Figure 4 - uploaded by Bharat Lakhani
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Heat map of the covariance matrix for fluctuations in MD calculated motional displacements <Dr ij >. The indices i and j run over amino acid residues in PDZ3. The regions in blue correspond to neighboring inter-residue contacts, based on an interatomic distance cutoff <10 Å.
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The idea of protein “sectors” posits that sparse subsets of amino acid residues form cooperative networks that are key elements of protein stability, ligand binding, and allosterism. To date, protein sectors have been calculated by the statistical coupling analysis (SCA) method of Ranganathan and co-workers via the spectral analysis of conservation...
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Context 1
... heat map for MD-calculated motional covariance matrices in PDZ3 is shown in Figure 4. This matrix is the essential data structure submitted for spectral analysis. ...
Context 2
... matrix is the essential data structure submitted for spectral analysis. Correlations that arise from residues adjacent in the backbone of PDZ3 (blue regions of Figure 4) are expected de facto, and are not included in the subsequent analysis. Spectral analysis of the MD-calculated motional covariance matrix resulted in an MD sector of 21 residue positions. ...
Citations
... 30 SCA has been instrumental in identifying networks of coevolved amino acids in proteins, such as in the MutS DNA mismatch repair protein, explaining the allosteric regulation and protein dynamics. [30][31][32] This method allows for the quantitative examination of the long-term correlated evolution of amino acids within protein families, highlighting the statistical signature of functional constraints arising from conserved communication between positions. 33 While SCA has previously been used to successfully identify allosteric networks within a variety of proteins, 34-39 the physicochemical interactions that enable communication through these allosteric networks and how evolutionary constraints are defined by these interactions are still poorly understood. ...
... One study did find a strong overlap between sectors identified by a decomposition of a covariance matrix of structural dynamics and the sectors identified by SCA. 32 However, this study was limited to a PDZ domain, which primarily serve as anchoring domains and do not have enzymatic activity. Therefore, it is unclear if the same relationship between SCA sectors and dynamic networks would be present in enzymes such as DHFR. ...
The role of dynamics in enzymatic function is a highly debated topic. Dihydrofolate reductase (DHFR), due to its universality and the depth with which it has been studied, is a model system in this debate. Myriad previous works have identified networks of residues in positions near to and remote from the active site that are involved in dynamics and others that are important for catalysis. For example, specific mutations on the Met20 loop in E. coli DHFR (N23PP/S148A) are known to disrupt millisecond-timescale motions and reduce catalytic activity. However, how and if networks of dynamically coupled residues influence the evolution of DHFR is still an unanswered question. In this study, we first identify, by statistical coupling analysis and molecular dynamic simulations, a network of coevolving residues, which possess increased correlated motions. We then go on to show that allosteric communication in this network is selectively knocked down in N23PP/S148A mutant E. coli DHFR. Finally, we identify two sites in the human DHFR sector which may accommodate the Met20 loop double proline mutation while preserving dynamics. These findings strongly implicate protein dynamics as a driving force for evolution.
... Distance covariance was used to measure the correlations of the joint independence of any two vectors. By computing the pairwise covariance between all residues over the simulation time, pairwise covariance was mapped for all residues as defined by Lakhani et al. 27 Molecular Visualization. Molecular dynamics simulations were animated for visual inspection using the visual molecular dynamics (VMD) software package. ...
Allosteric regulation of protein dynamics infers a long-range deliberate propagation of information via micro- and macroscale interactions. The Y220C structural mutant is one of the most frequent cancerous p53 mutants. The mutation is distally located from the DNA-binding site of the p53 DNA-binding domain yet causes changes in DNA recognition. This system presents a unique opportunity to examine the allosteric control of mutated proteins under a drug design paradigm. We focus on the key case study of p53 Y220C mutation restoration by a series of new compounds suggested to have Y220C reactivation properties in comparison to our previous findings on the restorative potential of PK11000, a compound studied extensively for reactivation in vitro and in vivo. Previously, we implemented all-atom molecular dynamics (MD) simulations and our lab’s techniques of MD-Sectors and MD-Markov state models on the wild type, the Y220C mutant, and Y220C with PK11000 to characterize the effector’s restorative properties in terms of conformational dynamics and hydrogen bonding. In this study, we turn to probing the effects made by docking the battery of a new but less well-tested set of aminobenzothiazole derivative compounds reported by Baud et al., which show promise of Y220C rescue. We find that while complete and precise reconstitution of p53 WT molecular dynamics may not be observed as was the case with PK11000, dispersed local reconstitution of loop dynamics provides evidence of rescuing effects by aminobenzothiazole derivative N,2-dihydroxy-3,5-diiodo-4-(1H-pyrrol-1-yl)benzamide, Effector 22, like what we observed for PK11000. Generalizable insights into the mutation and allosteric reactivation of p53 by various effectors by reconstitution of WT dynamics observed in statistical conformational ensemble analysis and network inference are discussed, considering the development of allosteric drug design rooted in first principles.
