Deciphering Protein–Protein Interactions. Part II. Computational Methods to Predict Protein and Domain Interaction Partners

Recent advances in high-throughput experimental methods for the identification of protein interactions have resulted in a large amount of diverse data that are somewhat incomplete and contradictory. As valuable as they are, such experimental approaches studying protein interactomes have certain limitations that can be complemented by the computational methods for predicting protein interactions. In this review we describe different approaches to predict protein interaction partners as well as highlight recent achievements in the prediction of specific domains mediating protein-protein interactions. We discuss the applicability of computational methods to different types of prediction problems and point out limitations common to all of them

Phase relations in K_xFe_{2-y}Se_2 and the structure of superconducting K_xFe_2Se_2 via high-resolution synchrotron diffraction

Superconductivity in iron selenides has experienced a rapid growth, but not without major inconsistencies in the reported properties. For alkali-intercalated iron selenides, even the structure of the superconducting phase is a subject of debate, in part because the onset of superconductivity is affected much more delicately by stoichiometry and preparation than in cuprate or pnictide superconductors. If high-quality, pure, superconducting intercalated iron selenides are ever to be made, the intertwined physics and chemistry must be explained by systematic studies of how these materials form and by and identifying the many coexisting phases. To that end, we prepared pure K_2Fe_4Se_5 powder and superconductors in the K_xFe_{2-y}Se_2 system, and examined differences in their structures by high-resolution synchrotron and single-crystal x-ray diffraction. We found four distinct phases: semiconducting K_2Fe_4Se_5, a metallic superconducting phase K_xFe_2Se_2 with x ranging from 0.38 to 0.58, an insulator KFe_{1.6}Se_2 with no vacancy ordering, and an oxidized phase K_{0.51(5)}Fe_{0.70(2)}Se that forms the PbClF structure upon exposure to moisture. We find that the vacancy-ordered phase K_2Fe_4Se_5 does not become superconducting by doping, but the distinct iron-rich minority phase K_xFe_2Se_2 precipitates from single crystals upon cooling from above the vacancy ordering temperature. This coexistence of metallic and semiconducting phases explains a broad maximum in resistivity around 100 K. Further studies to understand the solubility of excess Fe in the K_xFe_{2-y}Se_2 structure will shed light on the maximum fraction of superconducting K_xFe_2Se_2 that can be obtained by solid state synthesis.Comment: 12 pages, 16 figures, supplemental materia

Long-term trends in evolution of indels in protein sequences

BACKGROUND: In this paper we describe an analysis of the size evolution of both protein domains and their indels, as inferred by changing sizes of whole domains or individual unaligned regions or "spacers". We studied relatively early evolutionary events and focused on protein domains which are conserved among various taxonomy groups. RESULTS: We found that more than one third of all domains have a statistically significant tendency to increase/decrease in size in evolution as judged from the overall domain size distribution as well as from the size distribution of individual spacers. Moreover, the fraction of domains and individual spacers increasing in size is almost twofold larger than the fraction decreasing in size. CONCLUSION: We showed that the tolerance to insertion and deletion events depends on the domain's taxonomy span. Eukaryotic domains are depleted in insertions compared to the overall test set, namely, the number of spacers increasing in size is about the same as the number of spacers decreasing in size. On the other hand, ancient domain families show some bias towards insertions or spacers which grow in size in evolution. Domains from several Gene Ontology categories also demonstrate certain tendencies for insertion or deletion events as inferred from the analysis of spacer sizes

