dataesr/scientific-paragraphs-categorization huggingface dataset:
One major difficulty with some large-scale data is that PPIs detected using the same methods or with different methods by different research groups but on the same organism can display very limited overlap (false positives: detectable interactions but functionally irrelevant; false negatives: miss interactions that do occur) calling for major curation efforts to filter out unreliable interactions (remove the noise) and quantification of errors. These differences also suggest that the techniques could be providing complementary descriptions. [48] There are in fact many reasons for such discrepancies including the obvious differences between the experimental methods used (i. e. , some methods can capture transient interactions, others are geared towards identification of stable interactions, etc), but yet, at present, these differences represent a serious concern for the PPI field as downstream analyses of the resulting networks can preclude meaningful estimates of the size of the functional interactome or of the importance of some interactions in a given disease condition.
Adoption of the Proteomics Standards Initiative Molecular Interaction (PSI-MI) format  [49]  and the related directives of the IMEx consortium should help improve the quality of the data,  [50]  but much work is needed in this area. It has been assumed until recently that literature curated PPI data were of higher accuracy than those Special Issue STRASBOURG produced by high throughput studies because they were derived from focused investigations, but recent analyses are suggesting that this is not longer the case. [51] ome well-known databases include, the Database of Interacting Proteins (DIP),  [52]  the Biomolecular Interaction Network (BIND),  [53]  the Molecular Interaction (MINT),  [54]  the Mammalian ProteinÀProtein Interaction (MIPS),  [55]  the hostpathogen interaction database (HPIDB),  [56]  IntAct,  [57]  Bio-GRID,  [58]  STRING.
[59] Structural information can be found at the Protein Data Bank (PDB) and for instance PISite collects interface data from the PDB. [60] Structures of domaindomain interactions are available from 3did,  [61]  and iPfam,  [62]  while a spatial classification of 3D protein domain interaction database, KBDOCK, has recently been reported. [63] The database Instruct contains high-quality 3D structurally resolved protein interactome networks. [64] Homology models can also be used to study further PPIs and increase coverage. The Interactome3D database  [46]  provides 12 000 structurally resolved PPIs in 8 organisms while Instruct contains over 6500 human PPIs. [64] Another resource, the Protein Interaction and Molecule Search (PRIMOS) platform, represents a novel web portal that unifies six primary PPI databases (BIND; DIP; HPRD (Human Protein Reference Database); IntAct; MINT and MIPS, Munich Information Center for Protein Sequences) into a single consistent repository.
[65] long the same line, iRefWeb is a bioinformatics resource that offers access to a large collection of data on proteinÀ protein interactions in over a thousand organisms. This collection is consolidated from 14 major public databases. [51] imilarly, curated PPI data can also be found via the PSIC-QUIC Web service, which provide access on the fly to files made available by over 28 source databases. [66] Also, the DASMIweb service currently has access to 36 distributed data sources. Ten of these provide interaction data that have been experimentally determined or curated from the scientific literature, 24 data sources contain computational predictions, and 2 data sources can be used for scoring the quality of the interactions. [67] Another example is UniHI 7 (Unified Human Interactome). The online tool integrates about 350 000 molecular interactions for more than 30 000 human proteins.
Besides proteinÀprotein interactions from 12 different resources (including HPRD, BioGrid, IntAct, DIP, BIND and Reactome databases) as well as four interaction maps produced by computational predictions and two high-throughput yeast-2-hybrid screens, UniHI 7 also comprises curated transcriptional regulatory interactions from three complementary databases TRANSFAC, miRTarBase and HTRIdb. In addition to these interactions, the service also integrated drug target information from DrugBank that can be mapped and visualized online without having to download, manually process and load the data into an external standalone application. [68] The data can be filtered by the users (e. g. , number of PubMed references, smallscale or large-scale experiments, direct or indirect connection, binary or complex interaction).
