A common challenge that arises in these studies is to explain how genes identified as relevant in the given experiment are organized into a subnetwork that accounts for the response of interest. The task of inferring a subnetwork is typically dependent on the information available in publicly available, structured databases, which suffer from incompleteness. However, a wealth of potentially relevant information resides in the scientific literature, such as information about genes associated with certain concepts of interest, as well as interactions that occur among various biological entities. We contend that by exploiting this information, we can improve the explanatory power and accuracy of subnetwork inference in multiple applications. Here we propose and investigate several ways in which information extracted from the scientific literature can be used to augment subnetwork inference. We show that we can use literature-extracted information to (i) augment the set of entities identified as being relevant in a subnetwork inference task, (ii) augment the set of interactions used in the process, and (iii) support targeted browsing of a large inferred subnetwork by identifying entities and interactions that are closely related to concepts of interest. We use this approach to uncover the pathways involved in interactions between a virus and a host cell, and the pathways that are regulated by a transcription factor associated with breast cancer. Our experimental results demonstrate that these approaches can provide more accurate and more interpretable subnetworks. Integer program code, background network data, and pathfinding code are available at  There is a multitude of publicly available databases that contain information about biological entities  as well as information about how these entities interact together. However, these databases are often incomplete. There is a wealth of information present in the text of the scientific literature that is not yet available in these databases. Using tools that mine the scientific literature we are able to extract some of this potentially relevant information. In this work we show how we can use publicly available databases in conjunction with the information extracted from the scientific literature to infer the networks that are involved in specific biological processes, such as viral replication and cancer tumor growth. It returns as output a subset of the entities and interactions (a subnetwork) that are predicted to be centrally involved in the response. This approach has been shown to lend insight and make accurate predictions in a wide range of biological applications. However, it suffers from several key limitations that arise due to the immense search space, and the reliance of the approach on curated databases of interactions. In this work, we explore several ways in which the subnetwork inference approach can be augmented with information that is automatically elicited from the scientific literature, and we empirically demonstrate that such literature extracted information can lead to more accurate and interpretable subnetworks.An important and pervasive type of analysis in systems biology research is to characterize the set of molecular entities and interactions that are involved in a biological process or response of interest. This type of analysis, which we refer to as background network consisting of (i) entities such as genes/proteins and complexes, and (ii) intracellular interactions such as protein-protein interactions, protein-DNA interactions, protein constituents of a complex, etc. The background network is commonly assembled by integrating interactions from publicly accessible, curated databases such as BioGRID [source and target nodes. These sets might be identified by experimental data or they might be defined using background knowledge. The computational task of subnetwork inference is to (i) identify a subset of edges and nodes in the graph that enable the sources to be connected to the targets, while (ii) adhering to constraints that specify required properties of the subnetwork , and (iii) optimizing an objective function that describes desirable properties of the subnetwork . BioGRID  and Reac BioGRID . Note thHere, we consider two applications of subnetwork inference that are representative of the general task. The first application is to infer the host-cell subnetwork that is exploited by HIV during virus replication. In this analysis, the source nodes are host genes that have been identified as playing a significant role in viral replication via RNAi assays that systematically knock the genes down. The target nodes are HIV proteins. The goal of the inference task is to determine a subnetwork that explains how the RNAi-identified genes might be affecting HIV replication , 4.The second application we consider is focused on characterizing a nuclear receptor, NR2F1, which is an important factor mediating the activity of the Mcs1a Mammary Carcinoma Susceptibility locus, and may have therapeutic relevance to triple-negative breast cancer . In thisAlthough the subnetwork inference approach has proven to have significant value in a broad range of applications , 16, 17,views of the subnetwork which consist of nodes and edges that are closely related to concepts of interest. Whereas an inferred subnetwork represents a process of interest , views can be used to identify and inspect more specific concepts within the inferred subnetwork . We argue that subnetwork inference methods are hindered by failing to take advantage of the wealth of knowledge that is represented only in the scientific literature, as opposed to structured databases. The hypothesis driving the research presented here is that we can more accurately characterize responses of interest by automatically extracting and leveraging information from the scientific literature. In this article, we investigate the use of literature-extracted information to (i) augment the set of nodes identified as sources in a subnetwork inference task, (ii) augment the set of interactions used in the background network, and (iii) support targeted browsing of a large inferred subnetwork by computing In this section, we describe two subnetwork-inference applications considered in this article that we address using integer programming (IP). We also describe three ways in which we have augmented this approach by using literature-extracted information.hits, whose loss has a significant effect on the virus. However, the screens themselves do not reveal how the gene products of these hits are organized into the pathways that modulate the virus. Moreover, they may fail to detect a large number of host genes that are involved in the process [One approach to characterizing the host cellular machinery that is hijacked by a virus is to systematically suppress host gene products using techniques such as RNA interference , mutant  process . Here, w process , 4.interfaces. The goal is to infer a subnetwork consisting of paths, each of which is a linear chain of interactions that begins with a hit (a source) and ends with a host gene product that directly interacts with a viral protein (a target).In this task, which is illustrated in The hits come from five RNAi screening studies that have identified human genes involved in HIV replication , 25, 26.A background network is assembled from publicly accessible databases. We retrieve protein-kinase interactions, post-translational modification, and protein-protein interactions from a variety of sources , 29, 30.The first step in our subnetwork inference approach is to generate a set of candidate paths. Using the hits as source nodes, we use a depth-first traversal to find all directed acyclic paths that lead from the source nodes to the targets. The search is conducted to a maximum depth of three interactions, so all paths have at most four nodes. All paths have the same directionality, going from the source node to an interface target node.relevant. The integer program identifies a subnetwork by determining values for a set of binary variables that represent the relevance of paths, edges, and nodes. The relevance of a path p is represented with a binary variable σp, which takes the value of 1 if the particular path is included in the subnetwork, and 0 otherwise. The relevance of an edge e is represented by the binary variable xe, which takes a value of 1 if the edge is in at least one relevant path, and 0 otherwise. The relevance of a node n is represented by the binary variable yn, which takes a value of 1 if the node is present in any relevant paths, and 0 otherwise.We refer to paths, nodes, and edges that are included in an inferred subnetwork as being unconfirmed genes—those that are neither hits nor interfaces. n, and e. The set of paths is denoted as p, p, n, and e.The integer program determines the settings for these variables using the objective functions and constraints shown in The two objective functions are optimized sequentially. We use the first objective function to select the relevant nodes, and the second to identify all possible paths among those nodes. The rationale for the second objective function is to avoid arbitrarily selecting the paths that are included in the subnetwork. Instead, we opt to include all paths that satisfy the other constraints and connect the selected nodes. We know that the set of hits is incomplete given that RNAi screens typically have many false negatives , and thuDue to the fact that there are multiple solutions that satisfy all the constraints and maximize the objective functions, we generate an ensemble of solutions and then return a consensus subnetwork characterizing the nodes and edges that occur with high frequency in the ensemble. We first construct 100 subsampled data sets by holding aside 25% of the hits and interfaces in each, thus treating them as unconfirmed genes. To construct the ensemble, we run the IP independently on each subsampled data set.As mentioned above, the intersection among the hit sets identified by the RNAi knockdown screens is quite small. Prior research has indicated that this is due to the screens having many false negatives, and thus the number of genes involved in viral replication is likely to be much larger than even the number represented by the union of the HIV RNAi screens . To addrAg represent the set of abstracts mentioning gene g, Aq be the set of abstracts matching query q, and g and match the query q. We define adjusted precision as g. F1 is the default ranking criterion in GADGET since it prefers genes whose associated literature is both specific to the query ands cover many of the query-relevant abstracts.Our approach is based on a web-based tool called GADGET  that we To augment our set of hits in the HIV subnetwork-inference task, we query GADGET for “HIV” which returns an additional 738 human genes that appear in two or more query-matching abstracts and which were not already in our set of interfaces or RNAi-screen identified hits. Instead of employing GADGET’s ranking functions in this analysis, we simply use all 738 of these genes. In our view-generation experiments, which are described shortly, we make use of GADGET’s ranking capability. We add these 738 genes to the IP as additional sources, and generate an ensemble of subnetworks as described above. This idea is illustrated in In the second application we consider, our objective is to connect a nuclear receptor known as NR2F1 (the source node) to a list of genes that are differentially expressed when NR2F1 is over-expressed (the target nodes). Prior studies suggest NR2F1’s potential as a therapeutic agent in triple negative breast cancers (TNBC) since several lines of evidence indicate that NR2F1 may act as a tumor suppressor, given its association with decreased proliferation and less aggressive clinical subtypes , 34. TheRNA-Seq was used to identify a set of genes that were differentially expressed when NR2F1 was overexpressed in a TNBC cell line. This set of 340 DE genes served as the targets in our analysis. To assemble the background network, protein-protein interactions were gathered from HPRD  and BioGUsing NR2F1 as the source node, a depth-first traversal was used to find all directed acyclic paths that lead from NR2F1 to the DE genes. Each path is required to end in a direct protein-DNA interaction and the search was conducted to a maximum depth of three interactions, so all paths had at most four nodes. All paths have the same directionality, going from the NR2F1 source node to a DE target node.Given these paths, we infer an NR2F1 subnetwork by solving a series of integer programs. The role of the IP approach is to select a subset of the paths (and hence interactions and nodes) from the background network that reach as many of the DE genes as possible, while being parsimonious about incorporating non-DE genes, and taking into account RNA-Seq expression levels indicating the relevance of each included node. To incorporate RNA-Seq data into this process, normalized counts per million values were obtained from edgeR  and