Bacterial resistance to beta-lactam antibiotics: crystal structure of beta-lactamase from Staphylococcus aureus PC1 at 2.5 A resolution.

beta-lactamases are enzymes that protect bacteria from the lethal effects of beta-lactam antibiotics, and are therefore of considerable clinical importance. The crystal structure of beta-lactamase from the Gram-positive bacterium Staphylococcus aureus PC1 has been determined at 2.5 angstrom resolution. It reveals a molecule of novel topology, made up of two closely associated domains. The active site is located at the interface between the domains, with the key catalytic residue Ser70 at the amino terminus of a buried helix. Examination of the disposition of the functionally important residues within the active site depression leads to a model for the binding of a substrate and a functional analogy to the serine proteases. The unusual topology of the secondary structure units is relevant to questions concerning the evolutionary relation to the beta-lactam target enzymes of the bacterial cell wall.

Comparison of database potentials and molecular mechanics force fields.

The advantages and disadvantages of database and molecular mechanics force fields for the study of macromolecules are compared, with emphasis on the ability to distinguish between correct and incorrect structures. Molecular mechanics force fields have the advantage of resting on a clear theoretical basis, permitting an in-depth analysis of different contributions. On the other hand, large simplifications are necessary for tractable computing, and there has so far been little effective testing at the macromolecular level. Database potentials allow greater freedom of functional form and have been shown to be effective at discriminating between correct and incorrect complete structures. The principal negative is a controversial relationship to free energy. More testing and comparison of both sorts of potential are needed.

From fold to function.

A number of recent advances have been made in deriving function information from protein structure. A fold relationship to an already characterized protein will often allow general information about function to be deduced. More detailed information can be obtained using sequence relationships to already studied proteins. Methods of deducing function directly from structure, without the use of evolutionary relationships, are developing rapidly. All such methods may be used with models of protein structure, rather than with experimentally determined ones, but model accuracy imposes limitations. The rapid expansion of the structural genomics field has created a new urgency for improved methods of structure-based annotation of function.

Genetic algorithms for protein structure prediction.

Genetic algorithms are a general class of search methods that mimic natural gene-based optimization mechanisms. Mutation, cross-over and replication operations are performed on strings. When applied to structure prediction, each string describes a particular conformation of a protein molecule. There are many ways in which such search methods may be implemented. Recent results show potential for helping with protein structure prediction, but more data are needed before a complete assessment can be made.

Local interactions dominate folding in a simple protein model.

Recent computational studies of simple models of protein folding have concluded that a pronounced energy minimum (i.e. large gap in energy between low-energy states of the model) is a necessary and sufficient condition to ensure folding of a sequence to its lowest-energy conformation. Here, we show that this conclusion strongly depends on the particular temperature scheme selected to govern the simulations. On the other hand, we show that there is a dominant factor determining if a sequence is foldable. That is, the strength of possible interactions between residues close in the sequence. We show that sequences with many possible strong local interactions (either favorable or, more surprisingly, a mixture of strong favorable and unfavorable ones) are easy to fold. Progressively increasing the strength of such local interactions makes sequences easier and easier to fold. These results support the idea that initial formation of local substructures is important to the foldability of real proteins.

Genetic algorithms for protein folding simulations.

Genetic algorithms methods utilize the same optimization procedures as natural genetic evolution, in which a population is gradually improved by selection. We have developed a genetic algorithm search procedure suitable for use in protein folding simulations. A population of conformations of the polypeptide chain is maintained, and conformations are changed by mutation, in the form of conventional Monte Carlo steps, and crossovers in which parts of the polypeptide chain are interchanged between conformations. For folding on a simple two-dimensional lattice it is found that the genetic algorithm is dramatically superior to conventional Monte Carlo methods.

An all-atom distance-dependent conditional probability discriminatory function for protein structure prediction.

We present a formalism to compute the probability of an amino acid sequence conformation being native-like, given a set of pairwise atom-atom distances. The formalism is used to derive three discriminatory functions with different types of representations for the atom-atom contacts observed in a database of protein structures. These functions include two virtual atom representations and one all-heavy atom representation. When applied to six different decoy sets containing a range of correct and incorrect conformations of amino acid sequences, the all-atom distance-dependent discriminatory function is able to identify correct from incorrect more often than the discriminatory functions using approximate representations. We illustrate the importance of using a detailed atomic description for obtaining the most accurate discrimination, and the necessity for testing discriminatory functions against a wide variety of decoys. The discriminatory function is also shown to be capable of capturing the fine details of atom-atom preferences. These results suggest that the all-atom distance-dependent discriminatory function will be useful for protein structure prediction and model refinement.

