Allostery is conformation regulation by propagating a signal from one site to another distal site. This study focuses on the long-range communication in DNA mismatch repair proteins MutS and its homologs where intramolecular signaling has to travel over 70 Å to couple lesion detection to ATPase activity and eventual downstream repair. Using dynamic network analysis based on extensive molecular dynamics simulations, multiple preserved communication pathways were identified that would allow such long-range signaling. The pathways appear to depend on the nucleotides bound to the ATPase domain as well as the type of DNA substrate consistent with previously proposed functional cycles of mismatch recognition and repair initiation by MutS and homologs. A mechanism is proposed where pathways are switched without major conformational rearrangements allowing for efficient long-range signaling and allostery.
Allostery is a fundamental part of many if not most biological processes. It is classically defined as the induced regulation at one site by an event at another distal site. Venerable models for allostery, such as the MWC (Monod-Wyman-Changeux) [1] and KNF (Koshland-Nemethy-Filmer) [2] models emphasize a mostly static picture of induced conformational changes. The MWC model proposes coupled conformational changes via a population shift while the KNF model highlights the induced-fit of a binding of a ligand via common communication routes. A broader view of allostery [3–6] emphasizes communication pathways via protein motions but without requiring actual conformational changes. The idea of this model is that relatively minor perturbations may shift communication between multiple pre-existing pathways. Such a mechanism has been demonstrated by nuclear magnetic resonance (NMR) experiments for the binding of cyclic-adenosine monophosphate (cAMP) to the dimeric catabolite activator protein (CAP) [7] as well as for allosteric regulation in Pin1[8]. Recent work based on Markov state models that integrate energetics and kinetics has added further nuances to the discussion by emphasizing both conformational and kinetic selection as the main mechanism of allostery in signaling proteins protein kinase A [9] and NtrC [10]. The idea of kinetic selection is consistent with a pathway selection mechanism without significant conformational changes. Recent reviews have attempted to integrate the different ideas into a unified view [11,12] with the main question being to what degree conformational dynamics plays a role. Likely, the degree of dynamics will depend on a given system and the economics of achieving allosteric signaling within the thermodynamic and functional constraints in the biological environment. One particular question that is central to this work is how long-range allostery can be achieved in very large systems where larger conformational changes and global selection mechanisms that are conceptually straightforward in smaller proteins could be more challenging to realize. It is difficult to obtain detailed insight into allostery from experiments, especially for larger and more complex systems, because NMR spectroscopy is generally limited to small and soluble proteins that can be easily labeled and expressed in large quantities. On the other hand, crystallography is not well-suited for studying allosteric effects due to its inherent dynamic nature. Computational approaches such as statistical coupling analysis (SCA) [13], normal mode analysis (NMA) [14,15], dynamical network analysis [16], and Markov state model analysis based on extensive molecular dynamics simulations [9,10] offer complementary means for exploring allosteric mechanisms in biological systems. SCA, a bioinformatics-based method, obtains allosteric information by identifying coevolving residues from multiple sequence alignments, while NMA, a structure-based approach, suggests induced movements from a few robust low-frequency normal modes. Allosteric pathways obtained from these two methods would be encoded in the sequence and/or structure, but sensitivity to minor perturbations with this type of analysis is lacking. Dynamical network analysis [16] is based on molecular dynamics (MD) simulations and has been used to identify synchronous and/or asynchronous correlated residue motions in order to describe possible allosteric communication pathways. Examples of where this approach has been applied successfully to probe allosteric coupling include a tRNA-protein complex [16], the M2 muscarinic receptor [17], and cysteinyl tRNA synthetase [18]. Here, we used dynamical network analysis to develop a paradigm for allostery in very large multi-subunit complexes