Statistical Theory of Protein Combinatorial Libraries

Combinatorial experiments provide new ways to probe the determinants of protein folding and to identify novel folding amino acid sequences. These types of experiments, however, are complicated both by enormous conformational complexity and by large numbers of possible sequences. Therefore, a quantitative computational theory would be helpful in designing and interpreting these types of experiment. Here, we present and apply a statistically based, computational approach for identifying the properties of sequences compatible with a given main-chain structure. Protein side-chain conformations are included in an atom-based fashion. Calculations are performed for a variety of similar backbone structures to identify sequence properties that are robust with respect to minor changes in main-chain structure. Rather than specific sequences, the method yields the likelihood of each of the amino acids at preselected positions in a given protein structure. The theory may be used to quantify the characteristics of sequence space for a chosen structure without explicitly tabulating sequences. To account for hydrophobic effects, we introduce an environmental energy that it is consistent with other simple hydrophobicity scales and show that it is effective for side-chain modeling. We apply the method to calculate the identity probabilities of selected positions of the immunoglobulin light chain-binding domain of protein L, for which many variant folding sequences are available. The calculations compare favorably with the experimentally observed identity probabilities

ReadOut: structure-based calculation of direct and indirect readout energies and specificities for protein–DNA recognition

Protein–DNA interactions play a central role in regulatory processes at the genetic level. DNA-binding proteins recognize their targets by direct base–amino acid interactions and indirect conformational energy contribution from DNA deformations and elasticity. Knowledge-based approach based on the statistical analysis of protein–DNA complex structures has been successfully used to calculate interaction energies and specificities of direct and indirect readouts in protein–DNA recognition. Here, we have implemented the method as a webserver, which calculates direct and indirect readout energies and Z-scores, as a measure of specificity, using atomic coordinates of protein–DNA complexes. This server is freely available at . The only input to this webserver is the Protein Data Bank (PDB) style coordinate data of atoms or the PDB code itself. The server returns total energy Z-scores, which estimate the degree of sequence specificity of the protein–DNA complex. This webserver is expected to be useful for estimating interaction energy and DNA conformation energy, and relative contributions to the specificity from direct and indirect readout. It may also be useful for checking the quality of protein–DNA complex structures, and for engineering proteins and target DNAs

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要旨あり最適化技術に基づく統計的推論研究詳

Progress in the development and application of computational methods for probabilistic protein design

Proteins exhibit a wide range of physical and chemical properties, including highly selective molecular recognition and catalysis, and are also key components in biological metabolic, catabolic, and signaling pathways. Given that proteins are well-structured and can now be rapidly synthesized, they are excellent targets for engineering of both molecular structure and biological function. Computational analysis of the protein design problem allows scientists to explore sequence space and systematically discover novel protein molecules. Nonetheless, the complexity of proteins, the subtlety of the determinants of folding, and the exponentially large number of possible sequences impede the search for peptide sequences compatible with a desired structure and function. Directed search algorithms, which identify directly a small number of sequences, have achieved some success in identifying sequences with desired structures and functions. Alternatively, one can adopt a probabilistic approach. Instead of a finite number of sequences, such calculations result in a probabilistic description of the sequence ensemble. In particular, by casting the formalism in the language of statistical mechanics, the site-specific amino acid probabilities of sequences compatible with a target structure may be readily identified. The computational probabilities are well suited for both de novo protein design of particular sequences as well as combinatorial, library-based protein engineering. The computed site-specific amino acid profile may be converted to a nucleotide base distribution to allow assembly of a partially randomized gene library. The ability to synthesize readily such degenerate oligonucleotide sequences according to the prescribed distribution is key to constructing a biased peptide library genuinely reflective of the computational design. Herein we illustrate how a standard DNA synthesizer can be used with only a slight modification to the synthesis protocol to generate a pool of degenerate DNA sequences, which encodes a predetermined amino acid distribution with high fidelity

Phase retrieval from single biomolecule diffraction pattern

In this paper, we propose the SPR (sparse phase retrieval) method, which is a new phase retrieval method for coherent x-ray diffraction imaging (CXDI). Conventional phase retrieval methods effectively solve the problem for high signal-to-noise ratio measurements, but would not be sufficient for single biomolecular imaging which is expected to be realized with femto-second x-ray free electron laser pulses. The SPR method is based on the Bayesian statistics. It does not need to set the object boundary constraint that is required by the commonly used hybrid input-output (HIO) method, instead a prior distribution is defined with an exponential distribution and used for the estimation. Simulation results demonstrate that the proposed method reconstructs the electron density under a noisy condition even some central pixels are masked.Comment: 13 pages, 13 figures, submitted for a journa

Rational design of DNA sequence-specific zinc fingers

AbstractWe developed a rational scheme for designing DNA binding proteins. The scheme was applied for a zinc finger protein and the designed sequences were experimentally characterized with high DNA sequence specificity. Starting with the backbone of a known finger structure, we initially calculated amino acid sequences compatible with the expected structure and the secondary structures of the designed fingers were then experimentally confirmed. The DNA-binding function was added to the designed finger by reconsidering a section of the amino acid sequence and computationally selecting amino acids to have the lowest protein–DNA interaction energy for the target DNA sequences. Among the designed proteins, one had a gap between the lowest and second lowest protein–DNA interaction energies that was sufficient to give DNA sequence-specificity