A graph-theoretic algorithm for comparative modeling of protein structure.

The interconnected nature of interactions in protein structures appears to be the major hurdle in preventing the construction of accurate comparative models. We present an algorithm that uses graph theory to handle this problem. Each possible conformation of a residue in an amino acid sequence is represented using the notion of a node in a graph. Each node is given a weight based on the degree of the interaction between its side-chain atoms and the local main-chain atoms. Edges are then drawn between pairs of residue conformations/nodes that are consistent with each other (i.e. clash-free and satisfying geometrical constraints). The edges are weighted based on the interactions between the atoms of the two nodes. Once the entire graph is constructed, all the maximal sets of completely connected nodes (cliques) are found using a clique-finding algorithm. The cliques with the best weights represent the optimal combinations of the various main-chain and side-chain possibilities, taking the respective environments into account. The algorithm is used in a comparative modeling scenario to build side-chains, regions of main chain, and mix and match between different homologs in a context-sensitive manner. The predictive power of this method is assessed by applying it to cases where the experimental structure is not known in advance.

Structure, dynamics and energetics of initiation sites in protein folding: I. Analysis of a 1 ns molecular dynamics trajectory of an early folding unit in water: the helix I/loop I-fragment of barnase.

The dynamic and energetic behavior of an initiation site of protein folding (helix I/loop I fragment of barnase) isolated from the tertiary environment of the rest protein is investigated in a 1 ns molecular dynamics simulation. All atom representation, explicit solvent description, and periodic boundary conditions are applied. In the course of the simulation several steps of structural disintegration are observed, followed by events partially rebuilding the initial structure. The phase of disintegration results in a fragment conformation completely lacking hydrogen bonds, with one residue in the center of the helix changed from alpha to beta conformation. The transition state of helix disintegration is characterized by a complete i-->i + 4/i + 5 hydrogen bonding network which undergoes gradual hydrolysis starting at the solvent exposed flank and proceeding towards the interior of the fragment perpendicular to the axis of the helix. Energetic analysis of the helix transitions shows that the i-->i + 4/i-->i + 5 network of hydrogen bonds accommodates one helical residue in beta conformation with only slightly worse hydrogen bonding energy and Van der Waals packing compared to the regular alpha-helix. The stability of the fragment is primarily due to hydrophobic interactions of residues shown to be essential in mutagenesis experiments.

Docking by least-squares fitting of molecular surface patterns.

Molecular surfaces are fitted to each other by a new solution to the problem of docking a ligand into the active site of a protein molecule. The procedure constructs patterns of points on the surfaces and superimposes them upon each other using a least-squares best-fit algorithm. This brings the surfaces into contact and provides a direct measure of their local complementarity. The search over the ligand surface produces a large number of dockings, of which a small fraction having the best complementarity and the least steric hindrance are evaluated for electrostatic interaction energy. When applied to molecules taken from crystallographically observed complexes, this procedure consistently assigns the lowest electrostatic energies to correct dockings. On independently determined structures, the ability of the method to discern correct dockings depends on how much conformational difference there is between the free and complexed forms of the molecules. The procedure is found to be fast enough on contemporary workstation computers to permit many conformations to be considered, and tolerant enough to make rather coarse bond dihedral sampling a practicable way to overcome the problem of structural flexibility.

Protein folding simulations with genetic algorithms and a detailed molecular description.

We have explored the application of genetic algorithms (GA) to the determination of protein structure from sequence, using a full atom representation. A free energy function with point charge electrostatics and an area based solvation model is used. The method is found to be superior to previously investigated Monte Carlo algorithms. For selected fragments, up to 14 residues long, the lowest free energy structures produced by the GA are similar in conformation to the corresponding experimental structures in most cases. There are three main conclusions from these results. First, the genetic algorithm is an effective method for searching amongst the compact conformations of a polypeptide chain. Second, the free energy function is generally able to select native-like conformations. However, some deficiencies are identified, and further development is proposed. Third, the selection of native-like conformations for some protein fragments establishes that in these cases the conformation observed in the full protein structure is largely context independent. The implications for the nature of protein folding pathways are discussed.

Electron density calculations as an extension of protein structure refinement. Streptomyces griseus protease A at 1.5 A resolution.

