The Importance of Charge Transfer and Solvent Screening in the Interactions of Backbones and Functional Groups in Amino Acid Residues and Nucleotides.

Quantum mechanical (QM) calculations at the level of density-functional tight-binding are applied to a protein-DNA complex (PDB: 2o8b) consisting of 3763 atoms, averaging 100 snapshots from molecular dynamics simulations. A detailed comparison of QM and force field (Amber) results is presented. It is shown that, when solvent screening is taken into account, the contributions of the backbones are small, and the binding of nucleotides in the double helix is governed by the base-base interactions. On the other hand, the backbones can make a substantial contribution to the binding of amino acid residues to nucleotides and other residues. The effect of charge transfer on the interactions is also analyzed, revealing that the actual charge of nucleotides and amino acid residues can differ by as much as 6 and 8% from the formal integer charge, respectively. The effect of interactions on topological models (protein -residue networks) is elucidated.

Modifying the catalytic preference of alpha-amylase toward n-alkanes for bioremediation purposes using in silico strategies.

Since the beginning of oil exploration, whole ecosystems have been affected by accidents and bad practices involving petroleum compounds. In this sense, bioremediation stands out as the cheapest and most eco-friendly alternatives to reverse the damage done in oil-impacted areas. However, more efforts must be made to engineer enzymes that could be used in the bioremediation process. Interestingly, a recent work described that α-amylase, one of the most evolutionary conserved enzymes, was able to promiscuously degrade n-alkanes, a class of molecules abundant in the petroleum admixture. Considering that α-amylase is expressed in almost all known organisms, and employed in numerous biotechnological processes, using it can be a great leap toward more efficient applications of enzyme or microorganism-consortia bioremediation approaches. In this work, we employed a strict computational approach to design new α-amylase mutants with potentially enhanced catalytic efficiency toward n-alkanes. Using in silico techniques, such as molecular docking, molecular dynamics, metadynamics, and residue-residue interaction networks, we generated mutants potentially more efficient for degrading n-alkanes, L183Y, and N314A. Our results indicate that the new mutants have an increased binding rate for tetradecane, the longest n-alkane previously tested, which can reside in the catalytic center for more extended periods. Additionally, molecular dynamics and network analysis showed that the new mutations have no negative impact on protein structure than the WT. Our results aid in solidifying this enzyme as one more tool in the petroleum bioremediation toolbox.

Analyzing Cysteine Site Neighbors in Proteins to Reveal Dimethyl Fumarate Targets.

This work proposes a novel approach by which to consistently classify cysteine sites in proteins in terms of their reactivity toward dimethyl fumarate (DMF) and fumarate. Dimethyl fumarate-based drug products have been approved for use as oral treatments for psoriasis and relapsing-remitting multiple sclerosis. The adduction of DMF and its (re)active metabolites to certain cysteine residues in proteins is thought to underlie their effects. However, only a few receptors for these compounds have been discovered to date. Our approach takes advantage of the growing number of known DMF- and fumarate-sensitive proteins and sites to perform analyses by combining the concepts of network theory, for protein structure analyses, and machine-learning procedures. Wide-ranging and previously unforeseen variety is found in the analysis of the neighborhood composition (the first neighbors) of cysteine sites found in DMF- and fumarate-sensitive proteins. Furthermore, neighborhood composition has shown itself to be a network-type attribute that is endowed with remarkable predictive power when distinct classification algorithms are employed. In conclusion, when adopted in combination with other target identification/validation approaches, methods that are based on the analysis of cysteine site neighbors in proteins should provide useful information by which to decipher the mode of action of DMF-based drugs.

Network analysis of dynamically important residues in protein structures mediating ligand-binding conformational changes.

