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The physics-based united-residue (UNRES) model of proteins ( www.unres.pl ) has been designed to carry out large-scale simulations of protein folding. The force field has been derived and parameterized based on the principles of statistical-mechanics, which makes it independent of structural databases and applicable to treat nonstandard situations such as, proteins that contain D-amino-acid residues. Powered by Langevin dynamics and its replica-exchange extensions, UNRES has found a variety of applications, including ab initio and database-assisted protein-structure prediction, simulating protein-folding pathways, exploring protein free-energy landscapes, and solving biological problems. This chapter provides a summary of UNRES and a guide for potential users regarding the application of the UNRES package in a variety of research tasks.

The physics-based united-residue (UNRES) model of proteins ( www.unres.pl ) has been designed to carry out large-scale simulations of protein folding. The force field has been derived and parameterized based on the principles of statistical-mechanics, which makes it independent of structural databases and applicable to treat nonstandard situations such as, proteins that contain D-amino-acid residues. Powered by Langevin dynamics and its replica-exchange extensions, UNRES has found a variety of applications, including ab initio and database-assisted protein-structure prediction, simulating protein-folding pathways, exploring protein free-energy landscapes, and solving biological problems. This chapter provides a summary of UNRES and a guide for potential users regarding the application of the UNRES package in a variety of research tasks.

The recent NEWCT-9P version of the coarse-grained UNRES force field for proteins, with scale-consistent formulas for the local and correlation terms, has been tested in the CASP13 experiment of the blind-prediction of protein structure, in the ab initio, contact-assisted, and data-assisted modes. Significant improvement of the performance has been observed with respect to the CASP11 and CASP12 experiments (by over 10 GDT_TS units for the ab initio mode predictions and by over 15 GDT_TS units for the contact-assisted prediction, respectively), which is a result of introducing scale-consistent terms and improved handling of contact-distance restraints. As in previous CASP exercises, UNRES ranked higher in the free modeling category than in the general category that included template based modeling targets. Use of distance restraints from the predicted contacts, albeit many of them were wrong, resulted in the increase of GDT_TS by over 8 units on average and introducing sparse restraints from small-angle X-ray/neutron scattering and chemical cross-link-mass-spectrometry experiments, and ambiguous restraints from nuclear magnetic resonance experiments has also improved the predictions by 8.6, 9.7, and 10.7 GDT_TS units on average, respectively.

In this chapter the scale-consistent approach to the derivation of coarse-grained force fields developed in our laboratory is presented, in which the effective energy function originates from the potential of mean force of the system under consideration and embeds atomistically detailed interactions in the resulting energy terms through use of Kubo's cluster-cumulant expansion, appropriate selection of the major degrees of freedom to be averaged out in the derivation of analytical approximations to the energy terms, and appropriate expression of the interaction energies at the all-atom level in these degrees of freedom. Our approach enables the developers to find correct functional forms of the effective coarse-grained energy terms, without having to import them from all-atom force fields or deriving them on a heuristic basis. In particular, the energy terms derived in such a way exhibit correct dependence on coarse-grained geometry, in particular on site orientation. Moreover, analytical formulas for the multibody (correlation) terms, which appear to be crucial for coarse-grained modeling of many of the regular structures such as, e.g., protein α-helices and β-sheets, can be derived in a systematic way. Implementation of the developed theory to the UNIfied COarse-gRaiNed (UNICORN) model of biological macromolecules, which consists of the UNRES (for proteins), NARES-2P (for nucleic acids), and SUGRES-1P (for polysaccharides) components, and is being developed in our laboratory is described. Successful applications of UNICORN to the prediction of protein structure, simulating the folding and stability of proteins and nucleic acids, and solving biological problems are discussed.

A new approach to assisted protein-structure prediction has been proposed, which is based on running multiplexed replica exchange molecular dynamics simulations with the coarse-grained UNRES force field with restraints derived from knowledge-based models and distance distribution from small angle X-ray scattering (SAXS) measurements. The latter restraints are incorporated into the target function as a maximum-likelihood term that guides the shape of the simulated structures towards that defined by SAXS. The approach was first verified with the 1KOY protein, for which the distance distribution was calculated from the experimental structure, and subsequently used to predict the structures of 11 data-assisted targets in the CASP12 experiment. Major improvement of the GDT_TS was obtained for 2 targets, minor improvement for other 2 while, for 6 target GDT_TS deteriorated compared with that calculated for predictions without the SAXS data, partly because of assuming a wrong multimeric state (for Ts866) or because the crystal conformation was more compact than the solution conformation (for Ts942). Particularly good results were obtained for Ts909, in which use of SAXS data resulted in the selection of a correctly packed trimer and, subsequently, increased the GDT_TS of monomer prediction. It was found that running simulations with correct oligomeric state is essential for the success in SAXS-data-assisted prediction.

