A physical reference state unifies the structure-derived potential of mean force for protein folding and binding

Analysis of the quaternary structure of the MutL C-terminal domain

The dimeric DNA mismatch repair protein MutL has a key function in communicating mismatch recognition by MutS to downstream repair processes. Dimerization of MutL is mediated by the C-terminal domain, while activity of the protein is modulated by the ATP-dependent dimerization of the highly conserved N-terminal domain. Recently, a crystal structure analysis of the Escherichia coli MutL C-terminal dimerization domain has been reported and a model for the biological dimer was proposed. In this model, dimerization is mediated by the internal (In) subdomain comprising residues 475-569. Here, we report a computational analysis of all protein interfaces observed in the crystal structure and suggest that the biological dimer interface is formed by a hydrophobic surface patch of the external (Ex) subdomain (residues 432-474 and 570-615). Moreover, sequence analysis revealed that this surface patch is conserved among the MutL proteins. To test this hypothesis, single and double-cysteine variants of MutL were generated and tested for their ability to be cross-linked with chemical cross-linkers of various size. Finally, deletion of the C-terminal residues 605-615 abolished homodimerization. The biochemical data are fully compatible with a revised model for the biological dimer, which has important implications for understanding the heterodimerization of eukaryotic MutL homologues, modeling the MutL holoenzyme and predicting protein-protein interaction sites.

Pseudomonas aeruginosa LD-carboxypeptidase, a serine peptidase with a Ser-His-Glu triad and a nucleophilic elbow

LD-Carboxypeptidases (EC 3.4.17.13) are named for their ability to cleave amide bonds between l- and d-amino acids, which occur naturally in bacterial peptidoglycan. They are specific for the link between meso-diaminopimelic acid and d-alanine and therefore degrade GlcNAc-MurNAc tetrapeptides to the corresponding tripeptides. As only the tripeptides can be reused as peptidoglycan building blocks, ld-carboxypeptidases are thought to play a role in peptidoglycan recycling. Despite the pharmaceutical interest in peptidoglycan biosynthesis, the fold and catalytic type of ld-carboxypeptidases are unknown. Here, we show that a previously uncharacterized open reading frame in Pseudomonas aeruginosa has ld-carboxypeptidase activity and present the crystal structure of this enzyme. The structure shows that the enzyme consists of an N-terminal beta-sheet and a C-terminal beta-barrel domain. At the interface of the two domains, Ser(115) adopts a highly strained conformation in the context of a strand-turn-helix motif that is similar to the "nucleophilic elbow" in alphabeta-hydrolases. Ser(115) is hydrogen-bonded to a histidine residue, which is oriented by a glutamate residue. All three residues, which occur in the order Ser-Glu-His in the amino acid sequence, are strictly conserved in naturally occurring ld-carboxypeptidases and cannot be mutated to alanines without loss of activity. We conclude that ld-carboxypeptidases are serine peptidases with Ser-His-Glu catalytic triads.

Staphylococcus aureus Aminopeptidase S Is a Founding Member of a New Peptidase Clan

Staphylococcus aureus aminopeptidase S (AmpS) has been named for its predicted, but experimentally untested, aminopeptidase activity. The enzyme is homologous to biochemically characterized aminopeptidases that contain two cobalt or zinc ions in their active centers, but it is unrelated to all structurally characterized metallopeptidases. Here, we demonstrate AmpS aminopeptidase activity experimentally, and we present the 1.8-A crystal structure of the enzyme. Two metal ions with full occupancy and a third metal ion with low occupancy are present in the active site. A water molecule and Glu-319 serve as bridging ligands to the two metals with full occupancy. One of these metal ions is additionally coordinated by Glu-253 and His-348 and the other by His-381 and Asp-383. In addition, the metals are involved in weak metal-donor interactions to a water molecule and to Tyr-355. In the crystal, AmpS forms a dimer with a large internal cavity. The active sites are located at opposite ends of this internal cavity and are essentially inaccessible from the outside, suggesting that an inactive conformation was crystallized. Because gel filtration and analytical ultracentrifugation data also suggest dimer formation, the problem of substrate access to the active site cavity remains unresolved.