According to the generalized conformational selection model, ligand binding involves the co-existence of at least two conformers with different ligand-affinities in a dynamical equilibrium. Conformational transitions between them should be guaranteed by intramolecular vibrational dynamics associated to each conformation. These motions are, therefore, related to the biological function of a protein. Positions whose mutations are found to alter these vibrations the most can be defined as key positions, that is, dynamically important residues that mediate the ligand-binding conformational change. In a previous study, we have shown that these positions are evolutionarily conserved. They correspond to buried aliphatic residues mostly localized in regular structured regions of the protein like ß-sheets and α-helices. In the present paper, we perform a network analysis of these key positions for a large dataset of paired protein structures in the ligand-free and ligand-bound form. We observe that networks of interactions between these key positions present larger and more integrated networks with faster transmission of the information. Besides, networks of residues result that are robust to conformational changes. Our results reveal that the conformational diversity of proteins seems to be guaranteed by a network of strongly interconnected key positions rather than individual residues.

Interrogating Regulatory Mechanisms in Signaling Proteins by Allosteric Inhibitors and Activators: A Dynamic View Through the Lens of Residue Interaction Networks.

Computational studies of allosteric interactions have witnessed a recent renaissance fueled by the growing interest in modeling of the complex molecular assemblies and biological networks. Allosteric interactions in protein structures allow for molecular communication in signal transduction networks. In this chapter, we discuss recent developments in understanding of allosteric mechanisms and interactions of protein systems, particularly in the context of structural, functional, and computational studies of allosteric inhibitors and activators. Computational and experimental approaches and advances in understanding allosteric regulatory mechanisms are reviewed to provide a systematic and critical view of the current progress in the development of allosteric modulators and highlight most challenging questions in the field. The abundance and diversity of genetic, structural, and biochemical data underlies the complexity of mechanisms by which targeted and personalized drugs can combat mutational profiles in protein kinases. Structural and computational studies of protein kinases have generated in recent decade significant insights that allowed leveraging knowledge about conformational diversity and allosteric regulation of protein kinases in the design and discovery of novel kinase drugs. We discuss recent developments in understanding multilayered allosteric regulatory machinery of protein kinases and provide a systematic view of the current state in understanding molecular basis of allostery mediated by kinase inhibitors and activators. In conclusion, we highlight the current status and future prospects of computational biology approaches in bridging the basic science of protein kinases with the discovery of anticancer therapies.

Dynamics-based community analysis and perturbation response scanning of allosteric interaction networks in the TRAP1 chaperone structures dissect molecular linkage between conformational asymmetry and sequential ATP hydrolysis.

Allosteric interactions of the Hsp90 chaperones with cochaperones and diverse protein clients can often exhibit distinct asymmetric features that determine regulatory mechanisms and cellular functions in many signaling networks. The recent crystal structures of the mitochondrial Hsp90 isoform TRAP1 in complexes with ATP analogs have provided first evidence of significant asymmetry in the closed dimerized state that triggers independent activity of the chaperone protomers, whereby preferential hydrolysis of the buckled protomer is followed by conformational flipping between protomers and hydrolysis of the second protomer. Despite significant insights in structural characterizations of the TRAP1 chaperone, the atomistic details and mechanics of allosteric interactions that couple sequential ATP hydrolysis with asymmetric conformational switching in the TRAP1 protomers remain largely unknown. In this work, we explored atomistic and coarse-grained simulations of the TRAP1 dimer structures in combination with the ensemble-based network modeling and perturbation response scanning of residue interaction networks to probe salient features underlying allosteric signaling mechanism. This study has revealed that key effector sites that orchestrate allosteric interactions occupy the ATP binding region and N-terminal interface of the buckled protomer, whereas the main sensors of allosteric signals that drive functional conformational changes during ATPase cycle are consolidated near the client binding region of the straight protomer, channeling the energy of ATP hydrolysis for client remodeling. The community decomposition analysis of the interaction networks and reconstruction of allosteric communication pathways in the TRAP1 structures have quantified mechanism of allosteric regulation, revealing control points and interactions that coordinate asymmetric switching during ATP hydrolysis.

Intermediate-aided allostery mechanism for α-glucosidase by Xanthene-11v as an inhibitor using residue interaction network analysis.