The method for protein-structure prediction, which combines the physics-based coarse-grained UNRES force field with knowledge-based modeling, has been developed further and tested in the 13th Community Wide Experiment on the Critical Assessment of Techniques for Protein Structure Prediction (CASP13). The method implements restraints from the consensus fragments common to server models. In this work, the server models to derive fragments have been chosen on the basis of quality assessment; a fully automatic fragment-selection procedure has been introduced, and Dynamic Fragment Assembly pseudopotentials have been fully implemented. The Global Distance Test Score (GDT_TS), averaged over our "Model 1" predictions, increased by over 10 units with respect to CASP12 for the free-modeling category to reach 40.82. Our "Model 1" predictions ranked 20 and 14 for all and free-modeling targets, respectively (upper 20.2% and 14.3% of all models submitted to CASP13 in these categories, respectively), compared to 27 (upper 21.1%) and 24 (upper 18.9%) in CASP12, respectively. For oligomeric targets, the Interface Patch Similarity (IPS) and Interface Contact Similarity (ICS) averaged over our best oligomer models increased from 0.28 to 0.36 and from 12.4 to 17.8, respectively, from CASP12 to CASP13, and top-ranking models of 2 targets (H0968 and T0997o) were obtained (none in CASP12). The improvement of our method in CASP13 over CASP12 was ascribed to the combined effect of the overall enhancement of server-model quality, our success in selecting server models and fragments to derive restraints, and improvements of the restraint and potential-energy functions.

In this chapter the scale-consistent approach to the derivation of coarse-grained force fields developed in our laboratory is presented, in which the effective energy function originates from the potential of mean force of the system under consideration and embeds atomistically detailed interactions in the resulting energy terms through use of Kubo's cluster-cumulant expansion, appropriate selection of the major degrees of freedom to be averaged out in the derivation of analytical approximations to the energy terms, and appropriate expression of the interaction energies at the all-atom level in these degrees of freedom. Our approach enables the developers to find correct functional forms of the effective coarse-grained energy terms, without having to import them from all-atom force fields or deriving them on a heuristic basis. In particular, the energy terms derived in such a way exhibit correct dependence on coarse-grained geometry, in particular on site orientation. Moreover, analytical formulas for the multibody (correlation) terms, which appear to be crucial for coarse-grained modeling of many of the regular structures such as, e.g., protein α-helices and β-sheets, can be derived in a systematic way. Implementation of the developed theory to the UNIfied COarse-gRaiNed (UNICORN) model of biological macromolecules, which consists of the UNRES (for proteins), NARES-2P (for nucleic acids), and SUGRES-1P (for polysaccharides) components, and is being developed in our laboratory is described. Successful applications of UNICORN to the prediction of protein structure, simulating the folding and stability of proteins and nucleic acids, and solving biological problems are discussed.

Fullerene-based nanoparticles have been the subject of vital interest due to their unique properties and potential application in many areas, including medicine. Here we explore their characteristics that could make them prospective leads for known disease-related proteins. High-throughput virtual screening supported by comprehensive multi-software protein-ligand docking simulation and cheminformatics approaches has been applied in investigation of interactions of 1117 proteins with a 169 fullerene nanoparticles decorated with different small molecules. Moreover, obtained docking results were confirmed by the series of unrestricted all-atom molecular dynamics (MD) simulations. Hydrophobicity of fullerene core along with hydrophilic interaction of side chains plays a key role in binding with the studied proteins. We identified a series of nanoparticles that can lead to development of robust drugs for target proteins and another series that can behave as a highly toxic agent. The structure-activity relationship analysis revealed two significant molecular properties responsible for the binding score values. The application of carefully selected computational techniques and described outcome of the study facilitate development of functional fullerene nanoparticles for drug-like and drug delivery applications.

