEδ and subsequently inhibits the proliferation of human pancreatic ductal adenocarcinoma cells that depend on oncogenic KRas4B. Nevertheless, structural and energetic information about the inhibitory effects of Deltarasin on the KRas4B-PDEδ complex are not available. In this study, we explore the properties of Deltarasin in inhibiting the formation of wild-type and mutant KRas4B-PDEδ complexes present in different cell lines expressing mutant RAS genes (G12D, G12C, G12V, G13D, Q61L, and Q61R) using 1.7 μs molecular dynamics (MD) simulations in combination with the MMGBSA approach. Our results revealed the energetic and structural mechanisms that suggest a higher affinity of Deltarasin for PDEδ than the farnesylated HVR. Moreover, Deltarasin exerts another dissociative effect by binding to the protein-protein dimeric interface of wild-type KRas4B-PDEδ, whereas associative and dissociative effects were observed for mutant KRas4B-PDEδ, providing a mechanistic explanation for the inhibitory effects of Deltarasin on different cancer cell lines.     

Structural studies of receptor binding by cholera toxin mutants.

The wide range of receptor binding affinities reported to result from mutations at residue Gly 33 of the cholera toxin B-pentamer (CTB) has been most puzzling. For instance, introduction of an aspartate at this position abolishes receptor binding, whereas substitution by arginine retains receptor affinity despite the larger side chain. We now report the structure determination and 2.3-A refinement of the CTB mutant Gly 33-->Arg complexed with the GM1 oligosaccharide, as well as the 2.2-A refinement of a Gly 33-->Asp mutant of the closely related Escherichia coli heat-labile enterotoxin B-pentamer (LTB). Two of the five receptor binding sites in the Gly 33-->Arg CTB mutant are occupied by bound GM1 oligosaccharide; two other sites are involved in a reciprocal toxin:toxin interaction; one site is unoccupied. We further report a higher resolution (2.0 A) determination and refinement of the wild-type CTB:GM1 oligosaccharide complex in which all five oligosaccharides are seen to be bound in essentially identical conformations. Saccharide conformation and binding interactions are very similar in both the CTB wild-type and Gly 33-->Arg mutant complexes. The protein conformation observed for the binding-deficient Gly 33-->Asp mutant of LTB does not differ substantially from that seen in the toxin:saccharide complexes. The critical nature of the side chain of residue 33 is apparently due to a limited range of subtle rearrangements available to both the toxin and the saccharide to accommodate receptor binding. The intermolecular interactions seen in the CTB (Gly 33-->Arg) complex with oligosaccharide suggest that the affinity of this mutant for the receptor is close to the self-affinity corresponding to the toxin:toxin binding interaction that has now been observed in crystal structures of three CTB mutants.     

Protein-protein docking by simulating the process of association subject to biochemical constraints

We present a computational procedure for modeling protein-protein association and predicting the structures of protein-protein complexes. The initial sampling stage is based on an efficient Brownian dynamics algorithm that mimics the physical process of diffusional association. Relevant biochemical data can be directly incorporated as distance constraints at this stage. The docked configurations are then grouped with a hierarchical clustering algorithm into ensembles that represent potential protein-protein encounter complexes. Flexible refinement of selected representative structures is done by molecular dynamics simulation. The protein-protein docking procedure was thoroughly tested on 10 structurally and functionally diverse protein-protein complexes. Starting from X-ray crystal structures of the unbound proteins, in 9 out of 10 cases it yields structures of protein-protein complexes close to those determined experimentally with the percentage of correct contacts >30% and interface backbone RMSD <4 A. Detailed examination of all the docking cases gives insights into important determinants of the performance of the computational approach in modeling protein-protein association and predicting of protein-protein complex structures.     

Mechanism of DNA binding by the ADR1 zinc finger transcription factor as determined by SPR

The ADR1 protein recognizes a six base-pair consensus DNA sequence using two zinc fingers and an adjacent accessory motif. Kinetic measurements were performed on the DNA-binding domain of ADR1 using surface plasmon resonance. Binding by ADR1 was characterized to two known native binding sequences from the ADH2 and CTA1 promoter regions, which differ in two of the six consensus positions. In addition, non-specific binding by ADR1 to a random DNA sequence was measured. ADR1 binds the native sites with nanomolar affinities. Remarkably, ADR1 binds non-specific DNA with affinities only approximately tenfold lower than the native sequences. The specific and non-specific binding affinities are conferred mainly by differences in the association phase of DNA binding. The association rate for the complex is strongly influenced by the proximal accessory region, while the dissociation reaction and specificity of binding are controlled by the two zinc fingers. Binding kinetics of two ADR1 mutants was also examined. ADR1 containing an R91K mutation in the accessory region bound with similar affinity to wild-type, but with slightly less sequence specificity. The R91K mutation was observed to increase binding affinity to a suboptimal sequence by decreasing the complex dissociation rate. L146H, a change-of-specificity mutation at the +3 position of the second zinc finger, bound its preferred sequence with a slightly higher affinity than wild-type. The L146H mutant indicates that beneficial protein-DNA contacts provide similar levels of stabilization to the complex, whether they are hydrogen-bonding or van der Waals interactions.