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Accurate alignment of protein-protein binding sites can aid in protein docking studies and constructing templates for predicting structure of protein complexes, along with in-depth understanding of evolutionary and functional relationships. However, over the past three decades, structural alignment algorithms have focused predominantly on global alignments with little effort on the alignment of local interfaces. In this paper, we introduce the PBSalign ( P rotein-protein B inding S ite align ment) method, which integrates techniques in graph theory, 3D localized shape analysis, geometric scoring, and utilization of physicochemical and geometrical properties. Computational results demonstrate that PBSalign is capable of identifying similar homologous and analogous binding sites accurately and performing alignments with better geometric match measures than existing protein-protein interface comparison tools. The proportion of better alignment quality generated by PBSalign is 46, 56, and 70 percent more than iAlign as judged by the average match index (MI), similarity index (SI), and structural alignment score (SAS), respectively. PBSalign provides the life science community an efficient and accurate solution to binding-site alignment while striking the balance between topological details and computational complexity.

Macromolecular interactions are formed between proteins, DNA and RNA molecules. Being a principle building block in macromolecular assemblies and pathways, the interactions underlie most of cellular functions. Malfunctioning of macromolecular interactions is also linked to a number of diseases. Structural knowledge of the macromolecular interaction allows one to understand the interaction’s mechanism, determine its functional implications and characterize the effects of genetic variations, such as single nucleotide polymorphisms, on the interaction. Unfortunately, until now the interactions mediated by different types of macromolecules, e.g. protein–protein interactions or protein–DNA interactions, are collected into individual and unrelated structural databases. This presents a significant obstacle in the analysis of macromolecular interactions. For instance, the homogeneous structural interaction databases prevent scientists from studying structural interactions of different types but occurring in the same macromolecular complex. Here, we introduce DOMMINO 2.0, a structural Database Of Macro-Molecular INteractiOns. Compared to DOMMINO 1.0, a comprehensive database on protein-protein interactions, DOMMINO 2.0 includes the interactions between all three basic types of macromolecules extracted from PDB files. DOMMINO 2.0 is automatically updated on a weekly basis. It currently includes ∼1 040 000 interactions between two polypeptide subunits (e.g. domains, peptides, termini and interdomain linkers), ∼43 000 RNA-mediated interactions, and ∼12 000 DNA-mediated interactions. All protein structures in the database are annotated using SCOP and SUPERFAMILY family annotation. As a result, protein-mediated interactions involving protein domains, interdomain linkers, C- and N- termini, and peptides are identified. Our database provides an intuitive web interface, allowing one to investigate interactions at three different resolution levels: whole subunit network, binary interaction and interaction interface. Database URL: http://dommino.org

Integrating ‘omics, epidemiology and clinical data from healthy patients and their biologic samples is important for identifying the determinants of early carcinogenesis. In a cross-sectional study of 249 women with no history of cancer and who underwent reduction mammoplasty, whole transcriptome (i.e., gene expression) and epigenome (i.e., methylation and microRNA) data were collected from dissected breast tissues. An extensive epidemiologic questionnaire ascertained data on breast cancer risk factors. Subjects with benign lesions considered at risk for future cancer were excluded. In order to manage, analyze, and share these data efficiently, we organized the data into a relational database to support a series of web-based tools for analyzing and visualizing the multidimensional data in an intuitive graphical user interface (GUI). The current platform improves upon existing web resources that integrate ‘omics and clinical data by including novel features that allow for greater flexibility of bioinformatics analyses. It also focuses on the ‘omics as an outcome, rather, e.g., what epidemiology risk factor or particular gene is associated with downstream effects across the ‘omics. Such features include: 1) a plot function that can visualize up to 4 dimension data chosen by a user from the 3 types of genetic profiles and more than 100 clinical and biomedical variables; 2) a differentiation visualization tool based on the ‘omics data of 2 groups of samples; and 3) miRNA target gene network generated by the correlations between gene expression and miRNA expression of our patient samples and validated miRNA target gene database. The GUI facilitates the discovery of research scientists by reducing complex biological and clinical data into easily understandable views. Even without the expertise of computational biology, researchers can integrate and analyze the high dimensional data that they are interested conveniently on our user-friendly designed platform. Citation Format: Xingyan Kuang, Catalin Marian, Cenny Taslim, Min-Ae Song, Daniel Weng, Jo L. Freudenheim, Theodore Brasky, Kun Huang, Kevin Coombes, Peter G. Shields. A web-based platform for exploring ‘omics and clinical data from healthy breast tissues. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 5280.

PBSword is a web server designed for efficient and accurate comparisons and searches of geometrically similar protein–protein binding sites from a large-scale database. The basic idea of PBSword is that each protein binding site is first represented by a high-dimensional vector of ‘visual words’, which characterizes both the global and local shape features of the binding site. It then uses a scalable indexing technique to search for those binding sites whose visual words representations are similar to that of the query binding site. Our system is able to return ranked results of binding sites in short time from a database of 194 322 domain–domain binding sites. PBSword supports query by protein ID and by new structures uploaded by users. PBSword is a useful tool to investigate functional connections among proteins based on the local structures of binding site and has potential applications to protein–protein docking and drug discovery. The system is hosted at http://pbs.rnet.missouri.edu.

The ninth annual conference of the MidSouth Computational Biology and Bioinformatics Society (MCBIOS 2012), "Making Sense of the Omics Data Deluge", took place in Oxford, Mississippi February 17-8 2012. This year's Conference Chairs were Dr. Dawn Wilkins, of the University of Mississippi and Dr. Doris Kupfer, also the current MCBIOS President (2011-2), from the Federal Aviation Administration. There were 170 registrants and a total of 106 abstracts (34 oral presentations and 72 poster session abstracts). Keynote speakers for 2012 were Dr. Michael Gribskov, Purdue University, who gave the opening address, "After the Deluge: Bioinformatics meets big data"; Dr. David J. States, OncProTech LLC, who gave his presentation remotely via WebEx entitled "Data Intensive Proteomics"; and Sultan Meghji, Appistry Inc. presenting the Saturday morning address, entitled "Simple, Fast and Affordable - Turning the myriad of data into action - technologies to support personalized medicine" and invited speaker, Dr. William Slikker, Director of the Food and Drug Administration's, National Center for Toxicological Research, presented a talk entitled "Regulatory Science: Challenges and Progress" outlining the role research at the FDA plays in their regulatory responsibilities. Participants also had the opportunity to attend hands-on workshops on NCBI tools, presented by Dr. Peter Cooper, NCBI/NLM/NIH staff scientist, and a collaboration workshop focused on the timber rattlesnake genome, facilitated by Dr. Ed Perkins, Army Corp of Engineers. The winners of conference awards were: Best Oral Presentations (students): 1st Place: Shana Stoddard, University of Mississippi 2nd Place: Neal Platt, Mississippi State 3rd Place: Aleksandra Markovets, UALR Best Oral Presentations (Post-Doctoral fellows): 1st Place: Mikhail Dozmorov, OMRF 2nd Place: Zhichao Liu, NCTR Best Poster (Computation): 1st Place: Shraddha Thakkar, UAMS 2nd Place: Xingyan Kuang, University of Missouri 3rd Place: Sule Dogan, Mississippi State Best Poster (Biology): 1st Place: Tamer Aldwairi, Mississippi State 2nd Place: Dilip Gautam, Mississippi State 3rd Place: Bin Pang, University of Missouri