Residue-residue Interaction Network Research Articles

AlphaFold2 modeling and molecular dynamics simulations of an intrinsically disordered protein.

We use AlphaFold2 (AF2) to model the monomer and dimer structures of an intrinsically disordered protein (IDP), Nvjp-1, assisted by molecular dynamics (MD) simulations. We observe relatively rigid dimeric structures of Nvjp-1 when compared with the monomer structures. We suggest that protein conformations from multiple AF2 models and those from MD trajectories exhibit a coherent trend: the conformations of an IDP are deviated from each other and the conformations of a well-folded protein are consistent with each other. We use a residue-residue interaction network (RIN) derived from the contact map which show that the residue-residue interactions in Nvjp-1 are mainly transient; however, those in a well-folded protein are mainly persistent. Despite the variation in 3D shapes, we show that the AF2 models of both disordered and ordered proteins exhibit highly consistent profiles of the pLDDT (predicted local distance difference test) scores. These results indicate a potential protocol to justify the IDPs based on multiple AF2 models and MD simulations.

Long-timescale atomistic simulations uncover loss-of-function mechanisms of uncharacterized Angiogenin mutants associated with ALS

Amyotrophic Lateral Sclerosis (ALS) is a devastating neurodegenerative disease characterized by progressive degeneration of motor neurons, resulting in respiratory failure and mortality within 3–5 years. Mutations in the Angiogenin (ANG) cause loss of ribonucleolytic and nuclear translocation activities, contributing to ALS pathogenesis. This study focused on investigating two uncharacterized ANG mutations, T11S and R122H, newly identified in the Project Mine consortium. Using extensive computational analysis, including structural modeling and microsecond-timescale molecular dynamics (MD) simulations, we observed conformational changes in the catalytic residue His114 of ANG induced by T11S and R122H mutations. These alterations impaired ribonucleolytic activity, as inferred through molecular docking and binding free energy calculations. Gibbs free energy landscape and residue-residue interaction network analysis further supported our findings, revealing the energetic states and allosteric pathway from the mutated site to His114. Additionally, we assessed the binding of NCI-65828, an inhibitor of ribonucleolytic activity of ANG, and found reduced effectiveness in binding to T11S and R122H mutants when His114 assumed a non-native conformation. This highlights the crucial role of His114 and its association with ALS. Elucidating the relationship between physical structure and functional dynamics of frequently mutated ANG mutants is essential for understanding ALS pathogenesis and developing more effective therapeutic interventions.

A distant angiogenin variant causes amyotrophic lateral sclerosis through loss-of-function mechanisms: Insights from long-timescale atomistic simulations and conformational dynamics

Amyotrophic Lateral Sclerosis (ALS) is a progressive and incurable neurodegenerative disorder characterized by the degeneration of motor neurons leading to severe muscle atrophy, respiratory failure and death within 3–5 years of disease onset. Missense mutations in Angiogenin (ANG) cause ALS through loss of either ribonucleolytic activity or nuclear translocation activity or both of these functions. Although loss-of-function mechanisms of several rare and ALS-causing ANG variants have been studied before, the structure-function relationship and subsequent functional loss mechanisms of certain novel and uncharacterized rare variants have not been deciphered hitherto. In this study, the structural and dynamic properties of the distantly-located I71V variant, on the functional sites of ANG have been investigated to understand its role in ALS etiology and progression. The I71V variant has a minor allele frequency of <0.06% and thus is classified as a rare variant. Our extensive in silico investigation comprising 1-μs molecular dynamics (MD) simulations, conformational dynamics and related integrated analyses reveal that the I71V variant induces a characteristic conformational switching of catalytic His114 residue resulting in loss of ribonucleolytic activity. Molecular docking and a residue-residue interaction network propagated by an allosteric pathway further support these findings. Moreover, while no conformational alteration of nuclear localization signal governing the nuclear translocation activity was observed, an escalation in mutant plasticity was detected in the structural and essential dynamics simulations. Overall, our study emphasizes that the structure-function relationship of frequently mutating novel ANG variants needs to be established and prioritized in order to advance the pathophysiology and therapeutics of ALS.

