Residue interaction network analysis of Dronpa and a DNA clamp

Identifying Lymph Node Metastasis-Related Factors in Breast Cancer Using Differential Modular and Mutational Structural Analysis

Complex diseases are generally caused by disorders of biological networks and/or mutations in multiple genes. Comparisons of network topologies between different disease states can highlight key factors in their dynamic processes. Here, we propose a differential modular analysis approach that integrates protein-protein interactions with gene expression profiles for modular analysis, and introduces inter-modular edges and date hubs to identify the "core network module" that quantifies the significant phenotypic variation. Then, based on this core network module, key factors, including functional protein-protein interactions, pathways, and driver mutations, are predicted by the topological-functional connection score and structural modeling. We applied this approach to analyze the lymph node metastasis (LNM) process in breast cancer. The functional enrichment analysis showed that both inter-modular edges and date hubs play important roles in cancer metastasis and invasion, and in metastasis hallmarks. The structural mutation analysis suggested that the LNM of breast cancer may be the outcome of the dysfunction of rearranged during transfection (RET) proto-oncogene-related interactions and the non-canonical calcium signaling pathway via an allosteric mutation of RET. We believe that the proposed method can provide new insights into disease progression such as cancer metastasis.

Elastic network models and molecular dynamic simulations reveal the molecular basis of allosteric regulation in ubiquitin-specific protease 7 (USP7)

Ubiquitin-specific protease 7 (USP7) is one of the most abundant deubiquitinases and plays an important role in various malignant tumors. However, the molecular mechanisms underlying USP7's structures, dynamics, and biological significance are yet to be investigated. In this study, we constructed the full-length models of USP7 in both the extended and compact state, and applied elastic network models (ENM), molecular dynamics (MD) simulations, perturbation response scanning (PRS) analysis, residue interaction networks as well as allosteric pocket prediction to investigate allosteric dynamics in USP7. Our analysis of intrinsic and conformational dynamics revealed that the structural transition between the two states is characterized by global clamp motions, during which the catalytic domain (CD) and UBL4-5 domain exhibit strong negative correlations. The PRS analysis, combined with the analysis of disease mutations and post-translational modifications (PTMs) further highlighted the allosteric potential of the two domains. The residue interaction network based on MD simulations captured an allosteric communication path which starts at CD domain and ends at UBL4-5 domain. Moreover, we identified a pocket at the TRAF-CD interface as a high-potential allosteric site for USP7. Overall, our studies not only provide molecular insights into the conformational changes of USP7, but also aid in the design of allosteric modulators that target USP7.

Functional insight into Cordyceps militaris sugar transporters by structure modeling, network analysis and allosteric regulation

Insights into the structures, functions and dynamics of Cordyceps militaris (C. militaris) sugar transporters are necessary for understanding their versatile metabolic capability for fungal growth. The sequence-function relationship study of 85 C. militaris sugar transporters showed that there is a gap between phylogenetic-based subfamily classification and their functions. Beyond protein sequences, structural modeling and principal component analysis of the structural ensemble revealed the different folds of the Car and Org subfamilies. Performing channel detection and network analysis found that the Alp and Hex subfamilies can be specifically distinguished from others by the betweenness of channel residues. Signature dynamics analysis further suggested that the Hex subfamily demonstrates different dynamics, with high flexibility at the H1 region in TM11. Furthermore, the H1 region as an allosteric site was examined by network parameter calculations that guided allosteric pathways between this region and the channel cavity. Together with gene expression data of C. militaris, e.g., Hex06741 in the Hex subfamily, it was promisingly expressed when sugar utilization was altered. This work demonstrates an in silico framework for investigating C. militaris sugar transporters as an example case study of the allosteric activity of the Hex subfamily and can facilitate sugar transporter engineering design that can further optimize the preferable sugar utilization and fermentation process of C. militaris.

What is the potential impact of genetic divergence of plastid ribosomal genes between Silene nutans lineages in hybrids? An in silico approach using the 3D structure of the plastid ribosome

Introduction

Following the integration of cyanobacteria into the eukaryotic cells, many genes were transferred from the plastid to the nucleus. As a result, plastid complexes are encoded both by plastid and nuclear genes. Tight co-adaptation is required between these genes as plastid and nuclear genomes differ in several characteristics, such as mutation rate and inheritance patterns. Among these are complexes from the plastid ribosome, composed of two main subunits: a large and a small one, both composed of nuclear and plastid gene products. This complex has been identified as a potential candidate for sheltering plastid-nuclear incompatibilities in a Caryophyllaceae species, Silene nutans. This species is composed of four genetically differentiated lineages, which exhibit hybrid breakdown when interlineage crosses are conducted. As this complex is composed of numerous interacting plastid-nuclear gene pairs, in the present study, the goal was to reduce the number of gene pairs that could induce such incompatibilities.

Method

We used the previously published 3D structure of the spinach ribosome to further elucidate which of the potential gene pairs might disrupt plastid-nuclear interactions within this complex. After modeling the impact of the identified mutations on the 3D structure, we further focused on one strongly mutated plastid-nuclear gene pair: rps11-rps21. We used the centrality measure of the mutated residues to further understand if the modified interactions and associated modified centralities might be correlated with hybrid breakdown.

Results and discussion

This study highlights that lineage-specific mutations in essential plastid and nuclear genes might disrupt plastid-nuclear protein interactions of the plastid ribosome and that reproductive isolation correlates with changes in residue centrality values. Because of this, the plastid ribosome might be involved in hybrid breakdown in this system.

Biophysical Interpretation of Evolutionary Consequences on the SARS-CoV2 Main Protease through Molecular Dynamics Simulations and Network Topology Analysis

In this study, we present a combined analysis procedure between atomistic molecular dynamics (MD) simulations and network topology to obtain more understanding on the evolutionary consequences on protein stability and substrate binding of the main protease enzyme of SARS-CoV2. Communicability matrices of the protein residue networks (PRNs) were extracted from MD trajectories of both Mpro enzymes in complex with the nsp8/9 peptide substrate to compare the local communicability within both proteases that would affect the enzyme function, along with biophysical details on global protein conformation, flexibility, and contribution of amino acid side chains to both intramolecular and intermolecular interactions. The analysis displayed the significance of the mutated residue 46 with the highest communicability gain to the binding pocket closure. Interestingly, the mutated residue 134 with the highest communicability loss corresponded to a local structural disruption of the adjacent peptide loop. The enhanced flexibility of the disrupted loop connecting to the catalytic residue Cys145 introduced an extra binding mode that brought the substrate in proximity and could facilitate the reaction. This understanding might provide further help in the drug development strategy against SARS-CoV2 and prove the capability of the combined techniques of MD simulations and network topology analysis as a "reverse" protein engineering tool.

