webPSN v2.0: a webserver to infer fingerprints of structural communication in biomacromolecules

Computational Biophysical Characterization of the Effect of Gatekeeper Mutations on the Binding of Ponatinib to the FGFR Kinase

Fibroblast Growth Factor Receptor (FGFR) is connected to numerous downstream signalling cascades regulating cellular behavior. Any dysregulation leads to a plethora of illnesses, including cancer. Therapeutics are available, but drug resistance driven by gatekeeper mutation impedes the treatment. Ponatinib is an FDA-approved drug against BCR-ABL kinase and has shown effective results against FGFR-mediated carcinogenesis. Herein, we undertake molecular dynamics simulation-based analysis on ponatinib against all the FGFR isoforms having Val to Met gatekeeper mutations. The results suggest that ponatinib is a potent and selective inhibitor for FGFR1, FGFR2, and FGFR4 gatekeeper mutations. The extensive electrostatic and van der Waals interaction network accounts for its high potency. The FGFR3_VM mutation has shown resistance towards ponatinib, which is supported by their lesser binding affinity than wild-type complexes. The disengaged molecular brake and engaged hydrophobic spine were believed to be the driving factors for weak protein-ligand interaction. Taken together, the inhibitory and structural characteristics exhibited by ponatinib may aid in thwarting resistance based on Val-to-Met gatekeeper mutations at an earlier stage of treatment and advance the design and development of other inhibitors targeted at FGFRs harboring gatekeeper mutations.

Phage display identification of high-affinity ligands for human TSG101-UEV: A structural and thermodynamic study of PTAP recognition

The ubiquitin E2 variant domain of TSG101 (TSG101-UEV) plays a pivotal role in protein sorting and virus budding by recognizing PTAP motifs within ubiquitinated proteins. Disrupting TSG101-UEV/PTAP interactions has emerged as a promising strategy for the development of novel host-oriented antivirals with a broad spectrum of action. Nonetheless, finding inhibitors with good properties as therapeutic agents remains a challenge since the key determinants of binding affinity and specificity are still poorly understood. Here we present a detailed thermodynamic, structural, and dynamic characterization viral PTAP Late domain recognition by TSG101-UEV, combining isothermal titration calorimetry, X-ray diffraction structural studies, molecular dynamics simulations, and computational analysis of intramolecular communication pathways. Our analysis highlights key contributions from conserved hydrophobic contacts and water-mediated hydrogen bonds at the PTAP binding interface. We have identified additional electrostatic hotspots adjacent to the core motif that modulate affinity. Using competitive phage display screening we have improved affinity by 1-2 orders of magnitude, producing novel peptides with low micromolar affinities that combine critical elements found in the best natural binders. Molecular dynamics simulations revealed that optimized peptides engage new pockets on the UEV domain surface. This study provides a comprehensive view of the molecular forces directing TSG101-UEV recognition of PTAP motifs, revealing that binding is governed by conserved structural elements yet tuneable through targeted optimization. These insights open new venues to design inhibitors targeting TSG101-dependent pathways with potential application as novel broad-spectrum antivirals.

Identification of inhibitors for neurodegenerative diseases targeting dual leucine zipper kinase through virtual screening and molecular dynamics simulations

Neurodegenerative diseases lead to a gradual decline in cognitive and motor functions due to the progressive loss of neurons in the central nervous system. The role of dual leucine zipper kinase (DLK) in regulating stress responses and neuronal death pathways highlights its significance as a target against neurodegenerative diseases. The non-availability of FDA-approved drugs emphasizes a need to identify novel DLK-inhibitors. We screened NPAtlas (Natural products) and MedChemExpress (FDA-approved) libraries to identify potent ATP-competitive DLK inhibitors. ADMET analyses identified four compounds (two natural products and two FDA-approved) with favourable features. Subsequently, we performed molecular dynamics simulations to examine the binding-stability and ligand-induced conformational dynamics. Molecular mechanics Poisson Boltzmann surface area (MM-PBSA) calculations demonstrated CID139591660, dithranol, and danthron having greater affinity, while CID156581477 showed lower affinity than control sunitinib. PCA and network analysis results indicated structural and network alteration post-ligand binding. Furthermore, we identified an analogue of CID156581477 using the deep learning-based web server DeLA Drug which demonstrated a higher affinity than its parent compound and the control and identified several crucial interacting residues. Overall, our study provides significant theoretical guidance for designing potent novel DLK inhibitors and compounds that could emerge as promising drug candidates against DLK following laboratory validation.

Unveiling the intricacies of allosteric regulation in aspartate kinase from the Wolbachia endosymbiont of Brugia Malayi: Mechanistic and therapeutic insights

Aspartate kinase (AK), an enzyme from the Wolbachia endosymbiont of Brugia malayi (WBm), plays a pivotal role in the bacterial cell wall and amino acid biosynthesis, rendering it an attractive candidate for therapeutic intervention. Allosteric inhibition of aspartate kinase is a prevalent mode of regulation across microorganisms and plants, often modulated by end products such as lysine, threonine, methionine, or meso-diaminopimelate. The intricate and diverse nature of microbial allosteric regulation underscores the need for rigorous investigation. This study employs a combined experimental and computational approach to decipher the allosteric regulation of WBmAK. Molecular Dynamics (MD) simulations elucidate that ATP (cofactor) and ASP (substrate) binding induce a closed conformation, promoting enzymatic activity. In contrast, the binding of lysine (allosteric inhibitor) leads to enzyme inactivation and an open conformation. The enzymatic assay demonstrates the optimal activity of WBmAK at 28 °C and a pH of 8.0. Notably, the allosteric inhibition study highlights lysine as a more potent inhibitor compared to threonine. Importantly, this investigation sheds light on the allosteric mechanism governing WBmAK and imparts novel insights into structure-based drug discovery, paving the way for the development of effective inhibitors against filarial pathogens.

Molecular dynamics simulations reveal phosphorylation-induced conformational dynamics of the fibroblast growth factor receptor 1 kinase

The Fibroblast Growth Factor Receptor1 (FGFR1) kinase wields exquisite control on cell fate, proliferation, differentiation, and homeostasis. An imbalance of FGFR1 signaling leads to several pathogeneses of diseases ranging from multiple cancers to allergic and neurodegenerative disorders. In this study, we investigated the phosphorylation-induced conformational dynamics of FGFR1 in apo and ATP-bound states via all-atom molecular dynamics simulations. All simulations were performed for 2 × 2 µs. We have also investigated the energetics of the binding of ATP to FGFR1 using the molecular mechanics Poisson-Boltzmann scheme. Our study reveals that the FGFR1 kinase can reach a fully active configuration through phosphorylation and ATP binding. A 3-10 helix formation in the activation loop signifies its rearrangement leading to stability upon ATP binding. The interaction of phosphorylated tyrosine (pTyr654) with positively charged residues forms strong salt-bridge interactions, driving the compactness of the structure. The dynamic cross-correlation map reveals phosphorylation enhances correlated motions and reduces anti-correlated motions between different domains. We believe that the mechanistic understanding of large-conformational changes upon the activation of the FGFR1 kinase will aid the development of novel targeted therapeutics.Communicated by Ramaswamy H. Sarma.

