Applied to four independent systems, we successfully show that EpiScore and DCIasym can capture important biophysical properties in dual mutant substitution outcomes. We propose that allosteric regulation and the mechanisms underlying non-additive amino acid substitution outcomes  can be understood as emergent properties of an anisotropic network of interactions where the inclusion of the full network of interactions is critical for accurate modeling. Consequently, mutations which drive towards a new function may require a fine balance between functional site asymmetry and strength of dynamic coupling with the functional sites. These two tools will provide mechanistic insight into both understanding and predicting the outcome of dual mutations.Understanding the underlying mechanisms behind protein allostery and non-additivity of substitution outcomes  is critical when attempting to predict the functional impact of mutations, particularly at non-conserved sites. In an effort to model these two biological properties, we extend the framework of our metric to calculate dynamic coupling between residues, the Dynamic Coupling Index (DCI) to two new metrics: (i) EpiScore, which quantifies the difference between the residue fluctuation response of a functional site when two other positions are perturbed with random Brownian kicks simultaneously versus individually to capture the degree of cooperativity of these two other positions in modulating the dynamics of the functional site and (ii) DCI A growing body of data on the human genome suggest that within the exome (the protein coding region), one individual may possess 10,000 or more non-synonymous nucleotide variants, many of which occur at positions which are not evolutionarily conserved ,3. PrediOne reason why predicting the impact of mutations may fail is that extensive epistasis occurs during evolution ,5,6. EpiEpistatic relationships becomes crucial when comparing homologous protein families or protein domains, which can exhibit significant sequence variation and biochemical properties that may span orders of magnitude while still maintaining a similar three-dimensional (3-D) fold ,17,18,19On the other hand, when protein equilibrium dynamics and each individual position’s contribution to these dynamics are taken into consideration, we can shed light onto the mechanism of epistatic relations. This is because proteins sample many different conformations within the native state, and these conformational dynamics, governed by the strength of the 3-D network of interactions, underlie protein function. Within this dynamic view, we can simply treat a protein as a biological signal processor where the 3-D interaction network mediates long-range communication through amino acid fluctuations nascent to a given protein sequence. Therefore, the knowledge of how mutations may fine-tune this sequence-function relationship necessitates evaluating the role of each residue position in establishing a protein’s internal communication network through protein dynamics ,27. PartThe non-additive, epistatic interactions therefore can use dynamic features of a protein to modulate function. These dynamics features are similar to that found in allosteric modulation in which a protein is able to control catalytic function or regulate on/off states through the binding of a ligand to a site distal from a catalytic/active site. This distal binding has been shown to modulate catalytic site dynamics, sometimes without association to distinct conformational states. This type of allostery, which can impact function by manipulating the normal modes of the protein while retaining the conformation, is known as dynamic allostery ,29. We ni upon perturbation to a single position (or subset of positions) j, relative to the average fluctuation response of position i when all of the positions within a structure are perturbed. Expanding upon the dynamic coupling concept, here we develop a new metric called EpiScore. EpiScore measures the difference in the residue fluctuation response of an active site when two mutational sites are simultaneously perturbed by random forces versus the response when individual force perturbations are exerted one at a time to the mutational sites.The modeling of protein conformational dynamics using force perturbations and elastic networks has been previously used successfully in attempts to understand the role of long-range interactions in protein evolution ,31,32,338) [cat, KM, IC50) for a range of substrates are available. These two systems, P. falciparum DHFR (pfDHFR) and a β-lactamase (TEM-1), naturally confer resistance to drugs and the trajectories of these resistances as well as their epistatic relationships have been explored [In order to determine whether EpiScore can identify the degree and strength of epistatic relationships between position pairs, we first applied our analysis to the deep scanning database of double mutations between all positions in the IgG-binding domain of protein GB1 . These m8) ,35,36,37explored ,23. Impo50 values of pfDHFR dual mutants and the catalytic turnover rates for cefotaxime of TEM-1 dual mutants. Furthermore, each pfDHFR amino acid pair exhibits distinct distributions of EpiScore values showing the importance of how these two positions communicate with the active site through the anisotropic interaction network.We first observed that EpiScore can distinguish positive and negative epistasis in dual mutations when analysis was performed over 1045 single mutants and 509,963 double mutants of GB1. We also found that the average EpiScore value correlates well with experimental epistatic measures calculated using pyrimethamine ICi upon exerting random forces on j is not identical to the response of j when position i is perturbed; we calculate this asymmetry with DCIasym. We applied our DCIasym analysis to the TEM-1 dual-mutant sites and found that, indeed, a relationship exists between dynamic coupling asymmetry and EpiScore when all active sites in the TEM-1 system are considered. Specifically, two of the three dual mutant positions which exhibited the largest positive epistasis in cefotaxime kcat/KM from the wild-type had both EpiScore values < 1 (indicating strong non-additivity) with respect to active site S70. Additionally, these dual mutants also exhibit asymmetry in dynamic coupling based on DCIasym, with consistent unidirectionality from active sites site to mutation sites in long range communication. We propose that this communication directionality signature should be readily apparent in known allosteric systems as mentioned above. Therefore, we applied a similar analysis to a Pin1 protein well-studied for its dynamic allostery and showed that the DCIasym between the catalytic binding sites and non-catalytic distal binding sites presents a unique directionality in long distance dynamic coupling, leading to a cause-and-effect relationship between allosteric sites and active sites also observed in epistatic interactions.Interestingly, DCI is usually not symmetric, i.e., the fluctuation response of position We previously designed a unique way to capture site-specific coupling between residue pairs or groups of residues, the Dynamic Coupling Index (DCI). The underlying premise behind DCI is the importance of a system’s response to a force perturbation, be that protein-solvent, protein-protein, protein-ion or protein ligand interactions. Additionally, the point mutations here are modeled by the response of a system to a perturbation at a specific site, a.k.a. a single amino acid.R (Equation (1)) of each position and create response vector that measures the magnitude and direction  of displacement of every residue from its mean. As mentioned above, this (to the first order) mimics the effects of in vivo interactions of a protein. For example, a ligand binding event will apply a force to residues in the binding pocket of a receptor protein. In our perturbation residue scanning (PRS) approach, this is averaged over many unit force directions to simulate an isotropic perturbation.DCI is a combination of the Elastic Network Model (ENM) and Linear Response Theory (LRT) where the protein is modeled by representing the amino acids as nodes in a network connected by Hookean springs . The intH is the Hessian, a 3N × 3N matrix which can be constructed from 3-dimensional atomic coordinate information where it is composed of the second order derivatives of the harmonic potential energy with respect to the components of the position vector of length 3N. The Hessian matrix can be extracted directly from molecular dynamics simulations as the inverse of the covariance matrix. This method allows one to implicitly capture specific physiochemical properties and more accurate residue-residue interactions via atomistic force fields and subsequent all-atom simulation data. However, for the purposes of this paper, we wished to investigate only those relationships which could be derived solely from inter-atomic distances of single protein structures and thus we used the ENM version of our approach.Ai due to the perturbations at position