Using genome-wide measurements for computational prediction of SH2–peptide interactions

Peptide-recognition modules (PRMs) are used throughout biology to mediate protein–protein interactions, and many PRMs are members of large protein domain families. Recent genome-wide measurements describe networks of peptide–PRM interactions. In these networks, very similar PRMs recognize distinct sets of peptides, raising the question of how peptide-recognition specificity is achieved using similar protein domains. The analysis of individual protein complex structures often gives answers that are not easily applicable to other members of the same PRM family. Bioinformatics-based approaches, one the other hand, may be difficult to interpret physically. Here we integrate structural information with a large, quantitative data set of SH2 domain–peptide interactions to study the physical origin of domain–peptide specificity. We develop an energy model, inspired by protein folding, based on interactions between the amino-acid positions in the domain and peptide. We use this model to successfully predict which SH2 domains and peptides interact and uncover the positions in each that are important for specificity. The energy model is general enough that it can be applied to other members of the SH2 family or to new peptides, and the cross-validation results suggest that these energy calculations will be useful for predicting binding interactions. It can also be adapted to study other PRM families, predict optimal peptides for a given SH2 domain, or study other biological interactions, e.g. protein–DNA interactions.     

Ascochyta rabiei: A threat to global chickpea production

The necrotrophic fungus Ascochyta rabiei causes Ascochyta blight (AB) disease in chickpea. A. rabiei infects all aerial parts of the plant, which results in severe yield loss. At present, AB disease occurs in most chickpea‐growing countries. Globally increased incidences of A. rabiei infection and the emergence of new aggressive isolates directed the interest of researchers toward understanding the evolution of pathogenic determinants in this fungus. In this review, we summarize the molecular and genetic studies of the pathogen along with approaches that are helping in combating the disease. Possible areas of future research are also suggested.Taxonomykingdom Mycota, phylum Ascomycota, class Dothideomycetes, subclass Coelomycetes, order Pleosporales, family Didymellaceae, genus Ascochyta, species rabiei. Primary host A. rabiei survives primarily on Cicer species.Disease symptoms A. rabiei infects aboveground parts of the plant including leaves, petioles, stems, pods, and seeds. The disease symptoms first appear as watersoaked lesions on the leaves and stems, which turn brown or dark brown. Early symptoms include small circular necrotic lesions visible on the leaves and oval brown lesions on the stem. At later stages of infection, the lesions may girdle the stem and the region above the girdle falls off. The disease severity increases at the reproductive stage and rounded lesions with concentric rings, due to asexual structures called pycnidia, appear on leaves, stems, and pods. The infected pod becomes blighted and often results in shrivelled and infected seeds.Disease management strategiesCrop failures may be avoided by judicious practices of integrated disease management based on the use of resistant or tolerant cultivars and growing chickpea in areas where conditions are least favourable for AB disease development. Use of healthy seeds free of A. rabiei, seed treatments with fungicides, and proper destruction of diseased stubbles can also reduce the fungal inoculum load. Crop rotation with nonhost crops is critical for controlling the disease. Planting moderately resistant cultivars and prudent application of fungicides is also a way to combat AB disease. However, the scarcity of AB‐resistant accessions and the continuous evolution of the pathogen challenges the disease management process.Useful websites https://www.ndsu.edu/pubweb/pulse‐info/resourcespdf/Ascochyta%20blight%20of%20chickpea.pdf https://saskpulse.com/files/newsletters/180531_ascochyta_in_chickpeas‐compressed.pdf http://www.pulseaus.com.au/growing‐pulses/bmp/chickpea/ascochyta‐blight http://agriculture.vic.gov.au/agriculture/pests‐diseases‐and‐weeds/plant‐diseases/grains‐pulses‐and‐cereals/ascochyta‐blight‐of‐chickpea http://www.croppro.com.au/crop_disease_manual/ch05s02.php https://www.northernpulse.com/uploads/resources/722/handout‐chickpeaascochyta‐nov13‐2011.pdf http://oar.icrisat.org/184/1/24_2010_IB_no_82_Host_Plant https://www.crop.bayer.com.au/find‐crop‐solutions/by‐pest/diseases/ascochyta‐blight     

Tertiary motifs as building blocks for the design of protein‐binding peptides

Despite advances in protein engineering, the de novo design of small proteins or peptides that bind to a desired target remains a difficult task. Most computational methods search for binder structures in a library of candidate scaffolds, which can lead to designs with poor target complementarity and low success rates. Instead of choosing from pre‐defined scaffolds, we propose that custom peptide structures can be constructed to complement a target surface. Our method mines tertiary motifs (TERMs) from known structures to identify surface‐complementing fragments or “seeds.” We combine seeds that satisfy geometric overlap criteria to generate peptide backbones and score the backbones to identify the most likely binding structures. We found that TERM‐based seeds can describe known binding structures with high resolution: the vast majority of peptide binders from 486 peptide‐protein complexes can be covered by seeds generated from single‐chain structures. Furthermore, we demonstrate that known peptide structures can be reconstructed with high accuracy from peptide‐covering seeds. As a proof of concept, we used our method to design 100 peptide binders of TRAF6, seven of which were predicted by Rosetta to form higher‐quality interfaces than a native binder. The designed peptides interact with distinct sites on TRAF6, including the native peptide‐binding site. These results demonstrate that known peptide‐binding structures can be constructed from TERMs in single‐chain structures and suggest that TERM information can be applied to efficiently design novel target‐complementing binders.     

