Showing papers by "Michael Levitt published in 2009"

Nature of the protein universe.

Abstract: The protein universe is the set of all proteins of all organisms Here, all currently known sequences are analyzed in terms of families that have single-domain or multidomain architectures and whether they have a known three-dimensional structure Growth of new single-domain families is very slow: Almost all growth comes from new multidomain architectures that are combinations of domains characterized by ≈15,000 sequence profiles Single-domain families are mostly shared by the major groups of organisms, whereas multidomain architectures are specific and account for species diversity There are known structures for a quarter of the single-domain families, and >70% of all sequences can be partially modeled thanks to their membership in these families

Structural basis of transcription: backtracked RNA polymerase II at 3.4 angstrom resolution.

Abstract: Transcribing RNA polymerases oscillate between three stable states, two of which, pre- and posttranslocated, were previously subjected to x-ray crystal structure determination. We report here the crystal structure of RNA polymerase II in the third state, the reverse translocated, or "backtracked" state. The defining feature of the backtracked structure is a binding site for the first backtracked nucleotide. This binding site is occupied in case of nucleotide misincorporation in the RNA or damage to the DNA, and is termed the "P" site because it supports proofreading. The predominant mechanism of proofreading is the excision of a dinucleotide in the presence of the elongation factor SII (TFIIS). Structure determination of a cocrystal with TFIIS reveals a rearrangement whereby cleavage of the RNA may take place.

The normal modes of a protein: Native bovine pancreatic trypsin inhibitor

Abstract: A completely general treatment of normal mode analysis is developed that can be used with any potential energy function and any set of generalized coordinates. The method is applied to the calculation of the normal modes of the small protein bovine pancreatic trypsin inhibitor that has been the subject of many previous theoretical studies. The potential energy function used comprises a torsion angle potential, a van der Waals potential between nonbonded pairs of atoms, and a hydrogen bond potential. Therefore, the generalized coordinates used are the 208 Φ, ϕ, and χ torsion angles about single bonds. This eliminates the difficulties inherent in using internal or Cartesian coordinates for a large molecule. Many dynamic properties of the protein may now be calculated in the normal mode description. In particular, the rms magnitudes and pair correlations of the fluctuations in positions and velocities of the α-carbon atoms and various classes of torsion angles, such as backbone, side chain, β-sheet, and α-helix, are calculated and analyzed to identify the most correlated modes. In addition, the ir intensities are calculated.

Generalized ensemble methods for de novo structure prediction

Abstract: Current methods for predicting protein structure depend on two interrelated components: (i) an energy function that should have a low value near the correct structure and (ii) a method for searching through different conformations of the polypeptide chain. Identification of the most efficient search methods is essential if we are to be able to apply such methods broadly and with confidence. In addition, efficient search methods provide a rigorous test of existing energy functions, which are generally knowledge-based and contain different terms added together with arbitrary weights. Here, we test different search methods with one of the most accurate and predictive energy functions, namely Rosetta the knowledge-based force-field from Baker's group [Simons K, Kooperberg C, Huang E, Baker D (1997) J Mol Biol 268:209-225]. We use an implementation of a generalized ensemble search method to scale relevant parts of the energy function. This method, known as Hamiltonian Replica Exchange Monte Carlo, outperforms the original Monte Carlo Simulated Annealing used in the Rosetta package in terms of sampling low-energy states. It also outperforms another widely used generalized ensemble search method known as Temperature Replica Exchange Monte Carlo. Our results reveal clear deficiencies in the low-resolution Rosetta energy function in that the lowest energy structures are not necessarily the most native-like. By using a set of nonnative low-energy structures found by our extensive sampling, we discovered that the long-range and short-range backbone hydrogen-bonding energy terms of the Rosetta energy discriminate between the nonnative and native-like structures significantly better than the low-resolution score used in Rosetta.

Protein segment finder: an online search engine for segment motifs in the PDB

Abstract: Finding related conformations in the Protein Data Bank (PDB) is essential in many areas of bioscience. To assist this task, we designed a search engine that uses a compact database to quickly identify protein segments obeying a set of primary, secondary and tertiary structure constraints. The database contains information such as amino acid sequence, secondary structure, disulfide bonds, hydrogen bonds and atoms in contact as calculated from all protein structures in the PDB. The search engine parses the database and returns hits that match the queried parameters. The conformation search engine, which is notable for its high speed and interactive feedback, is expected to assist scientists in discovering conformation homologs and predicting protein structure. The engine is publicly available at http://ari.stanford.edu/psf and it will also be used in-house in an automatic mode aimed at discovering new protein motifs.

A geometric knowledge-based coarse-grained scoring potential for structure prediction evaluation

Abstract: Knowledge-based protein folding potentials have proven successful in the recent years. Based on statistics of observed interatomic distances, they generally encode pairwise contact information. In this study we present a method that derives multi-body contact potentials from measurements of surface areas using coarse-grained protein models. The measurements are made using a newly implemented geometric construction: the arrangement of circles on a sphere. This construction allows the definition of residue covering areas which are used as parameters to build functions able to distinguish native structures from decoys. These functions, encoding up to 5-body contacts are evaluated on a reference set of 66 structures and its 45000 decoys, and also on the often used lattice ssfit set from the decoys'R us database. We show that the most relevant information for discrimination resides in 2- and 3-body contacts. The potentials we have obtained can be used for evaluation of putative structural models; they could also lead to different types of structure refinement techniques that use multi-body interactions.