j. From this perturbation response matrix, we can construct DCI. DCIij, then, represents the displacement response of position i upon perturbation to a single functionally important position (or subset of positions) j, relative to the average fluctuation response of position i when all of the positions within a structure are perturbed.Repeating this process, each position in the structure is perturbed sequentially to generate a perturbation response matrix i and residue(s) j upon perturbation to residue(s) j.As such, DCI can be considered a measure of the dynamic coupling between residue ≤i is the number of positions with a DCI value ≤ DCIij for a system of N residues.It is often more convenient to represent DCI as a percentile rank,One of the most important aspects of DCI is that the entire network of interactions is explicitly included in subsequent calculations without the need of dimensionality reduction such as Normal Mode Analysis through principal component analysis. If one considers interactions such as allostery as an emergent property of an anisotropic interaction network, it is critical to include the interactions of the entire network to accurately model the effect one residue can have on another.asym, respectively. EpiScore can identify or describe potential non-additivity in substitution behavior between residue pairs. This metric can capture the differences in a normalized perturbation response to a position k when a force is applied at two residues i and j simultaneously versus the average additive perturbation response when each residue i, j, is perturbed individually (i and j generates a greater (lesser) response at position k than the effect of a simultaneous perturbation. This means that, when treated together with a simultaneous perturbation at both sites i and j, the displacement response of k is lower (higher) as compared to the average effect of individual perturbations to i and j, one at a time. As EpiScore is a linear scale, the further the value from 1, the greater the effect described above.Here, we present two further extensions of DCI which allow us to uniquely model allosteric interactions and epistatic effects; EpiScore and DCIvidually . EpiScori j pair of amino acids which do not have direct interactions  − DCIji (Equation (5)). Using DCIasym we can determine a cause-effect relationship between the i j pair in terms of force/signal propagation between these two positions.Interestingly, through the use of DCI we can capture asymmetry between different residues within a protein, as coupling in and of itself is asymmetric within an anisotropic network. That is, each amino acid has a set of positions to which it is highly coupled, and this anisotropy in connections gives rise to unique differences in coupling between a given ractions . DCIasymab) − ln(Wa) − ln(Wb), where Wab represents the fitness for the dual mutant and Wa and Wb are the fitness values for the single mutants. Here we investigate the relationship between the experimental epistasis and EpiScore by comparing the average EpiScore for each position pair with instances of positive (blue) or negative (red) epistasis using the skewness of the experimental epistasis distribution over the full mutational space available for a given pair , for whven pair . SkewnesPlasmodium falciparum, by competitively inhibiting DHFR. While exhibiting a particularly low sequence conservation between species, most differences in sequence are from flexible loop regions [P. falciparum DHFR (pfDHFR) mutants as a result of four key amino acid substitutions at positions N51, C59, S108 and I164 which have also exhibited significant epistasis between mutation combinations [The full system analysis of GB1 showed the existence of a general trend between epistasis and EpiScore; particularly, an inverse relationship between EpiScore above or below one and skewness in experimental epistasis, indicating that positions with EpiScore less than 1 more often work cooperatively towards beneficial protein function, whereas pairs yielding EpiScore values greater than 1 usually result in antagonistic interactions which impair function. In an effort to elucidate more specific mechanistic details or trends underlying epistatic interactions which may exist in other systems, we broaden our application of EpiScore to other known epistatic proteins with a focus on specific mutation pairs. As such, we next study DHFR, a protein involved in the development of anti-malarial resistances in malarial parasites. Anti-malarial drugs commonly target the DHFR, which catalyzes the reduction of dihydrofolate and is essential to cellular growth and proliferation. Pyrimethamine is one such drug, used to treat malaria caused by one of the most common malarial parasites,  regions , while t regions . Howeverinations   with IC50 epistasis, where the stronger the positive epistasis, the lower the average EpiScore value. For example, an EpiScore value of ~0 means the pairwise dynamic coupling to FG loop positions of a dual mutant pair is negligible as compared to the average individual dynamic coupling; that is, the distal sites can individually impact position the FG loop residues allosterically. However, when treated together with a simultaneous perturbation at both sites, the displacement response of the FG loop residues are significantly lower, and, subsequently, their joint ability to allosterically regulate these FG loop positions is effectively lost. Due to the interaction network between the two distal positions with the FG loop, they may antagonistically compensate the amplitude and direction of the response when the perturbations on these two sites are exerted at the same time. To the reverse, an EpiScore value >> 1 suggests that, simultaneously, two positions may exhibit dynamic coupling to the FG loop enough such that their pairwise mutational impact fundamentally alters the role the FG loop plays within the pfDHFR interaction network resulting in loss of function.At first, this relationship may seem somewhat counterintuitive, as one could reasonably expect that the higher the EpiScore value , the higher the experimental epistasis. However, when complexed with substrate, the functionally critical M20 loop  is stabiExpanding our study to another system important to the concept of antibiotic resistance, we analyze TEM-1, a protein which possesses antibiotic resistance largely driven by its high evolvability, with over 170 TEM-1 mutants discovered as clinical or hospital isolates . TEM-1 iPrevious work has shown that the majority of the resistance-conferring mutations in TEM-1 are both distal to (10 Å or further) and highly coupled with the active site residues , indicatHere we focus on the specific epistatic relationship between four TEM-1 mutation sites  which have exhibited significant non-additive behavior  with mutasym. Thus, we calculated DCIasym between each TEM-1 dual-mutant site and all main active sites for the relevant TEM-1 structure  are those which display the largest degree of positive epistasis.In TEM-1, mutational sites which confer incremental changes in biophysical activity are neither locally distributed with respect to one another, nor at important functional sites. Furthermore, they do not belong to an immediately identifiable allosteric inhibitor site, but they do, however, exhibit unique pairwise epistatic behavior which indicates that they likely regulate the active sites allosterically. In an effort to analyze whether the pairs having EpiScore less than 1 and associated with positive epistasis  exhibit long-range communication that is distinct from the pairs having EpiScore greater than 1 and associated with negative epistasis, we explored the degree of asymmetry in long-range communication between the mutational positions and the active site positions using DCIThus, as a test system, TEM-1 highlights the complex relationship between mutational positions, allosteric relationships, and epistatic interplay. These emergent properties of the anisotropic residue-residue interaction network within a protein must be accounted for when attempting to fully understand or predict mutation outcomes.Using the dynamical picture presented above, the modulation of protein dynamics through mutations  is similar to the modulation of dynamics through binding; this is the fundamental principle behind the concept of dynamic allostery. With the TEM-1 dual mutation positions showing unique coupling asymmetry to the active sites, it follows that there should be an obvious, unidirectional signature between allosteric sites and active sites in known allosteric proteins. Here we explore the role dynamic coupling directionality plays in allosteric regulations using an ideal model system, Pin1. Pin1 is a two-domain protein containing a catalytic PPIase domain and a distally-located WW domain, connected by a flexible (and highly disordered) interdomain linker ,55,56. WPrevious works propose the existence of communication networks between the WW domain and the PPIase domain, including a unique allosteric pathway which only becomes active when a substrate