... 10−15 PDZ domains are crucial for organizing a great diversity of signaling pathways 16,17 and implicated in clustering proteins into functional complexes at the plasma membrane 18 in addition to sorting, assembling, and anchoring multidomain interaction complexes. 19 Various studies on several PDZ domains, including normal mode analysis, 20 statistical coupling analysis, 12,21,22 NMR relaxation, 23 and site-directed mutational analysis, 14,24,25 have demonstrated PDZ's capacity for allosteric behavior. Structurally, PDZ domains are approximately 80− 100 amino acid residues long. ...
Allosteric signaling in proteins has been known for some half a century, yet how the signal traverses the protein remains an active area of research. Recently, the importance of electrostatics to achieve long-range signaling has become increasingly appreciated. Our laboratory has been working on developing network approaches to capture such interactions. In this study, we turn our attention to the well-studied allosteric model protein, PDZ. We study the allosteric dynamics on a per-residue basis in key constructs involving the PDZ domain, its allosteric effector, and its peptide ligand. We utilize molecular dynamics trajectories to create the networks for the constructs to explore the allosteric effect by plotting the heat kernel results onto axes defined by principal components. We introduce a new metric to quantitate the volume sampled by a residue in the latent space. We relate our findings to PDZ and the greater field of allostery.
... At this point, we have a complete sector, and analysis of the sector residues follows. 50 In contrast to slow and costly high-throughput methods such as screen-based assays, MD sectors can efficiently identify potentially functionally significant residues at a minimal cost. 52 We feel, however, that analyzing the MD sector as a network of residues would further validate the sector hypothesis and improve our ability to pinpoint allosteric pathways and pockets to target. ...
Design of allosteric regulators is an emergent field in the area of drug discovery holding promise for currently untreated diseases. Allosteric regulators bind to a protein in one location and affect a distant site. The ubiquitous presence of allosteric effectors in biology and the success of serendipitously identified allosteric compounds point to the potential they hold. Although the mechanism of transmission of an allosteric signal is not unequivocally determined, one hypothesis suggests that groups of evolutionarily covarying residues within a protein, termed sectors, are conduits. A long-term goal of our lab is to allosterically modulate the activity of proteins by binding small molecules at points of allosteric control. However, methods to consistently identify such points remain unclear. Sector residues on the surfaces of proteins are a promising source of allosteric targets. Recently, we introduced molecular dynamics (MD)-based sectors; MD sectors capitalize on covariance of motion, in place of evolutionary covariance. By focusing on motional covariance, MD sectors tap into the framework of statistical mechanics afforded by the Boltzmann ensemble of structural conformations comprising the underlying data set. We hypothesized that the method of MD sectors can be used to identify a cohesive network of motionally covarying residues capable of transmitting an allosteric signal in a protein. While our initial qualitative results showed promise for the method to predict sectors, that a network of cohesively covarying residues had been produced remained an untested assumption. In this work, we apply network theory to rigorously analyze MD sectors, allowing us to quantitatively assess the biologically relevant property of network cohesiveness of sectors in the context of the tumor suppressor protein, p53. We revised the methodology for assessing and improving MD sectors. Specifically, we introduce a metric to calculate the cohesive properties of the network. Our new approach separates residues into two categories: sector residues and non-sector residues. The relatedness within each respective group is computed with a distance metric. Cohesive sector networks are identified as those that have high relatedness among the sector residues which exceeds the relatedness of the residues to the non-sector residues in terms of the correlation of motions. Our major finding was that the revised means of obtaining sectors was more efficacious than previous iterations, as evidenced by the greater cohesion of the networks. These results are discussed in the context of the development of allosteric regulators of p53 in particular and the expected applicability of the method to the drug design field in general.
... In short, the method employs a spectral analysis technique to remove the highfrequency noise to reveal the concerted motion of the protein. 66 The extent to which the positional information covaries is quantitated in the vpica metric. The measurement is pairwise but integrated into a matrix approach to quantitate the covariance of all residues. ...
... In addition to the PDZ system in which the method was developed and validated, it has been applied to GPX4 previously. 66,67 H-Bond Analysis. Trajectories obtained from MD simulation were subjected to hydrogen-bond analysis in the standard implementation of the AMBER package's cpptraj suite of programs. ...