based on long-range signal propagating pathways in the MutS component of the methyl-directed DNA mismatch repair (MMR) system. MMR is responsible for correcting errors that escape immediate proofreading during DNA replication and the mechanism is widely conserved from prokaryotic to eukaryotic organisms. MMR alone can increase the accuracy of DNA replication by 20–400 fold [19]. While several components, such as MutS, MutL, MutH, nuclease, and polymerase, are needed to work together to complete DNA repair [20], MutS is responsible for the initial recognition of DNA lesions, in particular mismatches and insertions or deletions (IDLs). MutS is a homodimer, but, structurally and functionally, it acts as a heterodimer because only one subunit (termed the ‘A’ chain in this paper) directly contacts the lesion sites [21]. MutS homologs (MSH) in eukaryotes are heterodimers with differing substrate specificities. MutSα (MSH2-MSH6) preferentially recognizes base pair mismatches and single base IDLs [22], whereas MutSβ (MSH2-MSH3) has a higher affinity and specificity for small DNA loops composed of 2–13 bases [23]. The crystal structures of prokaryotic MutS and its eukaryotic homologs, complexed with mismatched DNA heteroduplexes, feature a similar overall Θ shape [22,24–26]. Each subunit of MutS and MSH is comprised of five distinct domains (see Fig 1): the mismatch-binding domain (MBD, domain I), the connector domain (domain II), the lever domain (domain III), the clamp domain (domain IV), and the nucleotide binding domain (ATPase, domain V) [24]. The MBD and clamp domains interact with the bound DNA directly. The MBD contains a conserved, mismatch-identifying Phe-X-Glu motif, forming specific interactions with mismatches. The phenylalanine forms an aromatic ring stack on the 3′ side of the mismatched base [24,25] although there is also evidence for base flipping of the mismatched or neighboring base during the mismatch recognition process [27,28]. MSHβ, which specializes in the recognition of longer insertions/deletions, lacks this motif. The lever and connector domains connect the MBD and clamp domains to the ATPase domain. The ATPase domain is a conserved domain in the ABC (ATP binding cassette) superfamily. Biochemical studies have provided evidence that ATPase activities are coupled with DNA scanning, mismatch recognition, and repair initiation [29–31]. The different functional states are assumed to involve different conformations of MutS. The major states are MutS without DNA with open clamps, MutS scanning DNA in search of a mismatch with the clamps closed, MutS bound to a mismatch in the tightly DNA-bound conformation seen in crystallography, and a sliding clamp configuration where MutS is able to move away from the mismatch without scanning or complete dissociation from the DNA [32]. Based on the biochemical data, nucleotide binding and exchange to the ATPase domain appear to be key allosteric effectors coupled to DNA mismatch recognition that at least in part trigger changes between those functional states. This implies allosteric coupling between the mismatch binding site and the ATPase site over a distance of 70 Å [33] is essential for the biological function of MutS. Mismatched binding promotes exchange from ADP to ATP based on kinetic measurements of ATP hydrolysis [30,34,35] and results in asymmetric activity of the two ATPase domains [30], whereas the sliding clamp state is supposed to be formed in ATP binding states [36–38]. Previous studies have examined MutS and eukaryotic analogs via molecular dynamics simulations [27,32,39–43], but many mechanistic questions remain. Here, we subjected previously generated simulations of MutS, MutSα (MSH2-MSH6) and MutSβ (MSH2-MSH3) to dynamical network analysis to elucidate allosteric communication pathways between the structural domains in MutS and MSHs. In particular, we addressed the questions of how intra-molecular signaling could be accomplished over very long distances via protein dynamics and how small perturbations could affect the signal propagation. Previous work has suggested coupling between the MBD and ATPase domains, but mechanistic details and in particular the role of exchanging NTPs still remain largely unclear [43,44]. The dynamic network analysis applied here allowed us to probe for pathways connecting the domains in contact with the DNA and the ATPase domain. Furthermore, by comparing pathways in simulations with different nucleotides bound to MutS and different DNA substrates bound to MutSα and MutSβ we were able to develop hypotheses for how communication along those pathways may be shifted during the functional cycle of MutS and its homologs.