is bound to the WW domain . A furthij) and non-catalytic WW domains (%DCIji) of Pin1 (PDB ID 3TCZ [asym” (%DCIij − %DCIji) the coupling asymmetry between PPIase domain binding positions  and WW domain binding positions   . Hence, asym can capture communication directionality in allosteric systems from structural dynamics encoded within a given set of atomic coordinates.This suggests a cause-and-effect relationship exists between the two domains. Using this framework, a ligand binding event is modeled as a force perturbation to the binding positions in each domain. Upon these random force perturbations, we find that, overall, the WW domain is able to induce a stronger perturbation response in the PPIase domain than the reverse. This is largely the expected relationship between an allosteric site and a catalytic site; communication between these sites should predominantly involve information transfer from the allosteric site to the catalytic site, indicating that %DCIasym to TEM-1, we found that EpiScore and epistasis both relate to dynamic coupling asymmetry, where position pairs which exhibited high EpiScores associated with negative epistasis also exhibited mutation-dominant coupling asymmetry. This suggests that %DCIasym and EpiScore may both capture factors which contribute towards the biochemical outcome of dual mutations. If both mutational sites dominate the dynamics coupling with the active site , then dual mutations on both sites lead to negative epistasis.In this work we showed how the anisotropic interaction network within a protein captures two essential emergent properties of protein evolution—epistasis and communication directionality—using the information stored in structural dynamics alone. Additionally, EpiScore can capture the behavior of dual-mutation epistatic outcomes with some consistent trends across different protein systems. As seen in pfDHFR, mutation pairs with a lower pairwise dynamic coupling versus average of individual couplings (EpiScore < 1) to FG loop positions are favorable, as dual mutations at these positions may be less likely to disrupt the FG loop’s interaction with the functionally critical M20 loop. A similar trend was also observed in the EpiScore analysis of TEM-1 dual mutants, where lower EpiScore to active site S70 was generally associated with higher positive experimental epistasis (R = −0.71) Further, the system-wide EpiScore analysis of GB1 dual mutants has shown that the position pairs with average EpiScore values > 1 were associated more frequently with negative epistasis, indicating that these positions might ultimately be more disruptive to the entire protein when mutated together. Furthermore, when dynamic coupling asymmetry analysis was applied via %DCIAs modulation of normal modes and protein dynamics is not only a tool used in evolution but also a principle exploited via allostery, we used an “ideal” allosteric system, Pin1, and observed the dynamic coupling asymmetry between a well-identified allosteric domain and an enzymatically active domain exhibits behavior that, as expected, showed the allosteric WW domain to dominate communication to the PPIase domain. Overall, these two novel protein dynamics-based metrics provide steps to mechanistically describe these complicated interactions, and also shed light on the complex anisotropic interaction network which ultimately gives rise to epistasis and allosteric regulation. They can be useful to predict mutational outcomes, particularly for those sites distal from the active site that can modulate function .


Top-down proteomics has enabled the elucidation of heterogeneous protein complexes with different cofactors, post-translational modifications, and protein membership. This heterogeneity is believed to play a previously unknown role in cellular processes. The different molecular forms of a protein complex have come to be called “complex isoform” or “complexoform”. Despite the elucidation of the complexoform, it remains unclear how and whether cellular circuits control the distribution of a complexoform. To help address this issue, we first simulate a generic three-protein complexoform to reveal the control of its distribution by the timing of gene transcription, mRNA translation, and protein transport. Overall, we ran 265 computational experiments: each averaged over 1,000 stochastic simulations. Based on the experiments, we show that genes arranged in a single operon, a cascade, or as two operons all give rise to the different protein composition of complexoform because of timing differences in protein-synthesis order. We also show that changes in the kinetics of expression, protein transport, or protein binding dramatically alter the distribution of the complexoform. Furthermore, both stochastic and transient kinetics control the assembly of the complexoform when the expression and assembly occur concurrently. We test our model against the biological cellulosome system. With biologically relevant rates, we find that the genetic circuitry controls the average final complexoform assembly and the variation in the assembly structure. Our results highlight the importance of both the genetic circuit architecture and kinetics in determining the distribution of a complexoform. Our work has a broad impact on our understanding of non-equilibrium processes in both living and synthetic biological systems. Multiple protein subunits can come together to form protein complexes that play critical functional roles in a cell. Recent advancement in measurement technologies has revealed tremendous variation in the members of protein complexes. The recent results motivate further research into the importance and the underlying mechanisms of the variation. Here, we study the arrangement of genes as a key factor that modulates the variation of protein complexes. We run computer simulations to investigate how various reaction parameters control the variation of a protein complex. Finally, we extend our framework to study the variation of an enzymatic complex that digests cellulose. Our work has a broad impact on the understanding of protein-complex assembly and set up the new research direction about the variation of protein complexes. Proteins are synthesized in specific orders to assemble large protein complexes, such as microtubule, proteasome, ribosomes, and cellulosome. These protein complexes are assembled both inside and outside cells through the coordination of gene expression, protein transport, and binding processes. Prior work has been assuming that protein complexes have a homogeneous composition of protein members. Yet, recent top-down proteomics shows that protein complexes can compose of different cofactors, post-translational modifications, and protein membership –3. The dWhile the elucidation of complexoforms is now possible, finding the mechanisms that control the distribution of complexoforms is challenging. There are two competing paradigms for understanding the formation of complexoforms. A classical but unproven paradigm assumes that temporal ordering of protein synthesis is essential for accurate assembly of protein complexes. Yet, most biochemical studies mix proteins in a test tube to study protein complex assembly, neglecting the underlying cellular networks that control synthesis and transport of the proteins. This approach suggests a second, contradicting paradigm: the underlying cellular networks are not essential for the assembly of quaternary protein complexes.One way to resolve the contradiction is to examine genome sequences for the spatial arrangement of genes in the genome: if genes are arranged in specific patterns that correlate with their synthesis order or the assembly sequence of protein complexes, then the underlying gene networks may be essential for the protein assembly. Indeed, a study of the human genome shows that housekeeping genes expressed in most tissues show strong clustering . The datA central question currently challenging the field is, therefore, what underlying rules govern protein-synthesis or assembly order, and the limit of the rules in controlling the distribution of a complexoform. Here, we use stochastic computational simulations to model protein expression and assembly from the bottom-up. We first simulate a three-protein model system to investigate how complexoform composition is affected by the timing of gene transcription, mRNA translation, and protein transport, as well as the gene circuit architecture. Due to the small number of chemical agents and proteins often involved in protein assembly processes, ordinary equilibrium statistical mechanics approaches are not expected to adequately describe the eventual protein assembly outcome. We show that genetic circuits, physical transport, and binding kinetic rates all modulate the distribution of complexoform. Comparison of the stochastic simulations to deterministic solutions reveal two distinct kinetic regimes: a slow equilibrium regime in which the average structure is well predicted by equilibrium statistical mechanics, and a fast nonequilibrium regime in which small-number statistics leads