Mechanism of Mutation-Induced Effects on the Catalytic Function of TEV Protease: A Molecular Dynamics Study

Tobacco etch virus protease (TEVp) is wildly exploited for various biotechnological applications. These applications take advantage of TEVp's ability to cleave specific substrate sequences to study protein function and interactions. A major limitation of this enzyme is its relatively slow catalytic rate. In this study, MD simulations were conducted on TEV enzymes and known highly active mutants (eTEV and uTEV3) to explore the relationship between mutation, conformation, and catalytic function. The results suggest that mutations distant from the active site can influence the substrate-binding pocket through interaction networks. MD analysis of eTEV demonstrates that, by stabilizing the orientation of the substrate at the catalytic site, mutations that appropriately enlarge the substrate-binding pocket will be beneficial for Kcat, enhancing the catalytic efficiency of the enzyme. On the contrary, mutations in uTEV3 reduced the flexibility of the active pocket and increased the hydrogen bonding between the substrate and enzyme, resulting in higher affinity. At the same time, the MD simulation demonstrates that mutations outside of the active site residues could affect the dynamic movement of the binding pocket by altering residue networks and communication pathways, thereby having a profound impact on reactivity. These findings not only provide a molecular mechanistic explanation for the excellent mutants, but also serve as a guiding framework for rational computational design.

Biophysical Analysis of Potential Inhibitors of SARS-CoV-2 Cell Recognition and Their Effect on Viral Dynamics in Different Cell Types: A Computational Prediction from In Vitro Experimental Data

Recent reports have suggested that the susceptibility of cells to SARS-CoV-2 infection can be influenced by various proteins that potentially act as receptors for the virus. To investigate this further, we conducted simulations of viral dynamics using different cellular systems (Vero E6, HeLa, HEK293, and CaLu3) in the presence and absence of drugs (anthelmintic, ARBs, anticoagulant, serine protease inhibitor, antimalarials, and NSAID) that have been shown to impact cellular recognition by the spike protein based on experimental data. Our simulations revealed that the susceptibility of the simulated cell systems to SARS-CoV-2 infection was similar across all tested systems. Notably, CaLu3 cells exhibited the highest susceptibility to SARS-CoV-2 infection, potentially due to the presence of receptors other than ACE2, which may account for a significant portion of the observed susceptibility. Throughout the study, all tested compounds showed thermodynamically favorable and stable binding to the spike protein. Among the tested compounds, the anticoagulant nafamostat demonstrated the most favorable characteristics in terms of thermodynamics, kinetics, theoretical antiviral activity, and potential safety (toxicity) in relation to SARS-CoV-2 spike protein-mediated infections in the tested cell lines. This study provides mathematical and bioinformatic models that can aid in the identification of optimal cell lines for compound evaluation and detection, particularly in studies focused on repurposed drugs and their mechanisms of action. It is important to note that these observations should be experimentally validated, and this research is expected to inspire future quantitative experiments.

Microsecond dynamics of H10N7 influenza neuraminidase reveals the plasticity of loop regions and drug resistance due to the R292K mutation

At the beginning of the last century, multiple pandemics caused by influenza (flu) viruses severely impacted public health. Despite the development of vaccinations and antiviral medications to prevent and control impending flu outbreaks, unforeseen novel strains and continuously evolving old strains continue to represent a serious threat to human life. Therefore, the recently identified H10N7, for which not much data is available for rational structure-based drug design, needs to be further explored. Here, we investigated the structural dynamics of neuraminidase N7 upon binding of inhibitors, and the drug resistance mechanisms against the oseltamivir (OTV) and laninamivir (LNV) antivirals due to the crucial R292K mutation on the N7 using the computational microscope, molecular dynamics (MD) simulations. In this study, each system underwent long 2 × 1 μs MD simulations to answer the conformational changes and drug resistance mechanisms. These long time-scale dynamics simulations and free energy landscapes demonstrated that the mutant systems showed a high degree of conformational variation compared to their wildtype (WT) counterparts, and the LNV-bound mutant exhibited an extended 150-loop conformation. Further, the molecular mechanics Poisson-Boltzmann surface area (MM/PBSA) calculation and MM/GBSA free energy decomposition were used to characterize the binding of OTV and LNV with WT, and R292K mutated N7, revealing the R292K mutation as drug-resistant, facilitated by a decline in binding interaction and a reduction in the dehydration penalty. Due to the broader binding pocket cavity of the smaller K292 mutant residue relative to the wildtype, the drug carboxylate to K292 hydrogen bonding was lost, and the area surrounding the K292 residue was more accessible to water molecules. This implies that drug resistance could be reduced by strengthening the hydrogen bond contacts between N7 inhibitors and altered N7, creating inhibitors that can form a hydrogen bond to the mutant K292, or preserving the closed cavity conformations.

Deciphering the molecular choreography of Janus kinase 2 inhibition via Gaussian accelerated molecular dynamics simulations: a dynamic odyssey

The Janus kinases (JAK) are crucial targets in drug development for several diseases. However, accounting for the impact of possible structural rearrangements on the binding of different kinase inhibitors is complicated by the extensive conformational variability of their catalytic kinase domain (KD). The dynamic KD contains mainly four prominent mobile structural motifs: the phosphate-binding loop (P-loop), the αC-helix within the N-lobe, the Asp-Phe-Gly (DFG) motif, and the activation loop (A-loop) within the C-lobe. These distinct structural orientations imply a complex signal transmission path for regulating the A-loop's flexibility and conformational preference for optimal JAK function. Nevertheless, the precise dynamical features of the JAK induced by different types of inhibitors still remain elusive. We performed comparative, microsecond-long, Gaussian accelerated molecular dynamics simulations in triplicate of three phosphorylated JAK2 systems: the KD alone, type-I ATP-competitive inhibitor (CI) bound KD in the catalytically active DFG-in conformation, and the type-II inhibitor (AI) bound KD in the catalytically inactive DFG-out conformation. Our results indicate significant conformational variations observed in the A-loop and αC helix motions upon inhibitor binding. Our studies also reveal that the DFG-out inactive conformation is characterized by the closed A-loop rearrangement, open catalytic cleft of N and C-lobe, the outward movement of the αC helix, and open P-loop states. Moreover, the outward positioning of the αC helix impacts the hallmark salt bridge formation between Lys882 and Glu898 in an inactive conformation. Finally, we compared their ligand binding poses and free energy by the MM/PBSA approach. The free energy calculations suggested that the AI's binding affinity is higher than CI against JAK2 due to an increased favorable contribution from the total non-polar interactions and the involvement of the αC helix. Overall, our study provides the structural and energetic insights crucial for developing more promising type I/II JAK2 inhibitors for treating JAK-related diseases.