Exploring allosteric inhibition and the discovery of new inhibitor binding sites are important studies in protein regulation mechanisms and drug discovery. Structural and network-based analyses of trajectories resulting from molecular dynamics (MD) simulations have been developed to discover protein dynamics, landscape, functions, and allosteric regions. Here, an experimentally suggested non-competitive inhibitor, xanthene-11v, was considered to explore its allosteric inhibition mechanism in α-glucosidase MAL12. Comparative structural and network analyses were applied to eight 250 ns independent MD simulations, four of which were performed in the free state and four of which were performed in ligand-bound forms. Projected two-dimensional free energy landscapes (FEL) were constructed from the probabilistic distribution of conformations along the first two principal components. The post-simulation analyses of the coordinates, side-chain torsion angles, non-covalent interaction networks, network communities, and their centralities were performed on α-glucosidase conformations and the intermediate sub-states. Important communities of residues have been found that connect the allosteric site to the active site. Some of these residues like Thr307, Arg312, TYR344, ILE345, Phe357, Asp406, Val407, Asp408, and Leu436 are the key messengers in the transition pathway between allosteric and active sites. Evaluating the probability distribution of distances between gate residues including Val407 in one community and Phe158, and Pro65 in another community depicted the closure of this gate due to the inhibitor binding. Six macro states of protein were deduced from the topology of FEL and analysis of conformational preference of free and ligand-bound systems to these macro states shows a combination of lock-and-key, conformational selection, and induced fit mechanisms are effective in ligand binding. All these results reveal structural states, allosteric mechanisms, and key players in the inhibition pathway of α-glucosidase by xanthene-11v.

Exploring species-specific inhibitors with multiple target sites on S. aureus pyruvate kinase using a computational workflow.

Experimental evidence indicated that bacterial pyruvate kinase of glycolysis can be evaluated as an alternative target to eliminate infections, while antibiotic resistance poses a global threat. Here, we use a computational workflow to reveal and investigate the potential allosteric sites of methicillin-resistant S. aureus PK, which can help in designing species-specific drugs to inhibit activity of this organism. Residue interaction networks point to a known allosteric site at the small C-C interface, a potential allosteric site near the small interface (site #1), and a second potential allosteric site at the large interface (site #2). 2 µs-long molecular dynamics (MD) simulations with AMBER16 generate different conformations of one narrow target site. Known and potential allosteric sites on the selected conformers are investigated using ensemble docking with AutoDock Vina and a library of 2447 FDA-approved drugs. We determine 18 hits, comprising ergot-alkaloids, anti-cancer-agents, antivirals, analgesics, cardiac glycosides, all with a high docking z-score for three sites. 5 selected compounds with high, average and low z-scores are subjected to 50 ns-long MD simulations for MM-GBSA calculations. ΔGbind values up to -49.3 kcal/mol at the C-C interface, up to -32.7 kcal/mol at site #1, and up to -53.3 kcal/mol at site #2 support the docking calculations. We investigate mitapivat and TT-232 as reference compounds under clinical trial, targeting human PK isomers. We suggest 18 FDA-approved hits from the docking calculations and TT-232 as potential inhibitors with multiple target sites on S. aureus PK. This study also proposes pharmacophores models for de novo drug design.Communicated by Ramaswamy H. Sarma.

Connecting the dots: A practical evaluation of web-tools for describing protein dynamics as networks.

Protein Structure Networks (PSNs) are a well-known mathematical model for estimation and analysis of the three-dimensional protein structure. Investigating the topological architecture of PSNs may help identify the crucial amino acid residues for protein stability and protein-protein interactions, as well as deduce any possible mutational effects. But because proteins go through conformational changes to give rise to essential biological functions, this has to be done dynamically over time. The most effective method to describe protein dynamics is molecular dynamics simulation, with the most popular software programs for manipulating simulations to infer interaction networks being RING, MD-TASK, and NAPS. Here, we compare the computational approaches used by these three tools-all of which are accessible as web servers-to understand the pathogenicity of missense mutations and talk about their potential applications as well as their advantages and disadvantages.