... To investigate how allosteric signaling may take place in p53 and its isoforms, we conducted MD-based sector analysis. 66 This method uses spectral decomposition to extract residues covarying in coordinate space over the course of the MD trajectory ( Figure 6). Such residues may form a cohesive network of residues capable of allosterically transmitting information within a macromolecule. ...
Allosteric regulation of protein activity pervades biology as the "second secret of life." We have been examining the allosteric regulation and mutant reactivation of the tumor suppressor protein p53. We have found that generalizing the definition of allosteric effector to include entire proteins and expanding the meaning of binding site to include the interface of a transcription factor with its DNA to be useful in understanding the modulation of protein activity. Here, we cast the variable regions of p53 isoforms as allosteric regulators of p53 interactions with its consensus DNA. We implemented molecular dynamics simulations and our lab's new techniques of molecular dynamics (MD) sectors and MD-Markov state models to investigate the effects of nine naturally occurring splice variant isoforms of p53. We find that all of the isoforms differ from wild type in their dynamic properties and how they interact with the DNA. We consider the implications of these findings on allostery and cancer treatment.
... SCA identifies functional residues from conservationweighted evolutionary covariance matrices obtained from multiple sequence alignments of homologous families of proteins. 13 The spectral method has been subsequently adapted by our lab to identify allosteric networks in MDcalculated covariance matrices based on two different modes: fluctuations of residue motional displacements, named MD-Sectors and our latest approach using nonbonded interaction energies and named MD-END (energetic network decomposition). While the MD-MSM approach introduces a methodology that can be used to assess the broad landscape of allosteric conformational change, MD-Sectors and the newly introduced MD-END reveal the nuanced signals at the molecular level. ...
... All-atom 1 microsecond MD simulations on the p53 DBD, residues 96−290, with explicit solvent were performed using the standard lab protocol 13,17 with the AMBER14.0 and 16.0 simulation packages and AMBERTOOLS14 suite. 18 FF14SB force fields for the protein 19 and the TIP3P potential for solvent water were used. ...
... Distance covariance was used to measure the correlations of the joint independence of any two vectors. By computing the pairwise covariance between all residues over the simulation time, pairwise covariance was mapped for all residues as defined by Lakhani et al. 13 MD-END and Pairwise Interaction Energy Network. Computing the pairwise interaction energy for every frame in the trajectory is very computationally costly. ...
The development of drugs to restore protein function has been a major advance facilitated by molecular medicine. Allosteric regulation, a phenomenon widely observed in nature, in which a molecule binds to control a distance active site, holds great promise for regulating proteins, yet how to rationally design such a molecule remains a mystery. Over the past few years, we and others have developed several techniques based on molecular dynamics (MD) simulations: MD-Markov state models to capture global conformational substates, and network theory approach utilizing the interaction energy within the protein to confer local allosteric control. We focus on the key case study of the p53 Y220C mutation restoration by PK11000, a compound experimentally shown to reactivate p53 native function in Y220C mutant present tumors. We gain insights into the mutation and allosteric reactivation of the protein, which we anticipate will be applicable to de novo design to engineer new compounds not only for this mutation, but in other macromolecular systems as well.
... The limited improvements achieved during these last rounds of evolution indicate that further fine-tuning would require many more mutations to reprogram the global dynamics encoded in the scaffold. Although this may be challenging for laboratory evolution, nature probably evolved similar networks as indicated by sectors of spatially proximal coevolving residues 38-40 with highly correlated dynamics 41 . Dynamical networks 22,[42][43][44][45][46] probably play a crucial role in enhancing preorganization, reducing non-productive conformations [16][17][18]22 and tuning protein flexibility for thermoadaptation 28,29,47 . ...
Activation heat capacity is emerging as a crucial factor in enzyme thermoadaptation, as shown by the non-Arrhenius behaviour of many natural enzymes. However, its physical origin and relationship to the evolution of catalytic activity remain uncertain. Here we show that directed evolution of a computationally designed Kemp eliminase reshapes protein dynamics, which gives rise to an activation heat capacity absent in the original design. These changes buttress transition-state stabilization. Extensive molecular dynamics simulations show that evolution results in the closure of solvent-exposed loops and a better packing of the active site. Remarkably, this gives rise to a correlated dynamical network that involves the transition state and large parts of the protein. This network tightens the transition-state ensemble, which induces a negative activation heat capacity and non-linearity in the activity–temperature dependence. Our results have implications for understanding enzyme evolution and suggest that selectively targeting the conformational dynamics of the transition-state ensemble by design and evolution will expedite the creation of novel enzymes.
