Dynamically driven correlations in elastic net models reveal sequence of events and causality in proteins.

An explicit analytic solution is given for the Langevin equation applied to the Gaussian Network Model of a protein subjected to both a random and a deterministic periodic force. Synchronous and asynchronous components of time correlation functions are derived and an expression for phase differences in the time correlations of residue pairs is obtained. The synchronous component enables the determination of dynamic communities within the protein structure. The asynchronous component reveals causality, where the time correlation function between residues i and j differs depending on whether i is observed before j or vice versa, resulting in directional information flow. Driver and driven residues in the allosteric process of cyclophilin A and human NAD-dependent isocitrate dehydrogenase are determined by a perturbation-scanning technique. Factors affecting phase differences between fluctuations of residues, such as network topology, connectivity, and residue centrality, are identified. Within the constraints of the isotropic Gaussian Network Model, our results show that asynchronicity increases with viscosity and distance between residues, decreases with increasing connectivity, and decreases with increasing levels of eigenvector centrality.

Dynamic correlations: exact and approximate methods for mutual information.

MOTIVATION: Proteins are dynamic entities that undergo conformational changes critical for their functions. Understanding the communication pathways and information transfer within proteins is crucial for elucidating allosteric interactions in their mechanisms. This study utilizes mutual information (MI) analysis to probe dynamic allostery. Using two cases, Ubiquitin and PLpro, we have evaluated the accuracy and limitations of different approximations including the exact anisotropic and isotropic models, multivariate Gaussian model, isotropic Gaussian model, and the Gaussian Network Model (GNM) in revealing allosteric interactions. RESULTS: Our findings emphasize the required trajectory length for capturing accurate mutual information profiles. Long molecular dynamics trajectories, 1 ms for Ubiquitin and 100 µs for PLpro are used as benchmarks, assuming they represent the ground truth. Trajectory lengths of approximately 5 µs for Ubiquitin and 1 µs for PLpro marked the onset of convergence, while the multivariate Gaussian model accurately captured mutual information with trajectories of 5 ns for Ubiquitin and 350 ns for PLpro. However, the isotropic Gaussian model is less successful in representing the anisotropic nature of protein dynamics, particularly in the case of PLpro, highlighting its limitations. The GNM, however, provides reasonable approximations of long-range information exchange as a minimalist network model based on a single crystal structure. Overall, the optimum trajectory lengths for effective Gaussian approximations of long-time dynamic behavior depend on the inherent dynamics within the protein's topology. The GNM, by showcasing dynamics across relatively diverse time scales, can be used either as a standalone method or to gauge the adequacy of MD simulation lengths. AVAILABILITY AND IMPLEMENTATION: Mutual information codes are available at https://github.com/kemaldemirtas/prc-MI.git.

Synergy and anti-cooperativity in allostery: Molecular dynamics study of WT and oncogenic KRAS-RGL1.

This study focuses on investigating the effects of an oncogenic mutation (G12V) on the stability and interactions within the KRAS-RGL1 protein complex. The KRAS-RGL1 complex is of particular interest due to its relevance to KRAS-associated cancers and the potential for developing targeted drugs against the KRAS system. The stability of the complex and the allosteric effects of specific residues are examined to understand their roles as modulators of complex stability and function. Using molecular dynamics simulations, we calculate the mutual information, MI, between two neighboring residues at the interface of the KRAS-RGL1 complex, and employ the concept of interaction information, II, to measure the contribution of a third residue to the interaction between interface residue pairs. Negative II indicates synergy, where the presence of the third residue strengthens the interaction, while positive II suggests anti-cooperativity. Our findings reveal that MI serves as a dominant factor in determining the results, with the G12V mutation increasing the MI between interface residues, indicating enhanced correlations due to the formation of a more compact structure in the complex. Interestingly, although II plays a role in understanding three-body interactions and the impact of distant residues, it is not significant enough to outweigh the influence of MI in determining the overall stability of the complex. Nevertheless, II may nonetheless be a relevant factor to consider in future drug design efforts. This study provides valuable insights into the mechanisms of complex stability and function, highlighting the significance of three-body interactions and the impact of distant residues on the binding stability of the complex. Additionally, our findings demonstrate that constraining the fluctuations of a third residue consistently increases the stability of the G12V variant, making it challenging to weaken complex formation of the mutated species through allosteric manipulation. The novel perspective offered by this approach on protein dynamics, function, and allostery has potential implications for understanding and targeting other protein complexes involved in vital cellular processes. The results contribute to our understanding of the effects of oncogenic mutations on protein-protein interactions and provide a foundation for future therapeutic interventions in the context of KRAS-associated cancers and beyond.

