Alexandre V. Morozov

An orientation-dependent hydrogen bonding potential improves prediction of specificity and structure for proteins and protein-protein complexes

Hydrogen bonding is a key contributor to the specificity of intramolecular and intermolecular interactions in biological systems. Here, we develop an orientation-dependent hydrogen bonding potential based on the geometric characteristics of hydrogen bonds in high-resolution protein crystal structures, and evaluate it using four tests related to the prediction and design of protein structures and protein-protein complexes. The new potential is superior to the widely used Coulomb model of hydrogen bonding in prediction of the sequences of proteins and protein-protein interfaces from their structures, and improves discrimination of correctly docked protein-protein complexes from large sets of alternative structures.

Protein–DNA binding specificity predictions with structural models

Protein–DNA interactions play a central role in transcriptional regulation and other biological processes. Investigating the mechanism of binding affinity and specificity in protein–DNA complexes is thus an important goal. Here we develop a simple physical energy function, which uses electrostatics, solvation, hydrogen bonds and atom-packing terms to model direct readout and sequence-specific DNA conformational energy to model indirect readout of DNA sequence by the bound protein. The predictive capability of the model is tested against another model based only on the knowledge of the consensus sequence and the number of contacts between amino acids and DNA bases. Both models are used to carry out predictions of protein–DNA binding affinities which are then compared with experimental measurements. The nearly additive nature of protein–DNA interaction energies in our model allows us to construct position-specific weight matrices by computing base pair probabilities independently for each position in the binding site. Our approach is less data intensive than knowledge-based models of protein–DNA interactions, and is not limited to any specific family of transcription factors. However, native structures of protein–DNA complexes or their close homologs are required as input to the model. Use of homology modeling can significantly increase the extent of our approach, making it a useful tool for studying regulatory pathways in many organisms and cell types.

Gene regulation by nucleosome positioning

To achieve high compaction, most genomic DNA in eukaryotes is incorporated into nucleosomes; however, regulatory factors and transcriptional machinery must gain access to chromatin to extract genetic information. This conflict is partially resolved by a particular arrangement of nucleosome locations on the genome. Across all eukaryotic species, promoters and other regulatory sequences are more nucleosome-depleted, whereas transcribed regions tend to be occupied with well-positioned, high-density nucleosomal arrays. This nucleosome positioning pattern, as well as its dynamic regulation, facilitates the access of transcription factors to their target sites and plays a crucial role in determining the transcription level, cell-to-cell variation and activation or repression dynamics.

An improved protein decoy set for testing energy functions for protein structure prediction.

We have improved the original Rosetta centroid/backbone decoy set by increasing the number of proteins and frequency of near native models and by building on sidechains and minimizing clashes. The new set consists of 1,400 model structures for 78 different and diverse protein targets and provides a challenging set for the testing and evaluation of scoring functions. We evaluated the extent to which a variety of all‐atom energy functions could identify the native and close‐to‐native structures in the new decoy sets. Of various implicit solvent models, we found that a solvent‐accessible surface area–based solvation provided the best enrichment and discrimination of close‐to‐native decoys. The combination of this solvation treatment with Lennard Jones terms and the original Rosetta energy provided better enrichment and discrimination than any of the individual terms. The results also highlight the differences in accuracy of NMR and X‐ray crystal structures: a large energy gap was observed between native and non‐native conformations for X‐ray structures but not for NMR structures. Proteins 2003.

Using DNA mechanics to predict in vitro nucleosome positions and formation energies

In eukaryotic genomes, nucleosomes function to compact DNA and to regulate access to it both by simple physical occlusion and by providing the substrate for numerous covalent epigenetic tags. While competition with other DNA-binding factors and action of chromatin remodeling enzymes significantly affect nucleosome formation in vivo, nucleosome positions in vitro are determined by steric exclusion and sequence alone. We have developed a biophysical model, DNABEND, for the sequence dependence of DNA bending energies, and validated it against a collection of in vitro free energies of nucleosome formation and a set of in vitro nucleosome positions mapped at high resolution. We have also made a first ab initio prediction of nucleosomal DNA geometries, and checked its accuracy against the nucleosome crystal structure. We have used DNABEND to design both strong and weak histone- binding sequences, and measured the corresponding free energies of nucleosome formation. We find that DNABEND can successfully predict in vitro nucleosome positions and free energies, providing a physical explanation for the intrinsic sequence dependence of histone–DNA interactions.

Protein-protein docking predictions for the CAPRI experiment.