PPI networks can be derived from data collected by the above-mentioned methods, namely, methods that probe binary interactions (e. g, Y2H), and approaches that detect multi-protein complexes (e. g. , AP-MS). Both, AP-MS and binary detection methods probe non-native protein constructs, where tags are appended to the native polypeptides, potentially altering their properties. [45] As mentioned above, networks can also be built from curated PPI data collected from the literature. All these data can be visualized using, for example, the Cytoscape package. [69] Such PPI networks, comprising in human approximately 130 000 to 650 000 protein interactions (only a small subset has been fully experimentally identified)  [70, 71]  can shed new light on human diseases. [72, 73] Further, monitoring portions of the network that change when cellular states and conditions are altered could also give new insights about the health and disease states.
[2] Essential proteins tend to be more interconnected than nonessential proteins. It would seem that human disease-associated proteins too, are more interconnected than non-disease proteins. [74] inally, as whole genome and transcriptome sequencing gets cheaper and faster, gene expression profile analyses of Special Issue STRASBOURG normal and pathological conditions should also contribute to the initial identification of clinically relevant PPIs that will then require further in depth investigation. For instance, the pro-survival IAP and BCL-2 proteins represent highly attractive PPI targets since their over-expression is associated with tumor progression and maintenance. Many comparative gene expression databases have therefore been developed, that allow the retrieval, analysis and comparison of gene expression patterns within or among species (see for instance the literature  [77] [78] [79]  ).
[3] Analysis of a novel PPI dataset suggest that the minimum protein surface that must be buried to form a functional complex is in the order of 900 2 (about 500 2 provided by each partner) and involves about 12 residues on each partner (of course these values can differ slightly depending on the datasets and the way the computations are carried out). A large majority of atoms in non-obligate interfaces are usually still accessible to the solvent. Relative to the accessible protein surface, the interfaces of such complexes are depleted in Glu, Asp and Lys and enriched in Met, Tyr and Trp. The rim made of residues in which none of the interface atoms are fully buried has a composition close to the protein accessible surface. The core comprises buried atoms and about 55 % of all interface residues.
g. , 1200 to 2000 to well over 3000 2 ). [3] In addition, molecular dynamics studies have shown that hotspots are relatively rigid as compared to the surrounding interface residues. [99] Of importance also is the recent estimation of the number of possible protein interaction types, estimated to be around 4000. [100]  By looking at the structure of the interface area and through investigations of the Protein Data Bank,  [101]  it seems that the interface space is limited and even chains with different folds often have similar interfaces. [104]  Further, it is important to note that many protein complexes seem to be dominated by a hot segment where the interaction is dominated by a continuous epitope and as such hot segments could be good predictors of PPI druggability. [34] Still along this line of attempting to predict PPI druggability in term of a region capable of binding a small molecule, a recently reported study attempts to define classes of PPIs that could be more easily modulated by low molecular weight compounds and suggests that the "tight and narrow" and "weak and narrow" proteinÀprotein complex categories are good candidates.
[117] Meta-servers, combining different tools have also been developed such as meta-PPISP. [119] Protein interface can also be probed by docking. [113] ther tools developed to predict specifically hotspots (the experimental approach commonly used is alaninescanning) require in general the 3D structure of the protein complex and can use, for instance, an empirical scoring function to assess the interface (e. g. , HotPoint  [120]  ) while others are energy-based like ROBETTA (or ROSETTA),  [121]  or FoldX,  [122]  or iPred. 123] Other methods for hotspot prediction use the unbound protein structures of each partner of a complex and docking e. g. , the module pyDockNIP of the pyDock software package  [97, 124]  ). Hotspots can also be investigated by molecular dynamics in water and in isopropanol/water cosolvent environment (see for instance the literature  [125]  ).
[165] ools to compare traditional binding pockets have been developed  [149]  and some examples of recently reported approaches include PocketAnnotate,  [166]  APoc  [167]  and Site-Comp. [168] All these pockets have been stored in databases like for instance the pocketome. [169] Somewhat related, in a recent study, interfaces were defined and clustered leading to the identification of 22 604 unique interface structures in the PDB. [170] PPIs are Challenging but Should be Tractable Molecular Targets: Supports from In Silico Methods Despite their therapeutic relevance, most small molecule drugs do not in general hit PPIs but rather enzymes, ion channels, nuclear hormone receptors and G-protein coupled receptors. In fact, these last 50 years, PPIs have been essentially modulated by therapeutic antibodies, therapeutic proteins and peptides (or modified peptides or more recently stapled peptides).