5-Fluoro/(trifluoromethoxy)-2-indolinone derivatives with anti-interleukin-1 activity.

The pro-inflammatory cytokine interleukin-1 (IL-1) drives the pathogenesis of several inflammatory diseases. Recent studies have revealed that 2-indolinones can modulate cytokine responses. Therefore, we screened several 2-indolinone derivatives in preliminary studies to develop agents with anti-IL-1 activity. First, the putative efficacies and binding interactions of 2-indolinones were evaluated by docking studies. Second, previously synthesized 5-fluoro/(trifluoromethoxy)-1H-indole-2,3-dione 3-(4-phenylthiosemicarbazones) (compounds 47-69) which had the highest inhibitory effect in the screening were evaluated for inhibitory effects on the IL-1 receptor (IL-1R). Compounds 52 (IC50 = 0.09 µM) and 65 (IC50 = 0.07 µM) were selected as lead compounds for the subsequent synthesis of new derivatives. The novel 5-fluoro/(trifluoromethoxy)-1H-indole-2,3-dione 3-(4-phenylthiosemicarbazones) (compounds 70-116) were designed, synthesized, and in vitro studies were completed. The compounds 76, 78, 81, 91, 100, 105, and 107 tested showed nontoxic inhibitory effects on IL-1R-dependent responses in the range of 0.01-0.06 µM and stronger than the lead compounds 52 and 65. In vitro and in silico findings showed that compounds 78 (IC50 = 0.01 µM) and 81 (IC50 = 0.02 µM) had the strongest IL-1R inhibitory effects and the most favorable drug-like properties. Molecular modeling studies of the compounds 78 and 81 were carried out to determine the possible binding interactions at the active site of the IL-1R.

Fine tuning rigid body docking results using the Dreiding force field: A computational study of 36 known nanobody-protein complexes.

This paper aims to understand the binding strategies of a nanobody-protein pair by studying known complexes. Rigid body protein-ligand docking programs produce several complexes, called decoys, which are good candidates with high scores of shape complementarity, electrostatic interactions, desolvation, buried surface area, and Lennard-Jones potentials. However, the decoy that corresponds to the native structure is not known. We studied 36 nanobody-protein complexes from the single domain antibody database, sd-Ab DB, http://www.sdab-db.ca/. For each structure, a large number of decoys are generated using the Fast Fourier Transform algorithm of the software ZDOCK. The decoys were ranked according to their target protein-nanobody interaction energies, calculated by using the Dreiding Force Field, with rank 1 having the lowest interaction energy. Out of 36 protein data bank (PDB) structures, 25 true structures were predicted as rank 1. Eleven of the remaining structures required Ångstrom size rigid body translations of the nanobody relative to the protein to match the given PDB structure. After the translation, the Dreiding interaction (DI) energies of all complexes decreased and became rank 1. In one case, rigid body rotations as well as translations of the nanobody were required for matching the crystal structure. We used a Monte Carlo algorithm that randomly translates and rotates the nanobody of a decoy and calculates the DI energy. Results show that rigid body translations and the DI energy are sufficient for determining the correct binding location and pose of ZDOCK created decoys. A survey of the sd-Ab DB showed that each nanobody makes at least one salt bridge with its partner protein, indicating that salt bridge formation is an essential strategy in nanobody-protein recognition. Based on the analysis of the 36 crystal structures and evidence from existing literature, we propose a set of principles that could be used in the design of nanobodies.

Mutual information analysis of mutation, nonlinearity, and triple interactions in proteins.