EDock‐ML: A web server for using ensemble docking with machine learning to aid drug discovery

EDock‐ML is a web server that facilitates the use of ensemble docking with machine learning to help decide whether a compound is worthwhile to be considered further in a drug discovery process. Ensemble docking provides an economical way to account for receptor flexibility in molecular docking. Machine learning improves the use of the resulting docking scores to evaluate whether a compound is likely to be useful. EDock‐ML takes a bottom‐up approach in which machine‐learning models are developed one protein at a time to improve predictions for the proteins included in its database. Because the machine‐learning models are intended to be used without changing the docking and model parameters with which the models were trained, novice users can use it directly without worrying about what parameters to choose. A user simply submits a compound specified by an ID from the ZINC database (Sterling, T.; Irwin, J. J., J Chem Inf Model 2015, 55[11], 2,324–2,337.) or upload a file prepared by a chemical drawing program and receives an output helping the user decide the likelihood of the compound to be active or inactive for a drug target. EDock‐ML can be accessed freely at edock‐ml.umsl.edu     

PDBsum extras: SARS‐CoV‐2 and AlphaFold models

The PDBsum web server provides structural analyses of the entries in the Protein Data Bank (PDB). Two recent additions are described here. The first is the detailed analysis of the SARS‐CoV‐2 virus protein structures in the PDB. These include the variants of concern, which are shown both on the sequences and 3D structures of the proteins. The second addition is the inclusion of the available AlphaFold models for human proteins. The pages allow a search of the protein against existing structures in the PDB via the Sequence Annotated by Structure (SAS) server, so one can easily compare the predicted model against experimentally determined structures. The server is freely accessible to all at http://www.ebi.ac.uk/pdbsum.     

Seed Pro-Nutra Care: A tool for characterization of seed storage proteins and database of bioactive peptides having potential health benefits

Seed storage proteins, the major food proteins, possess unique physicochemical characteristics which determine their nutritional importance and influence their utilization by humans. Here, we describe a database driven tool named Seed Pro-Nutra Care which comprises a systematic compendium of seed storage proteins and their bioactive peptides influencing several vital organ systems for maintenance of health. Seed Pro-Nutra Careis an integrated resource on seed storage protein. This resource help in the (I) Characterization of proteins whether they belong to seed storage protein group or not. (II) Identification the bioactive peptides with their sequences using peptide name (III) Determination of physico chemical properties of seed storage proteins. (IV) Epitope identification and mapping (V) Allergenicity prediction and characterization. Seed Pro-Nutra Care is a compilation of data on bioactive peptides present in seed storage proteins from our own collections and other published and unpublished sources. The database provides an information resource of a variety of seed related biological information and its use for nutritional and biomedical application.

Availability

http://www.gbpuat-cbsh.ac.in/departments/bi/database/seed_pro_nutra_care/     

hu.MAP 2.0: integration of over 15,000 proteomic experiments builds a global compendium of human multiprotein assemblies

A general principle of biology is the self‐assembly of proteins into functional complexes. Characterizing their composition is, therefore, required for our understanding of cellular functions. Unfortunately, we lack knowledge of the comprehensive set of identities of protein complexes in human cells. To address this gap, we developed a machine learning framework to identify protein complexes in over 15,000 mass spectrometry experiments which resulted in the identification of nearly 7,000 physical assemblies. We show our resource, hu.MAP 2.0, is more accurate and comprehensive than previous state of the art high‐throughput protein complex resources and gives rise to many new hypotheses, including for 274 completely uncharacterized proteins. Further, we identify 253 promiscuous proteins that participate in multiple complexes pointing to possible moonlighting roles. We have made hu.MAP 2.0 easily searchable in a web interface (http://humap2.proteincomplexes.org/), which will be a valuable resource for researchers across a broad range of interests including systems biology, structural biology, and molecular explanations of disease.     