We predicted structures for all seven targets in the CAPRI experiment using a new method in development at the time of the challenge. The technique includes a low‐resolution rigid body Monte Carlo search followed by high‐resolution refinement with side‐chain conformational changes and rigid body minimization. Decoys (∼106 per target) were discriminated using a scoring function including van der Waals and solvation interactions, hydrogen bonding, residue–residue pair statistics, and rotamer probabilities. Decoys were ranked, clustered, manually inspected, and selected. The top ranked model for target 6 predicted the experimental structure to 1.5 Å RMSD and included 48 of 65 correct residue–residue contacts. Target 7 was predicted at 5.3 Å RMSD with 22 of 37 correct residue–residue contacts using a homology model from a known complex structure. Using a preliminary version of the protocol in round 1, target 1 was predicted within 8.8 Å although few contacts were correct. For targets 2 and 3, the interface locations and a small fraction of the contacts were correctly identified. Proteins 2003;52:118–122.

Simple physical models connect theory and experiment in protein folding kinetics.

Our understanding of the principles underlying the protein-folding problem can be tested by developing and characterizing simple models that make predictions which can be compared to experimental data. Here we extend our earlier model of folding free energy landscapes, in which each residue is considered to be either folded as in the native state or completely disordered, by investigating the role of additional factors representing hydrogen bonding and backbone torsion strain, and by using a hybrid between the master equation approach and the simple transition state theory to evaluate kinetics near the free energy barrier in greater detail. Model calculations of folding phi-values are compared to experimental data for 19 proteins, and for more than half of these, experimental data are reproduced with correlation coefficients between r=0.41 and 0.88; calculations of transition state free energy barriers correlate with rates measured for 37 single domain proteins (r=0.69). The model provides insight into the contribution of alternative-folding pathways, the validity of quasi-equilibrium treatments of the folding landscape, and the magnitude of the Arrhenius prefactor for protein folding. Finally, we discuss the limitations of simple native-state-based models, and as a more general test of such models, provide predictions of folding rates and mechanisms for a comprehensive set of over 400 small protein domains of known structure.

Connecting protein structure with predictions of regulatory sites

A common task posed by microarray experiments is to infer the binding site preferences for a known transcription factor from a collection of genes that it regulates and to ascertain whether the factor acts alone or in a complex. The converse problem can also be posed: Given a collection of binding sites, can the regulatory factor or complex of factors be inferred? Both tasks are substantially facilitated by using relatively simple homology models for protein–DNA interactions, as well as the rapidly expanding protein structure database. For budding yeast, we are able to construct reliable structural models for 67 transcription factors and with them redetermine factor binding sites by using a Bayesian Gibbs sampling algorithm and an extensive protein localization data set. For 49 factors in common with a prior analysis of this data set (based largely on phylogenetic conservation), we find that half of the previously predicted binding motifs are in need of some revision. We also solve the inverse problem of ascertaining the factors from the binding sites by assigning a correct protein fold to 25 of the 49 cases from a previous study. Our approach is easily extended to other organisms, including higher eukaryotes. Our study highlights the utility of enlarging current structural genomics projects that exhaustively sample fold structure space to include all factors with significantly different DNA-binding specificities.

Chromatin-dependent Transcription Factor Accessibility Rather than Nucleosome Remodeling Predominates during Global Transcriptional Restructuring in Saccharomyces cerevisiae

Several well-studied promoters in yeast lose nucleosomes upon transcriptional activation and gain them upon repression, an observation that has prompted the model that transcriptional activation and repression requires nucleosome remodeling of regulated promoters. We have examined global nucleosome positioning before and after glucose-induced transcriptional reprogramming, a condition under which more than half of all yeast genes significantly change expression. The majority of induced and repressed genes exhibit no change in promoter nucleosome arrangement, although promoters that do undergo nucleosome remodeling tend to contain a TATA box. Rather, we found multiple examples where the pre-existing accessibility of putative transcription factor binding sites before glucose addition determined whether the corresponding gene would change expression in response to glucose addition. These results suggest that selection of appropriate transcription factor binding sites may be dictated to a large extent by nucleosome prepositioning but that regulation of expression through these sites is dictated not by nucleosome repositioning but by changes in transcription factor activity.

High-throughput sequencing reveals a simple model of nucleosome energetics

We use genome-wide nucleosome maps to study sequence specificity of intrinsic histone-DNA interactions. In contrast with previous approaches, we employ an analogy between a classical one-dimensional fluid of finite-size particles in an arbitrary external potential and arrays of DNA-bound histone octamers. We derive an analytical solution to infer free energies of nucleosome formation directly from nucleosome occupancies measured in high-throughput experiments. The sequence-specific part of free energies is then captured by fitting them to a sum of energies assigned to individual nucleotide motifs. We have developed hierarchical models of increasing complexity and spatial resolution, establishing that nucleosome occupancies can be explained by systematic differences in mono- and dinucleotide content between nucleosomal and linker DNA sequences, with periodic dinucleotide distributions and longer sequence motifs playing a minor role. Furthermore, similar sequence signatures are exhibited by control experiments in which nucleosome-free genomic DNA is either sonicated or digested with micrococcal nuclease, making it possible that current predictions based on high-throughput nucleosome-positioning maps are biased by experimental artifacts.