Structural plasticity of omicron BA.5 and BA.2.75 for enhanced ACE-dependent entry into cells

The current study investigated the binding variations among the wilt type, Omicron sub-variants BA.2.75 and BA.5, using protein-protein docking, protein structural graphs (P SG), and molecular simulation methods. HADDOCK predicted docking scores and dissociation constant (KD) revealed tighter binding of these sub-variants in contrast to the WT. Further investigation revealed variations in the hub residues, protein sub-networks, and GlobalMetapath in these variants as compared to the WT. A very unusual dynamic for BA.2.75 and BA.5 was observed, and secondary structure transition can also be witnessed in the loops (44-505). The results show that the flexibility of these three loops is increased by the mutations as an allosteric effect and thus enhances the chances of bonding with the nearby residues to connect and form a stable connection. Furthermore, the additional hydrogen bonding contacts steer the robust binding of these variants in contrast to the wild type. The total binding free energy for the wild type was calculated to be -61.38 kcal/mol, while for BA.2.75 and BA.5 variants the T BE was calculated to be -70.42 kcal/mol and 69.78 kcal/mol, respectively. We observed that the binding of BA.2.75 is steered by the electrostatic interactions while the BA.5 additional contacts are due to the vdW (Van der Waal) energy. From these findings, it can be observed the Spike (S) protein is undergoing structural adjustments to bind efficiently to the hACE2 (human angiotensin-converting enzyme 2) receptor and, in turn, increase entry to the host cells. The current study will aid the development of structure-based drugs against these variants.Communicated by Ramaswamy H. Sarma.

Conformational plasticity and allosteric communication networks explain Shelterin protein TPP1 binding to human telomerase

The Shelterin complex protein TPP1 interacts with human telomerase (TERT) by means of the TEL-patch region, controlling telomere homeostasis. Aberrations in the TPP1-TERT heterodimer formation might lead to short telomeres and severe diseases like dyskeratosis congenita and Hoyeraal-Hreidarsson syndrome. In the present study, we provide a thorough characterization of the structural properties of the TPP1's OB-domain by combining data coming from microsecond-long molecular dynamics calculations, time-series analyses, and graph-based networks. Our results show that the TEL-patch conformational freedom is influenced by a network of long-range amino acid communications that together determine the proper TPP1-TERT binding. Furthermore, we reveal that in TPP1 pathological variants Glu169Δ, Lys170Δ and Leu95Gln, the TEL-patch plasticity is reduced, affecting the correct binding to TERT and, in turn, telomere processivity, which eventually leads to accelerated aging of affected cells. Our study provides a structural basis for the design of TPP1-targeting ligands with therapeutic potential against cancer and telomeropathies.

Cytotoxicity and reversal effect of sertraline, fluoxetine, and citalopram on MRP1- and MRP7-mediated MDR

Cancer is one of the leading causes of death worldwide, and the development of resistance to chemotherapy drugs is a major challenge in treating malignancies. In recent years, researchers have focused on understanding the mechanisms of multidrug resistance (MDR) in cancer cells and have identified the overexpression of ATP-binding cassette (ABC) transporters, including ABCC1/MRP1 and ABCC10/MRP7, as a key factor in the development of MDR. In this study, we aimed to investigate whether three drugs (sertraline, fluoxetine, and citalopram) from the selective serotonin reuptake inhibitor (SSRI) family, commonly used as antidepressants, could be repurposed as inhibitors of MRP1 and MRP7 transporters and reverse MDR in cancer cells. Using a combination of in silico predictions and in vitro validations, we analyzed the interaction of MRP1 and MRP7 with the drugs and evaluated their ability to hinder cell resistance. We used computational tools to identify and analyze the binding site of these three molecules and determine their binding energy. Subsequently, we conducted experimental assays to assess cell viability when treated with various standard chemotherapies, both with and without the presence of SSRI inhibitors. Our results show that all three SSRI drugs exhibited inhibitory/reversal effects in the presence of chemotherapies on both MRP1-overexpressed cells and MRP7-overexpressed cells, suggesting that these medications have the potential to be repurposed to target MDR in cancer cells. These findings may open the door to using FDA-approved medications in combination therapy protocols to treat highly resistant malignancies and improve the efficacy of chemotherapy treatment. Our research highlights the importance of investigating and repurposing existing drugs to overcome MDR in cancer treatment.

PyPCN: protein contact networks in PyMOL

MOTIVATION

Protein contact networks (PCNs) represent the 3D structure of a protein using network formalism. Inter-residue contacts are described as binary adjacency matrices, which are derived from the graph representation of residues (as α-carbons, β-carbons or centroids) and Euclidean distances according to defined thresholds. Functional characterization algorithms are computed on binary adjacency matrices to unveil allosteric, dynamic, and interaction mechanisms in proteins. Such strategies are usually applied in a combinatorial manner, although rarely in seamless and user-friendly implementations.

RESULTS

PyPCN is a plugin for PyMOL wrapping more than twenty PCN algorithms and metrics in an easy-to-use graphical user interface, to support PCN analysis. The plugin accepts 3D structures from the Protein Data Bank, user-provided PDBs, or precomputed adjacency matrices. The results are directly mapped to 3D protein structures and organized into interactive diagrams for their visualization. A dedicated graphical user interface combined with PyMOL visual support makes analysis more intuitive and easier, extending the applicability of PCNs.

AVAILABILITY AND IMPLEMENTATION

https://github.com/pcnproject/PyPCN.