Web-based toolkits for topology prediction of transmembrane helical proteins, fold recognition, structure and binding scoring, folding-kinetics analysis and comparative analysis of domain combinations

We have developed the following web servers for protein structural modeling and analysis at http://theory.med.buffalo.edu: THUMBUP, UMDHMM(TMHP) and TUPS, predictors of transmembrane helical protein topology based on a mean-burial-propensity scale of amino acid residues (THUMBUP), hidden Markov model (UMDHMM(TMHP)) and their combinations (TUPS); SPARKS 2.0 and SP3, two profile-profile alignment methods, that match input query sequence(s) to structural templates by integrating sequence profile with knowledge-based structural score (SPARKS 2.0) and structure-derived profile (SP3); DFIRE, a knowledge-based potential for scoring free energy of monomers (DMONOMER), loop conformations (DLOOP), mutant stability (DMUTANT) and binding affinity of protein-protein/peptide/DNA complexes (DCOMPLEX & DDNA); TCD, a program for protein-folding rate and transition-state analysis of small globular proteins; and DOGMA, a web-server that allows comparative analysis of domain combinations between plant and other 55 organisms. These servers provide tools for prediction and/or analysis of proteins on the secondary structure, tertiary structure and interaction levels, respectively.

The dependence of all-atom statistical potentials on structural training database

An accurate statistical energy function that is suitable for the prediction of protein structures of all classes should be independent of the structural database used for energy extraction. Here, two high-resolution, low-sequence-identity structural databases of 333 alpha-proteins and 271 beta-proteins were built for examining the database dependence of three all-atom statistical energy functions. They are RAPDF (residue-specific all-atom conditional probability discriminatory function), atomic KBP (atomic knowledge-based potential), and DFIRE (statistical potential based on distance-scaled finite ideal-gas reference state). These energy functions differ in the reference states used for energy derivation. The energy functions extracted from the different structural databases are used to select native structures from multiple decoys of 64 alpha-proteins and 28 beta-proteins. The performance in native structure selections indicates that the DFIRE-based energy function is mostly independent of the structural database whereas RAPDF and KBP have a significant dependence. The construction of two additional structural databases of alpha/beta and alpha + beta-proteins further confirmed the weak dependence of DFIRE on the structural databases of various structural classes. The possible source for the difference between the three all-atom statistical energy functions is that the physical reference state of ideal gas used in the DFIRE-based energy function is least dependent on the structural database.

ProPose: a docking engine based on a fully configurable protein–ligand interaction model

Virtual high-throughput screening of molecular databases and in particular high-throughput protein-ligand docking are both common methodologies that identify and enrich hits in the early stages of the drug design process. Current protein-ligand docking algorithms often implement a program-specific model for protein-ligand interaction geometries. However, in order to create a platform for arbitrary queries in molecular databases, a new program is desirable that allows more manual control of the modeling of molecular interactions. For that reason, ProPose, an advanced incremental construction docking engine, is presented here that implements a fast and fully configurable molecular interaction and scoring model. This program uses user-defined, discrete, pharmacophore-like representations of molecular interactions that are transformed on-the-fly into a continuous potential energy surface, allowing for the incorporation of target specific interaction mechanisms into docking protocols in a straightforward manner. A torsion angle library, based on semi-empirical quantum chemistry calculations, is used to provide minimum energy torsion angles for the incremental construction algorithm. Docking results of a diverse set of protein-ligand complexes from the Protein Data Bank demonstrate the feasibility of this new approach. As a result, the seamless integration of pharmacophore-like interaction types into the docking and scoring scheme implemented in ProPose opens new opportunities for efficient, receptor-specific screening protocols. [figure: see text]. ProPose--a fully configurable protein-ligand docking program--transforms pharmacophores into a smooth potential energy surface.

