Formaldehyde has long been recognized as a hazardous environmental agent highly reactive with DNA. Recently, it has been realized that due to the activity of histone demethylation enzymes within the cell nucleus, formaldehyde is produced endogenously, in direct vicinity of genomic DNA. Should it lead to extensive DNA damage? We address this question with the aid of a computational mapping method, analogous to X-ray and nuclear magnetic resonance techniques for observing weakly specific interactions of small organic compounds with a macromolecule in order to establish important functional sites. We concentrate on the leading reaction of formaldehyde with free bases: hydroxymethylation of cytosine amino groups. Our results show that in B-DNA, cytosine amino groups are totally inaccessible for the formaldehyde attack. Then, we explore the effect of recently discovered transient flipping of Watson–Crick (WC) pairs into Hoogsteen (HG) pairs (HG breathing). Our results show that the HG base pair formation dramatically affects the accessibility for formaldehyde of cytosine amino nitrogens within WC base pairs adjacent to HG base pairs. The extensive literature on DNA interaction with formaldehyde is analyzed in light of the new findings. The obtained data emphasize the significance of DNA HG breathing.

Binding hot spots, protein sites with high-binding affinity, can be identified using X-ray crystallography or NMR by screening libraries of small organic molecules that tend to cluster at such regions. FTMAP, a direct computational analog of the experimental screening approaches, globally samples the surface of a target protein using small organic molecules as probes, finds favorable positions, clusters the conformations and ranks the clusters on the basis of the average energy. The regions that bind several probe clusters predict the binding hot spots, in good agreement with experimental results. Small molecules discovered by fragment-based approaches to drug design also bind at the hot spot regions. To identify such molecules and their most likely bound positions, we extend the functionality of FTMAP (http://ftmap.bu.edu/param) to accept any small molecule as an additional probe. In its updated form, FTMAP identifies the hot spots based on a standard set of probes, and for each additional probe shows representative structures of nearby low energy clusters. This approach helps to predict bound poses of the user-selected molecules, detects if a compound is not likely to bind in the hot spot region, and provides input for the design of larger ligands.

Our approach to protein-protein docking includes three main steps. First we run PIPER, a rigid body docking program based on the Fast Fourier Transform (FFT) correlation approach, extended to use pairwise interactions potentials. Next, the 1000 best energy conformations are clustered, and the 30 largest clusters are retained for refinement. Third, the stability of the clusters is analyzed by short Monte Carlo simulations, and the structures are refined by the medium-range optimization method SDU. The first two steps of this approach are implemented in the ClusPro 2.0 protein-protein docking server. Despite being fully automated, the last step is computationally too expensive to be included in the server. Comparing the models obtained in CAPRI rounds 13–19 by ClusPro, by the refinement of the ClusPro predictions, and by all predictor groups, we arrived at three conclusions. First, for the first time in the CAPRI history, our automated ClusPro server was able to compete with the best human predictor groups. Second, selecting the top ranked models, our current protocol reliably generates high quality structures of protein-protein complexes from the structures of separately crystallized proteins, even in the absence of biological information, provided that there is limited backbone conformational change. Third, despite occasional successes, homology modeling requires further improvement to achieve reliable docking results.

Structures of the influenza A virus M2 proton channel have been determined by X-ray crystallography in the open conformation, and by NMR in the closed state. Whereas the X-ray structure shows a single inhibitor molecule in the middle of the channel, four inhibitor molecules bind the channel’s outer surface in the NMR structure. Although in both structures the strongest hot spots (i.e., regions which substantially contribute to the free energy of binding any potential ligand) lie inside the pore, hot spots also are found at exterior locations. By considering all available models, we propose the primary drug binding site is inside the pore, but that exterior binding also occurs under appropriate conditions.