Molecular level investigation for identifying potential inhibitors against thymidylate kinase of monkeypox through in silico approaches

The need for more advanced and effective monkeypox (Mpox) treatments has become evident with numerous Mpox virus (MPXV) outbreaks. Over the years, interest has increased in developing targeted medicines that are efficient, safe, and precise while avoiding adverse effects. Here, we screened 32409 compounds against thymidylate kinase (TMPK), an emerging target for Mpox treatment. We studied their pharmacological characteristics and analyzed those through all-atom molecular dynamics simulations followed by molecular mechanics Poisson Boltzmann surface area (MM-PBSA) based free energy calculations. According to our findings, the leads CID40777874 and CID28960001 had the highest binding affinities towards TMPK with ΔGbind of -8.04 and -5.58 kcal/mol, respectively, which outperformed our control drug cidofovir (ΔGbind = -2.92 kcal/mol) in terms of binding favourability. Additionally, we observed crucial TMPK dynamics brought on by ligand-binding and identified key residues such as Phe68 and Tyr101 as the critical points of the protein-ligand interaction. The DCCM analysis revealed the role of ligand binding in stabilizing TMPK's binding region, as indicated by residual correlation motions. Moreover, the PSN analysis revealed that the interaction with ligand induces changes in residual network properties, enhancing the stability of complexes. We successfully identified novel compounds that may serve as potential building blocks for constructing contemporary antivirals against MPXV and highlighted the molecular mechanisms underlying their binding with TMPK. Overall, our findings will play a significant role in advancing the development of new therapies against Mpox and facilitating a comprehensive understanding of their interaction patterns.Communicated by Ramaswamy H. Sarma.

Exploring molecular interactions of potential inhibitors against the spleen tyrosine kinase implicated in autoimmune disorders via virtual screening and molecular dynamics simulations

The spleen tyrosine kinase (Syk) plays a pivotal role in immune cells' signal transduction mechanism. While fostamatinib, an FDA-approved Syk inhibitor, is currently used to treat immune thrombocytopenia, the search for improved Syk-targeted medications to treat autoimmune diseases is still underway. Herein, we screened 38,493 compounds against Syk and selected eight leads based on the docking score and ADMET properties, and performed 3× 200 ns long molecular dynamics simulations of the apo and Syk-ligand complexes. We considered R406, the active component of fostamatinib, as a control. The molecular mechanics Poisson-Boltzmann surface area (MM-PBSA) calculations demonstrated the lead1 (Δ G b i n d  = -30.35 kcal/mol) exhibited a similar binding free energy as the control (Δ G b i n d = -29.82 kcal/mol). The Syk stabilizing effect of lead1 was also indicated in its network features, sampling space, and residual correlation motion analysis. We further generated 100 structural analogues of lead1 using deep learning, and one of the analogues displayed a better binding free energy (Δ G b i n d = -47.58 kcal/mol) compared to the control or lead1, facilitated by more favourable van der Waals interactions and lesser binding-opposing net polar forces. This analogue may be further exploited to develop effective therapeutics against Syk-associated diseases after validation in vitro and in vivo.

Plant derived active compounds of ayurvedic neurological formulation, Saraswatharishta as a potential dual leucine zipper kinase inhibitor: an in-silico study

Recent findings have highlighted the essential role of dual leucine zipper kinase (DLK) in neuronal degeneration. Saraswatharishta (SWRT), an ayurvedic formulation utilized in traditional Indian medicine, has demonstrated effectiveness in addressing neurodegenerative diseases. Herein, we aim to delve into the atomistic details of the mode of action of phytochemicals present in SWRT against DLK. Our screening process encompassed over 500 distinct phytochemicals derived from the main ingredients of the SWRT formulation. Through a comparative analysis of docking scores and relative poses, we successfully identified four novel compounds, which underwent further investigation via 2 × 500 ns long molecular dynamics (MD) simulations. Among the top four compounds, CID16066851 sourced from the Acorus calamus displayed the most stable complex with DLK. The molecular mechanics Poisson - Boltzmann surface area (MM-PBSA) calculations highlighted the significance of electrostatic and van der Waals interactions in the binding recognition process. Additionally, we identified key residues, namely Phe192, Leu243, Val139, and Leu141, as hotspots that predominantly govern the DLK-inhibitor interaction. Notably, the leading compounds are sourced from the Acorus calamus, Syzygium aromaticum, Zingiber officinale, and Anethum sowa plants present in the SWRT formulation. Overall, the findings of our study hold promise for future drug development endeavors combating neurodegenerative conditions.Communicated by Ramaswamy H. Sarma.

New Insights into the Cooperativity and Dynamics of Dimeric Enzymes

A survey of protein databases indicates that the majority of enzymes exist in oligomeric forms, with about half of those found in the UniProt database being homodimeric. Understanding why many enzymes are in their dimeric form is imperative. Recent developments in experimental and computational techniques have allowed for a deeper comprehension of the cooperative interactions between the subunits of dimeric enzymes. This review aims to succinctly summarize these recent advancements by providing an overview of experimental and theoretical methods, as well as an understanding of cooperativity in substrate binding and the molecular mechanisms of cooperative catalysis within homodimeric enzymes. Focus is set upon the beneficial effects of dimerization and cooperative catalysis. These advancements not only provide essential case studies and theoretical support for comprehending dimeric enzyme catalysis but also serve as a foundation for designing highly efficient catalysts, such as dimeric organic catalysts. Moreover, these developments have significant implications for drug design, as exemplified by Paxlovid, which was designed for the homodimeric main protease of SARS-CoV-2.

Macromolecular crowding impact on anti-CRISPR AcrIIC3/NmeCas9 complex: Insights from scaled particle theory, molecular dynamics, and elastic networks models

The coupling of Cas9 and its inhibitor AcrIIC3, both from the bacterium Neisseria meningitidis (Nme), form a homodimer of the (NmeCas9/AcrIIC3)₂ type. This coupling was studied to assess the impact of their interaction with the crowders in the following environments: (1) homogeneous crowded, (2) heterogeneous, and (3) microheterogeneous cytoplasmic. For this, statistical thermodynamic models based on the scaled particle theory (SPT) were used, considering the attractive and repulsive protein-crowders contributions and the stability of the formation of spherocylindrical homodimers and the effects of changes in the size of spherical dimers were estimated. Studies based on models of dynamics, elastic networks, and statistical potentials to the formation of complexes NmeCas9/AcrIIC3 using PEG as the crowding agent support the predictions from SPT. Macromolecular crowding stabilizes the formation of the dimers, being more significant when the attractive protein-crowder interactions are weaker and the crowders are smaller. The coupling is favored towards the formation of spherical and compact dimers due to crowding addition (excluded-volume effects) and the thermodynamic stability of the dimers is markedly dependent on the size of the crowders. These results support the experimental mechanistic proposal of inhibition of NmeCas9 mediated by AcrIIC3.