weren, represents either a protein or a target gene. n, and n. e. The set of paths is denoted as p. n, and e. The RNA-Seq determined score for a node n is given by rn.A description of the constraints and objective functions used in the IPs is shown in p is represented with a binary variable σp, which takes the value of 1 if the particular path was predicted to be included, and 0 otherwise. The predicted relevance of an edge is represented by the binary variable xe, which takes a value of 1 if the edge was in at least one relevant path, and 0 otherwise. The predicted relevance of a node is represented by the binary variable yn. The variable receives a value of 1 if it is present in any relevant paths, and 0 otherwise.The integer program identifies a subnetwork by determining values for a set of binary variables that represent the relevance of paths, edges, and nodes. The relevance of a path We use the IP approach to infer an ensemble of subnetworks from subsampled datasets and then construct a consensus subnetwork as we did in the host-virus analysis.As previously discussed, one of the key limitations of the standard subnetwork-inference approach is that it relies on existing, structured databases of interactions which may be highly incomplete. Although the interactions present in publicly available databases allow us to generate paths connecting NR2F1 to many of the DE genes, we are not able to reach all of them. To address this limitation, we exploit information extracted automatically from the scientific literature. There is a large body of prior work addressing the tasks of extracting binary relations and more complex events from text sources , 39, 40,In order to augment our existing interactions, we use the Literome system , 44 whicview of a subnetwork, which is a graphical representation of the part of an inferred subnetwork that is highly related to a given concept of interest. The concept might represent, for example, a process, subcellular location, or stage of the viral lifecycle.Given the large number of genes and other entities involved in many biological processes, even the most stringently-defined subnetworks can be large and difficult to manually inspect. In our host-virus study, for example, there are more than two thousand hits and interfaces . Here we present a methodology for generating a NQ representing a concept of interest, the view generation process returns a set of nodes, and associated edges and paths, that are enriched for gene set. The set of genes could be specified (i) manually, (ii) by selecting genes that have been annotated with an ontology term of interest, or (iii) by identifying genes associated with the concept of interest in the scientific literature. Here we explore the third approach by using queries to GADGET to define gene sets that are closely related to specific concepts.Given a subnetwork and a set of genes n in the inferred subnetwork for its predicted functional similarity to the given query set of genes NQ. Nodes are considered functionally similar if they share relevant paths. Our similarity function, n and at least one query node n or any query node q. Let n. Our similarity function is defined as:To compute a view, we first rank every node k as predicted additions to the query set. We then extract all of the paths that consist exclusively of query nodes, predicted additions, and targets.After ranking the consensus nodes by this similarity function, we take the top HIV. We evaluated the resulting subnetworks by measuring their ability to predict the relevance of genes to HIV replication as determined by whether they were included in an inferred subnetwork.In the HIV subnetwork application, our baseline set of source nodes were those genes identified as important to HIV replication in RNAi studies. Here consider the effect of augmenting this set of source nodes with human genes returned by GADGET for the query We used a methodology in which information about the relevance of 25% of the hits and interfaces  was held aside on each iteration. That is, although the genes were still included in the background network, information about whether they were hits, interfaces, or neither was hidden. We can estimate the accuracy of our approach by checking each inferred subnetwork for the presence of the hits and interfaces that have been held aside from their input. Given the absence of a set of genes that known not to be involved in HIV replication, we used the set of all unconfirmed background-network human genes as the set of negatives. For each gene, we calculated a confidence value as its frequency of being included in the inferred subnetworks when held aside. By varying a threshold on these confidence values, we plotted a precision-recall curve characterizing the predictive accuracy of our method. Recall is defined as the fraction of truly relevant genes (hits and interfaces) that are predicted to be relevant, and precision is defined as the fraction of genes predicted to be relevant that truly are relevant. In this context, we consider precision to be the more important of the two measures, as it is better to avoid devoting follow-up experiments to false positives.We inferred consensus subnetworks and generated precision-recall curves for both the baseline approach and the GADGET-augmented approach. These results are shown in We also considered augmenting our background networks with interactions extracted from the scientific literature. We queried Literome with our list of relevant genes and returned all interactions that contained at least one of those genes. We then incorporated these interactions into our baseline background network. Comparing the precision-recall curves from the baseline approach and the Literome-augmented approach, there was no significant improvement in precision at any point along the curve. We also added literature-extracted interactions to our GADGET-augmented background network. When comparing the precision-recall curves from the GADGET-augmented approach and the GADGET and Literome augmented approach, there was once again no significant improvement in precision. However, we note that the addition of the Literome interactions did not diminish the accuracy of the inferred subnetworks, suggesting that there is little risk of overfitting when including literature extracted interactions. For the breast cancer task, our goal was to infer a parsimonius subnetwork that connects the nuclear receptor NR2F1 to a set of genes that are differentially expressed (DE) when NR2F1 is overexpressed. Our IP attempts to find the paths connecting NR2F1 to as many differentially expressed genes as possible. With our baseline background network (which does not include edges from Literome), we were able to reach 314 out of 340 differentially expressed genes. In order to reach more DE genes, we used Literome to incorporate additional interactions into the background network. We queried Literome for our 340 DE genes and found all interactions that included one of those genes as a downstream target. We re-ran our IP and discovered that we were subsequently able to reach 326 out of 340 differentially expressed genes from NR2F1. The number of intermediate nodes used to connect the genes also increased when we used Literome due to the fact that more nodes were necessary to reach the additional differentially expressed genes.We use a set of genes that are essential for basal tumor cell generation  to deterWe also use RNA-Seq data to evaluate the subnetworks. Genes that are highly expressed may be more essential for the differences within certain cell types. We obtained RNA-Seq data in counts per million (CPM) for every gene expressed in our cells and ranked the genes based on sequence read abundance. Similar to the analysis above, we varied a threshold on the ranked list of genes and counted the number of genes above the threshold that were in the top 10% of genes in terms of RNA-Seq abundance. In The results of this study indicate that literature-extracted interactions may provide value by enabling an inferred subnetwork to “explain” additional data. In this application, we were able to include more DE genes into the inferred subnetwork when including literature-extracted interactions in our background network. Moreover, the augmentation with literature-extracted interactions led to the incorporation of additional relevant genes in the inferred subnetwork.membrane scission and intrinsically disordered proteins. Whereas the inferred subnetwork represents the process of HIV replication, the views we generate isolate more specific concepts  represented within the subnetwork.To demonstrate the value of our view-generation approach in browsing and understanding a large subnetwork, we consider the case of computing views onto our inferred HIV consensus subnetwork. This subnetwork includes 14,426 edges connecting 948 nodes and thus is quite large and complex to comprehend. The inherent complexity of HIV subnetwork is due to the fact that a large number of host genes and processes are involved across the multiple stages of viral replication. We illustrate the view-generation approach by computing views that focus on genes related to the concepts of membrane scission view, we issued the query HIV AND “membrane scission” to GADGET. This query returns 16 genes which GADGET has determined are associated with the query. We used our similarity function to rank all nodes in the HIV consensus subnetwork based on the frequency with which they are found in the same paths as these GADGET query genes. We took the top 10 of these ranked genes to be used as predicted additions to the membrane scission concept. We then assembled the consensus paths that consisted entirely of our GADGET query genes, predicted additions, and HIV proteins.In order to assemble a set of genes for the membrane scission view. HIV and “membrane scission”), or a concept that is not defined in any ontology.Although the gene set that served as the basis of this view came from a GADGET query, there are multiple sources which could provide a gene set such as the Gene Ontology . The advintrinsically disordered proteins. We queried GADGET for the terms HIV AND “intrinsically disordered.” Using the same methodology as above, we inferred a view that includes of 8 of the 36 genes returned by GADGET. The view, which is shown in In order to demonstrate that queries are not limited to terms defined in an ontology, we computed a view based on the concept The experiments presented in this section show how literature-extracted information can be used to explore and gain insight into a large inferred subnetwork by generating different views of the subnetwork. The key idea of a view is to select a subset of the genes in an inferred subnetwork that are enriched for a concept of interest, such as a cellular process or gene products sharing a specific physical property.views of the subnetwork that are closely related to concepts of interest.We have investigated the use of information automatically extracted from the scientific literature to augment the process of inferring subnetworks characterizing biological responses of interest. Specifically, we have used literature-extracted information to (i) enlarge the set of nodes identified as sources in a subnetwork inference task, (ii) enlarge the set of interactions used in the background network, and (iii) support targeted browsing of a large inferred subnetwork by computing The empirical studies we present demonstrate that literature-extracted information can improve the explanatory power and accuracy of subnetwork inference in both of the applications considered. However, we argue that our general approaches are relevant to a range of other network analysis tasks, including predicting and ranking genes that are likely to be involved in the same response as a given set of genes , 51, 52,Although the specific constraints and objective functions used in subnetwork inference are somewhat application-dependent, the integer programs typically incorporate several common elements. Among these elements are a background network consisting of subcellular entities and interactions, and a procedure that aims to connect source and target nodes in the network in order to optimize various objectives. Our approach is applicable to any network analysis task that shares these common elements. In cases in which either the source or target nodes are believed to be incomplete, tools like GADGET can be used to augment the sources or targets by mining the scientific literature. In cases in which the relevant interactions are believed to be incomplete, tools like Literome can be used to augment the set of interactions in the background network.We consider this work as an initial foray into exploring the range of ways in which text mining can boost the subnetwork-inference process. For example, we also plan to explore using literature-extracted information to extend the types of relationships that are represented in the background network, and to prioritize the inclusion of entities and interactions into subnetworks.