Thurincin H is a small protein produced by Bacillus thuringiensis SF361 with gram-positive antimicrobial properties. The toxins produced by B. thuringiensis are widely used in the agriculture as, e.g. natural preservatives in dairy products. The structure of thurincin H possesses four covalent sulfur to [Formula: see text]-carbon bonds that involve the cysteine side-chains; these bonds are probably responsible for the shape and stability of the protein and, thereby, for its antimicrobial properties. To examine the influence of the formation of the sulfur-carbon bonds on the folding pathways and stability of the protein, a series of canonical and multiplexed replica-exchange simulations with the coarse-grained UNRES force field was carried out without and with distance restraints imposed on selected S-C[Formula: see text] atom pairs. It was found that the order of the formation and breaking of the S-C[Formula: see text] thioether bonds significantly impacts on the foldability and stability of the thurincin H. It was also observed that thioether bridges play a major role in stabilizing the global fold of the protein, although it significantly diminishes the entropy of the system. The maximum foldability of thurincin H was observed in the presence of the optimal set of three out of four thioether bridges. Thus, the results suggest that the presence of ThnB enzyme and other agents that catalyze the formation of thioether bridges can be essential for correct folding of thurincin H and that the formation of the fourth bridge does not seem to facilitate folding; instead, it seems to rigidify the loop and prevent proteolysis.

Every two years groups worldwide participate in the Critical Assessment of Protein Structure Prediction (CASP) experiment to blindly test the strengths and weaknesses of their computational methods. CASP has significantly advanced the field but many hurdles still remain, which may require new ideas and collaborations. In 2012 a web-based effort called WeFold, was initiated to promote collaboration within the CASP community and attract researchers from other fields to contribute new ideas to CASP. Members of the WeFold coopetition (cooperation and competition) participated in CASP as individual teams, but also shared components of their methods to create hybrid pipelines and actively contributed to this effort. We assert that the scale and diversity of integrative prediction pipelines could not have been achieved by any individual lab or even by any collaboration among a few partners. The models contributed by the participating groups and generated by the pipelines are publicly available at the WeFold website providing a wealth of data that remains to be tapped. Here, we analyze the results of the 2014 and 2016 pipelines showing improvements according to the CASP assessment as well as areas that require further adjustments and research. Funding Agencies|Office of Science of the U.S. Department of Energy [DE-AC02-05CH11231]; U.S. Department of Energy, Office of Science, Office of Workforce Development for Teachers and Scientists (WDTS) under the Science Undergraduate Laboratory Internship (SULI) program; U.S. Department of Energy, Office of Science, Office of Workforce Development for Teachers and Scientists (WDTS) under the Visiting Faculty Program (VFP); United States-Israel Binational Science Foundation (BSF) [2009432]; Israel Science Foundation (ISF) [1122/14]; National Institute of General Medical Sciences [R01GM093123, GM083107, GM116960]; Purdue University start-up funds; Ralph W. and Grace M. Showalter Trust Award; Jim and Diann Robbers Cancer Research Grant for New Investigators Award; Brazilian agency: FAPESP; Brazilian agency: CAPES; Brazilian agency: CNPq; NIH [GM-14312]; NSF [MCB-10-19767]; National Institutes of Medicine [GM11574901]; Swedish Research Council [2012-5270, 2016-05369]; Swedish e-Science Research Center; Polish National Science Center [UMO-2013/10/M/ST4/00640]; IISc Mathematical Initiative Assistantship; National Academy of Sciences, India; National Institutes of Health [R01-GM100701, R01GM052032]; National Science Foundation; National Science Foundation Graduate Research Fellowship [DGE-1148900]; Princeton Institute for Computational Science and Engineering (PICSciE); Princeton University Office of Information Technology; UK Engineering and Physical Sciences Research Council [EP/M020576/1, EP/N031962/1]; National Research Foundation of Korea [2016R1A2A1A05005485]

