Intracellular condensates are highly multi-component systems in which complex phase behaviour can ensue, including the formation of architectures comprising multiple immiscible condensed phases. Relying solely on physical intuition to manipulate such condensates is difficult because of the complexity of their composition, and systematically learning the underlying rules experimentally would be extremely costly. We address this challenge by developing a computational approach to design pairs of protein sequences that result in well-separated multilayered condensates and elucidate the molecular origins of these compartments. Our method couples a genetic algorithm to a residue-resolution coarse-grained protein model. We demonstrate that we can design protein partners to form multiphase condensates containing naturally occurring proteins, such as the low-complexity domain of hnRNPA1 and its mutants, and show how homo- and heterotypic interactions must differ between proteins to result in multiphasicity. We also show that in some cases the specific pattern of amino-acid residues plays an important role. Our findings have wide-ranging implications for understanding and controlling the organisation, functions and material properties of biomolecular condensates. We develop a computational method integrating a genetic algorithm with a residue-level coarse-grained model of intrinsically disordered proteins in order to uncover the molecular origins of multiphase condensates and enable their controlled design. Anti-cancer drugs have already been shown to concentrate preferentially into condensates,33 and some small molecules can modulate LLPS,34 making biomolecular condensates potential drug targets.Biomolecular condensates are involved in controlling many aspects of cell biology and pathology. By dynamically segregating particular biomolecules,in vitro and in cells have been observed to display multiphase architectures: structural heterogeneity over mesoscopic length scales, where immiscible phases with different compositions coexist within the same liquid droplet. In vitro, multiphase droplets have been observed in various multi-component mixtures, all involving RNA and at least two different proteins, e.g. those of poly(PR) and RNA homopolymers,35 of polyR, polyK and polyU,36 and of prion-like and arginine-rich polypeptides, and RNA.37 Inside cells, a prime example is the nucleolus, a highly multi-component system, which exhibits a multilayered architecture38 that is thought to be important for sequential processing of nascent rRNA transcripts.10 Similar internal structuring is also found in stress13 and P granules.39–41 The presence of multiple condensed phases in a single condensate may reflect different biological processes taking place in physically separated regions within the same compartment.42,43A number of biomolecular condensates 36,37 For instance, competing protein–protein and protein–RNA interactions can provide a regulatory mechanism for the organisation of multiphase condensates.37 Simulations of multi-component systems44,45 have further revealed that the phase boundary for demixed phases is sensitive to the variance of intermolecular interaction strengths: if it is sufficiently large, multiple distinct phases can form.45 From the physicochemical point of view, the formation of an interface between two phases is thermodynamically unfavourable and must be compensated for by free-energetically favourable interactions in the demixed system. The extent to which an interface is unfavourable is quantified by the interfacial free-energy density, which gives the free-energy penalty per unit area of the interface. Unsurprisingly, the various forms of structuring and morphological patterns of multiphase condensates have been suggested to be modulated by the difference in the interfacial free-energy densities of the phases,38,46–49 which in turn depend on the sequence-encoded molecular interactions of the components.35–37,48 Phases with high interfacial free-energy densities are expected to be engulfed by those with lower ones,50 while some phases form completely separate droplets due to high interfacial tensions.51 These observations, together, might suggest that RNA is essential to sustain multiphase condensates, or that a large number of components are needed. But is that the case? To be able to identify the rules governing multiphasicity, here, we focus on systems that are rather simpler, with only two protein components. We develop a molecular-simulations approach that allows us to understand the physicochemical characteristics they must have to form multiphase condensates.The emergence of a multiphase organisation has been associated with the physicochemical diversity of the various molecular components. One hypothesis is that multiphase condensates emerge in multicomponent systems when there is competition for a shared binding partner.52–54 and have long been used in optimisation problems in many fields,55–65 including those with biological applications such as protein engineering66–68 and drug design.69,70 Genetic algorithms have recently been applied to evolve protein sequences to (de)stabilise their condensates.71,72 However, in order to use genetic algorithms in the context of LLPS, we first need to quantify the protein properties we wish to optimise. Recently, computer simulations have connected features of individual biomolecules to their phase behaviour.73 Depending on the question being addressed, models from atomistic74–80via residue-level81–87 to minimal,80,88–90 alongside other computational approaches such as predictive algorithms and machine-learning methods,85–87,91–95 have all been used with success. Simple models for phase separation of multi-component mixtures and multiphase organisation have also been studied in detail with a combination of simulations and theory.96–100Although one could in principle speculate, based on physical intuition, which pairs of protein sequences might give rise to multiphase architectures, this strategy is likely only feasible for simple amino-acid sequences. Even then, the phase behaviour of multi-component systems with multiple coexisting condensed phases is far more challenging to predict than that of single-component condensates, especially given the complexity and diversity of biologically relevant proteins. Computational approaches, such as genetic algorithms, can help explore the vast size of protein sequence space by automating the design of protein sequence mutations. Broadly speaking, genetic algorithms use mechanisms inspired by biological evolution, such as crossovers and mutations, to optimise properties of a system,84 with a genetic algorithm71 to evolve protein sequences towards increasing ‘multiphasicity’, which we define to be the difference in the compositions of the two coexisting phases of a multiphase condensate. The multiphasicity of a condensate increases with the purity of the two coexisting phases. We first demonstrate that we can increase the multiphasicity of a protein mixture using a genetic algorithm with an appropriate fitness function to evolve the amino-acid sequence of one of the two proteins (Section 2.1). We then show that we can design a protein sequence to act as a multiphase partner for some other protein of choice by coevolution (Section 2.2), including proteins of biological relevance such as the low-complexity domain (LCD) of heterogeneous nuclear ribonucleoprotein A1 (hnRNPA1). Finally, we analyse the changes in interaction energies (Section 2.3) and amino-acid patterning (Section 2.4) to probe the factors driving the formation of multilayered condensates.Motivated by these ideas, here we develop an evolutionary algorithm that goes beyond manipulating the stability of condensates and allows us to enforce or inhibit a desired spatial organisation of biomolecules inside multi-component condensates. We couple molecular-dynamics simulations of a residue-resolution coarse-grained protein model that achieves near quantitative agreement with experiments22.171,72 During the evolution, each protein in a population is associated with a fitness value, as computed with a fitness function, and the population is evolved with local mutations and crossovers between individual sequences towards a fitter – albeit not necessarily the fittest – solution. Rather than determining an optimal solution (however defined), the genetic algorithm follows local gradients in fitness space towards a better solution that is not drastically dissimilar to the starting sequence. The success and efficiency of the genetic algorithm depend on the quality of the fitness function, which should be relatively simple and inexpensive to compute, while at the same time sufficiently complex to act as a suitable proxy for the property being evolved. For simplicity, here we focus on evolving two-component protein condensates towards higher multiphasicity (as defined above). Nonetheless, our approach can readily be generalised to condensates with a larger number of components, which are in any case expected to behave similarly.45Genetic algorithms can effectively evolve amino-acid sequences to find mutations that give rise to desired changes in phase behaviour.i.e. low multiphasicity) and large when it phase separates into a multilayered condensate with each layer enriched in a different protein (i.e. high multiphasicity) , since aromatic residues exhibit strong favourable interactions. For the outer sequence, we choose a simple sequence that combines some sticker residues with spacer residues [e.g. (FAFAA)10], so that the overall interaction is less strong compared to the inner sequence. To arrive at our protein of interest, we then systematically mutate one of the two reference sequences throughout the coevolution run. These systematic mutations are done gradually, once every 5 rounds, by randomly changing ∼10% of the residues of this protein selected from those that have yet to be changed to the amino acid of the target protein. Simultaneously, we evolve the other sequence with the genetic algorithm using our fitness function  to estimate these changes. The average of εi,Mpipi across all residues in the evolved sequence decreases over the course of the genetic-algorithm run when the outer sequence is evolved , in Fig. S17;The phase behaviour of IDPs has been observed to be affected by the patterning of not only aromatic residues, but also charged ones.a priori when the patterning of residues is likely to affect the phase behaviour of multilayered condensates, the use of a genetic algorithm and the coevolution approach as a predictive tool, where any relevant patterning is optimised alongside the interaction strengths, is especially attractive.Since it is difficult to know 384 We demonstrate the utility of our approach in a biological context by applying it to predict different protein partners capable of forming two-component multiphase condensates when mixed with A1 LCD or its variants. We show that our method can be adapted to produce condensates that concentrate the protein of interest (e.g. A1 LCD) either at the centre of the multilayered condensate or in the outer layer, as desired.We have developed a computational approach to design multi-component multilayered condensates that contain a target protein of interest. Our approach integrates a genetic algorithm, anchored in an innovative fitness function for automated evolution of multiphasicity, with our near-quantitative residue-resolution coarse-grained protein model, Mpipi.In addition to enabling the design of multiphase condensates, our approach helps uncover the biophysical mechanisms that drive the formation of complex multilayered organisations. In all cases, we find that multiphasicity in multi-component protein systems is favoured if the difference between homotypic interactions among different components is large, and the strength of heterotypic interactions is small but comparable with that of the weaker homotypic interactions in the mixture. In a two-component system, proteins that establish stronger homotypic interactions are concentrated at the core of the multiphase condensate, as saturating their bonds enhances the overall enthalpic gain for condensate formation. Similarly, the outer layer of the