Literature from the past two decades has outlined the existence of a trade-off between protein stability and function. This trade-off creates a unique challenge for protein engineers who seek to introduce new functionality to proteins. These engineers must carefully balance the mutation-mediated creation and/or optimization of function with the destabilizing effect of those mutations. Subsequent research has shown that protein stability is positively correlated with “evolvability” or the ability to support mutations which bestow new functionality on the protein. Since the ultimate goal of protein engineering is to create and/or optimize a protein’s function, highly stable proteins are preferred as potential scaffolds for protein engineering. This review focuses on the application potential for thermophilic proteins as scaffolds for protein engineering. The relatively high inherent thermostability of these proteins grants them a great deal of mutational robustness, making them promising scaffolds for various protein engineering applications. Comparative studies on the evolvability of thermophilic and mesophilic proteins have strongly supported the argument that thermophilic proteins are more evolvable than mesophilic proteins. These findings indicate that thermophilic proteins may represent the scaffold of choice for protein engineering in the future. The field of protein engineering focuses on rational modification or combinatorial design of proteins to enhance or otherwise alter protein functionality . Each prOver the course of the past decade and a half, a great deal of research has been done attempting to better understand this negative correlation between protein stability and functionality. The most prevalent explanation for this stems from the fact that the amino acid residues which promote functionality are often destabilizing, either inherently or as a function of their location in the folded protein . It is kfolding between the native protein and the mutated protein [folding is defined as the change in ΔGfolding between the mutant protein and the parental, or wild-type, protein: ΔΔGfolding = ΔGfolding(mutant) − ΔGfolding(wild type). ΔΔGfolding values are direct measures of thermodynamic protein stability: once a mutation has been introduced, the higher (or more positive) the ΔΔGfolding, the less stable the protein has been rendered as a result of that introduction. Since most function-inducing mutations increase the ΔGfolding of a protein, after the introduction of a functional mutation the more robust proteins will have a ΔΔGfolding lower than that of less robust proteins [To address the challenge associated with the trade-off between protein function and stability, protein engineers have spent the past decade and a half researching ways to manipulate highly stable proteins that are better able to tolerate the mutations necessary to confer new functionality. The concept of “mutational robustness” is one which has particularly interested researchers—a robust protein is better able to tolerate a given point mutation than is a less robust protein . This “r protein . ΔΔGfoldproteins .thermostability are much more evolvable than are less thermostable proteins. Protein engineers have explored various ways to capitalize upon the correlation between thermostability and evolvability by introducing mutations to thermophilic proteins [A positive correlation between protein robustness, thermodynamic stability, and “evolvability” has directed research towards utilizing thermophilic proteins as scaffolds for protein engineering . Proteinproteins . The hopThe present article overviews the research that has been performed on thermophilic proteins as potential scaffolds for protein engineering. First, it reviews research which has defined the trade-off between protein activity and stability, and highlights the challenge this trade-off poses to the field of protein engineering. Next, it discusses research describing how protein stability and mutational robustness directly promote evolvability. It then proceeds to introduce thermophilic proteins to this discussion and outline the structural characteristics of these proteins that contribute to their increased stability and mutational robustness. Finally, it highlights recent research which has utilized highly thermostable proteins as effective scaffolds for engineered functional evolution and, when possible, compares the evolvability of these proteins with that of their mesophilic analogues.Over the last decade and a half, numerous studies have demonstrated that there is a distinct trade-off between protein activity and protein stability ,3. This Wang et al. studied the evolution of an enzyme, Temoniera-1 (TEM-1) β-lactamase, which is responsible for bacterial antibiotic resistance . In thisfolding for these mutated proteins was higher than that observed for the wild-type protein [folding is a less negative value than it had been for the original protein.Bloom et al. computationally evaluated the destabilizing effects of functional mutations on protein structure. Specifically, the authors simulated protein evolution for improved ligand binding affinity by introducing mutations to 20 “lattice” proteins . These l protein . The datkcat/Km) for the catalytic process than MMP-3 [Liang et al. examined the tradeoff between protein activity and stability with two related metalloproteinases (MMP): MMP-3 and MMP-12. The authors evaluated the proteinase activity of these enzymes, and found that MMP-12 demonstrated a higher second order rate constant  became resistant to a wider range of penicillin derivatives [This correlation is supported by data from a number of studies which have confirmed that protein mutational robustness and stability increases evolvability. Caetano-Anolles et al. employed an interesting experimental method to evaluate the influence of protein stability on evolvability. The authors inserted a “test protein” into the amino acid sequence of various β-lactamases, which were then expressed in ia coli. . It was ivatives . Their rivatives .E. coli strains. Since PYP is a photoreceptor, the authors were able to effectively measure alterations in protein functionality based upon variations in four criteria: visible absorbance maximum, pKa, fluorescence quantum yield, and lifetime of the unstable “pB”—or blue-shifted absorbance—state [U values (a measure of thermodynamic stability against unfolding) and protein production level [U of the PYP protein or decreased overall protein production [Another study confirming the existence of the positive correlation between protein stability and evolvability was performed by Philip et al. who experimentally manipulated the functionality of the photoactive yellow protein (PYP)—a 125 residue photoreceptor prototype of the period circadian protein-aryl hydrocarbon receptor nuclear translocator protein-single-minded protein, or PER-ARNT-SIM (PAS) signaling superfamily. PAS proteins are widely known to be extremely diverse—over 20,000 different individual PAS domains have been defined in thousands of signaling proteins in a diverse array of organisms ranging from bacteria to humans . The autce—state . These von level . The auton level . They aloduction . From thoduction .Due to the trade-off between protein stability and function, it is highly difficult to introduce mutations necessary for functional evolution without significant compromise of protein stability. Since proteins characterized by higher mutational robustness are more evolvable, as described above, those featuring high mutational robustness and stability could be an important structural framework for functional evolution. One group of proteins which could satisfy the stability requirements is the group of proteins stable at very high temperatures, namely thermophilic and hyperthermophilic proteins. Thermophilic proteins are defined as proteins derived from organisms with optimum growth temperature (OGT) from 45–80 °C, while hyperthermophilic proteins are derived from organisms with OGT above 80 °C  . MesophiThe first major structural difference between thermophilic proteins and their mesophilic analogues is the number of ion pairs . SzilagyThe second major structural difference between thermophilic proteins and their mesophilic analogues is the average hydrophobicity of the amino acid side chains buried within the protein ,18. GromThe third major structural difference between thermophilic proteins and their mesophilic analogues is the presence of compact, stable structural cores ,20,21. MThe fourth major structural difference between thermophilic proteins and their mesophilic counterparts is the number of hydrogen bonds bridging a protein’s buried and exposed regions. The study performed by Tompa et al. showed that the distribution of hydrogen bonds is different in thermophilic proteins compared to that seen in their mesophilic counterparts . The resIn summary, the increased thermostability of thermophilic proteins appears to be attributable to an increased number of strong ion pairs, an increased surrounding hydrophobicity of buried amino acid side chains, more compact cores, and a more consolidating distribution of hydrogen bonds with respect to similar mesophilic proteins . It mighAll of the above features contribute to the stabilization of thermophilic and hyperthermophilic proteins by increasing the rigidity of the corresponding tertiary structures. Rigidity and stability are correlated when it comes to protein structures; thermophilic and hyperthermophilic proteins can afford to be rigid, because they operate by definition in high temperature environments which develop sufficient interactions between substrates and active sites with limited flexibility . These pIt is worth noting at this point that the magnitude of contribution from denaturation entropy changes to the enhanced stability of thermophilic and hyperthermophilic proteins is a topic of debate. This is an empirically observed phenomenon which specifies that the ΔS values associated with the denaturation of thermostable proteins are smaller than those associated with denaturation of mesophilic proteins . The hypSince thermophilic proteins generally exhibit a higher level of mutational robustness when compared with their mesophilic counterparts, it stands to reason that these proteins are natural candidates to become scaffolds for evolutionary engineering. A number of studies have therefore been performed over the past decade evaluating thermophilic proteins as scaffolds for protein engineering and exploring options for application. This literature has become more robust recently as studies have begun to directly compare the evolvability of thermophilic proteins with that of analogous mesophilic proteins.T50) = 47 °C) and the thermophilic 5H6 enzyme (T50 = 62 °C) [T50 values were isolated from the rest and subsequently tested for new functionality. Interestingly, the isolated variants were found to have evolved new functionality against cephalosporin antibiotics: these thermophilic variants were roughly 800 times more efficient at degrading the cephalosporin cefotaxime than were the original parent TEM-1 β-lactamase [Bloom et al. randomly mutated two separate variants of a cytochrome P450 BM3 heme domain peroxygenase and screened the resulting mutants for new functionality  [= 62 °C) . The aut= 62 °C) . The mutactamase . Since tactamase .E. coli bacteria [folding of up to 3.5 kcal/mol, whereas those developed without chaperonins were only able to tolerate destabilizing mutations with a ΔΔGfolding of up to 1 kcal/mol [Another experimental example of thermophilic proteins being utilized as scaffolds for protein evolution was reported by Tokuriki and Tawfik . In thisbacteria . Previoubacteria . The prokcal/mol . In addikcal/mol .Subsequent research performed by Aledo et al. compared the thermostability of two mammalian protein analogues: cytochrome b and cyclooxygenase I (COX I) to examine a correlation between evolvability and thermostability. Whereas previous studies done to evaluate the relationship between thermostability and evolvability started with a known thermophilic protein and a known mesophilic protein and compared their evolvability, this study does the opposite. The authors started with a pair of proteins, cytochrome b and COX I, known respectively to be highly evolvable and poorly evolvable, and compared their thermostability . Both prRubrobacter xylanophilus and explored its applicability as a stable substitute for the ubiquitous DAO variant found in eukaryotes [Takahashi et al. identified a thermophilic variant of D-amino acid oxidase (DAO) in the thermophilic bacterium karyotes . DAO is karyotes . The autkaryotes .Thermotoga neapolitana were able to form functional complements which efficiently supported the growth of E. coli in culture; similar fragments from the mesophilic Bacillus subtilis were not able to do the same [Tm)—a measure of protein themostability) of the parent enzyme: a consequence of the fact that the enzyme fragments derived from the hyperthermophilic parents retained more of the original tertiary structure of their parents than did their mesophilic fragment counterparts [E. coli growth, whereas only 6% of mesophilic fragments were capable of doing the same [Thermophilic proteins have also proven themselves superior scaffolds for the branch of protein engineering that explores protein fragmentation and cooperative function. This is a particularly challenging area of protein engineering which involves the destruction of parent proteins in such a manner that the resulting fragments are capable of reassembling themselves into stable, functional tertiary structures similar to the parents . It is uthe same . The autterparts . A follothe same . The autthe same .Pyrococcus furiosus as a host protein, into which various guest enzyme domains, such as exoinulinase, TEM-1 β-lactamase and xylanase, were successfully inserted. The insertional fusions led to the creation of chimeric protein complexes, where thermostability of guest enzyme domains was improved by various mechanisms [E. coli lowered thermostability and expression levels of a guest enzyme domain [Bacillus circulans xylanase was greatly assisted by its enhanced thermostability acquired by the insertional fusion [More recently, a series of studies performed by Kim and coworkers demonstrated that domain insertion of a guest protein may benefit from high thermostability of a host protein ,39,40,41chanisms . In contl fusion . The impl fusion .The research discussed above strongly supports the notion that high stability of thermophilic proteins makes them greatly suitable for protein engineering, when compared to their mesophilic analogues. The applications for these highly stable proteins are clearly manifold in the field of protein engineering as they provide a broad array of exciting opportunities for the protein engineers of the new century.The field of protein engineering is one which endeavors to introduce new functionality to protein domains. Since there is an inherent trade-off between protein functionality and protein stability, protein engineers must constantly balance the destabilizing effect of the mutations they introduce to protein domains with the anticipated modifications they are attempting to make to the protein. Over the course of the past decade and a half, many studies have shown that one of the best ways to increase a protein’s chance of accommodating a functional mutation is to have an increased stability or mutational robustness to begin with. Numerous studies first confirmed that proteins characterized by greater stability were better able to withstand mutations, and later demonstrated that they were better able to support the type of mutations required to enhance existing function or introduce new function. The positive correlation between protein stability and evolvability was thus established.Thermophilic proteins are characterized by certain significant structural differences from mesophilic proteins and often are accompanied by “chaperone proteins” which help the protein maintain its three-dimensional structure at high temperatures. Over the past 20 years, a nascent but increasingly robust literature has been developed directly comparing the evolvability of thermophilic proteins to that of mesophilic proteins; this literature clearly demonstrates that thermophilic proteins are indeed more evolvable and more tolerant of mutations introduced to enhance or modify functionality. This renders the thermophilic proteins superior scaffolds for protein engineering.Thermophilic proteins thus appear to be an efficient and effective solution to the challenge posed by the simultaneous manipulation of protein function and maintenance of protein stability. Functionality and stability do indeed trade off, stability does indeed promote evolvability, and the increased stability of thermophilic proteins does indeed make this kind of protein a uniquely effective scaffold for evolving protein function, as summarized in


SEC) to fractionate native protein complexes, SWATH/DIA mass spectrometry to precisely quantify the proteins in each SEC fraction, and the computational framework CCprofiler to detect and quantify protein complexes by error‐controlled, complex‐centric analysis using prior information from generic protein interaction maps. Our analysis of the HEK293 cell line proteome delineates 462 complexes composed of 2,127 protein subunits. The technique identifies novel sub‐complexes and assembly intermediates of central regulatory complexes while assessing the quantitative subunit distribution across them. We make the toolset CCprofiler freely accessible and provide a web platform, SECexplorer, for custom exploration of the HEK293 proteome modularity.Proteins are major effectors and regulators of biological processes that can elicit multiple functions depending on their interaction with other proteins. The organization of proteins into macromolecular complexes and their quantitative distribution across these complexes is, therefore, of great biological and clinical significance. In this paper, we describe an integrated experimental and computational technique to quantify hundreds of protein complexes in a single operation. The method consists of size exclusion chromatography ( A range of high‐throughput technologies to analyze genomes, transcriptomes, metabolomes, and proteomes now provide accurate molecular inventories of biological samples at high throughput. Yet, the notion of a modular biology  the use of prior information from generic protein interaction maps to reduce the erroneous assignment of co‐eluting proteins to a complex.Complex‐centric proteome profiling consists of the robust and proven technique of size exclusion chromatography (SEC) to fractionate native protein complexes, SWATH/DIA mass spectrometry to precisely and reproducibly quantify proteins across SEC fractions and a new computational analysis strategy implemented in et al, et al, et al, et al, et al, CCprofiler.A range of generic protein complex compendia have been generated by different approaches that can be used as prior information for complex‐centric analysis. They include (i) the CORUM reference database of complexes  at an FDR of 5%. The data further show superior performance of the complex‐centric approach in recalling protein complexes compared to the reference method, achieved at a significantly reduced experimental effort . We applied the complex‐centric proteome profiling strategy to quantify complexes in a native extract from HEK293 cells in exponential growth state. The results indicate that 55% of the protein mass is present in the form of complexes that distribute across distinct states of complex formation. The data indicated quantitative complex signals for 462 cellular assemblies if prior knowledge from the CORUM, BioPlex, and StringDB reference databases was used and the results were cumulatively integrated. The utility of quantifying the distribution of specific proteins across different resolved sub‐modules is exemplified by the identification of previously unknown substructures of cellular effector complexes such as the proteasome. Finally, we describe and provide access to SECexplorer, an interactive online platform for customized expert interpretation of quantitative co‐fractionation protein profiles generated by SEC‐SWATH‐MS. We expect that the complex‐centric analysis method, the SEC‐SWATH dataset representing the organization of the proteome of the cycling HEK 293 cell line, and the computational tools to explore the data will find wide application in life science research.We benchmark the method, including the et al, et al, CCprofiler is used to infer quantitative protein elution profiles from the peptide elution profiles across SEC fractions  proails Fig .CCprofiler algorithm and error model against a manually curated reference dataset, (ii) by comparing its performance with the performance of a reference method consisting of multidimensional co‐fractionation of native complexes and the proteomic analysis of 1,163 fractions by data‐dependent mass spectrometry  by benchmarking the CCprofiler against a manually curated reference set . However, both datasets also uniquely recall parts of the CORUM complexes . Due to a lack of ground truth in terms of complexes truly present in the respective sample, ultimate conclusions on the correctness of each set of reported complexes remain challenging and performance comparisons rest on the assumption of the reference complexes being equally expressed across the different samples and cell lines analyzed in the respective studies. Under this limitation, complex‐centric analysis under equivalent error control allows direct comparison of dataset information content between previously deployed SEC‐DDA‐MS and our SEC‐SWATH‐MS data, indicating substantial improvements with 249 and 565 reference complexes recalled, respectively, partially attributable to improved SEC fractionation and sampling.As a metric, we evaluated the ability of either method to recall complexes reported in the CORUM knowledgebase which consists of a total of 1,753 non‐redundant complexes. We considered a complex as recalled if at least 50% of its CORUM annotated protein subunits were stated as part of a reported complex by either method  subunit composition and (ii) resolution in the chromatographic dimension. Taking the simple example from above, the signal collapsing step will merge the two features from complex query A (XYZ discovered from querying WXYZ) and B (XYZ discovered from querying VXYZ) to one unique protein signal XYZ that is independent from the original complex queries  provided evidence of proteins common to two (152) and up to five or more (27) distinct, chromatographically separated complex instances  testing of novel predicted models on complex formation between candidate proteins or (ii) interrogating the profile sets of known modules for evidence pointing toward new variants or (iii) manual refinement and extension of results obtained from automated complex‐centric profiling, for example, by extending the set of automatically detected complex components with additional proteins, e.g., derived from the literature or from interaction network context. Analyses are assisted by the CCprofiler algorithm suggesting distinct co‐elution signals and calculating their expected to apparent molecular weight mismatch, among other metrics, in order to speed up data interpretation by expert users. SECexplorer can be accessed at https://sec-explorer.ethz.ch/  of the observed protein species appear engaged in higher order assemblies; a lower‐boundary estimate given inevitable losses of associations in the experimental procedure. Besides detecting cumulatively 462 cellular complexes upon targeted analysis, the method in many instances resolves distinct variants of the expected complexes, such as sub‐complexes that elute independently from the chromatographic column. While sub‐complex signals may originate from artifactual disruption of cellular complexes, we demonstrate in two cases that orthogonal pieces of evidence can build confidence in the biological relevance of substructures assigned from defined subunit co‐elution. First, we identified a new complex CSN1/3/8 as a sub‐complex of the COP signalosome (CSN) holo‐complex that elicits crucial regulatory functions toward E3 ligase complexes and the ubiquitin proteasome system . Consequently, complex detectability is limited by thermodynamic stability and despite best efforts toward minimizing complex disintegration , thermodynamically labile interactions, particularly those with fast off‐rates, are likely inaccessible by correlation profiling methods, including complex‐centric proteome profiling. While first studies have evaluated chemical crosslinking as means to stabilize cellular modules for chromatographic analyses  The balance of stability of complexes and extractability in native form. Inevitably, associations are lost in the experimental procedure, most notably upon dilution imposed during lysis and subsequent size exclusion chromatography, reducing protein concentration by SEC‐SWATH‐MS accelerates the mapping of cellular complexes. Whereas the