Prediction of viable circular permutants using a graph theoretic approach

Design of stable circular permutants of the GroEL chaperone apical domain

Enhancing protein stability holds paramount significance in biotechnology, therapeutics, and the food industry. Circular permutations offer a distinctive avenue for manipulating protein stability while keeping intra-protein interactions intact. Amidst the creation of circular permutants, determining the optimal placement of the new N- and C-termini stands as a pivotal, albeit largely unexplored, endeavor. In this study, we employed PONDR-FIT's predictions of disorder propensity to guide the design of circular permutants for the GroEL apical domain (residues 191-345). Our underlying hypothesis posited that a higher predicted disorder value would correspond to reduced stability in the circular permutants, owing to the increased likelihood of fluctuations in the novel N- and C-termini. To substantiate this hypothesis, we engineered six circular permutants, positioning glycines within the loops as locations for the new N- and C-termini. We demonstrated the validity of our hypothesis along the set of the designed circular permutants, as supported by measurements of melting temperatures by circular dichroism and differential scanning microcalorimetry. Consequently, we propose a novel computational methodology that rationalizes the design of circular permutants with projected stability. Video Abstract.

Tandem domain swapping: determinants of multidomain protein misfolding

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Domain swapping refers to the exchange of structural elements between protein domains.

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Experiments show that tandem homologous domains are prone to domain swapping.

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Recent studies establish a framework to understand the formation of tandem domain swaps.

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Prediction of tandem domain swaps is possible but hindered by the amount of available data.

Tandem homologous domains in proteins are susceptible to misfolding through the formation of domain swaps, non-native conformations involving the exchange of equivalent structural elements between adjacent domains. Cutting-edge biophysical experiments have recently allowed the observation of tandem domain swapping events at the single molecule level. In addition, computer simulations have shed light into the molecular mechanisms of domain swap formation and serve as the basis for methods to systematically predict them. At present, the number of studies on tandem domain swaps is still small and limited to a few domain folds, but they offer important insights into the folding and evolution of multidomain proteins with applications in the field of protein design.

Computational Prediction of New Intein Split Sites

Split inteins have emerged as a powerful tool in protein engineering. We describe a reliable in silico method to predict viable split sites for the design of new split inteins. A computational circular permutation (CP) prediction method facilitates the search for internal permissive sites to create artificial circular permutants. In this procedure, the original amino- and carboxyl-termini are connected and new termini are created. The identified new terminal sites are promising candidates for the generation of new split sites with the backbone opening being tolerated by the structural scaffold. Here we show how to integrate the online usage of the CP predictor, CPred, in the search of new split intein sites.

Small world network strategies for studying protein structures and binding

Small world network concepts provide many new opportunities to investigate the complex three dimensional structures of protein molecules. This mini-review explores the published literature on using small-world network approaches to study protein structure, with emphasis on the different combinations of descriptors that have been tested, on studies involving ligand binding in protein-ligand complexes, and on protein-protein complexes. The benefits and success of small world network approaches, which change the focus from specific interactions to the local environment, even to non-local phenomenon, are described. The purpose is to show the different ways that small world network concepts have been used for building new computational models for studying protein structure and function, and for extending and improving existing modelling approaches.

CPred: a web server for predicting viable circular permutations in proteins

Circular permutation (CP) is a protein structural rearrangement phenomenon, through which nature allows structural homologs to have different locations of termini and thus varied activities, stabilities and functional properties. It can be applied in many fields of protein research and bioengineering. The limitation of applying CP lies in its technical complexity, high cost and uncertainty of the viability of the resulting protein variants. Not every position in a protein can be used to create a viable circular permutant, but there is still a lack of practical computational tools for evaluating the positional feasibility of CP before costly experiments are carried out. We have previously designed a comprehensive method for predicting viable CP cleavage sites in proteins. In this work, we implement that method into an efficient and user-friendly web server named CPred (CP site predictor), which is supposed to be helpful to promote fundamental researches and biotechnological applications of CP. The CPred is accessible at http://sarst.life.nthu.edu.tw/CPred.