Mutations are the cause of several diseases as well as the underlying force of evolution. A thorough understanding of their biophysical consequences is essential. We present a computational framework for evaluating different levels of mutual information (MI) and its dependence on mutation. We used molecular dynamics trajectories of the third PDZ domain and its different mutations. Nonlinear MI between all residue pairs are calculated by tensor Hermite polynomials up to the fifth order and compared with results from multivariate Gaussian distribution of joint probabilities. We show that MI is written as the sum of a Gaussian and a nonlinear component. Results for the PDZ domain show that the Gaussian term gives a sufficiently accurate representation of MI when compared with nonlinear terms up to the fifth order. Changes in MI between residue pairs show the characteristic patterns resulting from specific mutations. Emergence of new peaks in the MI versus residue index plots of mutated PDZ shows how mutation may change allosteric pathways. Triple correlations are characterized by evaluating MI between triplets of residues. We observed that certain triplets are strongly affected by mutation. Susceptibility of residues to perturbation is obtained by MI and discussed in terms of linear response theory.

Subsets of Slow Dynamic Modes Reveal Global Information Sources as Allosteric Sites.

Allostery is a key biological control mechanism, and dynamic information flow provides a perspective to describe allosteric interactions in causal relationships. Here, as a novel implementation of the Gaussian Network Model (GNM) based Transfer Entropy (TE) calculations, we show that the dissection of dynamic information into subsets of slow dynamic modes discloses different layers of multi-directional allosteric pathways inherent in a given protein structure. In these subsets of slow modes, the degree of collectivity (Col) in the information transfer of residues with their TE values (TECol score) identifies distinct residues as powerful effectors, global information sources; showing themselves with a high dynamic capacity to collectively disseminate information to others. As exemplified on aspartate transcarbamoylase (ATCase), Na+/K+-adenosine triphosphatase (Na+/K+-ATPase), and human transient receptor potential melastatin 2 (TRPM2) along with a dataset of 20 proteins, these specific residues are associated with known active and allosteric sites. These information source residues, which collectively control others and lead allosteric communication pathways, hint at plausible binding sites for structure-based rational drug design.

Prediction of allosteric communication pathways in proteins.

MOTIVATION: Allostery in proteins is an essential phenomenon in biological processes. In this article, we present a computational model to predict paths of maximum information transfer between active and allosteric sites. In this information theoretic study, we use mutual information as the measure of information transfer, where transition probability of information from one residue to its contacting neighbors is proportional to the magnitude of mutual information between the two residues. Starting from a given residue and using a Hidden Markov Model, we successively determine the neighboring residues that eventually lead to a path of optimum information transfer. The Gaussian approximation of mutual information between residue pairs is adopted. The limits of validity of this approximation are discussed in terms of a nonlinear theory of mutual information and its reduction to the Gaussian form. RESULTS: Predictions of the model are tested on six widely studied cases, CheY Bacterial Chemotaxis, B-cell Lymphoma extra-large (Bcl-xL), Human proline isomerase cyclophilin A (CypA), Dihydrofolate reductase (DHFR), HRas GTPase and Caspase-1. The communication transmission rendering the propagation of local fluctuations from the active sites throughout the structure in multiple paths correlate well with the known experimental data. Distinct paths originating from the active site may likely represent a multi functionality such as involving more than one allosteric site and/or pre-existence of some other functional states. Our model is computationally fast and simple and can give allosteric communication pathways, which are crucial for the understanding and control of protein functionality. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Targeting mitochondrial DNA polymerase gamma for selective inhibition of MLH1 deficient colon cancer growth.

