GeneSplicer: a new computational method for splice site prediction

GeneSplicer is a new, flexible system for detecting splice sites in the genomic DNA of various eukaryotes. The system has been tested successfully using DNA from two reference organisms: the model plant Arabidopsis thaliana and human. It was compared to six programs representing the leading splice site detectors for each of these species: NetPlantGene, NetGene2, HSPL, NNSplice, GENIO and SpliceView. In each case GeneSplicer performed comparably to the best alternative, in terms of both accuracy and computational efficiency.

Computational method to reduce the search space for directed protein evolution

We introduce a computational method to optimize the in vitro evolution of proteins. Simulating evolution with a simple model that statistically describes the fitness landscape, we find that beneficial mutations tend to occur at amino acid positions that are tolerant to substitutions, in the limit of small libraries and low mutation rates. We transform this observation into a design strategy by applying mean-field theory to a structure-based computational model to calculate each residue's structural tolerance. Thermostabilizing and activity-increasing mutations accumulated during the experimental directed evolution of subtilisin E and T4 lysozyme are strongly directed to sites identified by using this computational approach. This method can be used to predict positions where mutations are likely to lead to improvement of specific protein properties.

Prediction of operons in microbial genomes

Operon structure is an important organization feature of bacterial genomes. Many sets of genes occur in the same order on multiple genomes; these conserved gene groupings represent candidate operons. This study describes a computational method to estimate the likelihood that such conserved gene sets form operons. The method was used to analyze 34 bacterial and archaeal genomes, and yielded more than 7600 pairs of genes that are highly likely (P ≥ 0.98) to belong to the same operon. The sensitivity of our method is 30–50% for the Escherichia coli genome. The predicted gene pairs are available from our World Wide Web site http://www.tigr.org/tigr-scripts/operons/operons.cgi.

Unraveling principles of lead discovery: from unfrustrated energy landscapes to novel molecular anchors.

The search for novel leads is a critical step in the drug discovery process. Computational approaches to identify new lead molecules have focused on discovering complete ligands by evaluating the binding affinity of a large number of candidates, a task of considerable complexity. A new computational method is introduced in this work based on the premise that the primary molecular recognition event in the protein binding site may be accomplished by small core fragments that serve as molecular anchors, providing a structurally stable platform that can be subsequently tailored into complete ligands. To fulfill its role, we show that an effective molecular anchor must meet both the thermodynamic requirement of relative energetic stability of a single binding mode and its consistent kinetic accessibility, which may be measured by the structural consensus of multiple docking simulations. From a large number of candidates, this technique is able to identify known core fragments responsible for primary recognition by the FK506 binding protein (FKBP-12), along with a diverse repertoire of novel molecular cores. By contrast, absolute energetic criteria for selecting molecular anchors are found to be promiscuous. A relationship between a minimum frustration principle of binding energy landscapes and receptor-specific molecular anchors in their role as "recognition nuclei" is established, thereby unraveling a mechanism of lead discovery and providing a practical route to receptor-biased computational combinatorial chemistry.

Computer determination of peptide conformations in water: different roads to structure.

Fragments of proteins (short peptides) that "fold" suggest a mechanism of how complete conformational search in protein folding is avoided. We used a computational method to determine structures of two foldable peptides in explicit water: RVEW and CSVTC. The optimization starts from random structures and no experimental constraints are used. In agreement with NMR data, the simulations find a hydrophobic pair (Val/Trp) in REVW. The structure of CSVTC is induced by a surface water that bridges two amide hydrogens, a drive to structure hypothesized by Ben-Naim [Ben-Naim, A. (1990) J. Chem. Phys. 93, 8196-8210] that is largely ignored in studies of folding. Tendency to structure in short peptide chains suggests a mechanism for the formation of short-range nucleation sites in protein folding.

Patterned library analysis: A method for the quantitative assessment of hypotheses concerning the determinants of protein structure

Site-directed mutagenesis and combinatorial libraries are powerful tools for providing information about the relationship between protein sequence and structure. Here we report two extensions that expand the utility of combinatorial mutagenesis for the quantitative assessment of hypotheses about the determinants of protein structure. First, we show that resin-splitting technology, which allows the construction of arbitrarily complex libraries of degenerate oligonucleotides, can be used to construct more complex protein libraries for hypothesis testing than can be constructed from oligonucleotides limited to degenerate codons. Second, using eglin c as a model protein, we show that regression analysis of activity scores from library data can be used to assess the relative contributions to the specific activity of the amino acids that were varied in the library. The regression parameters derived from the analysis of a 455-member sample from a library wherein four solvent-exposed sites in an α-helix can contain any of nine different amino acids are highly correlated (P < 0.0001, R2 = 0.97) to the relative helix propensities for those amino acids, as estimated by a variety of biophysical and computational techniques.

Estimating the probability for a protein to have a new fold: A statistical computational model

Structural genomics aims to solve a large number of protein structures that represent the protein space. Currently an exhaustive solution for all structures seems prohibitively expensive, so the challenge is to define a relatively small set of proteins with new, currently unknown folds. This paper presents a method that assigns each protein with a probability of having an unsolved fold. The method makes extensive use of protomap, a sequence-based classification, and scop, a structure-based classification. According to protomap, the protein space encodes the relationship among proteins as a graph whose vertices correspond to 13,354 clusters of proteins. A representative fold for a cluster with at least one solved protein is determined after superposition of all scop (release 1.37) folds onto protomap clusters. Distances within the protomap graph are computed from each representative fold to the neighboring folds. The distribution of these distances is used to create a statistical model for distances among those folds that are already known and those that have yet to be discovered. The distribution of distances for solved/unsolved proteins is significantly different. This difference makes it possible to use Bayes' rule to derive a statistical estimate that any protein has a yet undetermined fold. Proteins that score the highest probability to represent a new fold constitute the target list for structural determination. Our predicted probabilities for unsolved proteins correlate very well with the proportion of new folds among recently solved structures (new scop 1.39 records) that are disjoint from our original training set.

Computational adaptive optics for live three-dimensional biological imaging

Light microscopy of thick biological samples, such as tissues, is often limited by aberrations caused by refractive index variations within the sample itself. This problem is particularly severe for live imaging, a field of great current excitement due to the development of inherently fluorescent proteins. We describe a method of removing such aberrations computationally by mapping the refractive index of the sample using differential interference contrast microscopy, modeling the aberrations by ray tracing through this index map, and using space-variant deconvolution to remove aberrations. This approach will open possibilities to study weakly labeled molecules in difficult-to-image live specimens.