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.

Frustration and Direct-Coupling Analyses to Predict Formation and Function of Adeno-Associated Virus

Adeno-associated virus (AAV) is a promising gene therapy vector because of its efficient gene delivery and relatively mild immunogenicity. To improve delivery target specificity, researchers use combinatorial and rational library design strategies to generate novel AAV capsid variants. These approaches frequently propose high proportions of nonforming or noninfective capsid protein sequences that reduce the effective depth of synthesized vector DNA libraries, thereby raising the discovery cost of novel vectors. We evaluated two computational techniques for their ability to estimate the impact of residue mutations on AAV capsid protein-protein interactions and thus predict changes in vector fitness, reasoning that these approaches might inform the design of functionally enriched AAV libraries and accelerate therapeutic candidate identification. The Frustratometer computes an energy function derived from the energy landscape theory of protein folding. Direct-coupling analysis (DCA) is a statistical framework that captures residue coevolution within proteins. We applied the Frustratometer to select candidate protein residues predicted to favor assembled or disassembled capsid states, then predicted mutation effects at these sites using the Frustratometer and DCA. Capsid mutants were experimentally assessed for changes in virus formation, stability, and transduction ability. The Frustratometer-based metric showed a counterintuitive correlation with viral stability, whereas a DCA-derived metric was highly correlated with virus transduction ability in the small population of residues studied. Our results suggest that coevolutionary models may be able to elucidate complex capsid residue-residue interaction networks essential for viral function, but further study is needed to understand the relationship between protein energy simulations and viral capsid metastability.

A Comparative Study of All-atom Molecular Dynamics Simulation and Coarse-grained Normal Mode Analysis in Identifying Pre-existing Residue Interaction Networks that Promote Coupled-Domain Dynamics in Escherichia coli Methionyl-tRNA Synthetase

Inter-domain communication plays a key role in the function of modular proteins. Earlier studies have demonstrated that the coupling of domain motions is important in mediating site-to-site communications in modular proteins. In the present study, bioinformatics and molecular simulations were usedto trace “pre-existing” residue-residue interaction networks that mediate coupled-domain dynamics in multi-domain Escherichia coli methionyl-tRNA synthetase (Ec MetRS). In particular, a comparative study was carried out to evaluate the effectiveness of coarse-grained normal mode analysis and all-atom molecular dynamic simulation in predicting pre-existing pathways of inter-domain communications in this enzyme. Integration of dynamic information of residues with their evolutionary features (conserved and coevolved) demonstrated that multiple residue-residue interaction networks exist in Ec MetRS that promote dynamic coupling between the anticodon binding domain and the connective polypeptide I domain, which are &gt; 50Å apart, through correlated motions. Mutation of residues on these pathways have distinct impact on the dynamics and function of this enzyme. Moreover, the present study revealed that the dynamic information obtained from the coarse-grained normal mode analysis is comparable to the atomistic molecular dynamics simulations in predicting the interaction networks that are essential for promoting coupled-domain dynamics in Ec MetRS. KEYWORDS: Domain-domain Communication; Molecular Dynamics; Methionyl-tRNA Synthetase; Normal Mode Analysis; Coupled-domain Dynamics; Course-grained Normal Mode Analysis; Aminoacyl tRNA Synthetases; Statistical Coupling Analysis

Alterations in conformational topology and interaction dynamics caused by L418A mutation leads to activity loss of Mycobacterium tuberculosis isocitrate lyase