Knowledge-based annotation of small molecule binding sites in proteins

<p>Abstract</p> <p>Background</p> <p>The study of protein-small molecule interactions is vital for understanding protein function and for practical applications in drug discovery. To benefit from the rapidly increasing structural data, it is essential to improve the tools that enable large scale binding site prediction with greater emphasis on their biological validity.</p> <p>Results</p> <p>We have developed a new method for the annotation of protein-small molecule binding sites, using inference by homology, which allows us to extend annotation onto protein sequences without experimental data available. To ensure biological relevance of binding sites, our method clusters similar binding sites found in homologous protein structures based on their sequence and structure conservation. Binding sites which appear evolutionarily conserved among non-redundant sets of homologous proteins are given higher priority. After binding sites are clustered, position specific score matrices (PSSMs) are constructed from the corresponding binding site alignments. Together with other measures, the PSSMs are subsequently used to rank binding sites to assess how well they match the query and to better gauge their biological relevance. The method also facilitates a succinct and informative representation of observed and inferred binding sites from homologs with known three-dimensional structures, thereby providing the means to analyze conservation and diversity of binding modes. Furthermore, the chemical properties of small molecules bound to the inferred binding sites can be used as a starting point in small molecule virtual screening. The method was validated by comparison to other binding site prediction methods and to a collection of manually curated binding site annotations. We show that our method achieves a sensitivity of 72% at predicting biologically relevant binding sites and can accurately discriminate those sites that bind biological small molecules from non-biological ones.</p> <p>Conclusions</p> <p>A new algorithm has been developed to predict binding sites with high accuracy in terms of their biological validity. It also provides a common platform for function prediction, knowledge-based docking and for small molecule virtual screening. The method can be applied even for a query sequence without structure. The method is available at <url>http://www.ncbi.nlm.nih.gov/Structure/ibis/ibis.cgi</url>.</p

A 3000-year record of vegetation changes and fire at a high-elevation wetland on Kilimanjaro, Tanzania

Kilimanjaro is experiencing the consequences of climate change and multiple land-use pressures. Few paleoenvironmental and archeological records exist to examine historical patterns of late Holocene ecosystem changes on Kilimanjaro. Here we present pollen, phytolith, and charcoal (>125 μm) data from a palustrine sediment core that provide a 3000-year radiocarbon-dated record collected from a wetland near the headwaters of the Maua watershed in the alpine and ericaceous vegetation zones. From 3000 to 800 cal yr BP, the pollen, phytolith, and charcoal records show subtle variability in ericaceous and montane forest assemblages with apparent multicentennial secular variability and a long-term pattern of increasing Poaceae and charcoal. From 800 to 600 cal yr BP, montane forest taxa varied rapidly, Cyperaceae abundances increased, and charcoal remained distinctly low. From 600 yr cal BP to the present, woody taxa decreased, and ericaceous taxa and Poaceae dominated, with a conspicuously increased charcoal influx. Uphill wetland ecosystems are crucial for ecological and socioeconomic resilience on and surrounding the mountain. The results were synthesized with the existing paleoenvironmental and archaeological data to explore the high spatiotemporal complexity of Kilimanjaro and to understand historical human-environment interactions. These paleoenvironmental records create a long-term context for current climate, biodiversity, and land-use changes on and around Kilimanjaro

Integrating stakeholders' perspectives and spatial modelling to develop scenarios of future land use and land cover change in northern Tanzania.

Rapid rates of land use and land cover change (LULCC) in eastern Africa and limited instances of genuinely equal partnerships involving scientists, communities and decision makers challenge the development of robust pathways toward future environmental and socioeconomic sustainability. We use a participatory modelling tool, Kesho, to assess the biophysical, socioeconomic, cultural and governance factors that influenced past (1959-1999) and present (2000-2018) LULCC in northern Tanzania and to simulate four scenarios of land cover change to the year 2030. Simulations of the scenarios used spatial modelling to integrate stakeholders' perceptions of future environmental change with social and environmental data on recent trends in LULCC. From stakeholders' perspectives, between 1959 and 2018, LULCC was influenced by climate variability, availability of natural resources, agriculture expansion, urbanization, tourism growth and legislation governing land access and natural resource management. Among other socio-environmental-political LULCC drivers, the stakeholders envisioned that from 2018 to 2030 LULCC will largely be influenced by land health, natural and economic capital, and political will in implementing land use plans and policies. The projected scenarios suggest that by 2030 agricultural land will have expanded by 8-20% under different scenarios and herbaceous vegetation and forest land cover will be reduced by 2.5-5% and 10-19% respectively. Stakeholder discussions further identified desirable futures in 2030 as those with improved infrastructure, restored degraded landscapes, effective wildlife conservation, and better farming techniques. The undesirable futures in 2030 were those characterized by land degradation, poverty, and cultural loss. Insights from our work identify the implications of future LULCC scenarios on wildlife and cultural conservation and in meeting the Sustainable Development Goals (SDGs) and targets by 2030. The Kesho approach capitalizes on knowledge exchanges among diverse stakeholders, and in the process promotes social learning, provides a sense of ownership of outputs generated, democratizes scientific understanding, and improves the quality and relevance of the outputs