The steroid and xenobiotic-responsive human pregnane X receptor (PXR) binds a broad range of structurally diverse compounds. The structures of the apo and ligand-bound forms of PXR are very similar, in contrast to most promiscuous proteins that generally adapt their shape to different ligands. We investigated the structural origins of PXR's recognition promiscuity using computational solvent mapping, a technique developed for the identification and characterization of hot spots, i.e., regions of the protein surface that are major contributors to the binding free energy. Results reveal that the smooth and nearly spherical binding site of PXR has a well-defined hot spot structure, with four hot spots located on four different sides of the pocket and a fifth close to its center. Three of these hot spots are already present in the ligand-free protein. The most important hot spot is defined by three structurally and sequentially conserved residues, W299, F288, and Y306. This largely hydrophobic site is not very specific, and interacts with all known PXR ligands. Depending on their sizes and shapes, individual PXR ligands extend into 2, 3, or 4 more hot spot regions. The large number of potential arrangements within the binding site explains why PXR is able to accommodate a large variety of compounds. All five hot spots include at least one important residue, which is conserved in all mammalian PXRs, suggesting that the hot spot locations have remained largely invariant during mammalian evolution. The same side chains also show a high level of structural conservation across hPXR structures. However, each of the hPXR hot spots also includes residues with moveable side chains, further increasing the size variation in ligands that PXR can bind. Results also suggest a unique signal transduction mechanism between the PXR homodimerization interface and its co-activator binding site.

The interactions of beta2 glycoprotein I (B2GPI) with the receptors of the low-density lipoprotein receptor (LDLR) family are implicated in the clearance of negatively charged phospholipids and apoptotic cells and, in the presence of autoimmune anti-B2GPI antibodies, in cell activation, which might play a role in the pathology of antiphospholipid syndrome (APS). The ligand-binding domains of the lipoprotein receptors consist of multiple homologous LA modules connected by flexible linkers. In this study, we investigated at the atomic level the features of the LA modules required for binding to B2GPI. To compare the binding interface in B2GPI/LA complex to that observed in the high-resolution co-crystal structure of the receptor associated protein (RAP) with the LA modules 3 and 4 from the LDLR, we used the LA module 4 from the LDLR in our studies. Using solution NMR spectroscopy, we found that LA4 interacts with B2GPI and the binding site for B2GPI on the 15N-labeled LA4 is formed by the calcium coordinating residues of the LA module. We built a model for the complex between domain V of B2GPI (B2GPI-DV) and LA4 without introducing any experimentally derived constraints into the docking procedure. Our model, which is in the agreement with the NMR data, suggests that the binding interface of B2GPI for the lipoprotein receptors is centered at three lysine residues of B2GPI-DV, Lys 308, Lys 282 and Lys317.

Motivation: The binding sites of proteins generally contain smaller regions that provide major contributions to the binding free energy and hence are the prime targets in drug design. Screening libraries of fragment-sized compounds by NMR or X-ray crystallography demonstrates that such ‘hot spot’ regions bind a large variety of small organic molecules, and that a relatively high ‘hit rate’ is predictive of target sites that are likely to bind drug-like ligands with high affinity. Our goal is to determine the ‘hot spots’ computationally rather than experimentally.

Results: We have developed the FTMAP algorithm that performs global search of the entire protein surface for regions that bind a number of small organic probe molecules. The search is based on the extremely efficient fast Fourier transform (FFT) correlation approach which can sample billions of probe positions on dense translational and rotational grids, but can use only sums of correlation functions for scoring and hence is generally restricted to very simple energy expressions. The novelty of FTMAP is that we were able to incorporate and represent on grids a detailed energy expression, resulting in a very accurate identification of low-energy probe clusters. Overlapping clusters of different probes are defined as consensus sites (CSs). We show that the largest CS is generally located at the most important subsite of the protein binding site, and the nearby smaller CSs identify other important subsites. Mapping results are presented for elastase whose structure has been solved in aqueous solutions of eight organic solvents, and we show that FTMAP provides very similar information. The second application is to renin, a long-standing pharmaceutical target for the treatment of hypertension, and we show that the major CSs trace out the shape of the first approved renin inhibitor, aliskiren.

Availability: FTMAP is available as a server at http://ftmap.bu.edu/.

Contact: vajda@bu.edu

Supplementary information: Supplementary Material is available at Bioinformatics online.