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W4310750147 abstract "Article Figures and data Abstract Editor's evaluation Introduction Results and discussion Methods Data availability References Decision letter Author response Article and author information Abstract We develop integrated co-evolution and dynamic coupling (ICDC) approach to identify, mutate, and assess distal sites to modulate function. We validate the approach first by analyzing the existing mutational fitness data of TEM-1 β-lactamase and show that allosteric positions co-evolved and dynamically coupled with the active site significantly modulate function. We further apply ICDC approach to identify positions and their mutations that can modulate binding affinity in a lectin, cyanovirin-N (CV-N), that selectively binds to dimannose, and predict binding energies of its variants through Adaptive BP-Dock. Computational and experimental analyses reveal that binding enhancing mutants identified by ICDC impact the dynamics of the binding pocket, and show that rigidification of the binding residues compensates for the entropic cost of binding. This work suggests a mechanism by which distal mutations modulate function through dynamic allostery and provides a blueprint to identify candidates for mutagenesis in order to optimize protein function. Editor's evaluation A computational approach is proposed to identify mutations in enzymes that might impact their interactions with substrates. For one enzyme, in particular, the predictions are validated through experiments, using multiple techniques. Taken together, these data lead to non-trivial conclusions in regard to the nature of allosteric effects, albeit it remains unclear whether these conclusions will apply more broadly when other enzymes are examined. https://doi.org/10.7554/eLife.67474.sa0 Decision letter eLife's review process Introduction The evolutionary history of a protein comprises the ensemble of mutations acquired during the course of its evolutionary trajectory across different species, and contains valuable information on which residue positions contribute the most to a given protein’s 3D-fold and function based on their conservation (Campbell et al., 2016; Rivoire et al., 2016; Yang et al., 2016). Furthermore, the subset of positions that are co-evolved (i.e., correlated mutational sites) provide clues on specific, native-state interactions. Pairwise residue contacts inferred from co-evolved positions within a protein family can be used as distance restraints to accurately model 3D structures (de Juan et al., 2013; Hopf et al., 2019; Kamisetty et al., 2013; Kim et al., 2014; Tripathi et al., 2015). Recent revolutionary successes in accurate predictions of 3D protein structures combine these methods with machine learning strategies, that is, deep learning (Jumper et al., 2021; Wang et al., 2016; Xu, 2019). Co-evolved positions also embed information on protein function, for example, revealing how factors such as binding affinity and specificity are modulated across evolutionary history and species (Rivoire et al., 2016; Salinas and Ranganathan, 2018; Torgeson et al., 2022). However, accessing, interpreting, and applying this information in a predictive manner is very challenging; mutations observed in the evolutionary history are often distal from the functional sites, implying that protein dynamics are responsible for their effects on function and that these sites act as distal allosteric regulators of function (Campitelli et al., 2020a; Modi et al., 2021a; Romero and Arnold, 2009; Salinas and Ranganathan, 2018; Tokuriki et al., 2012; Torgeson et al., 2022; Wei et al., 2016). Molecular dynamics (MD) simulations can capture protein dynamics and reveal the impact of distal mutations on function (Bowman and Geissler, 2012; Campbell et al., 2016; Campitelli et al., 2020a; Jiménez-Osés et al., 2014; Kolbaba-Kartchner et al., 2021; Modi et al., 2021a; Yang et al., 2016). However, the computational cost of MD simulations of sufficient length can be prohibitively high; further, it’s often far from straightforward to forge a clear connection to function. To bridge this gap, we developed a framework to quickly evaluate MD trajectories and identify the sensitivity of a given position to mutation based on its