Carbon nanotubes (CNTs) have recently received considerable attention because of their possible applications in various branches of nanotechnology. For their cogent application, knowledge of their interactions with biological macromolecules, especially proteins, is essential and computer simulations are very useful for such studies. Classical all-atom force fields limit simulation time scale and size of the systems significantly. Therefore, in this work, we implemented CNTs into the coarse-grained UNited RESidue (UNRES) force field. A CNT is represented as a rigid infinite-length cylinder which interacts with a protein through the Kihara potential. Energy conservation in microcanonical coarse-grained molecular dynamics simulations and temperature conservation in canonical simulations with UNRES containing the CNT component have been verified. Subsequently, studies of three proteins, bovine serum albumin (BSA), soybean peroxidase (SBP), and α-chymotrypsin (CT), with and without CNTs, were performed to examine the influence of CNTs on the structure and dynamics of these proteins. It was found that nanotubes bind to these proteins and influence their structure. Our results show that the UNRES force field can be used for further studies of CNT-protein systems with 3–4 order of magnitude larger timescale than using regular all-atom force fields. Graphical abstractBovine serum albumin (BSA), soybean peroxidase (SBP), and α-chymotrypsin (CT), with and without CNTsᅟ Electronic supplementary material The online version of this article (10.1007/s00894-018-3656-1) contains supplementary material, which is available to authorized users.

The origins of formation of an intermediate state involved in amyloid formation and ways to prevent it are illustrated with the example of the Formin binding protein 28 (FBP28) WW domain, which folds with biphasic kinetics. Molecular dynamics of protein folding trajectories are used to examine local and global motions and the time dependence of formation of contacts between C(α)s and C(β)s of selected pairs of residues. Focus is placed on the WT FBP28 WW domain and its six mutants (L26D, L26E, L26W, E27Y, T29D, and T29Y), which have structures that are determined by high-resolution NMR spectroscopy. The origins of formation of an intermediate state are elucidated, viz. as formation of hairpin 1 by a hydrophobic collapse mechanism causing significant delay of formation of both hairpins, especially hairpin 2, which facilitates the emergence of an intermediate state. It seems that three-state folding is a major folding scenario for all six mutants and WT. Additionally, two-state and downhill folding scenarios were identified in ∼ 15% of the folding trajectories for L26D and L26W, in which both hairpins are formed by the Matheson-Scheraga mechanism much faster than in three-state folding. These results indicate that formation of hairpins connecting two antiparallel β-strands determines overall folding. The correlations between the local and global motions identified for all folding trajectories lead to the identification of the residues making the main contributions in the formation of the intermediate state. The presented findings may provide an understanding of protein folding intermediates in general and lead to a procedure for their prevention.

The iron-sulfur protein 1 (Isu1) and the J-type co-chaperone Jac1 from yeast are part of a huge ATP-dependent system, and both interact with Hsp70 chaperones. Interaction of Isu1 and Jac1 is a part of the iron-sulfur cluster biogenesis system in mitochondria. In this study, the structure and dynamics of the yeast Isu1-Jac1 complex has been modeled. First, the complete structure of Isu1 was obtained by homology modeling using the I-TASSER server and YASARA software and thereafter tested for stability in the all-atom force field AMBER. Then, the known experimental structure of Jac1 was adopted to obtain initial models of the Isu1-Jac1 complex by using the ZDOCK server for global and local docking and the AutoDock software for local docking. Three most probable models were subsequently subjected to the coarse-grained molecular dynamics simulations with the UNRES force field to obtain the final structures of the complex. In the most probable model, Isu1 binds to the left face of the Γ-shaped Jac1 molecule by the β-sheet section of Isu1. Residues L105 , L109 , and Y163 of Jac1 have been assessed by mutation studies to be essential for binding (Ciesielski et al., J Mol Biol 2012; 417:1-12). These residues were also found, by UNRES/molecular dynamics simulations, to be involved in strong interactions between Isu1 and Jac1 in the complex. Moreover, N(95), T(98), P(102), H(112), V(159), L(167), and A(170) of Jac1, not yet tested experimentally, were also found to be important in binding.

In order to find clinically useful prognostic markers for glioma patients’ survival, we employed Monte Carlo Feature Selection and Interdependencies Discovery (MCFS-ID) algorithm on DNA methylation (HumanMethylation450 platform) and RNA-seq datasets from The Cancer Genome Atlas (TCGA) for 88 patients observed until death. The input features were ranked according to their importance in predicting patients’ longer (400+ days) or shorter (≤400 days) survival without prior classification of the patients. Interestingly, out of the 65 most important features found, 63 are methylation sites, and only two mRNAs. Moreover, 61 out of the 63 methylation sites are among those detected by the 450 k array technology, while being absent in the HumanMethylation27. The most important methylation feature (cg15072976) overlaps with the RE1 Silencing Transcription Factor (REST) binding site, and was confirmed to intersect with the REST binding motif in human U87 glioma cells. Six additional methylation sites from the top 63 overlap with REST sites. We found that the methylation status of the cg15072976 site affects transcription factor binding in U87 cells in gel shift assay. The cg15072976 methylation status discriminates ≤400 and 400+ patients in an independent dataset from TCGA and shows positive association with survival time as evidenced by Kaplan-Meier plots.