to structures far from equilibrium predictions. This work points to new and previously unappreciated mechanisms of modulating the distribution of a complexoform. Our work also highlights the importance of both gene circuit architecture and nonequilibrium stochasticity in regulating the formation of complexoforms. Our work provides a possible resolution of the gene-circuit vs. protein-only paradigms: gene-circuit-based regulation of complexoform distribution may be important only under the select kinetic regime and cellular context.Protein expression and assembly typically involve a relatively small number of each type of molecule (<1000 per type of protein ). In sucX and Y. The proteins are exported to a sub-cellular location and then bind to scaffold proteins, each consisting of two docking sites. This model system allows us to investigate the role of timing and genetic circuit architecture in modulating the protein assembly, irrespective of the exact number of types of proteins and binding sites. In this model, the binding between the proteins creates a protein complex  and the cascade circuit . The results arise because these circuits produce the mRNA for X or protein X first. For the series uncoupled circuit, protein X is less prevalent in the final complexoform, but still exceeds protein Y . The parallel circuit stochastically produces similar amounts of X and Y in the final complexoform . These results indicate that even with identical rate constants and initial concentrations of all the reactants, the genetic circuit architecture can significantly modulate the final distribution of the complexoform.We first investigate the role of the specific genetic circuit architecture in determining the protein assembly by assuming that the rate constants are identical. This assumption helps to isolate the effect of gene circuit architecture on the formation of complexoform. It will be relaxed in subsequent simulations. Each genetic circuit modulates the distribution of the complexoforms differently . For theXX, 50% XY, and 25% YY. Our results show that varying the kinetic rates in the stochastic model alters the distribution of the complexoform, demonstrating that the genetic and physical rate constants, as well as the exact genetic circuit architecture, can modulate the eventual protein assembly. We note that the rate of mRNA degradation also alters the distribution of the complexoform, but that the effect is captured by altering the rate of translation , as noted in Next, we determine the impact of the reaction constants on protein assembly. We simulate each genetic circuit while individually varying the translation rate, the export rate, and the binding rate. For comparison, we also solve each condition deterministically. X and Y in the assemblies. For the parallel circuit, any single stochastic simulation produces either predominantly XX or YY assemblies, each with a 50/50 chance, and has a very low chance of producing any heterogeneous XY complexoforms. Since the structure of the cascade, series uncoupled, and series coupled genetic circuits all favor protein X expression before protein Y, higher kinetic rates tend to produce XX complexoform in these circuits. In contrast, if a sufficiently large number of both types of proteins reach the scaffold protein before the docking sites are occupied, any binding site has an equal chance of binding either protein X or Y, and the four different complexoforms  have an equal chance of occurring. A homogeneous XX or YY should, therefore, each occur 25% of the time, while a heterogeneous XY or YX assembly should account for 50% of the final scaffold proteins. This 25/50/25 distribution in the final protein complex is seen in almost all cases when the rates are sufficiently slow    togetherasb). The average fraction of XX, XY, and YY complexoform in the stochastic simulation is shown as a function of the average Tasb in asb, the total Tasb is highly correlated with the complexoform composition, and the average assembly outcome from the stochastic simulation can be correlated to a single parameter, the Tasb. Here again, however, the series coupled genetic circuit is an exception; when the translation rate in this genetic circuit is lowered, the sequential architecture of the circuit results predominantly in XX complexoform. This result again suggests that the gene circuit architecture can exert a high level of control over the protein assembly structure, even when the assembly is slow, and a large number of proteins are involved in stochastically forming the structure.Our results also show that altering any of the rates of the system, in turn, changes the average total time to fully occupy the available docking sites, which we term time to assembly , the fraction of protein X in the assembly drops to 77%, and a 1000-fold change predicts a fraction of 56% X, i.e., almost equal amounts of X and Y. Both protein binding and protein loss could depend on the exact extracellular conditions , thus impacting the assembly purely by altering the kinetic rates involved. Thus, our data demonstrate that both the kinetic rates and the genetic circuit can moderate the eventual assembly outcome in the case of the bacterial cellulosome.Our simulations show that, as observed for the generic model system, the type of genetic circuit exerts a great deal of control over the eventual protein assembly. For example, a parallel circuit will result in an average assembly of 50% see also . OverallIn summary, our work shows that the underlying genetic circuit architecture does modulate the protein assembly. However, it is the interplay between the circuit architecture and the genetic and physical rate kinetics that together determine the protein assembly structure. We demonstrate two distinct behaviors of kinetic assembly: a slow equilibrium regime, in which the average assembly is well described by equilibrium statistical mechanics, and a fast non-equilibrium regime, in which the average assembly arrests before the system reaches equilibrium. Regardless of the equilibrium or non-equilibrium regime, the cumulative protein concentrations  determine the eventual complexoform distribution . FurtherThere are several future considerations for our results. First, our work did not consider the details of how genetic context affects the transcription or translation rate of genes. For instance, a prior work  shows thSecond, one challenge in our work is to obtain the analytical solution of the stochastic equations. For instance, previous work by Laurenzi, Renyi, Mcquarrie, and Ishida –39 have Third, the arrangement of two genes in the series coupled module may affect the expression level of the genes. Typically, the second gene is expressed at a lower level than the first gene. A prior work  has quan1, α2) and beta  subunits  A letter containing a detailed list of your responses to all review comments, and a description of the changes you have made in the manuscript. Please note while forming your response, if your article is accepted, you may have the opportunity to make the peer review history publicly available. The record will include editor decision letters (with reviews) and your responses to reviewer comments. If eligible, we will contact you to opt in or out[2] Two versions of the revised manuscript: one with either highlights or tracked changes denoting where the text has been changed; the other a clean version (uploaded as the manuscript file).Important additional instructions are given below your reviewer comments.Thank you again for your submission to our journal. We hope that our editorial process has been constructive so far, and we welcome your feedback at any time. Please don't hesitate to contact us if you have any questions or comments.Sincerely,James R. FaederAssociate EditorPLOS Computational BiologyWilliam NobleDeputy EditorPLOS Computational Biology***********************ploscompbiol@plos.org immediately:A link appears below if there are any accompanying review attachments. If you believe any reviews to be missing, please contact [LINK]Reviewer's Responses to QuestionsComments to the Authors:Please note here if the review is uploaded as an attachment.Reviewer #1: This is a nice study elucidating the role of biological circuitry in control and assembly of complexoform proteins. With a few modifications, it should be accepted for publication. Please take the following suggestions into account:• The authors should take into account the effect of different promoter strengths and different ribosome binding site strengths for each separate gene. The authors should analyze the effect of changing the promoter and RBS strengths for an individual gene, and compare this to the effect of changing the genetic circuit architecture. If circuit architecture effects tend to dominate over promoter and RBS binding site effects, this should be explained.• The authors should include some discussion of the relative abundance of each type of circuit architecture found in natural genomes, for assembling known complexoforms. Is there a particular circuit layout which tends to appear more often than the others?