intrinsic flexibility, which we assess using our dynamic flexibility index (DFI) metric, and on its dynamic coupling with functionally critical positions assessed by dynamic coupling index (DCI) (Campitelli et al., 2018; Gerek and Ozkan, 2011; Kumar et al., 2015b; Larrimore et al., 2017). DFI measures the resilience of a position by computing the total fluctuation response and thus captures the flexibility/rigidity of a given position. Applying DFI to several systems, we showed that rigid positions such as hinge sites contribute the most to equilibrium dynamics, and that mutations at hinge sites significantly impact function regardless of the distance from active sites (Kim et al., 2015; Kolbaba-Kartchner et al., 2021; Modi et al., 2021b, Modi et al., 2018; Modi and Ozkan, 2018; Zou et al., 2021; Zou et al., 2015). DCI measures the dynamic coupling between residue pairs and thus identifies positions most strongly coupled to active/binding sites; these positions point to possible allosteric regulation sites important for modulating function in adaptation and evolution (Butler et al., 2015; Modi et al., 2021a, Campitelli et al., 2021; Kuriyan and Eisenberg, 2007; Lu and Liang, 2009; Modi and Ozkan, 2018; Ose et al., 2020; Risso et al., 2018; Wodak et al., 2019). In this paper, we present integrated co-evolution and dynamic coupling (ICDC) approach to identify distal allosteric sites, and to assess and predict the effects of mutations on these sites on function. We propose a system to classify residue positions in a binary fashion based on co-evolution (co-evolved, 1 or not, 0) and dynamic coupling by DFI and DCI (dynamically coupled 1, or not, 0) with the functionally critical sites. This classification captures the complementarity of dynamics-based and sequence-based methods. We hypothesize that positions belonging to category (1,1), that is, positions both co-evolved and dynamically coupled with the functional sites, will have the largest effect on function. We validate our hypothesis first by analyzing the existing mutational fitness data for TEM-1 β-lactamase, available for every position of the protein (Stiffler et al., 2015). In agreement with our hypothesis, we find that mutations on category (1,1) positions significantly modulate the function. A large fraction of mutations enhancing enzymatic activity correspond to category (1,1) irrespective of distance from the active site. Second, we apply our ICDC approach to blindly predict and experimentally validate mutations that allosterically modulate dimannose binding in a natural lectin, cyanovirin-N (CV-N). CV-N binds dimannose with nanomolar affinity and remarkable specificity (Barrientos et al., 2003; Botos and Wlodawer, 2005; Botos and Wlodawer, 2003; Mori and Boyd, 2001; O’Keefe et al., 2003). It is part of the CV-N family, found in a wide range of organisms including cyanobacterium, ascomycetous fungi, and fern (Koharudin et al., 2008; Koharudin and Gronenborn, 2013; Patsalo et al., 2011; Percudani et al., 2005; Qi et al., 2009). While the 3D folds is remarkably conserved in all experimentally characterized members, the affinity and specificity for different glycans and, in particular, to dimannose varies significantly (Koharudin et al., 2009; Koharudin et al., 2008; Matei et al., 2016; Woodrum et al., 2013). To design CV-N variants with improved binding affinities for dimannose based on distal allosteric coupling, we binned each position in one of the four categories based on computed DFI, DCI, and co-evolution rates. We explored mutations at these sites based on frequency in the sequence alignment. After obtaining the mutant models through MD simulations, we assessed the impact of each naturally observed mutation on binding affinity by docking dimannose to the mutant models via Adaptive BP-Dock (Bolia et al., 2014a; Bolia et al., 2014b; Bolia and Ozkan, 2016). We chose position I34, which belongs to category (1,1) and is 16 Å away from the binding pocket, for experimental validation. We found that mutations I34K/L/Y had a diverse effect on glycan binding, either improving by twofold or abolishing completely. Through experimental and MD studies we show that the observed improvement in binding affinity is due to changes in the dynamics of residues in the binding