Exploring the mechanism of C473D mutation on CDC25B causing weak binding affinity with CDK2/CyclinA by molecular dynamics study

CDC25B belongs to the CDC25 family, and it plays an important part in regulating the activity of CDK/CyclinA. Studies have shown that CDC25B is closely related to cancer development. When CYS473 on CDC25B is mutated into ASP, the affinity between CDC25B and CDK2/CyclinA weakens, and their dissociation speed is greatly improved. However, the mechanism by which the CDC25BC473D mutant weakens its binding to CDK2/CyclinA is unclear. In order to study the effect of CDC25BC473D mutants on CDK2/CyclinA substrates, we constructed and verified the rationality of the CDC25BWT:CDK2/CyclinA system and CDC25BC473D:CDK2/CyclinA system and conducted molecular dynamics (MD) simulation analysis. In the post-analysis, the fluctuations of residues ARG488-SER499, LYS541-TRP550 on CDC25B and residues ASP206-ASP210 on CDK2 were massive in the mutant CDC25BC473D:CDK2/CyclinA system. And the interactions between residue ARG492 and residue GLU208, residue ARG544 and residue GLU42, residue ARG544 and TRP550 were weakened in the mutant CDC25BC473D:CDK2/CyclinA system. The results showed that when CYS473 on CDC25B was mutated into ASP473, the mutant CDC25BC473D:CDK2/CyclinA system was less stable than the wild-type CDC25BWT:CDK2/CyclinA system. Finally, active site CYS473 of CDC25B was speculated to be the key residue, which had great effects on the binding between CDC25BCYS473 and CDK2 in the CDC25BC473D:CDK2/CyclinA system. Consequently, overall analyses appeared in this study ultimately provided a useful understanding of the weak interactions between CDC25BCYS473D and CDK2/CyclinA.Communicated by Ramaswamy H. Sarma.

Comparative molecular dynamics study of the receptor-binding domains in SARS-CoV-2 and SARS-CoV and the effects of mutations on the binding affinity

Here, we report on a computational comparison of the receptor-binding domains (RBDs) on the spike proteins of severe respiratory syndrome coronavirus-2 (SARS-CoV-2) and SARS-CoV in free forms and as complexes with angiotensin-converting enzyme 2 (ACE2) as their receptor in humans. The impact of 42 mutations discovered so far on the structure and thermodynamics of SARS-CoV-2 RBD was also assessed. The binding affinity of SARS-CoV-2 RBD for ACE2 is higher than that of SARS-CoV RBD. The binding of COVA2-04 antibody to SARS-CoV-2 RBD is more energetically favorable than the binding of COVA2-39, but also less favorable than the formation of SARS-CoV-2 RBD-ACE2 complex. The net charge, the dipole moment and hydrophilicity of SARS-CoV-2 RBD are higher than those of SARS-CoV RBD, producing lower solvation and surface free energies and thus lower stability. The structure of SARS-CoV-2 RBD is also more flexible and more open, with a larger solvent-accessible surface area than that of SARS-CoV RBD. Single-point mutations have a dramatic effect on distribution of charges, most prominently at the site of substitution and its immediate vicinity. These charge alterations alter the free energy landscape, while X→F mutations exhibit a stabilizing effect on the RBD structure through π stacking. F456 and W436 emerge as two key residues governing the stability and affinity of the spike protein for its ACE2 receptor. These analyses of the structural differences and the impact of mutations on different viral strains and members of the coronavirus genera are an essential aid in the development of effective therapeutic strategies. Communicated by Ramaswamy H. Sarma.

Impact of compound mutations I1171N + F1174I and I1171N + L1198H on the structure of ALK in NSCLC pathogenesis: atomistic insights

Anaplastic lymphoma kinase (ALK) fusion genes are found in 3%-5% of non-small cell lung cancers (NSCLCs). NSCLC is the most common type of lung cancer, accounting for 84% of all lung cancer diagnoses. Available treatment options for ALK-positive NSCLCs involve the use of ALK tyrosine kinase inhibitors (ALK-TKIs) which have shown to be effective with a high response rate. Nonetheless, the emergence of multiple compound mutations such as I1171N + F1174I or I1171N + L1198H has been reported to cause resistance to all approved ALK-TKIs. However, the underlying molecular mechanisms surrounding the impact of these compound mutants remain poorly understood. Hence, we performed molecular dynamics simulations to characterize the structural effects and functional implications of these compound mutations. Findings revealed a destabilizing effect on ALK by mutants as compared to the wild-type ALK structure. Also, further insights revealed a lower root-mean-squared fluctuation, radius of gyration, and solvent-accessible surface area values of I1171N + F1174I and I1171N + L1198H ALK compound mutations suggesting that the mutants have a more compact structure and a smaller surface area than the wild-type protein. The mutants also distorted the activation loop residues (Tyr1278, Tyr1282, and Tyr1283) in the ALK structure, which further identify them as possible disruptors of phosphorylation. In contrast to wild conformation, the mutant conformations exhibited a reduced node degree in their residue interaction networks. Collectively, our findings provide deeper insights into the deleterious effects of I1171N + F1174I and I1171N + L1198H ALK compound mutations, which may contribute to NSCLC pathogenesis.Communicated by Ramaswamy H. Sarma.

Landscape-Based Protein Stability Analysis and Network Modeling of Multiple Conformational States of the SARS-CoV-2 Spike D614G Mutant: Conformational Plasticity and Frustration-Induced Allostery as Energetic Drivers of Highly Transmissible Spike Variants

The structural and functional studies of the SARS-CoV-2 spike protein variants revealed an important role of the D614G mutation that is shared across many variants of concern (VOCs), suggesting the effect of this mutation on the enhanced virus infectivity and transmissibility. The recent structural and biophysical studies provided important evidence about multiple conformational substates of the D614G spike protein. The development of a plausible mechanistic model that can explain the experimental observations from a more unified thermodynamic perspective is an important objective of the current work. In this study, we employed efficient and accurate coarse-grained simulations of multiple structural substates of the D614G spike trimers together with the ensemble-based mutational frustration analysis to characterize the dynamics signatures of the conformational landscapes. By combining the local frustration profiling of the conformational states with residue-based mutational scanning of protein stability and network analysis of allosteric interactions and communications, we determine the patterns of mutational sensitivity in the functional regions and sites of variants. We found that the D614G mutation may induce a considerable conformational adaptability of the open states in the SARS-CoV-2 spike protein without compromising the folding stability and integrity of the spike protein. The results suggest that the D614G mutant may employ a hinge-shift mechanism in which the dynamic couplings between the site of mutation and the interprotomer hinge modulate the interdomain interactions, global mobility change, and the increased stability of the open form. This study proposes that mutation-induced modulation of the conformational flexibility and energetic frustration at the interprotomer interfaces may serve as an efficient mechanism for allosteric regulation of the SARS-CoV-2 spike proteins.

Dissecting mutational allosteric effects in alkaline phosphatases associated with different Hypophosphatasia phenotypes: An integrative computational investigation

Hypophosphatasia (HPP) is a rare inherited disorder characterized by defective bone mineralization and is highly variable in its clinical phenotype. The disease occurs due to various loss-of-function mutations in ALPL, the gene encoding tissue-nonspecific alkaline phosphatase (TNSALP). In this work, a data-driven and biophysics-based approach is proposed for the large-scale analysis of ALPL mutations-from nonpathogenic to severe HPPs. By using a pipeline of synergistic approaches including sequence-structure analysis, network modeling, elastic network models and atomistic simulations, we characterized allosteric signatures and effects of the ALPL mutations on protein dynamics and function. Statistical analysis of molecular features computed for the ALPL mutations showed a significant difference between the control, mild and severe HPP phenotypes. Molecular dynamics simulations coupled with protein structure network analysis were employed to analyze the effect of single-residue variation on conformational dynamics of TNSALP dimers, and the developed machine learning model suggested that the topological network parameters could serve as a robust indicator of severe mutations. The results indicated that the severity of disease-associated mutations is often linked with mutation-induced modulation of allosteric communications in the protein. This study suggested that ALPL mutations associated with mild and more severe HPPs can exert markedly distinct effects on the protein stability and long-range network communications. By linking the disease phenotypes with dynamic and allosteric molecular signatures, the proposed integrative computational approach enabled to characterize and quantify the allosteric effects of ALPL mutations and role of allostery in the pathogenesis of HPPs.