multiphase condensate is formed by the proteins that establish weaker homotypic interactions, as this reduces the overall interfacial free-energy density of the two-component system. Although the specific predicted partner sequences can differ across independent (co)evolution runs and several sequences can have similar fitness values, these general trends in interaction energies remain consistent in the cases we have tested, suggesting that the rules we have identified may be universal in driving the formation of multiphase condensates. The diversity of partner sequences obtained may also suggest that a given protein can form multiphase condensates with a wide range of different partners, rather than exclusively with a unique complementary sequence: multiphasic organisation thus appears to be a robust property of multi-component condensates.i.e. FUS, Ddx4, LAF-1 and their variants) and of polyR/polyK/polyU mixtures, and predicts the experimental radii of gyration of a large set of IDPs with high accuracy.84 We are therefore hopeful that the predictions of our approach will be robust against experimental validation, which is an essential next step. An important factor to consider for such validation is that the specific amino-acid sequences we predict to from multiphase condensates are only applicable at the fixed temperature and salt concentration at which the simulations are run; however, these could of course be changed to design multilayered condensates that are stable under different conditions. One possible limitation of using residue-resolution coarse-grained models like Mpipi is that they are typically unable to consider the emergence of secondary or tertiary structural transitions from specific changes in amino-acid sequence. In this regard, our evolutionary approach is flexible enough to incorporate knowledge-based constraints to bypass selected patterns of amino-acid sequences known to favour, for instance, the folding of protein regions into α-helices or β-sheets in specific contexts, or limit the number of certain residues such as cysteine, which forms disulphide bridges. Our approach can also easily be modified to consider special requirements for each protein system by introducing further constraints in the algorithm: for instance to introduce tailored replacement probabilities and outcomes for different residues  or protein regions (e.g. to avoid mutations at the N-terminus or to favour the concentration of aromatics at the centre) when proposing mutations.Since the genetic algorithm is coupled to a residue-resolution coarse-grained model for proteins, the accuracy of our predictions is contingent on that of the model. Reassuringly, we have previously demonstrated that Mpipi reproduces the experimental phase diagrams of A1 LCD and its variants with near-quantitative accuracy, achieves excellent agreement with experiments probing the phase behaviour of naturally occurring proteins  Choose an initial system with a degree of multiphasicity, such as one of the minimal systems we have outlined.(2) Choose a target protein for which to design a partner.(3) Decide whether the target protein should be on the inside or the outside of the multiphase condensate.(4) Run a genetic algorithm on one protein and systematically change the other protein to the target protein.x is assessed with a fitness function, and  are the average dimensionless number densities of the two different protein sequences A and B in the two-component mixtures.  and  denote the number density of protein A in the core of the multilayered condensate and in the dilute phase respectively, with analogous expressions for protein B [e.g. ρ* = ρ/nm−3, although the choice of unit is immaterial, since only the relative ordering in fitness is important, not the absolute numerical value. Finally, as discussed in the main text, the parameter s determines the trade-off between obtaining two stable liquid-like phases to give a stable multilayered condensate and the difference in compositions between the two phases. The value of s can be tuned as necessary depending on the specific system of interest; here, we have used s = 0, 0.5, 1 and 5.In our implementation of the genetic algorithm, we maintain a population of 20 sequences in each round, alongside a partner protein sequence that is not being evolved. To generate the initial population, we mutate the initial sequence by replacing, with 0.05 probability, each residue with a new one chosen from the 20 canonical amino acids with uniform probability. Each individual sequence rotein B . Details98 However, in our simulations of relatively small system sizes, the structure and arrangement of the coexisting phases introduce extra complexity and it is difficult to determine accurately the density of the two components, especially in the outer layer of the condensate. Calculating the compositional asymmetry in this way would therefore likely not be straightforward enough to be used routinely in the genetic algorithm. Another approach uses intra- and inter-species pair correlation functions with demixing characterised by the intra-species pair correlation function dominating at small separations compared to the inter-species one.99,100 We compute the pair correlation functions for a range of our simulation outputs and show in Fig. S18The fitness function we have introduced is facile to compute and works well even in relatively small systems, which is especially useful in genetic-algorithm simulations where the fitness must be evaluated for many systems. Other order parameters have also been shown to identify compositional demixing within the condensed phase well, such as by quantifying the compositional asymmetry by considering the fraction of the two sequences in the two coexisting phases.52,114 to select the fittest parents to cross over. We also apply a round of random mutations to explore previously unsampled regions of sequence space. Finally, we use a weak population replacement scheme to generate the population of sequences for the next round of the genetic-algorithm run. Our genetic-algorithm implementation is detailed in full in ref. Once the fitness of each individual is determined, we use the tournament selection algorithme.g. following a mutation or crossover; however, when a systematic change is made to the partner sequence in coevolution runs, this too affects the phase behaviour and the fitness of all individuals in the population must therefore be recalculated. In our coevolution runs, we do this at 5-round intervals, at which we change ∼10% of the residues of this partner protein to the target protein sequence, with residues to be changed chosen randomly from those that have not yet been changed with uniform probability.The fitness of each individual in the population is computed when it is first encountered, 4.284 combining (i) harmonic covalent bonds between residues, (ii) the Wang–Frenkel potential115 to account for non-bonded interactions between amino acids and (iii) Debye–Hückel electrostatic interactions.116 The Mpipi potential was shown to model LLPS of intrinsically disordered proteins well.84 For each amino-acid pair ij, the Mpipi model defines a Wang–Frenkel well-depth (εij), a characteristic length scale (σij) and values for ν and μ that determine the steepness of the potential well.To simulate protein chains, we use the Mpipi residue-resolution sequence-specific coarse-grained model,117,118 to model the vapour phase alongside the condensed phases in the same elongated tetragonal simulation box with explicit interfaces between phases. We use LAMMPS119 to run molecular-dynamics simulations with a typical time step of 10 fs and a coupling to a Langevin thermostat with a relaxation time of 10 ps. To estimate the densities of the dilute and condensed phases, we run each simulation for 40 ns for equilibration and an additional 20 ns to compute the densities. Each simulation takes around an hour on 76 CPU cores.We use direct-coexistence simulations120 rather than by forming truly immiscible bulk phases. If these phases are true thermodynamic phases, although they may of course have a preferred ordering in direct-coexistence simulations because of interfacial free-energy considerations, each of the three phases in question should be able to coexist independently with any one of the others under the same thermodynamic conditions. We test whether this holds for representative systems with different underlying multiphasic behaviour and confirm that each of the condensed phases coexists with the vapour-like phase under the same conditions as in the multiphasic regime [Fig. S23We use 96 chains of each protein for the evolution runs with generic sequences with a box size of 11.4 nm × 11.4 nm × 56.9 nm. For coevolution runs, we use 45 chains of the protein that is changed to A1 LCD, and either 90 chains of 50 residues or 45 chains of 100 residues of the other protein, in a box of size 10.9 nm × 10.9 nm × 54.7 nm. Although finite-size effects were examined in ref.  Fig. S23. For the Fig. S23.To ensure that our predictions are robust, we have confirmed that the final predicted sequences obtained from genetic-algorithm runs with the Mpipi potential exhibit similar multiphasic behaviour when simulated with another coarse-grained potential, namely Model 2 of ref. 4.3i.e. where the coevolved protein is the inner or the outer protein. To do this, we use the Kirkwood–Buff expression121 to relate the interfacial free-energy density γ to the normal and tangential components of the pressure tensor, and the mean value theorem to simplify the result122 for planar interfaces into γ = (Lz/2)(Pnorm − Ptang), where γ is the interfacial free-energy density, Lz is the length of the simulation box along which the interface occurs, Pnorm = Pzz is normal to the interface and Ptang = Pxx = Pyy is the tangential pressure, and the division by 2 accounts for the fact that there are two interfaces in our simulation set-up.120 Although the pressure tensor has many possible definitions, from virial to mechanical expressions, the interfacial-free energy density is independent of this arbitrary choice;123 we use the atomic virial pressure tensor in our calculation, which gives the same results as the molecular virial.124We compute interfacial free-energy densities for the interface between the vapour-like phase and the pure condensed phase for the final evolved maximum-fitness sequences in coevolution runs with A1 LCD for both cases, i.e. a surface tension using our coarse-grained model), since as long as a multilayered condensate forms, the interface between the two condensed phases of different compositions is always present and therefore does not affect the thermodynamics. We assume for simplicity that the resulting phases are pure and, in this back-of-the-envelope calculation, we do not consider the possible dependence of γ on the interface width or the curvature of the droplet. We determine the interfacial free-energy density for each system at several temperatures. The resulting data are well fitted with a linear function [Fig. S12aγ/∂T) = −Sint, the interfacial entropy,106 this approach allows us to extract the interfacial energy and interfacial entropy for each component, as discussed in the main text. We test for finite-size effects in the interfacial free-energy density as a function of both the surface area and the bulk depth and see that the values are the same within error bars across different system sizes [Fig. S12bWe compute only the interfacial free-energy density for the interface between the dense and dilute phases (Fig. S12a; since (Fig. S12b.https://doi.org/10.6084/m9.figshare.21926154.All relevant data are within the manuscript, its ESIhttps://doi.org/10.6084/m9.figshare.21926154.LAMMPS input scripts are available in the Figshare data repository at PYC, JAJ, RC-G and AR designed the research. PYC performed the research. PYC, JAJ, RC-G and AR analysed the results and wrote the paper.The authors declare no competing interests.SC-014-D2SC05873A-s001