Intrinsic Dynamics of the ClpXP Proteolytic Machine Using Elastic Network Models

ClpXP complex is an ATP-dependent mitochondrial matrix protease that binds, unfolds, translocates, and subsequently degrades specific protein substrates. Its mechanisms of operation are still being debated, and several have been proposed, including the sequential translocation of two residues (SC/2R), six residues (SC/6R), and even long-pass probabilistic models. Therefore, it has been suggested to employ biophysical-computational approaches that can determine the kinetics and thermodynamics of the translocation. In this sense, and based on the apparent inconsistency between structural and functional studies, we propose to apply biophysical approaches based on elastic network models (ENM) to study the intrinsic dynamics of the theoretically most probable hydrolysis mechanism. The proposed models ENM suggest that the ClpP region is decisive for the stabilization of the ClpXP complex, contributing to the flexibility of the residues adjacent to the pore, favoring the increase in pore size and, therefore, with the energy of interaction of its residues with a larger portion of the substrate. It is predicted that the complex may undergo a stable configurational change once assembled and that the deformability of the system once assembled is oriented, to increase the rigidity of the domains of each region (ClpP and ClpX) and to gain flexibility of the pore. Our predictions could suggest under the conditions of this study the mechanism of the interaction of the system, of which the substrate passes through the unfolding of the pore in parallel with a folding of the bottleneck. The variations in the distance calculated by molecular dynamics could allow the passage of a substrate with a size equivalent to ∼3 residues. The theoretical behavior of the pore and the stability and energy of binding to the substrate based on ENM models suggest that in this system, there are thermodynamic, structural, and configurational conditions that allow a possible translocation mechanism that is not strictly sequential.

GSNOR deficiency promotes tumor growth via FAK1 S-nitrosylation

Nitric oxide (NO) production in the tumor microenvironment is a common element in cancer. S-nitrosylation, the post-translational modification of cysteines by NO, is emerging as a key transduction mechanism sustaining tumorigenesis. However, most oncoproteins that are regulated by S-nitrosylation are still unknown. Here we show that S-nitrosoglutathione reductase (GSNOR), the enzyme that deactivates S-nitrosylation, is hypo-expressed in several human malignancies. Using multiple tumor models, we demonstrate that GSNOR deficiency induces S-nitrosylation of focal adhesion kinase 1 (FAK1) at C658. This event enhances FAK1 autophosphorylation and sustains tumorigenicity by providing cancer cells with the ability to survive in suspension (evade anoikis). In line with these results, GSNOR-deficient tumor models are highly susceptible to treatment with FAK1 inhibitors. Altogether, our findings advance our understanding of the oncogenic role of S-nitrosylation, define GSNOR as a tumor suppressor, and point to GSNOR hypo-expression as a therapeutically exploitable vulnerability in cancer.

Wordom update 2: A user-friendly program for the analysis of molecular structures and conformational ensembles

We present the second update of Wordom, a user-friendly and efficient program for manipulation and analysis of conformational ensembles from molecular simulations. The actual update expands some of the existing modules and adds 21 new modules to the update 1 published in 2011. The new adds can be divided into three sets that: 1) analyze atomic fluctuations and structural communication; 2) explore ion-channel conformational dynamics and ionic translocation; and 3) compute geometrical indices of structural deformation. Set 1 serves to compute correlations of motions, find geometrically stable domains, identify a dynamically invariant core, find changes in domain-domain separation and mutual orientation, perform wavelet analysis of large-scale simulations, process the output of principal component analysis of atomic fluctuations, perform functional mode analysis, infer regions of mechanical rigidity, analyze overall fluctuations, and perform the perturbation response scanning. Set 2 includes modules specific for ion channels, which serve to monitor the pore radius as well as water or ion fluxes, and measure functional collective motions like receptor twisting or tilting angles. Finally, set 3 includes tools to monitor structural deformations by computing angles, perimeter, area, volume, β-sheet curvature, radial distribution function, and center of mass. The ring perception module is also included, helpful to monitor supramolecular self-assemblies. This update places Wordom among the most suitable, complete, user-friendly, and efficient software for the analysis of biomolecular simulations. The source code of Wordom and the relative documentation are available under the GNU general public license at http://wordom.sf.net.

Comparative Protein Structural Network Analysis Reveals C-Terminal Tail Phosphorylation Structural Communication Fingerprint in PTEN-Associated Mutations in Autism and Cancer

PTEN (phosphatase and tensin homolog deleted on chromosome 10) is a tightly regulated dual-specificity phosphatase and key regulator of the PI3K/AKT/mTOR signaling pathway. PTEN phosphorylation at its carboxy-terminal tail (CTT) serine/threonine cluster negatively regulates its tumor suppressor function by inducing a stable, closed, and inactive conformation. Germline PTEN mutations predispose individuals to PTEN hamartoma tumor syndrome (PHTS), a rare inherited cancer syndrome and, intriguingly, one of the most common causes of autism spectrum disorder (ASD). However, the mechanistic details that govern phosphorylated CTT catalytic conformational dynamics in the context of PHTS-associated mutations are unknown. Here, we utilized a comparative protein structure network (PSN)-based approach to investigate PTEN CTT phosphorylation-induced conformational dynamics specific to PTEN-ASD compared to PTEN-cancer phenotypes. Results from our study show differences in structural flexibility, inter-residue contacts, and allosteric communication patterns mediated by CTT phosphorylation, differentiating PTEN-ASD and PTEN-cancer phenotypes. Further, we identified perturbations among global metapaths and community network connections within the active site and inter-domain regions, indicating the significance of these regions in transmitting information across the PSN. Together, our studies provide a mechanistic underpinning of allosteric regulation through the coupled interplay of CTT phosphorylation conformational dynamics in PTEN-ASD and PTEN-cancer mutations. Importantly, the detailed atomistic interactions and structural consequences of PTEN variants reveal potential allosteric druggable target sites as a viable and currently unexplored treatment approach for individuals with different PHTS-associated mutations.