A number of very similar MutS and MSH crystal structures are available with different nucleotide bound states and different mismatches or IDLs. The structural variations that can be discerned primarily focus on the MBD, ATPase and clamp domains and involve mostly local side-chain displacements rather than larger conformational changes of the main chain. For example, the MutS crystal structures 1E3M (with a single ADP) [24] and 1W7A (with bound ATP) [33] differ by only 0. 35 Å in the Cα coordinates after superposition. MD simulations paint a similar picture. In previous work from our group, MD simulations of MutS with all possible combinations of nucleotides bound to the ATPase dimer did not reveal large conformational changes of the overall MutS structure based on RMSD and clustering analysis [27]. A similar conclusion was found for Thermus aquaticus MutS in a recent study [44], although different nucleotides bound to the ATPase domain were not examined. Taken together, this information has suggested that allosteric communication in this system likely takes place via subtle changes in local dynamics to achieve signaling in MutS rather than via conformational selection or induced conformational changes [3,44]. Average dynamical cross-correlation matrices (DCCM) were calculated from the MD simulations. Fig 1 compares the DCCMs between MutS simulations with different nucleotides. A comparison of the DCCMs after 50,100,150, and 200 ns generally shows little change after 50 ns (S1 Fig). This suggests that the correlations based on 200 ns trajectories are well converged consistent with a previous study [45]. In all cases, we found strong local correlation within domains but also weaker coupling between distant parts of the complex (Fig 1). Overall, different nucleotide bound states resulted in similar coupling patterns, but differences as a function of different nucleotide bound states can be discerned. For example, the positive MBD (I) -connector (II) A coupling is strongest in ADP-None, while the strongest positive MBD (I) -connector (II) B coupling is observed in None-ADP. Also in the case of ADP-None, the MBD and connector domains of subunit A are strongly negatively coupled with the lever and clamp domains of subunit B. The two clamp domains are positively coupled in cases of ATP-ADP and None-None, which are stronger than the others. The positive coupling between the two ATPase domains is strongest in ATP-ADP. Similar direct correlations between MutS domains have also been observed in other work based on MD simulations of Thermus aquaticus MutS [44]. However, while a direct correlation analysis suggests coupling, it does not provide complete information about the pathway (s) along which allosteric communication take place and it discounts the possibility of asynchronous communication via stochastic steps that would introduce a variable time delay between signal input and output along a given communication pathway. Next, we turned to dynamical network analysis to allow for a more dynamic model of allostery where direct correlations between distant sites are not required. In this approach, pathways connecting residue pairs along the shortest path with the highest pairwise local correlations based on the converged DCCMs from 200 ns MD sampling are determined. We focused our analysis on the functionally most relevant signal propagation between the MBD, ATPase, and clamp domains using specific key residues as anchor points (S2 Table). A first set of pathways was determined between MutS-F36, the key residue in direct contact at the mismatch site, and MutS-K620, the key residue involved in binding the phosphate tails of NTPs in the ATPase domain. A second set of pathways was focused on the communication between the two ATPase domains connecting MutS-K620 in the A and B chains and a third set of pathways was constructed from MutS-K620 to MutS-N497, which is the contact point of the clamp domain with the DNA opposite the mismatch site in the B subunit of MutS. Mapping of the resulting pathways onto the MutS structure is shown in Fig 2. The computational analysis suggests multiple major pathways that vary as a function of the nucleotides bound to the ATPase domain. Within each major pathway, there are ensembles of similarly optimal minor pathways. The variability in the pathways was greatest within a given structural domain, where strong coupling between many residues allowed for many alternate, equivalent routes. However, connections between domains were limited to certain key residue pairs (S3 Table) that presented bottlenecks in the respective pathways. When employing network analysis to group strongly coupled residues into communities (S2 Fig), these communication bottlenecks appear as critical inter-community edges that are hypothesized to correspond to switching points between major pathways when perturbed. Tables 1–3 quantify the features of the optimal pathways in terms of the number of steps (hops) required to traverse a path from the beginning to the end, a weight reflecting the degree of correlation along the optimal path, and the minimum pairwise correlation for any residue pair along the path. This analysis was carried out for each of the three sets of pathways as a function of different nucleotides bound in the ATPase domains. The algorithm employed here is designed to always find an optimal path connecting two given residues. In order to identify paths that are functionally relevant we focused on paths that stand out by having significantly lower weights than other paths while also requiring that the minimum correlation along the path was at least 0. 