Deciphering the Effect of Lysine Acetylation on the Misfolding and Aggregation of Human Tau Fragment 171IPAKTPPAPK180 Using Molecular Dynamic Simulation and the Markov State Model

The formation of neurofibrillary tangles (NFT) with β-sheet-rich structure caused by abnormal aggregation of misfolded microtubule-associated protein Tau is a hallmark of tauopathies, including Alzheimer’s Disease. It has been reported that acetylation, especially K174 located in the proline-rich region, can largely promote Tau aggregation. So far, the mechanism of the abnormal acetylation of Tau that affects its misfolding and aggregation is still unclear. Therefore, revealing the effect of acetylation on Tau aggregation could help elucidate the pathogenic mechanism of tauopathies. In this study, molecular dynamics simulation combined with multiple computational analytical methods were performed to reveal the effect of K174 acetylation on the spontaneous aggregation of Tau peptide ¹⁷¹IPAKTPPAPK¹⁸⁰, and the dimerization mechanism as an early stage of the spontaneous aggregation was further specifically analyzed by Markov state model (MSM) analysis. The results showed that both the actual acetylation and the mutation mimicking the acetylated state at K174 induced the aggregation of the studied Tau fragment; however, the effect of actual acetylation on the aggregation was more pronounced. In addition, acetylated K174 plays a major contributing role in forming and stabilizing the antiparallel β-sheet dimer by forming several hydrogen bonds and side chain van der Waals interactions with residues I171, P172, A173 and T175 of the corresponding chain. In brief, this study uncovered the underlying mechanism of Tau peptide aggregation in response to the lysine K174 acetylation, which can deepen our understanding on the pathogenesis of tauopathies.

Identification of Oleanolic Acid as Allosteric Agonist of Integrin αM by Combination of In Silico Modeling and In Vitro Analysis

Oleanolic acid is a widely distributed natural product, which possesses promising antitumor, antiviral, antihyperlipidemic, and anti-inflammatory activities. A heterodimeric complex formed by integrin α_M (CD11b) and integrin β₂ (CD18) is highly expressed on monocytes and macrophages. In the current study, we demonstrate that the I domain of α_M (α_M-I domain) might present a potential cellular target for oleanolic acid. In vitro data show that oleanolic acid induces clustering of α_M on macrophages and reduces their non-directional migration. In accordance with experimental data, molecular docking revealed that oleanolic acid binds to the α_M-I domain in its extended-open form, the dominant conformation found in α_M clusters. Molecular dynamics simulation revealed that oleanolic acid can increase the flexibility of the α7 helix and promote its movement away from the N-terminus, indicating that oleanolic acid may facilitate the conversion of the α_M-I domain from the extended-closed to the extended-open conformation. As demonstrated by metadynamics simulation, oleanolic acid can destabilize the local minimum of the α_M-I domain in the open conformation partially through disturbance of the interactions between α1 and α7 helices. In summary, we demonstrate that oleanolic acid might function as an allosteric agonist inducing clustering of α_M on macrophages by shifting the balance from the closed to the extended-open conformation. The molecular target identified in this study might hold potential for a purposeful use of oleanolic acid to modulate chronic inflammatory responses.

Insights into Conformational Dynamics and Allostery in DNMT1-H3Ub/USP7 Interactions

DNA methyltransferases (DNMTs) including DNMT1 are a conserved family of cytosine methylases that play crucial roles in epigenetic regulation. The versatile functions of DNMT1 rely on allosteric networks between its different interacting partners, emerging as novel therapeutic targets. In this work, based on the modeling structures of DNMT1-ubiquitylated H3 (H3Ub)/ubiquitin specific peptidase 7 (USP7) complexes, we have used a combination of elastic network models, molecular dynamics simulations, structural residue perturbation, network modeling, and pocket pathway analysis to examine their molecular mechanisms of allosteric regulation. The comparative intrinsic and conformational dynamics analysis of three DNMT1 systems has highlighted the pivotal role of the RFTS domain as the dynamics hub in both intra- and inter-molecular interactions. The site perturbation and network modeling approaches have revealed the different and more complex allosteric interaction landscape in both DNMT1 complexes, involving the events caused by mutational hotspots and post-translation modification sites through protein-protein interactions (PPIs). Furthermore, communication pathway analysis and pocket detection have provided new mechanistic insights into molecular mechanisms underlying quaternary structures of DNMT1 complexes, suggesting potential targeting pockets for PPI-based allosteric drug design.

Exploring the cause of the dual allosteric targeted inhibition attaching to allosteric sites enhancing SHP2 inhibition

SHP2 is a protein tyrosine phosphatase (PTP) that can regulate the tyrosine phosphorylation level. Overexpression of SHP2 will promote the development of cancer diseases, so SHP2 has become one of the popular targets for the treatment of cancer. Studies have reported that both SHP099 and SHP844 are inhibitors of SHP2 and bind to different allosteric sites 1 and 2, respectively. Studies have shown that combining SHP099 with SHP844 will enhance pharmacological pathway inhibition in cells. This study uses molecular dynamic simulations to explore the dual allosteric targeted inhibition mechanism. The result shows that the residues THR108-TRP112 (allosteric site 1) move to LEU236-GLN245 (αB-αC link loop in PTP domain) , the residues of GLN79-GLN87 (allosteric site 2) get close to LEU262-GLN269 (αA-αB link loop in PTP domain) and HIS458-ARG465 (P-loop) come near to ARG501-THR507 (Q-loop) in SHP2-SHP099-SHP844 system, which makes the "inactive conformation" more stable and prevents the substrate from entering the catalytic site. Meanwhile, residue GLU110 (allosteric site 1), ARG265 (allosteric site 2), and ARG501 (Q-loop) are speculated to be the key residues that causing the SHP2 protein in auto-inhibition conformation. It is hoped that this study will provide clues for the development of the dual allosteric targeted inhibition of SHP2.

Residue interaction networks of K-Ras protein with water molecules identifies the potential role of switch II and P-loop

The mutant K-Ras with aberrant signaling is the primary cause of several cancers. The proposed study investigated the influence of water molecules in K-Ras crystal structure, where they have a significant function by understanding their residue interaction networks (RINs). We analyzed the RINs of K-Ras with and without water molecules and determined their interaction properties. RINs were developed with the help of StructureViz2 and RINspector; further, the changes in K-Ras backbone flexibility were predicted with the DynaMine. We found that the residues K42, I142, and L159 are the hotspots from water, including the K-Ras-GTP complex with the highest residue centrality analysis (RCA) Z-score. The DynaMine prediction calculated the NMR S² value for the frequently mutated positions G12, G13, and Q61 showing a minor shift in flexibility, which make up the P-Loop and switch II of the K-Ras protein. This flexibility shift can account for changes in conformational activity and the protein's GTPase activity, making it difficult to recognize by the effectors and exchange factors. Taken together, our study helps in understanding the functional importance of the water molecules in K-Ras protein and the impact of mutation that modulate the conformational state of the protein.

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.