Deciphering the preference and predicting the viability of circular permutations in proteins

Circular permutation (CP) refers to situations in which the termini of a protein are relocated to other positions in the structure. CP occurs naturally and has been artificially created to study protein function, stability and folding. Recently CP is increasingly applied to engineer enzyme structure and function, and to create bifunctional fusion proteins unachievable by tandem fusion. CP is a complicated and expensive technique. An intrinsic difficulty in its application lies in the fact that not every position in a protein is amenable for creating a viable permutant. To examine the preferences of CP and develop CP viability prediction methods, we carried out comprehensive analyses of the sequence, structural, and dynamical properties of known CP sites using a variety of statistics and simulation methods, such as the bootstrap aggregating, permutation test and molecular dynamics simulations. CP particularly favors Gly, Pro, Asp and Asn. Positions preferred by CP lie within coils, loops, turns, and at residues that are exposed to solvent, weakly hydrogen-bonded, environmentally unpacked, or flexible. Disfavored positions include Cys, bulky hydrophobic residues, and residues located within helices or near the protein's core. These results fostered the development of an effective viable CP site prediction system, which combined four machine learning methods, e.g., artificial neural networks, the support vector machine, a random forest, and a hierarchical feature integration procedure developed in this work. As assessed by using the hydrofolate reductase dataset as the independent evaluation dataset, this prediction system achieved an AUC of 0.9. Large-scale predictions have been performed for nine thousand representative protein structures; several new potential applications of CP were thus identified. Many unreported preferences of CP are revealed in this study. The developed system is the best CP viability prediction method currently available. This work will facilitate the application of CP in research and biotechnology.

Kinetics and reaction coordinates of the reassembly of protein fragments via forward flux sampling

We studied the mechanism of the reassembly and folding process of two fragments of a split lattice protein by using forward flux sampling (FFS). Our results confirmed previous thermodynamics and kinetics analyses that suggested that the disruption of the critical core (of an unsplit protein that folds by a nucleation mechanism) plays a key role in the reassembly mechanism of the split system. For several split systems derived from a parent 48-mer model, we estimated the reaction coordinates in terms of collective variables by using the FFS least-square estimation method and found that the reassembly transition is best described by a combination of the total number of native contacts, the number of interchain native contacts, and the total conformational energy of the split system. We also analyzed the transition path ensemble obtained from FFS simulations using the estimated reaction coordinates as order parameters to identify the microscopic features that differentiate the reassembly of the different split systems studied. We found that in the fastest folding split system, a balanced distribution of the original-core amino acids (of the unsplit system) between protein fragments propitiates interchain interactions at early stages of the folding process. Only this system exhibits a different reassembly mechanism from that of the unsplit protein, involving the formation of a different folding nucleus. In the slowest folding system, the concentration of the folding nucleus in one fragment causes its early prefolding, whereas the second fragment tends to remain as a detached random coil. We also show that the reassembly rate can be either increased or decreased by tuning interchain cooperativeness via the introduction of a single point mutation that either strengthens or weakens one of the native interchain contacts (prevalent in the transition state ensemble).

Optimized null model for protein structure networks

Much attention has recently been given to the statistical significance of topological features observed in biological networks. Here, we consider residue interaction graphs (RIGs) as network representations of protein structures with residues as nodes and inter-residue interactions as edges. Degree-preserving randomized models have been widely used for this purpose in biomolecular networks. However, such a single summary statistic of a network may not be detailed enough to capture the complex topological characteristics of protein structures and their network counterparts. Here, we investigate a variety of topological properties of RIGs to find a well fitting network null model for them. The RIGs are derived from a structurally diverse protein data set at various distance cut-offs and for different groups of interacting atoms. We compare the network structure of RIGs to several random graph models. We show that 3-dimensional geometric random graphs, that model spatial relationships between objects, provide the best fit to RIGs. We investigate the relationship between the strength of the fit and various protein structural features. We show that the fit depends on protein size, structural class, and thermostability, but not on quaternary structure. We apply our model to the identification of significantly over-represented structural building blocks, i.e., network motifs, in protein structure networks. As expected, choosing geometric graphs as a null model results in the most specific identification of motifs. Our geometric random graph model may facilitate further graph-based studies of protein conformation space and have important implications for protein structure comparison and prediction. The choice of a well-fitting null model is crucial for finding structural motifs that play an important role in protein folding, stability and function. To our knowledge, this is the first study that addresses the challenge of finding an optimized null model for RIGs, by comparing various RIG definitions against a series of network models.

A Ca2+-sensing molecular switch based on alternate frame protein folding

Existing strategies for creating biosensors mainly rely on large conformational changes to transduce a binding event to an output signal. Most molecules, however, do not exhibit large-scale structural changes upon substrate binding. Here, we present a general approach (alternate frame folding, or AFF) for engineering allosteric control into ligand binding proteins. AFF can in principle be applied to any protein to establish a binding-induced conformational change, even if none exists in the natural molecule. The AFF design duplicates a portion of the amino acid sequence, creating an additional "frame" of folding. One frame corresponds to the wild-type sequence, and folding produces the normal structure. Folding in the second frame yields a circularly permuted protein. Because the two native structures compete for a shared sequence, they fold in a mutually exclusive fashion. Binding energy is used to drive the conformational change from one fold to the other. We demonstrate the approach by converting the protein calbindin D(9k) into a molecular switch that senses Ca2+. The structures of Ca2+-free and Ca2+-bound calbindin are nearly identical. Nevertheless, the AFF mechanism engineers a robust conformational change that we detect using two covalently attached fluorescent groups. Biological fluorophores can also be employed to create a genetically encoded sensor. AFF should be broadly applicable to create sensors for a variety of small molecules.