Ab initio quantum mechanical calculations have been used to obtain details of the electron density distribution in a high-resolution refined protein structure. It is shown that with accurate atomic co-ordinates, electron density may be calculated with a quality similar to that which can be obtained directly from crystallographic studies of small organic molecules, and that this density contains information relevant to the understanding of catalysis. Atomic co-ordinates from the 1.8 A and 1.5 A resolution refinements of the crystal structure of protease A from Streptomyces griseus have been used to examine the influence of the environment on the electron density in the side-chain of the active site histidine (His57). The neighbouring aspartic acid 102 is the dominant factor in the environment, and quantum mechanical calculations have been performed on these two residues. Most interesting from the point of view of understanding the catalytic process is the effect that Asp102 has on the electron density in the region of the imidazole nitrogen (N epsilon 2) adjacent to the active site serine 195. In the positively charged imidazolium species, there is a polarization of the N epsilon 2-H bond, reducing the bonding density in a manner that may lower the height of the energy barrier for proton transfer. In the uncharged imidazole species, the proximity of Asp102 causes a movement of density from the lone pair region of the N epsilon 2 into the pi bonding region above and below the plane of the ring. Although it is shown that the primary effect of the aspartic acid is electrostatic, this movement is perpendicular to the direction of the electric field inducing it.

The current state of the art in protein structure prediction.

The capabilities of current protein structure prediction methods have been assessed from the outcome of a set of blind tests. In comparative modeling, many of the numerical methods did not perform as well as expected, although the resulting structures are still of great practical use. The new methods of fold identification ('threading') were partially successful, and show considerable promise for the future. Except for secondary structure data, results from traditional ab initio methods were poor. A second blind prediction experiment is underway, and progress in all areas is expected.

Predicting protein three-dimensional structure.

The current state of the art in modeling protein structure has been assessed, based on the results of the CASP (Critical Assessment of protein Structure Prediction) experiments. In comparative modeling, improvements have been made in sequence alignment, sidechain orientation and loop building. Refinement of the models remains a serious challenge. Improved sequence profile methods have had a large impact in fold recognition. Although there has been some progress in alignment quality, this factor still limits model usefulness. In ab initio structure prediction, there has been notable progress in building approximately correct structures of 40-60 residue-long protein fragments. There is still a long way to go before the general ab initio prediction problem is solved. Overall, the field is maturing into a practical technology, able to deliver useful models for a large number of sequences.

Biological function made crystal clear - annotation of hypothetical proteins via structural genomics.

Many of the gene products of completely sequenced organisms are 'hypothetical' - they cannot be related to any previously characterized proteins - and so are of completely unknown function. Structural studies provide one means of obtaining functional information in these cases. A 'structural genomics' project has been initiated aimed at determining the structures of 50 hypothetical proteins from Haemophilus influenzae to gain an understanding of their function. Each stage of the project - target selection, protein production, crystallization, structure determination, and structure analysis - makes use of recent advances to streamline procedures. Early results from this and similar projects are encouraging in that some level of functional understanding can be deduced from experimentally solved structures.

SNPs, protein structure, and disease.

Inherited disease susceptibility in humans is most commonly associated with single nucleotide polymorphisms (SNPs). The mechanisms by which this occurs are still poorly understood. We have analyzed the effect of a set of disease-causing missense mutations arising from SNPs, and a set of newly determined SNPs from the general population. Results of in vitro mutagenesis studies, together with the protein structural context of each mutation, are used to develop a model for assigning a mechanism of action of each mutation at the protein level. Ninety percent of the known disease-causing missense mutations examined fit this model, with the vast majority affecting protein stability, through a variety of energy related factors. In sharp contrast, over 70% of the population set are found to be neutral. The remaining 30% are potentially involved in polygenic disease.

Studies on the variability of glutathione S-transferase from human erythrocytes.

Glutathione S-transferase activity has been measured in erythrocytes from 101 subjects. Specific activity in the female subjects was significantly greater than that in the males, but there was no relationship in either sex between enzyme activity and age or between enzyme activity and the most common erythrocyte antigens. Separation of erythrocytes by age using isopycnic centrifugation showed that enzyme activity was constant during the life of the cell.

Fitting electron density by systematic search.

A systematic search approach to the automatic refinement of protein structures could reduce the need for manual intervention. In this approach, possible conformations for a segment of the polypeptide chain are generated systematically and the trial segments are scored for their agreement with the observed diffraction data. The sampling of conformational space is sufficiently exhaustive that reasonable conformations should be included. A number of score functions have been tested, including local electron-density correlations and global structure-factor agreements. The score functions vary in their predictive power as well as in their bias toward the conformation found in the current refined model, but the best score functions have reasonable predictive power. Related functions can be used to indicate which regions of the model fit poorly, reducing the need for manual inspection of models in electron density.