Computational approaches to molecular recognition

Recent advances in the computation of free energies have facilitated the understanding of host—guest and protein—ligand recognition. Rigorous perturbation methods have been assessed and expanded, and more approximate techniques have been developed that allow faster treatment of diverse systems.     

Computational recognition of potassium channel sequences

MOTIVATION: Potassium channels are mainly known for their role in regulating and maintaining the membrane potential. Since this is one of the key mechanisms of signal transduction, malfunction of these potassium channels leads to a wide variety of severe diseases. Thus potassium channels are priority targets of research for new drugs, despite the fact that this protein family is highly variable and closely related to other channels, which makes it very difficult to identify new types of potassium channel sequences. RESULTS: Here we present a new method for identifying potassium channel sequences (PSM, Property Signature Method), which-in contrast to the known methods for protein classification-is directly based on physicochemical properties of amino acids rather than on the amino acids themselves. A signature for the pore region including the selectivity filter has been created, representing the most common physicochemical properties of known potassium channels. This string enables genome-wide screening for sequences with similar features despite a very low degree of amino acid similarity within a protein family.     

Computational object recognition: a biologically motivated approach

We propose a conceptual framework for artificial object recognition systems based on findings from neurophysiological and neuropsychological research on the visual system in primate cortex. We identify some essential questions, which have to be addressed in the course of designing object recognition systems. As answers, we review some major aspects of biological object recognition, which are then translated into the technical field of computer vision. The key suggestions are the use of incremental and view-based approaches together with the ability of online feature selection and the interconnection of object-views to form an overall object representation. The effectiveness of the computational approach is estimated by testing a possible realization in various tasks and conditions explicitly designed to allow for a direct comparison with the biological counterpart. The results exhibit excellent performance with regard to recognition accuracy, the creation of sparse models and the selection of appropriate features.     

Computational approaches to gene prediction

The problems associated with gene identification and the prediction of gene structure in DNA sequences have been the focus of increased attention over the past few years with the recent acquisition by large-scale sequencing projects of an immense amount of genome data. A variety of prediction programs have been developed in order to address these problems. This paper presents a review of the computational approaches and gene-finders used commonly for gene prediction in eukaryotic genomes. Two approaches, in general, have been adopted for this purpose: similarity-based and ab initio techniques. The information gleaned from these methods is then combined via a variety of algorithms, including Dynamic Programming (DP) or the Hidden Markov Model (HMM), and then used for gene prediction from the genomic sequences.     

Computational principles underlying the recognition of acoustic signals in insects

Many animals produce pulse-like signals during acoustic communication. These signals exhibit structure on two time scales: they consist of trains of pulses that are often broadcast in packets—so called chirps. Temporal parameters of the pulse and of the chirp are decisive for female preference. Despite these signals being produced by animals from many different taxa (e.g. frogs, grasshoppers, crickets, bushcrickets, flies), a general framework for their evaluation is still lacking. We propose such a framework, based on a simple and physiologically plausible model. The model consists of feature detectors, whose time-varying output is averaged over the signal and then linearly combined to yield the behavioral preference. We fitted this model to large data sets collected in two species of crickets and found that Gabor filters—known from visual and auditory physiology—explain the preference functions in these two species very well. We further explored the properties of Gabor filters and found a systematic relationship between parameters of the filters and the shape of preference functions. Although these Gabor filters were relatively short, they were also able to explain aspects of the preference for signal parameters on the longer time scale due to the integration step in our model. Our framework explains a wide range of phenomena associated with female preference for a widespread class of signals in an intuitive and physiologically plausible fashion. This approach thus constitutes a valuable tool to understand the functioning and evolution of communication systems in many species.     

Computational methods for gene expression-based tumor classification

Gene expression profiles may offer more or additional information than classic morphologic- and histologic-based tumor classification systems. Because the number of tissue samples examined is usually much smaller than the number of genes examined, efficient data reduction and analysis methods are critical. In this report, we propose a principal component and discriminant analysis method of tumor classification using gene expression profile data. Expression of 2000 genes in 40 tumor and 22 normal colon tissue samples is used to examine the feasibility of gene expression-based tumor classification systems. Using this method, the percentage of correctly classified normal and tumor tissue was 87.0%. The combined approach using principal components and discriminant analysis provided superior sensitivity and specificity compared to an approach using simple differences in the expression levels of individual genes.     