Structure elements can be predicted using the contact volume among protein residues

Previously, the structure elements of dihydrofolate reductase (DHFR) were determined using comprehen­sive Ala-insertion mutation analysis, which is assumed to be a kind of protein “building blocks.” It is hypo­thesized that our comprehension of the structure elements could lead to understanding how an amino acid sequence dictates its tertiary structure. However, the comprehensive Ala-insertion mutation analysis is a time- and cost-consuming process and only a set of the DHFR structure elements have been reported so far. Therefore, developing a computational method to predict structure elements is an urgent necessity. We focused on intramolecular residue–residue contacts to predict the structure elements. We introduced a simple and effective parameter: the overlapped contact volume (CV) among the residues and calculated the CV along the DHFR sequence using the crystal structure. Our results indicate that the CV profile can recapitulate its precipitate ratio profile, which was used to define the structure elements in the Ala-insertion mutation analysis. The CV profile allowed us to predict structure elements like the experimentally determined structure elements. The strong correlation between the CV and precipitate ratio profiles indicates the importance of the intramolecular residue–residue contact in maintaining the tertiary structure. Additionally, the CVs between the structure elements are considerably more than those between a structure element and a linker or two linkers, indicating that the structure elements play a funda­mental role in increasing the intramolecular adhesion. Thus, we propose that the structure elements can be considered a type of “building blocks” that maintain and dictate the tertiary structures of proteins.

Screening potential FDA-approved inhibitors of the SARS-CoV-2 major protease 3CLpro through high-throughput virtual screening and molecular dynamics simulation

It has been confirmed that the new coronavirus SARS-CoV-2 caused the global pandemic of coronavirus disease 2019 (COVID-19). Studies have found that 3-chymotrypsin-like protease (3CL^pro) is an essential enzyme for virus replication, and could be used as a potential target to inhibit SARS-CoV-2. In this work, 3CL^pro was used as the target to complete the high-throughput virtual screening of the FDA-approved drugs, and Indinavir and other 10 drugs with high docking scores for 3CL^pro were obtained. Studies on the binding pattern of 3CL^pro and Indinavir found that Indinavir could form the stable hydrogen bond (H-bond) interactions with the catalytic dyad residues His41-Cys145. Binding free energy study found that Indinavir had high binding affinity with 3CL^pro. Subsequently, molecular dynamics simulations were performed on the 3CL^pro and 3CL^pro-Indinavir systems, respectively. The post-dynamic analyses showed that the conformational state of the 3CLpro-Indinavir system transformed significantly and the system tended to be more stable. Moreover, analyses of the residue interaction network (RIN) and H-bond occupancy revealed that the residue-residue interaction at the catalytic site of 3CL^pro was significantly enhanced after binding with Indinavir, which in turn inactivated the protein. In short, through this research, we hope to provide more valuable clues against COVID-19.

Probing the Relation Between Community Evolution in Dynamic Residue Interaction Networks and Xylanase Thermostability

Residue-residue interactions are the basis of protein thermostability. The molecular conformations of Streptomyces lividans xylanase (xyna_strli) and Thermoascus aurantiacus xylanase (xyna_theau) at 300K, 325K and 350K were obtained by Molecular Dynamics (MD) simulations. Dynamic weighted residue interaction networks were constructed and the rigid-communities were detected using the ESPRA algorithm and the Evolving Graph+Fast-Newman algorithm. The residues in the rigid-communities are primarily located in loop2, short helixes α2^', α3^', α4^' and helixes α3 and α4. Thus, the rigid-community is close to the N-terminus of xylanase, which is usually stabilized to increase thermostability using site-directed mutagenesis. The evolution of the rigid-community with increasing temperature shows a stable synergistic interaction between loop2, α2^', α3^' and α4^' in xyna_theau. In particular, the short helixes α2^' and α3^' form a "thermo helix" to promote thermostability. In addition, tight global interactions between loop2, α2^', α3^', α3, α4^' and α4 of xyna_theau are identified, consisting mainly of hydrogen bonds, van der Waals forces and π-π stacking. These residue interactions are more resistant to high temperatures than those in xyna_strli. Robust residue interactions within these secondary structures are key factors influencing xyna_strli and xyna_theau thermostability. Analyzing the rigid-community can elucidate the cooperation of secondary structures, which cannot be discovered from sequence and 3D structure alone.

Residue Interaction Network Analysis Predicts a Val24-Ile31 Interaction May be Involved in Preventing Amyloid-Beta (1-42) Primary Nucleation

Alzheimer's disease (AD) patients could benefit from a more effective treatment than the current FDA-approved options. Because amyloid-beta (Aβ) is thought to play a central role in AD pathogenesis, many experimental drugs attempt to reduce Aβ-induced pathology. Preventing amyloid accumulation may be a more effective strategy than clearing Aβ plaques after they form. If preventing Aβ accumulation can treat or prevent AD, then understanding Aβ primary nucleation may aid rational drug design. This study examines Aβ residue interaction networks and reports network and structural observations that may provide insight into primary nucleation. While many studies identify structural features of Aβ that promote aggregation, this study reports features that may resist primary nucleation by examining Aβ42 studies in more and less polar solvents. In Aβ42 in a less polar solvent (PDB ID: 1IYT), Val24 and Ile31 have higher betweenness and residue centrality values. This may be due to a predicted interaction between Val24 and Ile31. Residues in the central hydrophobic cluster (CHC) of Aβ40 and Aβ42 had significantly higher betweenness values compared to the average betweenness of the structures, highlighting the CHC's reported role in oligomerization. The predicted interaction between Val24 and Ile31 may reduce the likelihood of primary nucleation of Aβ.

Is Crocin a Potential Anti-tumor Candidate Targeting Microtubules? Computational Insights From Molecular Docking and Dynamics Simulations

Although it is known crocin, a hydrophilic compound from the herbal plant Crocus sativus L., has promising antitumor activity, the detailed mechanism of its antitumor activity was not well understood. Recent experiments suggested tubulin as the primary target for the antitumor activity of crocin. However, due to a lack of crystal structure of tubulin bound with crocin, the exact binding mode and interaction between crocin and tubulin remains exclusive. In the present work, a computational study by integrating multiple conformation docking, molecular dynamics simulation as well as residue interaction network analysis was performed to investigate the molecular mechanism of crocin-tubulin interaction. By comparing the docking score, the most likely binding mode CRO_E1 were identified from 20 different binding modes of crocin in the vinca binding pockets. Further molecular dynamics simulation of CRO_E1 complex showed the binding of crocin is more stable than the inhibitor soblidotin and vinblastine. During the simulation course, an excessive number of hydrogen bonds were observed for the ligand crocin. The binding free energy of crocin-tubulin complex was calculated as -79.25 ± 7.24 kcal/mol, which is almost twice of the ligand soblidotin and vinblastine. By using energy decomposition, hot residues for CRO_E1 were identified as Gln¹¹, Gln¹⁵, Thr⁷², Ser⁷⁵, Pro¹⁷³-Lys¹⁷⁴-Val¹⁷⁵-Ser¹⁷⁶-Asp¹⁷⁷, Tyr²²², and Asn²²⁶ in the β-chain, and Asp²⁴⁵, Ala²⁴⁷-Leu²⁴⁸, Val²⁵⁰, Asn³²⁹, and Ile³³² in the α-chain. Residue interaction network analysis also showed the importance of these hot residues in the interaction network of crocin-tubulin complex. In addition, a common residue motif Val¹⁷⁵-Xxx¹⁷⁶-Asp¹⁷⁷ was discovered for all three bindings, suggesting its importance in future drug design. The study could provide valuable insights into the interaction between crocin and tubulin, and give suggestive clues for further experimental studies.