Enhanced metastable state models of TAM kinase binding to cabozantinib explains the dynamic nature of receptor tyrosine kinases.

Receptor tyrosine kinases (RTKs) are essential proteins in the regulation of cell signaling. Tyro3, Axl and Mer are members of TAM RTKs and are overexpressed in several cancer forms. Kinase inhibitors such as cabozantinib, foretinib are reported to inhibit TAM kinases at nanomolar concentrations. The atomistic details of structure and mechanism of functional regulation is required to understand their normal physiological process and when bound to an inhibitor. The docking of cabozantinib into the active state conformations of TAM kinases (crystal structure and computational models) revealed the best binding pose and the complex formation that is mediated through non-bonding interactions involving the hinge region residues. The alterations in the conformations and the regions of flexibility in apo and complexed TAM kinases as a course of time are studied using 250 ns molecular dynamics (MD) simulations. The post-MD trajectory analysis using Python libraries like ProDy, MDTraj and PyEMMA revealed encrypted protein dynamic motions in active kinetic metastable states. Comparison between Principal component analysis and Anisotropic mode analysis deciphered structural residue interactions and salt bridge contacts between apo and inhibitor bound TAM kinases. Various structural changes occurred in αC-helix and activation loop involving hydrogen bonding between residues from Lys-(ß3 sheet), Glu-(αC-helix) and Asp-(DFG-motif) resulting in higher RMSD. Mechanical stiffness plots revealed that similar regions in apo and cabozantinib bound Axl fluctuated during MD simulations whereas different regions in Tyro3 and Mer kinases. The residue interaction network plots revealed important salt bridges that lead to constrained domain motions in the TAM kinases.Communicated by Ramaswamy H. Sarma.

Centrality Measures in Residue Interaction Networks to Highlight Amino Acids in Protein-Protein Binding.

Residue interaction networks (RINs) describe a protein structure as a network of interacting residues. Central nodes in these networks, identified by centrality analyses, highlight those residues that play a role in the structure and function of the protein. However, little is known about the capability of such analyses to identify residues involved in the formation of macromolecular complexes. Here, we performed six different centrality measures on the RINs generated from the complexes of the SKEMPI 2 database of changes in protein-protein binding upon mutation in order to evaluate the capability of each of these measures to identify major binding residues. The analyses were performed with and without the crystallographic water molecules, in addition to the protein residues. We also investigated the use of a weight factor based on the inter-residue distances to improve the detection of these residues. We show that for the identification of major binding residues, closeness, degree, and PageRank result in good precision, whereas betweenness, eigenvector, and residue centrality analyses give a higher sensitivity. Including water in the analysis improves the sensitivity of all measures without losing precision. Applying weights only slightly raises the sensitivity of eigenvector centrality analysis. We finally show that a combination of multiple centrality analyses is the optimal approach to identify residues that play a role in protein-protein interaction.

The impact of long-distance mutations on the Ω-loop conformation in TEM type ß-lactamases.

ß-lactamases are hydrolytic enzymes primarily responsible for occurrence and abundance of bacteria resistant to ß-lactam antibiotics. TEM type ß-lactamases are formed by the parent enzyme TEM-1 and more than two hundred of its mutants. Positions for the known amino acid substitutions cover ∼30% of TEM type enzyme's sequence. These substitutions are divided into the key mutations that lead to changes in catalytic properties of ß-lactamases, and the secondary ones, which role is poorly understood. In this study, Residue Interaction Networks were constructed from molecular dynamic trajectories of ß-lactamase TEM-1 and its variants with two key substitutions, G238S and E240K, and their combinations with secondary ones (M182T and Q39K). Particular attention was paid to a detailed analysis of the interactions that affect conformation and mobility of the Ω-loop, representing a part of the ß-lactamase active site. It was shown that key mutations weakened the stability of contact inside the Ω-loop thus increasing its mobility. Combination of three amino acid substitutions, including the 182 residue, leads to the release of R65 promoting its new contacts with N175 and D176. As a result, Ω-loop is fixed on the protein globule. The second distal mutation Q39K prevents changes in spatial position of R65, which lead to the weakening of the effect of M182T substitution and the recovery of the Ω-loop mobility. Thus, the distal secondary mutations are directed for recovering the mobility of enzyme disturbed by the key mutations responsible for expansion of substrate specificity. AbbreviationsESBLextended spectrum beta-lactamasesIRinhibitor resistant beta-lactamasesMDmolecular dynamicsRINresidue interaction networksRMSDroot mean square deviationRMSFroot mean square fluctuations.Communicated by Ramaswamy H. Sarma.