An overview of computational docking and mapping approaches, and their biological applications to the problems of drug discovery, is presented. ClusPro and the FTMap and FTSite programs for protein hotspot identification. These methods have been widely used by the research community by means of publicly available online servers, and models built using these automated tools have been reported in a large number of publications. Importantly, additional experimental information can be leveraged to further improve the predictive power of these approaches. Here, an overview of the methods and their biological applications is provided together with a brief interpretation of the results.Starting with a crystal structure of a macromolecule, computational structural modeling can help to understand the associated biological processes, structure and function, as well as to reduce the number of further experiments required to characterize a given molecular entity. In the past decade, two classes of powerful automated tools for investigating the binding properties of proteins have been developed: the protein–protein docking program  However, some applications such as drug discovery or the determination of protein–protein complexes may require further experiments and additional structures to answer all questions. In these instances, computational structural modeling tools can serve as an important alternative method to gain structural insights, as well as to guide or minimize the amount of further experiments.ClusPro, a protein–protein docking web server, and FTMap, a family of web servers for determining and characterizing ligand-binding hotspots of proteins. Advanced features may be enabled to leverage pertinent a priori or experimental data, thereby offering more accurate predictions. Recently, ClusPro has been used to explore additional applications with AlphaFold2, including high-accuracy prediction of protein–protein interactions.This paper aims to briefly outline several state-of-the-art computational approaches that are used to help understand biological processes, structure and function, including 1.1.ClusPro is a web server based on a rigid-body docking method, PIPER, that firstly samples all translations and rotations of a ligand protein with respect to a receptor protein and secondly uses the fast Fourier transform (FFT) correlation approach using knowledge-based or statistical potentials as the scoring function to sort the samples in order to select the best model of the complex  simulations. In spite of these approaches, we admit that without access to multiple representative structures, docking proteins that substantially alter their conformation upon binding is a difficult and not entirely solved problem.ClusPro. If interface restraints are available then only portions of conformational space will be examined by the program  or mutational data, which can offer information regarding pairs of atoms or residues at a protein interface. Such information can be used to generate pairwise distance restraints that can be provided as input to ClusPro docking methodology has consistently been the top-performing server in Critical Assessment of Predicted Interactions ? Given the binding site how can a ligand be most optimally designed, or given a natural ligand how should it be modified or extended? Here we describe a computational solvent-mapping algorithm, FTMap, which provides answers to these questions . Furthermore, regions that bind several probe clusters are referred to as consensus sites and define binding hotspots that substantially contribute to the binding free energy. Analogous to experiments, the larger the probe population at a particular site the more important the hotspot is. The number of probe clusters forming a consensus site is strongly correlated with ‘druggability’ and the relative importance of the site. The hotspots can be further combined to identify protein binding sites. This approach is performed by FTSite  and its inhibitor turkey ovomucoid third domain . The unbound structures, PDB entries 1scn and 2gkr, are submitted to ClusPro without any additional information. The top ten results of this docking run are shown in Fig. 1a) superimposed onto an X-ray structure of the complex (PDB entry 1r0r). In Fig. 1b) the near-native ClusPro model ranked 2 is highlighted. The model provides a reasonable approximation of the binding found in the crystal structure (PDB entry 1r0r) and shows an r.m.s.d. of 2.09 Å to the native structure.Here, we demonstrate a case of protein–protein docking starting from separately crystallized subunits. As an example, we consider a complex of subtilisin Carlsberg protease (PDB entry 2.1.2.3rpg) binding to a nucleosome core particle (PDB entry 3lz0). When the docking run is submitted without the use of restraints the Bmi1/Ring1b–UbcH5c complex is modeled as binding to the DNA strand, which contradicts experimental evidence. The ubiquitination process indicates that the Cys85 residue on UbcH5c needs to be proximal to the Lys119 residue on H2A of the nucleosome  compared with the crystal structure of the complex found in PDB entry 4r8p. This can be compared with the unrestrained docking results shown in Fig. 3a). The restrained results provide a binding pose close to the reported structure among the top predictions: this is the pose ranked 2 and it has an iRMSD of 4.9 Å .To demonstrate docking with experimental restraints we consider the case of the Bmi1/Ring1b–UbcH5c complex  demonstrates the global mapping of Mpro shown in a gray surface representation. FTMap produced nine consensus sites or hotspots ranked by cluster population and shown as different carbon-color line representations. There are four mostly minor consensus sites outside the active site of Mpro, including two near the dimerization interface. The majority (4/5) of highly populated consensus sites with over ten probe clusters can be found in the active site of Mpro, including the consensus site with the highest population (26 probe clusters), which implies that the site is druggable. Indeed, to date, several compounds with submicromolar binding to Mpro have been reported in the literature. Enlarging the active site shown in Fig. 4b), one can see that the compounds depicted in stick representation overlap with FTMap hotspots in different combinations.As a first example of computational binding-site prediction with 2.2.2.FTMap can be used to identify ‘hotspots’ on the protein surface, the presence, strength and relative distance of which on the interface can indicate druggable sites. Fig. 5a) highlights the FTMap results of mapping interleukin-2 at its interface with the interleukin-2 receptor. There are strong hotspots present (≥16 probes) along with other hotspots that indicate a druggable site. Indeed, low-nanomolar inhibitors were found for this interface. Fig. 5b) highlights the contrasting results for ZipA at its interface with FtsZ, where although some hotspots are present they are weak and do not indicate a druggable site. In fact, only weak ligands were found for this interface, which supports the prediction.The low druggability of protein–protein interfaces for the binding of drug-like small molecules is a grand challenge in drug discovery. It is especially difficult due to the relatively shallow pockets on the protein surface compared with those found in traditional protein–ligand interactions, and the requirement of the ligand to compete with protein interactions. 2.2.3.FTMap searches for sites on the entire protein surface, it can be useful for finding such sites. Here, we demonstrate the application of the approach to the identification of allosteric sites on PDK1 kinase. The kinase example is also interesting since kinases are multi-domain proteins and FTMap was optimized to work on single domains. To address this, in addition to mapping the entire protein (PDB entry 1h1w) we separately map the domains (N and C lobes in this case). These two lobes are then submitted to FTMap. PDK1 binds ATP in its main pocket; in addition, an allosteric regulation site has also been identified, the PDK1-interacting fragment (PIF) pocket. The mapping results for the N lobe are located in Fig. 6a), and the two most populated identified pockets, corresponding to the ATP-binding site and the PIF site, are shown in Fig. 6b) along with a bound ligand (PDB entry 4xx9). Application of FTMap in the analysis to identify cryptic and allosteric sites is discussed in more detail in Beglov et al.  momordin. The protein is known to bind adenosine. We predict the binding site of the protein starting with unliganded momordin (PDB entry 1ahc). The top two pockets predicted by FTSite are shown in Fig. 71mrg). The adenosine pose lies within the first-ranked pocket.Nearby hotspots predicted by 2.3.AlphaFold2 has made landmark advances in protein structure prediction  for each sequence, which were then combined  for each residue of the model, which we used to calculate an average PAE score for those residues at the interface of the interacting proteins. The interface was defined to be those residues that were within 10 Å of the other protein. The AlphaFold2 model of the complex with the lowest average interface PAE score was selected and split into two separate structures representing the receptor and the ligand. As can be seen from Fig. 8AlphaFold2 was not able to generate an accurate protein complex in this case. However, when we provided the monomer model to ClusPro for docking, it was able to generate a high-accuracy model of the complex.Firstly, we demonstrate the docking of models of individual protein monomers using 2.3.2.FTMap to perform the mapping of predicted binding sites on protein surfaces. The binding properties of high-quality protein models produced by AlphaFold2  have been shown to correlate with the binding properties of experimental structures in the functional analysis of CASP14 targets  to the X-ray structure, and the ADP-binding pockets are nearly identical  of sites of biological interest and also provides information for the design of small-molecular inhibitors and modulators. We demonstrate that the tools can also be used with high-accuracy protein models provided by novel deep-learning algorithms such as AlphaFold2. The methods are available for free to academic users by means of public web servers. All of the input models for the <!?tlsb=-.017w>server are available at https://cluspro.bu.edu/examples/inputs.zip.In this work, we show various applications of computational docking using


However, beyond some large interconnected settlements in southern Amazonia9, there has been no such evidence for pre-Hispanic Amazonia. Here we present lidar data of sites belonging to the Casarabe culture (around ad 500 to ad 1400)13 in the Llanos de Mojos savannah–forest mosaic, southwest Amazonia, revealing the presence of two remarkably large sites (147 ha and 315 ha) in a dense four-tiered settlement system. The Casarabe culture area, as far as known today, spans approximately 4,500 km2, with one of the large settlement sites controlling an area of approximately 500 km2. The civic-ceremonial architecture of these large settlement sites includes stepped platforms, on top of which lie U-shaped structures, rectangular platform mounds and conical pyramids . The large settlement sites are surrounded by ranked concentric polygonal banks and represent central nodes that are connected to lower-ranked sites by straight, raised causeways that stretch over several kilometres. Massive water-management infrastructure, composed of canals and reservoirs, complete the settlement system in an anthropogenically modified landscape. Our results indicate that the Casarabe-culture settlement pattern represents a type of tropical low-density urbanism that has not previously been described in Amazonia.Archaeological remains of agrarian-based, low-density urbananism Two remarkably large sites in southwest Amazonia, belonging to the Casarabe culture, include complex civic-ceremonial architecture and large water-management infrastructure, representing a type of tropical low-density urbanism that has not previously been described in Amazonia. The southeast sector of the Llanos de Mojos (our study region) benefits from soils that have advantageous agricultural properties because of the deposition of a mid-Holocene sedimentary lobe that creates a slightly more elevated topography than the surrounding Llanos de Mojos, which in turn, provides base-rich, Andean-derived, well-drained soils18. The Casarabe