Dynamic human liver proteome atlas reveals functional insights into disease pathways

Deeper understanding of liver pathophysiology would benefit from a comprehensive quantitative proteome resource at cell type resolution to predict outcome and design therapy. Here, we quantify more than 150,000 sequence‐unique peptides aggregated into 10,000 proteins across total liver, the major liver cell types, time course of primary cell cultures, and liver disease states. Bioinformatic analysis reveals that half of hepatocyte protein mass is comprised of enzymes and 23% of mitochondrial proteins, twice the proportion of other liver cell types. Using primary cell cultures, we capture dynamic proteome remodeling from tissue states to cell line states, providing useful information for biological or pharmaceutical research. Our extensive data serve as spectral library to characterize a human cohort of non‐alcoholic steatohepatitis and cirrhosis. Dramatic proteome changes in liver tissue include signatures of hepatic stellate cell activation resembling liver cirrhosis and providing functional insights. We built a web‐based dashboard application for the interactive exploration of our resource (www.liverproteome.org).     

FastD: Fast detection of insecticide target‐site mutations and overexpressed detoxification genes in insect populations from RNA‐Seq data

Target‐site mutations and detoxification gene overexpression are two major mechanisms conferring insecticide resistance. Molecular assays applied to detect these resistance genetic markers are time‐consuming and with high false‐positive rates. RNA‐Seq data contains information on the variations within expressed genomic regions and expression of detoxification genes. However, there is no corresponding method to detect resistance markers at present. Here, we collected 66 reported resistance mutations of four insecticide targets (AChE, VGSC, RyR, and nAChR) from 82 insect species. Next, we obtained 403 sequences of the four target genes and 12,665 sequences of three kinds of detoxification genes including P450s, GSTs, and CCEs. Then, we developed a Perl program, FastD, to detect target‐site mutations and overexpressed detoxification genes from RNA‐Seq data and constructed a web server for FastD (http://www.insect-genome.com/fastd). The estimation of FastD on simulated RNA‐Seq data showed high sensitivity and specificity. We applied FastD to detect resistant markers in 15 populations of six insects, Plutella xylostella, Aphis gossypii, Anopheles arabiensis, Musca domestica, Leptinotarsa decemlineata and Apis mellifera. Results showed that 11 RyR mutations in P. xylostella, one nAChR mutation in A. gossypii, one VGSC mutation in A. arabiensis and five VGSC mutations in M. domestica were found to be with frequency difference >40% between resistant and susceptible populations including previously confirmed mutations G4946E in RyR, R81T in nAChR and L1014F in VGSC. And 49 detoxification genes were found to be overexpressed in resistant populations compared with susceptible populations including previously confirmed detoxification genes CYP6BG1, CYP6CY22, CYP6CY13, CYP6P3, CYP6M2, CYP6P4 and CYP4G16. The candidate target‐site mutations and detoxification genes were worth further validation. Resistance estimates according to confirmed markers were consistent with population phenotypes, confirming the reliability of this program in predicting population resistance at omics‐level.     

Membranome 3.0: Database of single‐pass membrane proteins with AlphaFold models

The Membranome database provides comprehensive structural information on single‐pass (i.e., bitopic) membrane proteins from six evolutionarily distant organisms, including protein–protein interactions, complexes, mutations, experimental structures, and models of transmembrane α‐helical dimers. We present a new version of this database, Membranome 3.0, which was significantly updated by revising the set of 5,758 bitopic proteins and incorporating models generated by AlphaFold 2 in the database. The AlphaFold models were parsed into structural domains located at the different membrane sides, modified to exclude low‐confidence unstructured terminal regions and signal sequences, validated through comparison with available experimental structures, and positioned with respect to membrane boundaries. Membranome 3.0 was re‐developed to facilitate visualization and comparative analysis of multiple 3D structures of proteins that belong to a specified family, complex, biological pathway, or membrane type. New tools for advanced search and analysis of proteins, their interactions, complexes, and mutations were included. The database is freely accessible at https://membranome.org.     

GraphBind: protein structural context embedded rules learned by hierarchical graph neural networks for recognizing nucleic-acid-binding residues

Knowledge of the interactions between proteins and nucleic acids is the basis of understanding various biological activities and designing new drugs. How to accurately identify the nucleic-acid-binding residues remains a challenging task. In this paper, we propose an accurate predictor, GraphBind, for identifying nucleic-acid-binding residues on proteins based on an end-to-end graph neural network. Considering that binding sites often behave in highly conservative patterns on local tertiary structures, we first construct graphs based on the structural contexts of target residues and their spatial neighborhood. Then, hierarchical graph neural networks (HGNNs) are used to embed the latent local patterns of structural and bio-physicochemical characteristics for binding residue recognition. We comprehensively evaluate GraphBind on DNA/RNA benchmark datasets. The results demonstrate the superior performance of GraphBind than state-of-the-art methods. Moreover, GraphBind is extended to other ligand-binding residue prediction to verify its generalization capability. Web server of GraphBind is freely available at http://www.csbio.sjtu.edu.cn/bioinf/GraphBind/.     