pocket; mutation I34Y leads to rigidification of binding sites, thus compensating the entropic cost of binding (Breiten et al., 2013; Chodera and Mobley, 2013; Cornish-Bowden, 2002; Fox et al., 2018). Mutations at an additional position (A71T/S) from category (1,1) showed evidence of the same allosteric mechanism governing the modulation of binding dynamics. Overall, this study provides not only a new approach to identify distal sites whose mutations modulate binding affinity, but also sheds light into mechanistic insights on how distal mutations modulate binding affinity through dynamics allostery. Results and discussion Combining long-range dynamic coupling analysis with co-evolution allows to identify distal sites that contribute to functional activity With our ICDC approach, we aim to explore the role of dynamics versus evolutionary coupling (EC) as well as the role of rigidity versus flexibility in allosterically modulating active/binding site dynamics. To this extent, we created four unique categories that classify residue positions based on residue DFI score, DCI score, and co-evolutionary score: category (1,1) is dynamically and co-evolutionarily coupled rigid sites (exhibiting %DFI values 0.2 or lower, showing 0.7 or higher %DCI with the binding site, and showing 0.6 or higher co-evolution scores with the binding site); category (1,0) is dynamically coupled but co-evolutionarily not coupled sites; category (0,1) is dynamically not coupled but co-evolutionarily coupled sites; category (0,0) is dynamically not coupled, and co-evolutionarily not coupled flexible sites (exhibiting %DFI values 0.7 or higher) (Supplementary file 1 and Supplementary file 2; ); importantly, this classification is based on two independent statistical approaches thus compensate the noise of individual approaches. Based on our evolutionary analysis (Campitelli et al., 2020a; Modi et al., 2021b; Modi and Ozkan, 2018), we hypothesize that category (1,1) would impact protein activity or binding affinity the most. To test our hypothesis, we first analyzed the deep mutational scanning data available for the TEM-1 β-lactamase, correlating changes in ampicillin degradation activity (e.g., MIC values) with mutations to all possible amino acids at each position (Stiffler et al., 2015). The experimental results showed that amino acid substitutions at the catalytic site residues of TEM-1 negatively impacted activity. Mutations at other positions also affected activity; while most mutations were deleterious, surprisingly, others resulted in increased activity. The impact of mutations on dynamics and function of TEM-1 have been heavily explored but the distal mutational effects are still poorly understood (Kolbaba-Kartchner et al., 2021; Modi et al., 2021b; Modi and Ozkan, 2018; Salverda et al., 2010; Schneider et al., 2021; Stiffler et al., 2015; Thomas et al., 2010; Zimmerman et al., 2017; Zou et al., 2015). We applied our approach by obtaining DFI, DCI, and co-evolution scores for every position of TEM-1 and binning residue positions into each ICDC category (Supplementary file 1 and Supplementary file 5). We constructed fitness distributions for each category using the experimentally measured single mutant relative fitness values for all mutations per position provided in the dataset (Figure 1). Figure 1 Download asset Open asset Integrated co-evolution and dynamic coupling (ICDC) categories based on the dynamics and co-evolutionary analyses applied on TEM-1 β-lactamase. (A) The distributions in the form of violin plots are obtained for each ICDC category using all available experimental mutational data (Stiffler et al., 2015). (B) Violin plots showing the fitness values for amino acid substitutions observed in the natural sequences. (C) The category (1,1) positions are mapped on 3D structure. The catalytic site residues are shown in dark gray whereas category (1,1) positions are shown in magenta color. The function altering category (1,1) positions are widely distributed over the 3D structure. We found that category (1,1) positions show the highest impact, both significantly enhancing and reducing ampicillin degradation by TEM-1 (Figure 1A&C). In addition, category (0,0) residue mutations (i.e., the exact opposite of category (1,1)) lie within the neutral-like