Deciphering the Structural Determinants Critical in Attaining the FXR Partial Agonism

Elucidation of structural determinants is pivotal for structure-based drug discovery. The Farnesoid X receptor (FXR) is a proven target for NASH; however, its full agonism causes certain clinical complications. Therefore, partial agonism (PA) appears as a viable alternative for improved therapeutics. Since the agonist and PA both share the same binding site, i.e., ligand-binding pocket (LBP), which is highly dynamic and has synergy with the substrate binding site, the selective designing of PA is challenging. The identification of structural and conformational determinants is critical for PA compared with an agonist. Furthermore, the mechanism by which PA modulates the structural dynamics of FXR at the residue level, a prerequisite for PA designing, is still elusive. Here, by using ∼4.5 μs of MD simulations and residue-wise communication network analysis, we identified the structural regions which are flexible with PA but frozen with an agonist. Also, the network analysis identified the considerable changes between an agonist and PA in biologically essential zones of FXR such as helix H10/H11 and loop L:H11/H12, which lead to the modulation of synergy between LBP and the substrate binding site. Furthermore, the thermodynamic profiling suggested the methionine residues, mainly M³²⁸, M³⁶⁵, and M⁴⁵⁰, seem to be responsible for the recruitment of PA. The other residues I³⁵⁷, Y³⁶¹, L⁴⁶⁵, F³⁰⁸, Q³¹⁶, and K³²¹ are also identified, exclusively interacting with PA. This study offers novel structural and mechanistic insights that are critical for FXR targeted drug discovery for PA designing.

psnGPCRdb: The Structure-network Database of G Protein Coupled Receptors

G protein coupled receptors (GPCRs) are critical eukaryotic signal transduction gatekeepers and represent the largest protein superfamily in the human proteome, with more than 800 members. They share seven transmembrane helices organized in an up-down bundle architecture. GPCR-mediated signaling pathways have been linked to numerous human diseases, and GPCRs are the targets of approximately 35% of all drugs currently on the market. Structure network analysis, a graph theory-based approach, represents a cutting-edge tool to deeply understand GPCR function, which strongly relies on communication between the extracellular and intracellular poles of their structure. psnGPCRdb stores the structure networks (i.e., linked nodes, hubs, communities and communication pathways) computed on all updated GPCR structures in the Protein Data Bank, in their isolated states or in complex with extracellular and/or intracellular molecules. The structure communication signatures of a sub-family or family of GPCRs as well as of their small-molecule activators or inhibitors are stored as consensus networks. The database stores also all meaningful structure network-based comparisons (i.e., difference networks) of functionally different states (i.e., inactive or active) of a given receptor sub-type, or of consensus networks representative of a receptor sub-type, type, sub-family or family. Single or consensus GPCR networks hold also information on amino acid conservation. The database allows to graphically analyze 3D structure networks together with interactive data-tables. Ligand-centric networks can be analyzed as well. psnGPCRdb is unique and represents a powerful resource to unravel GPCR function with important implications in cell signaling and drug design. psnGPCRdb is freely available at: http://webpsn.hpc.unimo.it/psngpcr.php.

In silico study of cox protein from P2 type enteric bacteriophages based on sequence, structure and dynamics to understand its functional integrity

Cox protein plays a critical role in deciding the lytic-lysogenic switch of P2 enteric phages. This phenomenon makes Cox protein one of the most important candidates in developing novel phage-based therapeutics against antibacterial resistant pathogens. The principle focus concerning protein and its decision making is a DNA binding event, which helps to regulate differential promoter expression. In the current study, we have attempted to understand the sequence, structural and dynamic features associated with Cox protein and its DNA binding. Unavailability of information was a big burden in further proceedings. We have done an extensive literature search to develop a database of Cox with relevant information. That information coupled with the methods of Sequence-based phylogenetic and conservation studies, Homology Modelling, Atomic-level Docking and Molecular Dynamics (MD) Simulation (50 ns each for 10 systems, i.e. total of 500 ns) were performed in the current study. Analysis of those extensive studies has provided us the required sequence to structure to dynamics to functional understanding. Our present study would indeed be very helpful in understanding the biochemical mechanism of Cox activation as well as designing potential phage therapeutics.

Role of Thylakoid Lipids in Protochlorophyllide Oxidoreductase Activation: Allosteric Mechanism Elucidated by a Computational Study

Light-dependent protochlorophyllide oxidoreductase (LPOR) is a chlorophyll synthetase that catalyzes the reduction of protochlorophyllide (Pchlide) to chlorophyllide (Chlide) with indispensable roles in regulating photosynthesis processes. A recent study confirmed that thylakoid lipids (TL) were able to allosterically enhance modulator-induced LPOR activation. However, the allosteric modulation mechanism of LPOR by these compounds remains unclear. Herein, we integrated multiple computational approaches to explore the potential cavities in the Arabidopsis thaliana LPOR and an allosteric site around the helix-G region where high affinity for phosphatidyl glycerol (PG) was identified. Adopting accelerated molecular dynamics simulation for different LPOR states, we rigorously analyzed binary LPOR/PG and ternary LPOR/NADPH/PG complexes in terms of their dynamics, energetics, and attainable allosteric regulation. Our findings clarify the experimental observation of increased NADPH binding affinity for LPOR with PGs. Moreover, the simulations indicated that allosteric regulators targeting LPOR favor a mechanism involving lid opening upon binding to an allosteric hinge pocket mechanism. This understanding paves the way for designing novel LPOR activators and expanding the applications of LPOR.

Identification of potential inhibitory analogs of metastasis tumor antigens (MTAs) using bioactive compounds: revealing therapeutic option to prevent malignancy

The deeper understanding of metastasis phenomenon and detection of drug targets could be a potential approach to minimize cancer mortality. In this study, attempts were taken to unmask novel therapeutics to prevent metastasis and cancer progression. Initially, we explored the physiochemical, structural and functional insights of three metastasis tumor antigens (MTAs) and evaluated some plant-based bioactive compounds as potent MTA inhibitors. From 50 plant metabolites screened, isoflavone, gingerol, citronellal and asiatic acid showed maximum binding affinity with all three MTA proteins. The ADME analysis detected no undesirable toxicity that could reduce the drug likeness properties of top plant metabolites. Moreover, molecular dynamics studies revealed that the complexes were stable and showed minimum fluctuation at molecular level. We further performed ligand-based virtual screening to identify similar drug molecules using a large collection of 376,342 compounds from DrugBank. The results suggested that several structural analogs (e.g., tramadol, nabumetone, DGLA and hydrocortisone) may act as agonist to block the MTA proteins and inhibit cancer progression at early stage. The study could be useful to develop effective medications against cancer metastasis in future. Due to encouraging results, we highly recommend further in vitro and in vivo trials for the experimental validation of the findings.