Computational gene knockout reveals transdisease-transgene association structure

Genome-wide association studies for a variety of diseases are identifying increasing numbers of candidate genes. Now we are confronted with the fact that some genes are common candidates across diseases. Thus there is a strong need to develop a hypothesis formulation methodology to comprehend multifaceted associations between genes and diseases. We have developed a computational method for building transdisease-transgene association structure. By introducing the basic rationale underlying the gene knockout approach as an information processing procedure to a network constructed on the basis of hyperlinks between disease and gene pages listed in the Online Mendelian Inheritance in Man (OMIM) database, relations of genes with diseases are computationally quantified. We did successively eliminate gene pages (called "computational gene knockout" in this paper) expected to contribute to metabolic syndrome, and catalogued each association with various disease pages. We thereby apply a co-clustering method to the gene-disease relations to obtain an association structure by classifying diseases and genes simultaneously. Observing an association structure between over 100 diseases and their related genes, we then found that the structure revealed gene classes that were commonly associated with diseases as well as gene classes that were selectively associated with a specific disease class.     

The lateral organ boundaries gene defines a novel,plant-specific gene family

The LATERAL ORGAN BOUNDARIES (LOB) gene in Arabidopsis defines a new conserved protein domain. LOB is expressed in a band of cells at the adaxial base of all lateral organs formed from the shoot apical meristem and at the base of lateral roots. LOB encodes a predicted protein that does not have recognizable functional motifs, but that contains a conserved domain (the LOB domain) that is present in 42 other Arabidopsis proteins and in proteins from a variety of other plant species. Proteins showing similarity to the LOB domain were not found outside of plant databases, indicating that this unique protein may play a role in plant-specific processes. Genes encoding LOB domain proteins are expressed in a variety of temporal- and tissue-specific patterns, suggesting that they may function in diverse processes. Loss-of-function LOB mutants have no detectable phenotype under standard growth conditions, suggesting that LOB is functionally redundant or required during growth under specific environmental conditions. Ectopic expression of LOB leads to alterations in the size and shape of leaves and floral organs and causes male and female sterility. The expression of LOB at the base of lateral organs suggests a potential role for LOB in lateral organ development.     

Computational modeling of gene structure in Arabidopsis thaliana

Computational gene identification by sequence inspection remains a challenging problem. For a typical Arabidopsis thaliana gene with five exons, at least one of the exons is expected to have at least one of its borders predicted incorrectly by ab initio gene finding programs. More detailed analysis for individual genomic loci can often resolve the uncertainty on the basis of EST evidence or similarity to potential protein homologues. Such methods are part of the routine annotation process. However, because the EST and protein databases are constantly growing, in many cases original annotation must be re-evaluated, extended, and corrected on the basis of the latest evidence. The Arabidopsis Genome Initiative is undertaking this task on the whole-genome scale via its participating genome centers. The current Arabidopsis genome annotation provides an excellent starting point for assessing the protein repertoire of a flowering plant. More accurate whole-genome annotation will require the combination of high-throughput and individual gene experimental approaches and computational methods. The purpose of this article is to discuss tools available to an individual researcher to evaluate gene structure prediction for a particular locus.     

Computational simulation study on the anion recognition properties of functionalized tetraphenyl porphyrins

Dedicated bond force constant and bulk modulus of C _n fullerenes (n = 20, 28, 36, 50, 60) are computed using density functional theory (DFT). DFT predicts bond force constants of 611, 648, 675, 686, and 691 N/m, for C₂₀, C₂₈, C₃₆, C₅₀, and C₆₀, respectively, indicating that the bond force constant increases for larger fullerenes. The bulk modulus predicted by DFT increases with decreased fullerene diameter, from 0.874 TPa for C₆₀ to 1.830 TPa for C₂₀. The bond force constants predicted by DFT are then used as an input for finite element analysis (FEA) of the fullerenes, considered as spatial frames in structural models where the bond stiffness is represented by the DFT-computed bond force constant. In agreement with DFT, FEA predicts that smaller fullerenes are stiffer, and underestimates the bulk modulus with respect to DFT. The difference between the FEA and DFT predictions of the bulk modulus decreases as the size of the fullerene increases, from 20.9 % difference for C₂₀ to only 4 % difference for C₆₀. Thus, it is concluded that knowing the appropriate bond force constant, FEA can be used as a plausible approximation to model the elastic behavior of small fullerenes.