Acidic groups docked to well defined wetted pockets at the core of the binding interface: a tale of scoring and missing protein interactions in CAPRI

Specificity and cooperativity at β-lactamase position 104 in TEM-1/BLIP and SHV-1/BLIP interactions

Establishing a quantitative understanding of the determinants of affinity in protein-protein interactions remains challenging. For example, TEM-1/β-lactamase inhibitor protein (BLIP) and SHV-1/BLIP are homologous β-lactamase/β-lactamase inhibitor protein complexes with disparate K(d) values (3 nM and 2 μM, respectively), and a single substitution, D104E in SHV-1, results in a 1000-fold enhancement in binding affinity. In TEM-1, E104 participates in a salt bridge with BLIP K74, whereas the corresponding SHV-1 D104 does not in the wild type SHV-1/BLIP co-structure. Here, we present a 1.6 Å crystal structure of the SHV-1 D104E/BLIP complex that demonstrates that this point mutation restores this salt bridge. Additionally, mutation of a neighboring residue, BLIP E73M, results in salt bridge formation between SHV-1 D104 and BLIP K74 and a 400-fold increase in binding affinity. To understand how this salt bridge contributes to complex affinity, the cooperativity between the E/K or D/K salt bridge pair and a neighboring hot spot residue (BLIP F142) was investigated using double mutant cycle analyses in the background of the E73M mutation. We find that BLIP F142 cooperatively stabilizes both interactions, illustrating how a single mutation at a hot spot position can drive large perturbations in interface stability and specificity through a cooperative interaction network.

Novel modulation factor quantifies the role of water molecules in protein interactions

Water molecules decrease the potential of mean force of a hydrogen bond (H-bond), as well as modulate (de)solvation forces, but exactly how much has not been easy to determine. Crystallographic water molecules provide snapshots of optimal solutions for the role of solvent in protein interactions, information that is often ignored by implicit solvent models. Motivated by high-resolution crystal structures, we describe a simple quantitative approach to explicitly incorporate the role of molecular water in protein interactions. Applications to protein-DNA interactions show that the accuracy of binding free-energy estimates improves significantly if a distinction is made between H-bonds that are desolvated (or only contact crystal waters), solvated by mobile waters trapped at the binding interface, or partially solvated through connections to bulk water. These different environments are modeled by a unique "water" scaling factor that decreases or increases the strength of hydrogen bonds depending on whether water contacts the acceptor or donor atoms or the bond is fully desolvated, respectively. Our empirical energies are fully consistent with mobile water molecules having a strong polarization effect in direct intermolecular interactions.

Computational mapping of anchoring spots on protein surfaces

Protein-protein and protein-peptide interactions are often controlled by few strong contacts that involve hot spot residues. Computational detection of such contacts, termed here anchoring spots, is important for understanding recognition processes and for predicting interactions; it is an essential step in designing interaction interfaces and therapeutic agents. We describe ANCHORSMAP, an algorithm for computational mapping of amino acid side chains on protein surfaces. The algorithm consists of two stages: A geometry based stage (LSMdet), in which sub-pockets adequate for binding single side chains are detected and amino acid probes are scattered near them, and an energy based stage in which optimal positions of the probes are determined through repeated energy minimization and clustering of nearby poses and their DeltaG are calculated. ANCHORSMAP employs a new function for DeltaG calculations, which is specifically designed for the context of protein-protein recognition by introducing a correction in the electrostatic energy term that compensates for the dielectric shielding exerted by a hypothetical protein bound to the probe. The algorithm successfully detects known anchoring sites and accurately positions the probes. The calculated DeltaG rank high the correct anchoring spots in maps produced for unbound proteins. We find that Arg, Trp, Glu and Tyr, which are favorite hot spot residues, are also more selective of their binding environment. The usefulness of anchoring spots mapping is demonstrated by detecting the binding surfaces in the protein-protein complex barnase/barstar and the protein-peptide complex kinase/PKI, and by identifying phenylalanine anchoring sites on the surface of the nuclear transporter NTF2, C-terminus anchors on PDZ domains and phenol anchors on thermolysin. Finally, we discuss the role of anchoring spots in molecular recognition processes.