Functional Loop Dynamics and Characterization of the Inactive State of the NS2B-NS3 Dengue Protease due to Allosteric Inhibitor Binding

Dengue virus, a flavivirus that causes dengue shock syndrome and dengue hemorrhagic fever, is currently prevalent worldwide. A two-component protease (NS2B-NS3) is essential for maturation, representing an important target for designing anti-flavivirus drugs. Previously, consideration has been centered on developing active-site inhibitors of NS2B-NS3pro. However, the flat and charged nature of its active site renders difficulties in developing inhibitors, suggesting an alternative strategy for identifying allosteric inhibitors. The allosterically sensitive site of the dengue protease is located near Ala125, between the 120s loop and 150s loop. Using atomistic molecular dynamics simulations, we have explored the protease's conformational dynamics upon binding of an allosteric inhibitor. Furthermore, characterization of the inherent flexible loops (71-75s loop, 120s loop, and 150s loop) is carried out for allosteric-inhibitor-bound wild-type and mutant A125C variants and a comparison is performed with its unbound state to extract the structural changes describing the inactive state of the protease. Our study reveals that compared to the unliganded system, the inhibitor-bound system shows large structural changes in the 120s loop and 150s loop in contrast to the rigid 71-75s loop. The unliganded system shows a closed-state pocket in contrast to the open state for the wild-type complex that locks the protease into the open and inactive-state conformations. However, the mutant complex fluctuates between open and closed states. Also, we tried to see how mutation and binding of an allosteric inhibitor perturb the connectivity in a protein structure network (PSN) at contact levels. Altogether, our study reveals the mechanism of conformational rearrangements of loops at the molecular level, locking the protein in an inactive conformation, which may be useful for developing allosteric inhibitors.

In silico analysis of the C-terminal domain of big defensin from the Pacific oyster

Defensins are antimicrobial peptides consisting of intramolecular disulphide bonds in a complex folded arrangement of two or three antiparallel β-sheets with or without an α-helical structure. They are produced by a vast range of organisms being constitutively expressed or induced in various tissues against different stimuli like infection, injury or other inflammatory factors. Two classes of invertebrate defensin exist, namely CS-αβ and big defensin, the latter being predominantly present in molluscs. Intriguingly, an invertebrate big defensin gene has been hypothesized as the most probable ancestor of vertebrate β-defensins. Here, conserved residues were identified for both big defensin and β-defensin. In silico mutation on conserved amino acid positions of the β-defensin-like domain of big defensin from Crassostrea gigas was carried out to understand the effects of mutation on the structure and function of the protein. R64A and E71A have been identified as deleterious as well as destabilizing for the protein. Changes in amino acid network and aggregation propensity were also observed upon mutating these two charged residues. 100 ns molecular dynamics simulations of wild-type, R64A and E71A structures revealed significant conformational changes in the case of mutants. Furthermore, molecular docking highlighted the significance of R64 in ligand interaction. In conclusion, these results provide the first in-depth understanding of the structural and functional importance imparted by two conserved charged residues in the C-terminal region of big defensin. It also enhances the existing knowledge about this antimicrobial peptide for application in therapeutics and other aspects of protein engineering.Communicated by Ramaswamy H. Sarma.

Coarse grained modelling highlights the binding differences in the two different allosteric sites of the Human Kinesin EG5 and its implications in inhibitor design

Kinesins involved in mitotic cell division have gained prominence as promising chemotherapy targets. One such kinesin, EG5, a motor protein responsible for cell division, is a validated chemotherapy target with several compounds at various stages of clinical trials. EG5 has an active site and two different allosteric sites that are known to have ligand specificity. Upon ligand binding, EG5's motor domain will no longer undergo nucleotide-dependent conformational changes required to complete the catalytic cycle. However, there is a lack of in-depth knowledge on the mechanism of inhibitor binding to the two different allosteric sites. To understand the EG5's inhibition mechanism and interactions at allosteric sites and other functionally important regions, we generated two coarse-grained models, Gaussian Network Model (GNM) and Anisotropic Network Model (ANM), to identify the dynamics and its correlation to EG5's function. The first three slowest modes of GNM showed marked differences between the various models of EG5. In the first mode, when the inhibitor is bound at allosteric site 1, there is a presence of a hinge region around residue 166, which is not found when the inhibitor is bound at allosteric site 2 or allosteric sites 1 and 2. The third slowest mode showed a distinctive positively correlated region when the inhibitor is bound at allosteric site 2. These differences indicated that the mechanism of binding at allosteric site 1 and allosteric site 2 are unique. Further, it was observed that the simultaneous ligand binding at allosteric sites 1 and 2 shares structural dynamics and interactions that were found while ligand binds at allosteric sites 1 and 2 independently, leading to a new mechanism. Taken together, our observations suggest that there are different mechanisms at play in each inhibitor bound system considered.

Deciphering the conformational transitions of LIMK2 active and inactive states to ponder specific druggable states through microsecond scale molecular dynamics simulation

LIMK2 inhibitors are one of the potential therapeutic modalities for treating various diseases. In the current scenario, there is a paucity of effective LIMK inhibitors that are highly specific with minimal off-target effects. To date, the conformational transitions of LIMK2 from DFG_inαC_in (CIDI) (active) to DFG_outαC_out (CODO) (inactive) states are yet to be probed and are essential for capturing the unique, druggable conformations. Therefore, this study was intended to capture the diverse conformational states of LIMK2 for accelerating the rational identification of conformation specific inhibitors through high-end structural bioinformatics protocols. Hence, in this study, molecular modelling followed by an extensive microsecond timescale of molecular dynamics simulation was performed encompassing perturbation response scanning, metapath, and community analysis towards the conformational sampling of LIMK2. Overall this study precisely identifies the conformational ensemble of LIMK2 the intermediate inactive states namely, CIDO, CinterDinter, CIDinter, CinterDI, CinterDO, CODI, CODinter apart from CIDI and CODO. This also facilitated observing that β8 preceding XDFG, αC (F373, L374), and αD (L413) as the major effectors that may facilitate the regulation of varying conformational transitions among the states. Additionally, the conserved β sheets and the loops namely, C.l, b.l, and G/P.loop were observed to be involved in the metapath for allosteric communication among the intermediates with CIDI and CODO state. Moreover, only the CODO state was observed to have closed type A.l, while the CIDI and other intermediate states except for CIDO were observed to have open-DFG out type A.l, thereby enabling the binding of substrate. Apart from these, the druggable site analysis inferred that the CIDI and CODO states harbor prominent druggable sites spanning the conserved N-lobe, while the intermediates were observed to have unraveled allosteric druggable sites distal from the ATP binding site, majorly spanning the C-lobe of LIMK2. Thus, this study provides potential insights into the intermediate conformational druggable states of LIMK2 and also the druggable conformations, especially the inactive states of LIMK2, as a specific therapeutic targeting mode. Thus, providing a widened avenue to ponder the allosteric sites or the isoform selectivity conformations for targeting LIMK2 in various disease conditions.

