Discovering gene functional relationships using a literature-based NMF model

The rapid growth of the biomedical literature and genomic information presents a major challenge for determining the functional relationships among genes. Several bioinformatics tools have been developed to extract and identify gene relationships from various biological databases. However, an intuitive user-interface tool that allows the biologist to determine functional relationships among genes is still not available. In this study, we develop a Web-based bioinformatics software environment called FAUN or Feature Annotation Using Nonnegative matrix factorization (NMF) to facilitate both the discovery and classification of functional relationships among genes. Both the computational complexity and parameterization of NMF for processing gene sets are discussed. We tested FAUN on three manually constructed gene document collections, and then used it to analyze several microarray-derived gene sets obtained from studies of the developing cerebellum in normal and mutant mice. FAUN provides utilities for collaborative knowledge discovery and identification of new gene relationships from text streams and repositories (e.g., MEDLINE). It is particularly useful for the validation and analysis of gene associations suggested by microarray experimentation. The FAUN site is publicly available at http://grits.eecs.utk.edu/faun

Discovering gene functional relationships using FAUN (Feature Annotation Using Nonnegative matrix factorization)

Background Searching the enormous amount of information available in biomedical literature to extract novel functional relationships among genes remains a challenge in the field of bioinformatics. While numerous (software) tools have been developed to extract and identify gene relationships from biological databases, few effectively deal with extracting new (or implied) gene relationships, a process which is useful in interpretation of discovery-oriented genome-wide experiments. Results In this study, we develop a Web-based bioinformatics software environment called FAUN or Feature Annotation Using Nonnegative matrix factorization (NMF) to facilitate both the discovery and classification of functional relationships among genes. Both the computational complexity and parameterization of NMF for processing gene sets are discussed. FAUN is tested on three manually constructed gene document collections. Its utility and performance as a knowledge discovery tool is demonstrated using a set of genes associated with Autism. Conclusions FAUN not only assists researchers to use biomedical literature efficiently, but also provides utilities for knowledge discovery. This Web-based software environment may be useful for the validation and analysis of functional associations in gene subsets identified by high-throughput experiments

Putting the Pieces Together: Integrative Modeling Platform Software for Structure Determination of Macromolecular Assemblies

A set of software tools for building and distributing models of macromolecular assemblies uses an integrative structure modeling approach, which casts the building of models as a computational optimization problem where information is encoded into a scoring function used to evaluate candidate models

Detection of specific solvent rearrangement regions of an enzyme: NMR and ITC studies with aminoglycoside phosphotransferase(3 ')-IIIa

This work describes differential effects of solvent in complexes of the aminoglycoside phosphotransferase(3')-IIIa (APH) with different aminoglycosides and the detection of change in solvent structure at specific sites away from substrates. Binding of kanamycins to APH occurs with a larger negative Delta H in H2O relative to D2O (Delta Delta H(H2O-D2O) < 0), while the reverse is true for neomycins. Unusually large negative Delta C-p values were observed for binding of aminoglycosides to APH. Delta C-p for the APH-neomycin complex was -1.6 kcal(.)mol(-1.)deg(-1). A break at 30 degrees C was observed in the APH-kanamycin complex yielding Delta C-p values of -0.7 kcal(.)mol(-1.)deg(-1) and -3.8 kcal(.)mol(-1.)deg(-1) below and above 30 degrees C, respectively. Neither the change in cessible surface area (Delta ASA) nor contributions from heats of ionization were sufficient to explain the large negative Delta C-p values. Most significantly, N-15-H-1 HSQC experiments showed that temperature-dependent shifts of the backkbone amide protons of Leu 88, Ser 91, Cys 98, and Leu143 revealed a break at 30 C only in the APH-kanamycin complex in spectra collected between 21 degrees C and 38 degrees C. These amino acids represent solvent reorganization sites that experience a change in solvent structure in their immediate environment as structurally different ligands bind to the enzyme. These residues were away from the substrate binding site and distributed in three hydrophobic patches in APH. Overall, our results show that a large number of factors affect Delta C-p and binding of structurally different ligand groups cause different solvent structure in the active site as well as differentially affecting specific sites away from the ligand binding site

SALIGN : a web server for alignment of multiple protein sequences and structures

Summary: Accurate alignment of protein sequences and/or structures is crucial for many biological analyses, including functional annotation of proteins, classifying protein sequences into families, and comparative protein structure modeling. Described here is a web interface to SALIGN, the versatile protein multiple sequence/structure alignment module of MODELLER. The web server automatically determines the best alignment procedure based on the inputs, while allowing the user to override default parameter values. Multiple alignments are guided by a dendrogram computed from a matrix of all pairwise alignment scores. When aligning sequences to structures, SALIGN uses structural environment information to place gaps optimally. If two multiple sequence alignments of related proteins are input to the server, a profile–profile alignment is performed. All features of the server have been previously optimized for accuracy, especially in the contexts of comparative modeling and identification of interacting protein partners