culture developed here between around ad 500 and ad 1400, spreading over an area of 4,500 km2 (see ‘Chronology’ in the 19 (Supplementary Table Zea mays) as the primary staple21, and who met their protein needs by hunting22 and fishing23.During the Late Holocene epoch, pre-Hispanic agriculturalists in the Llanos de Mojos, Bolivia, transformed the most-extensive, seasonally flooded, Amazonian savannahs  that have known concentrations of major settlements, totalling 204 km2  documented in detail the two large settlement sites and 24 smaller sites, of which only 15 were previously known to exist. The new data allowed us to define a four-tiered hierarchy classification of sites 27, the largest structure in Tiwanaku, capital of the eponymous expansive state that developed over several centuries in the Bolivian highlands, simultaneous with the Casarabe culture.Cotoca and Landívar were the primary centres of a regional settlement network connected by still-visible, straight causeways that radiate from these sites into the landscape for several kilometres Fig. . The preThe large settlement sites Cotoca and Landívar were primary centres in the settlement network of the Casarabe culture. Secondary centres )  within a 10-km radius of each settlement . Density is higher in the eastern sector, with an average distance of 1,800–3,970 m between settlements , two tertiary centres , and clusters of small fourth-tier sites to the southeast and the west  connected to the core by causeways 9. More recently, lidar has also been used to document archaeological sites in the Amazon regions of Brazil8 and Peru37.Lidar technology has revolutionized the documentation of archaeological sites worldwide, especially those hidden under dense forest2 distributed over 6 unconnected areas of varying size of the Casarabe culture area  LAS files were generated that had a mean point spacing of 0.3 m. From these, DEMs with 50 cm per pixel were generated using the ‘natural neighbours’ method (ArcMap). We used the visualization techniques provided by ArcMap  and the relief visualization toolbox (RVT_2.2.1.) developed by the Research Center of the Slovenian Academy of Sciences and Arts40. Display options were chosen in such a way that they led to an optimal visibility of the archaeological remains.In the present study, we mapped a total of 200 kmrea Fig. . The sen2 and has a volume of 323,988 m3. The base platform of Landívar measures 99,795 m2 with a volume of 276,030 m3. The size of the base platform of Cotoca is 159,649 m2 and has a volume of 384,228 m3. Volume calculations were done using ArcGIS by (1) extracting the area of the platforms into a new raster; (2) calculating the ΔZ value of the pixels of that raster; (3) calculating the volume for each pixel; and (4) summing the total volume using the ‘zonal statistics as table’ command.Volumes were calculated for the built structures of the core area  of the three major monumental sites of our study area Fig. , 3. At E42, Mendoza12, Salvatierra11 and Pancho Román43. For the two sites investigated by the PABAM, we have 94 radiocarbon dates (46 from Mendoza and 48 from Salvatierra; Supplementary Figs. α ranges, which prevents their integration into the corresponding list (Supplementary Table ad 0 and 220, which is unusually early and merits closer examination.In total, 144 radiocarbon dates are available for the absolute chronological dating of the Casarabe culture, but these are from a limited number of sites. Stratigraphic information on the dated contexts is available from only four sites (Loma Alta de CasarabeFurther information on research design is available in the Any methods, additional references, Nature Research reporting summaries, source data, extended data, supplementary information, acknowledgements, peer review information; details of author contributions and competing interests; and statements of data and code availability are available at 10.1038/s41586-022-04780-4.Supplementary InformationThis file contains the Supplementary Note, Supplementary Figs. 1–5, Supplementary Tables 1–5 and Supplementary Methods; see contents page for details.Reporting SummaryPeer Review File


AlphaFold 2 (AF2) has placed Molecular Biology in a new era where we can visualize, analyze and interpret the structures and functions of all proteins solely from their primary sequences. We performed AF2 structure predictions for various protein systems, including globular proteins, a multi-domain protein, an intrinsically disordered protein (IDP), a randomized protein, two larger proteins (> 1000 AA), a heterodimer and a homodimer protein complex. Our results show that along with the three dimensional (3D) structures, AF2 also decodes protein sequences into residue flexibilities via both the predicted local distance difference test (pLDDT) scores of the models, and the predicted aligned error (PAE) maps. We show that PAE maps from AF2 are correlated with the distance variation (DV) matrices from molecular dynamics (MD) simulations, which reveals that the PAE maps can predict the dynamical nature of protein residues. Here, we introduce the AF2-scores, which are simply derived from pLDDT scores and are in the range of . We found that for most protein models, including large proteins and protein complexes, the AF2-scores are highly correlated with the root mean square fluctuations (RMSF) calculated from MD simulations. However, for an IDP and a randomized protein, the AF2-scores do not correlate with the RMSF from MD, especially for the IDP. Our results indicate that the protein structures predicted by AF2 also convey information of the residue flexibility, i.e., protein dynamics. The success of AlphaFold2 (AF2) promises to fill this gap by predicting protein structures with experimental accuracy based solely on their primary amino acid sequences4. In a recent special issue published in the Journal of Molecular Biology, AF2 was highlighted as “a once-in-a-lifetime breakthrough in science”5, because AF2 successfully demonstrated that the structure of a protein can be determined based on its sequence using modeling. We are now in a position to demonstrate Anfinsen’s6 proposal: a protein’s sequence determines its structure, which in turn determines its function. Inspired by AF2, other tools have been announced, including RoseTTAFold7 and AlphaFold-Multimer (a recent update of AF2)8 which are sufficient to describe three-dimensional (3D) protein–protein interactions (PPIs) in the cells9. These tools have opened new avenues in biology and medicine11 and have shown promise in protein engineering and biodesign15.The protein sequence-structure gap had been observed for decades16, intrinsically disordered proteins (IDPs)19, misfolded and aggregated proteins22, glycoproteins23, large-size proteins4 and protein complexes24, as well as other proteins belonging to the dark proteome26. Nevertheless, compared to homology modeling methods28, the deep learning-based AF2 method has established the potential of constructing protein structures of whole organismal proteomes4, i.e., the structurome, with near-experimental accuracies. To better utilize the AF2 models, it is of considerable importance to understand how these models have been built and evaluated by AF2, i.e., what are the AF2 model construction procedures and its evaluation metrics? In this report we provide insight into the evaluation metrics, connecting them to protein structural dynamics.Despite the exciting progress, challenges persist, demanding further development of AF2 and other algorithms, to model membrane proteins3. The structural module of AF2 also creates the model confidence predictions, reported as the predicted local distance difference test (pLDDT) scores30. The pLDDT scores are in the range of . High pLDDT scores  indicate high confidence of the residue structure, and low pLDDT scores  may indicate that the residues are in intrinsically disordered protein regions (IDPRs)32. Another useful metric to qualify/quantify the structure prediction is the global superposition TM-score33. AF2 calculates the predicted template modeling, or TM-scores (pTM) based on a pairwise error prediction, and is used to calculate the predicted aligned error (PAE) that estimates the error of the position of each amino acid3. It was shown that the MSA depth from the protein sequence strongly affects prediction accuracy3. It was also established that using MSA alone is sufficient for fast and effective protein structure predictions34.Starting from a multisequence alignment (MSA), AF2 uses the Evoformer network that integrates both biological and physical information contained in its databases3. In reality, the users may only consider the best-ranked model  for further analysis. Both pLDDT scores and PAE matrices are estimated from a comparison of the predicted structure and the “real” (ground truth) structure in AF2 reports. However, the original AF2 manuscript recognized that there are actually no “real” structures3. The proteins perform their functions through interactions and movements, especially in the crowded cell environment. The diversities in structure models or protein movements are related to protein dynamics. Therefore, it is important to ask if AF2 can also predict the dynamic characters, or dynamics personalities, of proteins35, which adds the fourth dimension of time of all atoms in a protein.Proteins are not static, their configurations constantly change and they frequently make interactions with other molecules in the cell. The models that AF2 generates only represent single snapshots of the proteins, and AF2 developers suggest multiple runs to properly sample model configurations to represent biological diversity36. We found that for proteins with high MSA depth the pLDDT scores highly correlate with the RMSF scores. We also calculated the one-dimensional (1D) distance variations (DV) between the Cɑ carbons, and found that the DV matrix from MD is highly consistent with the PAE matrix from AF2, indicating that the PAE matrix originates from protein dynamics. We further tested an intrinsically disordered protein and a randomly constructed protein, whose sequences were found to have no MSA hits. In these cases, the pLDDT scores from AF2 poorly predict residue flexibilities. Therefore, in AF2 modeling, biological information through multisequence alignment, may not only be translated to structural information, but also contains other biophysical information, including information about which residues are mobile. Such information provides valuable insights into protein dynamics and clues about how they function.In the present work, we suggest an answer to this question. By comparing molecular dynamics (MD) simulations with the AF2 predictions, we show that AF2 not only predicts the protein 3D structure, but also gives clues about the protein dynamics, via both the pLDDT scores and PAE matrices. We compared the flexibility scores from MD  with the pLDDT scores reported by AF2, as well as the intrinsic disorder contents predicted by IUPRED23. Table AF2 V2.0.1) is used for structure predictions with the required databases downloaded from the AF2 GitHub repository is used 37. The protein atoms were typed according to the CHARMM force field39 (c36m) and a modified TIP3P model40 was used for the solvent water molecules. All hydrogen atoms were added using the HBuild function of CHARMM41. The proteins were placed in unit cells with at least 12 Å of solvent water molecules added to each edge. The solvation and neutralization (using Na+ and/or Cl−) were carried out by the Solvate and Autoionization packages of VMD42. After solvation and neutralization, the whole system was optimized by 50,000 steps. Next, the temperature of the system was increased to 300 K with a rate of 0.001 K/timestep, followed by constant-pressure, constant-temperature (NPT) equilibration at a pressure of 1 atm and temperature of 300 K maintained by Langevin piston controls.The SHAKE algorithm was applied to fix the bond lengths involving hydrogen atoms. The simulations were conducted using a timestep of 2 fs and a non-bonded interaction cutoff switching of 9 to 11 Å. The protonation states of all titratable residues of the protein were determined by ProPka43 at neutral pH of 7. For each protein, the system was