method yields a similar coverage of complexes compared to state of the art at over fourteen times less LC‐MS injections, it still required 81 fractions to be analyzed at 2‐h gradient time per fraction, culminating in 162 h of net MS acquisition time. This fact limits the scope for cohort studies. However, this issue may well be alleviated soon, given anticipated improvements SWATH/DIA sample throughput with minimal loss of protein coverage that seem achievable because in SWATH/DIA acquisition the number of analytes quantified does much less strongly depended on gradient length than is the case for DDA acquisition. As a consequence of the high quantitative accuracy of the SEC‐SWATH‐MS data and targeted, error‐controlled complex centric analysis, this study lays the foundation to confidently assess proteome organization and to conclusively follow its dynamics as a function of cell state. Ultimately, extensions of our workflow will support the detection of subtle re‐arrangements within proteomes that occur in response to perturbation or along central biological processes. Such insights will help foster our understanding of the importance of higher order organization of the parts to convey plasticity and regulation to cellular systems.Ca. 7e7 cells were mildly lysed by freeze–thawing into 0.5% NP‐40 detergent‐ and protease and phosphatase inhibitor containing buffer, essentially as described , and buffer was exchanged to SEC buffer  over 30‐kDa molecular weight cutoff membrane at a ratio of 1:50 and concentrated to 25–30 mg/ml (as judged by OD280). After 5 min of centrifugation at 16.9 × g, 4°C, the supernatant was directly subjected to fractionation on a Yarra‐SEC‐4000 column . Per SEC run, 1 mg native proteome (by OD280) was injected and fractionated at 500 μl/min flow rate at 4°C, collecting fractions at 0.19 min per fraction from 10 to 28 min post‐injection, fractions 3–83 of which were considered relevant proteome elution range and considered for further analysis with fractionation index 1–81. The fractions collected from two consecutive SEC fractionations of the same extract (2 × 1 mg) were pooled for subsequent bottom‐up proteomic analysis. Apparent molecular weight per fraction was log‐linearly calibrated based on column performance check protein mix analyzed prior and after each experimental replicate . An aliquot of the unfractionated mild proteome extract was included in peptide sample preparation and LC‐MS analysis. Proteins were proteolyzed to peptide level by trypsin digestion (Promega V5111) in the presence of 1% sodium deoxycholate (Sigma‐Aldrich D6750), reduced, alkylated, and de‐salted on C18 reversed phase , and each sample was supplemented with equal amounts of internal retention time calibration peptides .HEK293 cells were obtained from ATCC and cultured in DMEM containing 10% FCS and 50 μg/ml penicillin/streptomycin to 80% confluency. et al  or His6 tags were co‐expressed by co‐infection of Sf21VM cells with three baculoviral vectors obtained from Lingaraju et al . After 4et al, et al, et al, LC‐MS analysis of peptide samples was performed in both DDA and SWATH/DIA acquisition mode on an AB Sciex TripleTOF 5,600+ instrument , side‐by‐side per sample, sliding from early to late‐eluting fractions. Online reversed phase chromatography fractionated peptide samples delivering at 300 nl/min flow a 120‐min gradient from 2 to 35% buffer B  in buffer A  on a self‐packed picoFrit emitter packed with 20 cm column bed of 3 μm 200‐Å Magic C18 AQ stationary phase, essentially as described  mass spectrometry was performed using an updated scheme of 64 variably sized precursor co‐isolation windows optimized for human cell lysate MS signal density  with the human canonical SwissProt reference database (build Aug‐2014), standard parameters and variable methionine oxidation and N‐terminal acetylation enabled. Match between runs was enabled to facilitate ID transfer and more consistent MS1 quantification (from and to) between adjacent fractions. Raw peptide MS1 intensities of individual peptide precursor signals were further considered. For the generation of the peptide query parameter library employed for targeted analysis of the SWATH/DIA data, DDA‐MS data were processed as described  removing non‐proteotypic evidence, (ii) summing precursor signals per peptide to generate peptide‐level quantitative profiles , (iii) filtering the data based on chromatography‐informed scores to perform protein‐level error estimation and control, and (iv) to infer protein‐level quantitative profiles .The raw precursor‐level quantitative data from the peptide‐centric analysis pipeline above were next imported into CCprofiler framework by applying the importFromOpenSWATH function with following parameters: annotation_table = exampleFractionAnnotation, rm_requantified = TRUE, MS1Quant = FALSE, rm_decoy = FALSE. During import, non‐proteotypic evidence is removed and multiple precursor signals are summed to peptide level, generating a peptide‐level quantitative profiles (or: peptide traces) stored in a unified data container of class “traces”. Subsequently, the peptide traces were annotated with protein molecular weight and further information from the UniProt database  applying the annotateTraces function with following parameters: trace_annotation = exampleTraceAnnotation, traces_id_column = “protein_id”, trace_annotation_id_column = “Entry”, trace_annotation_mass_column = “Mass”, uniprot_mass_format = TRUE, replace_whitespace = TRUE. The peptide traces generated here are not yet strictly FDR‐filtered and thus represent a “raw” set of signals subject to further processing, see below.The precursor‐level data were imported into the calibrateMW using the CCprofiler exampleCalibrationTable containing apex fraction number and known molecular weight of the reference protein set  below 0.316 were discarded in order to achieve an estimated FDR of < 1% among the remaining 4,958 proteins. The proteins are then quantified based on summing the top2 peptides with highest cumulative signal intensity across the 81 fractions, generating the final protein‐level quantitative data matrix by applying the proteinQuantification function with the options: topN = 2, keep_less = FALSE, rm_decoys = TRUE. The resulting final protein traces entail 4,916 proteins quantifiable with at least two proteotypic peptides and form the basis for complex‐centric exploration, searching the data for hypothetical complexes inferred from public protein interaction databases.The peptide traces to this point have been generated under relatively relaxed FDR and related score cutoff criteria to ensure maximal sensitivity of analyte retrieval. To ensure highest possible data quality, protein‐level error control is postponed to this later stage in order to leverage additional information available through SEC fractionation for optimized protein analyte validation. We filter the peptide‐level data based on SEC‐informed filters regarding (i) the length of coherent identification stretches along consecutive SEC fractions and (ii) peptides’ quantitative fractionation pattern similarity to those of its sibling peptides (originating from the same parent protein). We monitor the impact of filtering on the decoy‐estimated FDR on protein level by the TDA  are directly employed to detect of protein elution events from the SEC column  based on sibling peptide co‐peaking in the SEC dimension, performed in the protein‐centric analysis module within CCprofiler.In addition to complex‐centric exploration of the protein‐level traces, the filtered peptide traces . Protein elution is detected based on based on sibling peptide co‐peaking “features” in the SEC dimension, based on the protein–FDR‐filtered peptide traces  grouped by parent protein and detecting elution signals via the CCprofiler algorithm. Algorithm parameters were aligned to the parameters optimized for complex‐centric analysis reasoning that correlation signal and peak width properties are generic attributes of the co‐fractionation data, regardless of the analyte level. Protein features were detected applying findProteinFeatures with following parameters: corr_cutoff = 0.95, window_size = 8, parallelized = TRUE, n_cores = 30, collapse_method = “apex_only”, perturb_cutoff = “5%”, rt_height = 3, smoothing_length = 9, useRandomDecoyModel = TRUE. These parameters correspond to the optimal parameters selected for the dataset with a grid search of the parameter space that was evaluated by performance metrics based on the complex‐level analysis and target‐decoy strategy (see below). All protein elution features were scored by calculateCoelutionScore and q‐values were estimated applying calculateQvalue (lambda = 0.5). The results were filtered for a maximal q‐value of 0.1, corresponding to an FDR of 10%.To evaluate complex assembly behavior of each protein individually, we employ the targeted analysis concept and The core module of complex‐centric proteome profiling is complex‐centric query of hypothetical complexes inferred from public databases in the protein‐level quantitative fractionation profiles (protein traces). The necessary steps are (i) formulation of protein complex queries from public databases, (ii) formulation of decoy complex queries to model and control error rates, (iii) optimization of processing parameters in a grid search using a subset of complex queries, (iv) detection and statistical scoring of complex subunit co‐elution evidence (“complex features”) across all queries, and (v) collapsing of overlapping and redundant co‐elution evidence to delineate complexes and complex families with defined co‐elution of subunits in SEC.et al, et al, et al, A crucial step in complex‐centric proteome profiling is the definition of target queries. Here, protein complex queries were generated based on CORUM .http://bioplex.hms.harvard.edu . Protein identifiers were mapped to Uniprot accessions via BioMart. The interactions were filtered for a minimal combined_score of 900. Pathlengths between any protein pair within the network were calculated by generateBinaryNetwork), followed by pathlength calculation . The decoys were generated by generateComplexDecoys with n_tries = 3, append=TRUE, and dist = 2 for CORUM and BioPlex and dist = 1 for StringDB.In order to enable an automated error estimation of the complex‐centric feature finding a decoy complex query is generated for each target. For all three protein complex query sets, decoys were generated separately, by first creating a binary network based on the respective complex queries , windows = c, smoothing = c, rt_heights = c. Only the best, most complete complex feature for each tested complex query was considered (getBestFeatures). Scores were calculated for each parameter set by calculateCoelutionScore and calculateQvalue (lambda = 0.5). The best parameter set is selected by only considering parameter combinations that achieve a decoy‐based FDR below a selected threshold, followed by taking the set that resulted at the highest number of detected features. These statistics for each parameter set were determined by qvaluePositivesPlotGrid and the optimal parameter set was selected by getBestQvalueParameters (FDR_cutoff = 0.05). The optimal parameters relating to chromatography and noise in the dataset are employed also for the task of protein‐centric detection of protein elution from peptide‐level traces (see above). We expect transferability because chromatographic parameters such as resolution in SEC are specific to the dataset and differences in noise levels should be neglectable when moving from protein profiles based on two peptides back to individual peptide signals. In complex‐centric analysis, the optimal parameter identified based on a subset of complex queries is then employed to detect protein co‐elution signals for the full set of complex queries in the global complex feature detection step.Optimal parameters for complex feature finding in the HEK293 SEC‐SWATH‐MS dataset were determined by a complex‐centric feature finding grid search based on the CORUM complex queries, as implemented in findComplexFeatures function was applied with following parameters: corr_cutoff = 0.95, window_size = 8, parallelized = TRUE, n_cores = 30, collapse_method = “apex_network”, perturb_cutoff = “5%”, rt_height = 3, smoothing_length = 9. The resulting protein complex features were initially filtered to contain only elution features eluting at a higher molecular weight than 2‐times the molecular weight of the largest monomer across all complex subunits, filterFeatures: complex_ids = NULL, protein_ids = NULL, min_feature_completeness = NULL, min_hypothesis_completeness = NULL, min_subunits = NULL, min_peak_corr = NULL, min_monomer_distance_factor = 2.The optimal parameter set determined in the complex feature finding grid search explained above was used to detect complex features for all three complex query sets based on CORUM, BioPlex, and StringDB. The getBestFeatures). Scores and q‐values were determined by calculateCoelutionScore and calculateQvalue (lambda = 0.5). The results were subsequently filtered for a maximal q‐value of 0.05, corresponding to an FDR of 5%. The analyses yield co‐elution evidence for 572, 951, and 1,810 complex queries from CORUM, Bioplex, and StringDB, respectively, which then needs to be integrated to remove redundancies in order to identify unique, chromatographically resolved co‐elution groups representing distinct complexes or complex families. Alternatively, individual complex signal sets can be interrogated for the retrieval of chromatographically resolved complex variants, e.g., assembly intermediates.For scoring and statistical evaluation, only the best, most complete complex elution feature was selected per complex query . While many of the recovered signals represent actual distinct complex variants, we suggest special care and in‐depth investigation when interpreting individual cases of multi‐complex‐feature queries, similar to the evaluation of COP9 signalosome and 20S proteasome subversions presented in the main text of the paper. We particularly encourage the use of SECexplorer to cross‐reference putative complex variant signals with further proteins known to engage in physical interactions with the protein set in question to help strengthen or disqualify the complex query extractable from the dataset at hand.Separate complex‐centric analysis of the CORUM, BioPlex, and StringDB‐derived sets of complex queries retrieves co‐elution evidence for 572, 951, and 1,810 queries, respectively. In order to identify unique, chromatographically resolved co‐elution groups representing distinct complexes or complex families, the signal sets need to be integrated and collapsed to unique signals.getUniqueFeatureGroups with following parameters: rt_height = 3, distance_cutoff = 1.25. The collapsing was then performed by applying callapseByUniqueFeatureGroups, rm_decoys = TRUE.To perform feature collapsing, only the best, most complete co‐elution signal per complex query was used for CORUM, BioPlex, and String results, each independently filtered for 5% estimated FDR. Complex features were mapped by CCprofiler analysis against manual analysis of a curated reference set of chromatographically co‐eluting proteins that are annotated in the CORUM knowledgebase as subunits of well‐defined complexes , and lower‐confidence positives, characterized by lower signal‐to‐noise and/or poor peak shape (Plow). All complex queries for which no co‐elution peak were visible in manual inspection were marked as negatives. Indeed, high‐quality signals were more effectively recovered in algorithmic processing  and FDR were taken as measures of the performance of CCprofiler compared to the manual analysis.The manual annotation was taken as reference set to test the performance of the TPall is the number of complex queries with an automatically detected feature that also got manually annotated as high‐ or low‐confidence positive (Phigh or Plow). TPhigh is the number of complex queries with an automatically detected feature that also got manually annotated as high‐confidence positive (Phigh).Here, The manual annotation‐based FDR was estimated as follows:Tall is the total number of complex queries with a detected feature from CCprofiler .Here, et al  a reference chromatographic complex analysis workflow implemented by Havugimana et al  that depl  is the maximum number of subunits annotated in the CORUM reference complex that are reported as co‐complex members by our complex‐centric profiling strategy or the other datasets respectively.Here, CCprofiler of StringDB‐derived complex queries. For Havugimana et al, all of their 622 reported complexes were taken. For Larance et al, both their reported 475 complexes and the complexes derived from complex‐centric analysis with CCprofiler usingStringDB prior connectivity information were considered.For our complex‐centric profiling strategy, we took the complex features derived from complex‐centric analysis with The number of retrieved CORUM complexes was determined by counting the number of CORUM reference complexes with a minimal overlap of 0.5.DDA‐MS data were processed using the MaxQuant software package . The optimal parameter set for both the spectral counting and MS1 quantification dataset were then used to perform complex feature finding, again with identical strategy and parameters as for the SEC‐SWATH‐MS complex feature finding (see above). The optimal parameters used for et al (http://www.mcponline.org/lookup/suppl/doi:10.1074/mcp.O115.055467/-/DC1/mcp.O115.055467-3.xlsx). In the case of protein groups, groups were reduced to a single UniProt entry by keeping the first protein only. Decoys were further removed from the dataset. The raw protein intensities were summed across all three replicates to generate a single combined protein quantification matrix across all 40 measured SEC fractions.The native SEC‐DDA‐MS data from Larance et al  were dowperformComplexGridSearch. Following parameters were tested: corrs = c, windows = c, smoothing = c, rt_heights = c. The optimal parameter set determined for an FDR_cutoff of 0.05 were corr_cutoff = 0.95, window_size = 12, rt_height = 5, smoothing_length = 7. Complex‐centric analysis was performed with these parameters by using both CorumDB and StringDB as prior connectivity information. The results were similarly processed as for the SEC‐SWATH‐MS dataset, achieving a 5% FDR for each complex query set respectively.Optimal parameters for complex feature finding in the native SEC‐DDA‐MS dataset were determined by a complex‐centric feature finding grid search based on the CORUM complex queries, as implemented in The whole workflow replicate R2, with measured SWATH‐MS quantitative profiles between fraction 23 and 46, was processed in an identical manner compared to workflow replicate R1. In contrast to replicate R1, replicate R2 was not filtered for sibling peptide correlation. Protein quantification was performed using the same two peptides as selected for replicate 1, in order to be quantitatively comparable.To validate the mass spectrometric observation of two distinctly eluting variants of the COP9 signalosome complex, we assayed CSN subunits in the relevant fractionation range by immunoblotting from two independent experimental replicates. 1 mg of HEK293 lysate was fractionated as described above, and 20 μl per fraction (21%) was submitted to SDS–PAGE , transferred onto a nitrocellulose membrane, and probed with antibodies against CSN1 , CSN3 , CSN8 , CSN4 , CSN5 , and CSN7A  according to supplier's instructions. Bound antibodies were detected with HRP‐conjugated goat anti‐rabbit IgG antibody  and visualized with the Amersham, ECL Prime Western Blotting Detection Reagent  according to the manufacturer's protocol.The datasets and computer code produced in this study are available in the following databases:http://proteomecentral.proteomexchange.org)Mass spectrometry proteomics data: ProteomeXchange Consortium PXD007038 (CCprofiler package: GitHub (https://github.com/CCprofiler/CCprofiler/)CCprofiler R package.A detailed vignette describing the main functionalities and usage of the software is provided in the Conceptualization: RA, MG, BCC, and MH; methodology: MH, IB, and GR; software: IB, MH, GR, RH, MF, and AB‐E; validation: MH and IB; formal analysis: MH and IB; investigation: MH; performed Western blot experiments: AvD; resources: MG and RA; data curation: IB and MH; writing—original draft: MH and RA; writing—review and editing: IB, MF, GR, RH, AB‐E, BCC, MG, and RA; completion submitted manuscript: MH, IB, and RA; visualization: MH, IB, and RH; supervision: BCC, MG, and RA; project administration: MH; funding acquisition: RA.The authors declare that they have no conflict of interest.AppendixClick here for additional data file.Dataset EV1Click here for additional data file.Dataset EV2Click here for additional data file.Dataset EV3Click here for additional data file.Dataset EV4Click here for additional data file.Dataset EV5Click here for additional data file.Dataset EV6Click here for additional data file.Dataset EV7Click here for additional data file.Dataset EV8Click here for additional data file.Review Process FileClick here for additional data file.


We overlaid the complex models of S100A9-S100A12 with the same orientation of S100A9 and the RAGE V-domain. This complex showed that S100A12 protein blocks the interaction between S100A9 and the RAGE V-domain. It means S100A12 may be used as an antagonist for S100A9. The results could be favorable for developing anti-cancer drugs based on S100 family proteins.The proteins S100A9 and S100A12 are associated with the human S100 calcium-binding protein family. These proteins promote interaction with target proteins and alter their conformation when they bind to calcium ions in EF-hand motifs. The V domain of RAGE (Receptor for Advanced Glycation End products) is crucial for S100A9 binding. The binding of RAGE with S100 family proteins aids in cell proliferation. In this report, we demonstrate that S100A12 protein hinders the binding of S100A9 with the RAGE V-domain. We used fluorescence and NMR spectroscopy to analyze the interaction of S100A9 with S100A12. The binary complex models of S100A9-S100A12 were developed using data obtained from  The interaction mechanisms of S100 family proteins could be useful for inhibiting their interaction in humans with the V-domain of RAGE (Receptor for Advanced Glycation End products). It was reported that the human S100A12 protein interacted with S100A9 protein . The S10The human S100A9 protein  has a molecular weight of 13.2 kDa and contains a total of 114 amino acids , and thaRAGE has been a crucial therapeutic target due to its impact and relevance in a diverse range of human diseases and tumor growth –21. The d) of the S100A9-S100A12 heterodimer complex by fluorescence spectroscopy. The HADDOCK (High Ambiguity Driven Protein-Protein Docking) program was used to examine the residues at the interfaces of two proteins which obtained from NMR (1H-15N HSQC) titrations  T and [D] T are the total concentration of S100A9 and S100A12 respectively.Fluorescence experiments are useful for calculating the dissociation constant of protein—protein interaction . The Flu1H-15N HSQC) NMR spectroscopy is a convenient method for determining protein-ligand or protein-protein interactions [15N-labeled S100A9 and that of 15N-labeled S100A9 complex with unlabeled S100A12 . To discern the residues involvement in S100A9’s complex formation with S100A12, we created bar diagrams for convenient correlation of the HSQC titration results with observed the chemical shift perturbation  of 1.30±0.36. This was in the micromolar range, in the same way it’s indicate that formation of a stable S100A9-S100A12 protein complex under physiological conditions. A previous report showed that Kd for S100A9 binding with the RAGE V domain was 5–6 μM [There is one tryptophan residue in the S100A9 protein at Trp-88 position. The NACCESS result indicated that Trp-88 is the located on the interface of the S100A9. We observed the emission maxima of tryptophan residue at 345 nm upon excitation at 295 nm. The titration experiment shows decreasing fluorescence intensity of S100A9 protein with increasing S100A12 protein addition . These rs 5–6 μM .The interaction of S100A9 protein with the RAGE V domain leads to downstream signaling and triggers cell proliferation. Blocking the interactions between S100A9 and RAGE can prevent the cell proliferation activity . After eRAGE is the most significant extracellular receptor and its interaction with S100 proteins can activate the transduction of cellular signals. This interaction is a noteworthy factor in the proliferation of cancer cells. A few studies have reported that the binding of the RAGE V domain and S100 proteins, such as S100P , S100A6 S1 Fig(TIF)Click here for additional data file.S2 Fig(TIF)Click here for additional data file.S3 Fig(TIF)Click here for additional data file.S4 Fig(TIF)Click here for additional data file.S5 FigAbout 84% of the residues are in the favoured region and the disallow region is 0.5%. The overall average of G-factor is -0.2, which is in the usual region.(TIF)Click here for additional data file.