Knowledge-based methods are, at present, the most effective ones for the prediction of protein structures; however, their results heavily depend on the similarity of a target sequence to those of proteins with known structures. On the other hand, the physics-based methods, although still less accurate and more expensive to execute, are independent of databases and give reasonable results where the knowledge-based methods fail because of weak sequence similarity. Therefore, a plausible approach seems to be the use of knowledge-based methods to determine the sections of the structures that correspond to sufficient sequence similarity and physics-based methods to determine the remaining structure. By participating in the 12th Community Wide Experiment on the Critical Assessment of Techniques for Protein Structure Prediction (CASP12) as the KIAS-Gdansk group, we tested our recently developed hybrid approach, in which protein-structure prediction is carried out by using the physics-based UNRES coarse-grained energy function, with restraints derived from the server models. Best predictions among all groups were obtained for 2 targets and 80% of our models were in the upper 50% of the models submitted to CASP. Our method was also able to exclude, with about 70% confidence, the information from the servers that performed poorly on a given target. Moreover, the method resulted in the best models of 2 refinement targets and performed remarkably well on oligomeric targets.

Ribonuclease A (RNase A) is the most studied member of ribonucleases - a group of enzymes responsible for catalyzing RNA degradation. RNase A contains four disulfide bonds, which were found to be necessary for the native structure of the protein to form. In this work the kinetics and thermodynamics of RNase unfolding were studied by means of a series of coarse-grained canonical molecular-dynamics simulations, run at various temperatures, and replica-exchange molecular dynamics simulations with the UNRES force field, which is capable of dynamic formation and breaking the disulfide bonds during the course of the simulations. It was found that the Cys40-Cys95 bond was the first to break, while the Cys26-Cys84 bond was the last to break, independent of temperature, in agreement with available experimental data. Except for the Cys40-Cys95 bond, all disulfide bonds were relatively stable during the simulations. The formation/disruption of disulfide bonds was found to be temperature dependent for three out of four disulfide bonds in RNase A, except for the most stable disulfide bond between Cys65 and Cys72. A stable intermediate without the Cys40-Cys95 disulfide bond, with structure similar to that of the most common folding intermediate observed experimentally, was found in simulated unfolding. In agreement with experiment, non-native disulfide bonds were also observed. By analyzing residue-position fluctuations, it was found that native disulfide bonds are located in the highly flexible regions of the protein, which is probably why their presence is necessary for the stability of RNase A.

The protein structure prediction problem continues to elude scientists. Despite the introduction of many methods, only modest gains were made over the last decade for certain classes of prediction targets. To address this challenge, a social-media based worldwide collaborative effort, named WeFold, was undertaken by 13 labs. During the collaboration, the laboratories were simultaneously competing with each other. Here, we present the first attempt at "coopetition" in scientific research applied to the protein structure prediction and refinement problems. The coopetition was possible by allowing the participating labs to contribute different components of their protein structure prediction pipelines and create new hybrid pipelines that they tested during CASP10. This manuscript describes both successes and areas needing improvement as identified throughout the first WeFold experiment and discusses the efforts that are underway to advance this initiative. A footprint of all contributions and structures are publicly accessible at http://www.wefold.org.

Ranking protein structure models is an elusive problem in bioinformatics. These models are evaluated on both the degree of similarity to the native structure and the folding pathway. Here, we simulated the use of the coarse-grained UNited RESidue (UNRES) force field as a tool to choose the best protein structure models for a given protein sequence among a pool of candidate models, using server data from the CASP11 experiment. Because the original UNRES was optimized for Molecular Dynamics simulations, we reoptimized UNRES using a deep feed-forward neural network, and we show that introducing additional descriptive features can produce better results. Overall, we found that the reoptimized UNRES performs better in selecting the best structures and tracking protein unwinding from its native state. We also found a relatively poor correlation between UNRES values and the model’s Template Modeling Score (TMS). This is remedied by reoptimization. We discuss some cases where our reoptimization procedure is useful. The reoptimized version of UNRES (OUNRES) is available at http://mamiris.com and http://www.unres.pl.

