Efrat Ben-Zeev

Weighted geometric docking: Incorporating external information in the rotation-translation scan

Weighted geometric docking is a prediction algorithm that matches weighted molecular surfaces. Each molecule is represented by a grid of complex numbers, storing information about the shape of the molecule in the real part and weight information in the imaginary part. The weights are based on experimental biochemical and biophysical data or on theoretical analyses of amino acid conservation or correlation patterns in multiple‐sequence alignments of homologous proteins. Only a few surface residues on either one or both molecules are weighted. In contrast to methods that use postscan filtering based on biochemical information, our method incorporates the external data in the rotation‐translation search, producing a different set of docking solutions biased toward solutions in which the up‐weighted residues are at the interface. Similarly, interactions involving specified residues can be impeded. The weighted geometric algorithm was applied to five systems for which regular geometric docking of the unbound molecules gave poor results. We obtained much better ranking of the nearly correct prediction and higher statistical significance when weighted geometric docking was used. The method was successful even when the weighted portion of the surface corresponded only partially and approximately to the binding site. Proteins 2003;52:24–27.

Docking to single-domain and multiple-domain proteins: Old and new challenges

The diverse selection of targets in the CAPRI experiments provides grounds for determining the limits of our rigid‐body docking program MolFit, and for extending it. We find that the sensitivity of MolFit is high, enabling it to produce reasonably accurate docking solutions when the structures undergo moderate local conformation changes upon complex formation or when the docked molecules are modeled. Yet the ranks of these solutions are sometimes too low to meet the requirements of CAPRI assessment. This indicates that the selectivity of MolFit, which was optimized for docking of unbound X‐ray structures, and which relies on the availability of external data from biochemical and bioinformatic sources, needs readjustment in order to meet the challenges presented by NMR or modeled structures. A different challenge is presented by large global conformation changes such as movements of domains. We show that such changes can be accommodated within the rigid‐body approximation by employing rigid multibody multistage docking procedures. We also address the difficulty of ranking results from 2‐body and multibody docking scans in cases in which there are no external data favoring one option over the other. Proteins 2005;60:195–201.

On the interaction of colicin E3 with the ribosome

Colicin E3 is a protein that kills Escherichia coli cells by a process that involves binding to a surface receptor, entering the cell and inactivating its protein biosynthetic machinery. Colicin E3 kills cells by a catalytic mechanism of a specific ribonucleolytic cleavage in 16S rRNA at the ribosomal decoding A-site between A1493 and G1494 (E. coli numbering system). The breaking of this single phosphodiester bond results in a complete cessation of protein biosynthesis and cell death. The inactive E517Q mutant of the catalytic domain of colicin E3 binds to 30S ribosomal subunits of Thermus thermophilus, as demonstrated by an immunoblotting assay. A model structure of the complex of the ribosomal subunit 30S and colicin E3, obtained via docking, explains the role of the catalytic residues, suggests a catalytic mechanism and provides insight into the specificity of the reaction. Furthermore, the model structure suggests that the inhibitory action of bound immunity is due to charge repulsion of this acidic protein by the negatively charged rRNA backbone

Prediction of the unknown: Inspiring experience with the CAPRI experiment

We submitted predictions for all seven targets in the CAPRI experiment. For four targets, our submitted models included acceptable, medium accuracy predictions of the structures of the complexes, and for a fifth target we identified the location of the binding site of one of the molecules. We used a weighted‐geometric docking algorithm in which contacts involving specified parts of the surfaces of either one or both molecules were up‐weighted or down‐weighted. The weights were based on available structural and biochemical data or on sequence analyses. The weighted‐geometric docking proved very useful for five targets, improving the complementarity scores and the ranks of the nearly correct solutions, as well as their statistical significance. In addition, the weighted‐geometric docking promoted formation of clusters of similar solutions, which include more accurate predictions. Proteins 2003;52:41–46.

A novel allosteric mechanism of NF-κB dimerization and DNA binding targeted by an anti-inflammatory drug

ABSTRACT The NF-κB family plays key roles in immune and stress responses, and its deregulation contributes to several diseases. Therefore its modulation has become an important therapeutic target. Here, we used a high-throughput screen for small molecules that directly inhibit dimerization of the NF-κB protein p65. One of the identified inhibitors is withaferin A (WFA), a documented anticancer and anti-inflammatory compound. Computational modeling suggests that WFA contacts the dimerization interface on one subunit and surface residues E285 and Q287 on the other. Despite their locations far from the dimerization site, E285 and Q287 substitutions diminished both dimerization and the WFA effect. Further investigation revealed that their effects on dimerization are associated with their proximity to a conserved hydrophobic core domain (HCD) that is crucial for dimerization and DNA binding. Our findings established NF-κB dimerization as a drug target and uncovered an allosteric domain as a target of WFA action.