activity range defined by Stiffler et al., 2015, suggesting that mutations on positions that neither co-evolve nor dynamically couple to active site do not affect the function significantly. Category (1,0) residues enhance activity more than those in the neutral category (0,0). Mutations in category (0,1) positions also modulate function in both positive and negative direction, albeit not as strongly as those in category (1,1). However, mutations that negatively impact activity are conspicuously under-represented in the multiple sequence alignment (MSA) of native sequences (Figure 1B), particularly in category (1,1). This finding implies nature mostly allows mutations that don’t compromise fold and function: Negative selection (i.e., elimination of amino acid types that are detrimental to the folding) is a major force in shaping the mutational landscape (Jana et al., 2014; Modi et al., 2021a; Morcos, 2020; Morcos et al., 2014; Morcos et al., 2013). Thus, the use of conservation information from MSA is a useful tool in eliminating deleterious amino acid substitutions in protein design. Our ICDC selection criteria effectively identifies residue positions and their amino acid substitutions that could fine-tune function without leading to a functional loss; and category (1,1) residues have the largest impact on function irrespective of their distance from active site (Figure 1C). Application of ICDC approach to modulate CV-N binding affinity through distal mutations CV-N is a small (11 kDa) natural lectin isolated from cyanobacterium Nostoc ellipsosporum which comprises two quasi-symmetric domains, A (residues 1–38/90–101) and B (residues 39–89 respectively), that are connected to each other by a short helical linker. Despite almost having identical structures, the domains show relatively low sequence homology (28% sequence identity and 52% similarity). Functionally, they both bind dimannose, yet the affinity is quite different, with domain B having tighter binding affinity (Kd = 15.3 µM), and domain A showing weak affinity (Kd = 400 µM) (Balzarini, 2007; Bolmstedt et al., 2001; Li et al., 2015). To simplify our analyses, we used a designed CV-N variant, P51G-m4, that contains a single high-affinity dimannose binding site (domain B), folds exclusively as a monomer in physiological conditions, and is more stable to thermal denaturation than wild type (Fromme et al., 2008; Fromme et al., 2007). The binding pocket of domain B of CV-N has been subjected to intense scrutiny to glean information on the origin of its binding specificity for dimannose (Bewley, 2001; Bolia et al., 2014b; Botos and Wlodawer, 2003; Li et al., 2015; Vorontsov and Miyashita, 2009). Previous mutational studies on the binding pocket residues have shown their importance in modulating interaction with dimannose (Barrientos et al., 2006; Bolia et al., 2014b; Chang and Bewley, 2002; Matei et al., 2008). All known substitutions of the binding residues led to decreased binding affinity for dimannose on domain B (Bolia et al., 2014b; Fujimoto and Green, 2012; Kelley et al., 2002; Matei et al., 2011; Ramadugu et al., 2014). Evolutionary analyses shows that the majority of the binding site residues are conserved in CV-N glycan interactions, suggesting that affinity is already optimized at the binding site (Koharudin et al., 2008; Percudani et al., 2005). We hypothesized that amino acid substitutions at distal positions could enhance the dimannose affinity of CV-N by rigidification of the binding site and applied our ICDC approach to CV-N to identify positions in each category (Supplementary file 2). We generated models of CV-N variants in each ICDC category by mutating these positions to amino acid types observed in the MSA of CV-N family members, choosing the subset of sequences that have binding sites with identical or similar amino acid composition to P51G-m4 CV-N. As discussed above, this approach allows us to identify amino acid substitutions with the least impact on fold. All the substitutions identified (104 variants in total) were modeled using the crystal structure of P51G-m4 CV-N (Fromme et al., 2008) and subjected to MD simulations (Abraham et al., 2015; Van Der Spoel et al., 2005). The best conformation sampled for each variant obtained from equilibrated production