• The authors should discuss their work in relation to the following literature:o Yeung et al. Biophysical Constraints Arising from Compositional Context in Synthetic Gene Networks. Cell Systems. 2017.o In particular, the authors should include a discussion of how the orientation of genetic circuits in a parallel configuration affects transcription, and how this might impact their model.• The term “complexoform” is of limited use in the literature (a Google Scholar search returns four relevant results). Accordingly, in order to assure clarity, the authors should introduce the term as “complex isoforms” as is done in Fonslow et al.Reviewer #2: This is an interesting and novel study, where computational simulations are used to analyze a recently appreciated phenomenon -- complexoforms. In particular, the authors thoroughly analyzed how gene circuit architecture and rate constants can affect distribution of complexoforms. They have also thoroughly compared deterministic and stochastic simulations to examine the potential contribution of noise in this dynamics.The study is well designed and the paper is well written and easy to follow. The model formulation and subsequent analysis are fairly standard. But the key innovation of the study is the application context . The work defines a useful blueprint for the dynamic analysis of complexoforms. The modeling framework (in particular) and the results can be useful for guiding future experimental analysis.I am in support of the publication of the work with some minor clarifications and revisions, as detailed below.Specific points:1. In essence, the different circuit architectures dictate the temporal dynamics of X and Y, which in turn bind the the different scaffold sites. As such, it is not entirely surprising that different architectures would modulate the find distribution of complexoforms differently. I wonder if it is possible to deduce some more general rules that govern the final distribution based on the temporal profiles of X and Y (outside the cell).2. Related to the point above, the binding kinetics of X and Y to scaffold site should be important as well. If I understand it correctly, the authors assume the binding to be irreversible (Table 1). I wonder what happens to the distributions if the binding is reversible, which appears to be more plausible.3. Does the number of binding sites matter? At it stands, the choice of the number seems ad hoc.4. There was a lot of focus on comparing deterministic modeling and stochastic modeling. The resulting insights are not well articulated and need to be clarified in a revision. Is there experimental data that the modeling results can be compared to?Reviewer #3: In the manuscript titled " Stochastic ordering of complexoform proteinassembly by genetic circuits", Jensen et al. used combination of stochasticsimulation and deterministic differential equation model to investigate howparameters such as timing of transcription, translation, and transport rateimpact formation preferences of complexoform. They found that in two differentextreme scenarios, complexoform variations are also vastly different. At theend, the authors used a real biological example to verify their framework. Ithink the authors did a good job explaining this relatively new topic and madethe text fairly easy to follow. Discussing a relatively less studied new area,this work used standard simulation methods to shed some new light on possiblemechanisms controlling complexoform preferences, attributing some of thecontrol to gene circuit layout. This finding hopefully would inspire morefuture work in this area to investigate this interesting and important problem.I found the manuscript to be informative, theapproaches used appropriate, and would recommend for its publication given myfollowing concerns/comments to be addressed:1. One major concern of mine is the lack of any analytical results. Numericalsimulations are definitely preferred in many scenarios, including this work.But careful analysis often is also needed. It would complement numericalsimulations well and often offer a fuller picture. I understand analyticalresults are not possible for complex systems, but in this case, there is nofeedback and an analysis of the simplified scenarios should be within reach.2. I am curious why the authors did not consider mRNA degradation as one keyparameters. mRNA degradation has been shown to be a key regulatory mechanismin biology. In addition, recent work showed that in the series coupled scenarios, the order of the gene X and Ywould affect their stability and hence could cause a change in thecomplexoform formation biases.**********Have all data underlying the figures and results presented in the manuscript been provided?PLOS Computational Biologydata availability policy, and numerical data that underlies graphs or summary statistics should be provided in spreadsheet form as supporting information.Large-scale datasets should be made available via a public repository as described in the Reviewer #1: YesReviewer #2: YesReviewer #3: Yes**********what does this mean?). 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RASSF5 as well as predict the structural changes associated with the mutants that hamper the normal protein–protein interactions. We adopted both sequence and structure based approaches to analyze the SNPs of RASSF5 protein. We also analyzed the putative post translational modification sites as well as the altered protein–protein interactions that encompass various cascades of signals. Out of all the SNPs obtained from the NCBI database, only 25 were considered as highly deleterious by six in silico SNP prediction tools. Among them, upon analyzing the effect of these nsSNPs on the stability of the protein, we found 17 SNPs that decrease the stability. Significant deviation in the energy minimization score was observed in P350R, F321L, and R277W. Besides this, docking analysis confirmed that P350R, A319V, F321L, and R277W reduce the binding affinity of the protein with H-Ras, where P350R shows the most remarkable deviation. Protein–protein interaction analysis revealed that RASSF5 acts as a hub connecting two clusters consisting of 18 proteins and alteration in the RASSF5 may lead to disassociation of several signal cascades. Thus, based on these analyses, our study suggests that the reported functional SNPs may serve as potential targets for different proteomic studies, diagnosis and therapeutic interventions.Ras association domain-containing protein 5 (RASSF5), one of the prospective biomarkers for tumors, generally plays a crucial role as a tumor suppressor. As deleterious effects can result from functional differences through SNPs, we sought to analyze the most deleterious SNPs of  RASSF5 is a proapoptotic component of Ras and induces p53 mediated apoptosis4. RASSF5 is implicated in a range of cell reactions including apoptosis, senescence, cell cycle regulation, differentiation and cell proliferation5. Inactivation of RASSF5 is found to be associated in the oncogenesis, proliferation and weak diagnosis of human cancers5. In various tumor tissues, low RASSF5 expression has been discovered and can therefore be considered as a prospective biomarker for tumors. RASSF5, in a range of human cancers, is shown to play crucial functions and is identified as a putative tumor suppressor7.Ras association domain-containing protein 5 (RASSF5) is the leading member of Ras effector super family protein that prevents tumor growth by facilitating GRASSF5 gene is 81 kb and it is located at the locus 1q32.1 that covers several isoforms resulting from alternative mRNA splicing and differential promoter usage, which may have different roles in oncogenesis3. NORE1A, with a molecular weight of around 47 kDa, is a bigger isoform of the RASSF5A gene that encodes a protein of 418 amino acids8. The RASSF5A protein structure includes an N-terminal proline-rich section containing prospective SH3-binding sites, and a nuclear localization signal, a cysteine-rich C1-type zinc finger (C1) domain, a Ras-association (RA) domain that interacts with GTP-bound H-Ras or several other Ras subfamily GTPases, and a Salvador-RASSF-Hippo9 domain that contains C-type carboxy-terminal tail 4.The complete length of 12. Among them, the most common type of mutation is single nucleotide polymorphism (SNP), which is a single base substitution in the alleles13. SNPs occur throughout the genome at the rate of 1 in 1,000 base pairs and the most significant SNPs are found in the coding regions12. Till now, about 500,000 SNPs are enlisted in the coding regions13. Amongst them, there are mainly two categories of SNPs observed, which are: Synonymous and Non-synonymous (nsSNP) SNPs. Synonymous SNPs refer to the conditions where mutations in the DNA sequences do not alter the amino acid sequences, whereas, non-synonymous SNPs, especially Missense SNPs, are responsible for amino acid substitutions and protein variations in human14. Previous studies show that nsSNPs are responsible for around 50% of the mutations involved in various genetic diseases16 along with