Comparative Dynamics and Functional Mechanisms of the CYP17A1 Tunnels Regulated by Ligand Binding

As an important member of cytochrome P450 (CYP) enzymes, CYP17A1 is a dual-function monooxygenase with a critical role in the synthesis of many human steroid hormones, making it an attractive therapeutic target. The emerging structural information about CYP17A1 and the growing number of inhibitors for these enzymes call for a systematic strategy to delineate and classify mechanisms of ligand transport through tunnels that control catalytic activity. In this work, we applied an integrated computational strategy to different CYP17A1 systems with a panel of ligands to systematically study at the atomic level the mechanism of ligand-binding and tunneling dynamics. Atomistic simulations and binding free energy computations identify the dynamics of dominant tunnels and characterize energetic properties of critical residues responsible for ligand binding. The common transporting pathways including S, 3, and 2c tunnels were identified in CYP17A1 binding systems, while the 2c tunnel is a newly formed pathway upon ligand binding. We employed and integrated several computational approaches including the analysis of functional motions and sequence conservation, atomistic modeling of dynamic residue interaction networks, and perturbation response scanning analysis to dissect ligand tunneling mechanisms. The results revealed the hinge-binding and sliding motions as main functional modes of the tunnel dynamic, and a group of mediating residues as key regulators of tunnel conformational dynamics and allosteric communications. We have also examined and quantified the mutational effects on the tunnel composition, conformational dynamics, and long-range allosteric behavior. The results of this investigation are fully consistent with the experimental data, providing novel rationale to the experiments and offering valuable insights into the relationships between the structure and function of the channel networks and a robust atomistic model of activation mechanisms and allosteric interactions in CYP enzymes.

SARS-CoV and SARS-CoV-2 main protease residue interaction networks change when bound to inhibitor N3

COVID-19 is a respiratory disease caused by the coronavirus SARS-CoV-2. SARS-CoV-2 has many similarities with SARS-CoV. Both viruses rely on a protease called the main protease, or M^pro, for replication. Therefore, inhibiting M^pro may be a successful strategy for treating COVID-19. Structures of the main proteases of SARS-CoV and SARS-CoV-2 with and without inhibitor N3 are available in the Protein Data Bank. Comparing these structures revealed residue interaction network changes associated with N3 inhibition. Comparing network clustering with and without inhibitor N3 identified the formation of a cluster of residues 17, 18, 30-33, 70, 95, 98, 103, 117, 122, and 177 as a network change in both viral proteases when bound to inhibitor N3. Betweenness and stress centrality differences as well as differences in bond energies and relative B-factors when comparing free M^pro to inhibitor-bound M^pro identified residues 131, 175, 182, and 185 as possibly conformationally relevant when bound to the inhibitor N3. Taken together, these results provide insight into conformational changes of betacoronavirus M^pros when bound to an inhibitor.

In Silico Repurposing of J147 for Neonatal Encephalopathy Treatment: Exploring Molecular Mechanisms of Mutant Mitochondrial ATP Synthase

BACKGROUND

Neonatal Encephalopathy (NE) is a mitochondrial ATP synthase (mATPase) disease, which results in the death of infants. The case presented here is reportedly caused by complex V deficiency as a result of mutation of Arginine to Cysteine at residue 329 in the mATPase. A recent breakthrough was the discovery of J147, which targets mATPase in the treatment of Alzheimer's disease. Based on the concepts of computational target-based drug design, this study investigated the possibility of employing J147 as a viable candidate in the treatment of NE.

OBJECTIVE/METHODS

The structural dynamic implications of this drug on the mutated enzyme are yet to be elucidated. Hence, integrative molecular dynamics simulations and thermodynamic calculations were employed to investigate the activity of J147 on the mutated enzyme in comparison to its already established inhibitory activity on the wild-type enzyme.

RESULTS

A correlated structural trend occurred between the wild-type and mutant systems whereby all the systems exhibited an overall conformational transition. Equal observations in favorable free binding energies further substantiated uniformity in the mobility, and residual fluctuation of the wild-type and mutant systems. The similarity in the binding landscape suggests that J147 could as well modulate mutant mATPase activity in addition to causing structural modifications in the wild-type enzyme.

CONCLUSION

Findings suggest that J147 can stabilize the mutant protein and restore it to a similar structural state as the wild-type which depicts functionality. These details could be employed in drug design for potential drug resistance cases due to mATPase mutations that may present in the future.

Identification of Selective Novel Hits against Plasmodium falciparum Prolyl tRNA Synthetase Active Site and a Predicted Allosteric Site Using in silico Approaches

Recently, there has been increased interest in aminoacyl tRNA synthetases (aaRSs) as potential malarial drug targets. These enzymes play a key role in protein translation by the addition of amino acids to their cognate tRNA. The aaRSs are present in all Plasmodium life cycle stages, and thus present an attractive malarial drug target. Prolyl tRNA synthetase is a class II aaRS that functions in charging tRNA with proline. Various inhibitors against Plasmodium falciparum ProRS (PfProRS) active site have been designed. However, none have gone through clinical trials as they have been found to be highly toxic to human cells. Recently, a possible allosteric site was reported in PfProRS with two possible allosteric modulators: glyburide and TCMDC-124506. In this study, we sought to identify novel selective inhibitors targeting PfProRS active site and possible novel allosteric modulators of this enzyme. To achieve this, virtual screening of South African natural compounds against PfProRS and the human homologue was carried out using AutoDock Vina. The modulation of protein motions by ligand binding was studied by molecular dynamics (MD) using the GROningen MAchine for Chemical Simulations (GROMACS) tool. To further analyse the protein global motions and energetic changes upon ligand binding, principal component analysis (PCA), and free energy landscape (FEL) calculations were performed. Further, to understand the effect of ligand binding on the protein communication, dynamic residue network (DRN) analysis of the MD trajectories was carried out using the MD-TASK tool. A total of ten potential natural hit compounds were identified with strong binding energy scores. Binding of ligands to the protein caused observable global and residue level changes. Dynamic residue network calculations showed increase in betweenness centrality (BC) metric of residues at the allosteric site implying these residues are important in protein communication. A loop region at the catalytic domain between residues 300 and 350 and the anticodon binding domain showed significant contributions to both PC1 and PC2. Large motions were observed at a loop in the Z-domain between residues 697 and 710 which was also in agreement with RMSF calculations that showed increase in flexibility of residues in this region. Residues in this loop region are implicated in ATP binding and thus a change in dynamics may affect ATP binding affinity. Free energy landscape (FEL) calculations showed that the holo protein (protein-ADN complex) and PfProRS-SANC184 complexes were stable, as shown by the low energy with very few intermediates and hardly distinguishable low energy barriers. In addition, FEL results agreed with backbone RMSD distribution plots where stable complexes showed a normal RMSD distribution while unstable complexes had multimodal RMSD distribution. The betweenness centrality metric showed a loss of functional importance of key ATP binding site residues upon allosteric ligand binding. The deep basins in average L observed at the allosteric region imply that there is high accessibility of residues at this region. To further analyse BC and average L metrics data, we calculated the ΔBC and ΔL values by taking each value in the holo protein BC or L matrix less the corresponding value in the ligand-bound complex BC or L matrix. Interestingly, in allosteric complexes, residues located in a loop region implicated in ATP binding had negative ΔL values while in orthosteric complexes these residues had positive ΔL values. An increase in contact frequency between residues Ser263, Thr267, Tyr285, and Leu707 at the allosteric site and residues Thr397, Pro398, Thr402, and Gln395 at the ATP binding TXE loop was observed. In summary, this study identified five potential orthosteric inhibitors and five allosteric modulators against PfProRS. Allosteric modulators changed ATP binding site dynamics, as shown by RMSF, PCA, and DRN calculations. Changes in dynamics of the ATP binding site and increased contact frequency between residues at the proposed allosteric site and the ATP binding site may explain how allosteric modulators distort the ATP binding site and thus might inhibit PfProRS. The scaffolds of the identified hits in the study can be used as a starting point for antimalarial inhibitor development with low human cytotoxicity.