7. Our assumption is that even if a path has an overall low weight, it would not be an effective route of communication if it contained one or more links with poorly coupled residues. The overall premise of this study is the development of a dynamic allostery model for MutS since structural and previous simulation data suggest little conformational change as a function of nucleotides bound to the ATPase domains. Such a model implies the presence of communication paths between key structural elements (MBD, ATPase, and clamp domains) and the main result of this work is the identification of such paths in a nucleotide-dependent manner. Integrating previous biochemical data with such a dynamic allosteric model allowed us to arrive at the mechanism depicted in Fig 7 and described in more detail in the following: In the absence of DNA, the ADP-ADP state is presumed to be dominant [54]. The ADP-ADP state dissociates directly from DNA, while the binding of DNA induces the dissociation of one ADP molecule, more likely to be the one in B subunit. Therefore, the ADP-None state is presumed to be the mismatch scanning state [34,55]. Crystal structures of MutS were also obtained mostly in the ADP-None state [56–59]. Our results also support this idea. The MBDA strongly couples with the connector and ATPase domains in the ADP-None state via the proposed pathway ② that consists of a broad ensemble of individual paths with a few bottlenecks at domain boundaries. This communication is then proposed to result in exchange of ADP for ATP in the ATPaseA domain upon mismatch recognition [34,35]. In our model, the presence of ATP in the A site would abolish the communication between the MBD and ATPaseA domains because an optimal or suboptimal path via the connector domain is either absent altogether (ATP-ADP) or present with less favorable weights or low minimum pairwise correlations (ATP-none, ATP-ATP in path ①) that suggest inefficient coupling. At the same time, the ATPaseA -ATPaseB communication would engage the Walker B motif of chain B when ATP is present in the A site. We further hypothesize that ADP or ATP binding to the B site would follow, leading to the ATP-ADP state. Since the lifetime of the ATP-None state is believed to be short [54] this would occur quickly. Once the ATP-ADP state is reached, our model suggests that the ATPaseA domain connects to the lever instead of the connector. Because the connector primarily connects with the MBD domain while the lever domain provides a route to the clamp domain, we hypothesize that in the ATP-ADP state communication from the ATPaseA domain would be switched from the MBD domain to the clamp. At the same time, strong coupling between the ATPaseA -ATPaseB domains via the signature loop could disrupt the strong ATPaseB-clamp connection present in the ADP-None state and allow release of MutS from the mismatch site. The mechanism above postulates communication routes within the context of a dynamic allosteric mechanism that could be tested further experimentally, e. g. via mutations of pathway residues. Based on the dynamics sampled in the underlying simulations we are able to propose a structural basis for how pathways are switched in the presence of different nucleotides, however, the model is still lacking a clear mechanism for how ADP would be exchanged for ATP following mismatch recognition, for how the clamp domains would respond to signaling resulting from nucleotide exchange as proposed here, and what role MutL binding plays in this process. We speculate that the altered correlated dynamics induces subtle shifts in the overall conformational landscape which would favor ADP-ATP exchange and lead to clamp opening. To address this idea in more detail, significant additional simulations are required to probe the DNA binding process and clamp dynamics leading to the sliding clamp conformation in excess of the scope of the present work. Such a model could also conceptually integrate recent conformational landscape-based ideas of allostery with the communication-focused analysis presented here into a complete model for allostery in a large, complex system such as MutS where simpler concepts of conformational selection or induced-fit may not be able to adequately describe the allosteric mechanism. MutS can recognize a broad range of lesions, mismatches and IDLs, but MSHs have differentiated substrate specificities. MutSα (MSH2-MSH6) primarily recognizes mismatches and single base IDLs, whereas MutSβ (MSH2-MSH3) recognizes DNA loops composed of 2–13 bases. Based on previous simulations of MutSα and MutSβ with native and swapped substrates and no DNA at all [60], we also analyzed how different DNA substrates would shift the signaling pathways identified via our computational analysis. In the MSH complexes we identified pathways analogous to paths ①, ②, and ③ in MutS (see Fig 8 and details in S8 Fig and S9 Fig) suggesting that the proposed communication pathways may be preserved in the eukaryotic homologs. There appears to be strong communication from the MBD through the connector domain when MutSα and MutSβ are bound to their native substrates (a G: T mismatch and a four-nucleotide insertion loop (IDL-4L), respectively). However, swapping the substrate would abolish that path in favor of coupling along the lever domain. Again, cancer-associated mutations in MSH6 and MSH2 map onto the paths, some at critical edges connecting different domains (see S7 Fig and S8 Fig). Interestingly, communication between the MBD and ATPase domain of MSH3/MSH6 through the connector would also be present in the absence of DNA. These findings expand our allosteric model where effective communication between the MBD and ATPaseA domains (and subsequent initiation of repair) would depend on both the nature of the DNA substrate and the nucleotides bound in the ATPase domains.
