Proteins do not function in isolation; it is their interactions with one another and also with other molecules (e.g. DNA, RNA) that mediate metabolic and signaling pathways, cellular processes, and organismal systems. Due to their central role in biological function, protein interactions also control the mechanisms leading to healthy and diseased states in organisms. Diseases are often caused by mutations affecting the binding interface or leading to biochemically dysfunctional allosteric changes in proteins. Therefore, protein interaction networks can elucidate the molecular basis of disease, which in turn can inform methods for prevention, diagnosis, and treatment. In this chapter, we will describe the computational approaches to predict and map networks of protein interactions and briefly review the experimental methods to detect protein interactions. We will describe the application of protein interaction networks as a translational approach to the study of human disease and evaluate the challenges faced by these approaches.

Histone proteins play essential structural and functional roles in the transition between active and inactive chromatin states. Although histones have a high degree of conservation due to constraints to maintain the overall structure of the nucleosomal octameric core, variants have evolved to assume diverse roles in gene regulation and epigenetic silencing. Histone variants, post-translational modifications and interactions with chromatin remodeling complexes influence DMA replication, transcription, repair and recombination. The authors review recent findings on the structure of chromatin that confirm previous interparticle interactions observed in crystal structures. Keywords chromatin structure; histone variants; nucleosomesThe eukaryotic cell stores its genetic information in DNA molecules that can be over 1 m in length. The DNA is hierarchically packed in the nucleus (up to ~2 × 10 −5 -times smaller in length) with the aid of proteins to form a complex called chromatin. The nucleosome core particle represents the first level of chromatin organization and is composed of two copies of each of histones H2A, H2B, H3 and H4, assembled in an octameric core with 146-147 bp of DNA tightly wrapped around it [1,2]. Nucleosome cores are separated by linker DNA of variable length and are associated with the linker histone H1. The next level of chromatin organization is the 30-nm fiber, which is composed of packed nucleosome arrays recently found to be arranged as a two-start helical model [3], and mediated by core histone internucleosomal interactions. Earlier models proposed by several groups have not been extensively tested (reviewed in [4]), probably due to technological limitations. Chromatin compaction can also be achieved by the polycomb repression complex (PRC); a multi-protein complex recently described by Francis and colleagues that can alter the chromatin structure of nucleosomal arrays [5].The overall structural state of chromatin and its domains controls both DNA replication and the expression of genes. Core histones are characterized by the presence of a histone fold domain [6] and N-terminal tails of variable length that are the subject of extensive posttranslational modifications (PTMs), which have been implicated in transcriptional activation, silencing, chromatin assembly and DNA replication (reviewed in [7]). PTMs are a component of the epigenome that includes changes to DNA and its connected proteins. These epigenetic modifications are switches for the regulation of gene expression and are chemical modifications of the DNA and histones that do not result in changes to the DNA sequence.Several chromatin states are likely to be regulated and maintained in a tissue-specific manner, making the DNA accessible to the transcription machinery during specific periods of time and at precise locations. Recent efforts to characterize the epigenome include the characterization † Author for correspondence National Institutes of Health, Computational Biology Branch, National Center for Biotechnology This re...

Variations and similarities in our individual genomes are part of our history, our heritage, and our identity. Some human genomic variants are associated with common traits such as hair and eye color, while others are associated with susceptibility to disease or response to drug treatment. Identifying the human variations producing clinically relevant phenotypic changes is critical for providing accurate and personalized diagnosis, prognosis, and treatment for diseases. Furthermore, a better understanding of the molecular underpinning of disease can lead to development of new drug targets for precision medicine. Several resources have been designed for collecting and storing human genomic variations in highly structured, easily accessible databases. Unfortunately, a vast amount of information about these genetic variants and their functional and phenotypic associations is currently buried in the literature, only accessible by manual curation or sophisticated text mining technology to extract the relevant information. In addition, the low cost of sequencing technologies coupled with increasing computational power has enabled the development of numerous computational methodologies to predict the pathogenicity of human variants. This review provides a detailed comparison of current human variant resources, including HGMD, OMIM, ClinVar, and UniProt/Swiss-Prot, followed by an overview of the computational methods and techniques used to leverage the available data to predict novel deleterious variants. We expect these resources and tools to become the foundation for understanding the molecular details of genomic variants leading to disease, which in turn will enable the promise of precision medicine.

Freely available at: http://bioinf.umbc.edu/EMU/ftp.