Mycobacterium tuberculosis isocitrate lyase (MtbICL) is a key enzyme of the glyoxylate cycle that catalyzes the cleavage of isocitrate to succinate and glyoxylate and is a potential antituberculosis drug target. The aim of this research was to explore the structural alterations induced by L418A point mutation that caused the loss of enzyme activity. In-depth structural analyses were carried out for understanding the influence of L418A mutation using techniques, viz. molecular dynamics, principal component analysis, time-dependent secondary structure, residue interaction network and molecular docking. Since L418A mutation site is structurally far from the active site, it cannot influence the binding of the substrate directly. Our results showed that collective motions, residual mobility, and flexibility of the enzyme increased upon mutation. The mutated residue changed the global conformational dynamics of the system along with the residue-residue interaction network, leading to a loss of the enzyme activity. The docking results suggest that L418A mutation influenced the binding interactions of the substrate with several residues in the active site of MtbICL. This study provides information on the structural dynamics of MtbICL and highlights the importance of residue level interactions in the protein. Thus, our results may provide significant guidance to the scientific community engaged in designing potent inhibitors targeting MtbICL.

Structural insights into conformational stability of both wild-type and mutant EZH2 receptor.

Polycomb group (PcG) proteins have been observed to maintain the pattern of histone by methylation of the histone tail responsible for the gene expression in various cellular processes, of which enhancer of zeste homolog 2 (EZH2) acts as tumor suppressor. Overexpression of EZH2 results in hyper activation found in a variety of cancer. Point mutation on two important residues were induced and the results were compared between the wild type and mutant EZH2. The mutation of Y641 and A677 present in the active region of the protein alters the interaction of the top ranked compound with the newly modeled binding groove of the SET domain, giving a GLIDE score of −12.26 kcal/mol, better than that of the wild type at −11.664 kcal/mol. In depth analysis were carried out for understanding the underlying molecular mechanism using techniques viz. molecular dynamics, principal component analysis, residue interaction network and free energy landscape analysis, which showed that the mutated residues changed the overall conformation of the system along with the residue-residue interaction network. The insight from this study could be of great relevance while designing new compounds for EZH2 enzyme inhibition and the effect of mutation on the overall binding mechanism of the system.

Predicting Designability of Small Proteins from Graph Features of Contact Maps.

Highly designable structures can be distinguished based on certain geometric graphical features of the interactions, confirming the fact that the topology of a protein structure and its residue-residue interaction network are important determinants of its designability. The most designable structures and least designable structures obtained for sets of proteins having the same number of residues are compared. It is shown that the most designable structures predicted by the graph features of the contact diagrams are more densely packed, whereas the poorly designable structures are more open structures or structures that are loosely packed. Interestingly enough, it can also be seen that the highly designable identified are also common structural motifs found in nature.

The Promiscuity of Allosteric Regulation of Nuclear Receptors by Retinoid X Receptor.

The promiscuous protein retinoid X receptor (RXR) displays essential allosteric regulation of several members in the nuclear hormone receptor superfamily via heterodimerization and (anti)cooperative binding of cognate ligands. Here, the structural basis of the positive allostery of RXR and constitutive androstane receptor (CAR) is revealed. In contrast, a similar computational approach had previously revealed the mechanism for negative allostery in the complex of RXR and thyroid receptor (TR). By comparing the positive and negative allostery of RXR complexed with CAR and TR respectively, we reported the promiscuous allosteric control involving RXR. We characterize the allosteric mechanism by expressing the correlated dynamics of selected residue-residue contacts which was extracted from atomistic molecular dynamics simulation and statistical analysis. While the same set of residues in the binding pocket of RXR may initiate the residue-residue interaction network, RXR uses largely different sets of contacts (only about one-third identical) and allosteric modes to regulate TR and CAR. The promiscuity of RXR control may originate from multiple factors, including (1) the frustrated fit of cognate ligand 9c to the RXR binding pocket and (2) the different ligand-binding features of TR (loose) versus CAR (tight) to their corresponding cognate ligands.

Deducing the functional characteristics of the human selenoprotein SELK from the structural properties of its intrinsically disordered C-terminal domain.