Probing Carbon Utilization of Cordyceps militaris by Sugar Transportome and Protein Structural Analysis

Beyond comparative genomics, we identified 85 sugar transporter genes in Cordyceps militaris, clustering into nine subfamilies as sequence- and phylogenetic-based functional classification, presuming the versatile capability of the fungal growths on a range of sugars. Further analysis of the global gene expression patterns of C. militaris showed 123 genes were significantly expressed across the sucrose, glucose, and xylose cultures. The sugar transporters specific for pentose were then identified by gene-set enrichment analysis. Of them, the putative pentose transporter, CCM_06358 gene, was highest expressed in the xylose culture, and its functional role in xylose transport was discovered by the analysis of conserved structural motifs. In addition, a battery of molecular modeling methods, including homology modeling, transport pathway analysis, residue interaction network combined with molecular mechanics Poisson–Boltzmann surface area simulation (MM-PBSA), was implemented for probing the structure and function of the selected pentose transporter (CCM_06358) as a representative of sugar transportome in C. militaris. Considering the network bottlenecks and structural organizations, we further identified key amino acids (Phe38 and Trp441) and their interactions with other residues, contributing the xylose transport function, as verified by binding free energy calculation. The strategy used herein generated remarkably valuable biological information, which is applicable for the study of sugar transportome and the structure engineering of targeted transporter proteins that might link to the production of bioactive compounds derived from xylose metabolism, such as cordycepin.

Disclosing the Mechanism of Spontaneous Aggregation and Template-Induced Misfolding of the Key Hexapeptide (PHF6) of Tau Protein Based on Molecular Dynamics Simulation

The microtubule-associated protein tau is critical for the development and maintenance of the nervous system. Tau dysfunction is associated with a variety of neurodegenerative diseases called tauopathies, which are characterized by neurofibrillary tangles formed by abnormally aggregated tau protein. Studying the aggregation mechanism of tau protein is of great significance for elucidating the etiology of tauopathies. The hexapeptide ³⁰⁶VQIVYK³¹¹ (PHF6) of R3 has been shown to play a vital role in promoting tau aggregation. In this study, long-term all-atom molecular dynamics simulations in explicit solvent were performed to investigate the mechanisms of spontaneous aggregation and template-induced misfolding of PHF6, and the dimerization at the early stage of nucleation was further specifically analyzed by the Markov state model (MSM). Our results show that PHF6 can spontaneously aggregate to form multimers enriched with β-sheet structure and the β-sheets in multimers prefer to exist in a parallel way. It is observed that PHF6 monomer can be induced to form a β-sheet structure on either side of the template but in a different way. In detail, the β-sheet structure is easier to form on the left side but does not extend well, but on the right side, the monomer can form the extended β-sheet structure. Furthermore, MSM analysis shows that the formation of dimer mainly occurs in three steps. First, the separated monomers collide with each other at random orientations, and then a dimer with short β-sheet structure at the N-terminal forms; finally, β-sheets elongate to form an extended parallel β-sheet dimer. During these processes, multiple intermediate states are identified and multiple paths can form a parallel β-sheet dimer from the disordered coil structure. Moreover, the residues I308, V309, and Y310 play an essential role in the dimerization. In a word, our results uncover the aggregation and misfolding mechanism of PHF6 from the atomic level, which can provide useful theoretical guidance for rational design of effective therapeutic drugs against tauopathies.

Integration of network models and evolutionary analysis into high-throughput modeling of protein dynamics and allosteric regulation: theory, tools and applications

Proteins are dynamical entities that undergo a plethora of conformational changes, accomplishing their biological functions. Molecular dynamics simulation and normal mode analysis methods have become the gold standard for studying protein dynamics, analyzing molecular mechanism and allosteric regulation of biological systems. The enormous amount of the ensemble-based experimental and computational data on protein structure and dynamics has presented a major challenge for the high-throughput modeling of protein regulation and molecular mechanisms. In parallel, bioinformatics and systems biology approaches including genomic analysis, coevolution and network-based modeling have provided an array of powerful tools that complemented and enriched biophysical insights by enabling high-throughput analysis of biological data and dissection of global molecular signatures underlying mechanisms of protein function and interactions in the cellular environment. These developments have provided a powerful interdisciplinary framework for quantifying the relationships between protein dynamics and allosteric regulation, allowing for high-throughput modeling and engineering of molecular mechanisms. Here, we review fundamental advances in protein dynamics, network theory and coevolutionary analysis that have provided foundation for rapidly growing computational tools for modeling of allosteric regulation. We discuss recent developments in these interdisciplinary areas bridging computational biophysics and network biology, focusing on promising applications in allosteric regulations, including the investigation of allosteric communication pathways, protein–DNA/RNA interactions and disease mutations in genomic medicine. We conclude by formulating and discussing future directions and potential challenges facing quantitative computational investigations of allosteric regulatory mechanisms in protein systems.

Disclosing the Template-Induced Misfolding Mechanism of Tau Protein by Studying the Dissociation of the Boundary Chain from the Formed Tau Fibril Based on a Steered Molecular Dynamics Simulation

The level of tau aggregation into neurofibrillary tangles, including paired helical filament (PHF) and straight filament (SF), is closely associated with Alzheimer's disease. Despite the pathological importance of misfolding and aggregation of tau, the corresponding mechanism remains unclear. Therefore, to uncover the misfolding mechanism of the tau monomer upon induction of formed PHF and SF, in this study, a conventional molecular dynamics simulation combined with a steered molecular dynamics simulation was performed to study the dissociation of the boundary chain. Interestingly, our results show that the dissociation mechanisms of the boundary chain in PHF and SF are different. In PHF, the boundary chain begins to dissociate from regions β2 and β3 and ends at β8. However, in SF, it is simultaneously dissociated from β1 and β8 and ends at β5. The dissociation of the boundary chain is the reverse of template-induced misfolding of the monomer. Therefore, we can deduce the misfolding mechanism of the monomer upon induction of the template. For PHF, β8 first interacts with the template by hydrophobic interaction. Then β7, β6, β5, β4, and β1 sequentially bind to the template by electrostatic and hydrophobic interactions. After β1 binds to the template, β2 and β3 very quickly bind to the template through hydrophobic interaction. For SF, β5 of the monomer first interacts with the template by electrostatic attraction. Then β4 and β6, β3 and β7, and β2 and β8 bind to the template in turn. Finally, β1 and β8 are fully bound to the template by hydrophobic interaction. The obtained results will be vital for understanding the earlier events during misfolding and aggregation of tau.