equilibrated for 10 ns, followed by a 100 ns production run with the trajectory saved every 10 ps. The analyses were based on the production runs (10 k frames each).Molecular dynamics (MD) simulations were performed using the NAMD program45 to analyze the 100 ns production run MD trajectories: the root mean square deviation (RMSD), root mean square fluctuation (RMSF) calculations and the mass-weighted principal component analysis (PCA) for the movement of the protein backbone atoms.We used the R package bio3d29. Here, for two residues x and y, we define the distance deviation (DV) as:rxy is the distance between the Cɑ atoms of residues x and y monitored through the MD trajectory. IQR is the interquartile range. The DV also has a unit of Å. If we assume the residue y is fixed, then the calculated IQR could be regarded as the (1D) variation of residue x. Use of IQR in Eq. (rxy). Note that the PAE matrix is asymmetric46 as for any  pair, the uncertainty assigned to x may be different than that to y. However, the DV matrix is symmetric because the distance variations neglected the residue compositions. We consider the dynamic assumption valid if the DV map matches the PAE map—that is, the PAE map from AF2 originates from the protein dynamics.Distance variations (DV) were calculated from the MD simulations in order to gain insight into the predicted aligned errors (PAE) provided by AF2.. With the assumption that the PAE map contains the dynamics information of the protein, the DV can be regarded as a 1D simplification of the PAE (see below). In the definition and estimation of the PAE, AF2 performs “alignments” between the predicted structure and the “true”  structure. The PAE between the residues x and y, i.e., the  element of the PAE matrix is estimated as the error of the x residue if the y residue is aligned  of the proteins during the MD simulations. PyMol (2.4.1) was used to plot the residue dynamic cross correlation matrix (DCCM) analyzed by bio3d44. All figures were prepared using R. The R codes used for the principal component analysis, and the DV calculations (together with the heatmap plot) are available from the GitHub repository: https://github.com/haoboguo/AF2.Res.Flex. This repository also contains the coordinates (PDB format) of the AF2 structures used in this work, and the animations of the primary movements (PC1) of the two domain protein GNE (vibration between the two domains) and the homodimeric MerR-family protein (opening-and-closing dynamics) from Mycobacterium tuberculosis, calculated by PCA. All heatmaps were plotted using the heatmap.2 function from the gplots package of R.Besides AF2 structure prediction and the MD simulations, IUPRED2B or RMSF value corresponds to higher flexibility of a residue. Similarly, the B-factors could be inferred from an ensemble of configurations detected by NMR47. The square root of B-factors was used in the comparisons with RMSF , indicating the models generated by AF2 also contain information about the residue flexibility. For PAE and DV heatmaps, the color gradually changes from the highest (white) to lowest (dark green) in 256 bins, only exception is for the DV heatmap of PAS-Kinase, the color bar is manually adjusted Fig.  caption AF2 provides the per-residue pLDDT  scores for each residue of the final model. This score is in the range of  and represents the confidence of the predicted structure compared to the “true” (ground truth) structure. We used the AF2-scores, as a reversed normalization of the pLDDT scores see Methods section), and found that the AF2-scores are highly correlated with the residue flexibility. In reality, even the “true” X-ray crystallographic structure represents an ensemble of protein configurations embedded in the crystal lattices. The flexibilities of the atoms are usually recorded as the temperature factors (or B-factors) in the PDB files. From MD simulations, the residue flexibility can be calculated as the root-mean-square fluctuation  cleaved51 (PDB ID 6MI5). For the apo LanM, MD simulation shows high-flexibility of the N-terminal tail, consistent with the AF2-scores. The RMSF of all residues is highly correlated with the AF2-scores . However, the disorder prediction by IUPRED2 interprets that the N-terminus is in a well-ordered state (the median disorder content of 0.07), contradicting both the AF2 score and the RMSF calculated from MD.Figure Delftia acidovorans strain D4B52. Because D. acidovorans had been cultured in presence of perfluorooctanoic acid (PFOA)52, this dehalogenase might be involved in defluorination of PFOA (or other fluorinated compounds). For this model, the RMSF from MD correlates well with the AF2-score for the dehalogenase , as shown in Fig. P = 0).The second protein studied (300 AA) is a dehalogenase recently sequenced from the bacterium 53. It is shown in Fig. The third protein is part of the human PAS-A-domain containing serine/threonine kinase . Here the PAS-A domain sequence  is used to build the PAS-A domain model. The PAS-A domain is speculated to regulate the kinase activity by sensing environmental stimuli. In general, PAS domains are found in all three domains of life and have a well-conserved tertiary structure, albeit with diverse sequences54, resolution 1.15 Å), such that the experimental B-factors are available for comparisons. In this example, the AF2 score has a near-perfect correlation with the RMSF . However, the IUPRED score does not show positive correlation with the RMSF. The crystal lattice effect in the X-ray structure may lead to rigid body motions which affect the B-factor profile .The fourth protein is an antifreeze protein . It should be pointed out that all protein sequences in Fig. 3  among the Cɑ atoms of residues were computed to compare with the PAE map.The predicted aligned errors (PAE) derived from the predicted template modeling (pTM) scores clearly show that the residues within the same domain exhibit lower PAEs than the inter-domain residues. The AF2 model that serves as the tutorial for the PAE is the human GNE protein, a two-domain, bifunctional enzyme playing a key role in sialic acid biosynthesis57 may be inappropriate. For these systems, a principal component analysis can be used to examine the relationship between the domains58. The AF2 profile of the two domain GNE protein  distances from the MD trajectory, observing large amplitude fluctuations during the 100 ns MD with the interdomain COM varying from 47.5 to 60 Å  profile with respect to the first frame of the trajectory, to determine whether the system has equilibrated. However, in the two-domain system, we observed that the principal movement (PC1 from PCA) corresponds to the anti-correlated movement of the two domains  among the Cɑ atoms of all residues. The DV map and PAE map from AF2 Fig. c show hiThe PAE and DV heatmaps for the models in Fig. Titin protein  which include a long IDPR of over 2100 AAs60. The AlphaFold database of the human structurome does contain 3D models for smaller fragmental (1400 AA) of the Titin4. Other proteins containing residues as large as 2,180 AA’s  have also been reported with the TM-score of 0.963. Here, we have also modeled two large proteins (> 1000 amino acids), including one with considerable disordered regions.It remains a challenge for AF2 to model extremely large proteins, such as the human H. sapiens53. We modeled the structure of the full length PAS-kinase, which contains 1,323 AAs. This structural model has been also modeled by the AF2 team4 (AF2 entry: Q96RG2) and can be obtained from the AF2 database at . In the present work, both the AF2-scores and the PAE map . This 1,200 amino acid protein enables the microbial organism to facilitate the crystallization of supercooled water61. The ice nucleating properties of P. syringae are key for their biological function63, and confer them a role in cloud glaciation and precipitation65, as well as in artificial snow making66. Fragments of the inaZ protein had been modeled67 but the structure of the full-length protein has never been predicted. The AF2 structure  in a living cell suggests that the protein functions are dependent on their interactionsIn the heterodimer model, we used the sequences of the PAS-A domain and kinase domain as two independent entries, and applied AlphaFold-Multimer to construct the dimer structure. In this model, consistent with the monomer models, the AF2-scores correlate well with the RMSF from MD, indicating that the AlphaFold-Multimer also captures the residue flexibility Fig. a. In addMycobacterium tuberculosis (UniProt ID O53384). The monomer of this protein is available at the AF2 database at https://alphafold.ebi.ac.uk/entry/O53384. Using AlphaFold-multimer, the homodimer structure of this protein was constructed. Again, the AF2-score profile is consistent with the RMSF from MD , consistent with the previous simulations of the MerR family homodimers59.We used the sequence of a probable MerR family transcriptional regulatory protein from 74. Proteins with structures deposited in the protein data bank75, even proteins with high-resolution X-ray structures, also contain significant disorder contents in their sequences76. It has been shown that the pLDDT scores provided by AF2 can also be applied to detect disorder19. For example, pLDDT scores lower than 50 are often indications of disorder in a protein31. The human structruome constructed by AF2 covers 58% of residues with a confident prediction (pLDDT > 70)4, indicating the prevalence of IDPs and IDPRs in proteomes77.Intrinsically disordered proteins (IDPs) or protein regions (IDPRs) are abundant in all organismsNereis virens78. For this protein, no MSA hit has been found by AF2. The NVJP-1 protein is fully disordered, reflected by the IUPRED2 scores and the pLDDT scores  scores, however, it shows a moderate correlation with the IUPRED scores . Unlike other globular proteins  among adjacent residues serve as a signature of disorder.We also compared the PAE and DV maps of NVJP-1 Fig. c, which 60. For the randomized protein, AF2 did not find any MSA hits, similar to NVJP-1. However, unlike the NVJP-1 model that did not show any folded elements in its structure, the randomized protein contained folded regions  of the random protein are featureless, and resemble those of NVJP-1. A recent study starts with the random “hallucination” sequences that also yields featureless 2D contact maps15. However, the contact maps can be optimized by interactions of Markov chain Monte Carlo simulations, and the optimized contact maps corresponded to highly featured protein folds validated by X-ray crystallography15. Therefore, the neural network used in AF2 and RoseTTAFold contains sufficiently rich structure and dynamics information for useful protein engineering.We randomly constructed the amino acid sequence of a protein using the methods described previouslyons Fig. , indicatSaccharomyces cerevisiae), as listed in Table 71). The PINs, similar to other natural networks, have power-law distributions of the node degrees82. The H-index83  centrality is a measure that connect both node degree and coreness84. Using these models, together with the models shown in Table Besides the 11 models discussed above, we analyzed 10 more models from the budding yeast  of the proteins, but also give important information regarding residue flexibility associated with the protein dynamics, which are comparable to the results from molecular dynamics simulations. For globular proteins and protein complexes, the AF2-scores derived from the pLDDT scores  are highly consistent with the RMSF profiles from MD. For these protein models, the PAE maps predicted by AF2 also showed consistent trends as the distance variation maps from MD. Anfinsen's rule illustrates that the protein structure is determined by its primary sequence. This rule also indicates that the protein structure determines its function, which is derived from protein dynamics. Our results suggest that scores from the AF2 models are able to infer protein dynamics, which is crucial to understand protein interactions and functions. The low pLDDT scores in the AF2 models might not result from “low confidence” but be attributed to high residue flexibility, which contributes to protein function.Supplementary Information.