Biodiversity 2030: a road paved with good intentions: The new EU Commission's biodiversity Strategy risks to remain an empty husk without proper implementation

The EU''s Biodiversity Strategy for 2030 makes great promises about halting the decline of biodiversity but it offers little in terms of implementation. Subject Categories: S&S: Economics & Business, Ecology, S&S: Ethics

Earth is teeming with a stunning variety of life forms. Despite hundreds of years of exploration and taxonomic research, and with 1.2 million species classified, we still have no clear picture of the real extent of global biodiversity, with estimates ranging from 3 to 100 million species. A highly quoted—although not universally accepted—study predicted some 8.7 million species, of which about 2.2 million are marine (Mora et al, 2011). Although nearly any niche on the surface of Earth has been colonized by life, species richness is all but evenly distributed. A large share of the known species is concentrated in relatively small areas, especially in the tropics (Fig 1). Ultimately, it is the network of the interactions among life forms and the physical environment that make up the global ecosystem we call biosphere and that supports life itself.Open in a separate windowFigure 1Biological hotspots of the worldA total of 36 currently recognized hotspots make up < 3% of the planet''s land area but harbor half of the world''s endemic plant species and 42% of all terrestrial vertebrates. Overall, hotspots have lost more than 80% of their original extension. Credit: Richard J. Weller, Claire Hoch, and Chieh Huang, 2017, Atlas for the End of the World, http://atlas‐for‐the‐end‐of‐the‐world.com/. Reproduced with permission.Driven by a range of complex and interwoven causes–such as changes in land and sea use, habitat destruction, overexploitation of organisms, climate change, pollution, and invasive species–biodiversity is declining at an alarming pace. A report by the Intergovernmental Science‐Policy Platform on Biodiversity and Ecosystem Services (IPBES) issued a clear warning: “An average of around 25 per cent of species in assessed animal and plant groups are threatened, suggesting that around 1 million species already face extinction, many within decades, unless action is taken to reduce the intensity of drivers of biodiversity loss. Without such action, there will be a further acceleration in the global rate of species extinction, which is already at least tens to hundreds of times higher than it has averaged over the past 10 million years” (IPBES, 2019) (Fig 2). Although focused on a smaller set of organisms, a more recent assessment by WWF has reached similar conclusions. Their Living Planet Index, that tracks the abundance of thousands of populations of mammals, birds, fish, reptiles, and amphibians around the world, shows a stark decline in monitored populations (WWF, 2020). As expected, the trend of biodiversity decline is not homogeneous with tropical areas paying a disproportionately high price, mostly because of unrestrained deforestation and exploitation of natural resources.Open in a separate windowFigure 2The global, rapid decline of biodiversity(A) Percentage of species threatened with extinction in taxonomic groups that have been assessed comprehensively, or through a “sampled” approach, or for which selected subsets have been assessed by the IUCN Red List of Threatened Species. Groups are ordered according to the best estimate, assuming that data‐deficient species are as threatened as non‐data deficient species. (B) Extinctions since 1500 for vertebrate groups. (C) Red List Index of species survival for taxonomic groups that have been assessed for the IUCN Red List at least twice. A value of 1 is equivalent to all species being categorized as Least Concern; a value of zero is equivalent to all species being classified as Extinct. Data for all panels from www.iucnredlist.org. Reproduced from (IPBES, 2019), with permission.

Driven by a range of complex and interwoven causes […] biodiversity is declining at an alarming pace.