... Therefore, AVM-B1b can affect rigid-ity, and therefore the stability and native interactions of the viral protease, with a greater propensity than that exhibited by AVM-B1a, as predicted by the PSN-ENM/NMA approaches (Table 4 and Fig. 6). Importantly, the SPECTRUS method based on domain decomposition does not consider the structural properties of the protein, but it allows the study of the variations in the clustering of protein residues in terms of dynamically correlated domain networks, based on the effective correlation times of the pair distance correlation functions [66], it has also been shown to be a useful approach to identify dynamic domains in complex proteins [67,68]. ...
The COVID-19 pandemic has accelerated the study of the potential of multi-target drugs (MTDs). The mixture of homologues called ivermectin (avermectin-B1a + avermectin-B1b) has been shown to be a MTD with potential antiviral activity against SARS-CoV-2 in vitro. However, there are few reports on the effect of each homologue on the flexibility and stiffness of proteins associated with COVID-19, described as ivermectin targets. We observed that each homologue was stably bound to identified proteinsthe proteins studied and were able to induce detectable changes with Elastic Network Models (ENMs). The perturbations induced by each homologue were characteristic of each compound and, in turn, were represented by a disruption of native intramolecular networks (interactions between residues). The homologues were able to slightly modify the conformation and stability of the connection points between the Cα atoms of the residues that make up the structural network of proteins (nodes), compared to free proteins. Each homologue was able to modified differently the distribution of quasi-rigid regions of the proteins, which could theoretically alter their biological activities. These results could provide a biophysical-computational view of the potential MTD mechanism that has been reported for ivermectin.
... The hierarchy of dICs for different SSQs are shown in Fig. 3CeF. Unlike for the PDZ domain [24], the sICs and dICs of SSQs hardly overlap (Fig. 3, Fig. S7). Furthermore, despite having homologous structures, TEAS and AaBOS (rmsd is 2.81) do not have identical dICs, i.e., the corresponding structures do not have structurally equivalent residues (Fig. 3D and E). ...
Sesquiterpene synthases catalyse cyclisation of farnesyl pyrophosphate to produce diverse sesquiterpenes. Despite utilising the same substrate and exhibiting significant sequence and structural homology, these enzymes form different products. Previous efforts were based on identifying the effect of divergent residues present at the catalytic binding pocket on the product specificity of these enzymes. However, the rationales deduced for the product specificity from these studies were not generic enough to be applicable to other phylogenetically distant members of this family. To address this problem, we have developed a novel approach combining sequence, structural and dynamical information of plant sesquiterpene synthases (SSQs) to predict product modulating residues (PMRs). We tested this approach on the SSQs with known PMRs and also on sesquisabinene synthase 1 (SaSQS1), a SSQ from Indian sandalwood. Our results show that the dynamical sectors of SSQs obtained from molecular dynamics simulation and their hydrophobicity and vicinity indices together provide leads for the identification of PMRs. The efficacy of the technique was tested on SaSQS1 using mutagenesis. To the best of our knowledge, this is a first technique of this kind which provides cues on PMRs of SSQs, with divergent phylogenetic relationship.
... While this may be challenging for laboratory evolution, Nature probably evolved similar networks to tailor activity, as indicated by phylogenetic analyses that revealed sectors of spatially proximal coevolving residues [32][33][34] . Notably, these sectors can also show correlated dynamics 35 , hinting at a catalytically relevant role of the underlying networks. Other allosteric effects apparently also rely on similar networks [36][37][38] , which may enhance preorganization, reduce non-productive conformations, and tune global scaffold flexibility for thermoadaptation [17][18][19][20]39 . ...
Activation heat capacity is emerging as a crucial factor in enzyme thermoadaptation, as shown by non-Arrhenius behaviour of many natural enzymes. However, its physical origin and relationship to evolution of catalytic activity remain uncertain. Here, we show that directed evolution of a computationally designed Kemp eliminase introduces dynamical changes that give rise to an activation heat capacity absent in the original design. Extensive molecular dynamics simulations show that evolution results in the closure of solvent exposed loops and better packing of the active site with transition state stabilising residues. Remarkably, these changes give rise to a correlated dynamical network involving the transition state and large parts of the protein. This network tightens the transition state ensemble, which induces an activation heat capacity and thereby nonlinearity in the temperature dependence. Our results have implications for understanding enzyme evolution (e.g. in explaining the role of distal mutations and evolutionary tuning of dynamical responses) and suggest that integrating dynamics with design and evolution will accelerate the development of efficient novel enzymes.