Background: Considering protein mutations in their biological context is essential for understanding their functional impact, interpretation of high-dimensional datasets and development of effective targeted therapies in personalized medicine.Methods: We combined the curated knowledge of biochemical reactions from Reactome with the analysis of interaction-mediating 3D interfaces from Mechismo. In addition, we provided a software tool for users to explore and browse the analysis results in a multi-scale perspective starting from pathways and reactions to protein-protein interactions and protein 3D structures.Results: We analyzed somatic mutations from TCGA, revealing several significantly impacted reactions and pathways in specific cancer types. We found examples of genes not yet listed as oncodrivers, whose rare mutations were predicted to affect cancer processes similarly to known oncodrivers. Some identified processes lack any known oncodrivers, which suggests potentially new cancer-related processes (e.g. complement cascade reactions). Furthermore, we found that mutations perturbing certain processes are significantly associated with distinct phenotypes  in specific cancer types (e.g. PIK3CA centered pathways in LGG and UCEC cancer types), suggesting the translational potential of our approach for patient stratification. Our analysis also uncovered several druggable processes  containing enriched reactions, providing support for new off-label therapeutic options.Conclusions: In summary, we have established a multi-scale approach to study genetic variants based on protein-protein interaction 3D structures. Our approach is different from previously published studies in its focus on biochemical reactions and can be applied to other data types  collected for many types of disease. While recent efforts have been devoted to development of integrated bioinformatics pipelines for the reliable prediction of cancer driver mutation, the mechanistic details from such predictions are usually not available, therefore hampering deeper biological interpretation and clinical translation. The majority of current approaches for these studies are at the gene or protein, protein-protein interaction or pathway levels. The gene-level analysis tells us what changes  occur in genes or proteins, but not the functional impact , while the pathway-level analysis may reveal potential overall impact of mutations in cancer drivers, but not how. The protein-protein interaction level analysis may help us infer possible impact on the interactions of mutated proteins, but such analysis does not extend to the pathway context because a protein-protein interaction may be involved in multiple pathways. For example, an interaction between GAB1 and EGFR has been annotated by BioGrid (https://thebiogrid.org/108824/summary/homo-sapiens/gab1.html) and involved in both Signaling by EGFR (https://reactome.org/PathwayBrowser/#/R-HSA-177929) and Signaling by ERBB2 (https://reactome.org/PathwayBrowser/#/R-HSA-1227986) as annotated in Reactome via different reactions.Somatic mutations in cancer driving genes are a primary cause of oncogenesis. Large-scale cancer sequencing projects, including TCGA and ICGC, have uncovered many somatic mutations in cancer related genes.–). However, these studies were all based on experimentally determined protein-protein 3D structures, which covers only 6% of currently known human protein-protein interactions based on the latest release of interactome3d (https://interactome3d.irbbarcelona.org/results.php?queryid=human). To increase the coverage of 3D structures for protein-protein interactions, several groups (e.g.–) have employed homology modeling approaches using 3D structures of homologous proteins as templates. Even using homology modeling, the structural coverage of the human interactome remains quite low (12% in interactome3d). The Mechismo approach was developed to predict the functional consequences of variants and post-translational modifications (PTMs), such as phosphorylations and acetylations, at biomolecular 3D interfaces. The method extends predictions to proteins with no 3D structures as a function of sequence similarity to 3D templates assessed through BLAST alignments. This tool allows for inspecting positional effects of mutations and modifications for protein-protein, protein-nucleic acids and a subset of protein-chemical interactions which we previously demonstrated to be as effective as comparative modeling in identifying residues at interaction interfaces without intensive computation.Several studies have been published to survey cancer somatic variants using protein-protein interaction 3D structures . Pathways in Reactome are annotated as linked biochemical reactions, covering all types of biological processes, including metabolism, signal transduction, cell cycle, DNA replication and repair, programming cell death, immune system, development biology and cell-cell interaction.Pathway and network-based approaches are routinely used in cancer data analysis. Projecting perturbed genes in the contexts of pathways and networks provides a knowledge framework for researchers to understand the potential roles of genetic aberrations that cause cancer. Pathway and network knowledgebases provide the foundation to perform pathway and network-based analysis. Reactome enabling researchers to browse and explore results from this study.Leveraging the protein-protein 3D structural information generated from Mechismo and the rich set of biochemical reactions in Reactome pathways, we conducted a systematic analysis of protein structural variants in cancers using the somatic mutation data from TCGA. Our analysis is focused on functional impact of variants at 3D interfaces mediating biochemical reactions, striking a balance between the gene or protein level and pathway level analyses and unraveling novel biochemical insights of oncogenesis. In addition, we have developed a new set of features for Reactome’s Cytoscape app, ReactomeFIViz, The mapped protein-protein interactions are then traced back to reactions annotated in Reactome, which are tested for statistical significance. Significant pathways can be detected through similar statistical analysis (Methods) or may be found by mapping significant reactions to pathways that contain them. By combining the analysis results from protein-protein interactions, reactions and pathways, we draw biological insights.We developed a robust analysis approach to analyze cancer somatic mutations in the context of protein-protein interactions, biological reactions, and pathways . We applWe considered a total of 1.79 M non-synonymous somatic mutations from TCGA across 32 cancer types and 7120 unique samples carrying somatic mutations. These were mapped onto canonical UniProt sequences, yielding 1.16 M unique protein variants (Methods). We considered high confidence predictions for protein-protein interactions including known structures or close (≥70% sequence identity) homologs and representing only very confident, physical protein-protein interactions. For chemical and DNA/RNA we also considered predictions with low/medium confidence (as low as 30% sequence identity). Mechismo analysis revealed that 405 K mutations (22%) mapped to known 3D structures, including single structural matches . A total of 103 K (25%) of them were found at interaction interfaces, for a total of 6668 (59%) samples carrying at least one interface perturbing mutation . The maj involving both protein-protein and protein-chemicals interactions (Methods). We matched 6559 of 17225 (38%) FIN Protein-chemical interactions and 24363 of 183672 (13%) FIN PPIs to 3D interaction interfaces. We assessed the statistical significance of mutation enrichment at interaction interfaces through a random background model obtained by shuffling the same substitutions between equivalent positions in the same proteins . Of the 2974 (26%) samples affected by significantly perturbed interfaces, 82% of them (2437 samples) carried mutations at significant FIN interfaces and 32% (3694 samples) carried mutations mapping either to non-significant interfaces or structural, non-interface regions. A total of 41% of samples (4705) carried mutations that couldn’t be mapped to any available 3D interaction interface ,terface .The analysis revealed wide differences among cancer types in terms of fraction of samples affected by interface mutations. Five cancer types displayed more than 50% of samples carrying significantly impacted Functional Interactions (FIs): UVM (91%), PAAD (75%), SKCM (61%), UCS (57%) and THCA (56%) . Other c at FI interfaces  display only mutations at Cancer Genes Census (CGC) geneserfaces . After lerfaces . Cancer erfaces . instead of individual genes, allowed us to provide a mechanistic interpretation framework for low frequency mutations participating in common biological processes, thus facilitating the understanding of the oncogenetic mechanisms of cancer driver mutations in the long tail distribution. A total of 25 cancer types displayed 556 significantly mutated reactions (corresponding to 460 interfaces and 150 genes) and 375 pathways (corresponding to 2397 interfaces and 759 genes) (seeExtended data: Tables S2 and S3). A total of 303 reactions displayed at least two genes affected by interface mutations and 20 reactions were found to be affected in at least five genes (Table S2). The reaction “MAP 2Ks and MAPKs bind to the activated RAF complex” in SKCM is the single reaction carrying the largest number of interface mutations  affecting participating members .We also assessed whether biological processes, via either individual reactions or whole pathways, were significantly affected by somatic mutations in terms of mediating interaction interfaces. Defining biological processes significantly affected by edgetic mutations,Extended data: Figure S1). Signal transduction is the single most affected top level pathway, showing significantly perturbed reactions and pathways in 92% of cancer types . Signal transduction processes also display high variability in terms of samples mutated and interfaces involved with melanoma (SKCM) involving the highest number of perturbed interfaces when considering either reactions or pathways (https://reactome.org/PathwayBrowser/#/R-HSA-9006934&PATH=R-HSA-162582) are affected through cancer-type specific mutation signatures.Signaling by EGFR is the most recurrently perturbed pathway, being affected in 65% (17 out of 26) cancer types. Several other signaling pathways are related to GPCR signaling either through canonical pathways  or through WNT signaling .We obtained a landscape of cancer type specific patterns of perturbed processes by mapping each reaction or pathway to its top level biological pathway (athways . As expeImmune System andGene Expression (Transcription) . Perturbed reactions linking toGene expression (Transcription) are dominated exclusively byTP53 reactions, which are affected in all the 14 cancer types, with the sole exception of UCS where an additional reaction is affected byPPP2R1A mutations . We found at least one reaction contributing to theImmune System process significantly affected in 84% (21 out of 25) of cancer types, for a total of 19 unique reactions. The majority of significantly perturbed reactions in Immune System (12 out of 19) appear selectively mutated in only one cancer type, suggesting shared steps of dysregulation of a key cancer process .Other top level processes that are widely perturbed are−3) through mutations affecting five genes , which are predicted to disable the interaction with binding partners (−2) atC5 mediated interfaces, likewise predicted to perturb the interaction with binding partners . For example, we found complement cascade reactions, e.g. “C5b:C6:C7 translocates to the plasma membrane” , signifiartners . Anotherartners , was fouartners . The comExtended data: Figure_6_Networks.cys and Figure S5) while others are widely perturbed in multiple tumor contexts. The network view also allows for enhanced visualization of reaction interdependencies. For example, we identified a major cluster suggesting crosstalk between Signal Transduction and Immune System reactions in multiple cancer types (the zoomed in region inPIK3CA andNRAS (orKRAS), which are recurrently mutated in a mutually exclusive fashion in multiple cancer types, also support a major crosstalk between Signal Transduction and Immune