Rewiring Ancient Residue Interaction Networks Drove the Evolution of Specificity in Steroid Receptors.

Understanding how changes in amino acid sequence alter protein dynamics and allosteric signaling would illuminate strategies for protein design. To gain insight into this process, we have combined molecular dynamics simulations with ancestral sequence reconstruction to explore conformational dynamics in two ancient steroid receptors (SRs) to determine how allosteric signaling pathways were altered over evolution to generate hormone specificity. In a broad panel of aromatized and non-aromatized hormones, we investigate inter-residue contacts that facilitate allosteric signaling. This work reveals interhelical interactions that act as ligand sensors and explain the structural and dynamical basis for ligand discrimination in SRs. These sensors are part of a conserved SR allosteric network and persist over long simulation time scales, indicating that evolutionary substitutions rewire ancient SR networks to achieve functional evolution. This powerful combination of computation, ancestral reconstruction, and biochemistry may illuminate allosteric mechanisms and functional evolution in other protein families.

Statistical analysis of mutational epistasis to reveal intramolecular interaction networks in proteins.

Epistasis occurs when the combined effect of two or more mutations differs from the sum of their individual effects, and reflects molecular interactions that affect the function and fitness of a protein. Epistasis is widely recognized as a key phenomenon that drives the dynamics of evolution. It can profoundly affect our ability to understand sequence-structure-function relationships, and thus has important implications for protein engineering and design. Characterizing higher-order epistasis, i.e., interactions between three or more mutations, can unveil hidden intramolecular interaction networks that underlie essential protein functions and their evolution. For this chapter, we developed an analytical pipeline that can standardize the study of intramolecular epistasis. We describe the generation and characterization of a combinatorial library, the statistical analysis of mutational epistasis, and finally, the depiction of epistatic networks on the 3D structure of a protein. We anticipate that this pipeline will benefit the increasing number of scientists that are interested in the functional characterization of mutational libraries to provide a deeper understanding of the molecular mechanisms of protein evolution.

Identification of Novel Interaction Partners of Ets-1: Focus on DNA Repair.

The transcription factor Ets-1 (ETS proto-oncogene 1) shows low expression levels except in specific biological processes like haematopoiesis or angiogenesis. Elevated levels of expression are observed in tumor progression, resulting in Ets-1 being named an oncoprotein. It has recently been shown that Ets-1 interacts with two DNA repair enzymes, PARP-1 (poly(ADP-ribose) polymerase 1) and DNA-PK (DNA-dependent protein kinase), through two different domains and that these interactions play a role in cancer. Considering that Ets-1 can bind to distinctly different domains of two DNA repair enzymes, we hypothesized that the interaction can be transposed onto homologs of the respective domains. We have searched for sequence and structure homologs of the interacting ETS(Ets-1), BRCT(PARP-1) and SAP(DNA-PK) domains, and have identified several candidate binding pairs that are currently not annotated as such. Many of the Ets-1 partners are associated to DNA repair mechanisms. We have applied protein-protein docking to establish putative interaction poses and investigated these using centrality analyses at the protein residue level. Most of the identified poses are virtually similar to our recently established interaction model for Ets-1/PARP-1 and Ets-1/DNA-PK. Our work illustrates the potentially high number of interactors of Ets-1, in particular involved in DNA repair mechanisms, which shows the oncoprotein as a potential important regulator of the mechanism.

π-π stacking interaction is a key factor for the stability of GH11 xylanases at low pH.