Synthetic lethality in DNA repair pathways is an important strategy for the selective treatment of cancer cells without harming healthy cells and developing cancer-specific drugs. The synthetic lethal interaction between the mismatch repair (MMR) protein, MutL homolog 1 (MLH1), and the mitochondrial base excision repair protein, DNA polymerase γ (Pol γ) was used in this study for the selective treatment of MLH1 deficient cancers. Germline mutations in the MLH1 gene and aberrant MLH1 promoter methylation result in an increased risk of developing many cancers, including nonpolyposis colorectal and endometrial cancers. Because the inhibition of Pol γ in MLH1 deficient cancer cells provides the synthetic lethal selectivity, we conducted a comprehensive small molecule screening from various databases and chemical drug library molecules for novel Pol γ inhibitors that selectively kill MLH1 deficient cancer cells. We characterized these Pol γ inhibitor molecules in vitro and in vivo, and identified 3,3'-[(1,1'-Biphenyl)-4',4'-diyl)bis(azo)]bis[4-amino-1-naphthalenesulfonic acid] (congo red; CR; Zinc 03830554) as a high-affinity binder to the Pol γ protein and potent inhibitor of the Pol γ strand displacement and one-nucleotide incorporation DNA synthesis activities in vitro and in vivo. CR reduced the cell proliferation of MLH1 deficient HCT116 human colon cancer cells and suppressed HCT116 xenograft tumor growth whereas it did not affect the MLH1 proficient cell proliferation and xenograft tumor growth. CR caused mitochondrial dysfunction and cell death by inhibiting Pol γ activity and oxidative mtDNA damage repair, increasing the production of reactive oxygen species and oxidative mtDNA damage in MLH1 deficient cells. This study suggests that the Pol γ inhibitor, CR may be further evaluated for the MLH1 deficient cancers' therapy.

Information flow and allosteric communication in proteins.

Based on Schreiber's work on transfer entropy, a molecular theory of nonlinear information transfer between residue pairs in proteins is developed. The joint distribution function for residue fluctuations required by the theory is expressed in terms of tensor Hermite polynomials that conveniently separate harmonic and nonlinear contributions to information transfer. The harmonic part of information transfer is expressed as the difference between time dependent and independent mutual information. Third order nonlinearities are discussed in detail. The amount and speed of information transfer between residues, which are important for understanding allosteric activity in proteins, are discussed. Mutual information between two residues is commonly used for information transfer. While mutual information shows the maximum amount of information that may be transferred between two residues, it does not explain the actual amount of transfer nor the transfer rate of information. For this, dynamic equations of the system are needed. The solution of the Langevin equation and molecular dynamics trajectories are used in the present work for this purpose. Allosteric communication in human NAD-dependent isocitrate dehydrogenase is studied as an example. Calculations show that several paths contribute collectively to information transfer. Important residues on these paths are identified. Time resolved information transfer between these residues, their amplitudes, and transfer rates, which are in agreement with time resolved ultraviolet resonance Raman measurements in general, are estimated. Peak values of calculated information transfer, ∼0.01-0.04 bits, are about two orders of magnitude smaller than the information content of residues. They are comparable to mutual information values, however. Estimated transfer rates are in the order of 1-20 megabits per second, and sustained transfer during the activity time-span of proteins may be significant. Information transfer from third order contributions is one to two orders of magnitude smaller than the harmonic terms, showing that harmonic analysis is a good approximation to information transfer.

Gaussian network model revisited: effects of mutation and ligand binding on protein behavior.

The coarse-grained Gaussian network model (GNM), considers only the alpha carbons of the folded protein. Therefore it is not directly applicable to the study of mutation or ligand binding problems where atomic detail is required. This shortcoming is improved by including all atom pairs within the coordination shell of each other into the Kirchoff adjacency matrix. Counting all contacts rather than only alpha carbon contacts diminishes the magnitude of fluctuations in the system. But more importantly, it changes the graph-like connectivity structure, i.e., the Kirchoff adjacency matrix of the protein. This change depends on amino acid type which introduces amino acid specific and position specific information into the classical coarse-grained GNM which was originally modeled in analogy with the phantom network model of rubber elasticity. With this modification, it is now possible to explain the consequences of mutation and ligand binding on residue fluctuations, their pair-correlations and mutual information shared by each pair. We refer to the new model as 'all-atom GNM'. Using examples from published data we show that the all-atom GNM givesB-factors that are in better agreement with experiment, can explain effects of mutation on long range communication in PDZ domains and can predict effects of GDP and GTP binding on the dimerization of KRAS.