several autoimmune and inflammatory diseases19.Human genome shares about 99.9% identical DNA sequences globally and the rest 0.1% of the genome contains individual variations. This variation in the individual genome is resulted from random mutations20. Detrimental effects consist of damage to protein structures and gene regulation21. Moreover, alterations in the protein sequence may ultimately affect alteration of protein charge, geometry, hydrophobicity22, dynamics, translation, and inter/intra protein interaction24 putting cells in danger25. This information confirms that nsSNPs, especially missense SNPs, are associated with various human diseases. Recent studies on the nsSNPs using computational approaches reveal the potential impact of mutation in understanding the molecular mechanisms of various diseases28. Structural and functional analysis of nsSNPs may facilitate the development of personalized medicine based on genomic variation. The impact of nsSNPs on RASSF5 protein regarding disease pathogenesis is not yet well-understood. Thus, in the present study, we explored various bioinformatics tools and servers to find out the functional effects of nsSNPs of RASSF5 protein.Deleterious or neutral impacts on protein structure or function can result from functional differences through SNPsWe retrieved RASSF5 SNPs using NCBI dbSNP database that contained 37,426 SNPs in intronic region, 215 SNPs in 5′UTR region, 1,720 SNPs in 3′UTR region, and 757 SNPs in coding sequence. Out of the SNPs in coding sequence, 495 SNPs were missense (nsSNPs) and 236 SNPs were synonymous SNPs. For our current study, we only selected nsSNPs for further analyses as the change in codon results in different amino acids. Overview of the whole methodological approaches is summarized in a schematic diagram  to find out the deleterious SNPs that can significantly alter the structure or function of RASSF5 protein. Out of 495 nsSNPs, a total of 25 were predicted to be deleterious SNPs in all computational algorithms Table .Table 1HInterPro, a domain identification tool, predicts the domains and active sites of a protein through the functional analysis of protein families. It predicted three functional domains of RASSF5, which are Zinc finger domain (122–170), Ras-association domain (274–364), and SARAH domain (366–413) and demonstrated that 20 out of 25 nsSNPs are positioned on these domains Figure .To predict the changes in the stability of RASSF5 in terms of RI and free energy change values (DDG), we used I-Mutant tool that introduced point mutation in the RASSF5 protein. The outcome revealed that 17 out of 20 deleterious nsSNPs decreased stability , 3ddc (205–362), 4lgd (366–405) templates for zinc finger domain, Ras-binding domain and SARAH domain respectively using Phyre2. The 20 deleterious nsSNPs were individually replaced in the native sequence of the three templates and 3D models for all of the mutants were predicted.Structural similarities between native and mutant models were investigated based on TM-score and RMSD scores using TM-align tool. The mutant models for the two nsSNPs found in zinc finger domain and seven nsSNPs found in SARAH domain showed high TM-score and low RMSD value in comparison with low TM-score and high RMSD value of the eleven nsSNPs found in Ras-binding domain Table . As high2 and − 1.03° (degree) respectively but the values deviated significantly for the mutant structures  formed by wild type buried Arginine residue and R345C substitution causes the enlargement of cavity volume by 98.064 Åue Table . Structuue Table .Figure 5Molecular docking studies represented the binding affinity of the mutated RASSF5 protein with H-Ras protein Fig. . Out of The GPS-MSP 1.0 tool predicted R277 as methylation site in RASSF5 protein with 90% specificity. We used NetPhos 3.1 and GPS 3.0 servers to predict the phosphorylation sites Fig. . MoreoveCytoscape (v3.7.2) was used to build up the protein interaction network of RASSF5 protein and it predicted that RASSF5 protein is functionally associated with 18 proteins among which, HRAS, KRAS, NRAS, RRAS, RALGDS, RAP1A, RRAS2, RAP1B, MRAS, RAP1GAP, MLLT4, RAPGEF4 form one cluster and RASSF1, STK3, STK4, SAV1, MOB1A, MOB1B form another cluster. RASSF5 protein acts as a hub of these two clusters of proteins Fig. , S10.Fig33. Inactivation of RASSF5 has been found in several human cancers, such as lung cancer5, colorectal cancer5, gastric cancer34 etc. Changes in the structural conformation of RASSF5 protein during bio-molecular interactions play a vital role in executing its function35 but nsSNPs may cause aberrant conformations, which may lead to inactivation of its tumor suppressive properties37. Therefore, it is necessary to find out the effects of deleterious nsSNPs of RASSF5 and thereby, their association with various diseases. Here, we attempted computational analysis to find out the most deleterious nsSNPs and their effects on the structure and function of RASSF5 protein.Ras association domain-containing protein 5 (RASSF5), through their interaction with active GTP-bound Ras, acts as tumor suppressor in various human malignant tumors38, eleven (11) nsSNPs were located in the RA domain3, which serves as the binding site for active GTP-bound Ras, and the rest seven (7) nsSNPs were present in the SARAH domain that is crucial for cell apoptosis39.Among 495 nsSNPs found in NCBI database, we finally screened out 25 significantly deleterious nsSNPs using six in silico SNP prediction tools: SIFT, PANTHER, Polyphen-2, SNP&GO, PROVEAN, and PredictSNP. These 25 deleterious nsSNPs were selected on the basis of prediction scores generated by these six tools. We further used InterPro tool to determine the location of these nsSNPs on different domains of RASSF5. It revealed 20 nsSNPs in three different domains of the protein, where two nsSNPs were positioned in the Zinc finger domain that interacts with Ras association (RA) domain40. By using the I-Mutant tool, we obtained the deleterious nsSNPs that may affect the stability of the RASSF5 protein. Therefore, we focused on the consequences of the aforementioned 20 deleterious nsSNPs on the RASSF5 protein stability. Among these 20 nsSNPs, 17 nsSNPs decreased the stability of the protein. Protein stability governs the conformational structure of the protein and thus determines the function. Any alteration in protein stability may cause misfolding, degradation or aberrant conglomeration of proteins41. Moreover, evolutionary conservancy in the protein sequence is vital to determine whether a mutation has any negative effect on the host. Using the ConSurf web server, we found that highly deleterious nsSNPs having high conservation scores were located in the highly conserved regions, therefore, increases the risk of tumorigenesis by inactivating RASSF5.Protein stability is essential for the structural and functional activity of a protein25. On the basis of these criteria, we finally selected 11 deleterious nsSNPs positioned in the RA domain. Remaining 9 nsSNPs show lower RMSD and higher TM score that indicates less deviation of mutant protein structure from wild type protein. I-TASSER provided more accurate structural and functional information of wild type and mutant protein models with C-score and Swiss Model provided the QMEN, solvation, torsion scores for our targeted 11 nsSNPs.The investigation continues with the analysis of the structural consequence of these deleterious nsSNPs using Phyre2 homology modeling tool. Using the three templates, we generated the wild type and mutant protein models for three domains of RASSF5 protein. Moreover, we used Tm Align tool that predicted RMSD and TM score of these wildtype and mutant protein models, which were generated by Phyre2. Higher RMSD and lower TM score signify higher deviation of mutant protein structure from native ones42. The energy minimization score of wild type RASSF5 protein  is significantly deviated in P350R , F321L , and R277W . It was found that P350 forms three H-bonds with L347 (3.21 Å), E353 (3.26 Å), and V354 (2.92 Å) whereas, the mutant R350 clashes with V354. Besides this, variation in the energy minimization score compared to the wild type protein was observed in A319V, L335P, R345C, and R345H mutants as well.Project Hope server predicted that these 11 highly risky nsSNPs negatively affect the structure of the protein among which, 7 nsSNPs were structural and 4 nsSNPs were functional residues according to Consurf. Furthermore, Missense 3D tool predicted the consequences of the 7 structural nsSNPs and showed that 5 nsSNPs deleteriously affect the structural conformation of RASSF5 protein. Later on, we analyzed the energy minimization state of these 7 structural and 4 functional residues using Swiss PDB viewer, as the lowest energy conformation of the modeled protein is closer to the experimental structureDocking analysis confirmed that out of the 11 nsSNPs, four variants  significantly reduced the binding affinity with H-Ras protein compared to the wild type residues. The most remarkable change in binding affinity was observed in P350R where noticeable loss