Acidic xylanases possess the unique features necessary for the tolerance of acidic environments, which may have great potentials for industrial purposes. However, factors controlling the pH-dependent stability of xylanases are only partially known. Here we proposed a residue interaction networks based method to analyze the differences of residue interactions between 6 pairs of experimentally verified acidic and neutral xylanases. They had very close numbers of aromatic amino acids, however extremely significant more (p < 0.001) π-π stacking interactions existed in acidic xylanases, which has not been reported before. Whereas the interactions between Tyrosine-Phenylalanine (Tyr-Phe) and Phenylalanine-Phenylalanine (Phe-Phe) were the main contributors. An equation quantitatively described the relationship between the optimal pH and the number of π-π stacking interactions was proposed. The predicted optimal pHs for three xylanases was 4.13, 6.7 and 6.1, while the experimental values of the optimum pHs were 4.6, 6.5 and 6.5, with an absolute error of 0.47, 0.2 and 0.4 pH unit, respectively. By counting the aromatic residue pairs forming π-π stacking in the 3D structure of an acidic (PDB ID: 1BK1, with an optimal pH of 2) and a neutral (PDB ID:1XXN, with an optimal pH of 6.5) xylanase, we found significant differences existed in the positions ranging from 145 to 166 in forming π-π stacking. Two phenylalanines at position 149 and 157 in the acidic xylanase, which involved in 7 π-π stacking interactions, played an important role in the stability of xylanase at low pH environment, which was further proved by a mutation experiment. A mutated xylanase with Phe149 → Ala149 and Phe157 → Ala157 was expressed and purified, resulting the optimal pH shifted from 2 to 4.5. The interaction networks based method paved a new way in underlying and engineering the acid-stability of xylanase, as well as the characteristics of other enzymes.

Data-driven computational analysis of allosteric proteins by exploring protein dynamics, residue coevolution and residue interaction networks.

BACKGROUND: Computational studies of allosteric interactions have witnessed a recent renaissance fueled by the growing interest in modeling of the complex molecular assemblies and biological networks. Allosteric interactions in protein structures allow for molecular communication in signal transduction networks. METHODS: In this work, we performed a large scale comprehensive and multi-faceted analysis of >300 diverse allosteric proteins and complexes with allosteric modulators. By modeling and exploring coarse-grained dynamics, residue coevolution, and residue interaction networks for allosteric proteins, we have determined unifying molecular signatures shared by allosteric systems. RESULTS: The results of this study have suggested that allosteric inhibitors and allosteric activators may differentially affect global dynamics and network organization of protein systems, leading to diverse allosteric mechanisms. By using structural and functional data on protein kinases, we present a detailed case study that that included atomic-level analysis of coevolutionary networks in kinases bound with allosteric inhibitors and activators. CONCLUSIONS: We have found that coevolutionary networks can form direct communication pathways connecting functional regions and can recapitulate key regulatory sites and interactions responsible for allosteric signaling in the studied protein systems. The results of this computational investigation are compared with the experimental studies and reveal molecular signatures of known regulatory hotspots in protein kinases. GENERAL SIGNIFICANCE: This study has shown that allosteric inhibitors and allosteric activators can have a different effect on residue interaction networks and can exploit distinct regulatory mechanisms, which could open up opportunities for probing allostery and new drug combinations with broad range of activities.

Computational simulations assessment of mutations impact on streptokinase (SK) from a group G streptococci with enhanced activity - insights into the functional roles of structural dynamics flexibility of SK and stabilization of SK-µplasmin catalytic complex.