The bunch of tools including SNPNEXUS, SNAP-2, PhD-SNP, SNPs&GO, I-Mutant, ConSurf, and ModPred were used to predict 23 highly confident damaging and disease causing nsSNPs  that could potentially alter the SLC26A4 gene. Moreover, protein structure prediction, protein-ligand docking and Molecular Dynamics simulation were performed to confirm the impact of two evident alterations (Y127H and G334A) on the protein structure and function.Single Nucleotide Polymorphisms (SNPs) are the most common candidate mutations in human beings that play a vital role in the genetic basis of certain diseases. Previous studies revealed that Solute Carrier Family 26 Member 4 (SLC26A4) being an essential gene of the multi-faceted transporter family SLC26 facilitates reflexive movement of Iodide into follicular lumen through apical membrane of thyrocyte. SLC26A4 gene encodes Pendred protein, a membrane glycoprotein, highly hydrophobic in nature, present at the apical membrane of thyrocyte functioning as transporter of iodide for thyroid cells. A minor genetic variation in SLC26A4 can cause Pendred syndrome, a syndrome associated with thyroid glands and deafness. In this study, we performed  In last few years, genome-wide association studies (GWAS) tested a huge number of SNPs in thousands of people and highlighted reproducibly distinguished numerous relationships among the common genetic variants and diseases with their traits . These sSingle nucleotide polymorphisms (SNPs) act as indicators in the association and linkage studies for detecting the part of genome involved in a particular disease . The polIn the present world of genetics, a major goal remains to comprehend the substantial part of the disease-causing genetic mutations and variants . CharactThe human population has moderately restricted genetic variations. Numerous uncommon hereditary variations exist in the humans, yet the majority of the heterozygosity in the populace is inferable from commonly existing alleles . The rarPendrin protein produced by the translation of SLC26A4 gene, is a profoundly hydrophobic protein comprising of total 780 amino acids . Along wThe study of candidate genes tends to decrease the quantity of SNPs to a set of mutations from which the genes are destined to build up the genetic foundation of disease under consideration . AlthougSLC26A4 gene, their disease associations and the effect of deleterious nsSNPs in the protein structural behavior. For the prediction of pathogenic nsSNPs in SLC26A4 gene, we used SNPNEXUS [These days, more refined computational methods are bieng created, that help in developing high-throughput, practical path in identification of change in structure, capacity and strength of protein as result of some variation ,21. KeepSNPNEXUS , SNAP2 [SNPNEXUS , SNPs&GOSNPNEXUS , PhD-SNPSNPNEXUS , I-MutanSNPNEXUS . For ideSNPNEXUS  was usedSNPNEXUS  was usedSNPNEXUS  providedSNPNEXUS . In ordeSNPNEXUS  was usedSNPNEXUS .The schematic illustration of the methodology utilized in this study is given in http://www.ncbi.nlm.nih.gov) and datasets related to SNPs of SLC26A4 gene were obtained from NCBI database of SNPs (dbSNP (http://www.ncbi.nlm.nih.gov/snp/). Whereas data regarding Pendrin gene and protein were obtained from OMIM database (http://www.omim.org).The nucleotide sequence in FASTA format (Accession number: NC_000007. 14), Pendrin protein amino acid sequence (NP_000432.1) were downloaded from NCBI (https://www.snp-nexus.org) and SNAP2 (https://www.rostlab.org/servces/snap/). server. Out of which SNPNEXUS has further embedded tools like SIFT and PolyPhen. Both of the servers take into account different inputs and classify the possible SNPs on the basis of their respective confidence scores. For non-synonymous single amino acid substitution, SNPNEXUS provides the predicted effect on protein function (Tolerated or Damaging) based on the SIFT and PolyPhen predictions [The effects of SNPs were predicted using certain online tools and servers including SNPNEXUS (dictions . SIFT cldictions . The comhttp://snps.biofold.org/phd-snp/phd-snp.html) and SNPS&GO (http://snps-and-go.biocomp.unibo.it/snps-and-go/) were used for determining association of filtered SNPs with disease. PhD-SNP is an online tool used to predict the association of diseases with the SNPs with an accuracy rate of 78%. Classifying the SNPs into disease associated or neutral it ranks them on a scale of 0 to 9 [PhD-SNP (f 0 to 9 . SNPs&GOf 0 to 9 . The inphttp://folding.biofold.org/i-mutant/i-mutant2.0.html), predicts the impact of SNPs in changing stable state of a protein. The accuracy of the tool goes up to 77% [The SNPs tend to affect protein strength by either decreasing or increasing the protein stability. To predict these effects, a pair of tools was used to maximize the confidence of the alterations caused. I-Mutant (p to 77% . The inpp to 77% . The inphttp://ConSurf.tau.ac.il/). ConSurf is an efficient tool for predicting the high-throughput functionalities of the target regions of the proteins. For every residue of the protein of interest, the conservation analysis is shown on a scale of 1 to 9. On the scale, 1–3 score is referred as variable, 4–6 as average and 7–9 scores are showing highly conserved regions [ConSurf, an online tool was used for Pendrin protein conservation analysis ( regions . The toohttp://www.modpred.org/). ModPred by using protein sequences predicts PTMs within them. It incorporates 34 gatherings of strategic relapse abstractions that are prepared autonomously using a group of 126,036 non-excess practically confirmed sites for 23 distinct variations, accomplished by consulting open databases [The Post-transcriptional modification sites comprise of numerous kinds of amino acid modifications which give rise to the broad spectrum of proteins being generated. Methylation, phosphorylation, acetylation and ubiquitination are some of the characterized PTM sites. These sites play a vital role in important aspects of cellular organization like protein-protein interactions and signaling pathways that are associated with diseases. Thus, predicting PTM information helps understand the effect of variations in terms of disease association or pathogenicity. ModPred was used for predicting the overall PTMs (atabases .http://servicesn.mbi.ucla.edu/SAVES/) was used. It has a set of six integrated modules including RAMACHANDRAN plot which provides information about conformation of residues in allowed and disallowed regions and is used for structural evaluation of protein [A structural analysis was performed to evaluate the structural stability of the native and mutant proteins. As the structure of PDS protein is not reported yet so we predicted the native protein structure using MODELLER 9.11 package. FOLDX was used for refinement, repairing, energy minimization and mutant generation using native predicted structure of VHL protein. For the verification of protein models SAVES server ( protein .http://zhanglab.ccmb.med.umich.edu/COACH/)which is a tool that uses binding site approaches or interactions prediction, particularly binding sites of proteins and ligands. COACH uses two comparative methods TM_SITE and SSITE for identification of binding sites for ligands [Molecular docking analysis was performed to identify structural variations of native and wild type PDS protein structures by comparing the binding affinities of six ligands  with these structures. In order to see the structural variations among native and mutant structures, we first predicted the binding sites for ligands in the Pendrin protein using COACH  of 300K and Parrinello-Rahman (pcouple) pressure of 1 bar were used to keep the system in stable environment whereas the coupling constant was adjusted at 0.1 and 2.0 ps for temperature and pressure respectively. Partial Mesh Ewald (PME) algorithm was used for all electrostatic interactions. Short-range cut-off for van der wall (rvdw) and electrostatic (rcoulomb) was fixed at 1.0 ns whereas; neighbor list (nstlist) was set at 10 ns. LINCS algorithm was used to constrain all bonds including heavy atom-H bonds and the time step was set at 0.002 ps. Isothermal compressibility was set at 4.5x10-5. Structures were equilibrated for 100 ps in NPT (pcoupl) and NVT (tcouple). Lastly, 10-ns MDS was performed for both native and mutant structures and trajectories were stored every 1 ps. We then computed the comparative analysis of structural deviations among wild type and mutant structures. RMSD, RMSF, SASA, Rg, temperature, pressure and density plot analysis were carried out using g_rms, g_rmsf, g_sasa, g_Rg and g_density tools, respectively. All plots were generated using XMGRACE program.In order to compare the structural changes with time, we performed Molecular Dynamics (MD) simulation of native and mutant structures using GROMACS version 5.1.2 that was executed on Z-book computing machine with 16 GB RAM and Ubuntu 18.04. Initial calculation was performed using GROMACS-OPLS-AA force field. Systems were solvated in a cubic box with water molecules at 1 nm (10 Å) marginal radius and electrical neutralize of systems was done by adding 10 sodium Napolymorphisms). Out of which 674 were non-synonymous SNPs (4.70%), 12494 lay in intronic regions (86.30%), 240 were reported to be synonymous SNPs (1.80%), 872 lay in the UTR regions of the protein (6.02%). The splice site mutations reported were 20 (0.14%), whereas 143 (0.99%) were known to be frame shift and 30 (0.20%) as stop gained mutations. Human SLC2A46 gene contains a total number of 14473 single nucleotide SNPNEXUS and SNAP2 servers were simultaneously used to predict the nature of the SNPs to get high confidence results. A total of 674 nsSNPs were the input for SNPNEXUS, it consults the databases integrated to it and assigns an index score to each reported nsSNP. According to SIFT indexing 298 (44.2%) of the nsSNPs were predicted to be deleterious with the score < = 0.05, out of these 189 nsSNPs were reported to be highly deleterious with a confidence score of 0.00. The PolyPhen indexing which is based on the structural information and Multiple Sequence Alignment categorized 182 of the nsSNPs as ‘probably damaging’ and 112 as ‘possibly damaging’. Hence, 294 (43.6%) of nsSNPs were predicted damaging by PolyPhen.For the purpose of attaining the highest confident results, 46 nsSNPs were filtered out by combining information from SIFT and PolyPhen. These 46 nsSNPs were then provided to SNAP2 server for further validation. This reported 43 of the nsSNPs to be causing some “effect” on function of protein and only 3 were predicted to be “neutral”. A representation of the results of functional consequences of nsSNPs is in The filtered and reduced number of polymorphisms then subjected to the question that whether they are disease associated or not. 46 high confidence nsSNPs were then provided to the SNP&GO and PhD-SNP servers. Both of them calculated a score to declare each nsSNP as neutral or disease associated. The prediction score <0.5 makes the SNP neutral and disease causing if the score is >0.5. Among 46 nsSNPs only one (E303Q) was found to be neutral and the rest 45 nsSNPs were disease causing. A cut off value (p> = 0.80) for PhD-SNP scores was applied to narrow down the number of nsSNPs