Against this dire background, the EU has drafted a Biodiversity Strategy 2030, an ambitious framework aimed to tackling the key reasons behind biodiversity loss. The plan hinges around a few main elements, such as the establishment of protected areas for at least 30% of Europe''s lands and seas (Fig 3); a significant increase of biodiversity‐rich landscape features on agricultural land by establishing buffer zones like hedges and fallow fields; halting and reversing the decline of pollinators; and planting 3 billion trees by 2030 (https://ec.europa.eu/info/strategy/priorities‐2019‐2024/european‐green‐deal/actions‐being‐taken‐eu/eu‐biodiversity‐strategy‐2030_en). The budget for implementing these measures was set at €20 billion per year.Open in a separate windowFigure 3Natura 2000, the EU''s network of protected areasIn 2019, 18% of land in the EU was protected as Natura 2000, with the lowest share of protected land in Denmark (8%) and the highest in Slovenia (38%). In 2019, the largest national network of terrestrial Natura 2000 sites was located in Spain, covering 138,111 km², followed by France (70,875 km²) and Poland (61,168 km²). Reproduced from Eurostat: https://ec.europa.eu/eurostat/statistics‐explained/index.php?title=Main_Page “Nature is vital for our physical and mental wellbeing, it filters our air and water, it regulates the climate and it pollinates our crops. But we are acting as if it didn''t matter, and losing it at an unprecedented rate”, said Virginijus Sinkevičius, Commissioner for the Environment, Oceans and Fisheries, at the press launch of the new EU action (https://ec.europa.eu/commission/presscorner/detail/en/ip_20_884). “This new Biodiversity Strategy builds on what has worked in the past, and adds new tools that will set us on a path to true sustainability, with benefits for all. The EU''s aim is to protect and restore nature, to contribute to economic recovery from the current crisis, and to lead the way for an ambitious global framework to protect biodiversity around the planet”.Environmental groups and other stakeholders have welcomed the EU''s pledge in principle. “This is a unique opportunity to shape a new society in harmony with nature”, applauded Wetlands International. “We must not forget that the biodiversity and climate crisis is a much bigger and persistent challenge for humanity than COVID‐19”, (https://europe.wetlands.org/news/welcoming‐the‐eu‐biodiversity‐strategy‐for‐2030/). EuroNatur, a foundation focused on conservation, stated that the goals set out by the new strategy provide a strong basis for improving the state of nature in the EU (www.euronatur.org).Alongside the voices of praise, however, many have expressed concerns that the strategy could turn into a little more than a wish list. “The big issue of the strategy is that while setting a goal for financial funds, the EU does not specify where the money is supposed to come from. It only says it should include ‘EU funds and national and private funding’”, commented the European Wilderness Society, an environmental advocacy non‐profit organization headquartered in Tamsweg, Austria. “Goals are important, but do not create change without an organized and sustainable implementation. It''s a good and ambitious document, but what is also obvious is the lack of strategy of how to implement it, and a lack of discussion of why previous documents of this type failed” (https://wilderness‐society.org/ambitious‐eu‐biodiversity‐strategy‐2030/).

Alongside the voices of praise, however, many have expressed concerns that the strategy could turn into a little more than a wish list.

The Institute for European Environmental Policy (IEEP) is on the same page. The sustainability think‐tank based in Brussels and London noted that the outgoing EU 2020 biodiversity strategy showed major implementation problems, especially because of lack of engagement at national level and of ad hoc legislation supporting the meeting of key targets. Therefore, “[it] can be argued that a legally binding approach to the biodiversity governance framework is urgently needed unless Member States and other key stakeholders can show greater intrinsic ownership to deliver on agreed objectives”, (https://ieep.eu/news/first‐impressions‐of‐the‐eu‐biodiversity‐strategy‐to‐2030). In addition, IEEP remarked that money is an issue, since the €20 billion figure appears more as an estimate than a certified obligation.“The intentions of the Commission are good and the strategy contains a number of measures and targets that can really make a difference. However, implementation depends critically on the member states and experiences with the Common Agricultural Policy the past decade or so have taught us that many of them are more interested in short‐term economic objectives than in safeguarding the natural wealth of their country for future generations”, commented David Kleijn, an ecologist and nature conservation expert at the Wageningen University, the Netherlands. “I think it is important that we now have an ambitious Biodiversity Strategy but at the same time I have little hope that we will be able to achieve its objectives”.

I think it is important that we now have an ambitious Biodiversity Strategy but at the same time I have little hope that we will be able to achieve its objectives.

There is further criticism against specific measures, such as the proposal of planting 3 billion trees. “To have lots of trees planted in an area does not necessarily translate into an increase of biodiversity. Biodiverse ecosystems are the result of million years of complex multi‐species interactions and evolutionary processes, which are not as easy to restore”, explained plant ecologist Susana Gómez‐González, from the University of Cádiz, Spain. Planting a large number of trees is a too simplistic approach for saving European forests from the combined effects of excessive anthropic pressure and climate change, and could even have detrimental effects (see Box 1). More emphasis should be placed instead in reducing tree harvesting in sensitive areas and in promoting natural forest renewal processes (Gómez‐González et al, 2020). “For a biodiversity strategy, increasing the number of trees, or even increasing the forest area, should not be an objective; priority should be given to the conservation and restoration of natural ecosystems, forests and non‐forests”, Gómez‐González said.In other cases, it could be difficult, if not impossible, to reach some of the goals because of lack of information. For example, one of the roadmap''s targets is to restore at least 25,000 km of Europe''s rivers back to free‐flowing state. However, the number of barriers dispersed along European rivers will probably prevent even getting close to the mark. An international research team has collected detailed information on existing instream barriers for 147 rivers in 36 European countries, coming up with the impressive figure of over 1.2 million obstacles that inevitably impact on river ecosystems, affecting the transport and dispersion of aquatic organisms, nutrients, and sediments (Belletti et al, 2020). Existing inventories mainly focused on dams and other large barriers, while, in fact, a large number of artificial structures are much smaller, such like weirs, locks, ramps, and fords. As a result, river fragmentation has been largely underestimated, and the models used to plan flow restoration might be seriously flawed. “To avoid ‘death by a thousand cuts’, a paradigm shift is necessary: to recognize that although large dams may draw most of the attention, it is the small barriers that collectively do most of the damage. Small is not beautiful”, concluded the authors (Belletti et al, 2020).