Long-range signaling and allostery is a key mechanistic component of many large biomolecular complexes. Here, we present a detailed analysis of E. coli MutS and MSHs where several long-range signaling steps are essential for initiating DNA repair following mismatch recognition. Using dynamic network analysis based on extensive molecular dynamics simulations we developed a model consisting of a number of communication pathways that depend on strong local pairwise residue dynamical coupling where signaling would be expected to progress stochastically along those paths. In this model, different combinations of ATPase-bound nucleotides would result in switching between different pathways to implement the functional cycle of MutS without significant conformational rearrangements. A signaling mechanism based on pre-existing pathways that are switched on or off by different nucleotides and/or different DNA substrates is consistent with previous crystallographic and simulation studies that show surprisingly little structural variations in mismatch-bound MutS and homologs. The benefit of such a mechanism could be energetic economy, especially when considering the very long range over which the pathways appear to operate. Experimental validation of the hypotheses presented here could involve mutations of key residues, but it will also be interesting to see whether similar mechanisms are at play in other large enzymes. However, further computational studies will also be necessary to develop a more complete mechanistic understanding of how exactly signaling along the proposed pathways would promote and depend on nucleotide exchange and how it would lead to sliding clamp formation and complex formation with MutL.
MD simulations of the E. coli MutS protein bound to a G: T mismatch DNA (PDB ID: 1W7A) [33] were previously performed [27]. Each ATPase site may have three states: ATP, ADP or no nucleotide. All combinations of the three states in either of the two ATPase domains were simulated. They are denoted as ATP-None, None-ATP, ATP-ATP, ADP-None, None-ADP, ADP-ADP, ATP-ADP, ADP-ATP and None-None (S1 Table). In this notation, the first nucleotide is present in the ATPase site of the mismatch-binding moiety (subunit ‘A’) and the second one in the ATPase site of the non-mismatch-binding moiety (subunit ‘B’). Additional new simulations were carried out for five mutants of the E. coli MutS system to test the mechanistic hypotheses developed in this study: E169P (ADP-None), L240D (ADP-None), and Q626A (ATP-ADP) in chain A as well as L558R in either chain A or B (ATP-ADP). These simulations were simulated using the same protocol as the previous simulations of the wild-type systems (see below). MD simulations of human MutSα and MutSβ were started from the crystal structure 2O8B [22] and 3THX [26] (MutSα/G: T and MutSβ/IDL-4L) [61]. In MutSα and MutSβ structures, MSH6 and MSH3 are the mismatch-bound moieties (equivalent to the A subunit in MutS), while MSH2 interacts with the DNA non-specifically (equivalent to subunit B in MutS). Additional simulations were carried out for apo structures, where the DNA heteroduplex was removed (MutSα/Apo and MutSβ/Apo), and for MutSα and MutSβ where the respective substrates were swapped (MutSα/IDL-4L and MutSβ/G: T) [61]. In total, 15 previous simulations and five new simulations (S1 Table), each for at least 200 ns, were analyzed. All of the simulations were carried out with NAMD 2. 