to 23. I-Mutant and Mu-Pro servers were used to predict the structural effect of 23 candidate nsSNPs, of which only 1 nsSNP (G209V) was predicted by I-Mutant to be increasing the protein stability and rest 22 nsSNPs were suppressing the protein activity by decreasing its stability. Whereas, Mu-Pro predicted none of the nsSNPs to be increasing the protein’s stability. A representation of results is shown in Post Transcriptional Modifications associated with our candidate nsSNPs were predicted by ModPred server by giving the protein sequence as an input. The results were downloaded as .csv format and then analyzed all 23 nsSNPs. ModPred server reported that only 6 nsSNPs  located at Proteolytic Cleavage sites and 2 nsSNPs (S427P and S427C) at O-Linked glycosylation sites. The description in given in Any mutation that causes a disease usually resides in highly conserved region of the gene. In order to see the conserved behavior of the 23 nsSNPs, ConSurf server was used. Of the 23 nsSNPs, 8 nsSNPs were highly conserved on the scale along with being structural and buried and 7 were predicted to be highly conserved being functional and exposed.ConSurf results showed the level of confidence for the sequence conservation in the form of color codes starting from the blue to purple, where blue color indicates the variability and the purple color indicates the highly conserved position. ConSurf predicted the variations to be buried (b) or exposed (e) as well as functional (f) or structural (s) on a range of score 1–9 as depicted by Literature revealed that mutations at Y127H and G334A in PDS protein, phenotypically cause diffuse goiter, impaired Thyroid Function Test (TFT) with positive Perchlorate Discharge Test (PDT) and Enlarged Vestibular Aqueduct (EVA). Based on the prediction scores we chose two highly conserved mutations Y127H and G334A to see the protein structural modifications being caused by them. The coiling and recoiling of the predicted structure resulted in the formation of PDS native 3-D structure using threading approaches. By running MODELLER 9.2, we got 7 templates from which 6rtc.1.A and 5da0.1.A showed 38.37% and 37.71% identity with query sequence. We used these templates for the backbone building of our model and structure prediction. The comparative models were generated using Discrete Optimised protein energy (DOPE) assessment score for distinguishing a “good” model from “bad “model. Interaction sites present in the protein were firstly identified using COACH along with their binding affinity. For further verification and analysis of the interactions of our native and mutant protein structures with their most suitable ligands, we selected six molecules 4KU, BCT, DMU, IOD, MG, XAN and docked with all the three, native and mutant, structures. The docking interactions were studied with LigPlot that shows the 2D representation of all interactions in particular docking system. From possible binding sites predicted, the positions 622, 674, 704 and 707 carrying amino acids GLU, ARG, GLU and GLY respectively were found to be present in the docking interactions out of which position 674 carrying ARG, position 707 carrying GLY and position 704 carrying GLU were also present in mutants. The interacting residues obtained from docking are presented in The interactions among ligand and protein residues in mutants were found to be different from the ones in native that indicates the change in functional properties caused by mutations . Here it-3 which was very close to the experimental value of 1000 kgm-3 while that of mutant Y127H and G334A was more than 1002.6 kgm-3.After energy minimization, we equilibrated solvents and ions around our protein structures and in order to establish the proper orientation of molecules in our structures, we performed NVT (thermostat) and NPT (bariostat) equilibration; the two phases to stabilize the temperature and pressure of the systems in isothermal-isobaric ensemble respectively. “gms grompp” tool was used to calculate NVT and NPT of the system. Due to the involvement of temperature, pressure and density in equilibrated process, we established density plot from NPT. Structural and functional behavior of wild type SLC gene and its two mutant G334A and Y127H was compared and visualized using Molecular Dynamics Simulation approach. The main chain root mean square deviations were calculated for the trajectories of native and mutants of RIPK4 protein structures. The RMSD graphs  were buiWild type structure attained maximum RMSD value after 3.3 ns whereas the mutants G334A and Y127H attained the maximum values at 3.75 ns and 4.55 ns respectively. The RMSD graph of wild type SLC and its mutants G334A and Y127H that were aligned to the backbone suggested that the wild type SLC gene was steadier than the mutants Y127H and G334A. Secondly these mutants affected the protein dynamics of wild type SLC leading to changes in RMSD graph of backbone of the mutants thus giving an appropriate foundation for further analysis. In order to determine the effect of mutations on dynamic behavior of residues at atomic level, RMSF (Root Mean Square Fluctuation) values of native and mutants were analyzed . RMSF vaThe radius of gyration (Rg) is defined as root-mean square distance of all atoms from their common center on the basis of mass-weight. It provides an understanding to study the overall dimensions of protein. Radius of gyration plot for alpha carbon atoms (with respect to time) of native and mutant protein structures at 300 K is shown in 2 to 484.5 nm2 with an average of 473 nm2. The SASA value of Y127H and G334A was noted between 445 nm2 to 480 nm2 with an average of 467 nm2 and 453 nm2 to 472.5 nm2 with an average of 465 nm2 respectively.Solvent accessibility surface area (SASA) is bio-molecular surface area accessibility to solvent molecules. A decrease in the SASA value indicates that the structure is shrunken and the surface area is reduced. Pendrin protein like all other proteins of the body holds an important role in the efficient functioning of the human body. It acts as an essential ion exchanger located in many types of cells of the body. Though in the inner ear and thyroid areas, high level of Pendrin expression is known to date. In the areas of expression Pendrin mediates the efflux of Iodide ions across the membranes, which is essential for the secretion or development of hormones including thyroid . Moreovein-silico techniques and tools were used to study the impact and association of the 674 reported nsSNPs. Web based tools including SNPNEXUS, SNAP2, SNP&GO, PhD-SNP, I-Mutant, Mu-Pro, ConSurf and ModPred were used. Each tool was provided with the required input and they helped for extracting useful findings. The methods used in the study basically focus upon the correlation between the variations and their effects on the protein at the molecular level. A variety of tools used for a single purpose make the results confident as each program/tool runs on a different algorithm. Thus, the results obtained from the consensus of all the tools are believed to have the highest accuracy [Among all types of mutations, nsSNPs have the greatest impact on protein structure and function . In Pendaccuracy .in-silico study, 23 nsSNP were found to be most deleterious as per the consensus results of all the tools. The mutations at these particular residues were also found to alter the protein stability. The Post Transcriptional modifications were observed on 8 out of 23 residues. Among deleterious nsSNPs of SLC26A4, two mutations Y127H and G334A were found causing diffuse goiter, impaired TFT with positive PDT and EVA. To examine the impact of these two mutations in the structure of PDS protein (SLC26A4) we built protein model of native and mutants and found that there is variation in structure at secondary and tertiary level. Superimposition of mutants with native PDS indicated a significant change in structure at positions G334A and Y127H. To support our conclusion, we carried out protein-ligand docking and Molecular Dynamics simulation of all three structures. The binding poses of the native and mutants with six ligands  were determined and the fitness score of each pose was generated based on the binding affinity of ligand-receptor complexes. The purpose of docking was to see the binding affinity, hydrogen bonds and hydrophobic interactions of the ligands with the protein 3D structures. The results showed that mutation at G334A reduced the hydrogen binding interaction with all six ligands. It was highlighted that, on binding with ligands, the mutation at Y127H and G334A not only affected the binding residues but also the hydrogen bonds and hydrophilic interactions. It has been clearly illustrated that the binding pockets of PDS native protein was accommodating the ligands in a very well manner by making a number of H-bonds and hydrophobic contacts with the active site residues as compared to Y127H and G334A mutants where the residues W500, L323 and R157 were also identified to be present in ligand binding sites. To compare protein dynamics of wild type and mutant SLC, Molecular Dynamics simulation was performed where RMSD, RMSF, SASA, Rg, temperature, pressure and density plots were compared and analyzed at 10 ns MDS.After the All these comparisons were performed to observe the conformational behaviors of all structures towards structure stability. After the minimization, we found a little difference between the temperature, pressure and density among native and mutants confirmations over the time. RMSD plots of backbone atoms depicted that wild type structure was much steadier than the mutants. RMSF plots showed that the greatest degree of flexibility was shown by native and mutant structures. The RMSF calculated over the trajectories and averaged over the residual elements showed that the fluctuation of atoms in structures was greater in last quartile. We also examined the radius of gyration and solvent accessibility of surface area analysis to determine that how the unfolding of the mutant protein structure reduces the surface area solubility and how the protein activity is reduced. Higher the Rg value indicates lower the stability . We obseThus, our study not only presented powerful platform for performing virtual screening of SLC via a consistent and comprehensive procedure, but also revealed the molecular platform for acknowledgment of changes in activity, stability, binding and other properties.The mutations in the SLC26A4 gene or Pendrin protein are the leading causes for the most common form of Syndromic deafness, Pendred Syndrome . Moreovein-silico analysis of nsSNPs of SLC26A4 gene highlighted that 23 nsSNPs  were the most deleterious among all known and reported SLC26A4 gene nsSNPs. All predicted nsSNPs were both, disease associated and pathological playing significant roles in causing different diseases like deafness, goiter diffusion, and thyroid functional impairment. These nsSNPs were also involved in affecting stability and function of protein. All of them being highly conserved residues also showed post transcriptional modification sites where the residues W500, L323 and R157 were also identified to be present in ligand binding sites.Current study comprising