CPDB: a database of circular permutation in proteins

Circular permutation (CP) in a protein can be considered as if its sequence were circularized followed by a creation of termini at a new location. Since the first observation of CP in 1979, a substantial number of studies have concluded that circular permutants (CPs) usually retain native structures and functions, sometimes with increased stability or functional diversity. Although this interesting property has made CP useful in many protein engineering and folding researches, large-scale collections of CP-related information were not available until this study. Here we describe CPDB, the first CP DataBase. The organizational principle of CPDB is a hierarchical categorization in which pairs of circular permutants are grouped into CP clusters, which are further grouped into folds and in turn classes. Additions to CPDB include a useful set of tools and resources for the identification, characterization, comparison and visualization of CP. Besides, several viable CP site prediction methods are implemented and assessed in CPDB. This database can be useful in protein folding and evolution studies, the discovery of novel protein structural and functional relationships, and facilitating the production of new CPs with unique biotechnical or industrial interests. The CPDB database can be accessed at http://sarst.life.nthu.edu.tw/cpdb

CPSARST: an efficient circular permutation search tool applied to the detection of novel protein structural relationships

CPSARST (Circular Permutation Search Aided by Ramachandran Sequential Transformation) is an efficient database search tool that provides a new way for rapidly detecting novel relationships among proteins.

Circular permutation of a protein can be visualized as if the original amino- and carboxyl termini were linked and new ones created elsewhere. It has been well-documented that circular permutants usually retain native structures and biological functions. Here we report CPSARST (Circular Permutation Search Aided by Ramachandran Sequential Transformation) to be an efficient database search tool. In this post-genomics era, when the amount of protein structural data is increasing exponentially, it provides a new way to rapidly detect novel relationships among proteins.

In silico protein fragmentation reveals the importance of critical nuclei on domain reassembly

Protein complementation assays (PCAs) based on split protein fragments have become powerful tools that facilitate the study and engineering of intracellular protein-protein interactions. These assays are based on the observation that a given protein can be split into two inactive fragments and these fragments can reassemble into the original properly folded and functional structure. However, one experimentally observed limitation of PCA systems is that the folding of a protein from its fragments is dramatically slower relative to that of the unsplit parent protein. This is due in part to a poor understanding of how PCA design parameters such as split site position in the primary sequence and size of the resulting fragments contribute to the efficiency of protein reassembly. We used a minimalist on-lattice model to analyze how the dynamics of the reassembly process for two model proteins was affected by the location of the split site. Our results demonstrate that the balanced distribution of the "folding nucleus," a subset of residues that are critical to the formation of the transition state leading to productive folding, between protein fragments is key to their reassembly.

Ligand binding and circular permutation modify residue interaction network in DHFR

Residue interaction networks and loop motions are important for catalysis in dihydrofolate reductase (DHFR). Here, we investigate the effects of ligand binding and chain connectivity on network communication in DHFR. We carry out systematic network analysis and molecular dynamics simulations of the native DHFR and 19 of its circularly permuted variants by breaking the chain connections in ten folding element regions and in nine nonfolding element regions as observed by experiment. Our studies suggest that chain cleavage in folding element areas may deactivate DHFR due to large perturbations in the network properties near the active site. The protein active site is near or coincides with residues through which the shortest paths in the residue interaction network tend to go. Further, our network analysis reveals that ligand binding has “network-bridging effects” on the DHFR structure. Our results suggest that ligand binding leads to a modification, with most of the interaction networks now passing through the cofactor, shortening the average shortest path. Ligand binding at the active site has profound effects on the network centrality, especially the closeness.

The cooperative movements within a protein concerning the binding and dissociation of the reactants and products could be important for protein function. Communication among the various parts of an enzyme can be achieved by the networks connecting amino acids through peptide backbone connections and nonbonded amino acid contact. We used dihydrofolate reductase (DHFR), a clinically important enzyme, as an example to explore the effects of amino acid communication on protein functions. We found that the peptide chain itself is an efficient “telephone wire” to transfer the communications. Breaking the telephone wire (peptide chain) at different points leads to differentiated behavior near the enzyme active site. The important points to keep the peptide chain communication are coupled with the place where protein folding occurs. On the other hand, ligand binding to the enzyme active site provides a “short cut” to the communication networks, with most of the interaction networks now passing through the added ligand and shortening the average communication path. We considered the short cuts to be “network-bridging effects” in the protein structure. The enzyme active site is the place where the short cut has the most dramatic effect in modifying protein communication networks.