Box 1: Why many trees don''t (always) make a forestForests are cathedrals of biodiversity. They host by far the largest number of species on land, which provide food and essential resources for hundreds of millions of people worldwide. However, forests are disappearing and degrading at an alarming pace. The loss of these crucial ecosystems has given new impulses to a variety of projects aimed at stopping this devastation and possibly reversing the trend.Once it is gone, can you rebuild a forest? Many believe the answer is yes, and the obvious solution is to plant trees. Several countries have thus launched massive tree‐planting programs, notably India and Ethiopia, where 350 million trees have been planted in single day (https://www.unenvironment.org/news‐and‐stories/story/ethiopia‐plants‐over‐350‐million‐trees‐day‐setting‐new‐world‐record). The World Economic Forum has set up its own One Trillion Tree initiative (https://www.1t.org/) “to conserve, restore, and grow one trillion trees by 2030”. Launched in January last year at Davos, 1t.org was conceived as a platform for governments, companies and NGOs/civil society groups to support the UN Decade on Ecosystem Restoration (2021–2030). The initiative has been christened by renowned naturalist Jane Goodall, who commented: “1t.org offers innovative technologies which will serve to connect tens of thousands of small and large groups around the world that are engaged in tree planting and forest restoration”, (https://www.weforum.org/agenda/2020/01/one‐trillion‐trees‐world‐economic‐forum‐launches‐plan‐to‐help‐nature‐and‐the‐climate/).However, things are way more complicated than they appear: large‐scale tree planting schemes are rarely a viable solution and can even be harmful. “[A] large body of literature shows that even the best planned restoration projects rarely fully recover the biodiversity of intact forests, owing to a lack of sources of forest‐dependent flora and fauna in deforested landscapes, as well as degraded abiotic conditions resulting from anthropogenic activities”, commented Karen Holl from the University of Caliornia, Santa Cruz, and Pedro Brancalion from the University of São Paulo (Holl & Brancalion, 2020). A common problem of tree plantations, for example, is the low survival rate of seedlings, mostly because the wrong tree species are selected and due to poor maintenance after planting. Moreover, grasslands and savannas, which are often targeted for establishing new forests, are themselves treasure troves of biodiversity. Ending indiscriminate deforestation, improving the protection of existing forests, and promoting their restoration would therefore be a more efficient strategy to preserve biodiversity in the shorter term. If tree planting is indeed necessary, it should be well planned by selecting the right areas for reforestation, using suitable tree species that can maximize biodiversity, and involving local populations to maintain the plantations, Holl and Brancalion argue (Holl & Brancalion, 2020).

…even the best planned restoration projects rarely fully recover the biodiversity of intact forests, owing to a lack of sources of forest‐dependent flora and fauna in deforested landscapes…

The health of soil, where a high proportion of biodiversity is hosted, is another problem the new strategy should address in a more focused manner. “In my opinion, the EU Biodiversity Strategy is already a leap forward in terms of policy interest in soils in general and in soil biodiversity in particular. Compared with other nations/regions of the world, Europe is by far in the forefront regarding this issue”, commented Carlos António Guerra at the German Centre for Integrative Biodiversity Research (iDiv) in Leipzig, Germany, and Co‐leader of the Global Soil Biodiversity Observation Network (https://geobon.org/bons/thematic‐bon/soil‐bon/). “Nevertheless, the connection between soil biodiversity and ecological functions needs further commitments. Soils allow for horizontal integration of several policy agendas, from climate to agriculture and, very importantly, nature conservation. This is not explicit in the EU Biodiversity Strategy in regard to soils”. It remains to be seen if EU restoration plan will emphasize soil biodiversity, or consider it as a mere side effect of other initiatives, Guerra added. “A soil nature conservation plan should be proposed”, he said. “Only such a plan, that implies that current and future protected areas have to consider, describe and protect their soil biodiversity would make a significant push to help protect such a valuable resource”.More generally, research shows that the current paradigm of protection must be shifted to prevent further losses to biodiversity. In fact, an analysis of LIFE projects—a cornerstone of EU nature protection—found that conservation efforts are extremely polarized and strongly taxonomically biased (Mammola et al, 2020). From 1992 to 2018, investment in vertebrates was sixfold higher than that for invertebrates, with birds and mammals alone accounting for 72% of the targeted species and 75% of the total budget. In relative terms, investment per species for vertebrates has been 468 times higher than for invertebrates (Fig 4). There is no sound scientific reasoning behind this uneven conservation attention, but just popularity. “[T]he species covered by a greater number of LIFE projects were also those which attracted the most interest online, suggesting that conservation in the EU is largely driven by species charisma, rather than objective features”, the researchers wrote (Mammola et al, 2020).Open in a separate windowFigure 4Taxonomic bias in EU fauna protection effortsBreakdown of the number of projects (A) and budget allocation (B) across main animal groups covered by the LIFE projects (n = 835). (C) The most covered 30 species of vertebrates (out of 410) and invertebrates (out of 78) in the LIFE projects analyzed (n = 835). The vertical bar represents monetary investment and the blue scatter line the number of LIFE projects devoted to each species. Reproduced from (Mammola et al, 2020), with permission.     

