Modern research in life sciences relies vitally on experimental 3D structures and also, undeniably, on models derived through computational and integrative modeling methods. Unfortunately, while high-resolution 3D models from experimental structure determination are well centralized and made easily discoverable by the wwPDB, the same is far from true for computational and integrative models, which remain largely unavailable, difficult to find and disconnected from literature. On the one hand, deposition of such data upon publication is not mandatory, and on the other hand, many existing resources of molecular models are not easily searchable. The purpose of this letter is to call for (i) design and implementation of policies to make computational models openly available and (ii) institution of a definitive, centralized resource that facilitates reuse and discovery of computational models associated to literature.AQ4 Only ∼13% of PubMed articles that used structural models of proteins at the core of the work, sampled from publications of the last year, made the models freely available online (Supplementary Table S1). This is despite the numerous databases available where authors could seamlessly share their models: Model Archive (Schwede et al., 2009), Protein Model Data Bank (Castrignanò et al., 2006), Protein Model Portal (Haas et al., 2013), SwissModel’s and ModBase’s repositories for pure computational models (Pieper et al., 2014; Waterhouse et al., 2018); PDB-Dev (Berman et al., 2019; Burley et al., 2017; Sali et al., 2015) for integrative models; and SASBDB for SAXS-based models (Valentini et al., 2015). Some authors do not even use these services even though they are willing to share their models, which they do as Supplementary Material at publisher’s and their own websites (examples in Supplementary Table S1). We argue that models should be no exception to science-opening policies as depicted by the FAIR principles (Stall et al., 2019; Wilkinson et al., 2016). Models should be made available upon publication just like any other methodological or supporting data that is mandatory for the sakes of reproducibility, reuse, reanalysis and criticism. Our proposal could well apply not only to homology and ab initio models but also to multi-molecule complexes obtained by docking and virtual screening, and to relevant conformations sampled in molecular simulations. Notably, open availability of such data would help not only to judiciously follow the written descriptions from publications in interactive 3D, but would also serve as material for further studies, including future experimental structure determinations [as in Garcia-Alai et al. (2018)]. Open model availability would be especially valuable for models generated with expensive computer resources and very specific expertise. This includes tertiary structure models based on contact/distance predictions plus extensive conformational sampling (Greener et al., 2019; Michel et al., 2017; Ovchinnikov et al., 2015, 2017; Wang et al., 2019) as recent CASP rounds have highlighted (Abriata et al., 2018, 2019), poses from molecular dynamics simulations, which enable investigations unparalleled by experimentation alone (Bottaro and Lindorff-Larsen, 2018), and models from integrative methods that take the most out of experimental data (Rout and Sali, 2019; Vallat et al., 2019). Storage is probably not a limitation, especially with modern repositories like CERN-based Zenodo, which can even host simulation trajectories at reasonable stride (as many researchers are now starting to share). Since the technology and resources required to build the hub that molecular models deserve are easily available, we are only faced with the rather political challenge of reaching agreement among publishers, funding agencies, groups running modeling services and competitions (CASP, CAPRI, etc.) and the varied existing databases of literature-associated models, to set up a definitive, centralized hub that facilitates model deposition, annotation, discovery and reutilization. Such hub (Fig. 1) would allow the community to better benefit from the recent improvements in protein structure prediction, the rise in tools for integrative modeling, and the deep exploratory power of modern molecular simulations. Working of a centralizing archive for models of biological macromolecules, containing only sequences and links to the model-hosting servers. The central archive is a database of protein sequences associated to URLs or DOIs that point to the corresponding citations and downloadable models. This way there is no duplication of model coordinates, keeping a compact web server and allowing authors to submit model coordinates to the most appropriate database together with other relevant data. From the point of view of the users, besides allowing them to directly download models for inspection (green), such database also allows them to find models that match or resemble a query sequence (red), which has useful applications as exemplified in Figure 2. (Color version of this figure is available at Bioinformatics online.) Although one can of course get models from authors upon request, this is far less efficient than direct downloads and does not solve the problem of discoverability. This takes us to the second point of this letter. It would be useful that model-hosting servers allow finding models by routes that do not require knowing about the reporting articles; for example models of proteins should ideally be discoverable through sequence queries. Surprisingly, however, many modern databases and datasets of models, especially those based on methodologies other than comparative modeling (PDB-Dev, SASBDB, published datasets of contact/distance prediction-based models), do not allow sequence-level searches. Some of the available model archives like the Protein Model Portal (Haas et al., 2013), the Protein Model Data Base (Castrignanò et al., 2006) and the Model Archive (Schwede et al., 2009) have no sequence-search functionalities, or have them but unsupported (in particular, the Protein Model Portal will soon be discontinued). One notable resource that has a working sequence-search system is ModBase (Pieper et al., 2014), but this resource is limited to models derived from homology modeling. Today, the most interesting models, i.e. those for proteins that share no detectable similarity to any PDB entry thus being unamenable to homology modeling, those observed in docking and molecular simulation studies and those involving larger assemblies derived through integrative modeling methods, are all hardly discoverable. For all these reasons, we call for agreement in creating a single repository with features that facilitate discovery, especially through sequence queries. An important effort is ongoing but specifically tailored to integrative models and not yet incorporating sequence-search functionalities (Berman et al., 2019). There is however no technological impediment, actually the simplest solution would be convincing journals of mandating model deposition to one of the existing archives, or even to Zenodo, and then setting up a sequence-searchable database that links each sequence to its location (Fig. 1). We have in fact devised a palliative solution following this idea at http://lucianoabriata.altervista.org/modelsearch/ that allows querying for >5000 sequences for which models are available