At present, at least 3 databases are dedicated to modulators of PPIs, the 2P2Idb (manually curated),  [161]  TIMBAL,  [184]  and iPPI-DB (manually curated). [185] 4, 186]  Two research groups provide in silico filters that help to design PPI-focused libraries enriched in PPI inhibitors starting from large traditional compound collections. A decision tree approach was used by Reynes et al. [187]  (PPI-HitProfiler which is now available online via the ADME-Tox filtering tool FAF-Drugs2,  [188]  ) while support vector machines were used by Hamon et al. [189]  (2P2I HUNTER ). Two studies reporting the rational design of compound collections dedicated to PPI that contain alpha helical binding epitopes integrating the concept of increasing the three-dimensionality of the compounds have been reported recently.
Principal Component Analysis (PCA) on a commercial and allosteric databases and a database of inhibitors of proteinÀprotein interactions. A chemical vendor database, an allosteric database (version 2013)  [270]  and the proteinÀprotein interaction inhibitor iPPI-DB (version 2013)  [185]  were used in this analysis. Redundant compounds, amino acids, salts, compounds with less than 10 or more than 140 heavy atoms, compounds with a molecular weight less than 150 g/mol, or more than 1000 g/mol, compounds with a logP less than À10 or more than 10, carbocation, compounds with phosphorus, and non-organic compounds were removed with PipelinePilot v. 8. 5. A diversity criterion was applied on our dataset using Bemis and Murcko assemblies on PipelinePilot v. 8. 5. The dataset contained 421 522 compounds for the commercial database, 5141 allosteric compounds and 685 proteinÀprotein inhibitor compounds.
The PCA calculations were run using 18 physico-chemical properties. (a) PCA of commercial database, allosteric database and iPPI-DB: individual map of compounds, PPI inhibitors are represented as color dots, commercial compounds are represented as black dots and allosteric compounds are represented in grey. (b) PCA of commercial database, allosteric database and iPPI-DB: variable map of axes 1 and 2. (c) PCA of commercial database, allosteric database and iPPI-DB: variable map of axes 1 and 3. (d) IPPI family colors used for the PCA. The three first axes of the PCA represent 62 % of the total variance. The first axis is represented by the compound's aromaticity (number of benzene-like rings and number of sp2 carbon) and the compound's complexity (number of sp3 carbon and Csp3 Ratio). The second axis is characterized by the number of hydrogen bond donors/acceptors.
The third axis is characterized by the compound's polarity (here evaluated by TPSA, topological surface area). The global position of the proteinÀprotein interaction inhibitors population is the upper part of the individual map showing that these compounds seems to be more hydrophobic and more aromatic. According to the global position of the commercial and the allosteric compounds, the two dataset seems to share the same chemical space, even if, a small number of allosteric compounds presents an unusual profile. This tendency was confirmed on 5 similar analyses with different commercial datasets from different chemical vendors. higher doses which in turn can increase the risk of adverse drug reactions (e. g. , increased promiscuity leading to increase binding to anti-targets). [199] Further, knowing that starting hits usually have to grow in size during the compound optimization phase with in general a further increase in log P (in fact compounds that have more chance to succeed tend to preserve a relatively low lipophilicity during the optimization program),  [200]  a clever design of PPI screening collection is required such as to obtain compounds with balanced ADME-Tox properties still compatible with the binding site features present at or near the pro-teinÀprotein interfaces.
Virtual Screening of PPIs PPIs can be probed using HTS or virtual screening experiments followed by in vitro assays of a small selected list of molecules resulting from these computations. ] [215] [216] [217] [218] [219] [220] [221] [222] [223]  In silico screening search can be performed on libraries that contain physically existing compounds, on PPI enriched focused collections or on virtual libraries, and thus on compounds that are not yet synthesized. Noteworthy is the fact that virtual screening can be used on very large databases that no experimental approaches can tackle. It is important to remember that the easily accessible drug-like space contains about 10 33 molecules  [224]  and that with 17 atoms and simple chemistry rules, it is already possible to generate 166 billion compounds.