trajectories was used as a model for dimannose docking analysis. We evaluated the variants using Adaptive BP-Dock (Bolia and Ozkan, 2016), a computational docking tool that incorporates both ligand and receptor flexibility to accurately sample binding-induced conformations, and ranks them using X-scores binding energy units (XEUs) (Figure 4—figure supplement 1). In previous work on CV-N this method yielded good correlations with experimentally measured binding affinities (Kd), and established –6.0 XEU as a good threshold to differentiate variants that bind dimannose from ‘non-binders’ (Bolia et al., 2014b; Li et al., 2015; Woodrum et al., 2013). Here, we applied Adaptive BP-Dock initially on wild-type CV-N and its variants, P51G-m4 and mutDB (a mutant in which binding by domain B has been obliterated) and the results recapitulate the success of previous studies (Supplementary file 3). This result shows that Adaptive BP-Dock can correctly assess the dimannose binding of CV-N and its variants, thus, we applied it on new P51G-m4 CV-N variants to predict the impact of mutations on dimannose binding. Figure 2 shows the distribution of changes in predicted binding energy scores relative to the P51G-m4 energy scores for mutations belonging to each binary category: a positive change in binding score represents an unfavorable effect on binding, and, conversely, a negative change in the score indicates an enhancement in binding. Figure 2 Download asset Open asset Predicted binding energies for each integrated co-evolution and dynamic coupling (ICDC) category. Mutations in category (1,1) positions comprise the highest number of binding energy enhancing mutations as well as deleterious mutations. Mutations in category (0,0) positions are mostly near neutral (category (1,1) and (0,0) p value <0.3). The substitutions on positions in category (1,1) (Figure 2) yield a wide range of change in binding energy scores: the tail of the distribution on the positive side reaches nearly a binding score change of 2.0 XEUs and on the negative site values below –0.5 XEUs. Strikingly, the positions in category (1,1) yield the most binding enhancing energy scores compared to all other categories, mirroring TEM-1 results. Additionally, the substitutions applied in category (1,0) also result in more favorable binding energy scores for dimannose. Mutations in both category (1,1) and (1,0) present favorable binding energy scores. However, the number of mutations predicted to be enhancing binding in category (1,1) is more than those in category (1,0) (26% of category (1,1) compared to 14% of category (1,0)). Interestingly, the mutations in category (1,0) that disrupt the binding energy scores is not as strong as category (1,1), but similar to category (0,1) and (0,0). The observed mostly neutral behavior with category (0,0) agrees with the same trend obtained with TEM-1 analyses. Overall, the distribution of computational binding scores of dimannose binding to CV-N in each category aligns with the distribution of experimentally characterized TEM-1 fitness results of the same category. However, there are some discrepancies, for example, there are beneficial mutations in category (0,1) in TEM-1, but we don’t observe the same trend in CV-N. This is due to the initial challenge faced in constructing the MSA of CV-N homologous proteins. There is limited sequence information, and most of the proteins in the CV-N family exhibits binding specificity to a different glycan (Fujimoto and Green, 2012; Koharudin et al., 2009). In contrast, β-lactamase family proteins exhibit highest activity toward penicillin, and they have been subjected to strong natural selection leading to conservation in both fold and function (Salverda et al., 2010; Zou et al., 2021). Hence, the less noise in evolutionary analysis in case of β-lactamase family of proteins allows us to correctly filter deleterious type of substitutions based on the MSA. Regardless, however, in both cases, as hypothesized, substitutions on category (1,1) residues impact the function most. To further investigate the mechanism of functional modulation of category (1,1) mutations, we chose the position with highest binding enhancing docking scores, I34, from category (1,1). I34 exhibits %DFI values lower than 0.2 (Figure 