An accurate, residue-level, pair potential of mean force for folding and binding based on the distance-scaled, ideal-gas reference state

Structure prediction on a genomic scale requires a simplified energy function that can efficiently sample the conformational space of polypeptide chains. A good energy function at minimum should discriminate native structures against decoys. Here, we show that a recently developed, residue-specific, all-atom knowledge-based potential (167 atomic types) based on distance-scaled, finite ideal-gas reference state (DFIRE-all-atom) can be substantially simplified to 20 residue types located at side-chain center of mass (DFIRE-SCM) without a significant change in its capability of structure discrimination. Using 96 standard multiple decoy sets, we show that there is only a small reduction (from 80% to 78%) in success rate of ranking native structures as the top 1. The success rate is higher than two previously developed, all-atom distance-dependent statistical pair potentials. Applied to structure selections of 21 docking decoys without modification, the DFIRE-SCM potential is 29% more successful in recognizing native complex structures than an all-atom statistical potential trained by a database of dimeric interfaces. The potential also achieves 92% accuracy in distinguishing true dimeric interfaces from artificial crystal interfaces. In addition, the DFIRE potential with the C(alpha) positions as the interaction centers recognizes 123 native structures out of a comprehensive 125-protein TOUCHSTONE decoy set in which each protein has 24,000 decoys with only C(alpha) positions. Furthermore, the performance by DFIRE-SCM on newly established 25 monomeric and 31 docking Rosetta-decoy sets is comparable to (or better than in the case of monomeric decoy sets) that of a recently developed, all-atom Rosetta energy function enhanced with an orientation-dependent hydrogen bonding potential.

Accurate and efficient loop selections by the DFIRE-based all-atom statistical potential

The conformations of loops are determined by the water-mediated interactions between amino acid residues. Energy functions that describe the interactions can be derived either from physical principles (physical-based energy function) or statistical analysis of known protein structures (knowledge-based statistical potentials). It is commonly believed that statistical potentials are appropriate for coarse-grained representation of proteins but are not as accurate as physical-based potentials when atomic resolution is required. Several recent applications of physical-based energy functions to loop selections appear to support this view. In this article, we apply a recently developed DFIRE-based statistical potential to three different loop decoy sets (RAPPER, Jacobson, and Forrest-Woolf sets). Together with a rotamer library for side-chain optimization, the performance of DFIRE-based potential in the RAPPER decoy set (385 loop targets) is comparable to that of AMBER/GBSA for short loops (two to eight residues). The DFIRE is more accurate for longer loops (9 to 12 residues). Similar trend is observed when comparing DFIRE with another physical-based OPLS/SGB-NP energy function in the large Jacobson decoy set (788 loop targets). In the Forrest-Woolf decoy set for the loops of membrane proteins, the DFIRE potential performs substantially better than the combination of the CHARMM force field with several solvation models. The results suggest that a single-term DFIRE-statistical energy function can provide an accurate loop prediction at a fraction of computing cost required for more complicate physical-based energy functions. A Web server for academic users is established for loop selection at the softwares/services section of the Web site http://theory.med.buffalo.edu/.

Quantifying the effect of burial of amino acid residues on protein stability

The average contribution of individual residue to folding stability and its dependence on buried accessible surface area (ASA) are obtained by two different approaches. One is based on experimental mutation data, and the other uses a new knowledge-based atom-atom potential of mean force. We show that the contribution of a residue has a significant correlation with buried ASA and the regression slopes of 20 amino acid residues (called the buriability) are all positive (pro-burial). The buriability parameter provides a quantitative measure of the driving force for the burial of a residue. The large buriability gap observed between hydrophobic and hydrophilic residues is responsible for the burial of hydrophobic residues in soluble proteins. Possible factors that contribute to the buriability gap are discussed.