Binding Mechanism of Neutralizing Nanobodies Targeting SARS-CoV-2 Spike Glycoprotein.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) enters human cells upon binding of its spike (S) glycoproteins to ACE2 receptors. Several nanobodies neutralize SARS-CoV-2 infection by binding to the receptor-binding domain (RBD) of the S protein, but how their binding antagonizes S-ACE2 interactions is not well understood. Here, we identified interactions between the RBD and nanobodies H11-H4, H11-D4, and Ty1 by performing all-atom molecular dynamics simulations. H11-H4 and H11-D4 can bind to RBD without overlapping with ACE2. H11-H4, and to a lesser extent H11-D4, binding dislocates ACE2 from its binding site due to electrostatic repulsion. In comparison, Ty1 overlaps with ACE2 on RBD and has a similar binding strength to ACE2. Mutations in the Alpha variant of SARS-CoV-2 had a minor effect in RBD binding strengths of ACE2 and nanobodies, but reduced the ability of H11-H4 and H11-D4 to dislocate ACE2 from RBD. In comparison, the Beta variant weakened the RBD binding strengths of H11-H4 and H11-D4, which were less effective to dislocate ACE2 binding. Unexpectedly, mutations in Beta strengthened Ty1 binding to RBD, suggesting that this nanobody may be more effective to neutralize the Beta variant of SARS-CoV-2.

Synchronous and Asynchronous Response in Dynamically Perturbed Proteins.

We present a dynamic perturbation-response model of proteins based on the Gaussian Network Model, where a residue is perturbed periodically, and the dynamic response of other residues is determined. The model shows that periodic perturbation causes a synchronous response in phase with the perturbation and an asynchronous response that is out of phase. The asynchronous component results from the viscous effects of the solvent and other dispersive factors in the system. The model is based on the solution of the Langevin equation in the presence of solvent, noise, and perturbation. We introduce several novel ideas: The concept of storage and loss compliance of the protein and their dependence on structure and frequency; the amount of work lost and the residues that contribute significantly to the lost work; new dynamic correlations that result from perturbation; causality, that is, the response of j when i is perturbed is not equal to the response of i when j is perturbed. As examples, we study two systems, namely, bovine rhodopsin and the class of nanobodies. The general results obtained are (i) synchronous and asynchronous correlations depend strongly on the frequency of perturbation, their magnitude decreases with increasing frequency, (ii) time-delayed mean-squared fluctuations of residues have only synchronous components. Asynchronicity is present only in cross correlations, that is, correlations between different residues, (iii) perturbation of loop residues leads to a large dissipation of work, (iv) correlations satisfy the hypothesis of pre-existing pathways according to which information transfer by perturbation rides on already existing equilibrium correlations in the system, (v) dynamic perturbation can introduce a selective response in the system, where the perturbation of each residue excites different sets of responding residues, and (vi) it is possible to identify nondissipative residues whose perturbation does not lead to dissipation in the protein. Despite its simplicity, the model explains several features of allosteric manipulation.

Molecular dynamics simulations provide molecular insights into the role of HLA-B51 in Behçet's disease pathogenesis.

Behçet's disease is an inflammatory disorder of unknown etiology. Genetic tendency has an important role in its pathogenesis, and HLA-B51, a class I MHC antigen, has been recognized as the strongest susceptibility factor for Behçet's disease. Despite the confirmation of the association of HLA-B51 with Behçet's disease in different populations, its pathogenic mechanisms remain elusive. HLA-B51 differs in only two amino acids from HLA-B52, other split antigen of HLA-B5, which is not associated with Behçet's disease. These two amino acids are located in the B pocket of the antigen-binding groove, which occupies the second amino acids of the bound peptides. To understand the nature of the HLA-peptide interactions, differences in structure and dynamics of two HLA alleles were investigated by molecular dynamics simulations using YAYDGKDYI, LPRSTVINI, and IPYQDLPHL peptides. For HLA-B51, all bound peptides fluctuated to larger extent than HLA-B52. Free energy profiles of unbinding process for YAYDGKDYI by steered molecular dynamics simulations showed that unbinding from HLA-B52 results in greater free energy differences than HLA-B51. These results suggest the possibility of an instability of HLA-B51 associated with the repertoire of peptides, and this finding may provide significant insight to its pathogenic role in Behçet's disease.

