Servers and Databases
Hydroxyl-radical footprinting (HRF) is a powerful method for probing structures of nucleic acid-protein complexes with single-nucleotide resolution in solution. Hydroxyl-radical footprinting interpretation for DNA (HYDROID) is a method to quantify HRF data and integrate them with atomistic structural models. The stages of the HYDROID protocol include the extraction of the lane profiles from gel images, quantification of the DNA cleavage frequency at each nucleotide and theoretical estimation of the DNA cleavage frequency from atomistic structural models. As input, HYDROID requires polyacrylamide gel electrophoresis (PAGE) images of HRF products and optionally can use a molecular model of the DNA-protein complex. The HYDROID protocol can be used to quantify HRF over DNA regions of up to 100 nucleotides per gel image. In addition, it can be applied to the analysis of RNA-protein complexes and free RNA or DNA molecules in solution. Compared with other methods reported to date, HYDROID is unique in its ability to simultaneously integrate HRF data with the analysis of atomistic structural models.
MutaGene allows to explore, compare, identify and analyze mutagenic factors in tumors. Linking molecular fingerprints of mutational processes in tumors with their genotype and clinical data provides a valuable new dimension of information for researchers to explore in attempting to understand cancer as a genetic disease. MutaGene includes several tools for the analysis of cancer context-dependent mutations helping to
identify the most likely mutagenic processes for any given set of mutations. Cancer type and primary tumor site can be predicted based on the combination of mutational processes in a sample.
It can calculate expected DNA and protein site mutability and trying to decouple relative contributions of mutagenesis and selection by applying mutagen- or cancer-specific mutational background models to any genes of interest.
MutaBind evaluates the effects of sequence variants and disease mutations on protein interactions and calculates the quantitative changes in binding affinity. It uses molecular mechanics force fields, statistical potentials and fast side-chain optimization algorithms. The server maps mutations on a structural protein complex, calculates the associated changes in binding affinity, determines the deleterious effect of a mutation, estimates the confidence of this prediction and produces a mutant structural model for download. MutaBind can be applied to a large number of problems, including determination of potential driver mutations in cancer and other diseases, elucidation of the effects of sequence variants on protein fitness in evolution and protein design.
It classified by histone types and variants. All entries in the database are supplemented by rich sequence and structural annotations with many interactive tools to explore and compare sequences of different variants from various organisms. The core of the database is a manually curated set of histone sequences grouped into different variant subsets with variant-specific annotations. The curated set is supplemented by an automatically extracted set of histone sequences from the non-redundant protein database using algorithms trained on the curated set. The interactive web site supports various searching strategies in both datasets: browsing of phylogenetic trees; on-demand generation of multiple sequence alignments with feature annotations; classification of histone-like sequences and browsing of the taxonomic diversity for every histone variant.
Exploring Protein-Protein Interactions as Drug Targets
Targeting of protein-protein interactions in drug discovery is very challenging because of the size and shape of their binding interfaces, e.g., planar interfaces lacking binding pockets, the difficulty to construct a functional assay to screen out affected interactions. We propose a computational framework to design small molecule and peptide drugs that target protein-protein interactions, particularly for anti-cancer therapy. We explore multiple strategies, including finding binding hot spots, incorporating chemical similarity and bioactivity data, and sampling similar binding sites from homologous protein complexes. We demonstrate how to combine existing interdisciplinary resources with examples of semi-automated workflows.
IBIS – Inferred Biomolecular Interaction Server
It reports interactions observed in structural complexes of a given protein, and infers binding sites and interacting partners (protein-protein, protein-chemical, protein-nucleic acid, protein-peptide and protein-ion types of interactions) by inspecting protein complexes formed by homologous proteins. Similar binding sites are clustered together based on their sequence and structure conservation. To emphasize biologically relevant binding sites, several algorithms are used for verification in terms of evolutionary conservation, biological importance of binding partners, size and stability of interfaces, as well as evidence from the published literature.