Systems .Significantly affected reactions can be visualized on a reaction network, where nodes represent reactions and edges represent temporal or preceding/following relationships between reactions. Certain network modules are affected in specific cancer types such as STAD , Reaction 679977  can be expanded into four functional interactions , two of which (PPP1R13B-TP53 and TP53-TP53BP2) are statistically significant after FDR correction. Conversely, the interaction betweenCASP8 andCFLAR is extracted from nine significant reactions that are annotated for programmed cell death (https://reactome.org/PathwayBrowser/#/R-HSA-5357801). Interestingly, this interaction itself is not significant, presumably caused by FDR correction because there were many more protein interactions subject to analysis than reactions. Other interesting examples can be found for STAD inhttps://reactome.org/PathwayBrowser/#/R-HSA-200421), which is significant, is mapped to nine FIs that are not significant. The functional interaction betweenPIK3CA andPIK3R1 is involved in 10 reactions and both the FI and all 10 reactions are significant.The analysis pipeline we developed was used to survey the protein variants in cancer with a multi-scale perspective, allowing us to uncover cancer driver mutations significantly related to protein interactions or biochemical reactions and providing complementary results to help us draw biological insights. A reaction may be expanded into a set of functional interactions while a functional interaction may be involved in multiple reactions. Using STAD as an example (PIK3CA (Extended data: Table S4). Grouping interface perturbing mutations through reactions or pathways increases the statistical power of survival analysis (Extended data: Tables S5 and S6). Indeed, we found six and ten significant pathways in LGG (Brain Lower Grade Glioma) and UCEC  (adj P (Cox) < 0.05) respectively, invariably involvingPIK3CA along with different combinations of other oncogenes  interfaces. Intriguingly, in both LGG and UCECSignaling by Interleukins pathway has the largest numbers of affected patients . LGG patients with mutations in this pathway exhibit shorter survival  while UCEC patients show longer survival , suggesting that patients carrying mutations of genes involved in the same pathways of recurrently mutated oncodrivers might cause different phenotypes  in different cancer contexts.To explore the translational potential of our approach, we checked whether patients carrying mutations perturbing either individual interfaces, reactions or pathways were found to be statistically associated with patient overall survival. When considering interfaces, we found only one instance and no significant interfaces involving is Reactome’s Cytoscape app, implementing a suite of features for users to conduct pathway- and network-based data analysis and visualization based on Reactome pathways and its functional interaction network. To assist users to explore and browse the analysis results from this study, we implemented a set of new features in ReactomeFIViz.https://reactome.org/userguide/reactome-fiviz#Structural_Variants_Visualization.ReactomeFIViz By focusing on biochemical reactions, we identified and provided mechanistic explanations for rare mutations impinging on reactions participating in established onco-pathways involving recurrently mutated oncodrivers . Our multiscale representation of biomolecular processes was instrumental in highlighting interactions or reactions playing roles in crosstalk between higher level pathways belonging to distinct domains of biological function . We also identified significantly affected processes not involving any CGC, thereby representing potentially new cancer-associated processes. For example, we identified several interface mutations affecting the complement cascade pathway, which is emerging as a key regulator of the tumor microenvironment fostering tumor growth and metastasis. Several of the identified pathways also represent new therapeutic opportunities readily targetable by off-label drugs. For instance, we identified several GPCR signaling pathways characterized by rare mutations on either the receptors or downstream signaling nodes, confirming our previous findings that sparse GPCR mutations might converge in dysregulating signaling cascades similar to recurrent mutations of coupled G-proteins.,In this study, we conduct a systematic analysis of protein variants in cancer by mapping non-synonymous cancer somatic mutations from TCGA to protein interaction interfaces using Mechismo and the Reactome functional interaction network first, and then mapping those interfaces back into biochemical reactions and pathways to search for statistically significant interactions, reactions and pathways. Mechismo offers the possibility to predict positional effects of mutations and PTMs at biomolecular interfaces without the need to homology model 3D complexes. Reactome is unique in providing a comprehensive set of manually annotated biochemical reactions which can be leveraged to interpret cancer somatic variants in the context of biochemical reactions and biological pathways. Our analysis is focused on biochemical reactions carried out by protein-protein and protein-chemical interactions and it differs from previous published studies, which were focused either on protein-protein interactions or biological pathways, e.g. users are able to conduct in-silico drug screening and modeling in search of potential therapies by targeting significantly impacted reactions and related functional interactions. We will explore the utility of leveraging ReactomeFIViz in this way.On top of this study, we have implemented a set of new features in ReactomeFIViz for the community to browse and explore the results of our analysis, facilitating the understanding of potential cancer driver mechanisms in a unique, multi-scale perspective that is built upon the Reactome pathway knowledgebase and extends from gene and protein to protein interactions, reactions and pathways. Together with ReactomeFIViz’s previously existing features for visualizing drug-target interactions in the same context of pathways and interaction networks and fuzzy logic simulation to model perturbation of drugs and somatic mutations on pathway activities, or prediction projects (e.g. CASP community) become available, we expect to obtain more insightful results by applying our approach.The study reported in this paper was based on protein-protein and protein-chemical interaction 3D structures. The limited coverage of protein-protein interaction 3D structures still constrains our survey to a small portion of protein-protein interactions extracted from biochemical reactions and pathways annotated in Reactome. In the future, when results and tools from ongoing large-scale protein structure determinationIn summary, we have developed a multi-scale approach to investigate protein structure variants in cancer in the context of protein interactions, reactions and pathways annotated in Reactome. This approach is generic and can be applied to study other types of abnormal protein omics features collected from cancer or other complex diseases. For example, abnormal up or down protein or gene expression data or post-translational modifications can be mapped to protein interactions, reactions, and pathways to investigate their potential impact. Our approach can also be easily extended to integrate other types of interactions, such as protein-DNA, protein-RNA and protein-membrane interactions, for which Reactome has annotated many complexes and reactions. We plan to conduct a more complete analysis by including all these interactions as well as integrated mutations and PTMs sets (e.g. from CPTAC proteogenomics studies) in the future.http://firebrowse.org/ (2016 release), for a total of 1.8M somatic mutations from 7120 patients. A total of 1.79M non-synonymous somatic mutations were mapped on canonical UniProt sequences, yielding 1.16M unique protein variants. Only Ensembl transcripts matching in length to corresponding UniProt sequences were retained using Oncotator “UniProt Exact Match” function and only predicted variants whose reference amino acids matched those at corresponding positions in SwissProt sequences were retained for further inspection. We employed standard TCGA study abbreviations (defined here:https://gdc.cancer.gov/resources-tcga-users/tcga-code-tables/tcga-study-abbreviations) for cancer type names.We downloaded the latest MAF files of 32 TCGA Cancer types from to extract functional interactions between proteins and proteins or proteins and chemicals from Reactome annotated reactions. The FI network used in this study is different from our previously constructed FI network. Here we limited our analyses to FIs extracted from annotated Reactome reactions only. To construct the Reactome reaction network, we connect two reactions (e.g. reaction1 and reaction2) together if the output from reaction1 is an input, catalyst, or regulator of reaction2. To control spurious connections among reactions, a set of small molecules, including ATP, ADP, Pi, H2O, GTP, GDP, CO2 and H+, is excluded for this reaction connection checking. The version of the Reactome knowledgebase used in this study is release 59, released in December 2016, which may have some annotations that don’t exist in the current Reactome release.We adapted the algorithm we developed to build the Reactome FI networkmechismo.russelllab.org). We used UniProt accessions to map protein entities and employed a chemoinformatics similarity search to map chemical entities. In more detail, we retrieved InChI identifiers for both ChEBI (cross-referenced to Reactome) and PDB (cross-referenced to Mechismo) chemical components. We then generated topological fingerprints for both sets through the RDKit package (https://www.rdkit.org/) using the Tanimoto score as a similarity metric. We considered only interactions with matching UniProt accessions and with chemical similarity greater than 0.5 as corresponding protein-chemicals interactions.We mapped protein-protein and protein-chemical functional interactions from Reactome FIN to 3D interaction interfaces from Mechismo  homologs and representing only very confident, physical protein-protein interactions. For chemical and DNA/RNA we also considered predictions with low/medium confidence (as low as 30% sequence identity).We predicted functional consequences of TCGA non-synonymous somatic mutations using Mechismo,We identified the most perturbed interactions in cancer by ranking each interacting pair based on the number of unique samples where a missense mutation was predicted to affect the interface. We tested the significance of the most perturbed interactions in TCGA by using an interactome perturbation random background model (BM). We defined it by first collecting the missense variants found in each sample and randomly shuffling the same substitutions between equivalent positions in the same protein. We obtained equivalent Mechismo data for BM and calculated the probability of obtaining the same number of observed perturbing events for each interaction by chance using a binomial test:N is the total number of samples,c is the number of unique samples in which a given interaction has been found perturbed andP is the probability of getting the same interface perturbed from the random background distribution. The obtainedP-values were adjusted using the FDR/Benjamini-Hochberg procedure. We considered instances with adjustedP-values below 0.05 as significant.whereWe similarly applied the same BM and statistical test to define reactions and pathways whose mediating FIs' interfaces are significantly affected. and considered “vital_ status” , “gender”,”days_to_death”, “days_to_last_follow_up”, “age_at_diagnosis”. Cox’s proportional hazard model was employed to predict hazard ratios and survival probability of patients affected by interface-, reaction- and pathway-perturbing mutations, employing age and sex as covariates. Samples with missing values (i.e. “nan”) in any of these fields were not considered for this analysis.We downloaded clinical records of TCGA patients using TCGAbiolinks Bioconductor packagewww.python.org) through scipy 1.6.2 (www.scipy.org), statsmodels 0.12.2 (statsmodels.sourceforge.net) and lifelines 0.25.9 (lifelines.readthedocs.org/en/latest) libraries.All the statistical analysis has been done in Python 3.8.11 (https://hibernate.org) and a RESTful API to serve the analysis results from the MySQL database to ReactomeFIViz using the Spring MVC framework . New user interfaces were added to ReactomeFIViz using Java Swing  by following the standard practice to develop apps for Cytoscape. We used Java (JDK 1.8) for software programming and host the source code at GitHub:https://github.com/reactome-fi/mechismows for the RESTful API andhttps://github.com/reactome-fi/CytoscapePlugIn for the front-end user interfaces in ReactomeFIViz.We developed a MySQL database to store the analysis results via a Hibernate API (http://firebrowse.org/ (2016 release)MAF files of the 32 TCGA cancer types available fromhttps://reactome.org/tools/reactome-fivizThe outcome of the analysis is available through the Reactome FIViz Cytoscape app:https://doi.org/10.5281/zenodo.5590953.The mysql database dump used for the server-side application:https://doi.org/10.5281/zenodo.7401670.Zenodo: Leveraging biochemical reactions to unravel functional impacts of cancer somatic variants affecting protein interaction interfaces,•Figure S1: Significantly affected pathways in individual TCGA cancer types mapped to top level Reactome pathways. Diameters are proportional to the numbers of unique samples having mutations in top level pathways and color shades are proportional to the numbers of FI interfaces.