[229, 230] irtual screening approaches have been traditionally subdivided into two main methods  [231] [232] [233] [234] [235] [236]  (Figure  7 ): first, ligand-based screening, in which 2D or 3D chemical structures or molecular descriptors of known actives (and sometimes inactive molecules) are used to retrieve other compounds of interest from a database using some types of similarity measure or by seeking a common substructure or pharmacophore between the query molecule and the compounds in the database; and second, structure-based (or 3D receptor-based) screening in which compounds from the database are docked into a binding site (or over the entire surface) and are ranked using one or several scoring functions. Structure-based virtual screening also includes tools to perform binding site-derived pharmacophore search.
[256] , VEGFR-2 is known to be a main initiator of signaling pathways in endothelial cells. [257] The VEGF-VEGFR system is a validated and promising target for anti-angiogenic treatments. Although the VEGF-VEGFR interface was found to be one of the flattest proteinÀprotein interfaces available in the investigated dataset (the interface is relatively large > 800 2 with a planarity value of 1. 7  [43]  ), well below those of most transient proteinÀprotein complexes (mean planarity value = 2. 7 ,  [258]  successful structure-based in silico screening was performed by targeting the VEGFbinding zone of the extracellular domain D2 of VEGFR-1. As flexibility is known to be important at proteinÀprotein interfaces, the DFprot server was used to investigate the possible plasticity of the D2 domain of VEGFR-1.
[259] Analysis of the X-ray structure (with the probe mapping algorithm Pro-toMol implemented in the screening package Surflex and with LigBuilder) and of the simulation suggested that this region of the D2 domain was essentially rigid and as such docking experiments were performed on only one 3D structure of the D2 domain. Then, 8000 proprietary druglike molecules (a subset of the French National Compound Collection) were docked with Surflex  [260]  onto the predicted binding pockets of the target (Figure  8 ). After the in silico analysis, 206 compounds were selected for in vitro assays. Twenty compounds inhibiting the formation of the VEGF-VEGFR complex in the micromolar range were identified. The bioactive molecules contained a (3-carboxy-2-ureido)thiophen unit and the best IC 50 was ~10 mM.
We have used a structure-based virtual screening approach to discover drug-like molecules that bind to an exosite of APC (the catalytic site should remain functional as much as possible to carry out the cytoprotective effect) and inhibit the interaction between APC and its substrate FVa. [44] Such molecules could potentially be developed into drugs to treat bleeding disorders. The experimentally determined 3D structure of APC was used and druggable binding pockets were search using several different in silico tools (FTsite, DoGSiteScorer and MetaPocket which combines 8 predictors: LIGSITEcs, PASS, Q-SiteFinder, SURFNET, Fpocket, GHECOM, ConCavity and POCASA). Potentially interesting sites on APC were identified in one exosite located next to the active site (Figure  9 ). Structure-based screening (with the package Surflex) of 50 000 compounds (ChemBridge Diversity set) resulted in the identification of 624 compounds that were then experimentally tested.
These in silico methods include PPI network analysis, structural analysis and prediction of the interfaces, druggability predictions, rational design of focused compound collections and various virtual screening computations. Drug re-Special Issue STRASBOURG Figure  9. Inhibition of the APC-FVa interaction. A schematic diagram represents the anticoagulant activated protein C (APC) (left). APC is composed of a Gla domain allowing interactions with the appropriate cell membranes, two EGF-like modules and a serine protease (SP) domain. Such organization positions the active site far above the membrane, in the right location to interact with its substrates and partner proteins. A diversity set containing 50 000 molecules was docked in an exosite of APC (right, top) and a possible pose for one active compound is shown (right, bottom).