Comparative effects of oncogenic mutations G12C, G12V, G13D, and Q61H on local conformations and dynamics of K-Ras.

K-Ras is the most frequently mutated protein in human cancers. However, until very recently, its oncogenic mutants were viewed as undruggable. To develop inhibitors that directly target oncogenic K-Ras mutants, we need to understand both their mutant-specific and pan-mutant dynamics and conformations. Recently, we have investigated how the most frequently observed K-Ras mutation in cancer patients, G12D, changes its local dynamics and conformations (Vatansever et al., 2019). Here, we extend our analysis to study and compare the local effects of other frequently observed oncogenic mutations, G12C, G12V, G13D and Q61H. For this purpose, we have performed Molecular Dynamics (MD) simulations of each mutant when active (GTP-bound) and inactive (GDP-bound), analyzed their trajectories, and compared how each mutant changes local residue conformations, inter-protein distance distributions, local flexibility and residue pair correlated motions. Our results reveal that in the four active oncogenic mutants we have studied, the α2 helix moves closer to the C-terminal of the α3 helix. However, P-loop mutations cause α3 helix to move away from Loop7, and only G12 mutations change the local conformational state populations of the protein. Furthermore, the motions of coupled residues are mutant-specific: G12 mutations lead to new negative correlations between residue motions, while Q61H destroys them. Overall, our findings on the local conformational states and protein dynamics of oncogenic K-Ras mutants can provide insights for both mutant-selective and pan-mutant targeted inhibition efforts.

ModiBodies: A computational method for modifying nanobodies in nanobody-antigen complexes to improve binding affinity and specificity.

Nanobodies are special derivatives of antibodies, which consist of single domain fragments. They have become of considerable interest as next-generation biotechnological tools for antigen recognition. They can be easily engineered due to their high stability and compact size. Nanobodies have three complementarity-determining regions, CDRs, which are enlarged to provide a similar binding surface to that of human immunoglobulins. Here, we propose a benchmark testing algorithm that uses 3D structures of already existing protein-nanobody complexes as initial structures followed by successive mutations on the CDR domains. The aim is to find optimum binding amino acids for hypervariable residues of CDRs. We use molecular dynamics simulations to compare the binding energies of the resulting complexes with that of the known complex and accept those that are improved by mutations. We use the MDM4-VH9 complex, (PDB id 2VYR), fructose-bisphosphate aldolase from Trypanosoma congolense (PDB id 5O0W) and human lysozyme (PDB id 4I0C) as benchmark complexes. By using this algorithm, better binding nanobodies can be generated in a short amount of time. We suggest that this method can complement existing immune and synthetic library-based methods, without a need for extensive experimentation or large libraries.

Oncogenic G12D mutation alters local conformations and dynamics of K-Ras.

K-Ras is the most frequently mutated oncoprotein in human cancers, and G12D is its most prevalent mutation. To understand how G12D mutation impacts K-Ras function, we need to understand how it alters the regulation of its dynamics. Here, we present local changes in K-Ras structure, conformation and dynamics upon G12D mutation, from long-timescale Molecular Dynamics simulations of active (GTP-bound) and inactive (GDP-bound) forms of wild-type and mutant K-Ras, with an integrated investigation of atomistic-level changes, local conformational shifts and correlated residue motions. Our results reveal that the local changes in K-Ras are specific to bound nucleotide (GTP or GDP), and we provide a structural basis for this. Specifically, we show that G12D mutation causes a shift in the population of local conformational states of K-Ras, especially in Switch-II (SII) and α3-helix regions, in favor of a conformation that is associated with a catalytically impaired state through structural changes; it also causes SII motions to anti-correlate with other regions. This detailed picture of G12D mutation effects on the local dynamic characteristics of both active and inactive protein helps enhance our understanding of local K-Ras dynamics, and can inform studies on the development of direct inhibitors towards the treatment of K-RasG12D-driven cancers.

Effects of Timolol Treatment on Pancreatic Antioxidant Enzymes in Streptozotocin-induced Diabetic Rats: An Experimental and Computational Study.