MAPS Database: Medicinal plant Activities,Phytochemical and Structural Database

Drug development from natural sources is an important and fast developing area. Natural sources (plants) have been used to cure a range of diseases for Thousands of years. Different online medicinal plant databases provide information about classifications, activities, phytochemicals and structure of phytochemicals in different formats. These databases do not cover all aspects of medicinal plants. MAPS (Medicinal plant Activities, Phytochemicals & structural database) has been constructed with uniqueness that it combines all information in one web resource and additionally provides test targets on which particular plant found to be effective with reference to the original paper as well. MAPS database is user friendly information resource, including the data of > 500 medicinal plants. This database includes phytochemical constituents, their structure in mol format, different activities possessed by the medicinal plant with the targets reported in literature.

Availability

http://www.mapsdatabase.com     

Reply to Bronstein and Vinogradov

The response by the author. Subject Categories: S&S: Economics & Business, S&S: Ethics

I thank Michael Bronstein and Sophia Vinogradov for their interest and comments. I would like to respond to a few of their points.First, I agree with the authors that empirical studies should be conducted to validate any approaches to prevent the spread of misinformation before their implementation. Nonetheless, I think that the ideas I have proposed may be worth further discussion and inspire empirical studies to test their effectiveness.Second, the authors warn that informing about the imperfections of scientific research may undermine trust in science and scientists, which could result in higher vulnerability to online health misinformation (Roozenbeek et al, 2020; Bronstein & Vinogradov, 2021). I believe that transparency about limitations and problems in research does not necessarily have to diminish trust in science and scientists. On the contrary, as Veit et al put it, “such honesty… is a prerequisite for maintaining a trusting relationship between medical institutions (and practitioners) and the public” (Veit et al, 2021). Importantly, to give an honest picture of scientific research, information about its limitations should be put in adequate context. In particular, the public also should be aware that “good science” is being done by many researchers; we do have solid evidence of effectiveness of many medical interventions; and efforts are being taken to address the problems related to quality of research.Third, Bronstein and Vinogradov suggest that false and dangerous information should be censored. I agree with the authors that “[c]ensorship can prevent individuals from being exposed to false and potentially dangerous ideas” (Bronstein & Vinogradov, 2021). I also recognize that some information is false beyond any doubt and its spread may be harmful. What I am concerned about are, among others, the challenges related to defining what is dangerous and false information and limiting censorship only to this kind of information. For example, on what sources should decisions to censor be based and who should make such decisions? Anyone, whether an individual or an organization, with a responsibility to censor information will likely not only be prone to mistakes, but also to abuses of power to foster their interests. Do the benefits we want to achieve by censorship outweigh the potential risks?Fourth, we need rigorous empirical studies examining the actual impact of medical misinformation. What exactly are the harms we try to protect against and what is their scale? This information is necessary to choose proportionte and effective measures to reduce the harms. Bronstein and Vinogradov give an example of a harm which may be caused by misinformation—an increase in methanol poisoning in Iran. Yet, as noticed by the authors, misinformation is not the sole factor in this case; there are also cultural and other contexts (Arasteh et al, 2020; Bronstein & Vinogradov, 2021). Importantly, the methods of studies exploring the effects of misinformation should be carefully elaborated, especially when study participants are asked to self‐report. A recent study suggests that some claims about the prevalence of dangerous behaviors, such as drinking bleach, which may have been caused by misinformation are largely exaggerated due to the presence of problematic respondents in surveys (preprint: Litman et al, 2021).Last but not least, I would like to call attention to the importance of how veracity of information is determined in empirical studies on misinformation. For example, in a study of Roozenbeek et al, cited by Bronstein and Vinogradov, the World Health Organization (WHO) was used as reliable source of information, which raises questions. For instance, Roozenbeek et al (2020) used a statement “the coronavirus was bioengineered in a military lab in Wuhan” as an example of false information, relying on the judgment of the WHO found on its “mythbusters” website (Roozenbeek et al, 2020). Yet, is there a solid evidence to claim that this statement is false? At present, at least some scientists declare that we cannot rule out that the virus was genetically manipulated in a laboratory (Relman, 2020; Segreto & Deigin, 2020). Interestingly, the WHO also no longer excludes such a possibility and has launched an investigation on this issue (https://www.who.int/health‐topics/coronavirus/origins‐of‐the‐virus, https://www.who.int/emergencies/diseases/novel‐coronavirus‐2019/media‐resources/science‐in‐5/episode‐21‐‐‐covid‐19‐‐‐origins‐of‐the‐sars‐cov‐2‐virus); the information about the laboratory origin of the virus being false is no longer present on the WHO “mythbusters” website (https://www.who.int/emergencies/diseases/novel‐coronavirus‐2019/advice‐for‐public/myth‐busters). Against this backdrop, some results of the study by Roozenbeek et al (2020) seem misleading. In particular, the perception of the reliability of the statement about bioengineered virus by study participants in Roozenbeek et al (2020) does not reflect the susceptibility to misinformation, as intended by the researchers, but rather how the respondents perceive reliability of uncertain information.I hope that discussion and research on these and related issues will continue.     