3A), is at least 16 Å away from binding residues (distal), dynamically coupled (Figure 3B) and co-evolved with the binding pocket (Supplementary file 2 and Supplementary file 6). Moreover, docking scores of I34 variants suggest that the mutations can modulate binding in a wide range: I34Y variant leads to an increase in binding affinity (beneficial), I34K decreases the binding affinity (deleterious), and I34L yields no change (neutral) (Table 1). Figure 3 Download asset Open asset DFI and DCI analyses on CV-N. (A) Dynamic flexibility index (DFI) profile mapped onto cyanovirin-N (CV-N) structure: red corresponds to high DFI (very flexibile sites), and blue to low DFI values (rigid sites). Position I34 (low DFI score) is highlighted. (B) Dynamic coupling index (DCI) profile projected on CV-N structure with green corresponding to sites exhibiting high coupling with binding site residues. Table 1 Predicted binding affinities of domain B, experimental ITC data, and chemical denaturation experiments for P51G-m4 and its I34 variants. ProteinPredictedbindingscore(X-score energy unit)ITC dimannoseKd (μM)ITC dimannoseΔH (kcal/mol)ITC dimannoseTΔS (kcal/mol) (T=298 K)ITC dimannoseΔG (kcal/mol)∆GH2O (kcal/mol)Cm (M)P51G-m4–6.62117±3–12.3±0.3–7.00±0.3–5.30±0.33.01±0.0471.46±0.019P51G-m4-I34K–5.85No bindingNo bindingNo bindingNo binding2.40±0.1240.68±0.015P51G-m4-I34L–6.19148±2–9.60±0.1–4.40±0.1–5.20±0.12.95±0.0771.39±0.009P51G-m4-I34Y–6.7564±5–4.35±0.11.32±0.2–5.67±0.22.91±0.1571.13±0.017 To verify the predictions of I34 variants, we first assessed the folding and thermal stability of these mutants by circular dichroism (CD) spectroscopy. Far-UV CD spectroscopy showed that all mutants are well folded and adopt a fold similar to the parent protein, characterized by spectra with a single negative band centered at 216 nm. We determined the stability of the mutants by CD monitored thermal denaturation; the thermal denaturation curves were analyzed to obtain apparent melting temperature (Tm) values. We found that the conservative mutation I34L is as stable as P51G-m4, with apparent Tm of 57.8°C and 58°C, respectively. In contrast, I34Y and I34K were less thermostable than P51G-m4 as shown by apparent Tm values of 54.7°C and 47°C, respectively. Not surprisingly, substituting a hydrophobic residue with a basic aliphatic amino acid (lysine) has a large destabilizing effect, while aromatic and polar tyrosine is better tolerated. The trend of thermostability is P51G-m4~I34 L> I34 Y> I34 K (Figure 4—figure supplement 2). Chemical denaturation experiments were used to extract thermodynamic values, after ensuring complete equilibration at each concentration of guanidinium hydrochloride by incubating the samples for 72 hr (Patsalo et al., 2011). The ∆GH20 values and Cm values of P51G-m4, I34L, I34Y, and I34K are found as 3.0, 2.94, 2.91, and 2.38 kcal/mol and of 1.45, 1.39, 1.13, and 0.68 M respectively (Table 1). The results align with the thermal denaturation results: P51G-m4 is the most stable to denaturant, followed by I34L, I34Y, and I34K (Figure 4—figure supplement 3). Next, we evaluated the impact of the mutations on the dimannose binding affinity by isothermal titration calorimetry (ITC) (Figure 4—figure supplement 4); data were analyzed to extract Kd values listed in Table 1. We found that I34Y binds dimannose with tightest affinity (Kd: 64 µM) of all the mutants tested, a twofold improvement over P51G-m4 (Kd: 117 µM). Binding by I34L is slightly weaker with a Kd of 148 µM. No binding was observed for I34K in these conditions. Thermodynamic values extracted from ITC experiments (Table 1), suggesting that entropy changes play an important role in the observed changes in binding affinity: surprisingly, entropy is positive for I34Y, indicating an increase in disorder upon binding. To glean more information on the mode of binding by I34Y, we determined the X-ray structure of the unbound and dimannose-bound form and compared it with the template protein P51G-m4. The fold is highly conserved (Figure 4) as shown by main chain RMSD of 0.16 and 0.20 Å with bound and unbound I34Y, respectively, and tyrosine is well tolerated at position I34. The binding pocket region is also structurally conserved compared to P51G-m4. Analysis of the polar contacts