•Figure S2: Significantly affected reactions belonging to the Gene expression top level pathway. Diameters are proportional to the numbers of unique samples and colors are proportional to the numbers of FI interfaces.•Figure S3: Significantly affected reactions belonging to the Immune System top level pathway. Diameters are proportional to the numbers of unique samples and colors are proportional to the numbers of FI interfaces.•Figure S4: Significantly affected reactions belonging to the Signal Transduction top level pathway. Diameters are proportional to the numbers of unique samples and colors are proportional to the numbers of FI interfaces.•Figure S5: Higher resolution of Figure 6 in PDF.•Figure_6_Networks.cys: The original Cytoscape session file containing networks shown in Figure 6. The file was generated using Cytoscape 3.8.2. The visual style of the functional interaction network in Figure 6B is different from the network labeled “Figure_6B_FIN” in this session file to better distinguish protein nodes from reaction nodes.•Table S1: Mutation enrichment at protein interaction interfaces.•Table S2: Mutation enrichment at Reactome Reactions interfaces.•Table S3: Mutation enrichment at Reactome Pathway interfaces.•Table S4: Survival analysis of significantly affected interactions.•Table S5: Survival analysis of significantly affected reactions.•Table S6: Survival analysis of significantly affected pathways.This project contains the following extended data:Creative Commons Attribution 4.0 International license (CC-BY 4.0).Data are available under the terms of thehttps://reactome.org/userguide/reactome-fiviz#Structural_Variants_VisualizationHome page for user guide describing procedures on how to use ReactomeFIViz features for visualizing cancer somatic mutations via a multi-scale perspective:http://apps.cytoscape.org/apps/reactomefipluginCytoscape app store:https://github.com/reactome-fi/CytoscapePlugIn (ReactomeFIViz app code) andhttps://github.com/reactome-fi/mechismows (Server-side code)Latest source code:https://github.com/reactome-fi/CytoscapePlugIn/releases/tag/f1000_cancer_2021 andhttps://github.com/reactome-fi/mechismows/releases/tag/F1000_Cancer_2021Source code as at the time of publication:https://doi.org/10.5281/zenodo.5590945 (ReactomeFIViz app code) andhttps://doi.org/10.5281/zenodo.5590949 (Server-side code)Archived source code as at the time of publication:https://reactome.org/license).License: Creative Commons Attribution 4.0 International (CC BY 4.0) License  - Legends of figures could be improved, adding details. In particular it is not clear what "donor" means in Figure 2. - Supplementary figures S1-S4 are unreadable in the current version.Is the work clearly and accurately presented and does it cite the current literature?YesIf applicable, is the statistical analysis and its interpretation appropriate?YesAre all the source data underlying the results available to ensure full reproducibility?YesIs the study design appropriate and is the work technically sound?YesAre the conclusions drawn adequately supported by the results?YesAre sufficient details of methods and analysis provided to allow replication by others?YesReviewer Expertise:Computational biology; Bioinformatics; Structural and functional characterization of proteins and their variants; Machine and deep learning.I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. Dear Dr. Pier Luigi Martelli,In my opinion, paper readability would benefit from a more extended review of the methods at the basis of Mechismo and of the definition of functional interfaces. In particular, it is unclear what is the expected number of variations that Mechismo can annotate on proteins without 3D structure, as reported in the introduction. For the 1.16 M unique variations from TCGA, authors only report the number of those mapped on known 3D structures, and it is unclear whether they include those deriving from homology-based analysis. If yes, it would be useful to provide some more detail and discuss possible differences of effect predictions depending on the similarity between the wildtype sequence and the adopted structure.We mapped mutations to 3D structures, either known or homologous, by considering UniProt alignment to PDB fasta sequences via BLAST, considering matches with E-value threshold of 0.0001, and storing the best match for each position in the UniProt sequence. Thanks a lot for reviewing our manuscript. We have made some changes according to your comments. Please see our reply to your comments below: Then we considered high confidence predictions for protein-protein interactions including known structures or close (>=70% sequence identity) homologs and representing only very confident, physical protein-protein interactions. For chemical and DNA/RNA we also considered predictions with low/medium confidence (as low as 30% sequence identity). We have made these points clearer both in the Results and Methods sections.Introduction: the sentence "The gene-level analysis tells us what changes occur in genes or proteins, but not the functional impact" is a bit misleading, since different approaches have been developed for predicting possible impact at different levels When we said “gene-level analysis”, we meant the analysis is about genes or proteins such as somatic mutations or structural variants. Indeed, many approaches have developed to predict impacts of mutations on protein stability and functions. But we’d like to argue that “stability” is still a protein-level feature. To know the impact of mutation on the protein’s function, extra information is needed  for higher level analysis . Regardless, we added some phrases (as shown below in parentheses) to the original sentence. Now this sentence reads as the following: The gene-level analysis tells us what changes  occur in genes or proteins, but not the functional impact …Legends of figures could be improved, adding details. In particular it is not clear what "donor" means in Figure 2.Thanks for pointing this out. We have changed “donors” to “patients” in Figure 2.Supplementary figures S1-S4 are unreadable in the current version.These figures have been fixed. It is our mistake. Thanks again for reviewing our manuscript and comments. In this impressive work, the authors investigated all available data on somatic cancer mutations by connecting 3D protein-protein and protein-chemical interface data with data on biochemical reactions and pathways, thereby revealing significantly impacted reactions and pathways. This novel approach has yielded numerous important insights. Analysis of patient survival times identified mutations perturbing processes affecting patient survival. The authors also created an updated visualization tool enabling the users to explore the results of the analyses.The supplementary figures (Extended data) cannot be evaluated because they got cropped, probably during some conversion step.The top part of Fig. 5 is not labeled, and the legend does not refer to it.On Page 3, 'ERBB2' is misspelled as 'ERRB2'.The resolution of Fig. 6 is poor and most labels are unreadable. Since this figure is very complex, a vector image (preferably SVG) would be better. The legend says some nodes contain bar charts, but in fact they are pie charts. Actually, bar charts would be better because there are too many similar colors in the pie charts.The supplementary tables are provided in an Excel file which is inconvenient to use as the column labels are not fully visible. Please increase the height of the row for the column labels and use wrapped text.The cancer types are indicated with acronyms, but they are not expanded anywhere. Please provide their expansions, or a link where these can be found. Regarding the survival analysis, the authors used the clinical records from TCGA. Were there any missing data? If yes, how were they handled? The methodology is sound, and the results support the conclusions. There are only a few technical problems which need to be fixed: This article is a very significant contribution. However, it cannot be fully evaluated until the supplementary figures are fixed.Is the work clearly and accurately presented and does it cite the current literature?YesIf applicable, is the statistical analysis and its interpretation appropriate?YesAre all the source data underlying the results available to ensure full reproducibility?YesIs the study design appropriate and is the work technically sound?YesAre the conclusions drawn adequately supported by the results?YesAre sufficient details of methods and analysis provided to allow replication by others?YesReviewer Expertise:Bioinformatics, protein structure, protein-protein interactions, pathway analysisI confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. Dear Dr. Szilagyi,The supplementary figures (Extended data) cannot be evaluated because they got cropped, probably during some conversion step.Our mistake. This has been fixed now.The top part of Fig. 5 is not labeled, and the legend does not refer to it.The top part of Fig 5 is deleted.On Page 3, 'ERBB2' is misspelled as 'ERRB2'.Thanks! Fixed.The resolution of Fig. 6 is poor and most labels are unreadable. Since this figure is very complex, a vector image (preferably SVG) would be better. The legend says some nodes contain bar charts, but in fact they are pie charts. Actually, bar charts would be better because there are too many similar colors in the pie charts.https://cytoscape.org/. Thanks for pointing this out. We replaced the figure with a PDF version, which is scalable. Furthermore, we provided the original Cytoscape file  in Extended Data for readers to investigate the networks using Cytoscape, which can be downloaded from Thanks a lot for reviewing our manuscript. We have made the following updates according to your comments: We tried to use bar charts in Cytoscape. However, we believe using pie charts is better. Also we fixed the name of the charts to “pie charts” from “bar charts”. Thanks a lot for your suggestion!The supplementary tables are provided in an Excel file which is inconvenient to use as the column labels are not fully visible. Please increase the height of the row for the column labels and use wrapped text.Thanks for the suggestions. We have increased the height of the row for the column labels and use wrapped text.The cancer types are indicated with acronyms, but they are not expanded anywhere. Please provide their expansions, or a link where these can be found.https://gdc.cancer.gov/resources-tcga-users/tcga-code-tables/tcga-study-abbreviations). We provided a reference in the “TCGA dataset” section of the Methods.Regarding the cancer types acronyms, these are the standard abbreviations provided by TCGA (Regarding the survival analysis, the authors used the clinical records from TCGA. Were there any missing data? If yes, how were they handled?Thanks for pointing this out. We didn’t consider in our analysis samples with missing values. We have now made this point clear in the “Survival analysis” section of the Methods.  Thanks again for your comments.