8 [62] using the CHARMM27 force field [63], the latest force field available at the time those simulations were initiated. All systems were solvated in explicit solvent using the TIP3P water model and sodium counterions to neutralize the systems. Simulations were carried out under periodic boundary conditions with the particle-mesh Ewald method [64] to calculate electrostatic interactions at constant temperature (300K) and constant pressure (1 atm) using a Langevin thermostat and barostat. The fully solvated systems consisted of about 165,000 atoms for the MutS systems and about 600,000 atoms for the larger MutSα and MutSβ systems. All of the systems remained overall stable with RMSD values of 3–5 Å for Cα atoms with respect to the initial experimental structures. Additional details of the system setup and simulation results are described in our previous papers [27,61]. VMD [65] was used to visualize and analyze simulations and generate structural figures. Allosteric networks within the proteins were identified using the NetworkView plugin of VMD [16,66]. The dynamic networks were constructed using data from our molecular dynamics simulations of the protein-DNA complexes described above, each sampled every 1 ps. For each molecular system, a network graph was constructed with two nodes for each nucleotide (at N1/N9 and Pα/P), while protein residues were represented with a single node at the Cα position. All of the conformations from a given trajectory were pooled to calculate the local-contact matrix. A contact between two nodes (excluding neighboring nodes) was defined as within a distance of 4. 5 Å for more than 75% of MD trajectories. The resulting contact matrix was then weighed by the correlation values of the two end nodes in the dynamical network as wij = −log (|Cij|), where Cij are the elements of the correlation matrix calculated as Cij=⟨Δri⋅Δrj⟩/⟨Δri2⟩1/2⟨Δrj2⟩1/2. The correlation matrices, also called dynamic cross-correlation matrices (DCCM), were calculated using the carma software [67]. The length of a path is the sum of the edge weights between the consecutive nodes along this path. And the optimal (shortest) paths between two nodes in the network were obtained by the Floyd-Warshall algorithm [68]. The number of optimal paths that cross one edge is termed as betweenness of the edge. Suboptimal paths within a certain limit (offset) between the two nodes were also determined in addition to the optimal path. The number of suboptimal paths shows the path degeneracy. Communities were calculated based on the dynamical network by the Girvan–Newman algorithm [69]. The nodes in one community are more compactly interconnected than other nodes. All pathways were determined between residues in the MBD (located within 10 Å of the mismatch site) and residues in the ATPase domain (located within 10 Å of a bound nucleotide) or between residues in the clamp domain (within 10 Å of DNA) and residues in the ATPase domain (within 10 Å of a bound nucleotide). The residue pairs with the shortest optimal path were finally selected as representative residues (S2 Table). Suboptimal paths between specific residue pairs were calculated with edge length offsets of 3,5 and 10 for the MBD-ATPase, ATPase-ATPase, and ATPase-clamp interactions, respectively.
We are proposing a new model for how long-range allosteric communication may be accomplished via switching of pre-existing pathway as a result of only minor structural perturbations. The systems studied here are the bacterial mismatch repair enzyme MutS and its eukaryotic homologs where we identified strong communication pathways connecting distant functional domains. The functionally-related exchange of nucleotides in a distant ATPase domain appears to be able to switch between those pathways providing a new paradigm for how long-range allostery may be accomplished in large biomolecular assemblies.
allosteric regulation
enzymes
dna clamps
enzymology
phosphatases
mutation
network analysis
mismatch repair
dna
dna structure
enzyme chemistry
computer and information sciences
proteins
enzyme regulation
biophysics
molecular biology
adenosine triphosphatase
physics
biochemistry
biochemical simulations
point mutation
nucleic acids
genetics
biology and life sciences
dna repair
physical sciences
computational biology
macromolecular structure analysis
biophysical simulations
Long-Range Signaling in MutS and MSH Homologs via Switching of Dynamic Communication Pathways