Docking flexible peptide to flexible protein by molecular dynamics using two implicit-solvent models: an evaluation in protein kinase and phosphatase systems

Reliable prediction of protein-ligand docking pose requires proper account of induced fit effects. Treating both the ligand and the protein as flexible molecules is still challenging because many degrees of freedom are involved. Peptides are one type of ligand that are particularly difficult to study because of their extreme flexibility. In this study, we tested a molecular dynamics-based simulated-annealing cycling protocol in docking peptides to four protein kinases and two phosphatases using two implicit-solvent models: a distance-dependent dielectric model (epsilon(r) = 4r) and a version of the Generalized Born model termed GBMV. We found that the simpler epsilon(r) = 4r model identified docking pose better than the more expensive GBMV model. In addition, rescoring structures obtained from one implicit-solvent model with the other identified good docking poses for all six systems studied. Including protein energy in scoring also improved results.

Optimization of minimum set of protein-DNA interactions: a quasi exact solution with minimum over-fitting

Motivation: A major limitation in modeling protein interactions is the difficulty of assessing the over-fitting of the training set. Recently, an experimentally based approach that integrates crystallographic information of C2H2 zinc finger–DNA complexes with binding data from 11 mutants, 7 from EGR finger I, was used to define an improved interaction code (no optimization). Here, we present a novel mixed integer programming (MIP)-based method that transforms this type of data into an optimized code, demonstrating both the advantages of the mathematical formulation to minimize over- and under-fitting and the robustness of the underlying physical parameters mapped by the code.

Results: Based on the structural models of feasible interaction networks for 35 mutants of EGR–DNA complexes, the MIP method minimizes the cumulative binding energy over all complexes for a general set of fundamental protein–DNA interactions. To guard against over-fitting, we use the scalability of the method to probe against the elimination of related interactions. From an initial set of 12 parameters (six hydrogen bonds, five desolvation penalties and a water factor), we proceed to eliminate five of them with only a marginal reduction of the correlation coefficient to 0.9983. Further reduction of parameters negatively impacts the performance of the code (under-fitting). Besides accurately predicting the change in binding affinity of validation sets, the code identifies possible context-dependent effects in the definition of the interaction networks. Yet, the approach of constraining predictions to within a pre-selected set of interactions limits the impact of these potential errors to related low-affinity complexes.

Contact:ccamacho@pitt.edu; droleg@pitt.edu

Supplementary information:Supplementary data are available at Bioinformatics online.

Experimentally based contact energies decode interactions responsible for protein-DNA affinity and the role of molecular waters at the binding interface

A major obstacle towards understanding the molecular basis of transcriptional regulation is the lack of a recognition code for protein–DNA interactions. Using high-quality crystal structures and binding data on the promiscuous family of C₂H₂ zinc fingers (ZF), we decode 10 fundamental specific interactions responsible for protein–DNA recognition. The interactions include five hydrogen bond types, three atomic desolvation penalties, a favorable non-polar energy, and a novel water accessibility factor. We apply this code to three large datasets containing a total of 89 C₂H₂ transcription factor (TF) mutants on the three ZFs of EGR. Guided by molecular dynamics simulations of individual ZFs, we map the interactions into homology models that embody all feasible intra- and intermolecular bonds, selecting for each sequence the structure with the lowest free energy. These interactions reproduce the change in affinity of 35 mutants of finger I (R² = 0.998), 23 mutants of finger II (R² = 0.96) and 31 finger III human domains (R² = 0.94). Our findings reveal recognition rules that depend on DNA sequence/structure, molecular water at the interface and induced fit of the C₂H₂ TFs. Collectively, our method provides the first robust framework to decode the molecular basis of TFs binding to DNA.