FastTrack: An open-source software for tracking varying numbers of deformable objects

Analyzing the dynamical properties of mobile objects requires to extract trajectories from recordings, which is often done by tracking movies. We compiled a database of two-dimensional movies for very different biological and physical systems spanning a wide range of length scales and developed a general-purpose, optimized, open-source, cross-platform, easy to install and use, self-updating software called FastTrack. It can handle a changing number of deformable objects in a region of interest, and is particularly suitable for animal and cell tracking in two-dimensions. Furthermore, we introduce the probability of incursions as a new measure of a movie’s trackability that doesn’t require the knowledge of ground truth trajectories, since it is resilient to small amounts of errors and can be computed on the basis of an ad hoc tracking. We also leveraged the versatility and speed of FastTrack to implement an iterative algorithm determining a set of nearly-optimized tracking parameters—yet further reducing the amount of human intervention—and demonstrate that FastTrack can be used to explore the space of tracking parameters to optimize the number of swaps for a batch of similar movies. A benchmark shows that FastTrack is orders of magnitude faster than state-of-the-art tracking algorithms, with a comparable tracking accuracy. The source code is available under the GNU GPLv3 at https://github.com/FastTrackOrg/FastTrack and pre-compiled binaries for Windows, Mac and Linux are available at http://www.fasttrack.sh.     

DOMINO: a network‐based active module identification algorithm with reduced rate of false calls

Algorithms for active module identification (AMI) are central to analysis of omics data. Such algorithms receive a gene network and nodes'' activity scores as input and report subnetworks that show significant over‐representation of accrued activity signal (“active modules”), thus representing biological processes that presumably play key roles in the analyzed conditions. Here, we systematically evaluated six popular AMI methods on gene expression and GWAS data. We observed that GO terms enriched in modules detected on the real data were often also enriched on modules found on randomly permuted data. This indicated that AMI methods frequently report modules that are not specific to the biological context measured by the analyzed omics dataset. To tackle this bias, we designed a permutation‐based method that empirically evaluates GO terms reported by AMI methods. We used the method to fashion five novel AMI performance criteria. Last, we developed DOMINO, a novel AMI algorithm, that outperformed the other six algorithms in extensive testing on GE and GWAS data. Software is available at https://github.com/Shamir‐Lab.     

The Overlap of Small Molecule and Protein Binding Sites within Families of Protein Structures

Protein–protein interactions are challenging targets for modulation by small molecules. Here, we propose an approach that harnesses the increasing structural coverage of protein complexes to identify small molecules that may target protein interactions. Specifically, we identify ligand and protein binding sites that overlap upon alignment of homologous proteins. Of the 2,619 protein structure families observed to bind proteins, 1,028 also bind small molecules (250–1000 Da), and 197 exhibit a statistically significant (p<0.01) overlap between ligand and protein binding positions. These “bi-functional positions”, which bind both ligands and proteins, are particularly enriched in tyrosine and tryptophan residues, similar to “energetic hotspots” described previously, and are significantly less conserved than mono-functional and solvent exposed positions. Homology transfer identifies ligands whose binding sites overlap at least 20% of the protein interface for 35% of domain–domain and 45% of domain–peptide mediated interactions. The analysis recovered known small-molecule modulators of protein interactions as well as predicted new interaction targets based on the sequence similarity of ligand binding sites. We illustrate the predictive utility of the method by suggesting structural mechanisms for the effects of sanglifehrin A on HIV virion production, bepridil on the cellular entry of anthrax edema factor, and fusicoccin on vertebrate developmental pathways. The results, available at http://pibase.janelia.org, represent a comprehensive collection of structurally characterized modulators of protein interactions, and suggest that homologous structures are a useful resource for the rational design of interaction modulators.