The intrinsically disordered proteins (IDPs) cannot be described by a single structural representation but, due to their high structural fluctuation, through conformational ensembles. Certainly, molecular dynamics (MD) simulations represent a useful tool to study their different conformations capturing the conformational distribution. Our group is focusing on the structural characterization of proteins belonging to the seleno-proteome due to their involvement in cancer. They present disordered domains central for their biological function, and, in particular, SELK is a single-pass transmembrane protein that resides in the endoplasmic reticulum membrane (ER) with a C-terminal domain exposed to the cytoplasm that is known to interact with different components of the endoplasmic reticulum associated to the protein degradation (ERAD) pathway. This protein is found to be up-expressed in hepatocellular carcinoma and in other cancers. In this work we performed a detailed analysis of the C-terminal domain sequence of SELK and discovered that it is characterized by many prolines, and four negatively and eleven positively charged residues, which are crucial for its biological activity. This region can be considered as a weak polyelectrolyte and, specifically, a polycation, with high disordered propensity and different phosphorylation sites dislocated along the sequence. Then, we modeled its three-dimensional structure by performing MD simulations in water at neutral pH to analyze the structural stability as well as to identify the presence of HUB residues that play a key structural role as evidenced by the residue-residue interaction network analysis. Through this approach, we demonstrate that the C-terminal domain of SELK (i) presents a poor content of regular secondary structure elements, (ii) is dynamically stabilized by a network of intra-molecular H-bonds and H-bonds with water molecules, (iii) is highly fluctuating and, therefore, can be described only through a conformational ensemble, where we evidenced a distribution of equilibrium conformers which continuously inter-change their conformations. Finally to verify the specific role played by the negative charges, we also performed MD simulations at acidic pH. Overall, all the obtained results evidenced that SELK has the dynamic structural features to be defined as a HUB protein able to interact with multiple members. Therefore, considering the possible role that this protein can have in cancer development and progression, it can represent a target for drug design studies.

Molecular-docking study of malaria drug target enzyme transketolase in Plasmodium falciparum 3D7 portends the novel approach to its treatment.

BackgroundMalaria has been a major life threatening mosquito borne disease from long since. Unavailability of any effective vaccine and recent emergence of multi drug resistant strains of malaria pathogen Plasmodium falciparum continues to cause persistent deaths in the tropical and sub-tropical region. As a result, demands for new targets for more effective anti-malarial drugs are escalating. Transketolase is an enzyme of the pentose phosphate pathway; a novel pathway which is involved in energy generation and nucleic acid synthesis. Moreover, significant difference in homology between Plasmodium falciparum transketolase (Pftk) and human (Homo sapiens) transketolase makes it a suitable candidate for drug therapy. Our present study is aimed to predict the 3D structure of Plasmodium falciparum transketolase and design an inhibitor against it.ResultsThe primary and secondary structural features of the protein is calculated by ProtParam and SOPMA respectively which revealed the protein is composed of 43.3 % alpha helix and 33.04 % random coils along with 15.62 % extended strands, 8.04 % beta turns. The three dimensional structure of the transketolase is constructed using homology modeling tool MODELLAR utilizing several available transketolase structures as templates. The structure is then subjected to deep optimization and validated by structure validation tools PROCHECK, VERIFY 3D, ERRAT, QMEAN. The predicted model scored 0.74 for global model reliability in PROCHECK analysis, which ensures the quality of the model. According to VERIFY 3D the predicted model scored 0.77 which determines good environmental profile along with ERRAT score of 78.313 which is below 95 % rejection limit. Protein-protein and residue–residue interaction networks are generated by STRING and RING server respectively. CASTp server was used to analyze active sites and His 109, Asn 108 and His 515 are found to be more positive site to dock the substrate, in addition molecular docking simulation with Autodock vina determined the estimated free energy of molecular binding was of −6.6 kcal/mol for most favorable binding of 6′-Methyl-Thiamin Diphosphate.ConclusionThis predicted structure of Pftk will serve first hand in the future development of effective Pftk inhibitors with potential anti-malarial activity. However, this is a preliminary study of designing an inhibitor against Plasmodium falciparum 3D7; the results await justification by in vitro and in vivo experimentations.