BACKGROUND: The study aimed to investigate whether timolol-treatment has a beneficial effect on pentose phosphate pathway enzyme activities such as glucose-6-phosphate dehydrogenase (G6PD), 6-phosphogluconate dehydrogenase (6PGDH) enzyme activities and cAMP level in streptozotocin-induced diabetic rats in pancreatic tissues. METHODS: Diabetes was induced by streptozotocin (STZ) in 3-month old male Wistar rats. The diabetic rats were treated with timolol (5 mg/kg body weight, for 12 weeks) while the control group received saline. Enzyme activities were determined in pancreas tissue. To support our results, we performed in silico calculations, using Protein Data Bank structures. RESULTS: Timolol treatment of STZ-induced diabetic rats had no noteworthy effect on high blood-glucose levels. However, this treatment induced activities of G6PD and 6PGDH in diabetic rats. Timolol treatment significantly increased cAMP level in diabetic pancreatic tissue. We found that timolol cannot bind strongly to either G6PD or 6PGD, but there is a relatively higher binding affinity to adenylyl cyclase, responsible for cAMP production, serving as a regulatory signal via specific cAMP-binding proteins. CONCLUSIONS: Our data point out that timolol treatment has beneficial effects on the antioxidant defence mechanism enzymes in the pancreas of STZ-induced diabetic rats.

A computational model for controlling conformational cooperativity and function in proteins.

We present a computational model that allows for rapid prediction of correlations among a set of residue pairs when the fluctuations of another set of residues are perturbed. The simple theory presented here is based on the knowledge of the fluctuation covariance matrix only. In this sense, the theory is model independent and therefore universal. Perturbation of any set of fluctuations and the resulting response of the remaining set are calculated using conditional probabilities of a multivariate normal distribution. The model is expected to rapidly and accurately map the consequences of mutations in proteins, as well as allosteric activity and ligand binding. Knowledge of triple correlations of fluctuations of residues i, j, and k, 〈 Δ R i Δ R j Δ R k 〉 emerges as the necessary source of information for controlling residue pairs by perturbing a distant residue. Triple correlations have not received wide attention in literature. Perturbation-response-function relations for ubiquitin (UBQ) are discussed as an example. Covariance matrix for UBQ obtained from the Gaussian Network Model combined with the present computational algorithm is able to reflect the millisecond molecular dynamics correlations and observed NMR results. © 2018 Wiley Periodicals, Inc.

Molecular dynamics simulations of site point mutations in the TPR domain of cyclophilin 40 identify conformational states with distinct dynamic and enzymatic properties.

Cyclophilin 40 (Cyp40) is a member of the immunophilin family that acts as a peptidyl-prolyl-isomerase enzyme and binds to the heat shock protein 90 (Hsp90). Its structure comprises an N-terminal cyclophilin domain and a C-terminal tetratricopeptide (TPR) domain. Cyp40 is overexpressed in prostate cancer and certain T-cell lymphomas. The groove for Hsp90 binding on the TPR domain includes residues Lys227 and Lys308, referred to as the carboxylate clamp, and is essential for Cyp40-Hsp90 binding. In this study, the effect of two mutations, K227A and K308A, and their combinative mutant was investigated by performing a total of 5.76 µs of all-atom molecular dynamics (MD) simulations in explicit solvent. All simulations, except the K308A mutant, were found to adopt two distinct (extended or compact) conformers defined by different cyclophilin-TPR interdomain distances. The K308A mutant was only observed in the extended form which is observed in the Cyp40 X-ray structure. The wild-type, K227A, and combined mutant also showed bimodal distributions. The experimental melting temperature, Tm, values of the mutants correlate with the degree of compactness with the K308A extended mutant having a marginally lower melting temperature. Another novel measure of compactness determined from the MD data, the "coordination shell volume," also shows a direct correlation with Tm. In addition, the MD simulations show an allosteric effect with the mutations in the remote TPR domain having a pronounced effect on the molecular motions of the enzymatic cyclophilin domain which helps rationalise the experimentally observed increase in enzyme activity measured for all three mutations.