of H-bond interactions was found in the binding pocket. In the docking complex, Glu353 formed H-bonds with G60, G13, and G15 with distances of 2.477 Å, 2.516 Å, 2.495 Å, and 2.163 Å respectively and L347 formed H-bond with E31 with a distance of 2.163 Å. These H-bonds were disrupted when Arginine replaced Proline at 350th position, which altered the binding affinity. In short, molecular docking analysis revealed that the aforementioned variants can significantly affect the functional activity of RASSF5 protein.GPS-MSP 1.0 predicted methylation site for R277 whereas, the variant W277 did not undergo methylation. Wild type residues  and mutant residues  were the phosphorylation sites predicted by both NetPhos 3.1 and GPS 3.0 tools.43. RAS bound with RASSF5, activate MST1/244 to form STK3/MST2 and STK4/MST1 complexes in the hippo signaling pathway that plays a crucial role in tumor suppression by limiting proliferation and stimulating apoptosis where SAV1, MOB1A/B act as effector molecules46. Due to non-synonymous mutations in RASSF5, the signaling cascade might be disrupted, which in turn, may lead to tumorigenesis.The protein interaction network of RASSF5 was determined based on degree, average shortest path length, betweenness centrality and closeness centrality using the Cytoscape tool. With the shortest path length as well as higher closeness and betweenness centrality, the result exhibited strong network interaction of RASSF5 with HRAS, KRAS, NRAS, RRAS, RALGDS, involved in the regulation of the signal transduction pathways, essential for cell growth, differentiation, migration, and apoptosis39. Activation of MST1/2 facilitates degradation of YAP1, whose over expression causes cancer35. If any deleterious mutation happens to occur in this RA domain, the overall tumor suppressive activity of RASSF5 will be significantly reduced. No such experimental study was documented till date for the protein RASSF5. So, this study will provide a substantial framework for the identification of functional SNPs.Ras association domain (RA) is required for the interaction between Ras family members and all Ras effectors. RASSF5 forms complex with active Ras through the RA domain, which thereby, activates MST1/2https://www.ncbi.nlm.nih.gov/projects/SNP), we retrieved the information of SNPs 47. We filtered all the 495 nsSNPs for screening.From the National Center for Biotechnology Information (NCBI) dbSNP database (SIFT (Sorting Intolerant From Tolerant)49 (https://sift.jcvi.org/www/SIFT_seq_submit2.html), PANTHER 50 (https://www.pantherdb.org/tools), PolyPhen-2 (Polymorphism Phenotyping v2)51 (https://genetics.bwh.harvard.edu/pph2/), and SNPs&GO52 (https://snps.biofold.org/snps-and-go/snps-and-go.html), PROVEAN 53(https://provean.jcvi.org/index.php), and PredictSNP54(https://loschmidt.chemi.muni.cz/predictsnp) to predict functional effects of the nsSNPs collected from dbSNP database. This ensured the accuracy and stringency of the results and we considered those SNPs as high-risk, which were anticipated as deleterious by all the six programs.We recruited six distinct bioinformatics tools namely, 29. PANTHER program classifies the proteins on the basis of evolutionary relationship, molecular functions and their interactions with other proteins. It analyzes substitution based on position specific evolutionary conservation scores, which is calculated from alignment of various proteins that are evolutionarily related50. PolyPhen-2 predicts the functional effect of amino acid substitutions on the protein structure and functions based on sequence based characterization51. SNPs&GO server estimates the human disease related mutations based on support vector machines (SVM)52. PROVEAN, a web server that uses sequence homology to predict the functional effect of an amino acid substitution. The cutoff value of PROVEAN is set at -2.5. Substitutions of amino acid that exceed the cut off value were considered as deleterious. PredictSNP tool is a consensus SNP classifier, developed by exploiting six prediction programs  to predict disease related mutations.SIFT server uses sequence homology to interpret the effect of amino acid substitution to identify the tolerated and deleterious SNPs. Substitution of amino acid at a particular position with a probability < 0.05 is considered as deleterious and intolerant, whereas, probability ≥ 0.05 is predicted as tolerantInterPro9 (https://www.ebi.ac.uk/interpro/) tool, which can recognize motifs, and domains of a protein and thereby identify the functional characterization of a protein by using the database consisting of protein families, domains and functional sites55.To identify the location of nsSNPs on the conserved domains of RASSF5 we used I-Mutant 2.056 (https://folding.biofold.org/i-mutant/i-mutant2.0.html), which is a web server based on a support vector machine that predicts the stability of a protein after being mutated. This program utilizes the ProTherm-derived dataset, which is the largest database of experimental data on protein mutations57. We submitted the input data of 20 nsSNPs of RASSF5 in FASTA format.While engineering a protein, it is important to observe the extent to which a mutation affects the structure and stability of the protein. Thus, to serve this purpose, we used ConSurf58 (https://consurf.tau.ac.il) that performs its function by analyzing the phylogenetic relationships between homologous sequences60. We considered those nsSNPs of RASSF5 that were found to be conserved for further analyses.To identify the evolutionary conservation of the amino acids in the protein sequence we employed HOPE61 (https://www3.cmbi.umcn.nl/hope). HOPE is a web-server that identifies the structural effects of the point mutations in a protein sequence. We used Q8WWW0 (UniProt-Accession Code of RASSF5) and the 20 SNPs individually as the input.To understand the effect of the nsSNPs on the structure of the protein we recruited Missense 3D62 (https://www.sbg.bio.ic.ac.uk/~missense3d/) and Swiss-PDB Viewer64 (v4.1.0)  to ensure the accuracy and stringency of our result. Missense 3D predicts the structural changes introduced by an amino acid substitution, whereas, Swiss-PDB Viewer is a stand-alone software that can generate mutated models of the proteins for the corresponding amino acid substitutions. The “Mutation Tool” of Swiss-PDB Viewer allows browsing through a rotamer library of the mutated models and the “best” rotamer of the mutated model can be selected. Then we performed energy minimization for the three-dimensional structures of the mutated models by utilizing Nomad-Ref server, which utilizes Gromacs as the ideal force field for energy minimization purpose41.Moreover, we also used SWISS-MODEL65 (https://swissmodel.expasy.org/), Phyre266 (https://www.sbg.bio.ic.ac.uk/phyre2), and I-TASSER69 (https://zhanglab.ccmb.med.umich.edu/I-TASSER/). As there was no crystal structure of RASSF5 available, we utilized Phyre2 and I-TASSER to predict the 3D structure and function of this protein. I-TASSER, especially, constructs full-length protein structures by splicing continuous threading alignments and then generates the structure by utilizing replica exchanged Monte Carlo simulations69. We used Ras domain-containing family protein 5 (3ddc.1.B), Ras association  domain family 5 (1rfh.1.A), Ras association domain-containing protein 5 (4lgd.H) as models for this purpose. The templates were selected based on the score of confidence and sequence identity, generated by Phyre2 modeling tool  to compare between native and mutated protein structures, which calculates template modeling-score (TM-score) and root mean square deviation (RMSD) and then superposes the structures. TM-score provides the result in 0 and 1, where 1 indicates perfect match between the structures. On the other hand, greater RMSD denotes greater variation between the native and the mutated structures. Finally, SWISS-MODEL was exploited, which is an automated protein structure homology-modeling server that uses the crystal structure of comparable protein as a model to assess native and mutant structures with various parameters.To create the 3D models of the mutated protein we used three different online tools: ol Table . These tPyRx virtual screening tool71 (https://pyrx.sourceforge.io/). We prepared suitable target protein from crystal structure complex of RASSF5 with active Ras protein found in PDB using Discovery Studio72 (v4.5). The peptide sequences from mutated RASSF5 protein containing nsSNPs are used as the ligands for the docking procedure. The PDB format of these input ligands were converted into pdbqt format using Python Molecular Viewer with Autodock instruments, AutodockVina74. We maximized the grid box size along the axes X = 38.709, Y = 43.839, Z = 43.929 respectively. The docking result and the binding interaction between ligand and receptor proteins were visualized by UCSF Chimera 1.13rc tool75.To determine the effect of deleterious point mutations over the binding affinity of RASSF5, we performed molecular docking using the ModPred76 (https://www.modpred.org/). To predict potential sites which can