The arginine-binding protein (ArgBP) from the hyperthermophilic eubacterium Thermotoga maritima (TmArgBP) is responsible for arginine transport through the bacterial cell membrane. The protein binds a single molecule of l-arginine, which results in conformational changes due to hinge bending. Thereby, TmArgBP acquires one of two possible conformations: open (without the presence of the arginine ligand) and closed (in the presence of the arginine ligand). Here we report a molecular dynamics study of the influence of the presence or absence of the ligand on the dynamics of TmArgBP, using the coarse-grained UNRES force field. The results of our studies indicate that binding of the arginine ligand promotes a closed conformation, which agrees with experimental data. However, the sensitivity of the TmArgBP conformation to the presence of arginine decreases and the protein becomes more flexible with increasing temperature, which might be related to the functionality of this protein in the thermophilic organism T. maritima.

A unified coarse-grained model of three major classes of biological molecules—proteins, nucleic acids, and polysaccharides—has been developed. It is based on the observations that the repeated units of biopolymers (peptide groups, nucleic acid bases, sugar rings) are highly polar and their charge distributions can be represented crudely as point multipoles. The model is an extension of the united residue (UNRES) coarse-grained model of proteins developed previously in our laboratory. The respective force fields are defined as the potentials of mean force of biomacromolecules immersed in water, where all degrees of freedom not considered in the model have been averaged out. Reducing the representation to one center per polar interaction site leads to the representation of average site–site interactions as mean-field dipole–dipole interactions. Further expansion of the potentials of mean force of biopolymer chains into Kubo’s cluster-cumulant series leads to the appearance of mean-field dipole–dipole interactions, averaged in the context of local interactions within a biopolymer unit. These mean-field interactions account for the formation of regular structures encountered in biomacromolecules, e.g., α-helices and β-sheets in proteins, double helices in nucleic acids, and helicoidally packed structures in polysaccharides, which enables us to use a greatly reduced number of interacting sites without sacrificing the ability to reproduce the correct architecture. This reduction results in an extension of the simulation timescale by more than four orders of magnitude compared to the all-atom representation. Examples of the performance of the model are presented. Figure Components of the Unified Coarse Grained Model (UCGM) of biological macromolecules

The performance of the physics-based protocol, whose main component is the United Residue (UNRES) physics-based coarse-grained force field, developed in our laboratory for the prediction of protein structure from amino acid sequence, is illustrated. Candidate models are selected, based on probabilities of the conformational families determined by multiplexed replica-exchange simulations, from the 10th Community Wide Experiment on the Critical Assessment of Techniques for Protein Structure Prediction (CASP10). For target T0663, classified as a new fold, which consists of two domains homologous to those of known proteins, UNRES predicted the correct symmetry of packing, in which the domains are rotated with respect to each other by 180° in the experimental structure. By contrast, models obtained by knowledge-based methods, in which each domain is modeled very accurately but not rotated, resulted in incorrect packing. Two UNRES models of this target were featured by the assessors. Correct domain packing was also predicted by UNRES for the homologous target T0644, which has a similar structure to that of T0663, except that the two domains are not rotated. Predictions for two other targets, T0668 and T0684_D2, are among the best ones by global distance test score. These results suggest that our physics-based method has substantial predictive power. In particular, it has the ability to predict domain-domain orientations, which is a significant advance in the state of the art.

The protein structure prediction problem continues to elude scientists. Despite the introduction of many methods, only modest gains were made over the last decade for certain classes of prediction targets. To address this challenge, a social-media based worldwide collaborative effort, named WeFold, was undertaken by 13 labs. During the collaboration, the laboratories were simultaneously competing with each other. Here, we present the first attempt at "coopetition" in scientific research applied to the protein structure prediction and refinement problems. The coopetition was possible by allowing the participating labs to contribute different components of their protein structure prediction pipelines and create new hybrid pipelines that they tested during CASP10. This manuscript describes both successes and areas needing improvement as identified throughout the first WeFold experiment and discusses the efforts that are underway to advance this initiative. A footprint of all contributions and structures are publicly accessible at http://www.wefold.org.