Understanding contact patterns of protein structures from protein contact map and investigation of unique patterns in the globin-like folded domains

Proteins are biochemical compounds made up of one or more polypeptides in a specific order, typically folded into a functionally active form. Proteins are categorized into four different structural classes according to the topology of α-helices and β-strands. In this study, we modeled these four structural classes as an undirected network depicting amino acids as nodes and interaction between them as edges. Results infer that basic protein classes can be easily recognized as well as distinguished by utilizing protein contact maps (PCM). Toward studying the globin-like fold, the helix-loop-helix region contacts were seen to be of a unique pattern, and these remained in all the folds. Further, the averaged diagonal contacts were analyzed and identified those contacts in α/β proteins were higher in comparison with the other class. Interesting, we noticed that anti-parallel beta sheets were dominant in all-β and α + β classes that lead to similar diagonal patterns. Network properties of all four basic classes were analyzed and found to possess small-world property. Findings infer that PCM may assist classify protein structure classes and it also helps in evaluating the predicted protein structures.

Computational characterization of the binding mode between oncoprotein Ets-1 and DNA-repair enzymes

The Ets-1 oncoprotein is a transcription factor that promotes target gene expression in specific biological processes. Typically, Ets-1 activity is low in healthy cells, but elevated levels of expression have been found in cancerous cells, specifically related to tumor progression. Like the vast majority of the cellular effectors, Ets-1 does not act alone but in association with partners. Given the important role that is attributed to Ets-1 in major human diseases, it is crucial to identify its partners and characterize their interactions. In this context, two DNA-repair enzymes, PARP-1 and DNA-PK, have been identified recently as interaction partners of Ets-1. We here identify their binding mode by means of protein docking. The results identify the interacting surface between Ets-1 and the two DNA-repair enzymes centered on the α-helix H1 of the ETS domain, leaving α-helix H3 available to bind DNA. The models highlight a hydrophobic patch on Ets-1 at the center of the interaction interface that includes three tryptophans (Trp338, Trp356, and Trp361). We rationalize the binding mode using a series of computational analyses, including alanine scanning, molecular dynamics simulation, and residue centrality analysis. Our study constitutes a first but important step in the characterization, at the molecular level, of the interaction between an oncoprotein and DNA-repair enzymes.

Dynamic perspectives into the mechanisms of mutation-induced p53-DNA binding loss and inactivation using active perturbation theory: Structural and molecular insights toward the design of potent reactivators in cancer therapy

The DNA-binding ability of p53 represents the crux of its tumor suppressive activities, which involves transcriptional activation of target genes responsible for apoptosis and cell-cycle arrest. Mutational occurrences within or in close proximity to the DNA-binding surface of p53 have accounted for the loss of direct DNA-binding ability and inactivation implicated in many cases of cancer. Moreover, the design of therapeutic compounds that can restore DNA-binding ability in p53 mutants has been identified as a way forward in curtailing their oncogenic activities. However, there is still the need for more insights into evaluate the perturbations that occur at the DNA-binding interface of mp53 relative to DNA-binding loss, inactivation, and design of potent reactivators, hence the purpose of this study. Therefore, we evaluated p53-structural (R175H) and contact (R273C) mutational effects using tunnel perturbation analysis and other computational tools. We identified significant perturbations in the active tunnels of p53, which resulted in altered geometry and loss, unlike in the wild-type p53. This corroborated with structural, DNA-binding, and interaction network analysis, which showed that loss of flexibility, repulsion of DNA-interactive residues, and instability occurred at the binding interface of both mutants. Also, these mutations altered bonding interactions and network topology at the DNA-binding interface, resulting in the reduction of p53-DNA binding proximity and affinity. Therefore, these findings would aid the structure-based design of novel chemical entities capable of restoring p53-DNA binding and activation.

In silico prediction of novel residues involved in amyloid primary nucleation of human I56T and D67H lysozyme

Background

Amyloidogenic proteins are most often associated with neurodegenerative diseases such as Alzheimer’s disease, Parkinson’s disease, and Huntington’s disease, but there are more than two dozen human proteins known to form amyloid fibrils associated with disease. Lysozyme is an antimicrobial protein that is used as a general model to study amyloid fibril formation. Studies aimed at elucidating the process of amyloid formation of lysozyme tend to focus on partial unfolding of the native state due to the relative instability of mutant amyloidogenic variants. While this is well supported, the data presented here suggest the native structure of the variants may also play a role in primary nucleation.

Results

Three-dimensional structural analysis identified lysozyme residues 21, 62, 104, and 122 as displaced in both amyloidogenic variants compared to wild type lysozyme. Residue interaction network (RIN) analysis found greater clustering of residues 112–117 in amyloidogenic variants of lysozyme compared to wild type. An analysis of the most energetically favored predicted dimers and trimers provided further evidence for a role for residues 21, 62, 104, 122, and 112–117 in amyloid formation.

Conclusions

This study used lysozyme as a model to demonstrate the utility of combining 3D structural analysis with RIN analysis for studying the general process of amyloidogenesis. Results indicated that binding of two or more amyloidogenic lysozyme mutants may be involved in amyloid nucleation by placing key residues (21, 62, 104, 122, and 112–117) in proximity before partial unfolding occurs. Identifying residues in the native state that may be involved in amyloid formation could provide novel drug targets to prevent a range of amyloidoses.

Allosteric binding sites in Rab11 for potential drug candidates

Rab11 is an important protein subfamily in the RabGTPase family. These proteins physiologically function as key regulators of intracellular membrane trafficking processes. Pathologically, Rab11 proteins are implicated in many diseases including cancers, neurodegenerative diseases and type 2 diabetes. Although they are medically important, no previous study has found Rab11 allosteric binding sites where potential drug candidates can bind to. In this study, by employing multiple clustering approaches integrating principal component analysis, independent component analysis and locally linear embedding, we performed structural analyses of Rab11 and identified eight representative structures. Using these representatives to perform binding site mapping and virtual screening, we identified two novel binding sites in Rab11 and small molecules that can preferentially bind to different conformations of these sites with high affinities. After identifying the binding sites and the residue interaction networks in the representatives, we computationally showed that these binding sites may allosterically regulate Rab11, as these sites communicate with switch 2 region that binds to GTP/GDP. These two allosteric binding sites in Rab11 are also similar to two allosteric pockets in Ras that we discovered previously.

Comparing protein structures with RINspector automation in Cytoscape

Residue interaction networks (RINs) have been shown to be relevant representations of the tertiary or quaternary structures of proteins, in particular thanks to network centrality analyses. We recently developed the RINspector 1.0.0 Cytoscape app, which couples centrality analyses with backbone flexibility predictions. This combined approach permits the identification of crucial residues for the folding or function of the protein that can constitute good targets for mutagenesis experiments. Here we present an application programming interface (API) for RINspector 1.1.0 that enables interplay between Cytoscape, RINspector and external languages, such as R or Python. This API provides easy access to batch centrality calculations and flexibility predictions, and allows for the easy comparison of results between different structures. These comparisons can lead to the identification of specific and conserved central residues, and show the impact of mutations to these and other residues on the flexibility of the proteins. We give two use cases to demonstrate the interest of these functionalities and provide the corresponding scripts: the first concerns NMR conformers, the second focuses on mutations in a structure.

RINspector: a Cytoscape app for centrality analyses and DynaMine flexibility prediction

Motivation

Protein function is directly related to amino acid residue composition and the dynamics of these residues. Centrality analyses based on residue interaction networks permit to identify key residues in a protein that are important for its fold or function. Such central residues and their environment constitute suitable targets for mutagenesis experiments. Predicted flexibility and changes in flexibility upon mutation provide valuable additional information for the design of such experiments.