between dimannose and P51G-m4 and I34Y (Figure 4B) shows an identical number of hydrogen bonds (11) with the ligand, indicating a conserved binding pose. We compared the docked pose of I34Y acquired from Adaptive BP-Dock with the bound X-ray structure. The ligand shows an RMSD value of 0.75 Å (Figure 4—figure supplement 5). These observations suggest that the increase in binding affinity of I34Y toward dimannose might be mediated by equilibrium dynamics, which are not captured by the crystal structure. This hypothesis is supported by the changes in entropy compensation measured experimentally (ITC) in dimannose binding by P51G-m4 (negative TΔS) and I34Y (positive TΔS). Figure 4 with 5 supplements see all Download asset Open asset The comparison of the crystal structures of P51G-m4 and I34Y. (A) The crystal structures of I34Y (bound in magenta and unbound in cyan) and its template protein P51G-m4 (green) are superimposed. (B) Overlay of bound structures of I34Y (magenta) and P51G-m4 (gray) (RMSD 0.15 Å); dashed lines depict polar interactions with dimannose. Molecular mechanism governing the binding dynamics in I34 variants It is interesting to observe that a distal site can modulate binding affinity to a wide range based on amino acid substitutions. This finding has also been observed for allosterically regulated enzymes such as LacI, for which different amino acid substitutions on non-conserved sites lead to gradual changes in function, acting like a rheostatic switch to modulate function through conformational dynamics (Campitelli et al., 2021; Campitelli et al., 2020b; Meinhardt et al., 2013; Miller et al., 2017; Swint-Kruse et al., 1998). To gather atomic level detail on how the substitutions on I34 dynamically modulate the binding affinity, we employed MD simulations in both bound and unbound forms (see Methods for details of the simulations). The unbound trajectories were analyzed for acquiring binding pocket hydrogen bond distances and pocket volume. Later, to learn about the ligand-induced conformational dynamic changes, the bound trajectories were utilized to estimate computational binding free energies (Deng and Roux, 2009; Okazaki et al., 2006). Previous computational work in our lab had linked binding affinity in the CV-N family to the accessibility of the binding pocket: A hydrogen bond between the amide hydrogen of N42 and carbonyl oxygen of N53 forms a closed pocket, hindering glycan accessibility, whereas the loss of this hydrogen bond leads to an open pocket (Li et al., 2015). Using the formation of this hydrogen bond in the trajectories of unbound WT and I34Y as metric for assessing open and closed conformations, we found that I34Y variant samples the open binding pocket more often than P51G-m4 (Figure 5—figure supplement 1). Another compelling evidence differentiating I34 variants from P51G-m4 is the change in their binding pocket volumes estimated by POVME pocket volume calculation tool (Wagner et al., 2017). The calculated pocket volumes for I34Y, I34K, and P51G-m4 were converted into frequencies to obtain probability distributions (Figure 5A), revealing that I34Y variant samples a more compact pocket volume compared to P51G-m4. If the pocket is too small or too large, dimannose cannot maximize its interaction with the protein, and a compact conformation enables dimannose to easily make the necessary hydrogen bond interactions with the protein. This optimum pocket volume sampled by I34Y may also explain the different binding energetics observed by ITC, in which a positive entropy change upon binding compensates for the loss in enthalpy compared to P51G-m4 (Table 1; Breiten et al., 2013; Cornish-Bowden, 2002). Pocket volume analysis reveals a larger value for I34K compared to P51G-m4, suggesting that this mutant cannot accommodate the necessary interactions with the dimannose resulting in loss of binding. We applied the same pocket volume calculation to the X-ray structures of P51G-m4 and I34Y variant, and we found volumes of 141 and 114 Å3 for P51G-m4 a" @default.
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W4310750147 title "Author response: Design of novel cyanovirin-N variants by modulation of binding dynamics through distal mutations" @default.
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