Exploring Protein-Protein Interactions as Drug Targets for Anti-cancer Therapy with In Silico Workflows

We describe a computational protocol to aid the design of small molecule and peptide drugs that target protein-protein interactions, particularly for anti-cancer therapy. To achieve this goal, 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. Finally, we discuss several major problems, including the occurrence of drug-resistant mutations, drug promiscuity, and the design of dual-effect inhibitors.Fil: Goncearenco, Alexander. National Institutes of Health; Estados UnidosFil: Li, Minghui. Soochow University; China. National Institutes of Health; Estados UnidosFil: Simonetti, Franco Lucio. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigaciones Bioquímicas de Buenos Aires. Fundación Instituto Leloir. Instituto de Investigaciones Bioquímicas de Buenos Aires; ArgentinaFil: Shoemaker, Benjamin A. National Institutes of Health; Estados UnidosFil: Panchenko, Anna R. National Institutes of Health; Estados Unido

mutation3D:Cancer Gene Prediction Through Atomic Clustering of Coding Variants in the Structural Proteome

A new algorithm and Web server, mutation3D (http://mutation3d.org), proposes driver genes in cancer by identifying clusters of amino acid substitutions within tertiary protein structures. We demonstrate the feasibility of using a 3D clustering approach to implicate proteins in cancer based on explorations of single proteins using the mutation3D Web interface. On a large scale, we show that clustering with mutation3D is able to separate functional from nonfunctional mutations by analyzing a combination of 8,869 known inherited disease mutations and 2,004 SNPs overlaid together upon the same sets of crystal structures and homology models. Further, we present a systematic analysis of whole-genome and whole-exome cancer datasets to demonstrate that mutation3D identifies many known cancer genes as well as previously underexplored target genes. The mutation3D Web interface allows users to analyze their own mutation data in a variety of popular formats and provides seamless access to explore mutation clusters derived from over 975,000 somatic mutations reported by 6,811 cancer sequencing studies. The mutation3D Web interface is freely available with all major browsers supported

Mapping genetic variations to three- dimensional protein structures to enhance variant interpretation: a proposed framework

The translation of personal genomics to precision medicine depends on the accurate interpretation of the multitude of genetic variants observed for each individual. However, even when genetic variants are predicted to modify a protein, their functional implications may be unclear. Many diseases are caused by genetic variants affecting important protein features, such as enzyme active sites or interaction interfaces. The scientific community has catalogued millions of genetic variants in genomic databases and thousands of protein structures in the Protein Data Bank. Mapping mutations onto three-dimensional (3D) structures enables atomic-level analyses of protein positions that may be important for the stability or formation of interactions; these may explain the effect of mutations and in some cases even open a path for targeted drug development. To accelerate progress in the integration of these data types, we held a two-day Gene Variation to 3D (GVto3D) workshop to report on the latest advances and to discuss unmet needs. The overarching goal of the workshop was to address the question: what can be done together as a community to advance the integration of genetic variants and 3D protein structures that could not be done by a single investigator or laboratory? Here we describe the workshop outcomes, review the state of the field, and propose the development of a framework with which to promote progress in this arena. The framework will include a set of standard formats, common ontologies, a common application programming interface to enable interoperation of the resources, and a Tool Registry to make it easy to find and apply the tools to specific analysis problems. Interoperability will enable integration of diverse data sources and tools and collaborative development of variant effect prediction methods

How Bacterial Chemoreceptors Evolve Novel Ligand Specificities

Chemoreceptor-based signaling pathways are among the major modes of bacterial signal transduction, and Pseudomonas aeruginosa PAO1 is an important model to study their function. Of the 26 chemoreceptors of this strain, PctA has a broad ligand range and responds to most of the proteinogenic amino acids, whereas PctB and PctC have a much narrower range and show strong ligand preference for L-glutamine and -aminobutyrate, respectively. Using several comparative genomics approaches, we show that these receptors are paralogs: pctA gene duplication in the common ancestor of the genus Pseudomonas led to pctC, whereas pctB originated through another, independent pctA duplication in the common ancestor of P. aeruginosa. Thus, the broad-range amino acid chemoreceptor was evolutionarily older, and chemoreceptors that complemented “missing” amino acid sensing abilities arose later in specific Pseudomonas lineages. Using comparative sequence analysis, newly solved crystal structures of PctA, PctB, and PctC ligand-binding domains, and their molecular dynamics simulations, we identified a conserved amino acid recognition motif and changes in the ligand-binding pocket that led to novel ligand specificities. In addition, we determined major forces driving the evolution of this group of chemoreceptors.This work was supported by FEDER funds and Fondo Social Europeo from the Spanish Ministry for Economy and Competitiveness (grants BIO2013-42297 and BIO2016-76779-P to T.K. and BIO2016-74875-P to J.A.G.), by the Junta de Andalucía (grant CVI-7335 to T.K.), and by the U.S. National Institutes of Health (grant R35GM131760 to I.B.Z.).Peer reviewe

Formation of hydrogen-related shallow donors in Gei1-xSix crystals implanted with protons

If is found that shallow hydrogen-related donors are formed in the proton-implanted dilute Ge1-xSix alloys (0 < x < 0.031) as well as in Si-free Ge samples upon heat-treatments in the temperature range 225-300°C. The maximum concentration of the donors is about 1.5x10^16 cm^-3 for a H+ implantation dose of 1 x10^15 cm^-2. Formation and annihilation temperatures of the protonimplantation-induced donors do not depend on the Si concentration in Ge1-xSix samples. However, the increase in Si content has resulted in a decrease of the concentration of the H-related donors. The possible origin of the H-related donors and mechanisms of Si-induced suppression of their formation are discussed

Formation of hydrogen-related shallow donors in Gei1-xSix crystals implanted with protons

If is found that shallow hydrogen-related donors are formed in the proton-implanted dilute Ge1-xSix alloys (0 < x < 0.031) as well as in Si-free Ge samples upon heat-treatments in the temperature range 225-300°C. The maximum concentration of the donors is about 1.5x10^16 cm^-3 for a H+ implantation dose of 1 x10^15 cm^-2. Formation and annihilation temperatures of the protonimplantation-induced donors do not depend on the Si concentration in Ge1-xSix samples. However, the increase in Si content has resulted in a decrease of the concentration of the H-related donors. The possible origin of the H-related donors and mechanisms of Si-induced suppression of their formation are discussed