Results

We combined centrality analyses with DynaMine flexibility predictions in a Cytoscape app called RINspector. The app performs centrality analyses and directly visualizes the results on a graph of predicted residue flexibility. In addition, the effect of mutations on local flexibility can be calculated.

Availability and implementation

The app is publicly available in the Cytoscape app store.

Supplementary information

Supplementary data are available at Bioinformatics online.

Prediction of interface residue based on the features of residue interaction network

Protein-protein interaction plays a crucial role in the cellular biological processes. Interface prediction can improve our understanding of the molecular mechanisms of the related processes and functions. In this work, we propose a classification method to recognize the interface residue based on the features of a weighted residue interaction network. The random forest algorithm is used for the prediction and 16 network parameters and the B-factor are acting as the element of the input feature vector. Compared with other similar work, the method is feasible and effective. The relative importance of these features also be analyzed to identify the key feature for the prediction. Some biological meaning of the important feature is explained. The results of this work can be used for the related work about the structure-function relationship analysis via a residue interaction network model.

Comparative Study of Elastic Network Model and Protein Contact Network for Protein Complexes: The Hemoglobin Case

The overall topology and interfacial interactions play key roles in understanding structural and functional principles of protein complexes. Elastic Network Model (ENM) and Protein Contact Network (PCN) are two widely used methods for high throughput investigation of structures and interactions within protein complexes. In this work, the comparative analysis of ENM and PCN relative to hemoglobin (Hb) was taken as case study. We examine four types of structural and dynamical paradigms, namely, conformational change between different states of Hbs, modular analysis, allosteric mechanisms studies, and interface characterization of an Hb. The comparative study shows that ENM has an advantage in studying dynamical properties and protein-protein interfaces, while PCN is better for describing protein structures quantitatively both from local and from global levels. We suggest that the integration of ENM and PCN would give a potential but powerful tool in structural systems biology.

The RING 2.0 web server for high quality residue interaction networks

Residue interaction networks (RINs) are an alternative way of representing protein structures where nodes are residues and arcs physico–chemical interactions. RINs have been extensively and successfully used for analysing mutation effects, protein folding, domain–domain communication and catalytic activity. Here we present RING 2.0, a new version of the RING software for the identification of covalent and non-covalent bonds in protein structures, including π–π stacking and π–cation interactions. RING 2.0 is extremely fast and generates both intra and inter-chain interactions including solvent and ligand atoms. The generated networks are very accurate and reliable thanks to a complex empirical re-parameterization of distance thresholds performed on the entire Protein Data Bank. By default, RING output is generated with optimal parameters but the web server provides an exhaustive interface to customize the calculation. The network can be visualized directly in the browser or in Cytoscape. Alternatively, the RING-Viz script for Pymol allows visualizing the interactions at atomic level in the structure. The web server and RING-Viz, together with an extensive help and tutorial, are available from URL: http://protein.bio.unipd.it/ring.

Protein-Protein Interface and Disease: Perspective from Biomolecular Networks

Protein-protein interactions are involved in many important biological processes and molecular mechanisms of disease association. Structural studies of interfacial residues in protein complexes provide information on protein-protein interactions. Characterizing protein-protein interfaces, including binding sites and allosteric changes, thus pose an imminent challenge. With special focus on protein complexes, approaches based on network theory are proposed to meet this challenge. In this review we pay attention to protein-protein interfaces from the perspective of biomolecular networks and their roles in disease. We first describe the different roles of protein complexes in disease through several structural aspects of interfaces. We then discuss some recent advances in predicting hot spots and communication pathway analysis in terms of amino acid networks. Finally, we highlight possible future aspects of this area with respect to both methodology development and applications for disease treatment.

Molecular Mechanism underlying PRMT1 Dimerization for SAM Binding and Methylase Activity

Protein arginine methyltransferases (PRMTs) catalyze the posttranslational methylation of arginine, which is important in a range of biological processes, including epigenetic regulation, signal transduction, and cancer progression. Although previous studies of PRMT1 mutants suggest that the dimerization arm and the N-terminal region of PRMT1 are important for activity, the contributions of these regions to the structural architecture of the protein and its catalytic methylation activity remain elusive. Molecular dynamics (MD) simulations performed in this study showed that both the dimerization arm and the N-terminal region undergo conformational changes upon dimerization. Because a correlation was found between the two regions despite their physical distance, an allosteric pathway mechanism was proposed based on a network topological analysis. The mutation of residues along the allosteric pathways markedly reduced the methylation activity of PRMT1, which may be attributable to the destruction of dimer formation and accordingly reduced S-adenosyl-L-methionine (SAM) binding. This study provides the first demonstration of the use of a combination of MD simulations, network topological analysis, and biochemical assays for the exploration of allosteric regulation upon PRMT1 dimerization. These findings illuminate the results of mechanistic studies of PRMT1, which have revealed that dimer formation facilitates SAM binding and catalytic methylation, and provided direction for further allosteric studies of the PRMT family.

Analysis of core-periphery organization in protein contact networks reveals groups of structurally and functionally critical residues

The representation of proteins as networks of interacting amino acids, referred to as protein contact networks (PCN), and their subsequent analyses using graph theoretic tools, can provide novel insights into the key functional roles of specific groups of residues. We have characterized the networks corresponding to the native states of 66 proteins (belonging to different families) in terms of their core-periphery organization. The resulting hierarchical classification of the amino acid constituents of a protein arranges the residues into successive layers - having higher core order - with increasing connection density, ranging from a sparsely linked periphery to a densely intra-connected core (distinct from the earlier concept of protein core defined in terms of the three-dimensional geometry of the native state, which has least solvent accessibility). Our results show that residues in the inner cores are more conserved than those at the periphery. Underlining the functional importance of the network core, we see that the receptor sites for known ligand molecules of most proteins occur in the innermost core. Furthermore, the association of residues with structural pockets and cavities in binding or active sites increases with the core order. From mutation sensitivity analysis, we show that the probability of deleterious or intolerant mutations also increases with the core order. We also show that stabilization centre residues are in the innermost cores, suggesting that the network core is critically important in maintaining the structural stability of the protein. A publicly available Web resource for performing core-periphery analysis of any protein whose native state is known has been made available by us at http://www.imsc.res.in/ ~sitabhra/proteinKcore/index.html.

Amino acid contact energy networks impact protein structure and evolution

One of the most challenging tasks in structural proteomics is to understand the relationship between protein structure, biological function, and evolution. An understanding of amino acid networks based on protein topology has an important role in the study of this relationship; however, the relationship between network parameters underlying protein topology with structural properties or evolutionary rate is still unknown. To investigate this further, we modeled the three dimensional structure of proteins as amino acid contact energy networks (AACENs) with nodes represented as amino acid residues and edges established according to environment-dependent residue-residue contact energies. Five other types of networks were also constructed to investigate their topological parameters and compare their effect on protein structure and evolution: (1) a random contact network (RCN), (2) a rewiring network with the same degree of distribution as AACEN (RNDD), (3) long-range contact energy networks with and without the backbone connectivity (LCEN_BBs and LCENs), and (4) short range contact energy networks (SCENs). The results indicated that the long-range link percentage and the network clustering coefficient showed a significantly positive and negative correlation, respectively, with protein secondary structure density. In addition, the long-range link percentage and network diameter had a significantly positive and negative correlation, respectively, with evolutionary rate. According to our knowledge, this is the first study to identify the potential role of long-range links and network diameter in protein evolution.