accommodate phosphorylation, we utilized NetPhos 3.177 (https://www.cbs.dtu.dk/services/NetPhos/) and GPS 5.078 (https://gps.biocuckoo.cn/). NetPhos 3.1 server predicts the Serine, Threonine and Tyrosine phosphorylation sites in the protein by using ensembles of neural networks. Residues in the protein those have the score greater than 0.5 indicates phosphorylation. Similarly, higher score in the GPS 5.0 corresponds to higher probability of getting phosphorylated.Post-translation modifications (PTM) influence a range of significant biological procedures including cell signaling, metabolic pathways etc. Putative PTM sites in the human RASSF5 protein were identified by using the web-server GPS-MSP 1.078 (https://msp.biocuckoo.org/), ubiquitylation by UbPred16 (https://www.ubpred.org) and BDM-PUB (https://www.bdmpub.biocuckoo.org), and SUMOylation by using the tool GPS-SUMO80 (https://sumosp.biocuckoo.org/).Apart from identifying the phosphorylation sites, we also predicted the potential sites for methylation by Cytoscape81 (v3.7.2) is a freely available Java based software program that was used to visualize molecular interaction networks of human RASSF5 protein with other associated proteins. Visualization of interacting networks of several types of a protein can be done only if the data is introduced in Simple Interaction Format (SIF) or GML format82.Maintaining the interactions among proteins is very crucial to conserve homeostasis of the system. RASSF5 provides significant insight into the deleterious effects that the nsSNPs might cause to the protein. This is the first study that predicts the impacts of nsSNPs on the structure and function of RASSF5 protein. Our study reports that four nsSNPs  in the RA domain reduce the binding affinity with H-Ras, where P350R shows the most significant reduction. As this protein has been found to be involved in various tumors, our findings will serve as important guide in the studies of potential diagnostic and therapeutic interventions which require experimental mutational validation and clinical trial-based studies on a large population.RASSF5 protein plays a vital role as tumor suppressor. One of the functional domains of this protein is the Ras-association (RA) domain that interacts with GTP-bound H-Ras to execute its tumor suppressive role. Thus, the structural conformation of this domain is very crucial for exerting its functional role. This in silico analysis of the functional SNPs of Supplementary Information 1.Supplementary Information 2.Supplementary Information 3.


We also provide a new dataset of 187 protein-DNA complex structures, along with a subset of 82 associated unbound structures. The set represents the largest body of high-resolution crystallographic structures of protein-DNA complexes, use biological protein assemblies as DNA-binding units, and covers all major types of protein-DNA interactions. It is available at: http://www.lcqb.upmc.fr/PDNAbenchmarks.Interactions between proteins and nucleic acids are at the heart of many essential biological processes. Despite increasing structural information about how these interactions may take place, our understanding of the usage made of protein surfaces by nucleic acids is still very limited. This is in part due to the inherent complexity associated to protein surface deformability and evolution. In this work, we present a method that contributes to decipher such complexity by predicting protein-DNA interfaces and characterizing their properties. It relies on three biologically and physically meaningful descriptors, namely evolutionary conservation, physico-chemical properties and surface geometry. We carefully assessed its performance on several hundreds of protein structures and compared it to several machine-learning state-of-the-art methods. Our approach achieves a higher sensitivity compared to the other methods, with a similar precision. Importantly, we show that it is able to unravel ‘hidden’ binding sites by applying it to unbound protein structures and to proteins binding to DNA via multiple sites and in different conformations. It is also applicable to the detection of RNA-binding sites, without significant loss of performance. This confirms that DNA and RNA-binding sites share similar properties. Our method is implemented as a fully automated tool,  Protein-DNA interactions are essential to living organisms and their impairment is associated to many diseases. For these reasons, they have become increasingly important therapeutic targets. Experimental structure determination has revealed different binding motifs and modes, associated to different functions. Yet, the available structural data gives us only a glimpse of the multiplicity and complexity of protein surface usage by DNA. In this work, we use a three-layer model to describe and predict DNA-binding sites at protein surfaces. Given a protein, we consider the way its residues are conserved through evolution, their physico-chemical properties and geometrical shapes to decrypt its surface. We are able to detect a large portion of interacting residues with good precision, even when they are ‘hidden’ by conformational changes. We highlight cases where one protein binds DNA via distinct regions to perform different functions. We are able to uncover the alternative binding sites and relate their properties with their specific roles. Our work can help guiding mutagenesis experiments and the development of new drugs specifically targeting one site while limiting possible side effects. Interactions between proteins and DNA play a fundamental role in essential biological processes  and theiOur understanding of protein-DNA interactions is hampered by the fact that protein surfaces are complex dynamical objects which may interact with several DNA molecules at the same time or at different moments but via the same region –28, accosupport-core-rim model for experimental protein-protein interfaces, where interacting residues are classified based on their structural role in the interaction .Conservation levels are computed using the Joint Evolutionary Trees (JET) method . This meereafter , 46. TreDNA) were taken from , where 0 represents a prediction no better than random. All the other measures return values in the range . We multiplied by 100 all the statistical values reported in tables and figures, representing them in percentages varying in the range  for the MCC, and  for the other measures.We used six standard measures of performance:t-test when the distributions were normal and on the Wilcoxon signed-rank test Reviewer's Responses to QuestionsComments to the Authors:Please note here if the review is uploaded as an attachment.Reviewer #1: Many different kinds of protein interact with nucleic acids to perform a variety of essential functions (eg enzymes and transcription factors); only a small fraction of the known 3D structures in the Protein Data Bank are of such complexes. This manuscript makes an important contribution to the field. It describes a computational method to identify and predict protein sites that can bind DNA or RNA molecules in a way that makes it possible to interpret the most prominent features underpinning the recognition. The performance of the methods is tested, and compared to other methods, using a carefully composed set of experimentally characterized protein-D/RNA complexes. Somewhat surprisingly the method, which was trained to detect DNA-binding sites, does quite well also regarding RNA-binding sites.I have only a couple of minor items that the authors should look at:The six performance measures are only presented as mathematical expressions (page 8) - to help the reader it would be useful to also explain what they measure, and how they relate to each other.What is on the y-axis in Fig 4 (% of what?)?Reference 32 is incomplete.Reviewer #2: uploaded**********Have all data underlying the figures and results presented in the manuscript been provided?PLOS Computational Biologydata availability policy, and numerical data that underlies graphs or summary statistics should be provided in spreadsheet form as supporting information.Large-scale datasets should be made available via a public repository as described in the Reviewer #1: YesReviewer #2: Yes**********what does this mean?). If published, this will include your full peer review and any attached files.PLOS authors have the option to publish the peer review history of their article .One of the goals of PLOS is to make science accessible to educators and the public. PLOS staff issue occasional press releases and make early versions of PLOS Computational Biology articles available to science writers and journalists. PLOS staff also collaborate with Communication and Public Information Offices and would be happy to work with the relevant people at your institution or funding agency. If your institution or funding agency is interested in promoting your findings, please ask them to coordinate their releases with PLOS  1223-442824 | ploscompbiol.org | @PLOSCompBiolPLOS Computational Biology | Carlyle House, Carlyle Road, Cambridge CB4 3DN | United Kingdom