His clinical research is dedicated to early-phases clinical trials in oncology. CØline LabbØ is an engineer at the Inserm 973 research unit. She has a master degree on bioinformatics. She works on new in silico methods that assist the rational design of low molecular weight inhibitors of protein-protein interactions. Currently, she is developing a unique manually curated database of PPI modulators. Dr. David Lagorce is Research Engineer at INSERM since 1995 and working at MTi unit 973. His research area is cheminformatics, small compound library design and in silico ADMET predictions. Dr. Olivier Sperandio is research scientist at INSERM-University Paris Diderot U973. He has a background in chemistry and chemoinformatics. He is the head of the iPPI-DB initiative (http:// www. ippidb. cdithem.
Introduction This paper, one of a set associated with the 2013 release of data from the Planck 1  mission (Planck Collaboration I 2013), describes a frequentist estimation of cosmological parameters using profile likelihoods. Parameter estimation in cosmology is predominantly performed using Bayesian inference, particularly following the introduction of Markov chain Monte Carlo (MCMC) techniques  (Christensen et al. 2001). Many scientists in the field use the so-phisticated CosmoMC 2  software  (Lewis & Bridle 2002)  to study cosmological parameters, and several experiments provide readyto-use plugins for it. The Planck satellite mission has recently released high-quality data on the cosmic microwave background (CMB) temperature anisotropies. 3 The analysis of the cosmological parameters  (Planck Coll.
Such a method has already been used by  Yèche et al. (2006)  with Wilkinson Microwave Anisotropy Probe (WMAP) data for a nine-parameter fit. The high sensitivity of data from Planck and from the ground-based South Pole Telescope (SPT) and Atacama Cosmology Telescope (ACT) projects requires the simultaneous fit of a larger number of parameters, up to about 40, with some nuisance ones being poorly constrained. We therefore need to precisely tune a high-quality minimizer, as will be described in this paper. MCMC sampling is sometimes used to perform a "poorman's" determination of the maximum likelihood (e. g. ,  Reid et al. 2010) : one bins a given parameter and reports the sample of maximum likelihood in other dimensions. As pointed out in  Hamann (2012) , in many dimensions it is most likely that the real maximum was never reached in any reasonably-sized chain.
2007; Gonzalez-Morales et al. 2011; Hamann 2012). Given the sensitivity of the Planck data, statistical methodologies may matter, and this issue is scrutinized in this work. In order to build precise profile likelihoods in a highdimensional space (up to about 40 dimensions), we need a powerful minimizer. We use the mature and widely-used Minuit software  (James & Roos 1975). We interfaced it to the modular class Boltzmann solver  (Blas et al. 2011 ) which, from a set of input cosmological parameters, computes the corresponding temperature and polarization power spectra that are tested against the Planck likelihood. This required that we tune the class precision parameters to a level where the numerical noise can be handled by our minimizer, as is described in Sect. 2. 1. In Sect. 2. 2, we describe our Minuit minimization strategy, and cover in Sect.
The Boltzmann solver: class To compute the relevant CMB power spectra from a cosmological model, we need a "Boltzmann solver" that numerically evolves the coupled perturbation equations in an expanding universe. While camb is used in the CosmoMC sampler, we prefer to use the class (v1. 6) software  (Blas et al. 2011). It offers a rigorous way to control the accuracy of output quantities through a comprehensive list of precision parameters  (Lesgourgues 2011a). While one can use some high-speed/low-quality settings to perform MCMC sampling because the random nature of the algorithm smooths out discontinuities, this is no longer the case here when searching for an extremum, which requires precise computation of numerical derivatives. Equally, due to computation time, one cannot use precision settings that are too extreme, so a trade-off with Minuit convergence has to be found.
Minimizing with Minuit We chose to work with the powerful Minuit package  (James & Roos 1975) , a well-known minimizer originally developed for high-energy physics and used recently for the Higgs mass determination with a simultaneous fit of 354 parameters (ATLAS Collaboration 2013). While its roots trace back to the 1970s, it has been continually improved and rewritten in C++ as Minuit2, which is the version we use. Minuit is a toolbox including several algorithms that can be deployed depending on the problem under consideration. We refer the reader to the user guide 4  for a detailed description of the procedures we used. For cosmological parameter estimation with the Planck data, we executed the following strategy   1970). All parameters are bounded by large (or physical) limits during this exploration.