Streptokinase (SK), a plasminogen activator (PA) that converts inactive plasminogen (Pg) to plasmin (Pm), is a protein secreted by groups A, C, and G streptococci (GAS, GCS, and GGS, respectively), with high sequence divergence and functional heterogeneity. While roles of some residual changes in altered SK functionality are shown, the underlying structural mechanisms are less known. Herein, using computational approaches, we analyzed the conformational basis for the increased activity of SK from a GGS (SKG132) isolate with four natural residual substitutions (Ile33Phe, Arg45Gln, Asn228Lys, Phe287Ile) compared to the standard GCS (SKC). Using the crystal structure of SK.Pm catalytic complex as main template SKC.µPm catalytic complex was modeled through homology modeling process and validated by several online validation servers. Subsequently, SKG132.µPm structure was constructed by altering the corresponding residual substitutions. Results of three independent MD simulations showed increased RMSF values for SKG132.µPm, indicating the enhanced structural flexibility compared to SKC.µPm, specially in 170 and 250 loops and three regions: R1 (149-161), R2 (182-215) and R3 (224-229). In parallel, the average number of Hydrogen bonds in 170 loop, R2 and R3 (especially for Asn228Lys) of SKG132 compared to that of the SKC was decreased. Accordingly, residue interaction networks (RINs) analyses indicated that Asn228Lys might induce more level of structural flexibility by generation of free Lys256, while Phe287Ile and Ile33Phe enhanced the stabilization of the SKG132.µPm catalytic complex. These results denoted the potential role of the optimal dynamic state and stabilized catalytic complex for increased PA potencies of SK as a thrombolytic drug.

RIP-MD: a tool to study residue interaction networks in protein molecular dynamics.

Protein structure is not static; residues undergo conformational rearrangements and, in doing so, create, stabilize or break non-covalent interactions. Molecular dynamics (MD) is a technique used to simulate these movements with atomic resolution. However, given the data-intensive nature of the technique, gathering relevant information from MD simulations is a complex and time consuming process requiring several computational tools to perform these analyses. Among different approaches, the study of residue interaction networks (RINs) has proven to facilitate the study of protein structures. In a RIN, nodes represent amino-acid residues and the connections between them depict non-covalent interactions. Here, we describe residue interaction networks in protein molecular dynamics (RIP-MD), a visual molecular dynamics (VMD) plugin to facilitate the study of RINs using trajectories obtained from MD simulations of proteins. Our software generates RINs from MD trajectory files. The non-covalent interactions defined by RIP-MD include H-bonds, salt bridges, VdWs, cation-π, π-π, Arginine-Arginine, and Coulomb interactions. In addition, RIP-MD also computes interactions based on distances between Cαs and disulfide bridges. The results of the analysis are shown in an user friendly interface. Moreover, the user can take advantage of the VMD visualization capacities, whereby through some effortless steps, it is possible to select and visualize interactions described for a single, several or all residues in a MD trajectory. Network and descriptive table files are also generated, allowing their further study in other specialized platforms. Our method was written in python in a parallelized fashion. This characteristic allows the analysis of large systems impossible to handle otherwise. RIP-MD is available at http://www.dlab.cl/ripmd.

Exploring the effect of D61G mutation on SHP2 cause gain of function activity by a molecular dynamics study.

Noonan syndrome (NS) is a common autosomal dominant congenital disorder which could cause the congenital cardiopathy and cancer predisposition. Previous studies reported that the knock-in mouse models of the mutant D61G of SHP2 exhibited the major features of NS, which demonstrated that the mutation D61G of SHP2 could cause NS. To explore the effect of D61G mutation on SHP2 and explain the high activity of the mutant, molecular dynamic simulations were performed on wild type (WT) of SHP2 and the mutated SHP2-D61G, respectively. The principal component analysis and dynamic cross-correlation mapping, associated with secondary structure, showed that the D61G mutation affected the motions of two regions (residues Asn 58-Thr 59 and Val 460-His 462) in SHP2 from ß to turn. Moreover, the residue interaction networks analysis, the hydrogen bond occupancy analysis and the binding free energies were calculated to gain detailed insight into the influence of the mutant D61G on the two regions, revealing that the major differences between SHP2-WT and SHP2-D61G were the different interactions between Gly 61 and Gly 462, Gly 61 and Ala 461, Gln 506 and Ile 463, Gly 61 and Asn 58, Ile 463 and Thr 466, Gly 462 and Cys 459. Consequently, our findings here may provide knowledge to understand the increased activity of SHP2 caused by the mutant D61G.