Prediction of the Structure of the Complex Between the 30S Ribosomal Subunit and Colicin E3 via Weighted-Geometric Docking

Abstract Colicin E3 kills Escherichia coli cells by ribonucleolytic cleavage in the 16S rRNA. The cleavage occurs at the ribosomal decoding A-site between nucleotides A1493 and G1494. The breaking of this single phosphodiester bond results in a complete termination of protein biosynthesis leading to cell death. A model structure of the complex of the ribosomal subunit 30S and colicin E3 was constructed by means of a new weighted-geometric docking algorithm, in which interactions involving specified parts of the molecular surface can be up-weighted, allowing incorporation of experimental data in the docking search. Our model, together with available experimental data, predicts the role of the catalytic residues of colicin E3. In addition, it suggests that bound acidic immunity protein inhibits the enzymatic activity of colicin E3 by electrostatic repulsion of the negatively charged substrate.

A computational combinatorial approach identifies a protein inhibitor of superoxide dismutase 1 misfolding, aggregation, and cytotoxicity

Molecular agents that specifically bind and neutralize misfolded and toxic superoxide dismutase 1 (SOD1) mutant proteins may find application in attenuating the disease progression of familial amyotrophic lateral sclerosis. However, high structural similarities between the wild-type and mutant SOD1 proteins limit the utility of this approach. Here we addressed this challenge by converting a promiscuous natural human IgG-binding domain, the hyperthermophilic variant of protein G (HTB1), into a highly specific aggregation inhibitor (designated HTB1M) of two familial amyotrophic lateral sclerosis–linked SOD1 mutants, SOD1G93A and SOD1G85R. We utilized a computational algorithm for mapping protein surfaces predisposed to HTB1 intermolecular interactions to construct a focused HTB1 library, complemented with an experimental platform based on yeast surface display for affinity and specificity screening. HTB1M displayed high binding specificity toward SOD1 mutants, inhibited their amyloid aggregation in vitro, prevented the accumulation of misfolded proteins in living cells, and reduced the cytotoxicity of SOD1G93A expressed in motor neuron–like cells. Competition assays and molecular docking simulations suggested that HTB1M binds to SOD1 via both its α-helical and β-sheet domains at the native dimer interface that becomes exposed upon mutated SOD1 misfolding and monomerization. Our results demonstrate the utility of computational mapping of the protein–protein interaction potential for designing focused protein libraries to be used in directed evolution. They also provide new insight into the mechanism of conversion of broad-spectrum immunoglobulin-binding proteins, such as HTB1, into target-specific proteins, thereby paving the way for the development of new selective drugs targeting the amyloidogenic proteins implicated in a variety of human diseases.

Pre-equilibrium competitive library screening for tuning inhibitor association rate and specificity toward serine proteases

High structural and sequence similarity within protein families can pose significant challenges to the development of selective inhibitors, especially toward proteolytic enzymes. Such enzymes usually belong to large families of closely similar proteases and may also hydrolyze, with different rates, protein- or peptide-based inhibitors. To address this challenge, we employed a combinatorial yeast surface display library approach complemented with a novel pre-equilibrium, competitive screening strategy for facile assessment of the effects of multiple mutations on inhibitor association rates and binding specificity. As a proof of principle for this combined approach, we utilized this strategy to alter inhibitor/protease association rates and to tailor the selectivity of the amyloid β-protein precursor Kunitz protease inhibitor domain (APPI) for inhibition of the oncogenic protease mesotrypsin, in the presence of three competing serine proteases, anionic trypsin, cationic trypsin and kallikrein-6. We generated a variant, designated APPIP13W/M17G/I18F/F34V, with up to 30-fold greater specificity relative to the parental APPIM17G/I18F/F34V protein, and 6500- to 230 000-fold improved specificity relative to the wild-type APPI protein in the presence of the other proteases tested. A series of molecular docking simulations suggested a mechanism of interaction that supported the biochemical results. These simulations predicted that the selectivity and specificity are affected by the interaction of the mutated APPI residues with nonconserved enzyme residues located in or near the binding site. Our strategy will facilitate a better understanding of the binding landscape of multispecific proteins and will pave the way for design of new drugs and diagnostic tools targeting proteases and other proteins.