Characterization of domain-peptide interaction interface: prediction of SH3 domain-mediated protein-protein interaction network in yeast by generic structure-based models.

Determination of the binding specificity of SH3 domain, a peptide recognition module (PRM), is important to understand their biological functions and reconstruct the SH3-mediated protein-protein interaction network. In the present study, the SH3-peptide interactions for both class I and II SH3 domains were characterized by the intermolecular residue-residue interaction network. We developed generic MIEC-SVM models to infer SH3 domain-peptide recognition specificity that achieved satisfactory prediction accuracy. By investigating the domain-peptide recognition mechanisms at the residue level, we found that the class-I and class-II binding peptides have different binding modes even though they occupy the same binding site of SH3. Furthermore, we predicted the potential binding partners of SH3 domains in the yeast proteome and constructed the SH3-mediated protein-protein interaction network. Comparison with the experimentally determined interactions confirmed the effectiveness of our approach. This study showed that our sophisticated computational approach not only provides a powerful platform to decipher protein recognition code at the molecular level but also allows identification of peptide-mediated protein interactions at a proteomic scale. We believe that such an approach is general to be applicable to other domain-peptide interactions.

Discovery of Intramolecular Signal Transduction Network Based on a New Protein Dynamics Model of Energy Dissipation

A novel approach to reveal intramolecular signal transduction network is proposed in this work. To this end, a new algorithm of network construction is developed, which is based on a new protein dynamics model of energy dissipation. A key feature of this approach is that direction information is specified after inferring protein residue-residue interaction network involved in the process of signal transduction. This enables fundamental analysis of the regulation hierarchy and identification of regulation hubs of the signaling network. A well-studied allosteric enzyme, E. coli aspartokinase III, is used as a model system to demonstrate the new method. Comparison with experimental results shows that the new approach is able to predict all the sites that have been experimentally proved to desensitize allosteric regulation of the enzyme. In addition, the signal transduction network shows a clear preference for specific structural regions, secondary structural types and residue conservation. Occurrence of super-hubs in the network indicates that allosteric regulation tends to gather residues with high connection ability to collectively facilitate the signaling process. Furthermore, a new parameter of propagation coefficient is defined to determine the propagation capability of residues within a signal transduction network. In conclusion, the new approach is useful for fundamental understanding of the process of intramolecular signal transduction and thus has significant impact on rational design of novel allosteric proteins.

Prediction by Graph Theoretic Measures of Structural Effects in Proteins Arising from Non-Synonymous Single Nucleotide Polymorphisms

Recent analyses of human genome sequences have given rise to impressive advances in identifying non-synonymous single nucleotide polymorphisms (nsSNPs). By contrast, the annotation of nsSNPs and their links to diseases are progressing at a much slower pace. Many of the current approaches to analysing disease-associated nsSNPs use primarily sequence and evolutionary information, while structural information is relatively less exploited. In order to explore the potential of such information, we developed a structure-based approach, Bongo (Bonds ON Graph), to predict structural effects of nsSNPs. Bongo considers protein structures as residue–residue interaction networks and applies graph theoretical measures to identify the residues that are critical for maintaining structural stability by assessing the consequences on the interaction network of single point mutations. Our results show that Bongo is able to identify mutations that cause both local and global structural effects, with a remarkably low false positive rate. Application of the Bongo method to the prediction of 506 disease-associated nsSNPs resulted in a performance (positive predictive value, PPV, 78.5%) similar to that of PolyPhen (PPV, 77.2%) and PANTHER (PPV, 72.2%). As the Bongo method is solely structure-based, our results indicate that the structural changes resulting from nsSNPs are closely associated to their pathological consequences.