in resources based primarily on methods other than comparative modeling (examples in Fig. 2). More precisely, >2900 entries come from contact prediction-based modeling of proteins of unknown structures and no clear templates [from Gremlin/Rosetta (Ovchinnikov et al., 2015, 2017), PconsC3 (Michel et al., 2017), DMPfold (Greener et al., 2019) and Tara (Wang et al., 2019)], >1200 come from CASP models (most corresponding to experimental structures already available at the wwPDB, but some were never released hence their value), 250 entries come from integrative models at PDB-Dev, ∼700 entries come from SASBDB and ∼100 were manually entered from the Supplementary Material of the literature inspected for the statistics commented above and other resources. Models become much more useful if discoverable through sequence searches. Example (A) is a target from Salmonella typhimurium for which no templates are currently available in the Protein Data Bank and therefore no structural models in standard databases of homology models. However, the search identifies a model derived through contact predictions for a protein with similar sequence to the target’s C-terminal domain, enabling confident structure prediction for at least this domain using the identified model as template. In example (B), searching ubiquitin’s sequence retrieves models of covalent ubiquitin dimers and oligomers derived through integrative modeling, from the PDB-Dev and SASBDB Clearly, an actual service could not rely on a web app at its core but would rather require server-side computing to efficiently search for sequences among very large numbers of models. This would require funding to maintain the website and a host group that could possibly branch from an existing structural biology resource like the wwPDB or CASP. At least in a first stage to reduce costs, model storage could rely on third parties like the repositories mentioned above. To ensure reliability, only models from peer-reviewed publications should be accepted, which implies close coordination with publishers and libraries like Pubmed. From the user viewpoint, it is of utmost importance that entries also include quality estimates, both provided by the authors of the model and from automated methods (Won et al., 2019). Importantly, quality estimate methods need themselves to be evaluated and agreed upon. Other features like those of wwPDB sites, such as online visualization and connection to other databases, would also be possible in a dedicated website, and very useful. Academia, industry, funding agencies and scholarly publishers have come together to design and jointly endorse the FAIR Data Principles. These principles emphasize on enhancing the ability of machines to automatically find and use data, and supporting data reuse by individuals (Stall et al., 2019; Wilkinson et al., 2016). As others have also recently highlighted (Abraham et al., 2019; Graham et al., 2019; Vallat et al., 2019), it is now about time that these sharing principles be applied to structural models of biological molecules too, which will ultimately help in the endeavor of structurally annotating genomes (Sillitoe et al., 2020). We acknowledge all the colleagues with whom we discussed in the CASP13 meeting about the issues of opening up models and making the community of biologists aware about the utility of models for their research. Financial Support: none declared. Conflict of Interest: none declared.

MOTIVATION: Understanding the molecular mechanisms of thermal stability is a challenge in protein biology. Indeed, knowing the temperature at which proteins are stable has important theoretical implications, which are intimately linked with properties of the native fold, and a wide range of potential applications from drug design to the optimization of enzyme activity. RESULTS: Here, we present a novel graph-theoretical framework to assess thermal stability based on the structure without any a priori information. In this approach we describe proteins as energy-weighted graphs and compare them using ensembles of interaction networks. Investigating the position of specific interactions within the 3D native structure, we developed a parameter-free network descriptor that permits to distinguish thermostable and mesostable proteins with an accuracy of 76% and area under the receiver operating characteristic curve of 78%. AVAILABILITY AND IMPLEMENTATION: Code is available upon request to [email protected]. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MOTIVATION: Predicting the structure of protein loops is very challenging, mainly because they are not necessarily subject to strong evolutionary pressure. This implies that, unlike the rest of the protein, standard homology modeling techniques are not very effective in modeling their structure. However, loops are often involved in protein function, hence inferring their structure is important for predicting protein structure as well as function. RESULTS: We describe a method, LoopIng, based on the Random Forest automated learning technique, which, given a target loop, selects a structural template for it from a database of loop candidates. Compared to the most recently available methods, LoopIng is able to achieve similar accuracy for short loops (4-10 residues) and significant enhancements for long loops (11-20 residues). The quality of the predictions is robust to errors that unavoidably affect the stem regions when these are modeled. The method returns a confidence score for the predicted template loops and has the advantage of being very fast (on average: 1 min/loop). AVAILABILITY AND IMPLEMENTATION: www.biocomputing.it/looping. CONTACT: [email protected]. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MOTIVATION: Antibodies are able to recognize a wide range of antigens through their complementary determining regions formed by six hypervariable loops. Predicting the 3D structure of these loops is essential for the analysis and reengineering of novel antibodies with enhanced affinity and specificity. The canonical structure model allows high accuracy prediction for five of the loops. The third loop of the heavy chain, H3, is the hardest to predict because of its diversity in structure, length and sequence composition. RESULTS: We describe a method, based on the Random Forest automatic learning technique, to select structural templates for H3 loops among a dataset of candidates. These can be used to predict the structure of the loop with a higher accuracy than that achieved by any of the presently available methods. The method also has the advantage of being extremely fast and returning a reliable estimate of the model quality. AVAILABILITY AND IMPLEMENTATION: The source code is freely available at http://www.biocomputing.it/H3Loopred/ .

MOTIVATION: The need for new drugs and new targets is particularly compelling in an era that is witnessing an alarming increase of drug resistance in human pathogens. The identification of new targets of known drugs is a promising approach, which has proven successful in several cases. Here, we describe a database that includes information on 5153 putative drug-target pairs for 150 human pathogens derived from available drug-target crystallographic complexes. AVAILABILITY AND IMPLEMENTATION: The TiPs database is freely available at http://biocomputing.it/tips. CONTACT: [email protected] or [email protected].