Probing structural basis for enhanced binding of SARS-CoV-2 P.1 variant spike protein with the human ACE2 receptor

The initial step of infection by severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) involves the binding of receptor binding domain (RBD) of the spike protein to the angiotensin converting enzyme 2 (ACE2) receptor. Each successive wave of SARS‐CoV‐2 reports emergence of many new variants, which is associated with mutations in the RBD as well as other parts of the spike protein. These mutations are reported to have enhanced affinity towards the ACE2 receptor as well as are also crucial for the virus transmission. Many computational and experimental studies have demonstrated the effect of individual mutation on the RBD‐ACE2 binding. However, the cumulative effect of mutations on the RBD and away from the RBD was not investigated in detail. We report here a comparative analysis on the structural communication and dynamics of the RBD and truncated S1 domain of spike protein in complex with the ACE2 receptor from SARS‐CoV‐2 wild type and its P.1 variant. Our integrative network and dynamics approaches highlighted a subtle conformational changes in the RBD as well as truncated S1 domain of spike protein at the protein contact level, responsible for the increased affinity with the ACE2 receptor. Moreover, our study also identified the commonalities and differences in the dynamics of the interactions between spike protein of SARS‐CoV‐2 wild type and its P.1 variant with the ACE2 receptor. Further, our investigation yielded an understanding towards identification of the unique RBD residues crucial for the interaction with the ACE2 host receptor. Overall, the study provides an insight for designing better therapeutics against the circulating P.1 variants as well as other future variants.

RING 3.0: fast generation of probabilistic residue interaction networks from structural ensembles

Residue interaction networks (RINs) are used to represent residue contacts in protein structures. Thanks to the advances in network theory, RINs have been proved effective as an alternative to coordinate data in the analysis of complex systems. The RING server calculates high quality and reliable non-covalent molecular interactions based on geometrical parameters. Here, we present the new RING 3.0 version extending the previous functionality in several ways. The underlying software library has been re-engineered to improve speed by an order of magnitude. RING now also supports the mmCIF format and provides typed interactions for the entire PDB chemical component dictionary, including nucleic acids. Moreover, RING now employs probabilistic graphs, where multiple conformations (e.g. NMR or molecular dynamics ensembles) are mapped as weighted edges, opening up new ways to analyze structural data. The web interface has been expanded to include a simultaneous view of the RIN alongside a structure viewer, with both synchronized and clickable. Contact evolution across models (or time) is displayed as a heatmap and can help in the discovery of correlating interaction patterns. The web server, together with an extensive help and tutorial, is available from URL: https://ring.biocomputingup.it/.

Phosphorylation-Induced Conformational Dynamics and Inhibition of Janus Kinase 1 by Suppressors of Cytokine Signaling 1

The dysfunction of the JAK/STAT (Janus kinase/signal transducers and activators of transcription) pathway results in several pathophysiological conditions, including autoimmune disorders. The negative feedback regulators of the JAK/STAT signaling pathway, suppressors of cytokine signaling (SOCS), act as a natural inhibitor of JAK and inhibit aberrant activity. SOCS1 is the most potent member of the SOCS family, whose kinase inhibitory region targets the substrate-binding groove of JAK with high affinity and blocks the phosphorylation of JAK kinases. Overall, we performed an aggregate of 13 μs molecular dynamics simulations on the activation loop's three different phosphorylation (double and single) states. Results from our simulations show that the single Tyr¹⁰³⁴ phosphorylation could stabilize the JAK1/SOCS1 complex as well as the flexible activation segment. The phosphate-binding loop (P-loop) shows conformational variability at dual and single phosphorylated states. Principal component analysis and protein structure network (PSN) analysis reveal that the different phosphorylation states and SOCS1 binding induce intermediate inactive conformations of JAK1, which could be a better target for future JAK1 selective drug design. PSN analysis suggests that the com-pY1034 system is stabilized due to higher values of network hubs than the other two complex systems. Moreover, the binding free energy calculations suggest that pTyr¹⁰³⁴ states show a higher affinity toward SOCS1 than the dual and pTyr¹⁰³⁵ states. We believe that the mechanistic understanding of JAK1/SOCS1 complexation will aid future studies related to peptide inhibitors based on SOCS1.

Unraveling the Molecular Mechanism of Recognition of Selected Next-Generation Antirheumatoid Arthritis Inhibitors by Janus Kinase 1

Rheumatoid arthritis (RA) is a chronic immune-related condition, primarily of joints, and is highly disabling and painful. The inhibition of Janus kinase (JAK)-related cytokine signaling pathways using small molecules is prevalent nowadays. The JAK family belongs to nonreceptor cytoplasmic protein tyrosine kinases (PTKs), including JAK1, JAK2, JAK3, and TYK2 (tyrosine kinase 2). JAK1 has received significant attention after being identified as a promising target for developing anti-RA therapeutics. Currently, no crystal structure is available for JAK1 in complex with the next-generation anti-RA drugs. In the current study, we investigated the mechanism of binding of baricitinib, filgotinib, itacitinib, and upadacitinib to JAK1 using a combined method of molecular docking, molecular dynamics simulation, and binding free energy calculation via the molecular mechanics Poisson-Boltzmann surface area (MM-PBSA) scheme. We found that the calculated binding affinity decreases in the order upadacitinib > itacitinib > filgotinib > baricitinib. Due to the increased favorable intermolecular electrostatic contribution, upadacitinib is more selective to JAK1 compared to the other three inhibitors. The cross-correlation and principal component analyses showed that different inhibitor bindings significantly affect the binding site dynamics of JAK1. Furthermore, our studies indicated that the hydrophobic residues and hydrogen bonds from the hinge region (Glu⁹⁵⁷ and Leu⁹⁵⁹) of JAK1 played an essential role in stabilizing the inhibitors. Protein structural network analysis reveals that the total number of links and hubs in JAK1/baricitinib (354, 48) is more significant than those in apo (328, 40) and the other three complexes. The JAK1/baricitinib complex shows the highest probability of the highest-ranked community, indicating a compact network of the JAK1/baricitinib complex, consistent with its higher stability than the rest of the four systems. Overall, our study may be crucial for the rational design of JAK1-selective inhibitors with better affinity.

Flavonoids improve the stability and function of P23H rhodopsin slowing down the progression of retinitis pigmentosa in mice

The balanced homeostasis of the G protein-coupled receptor (GPCR), rhodopsin (Rho), is required for vision. Misfolding mutations in Rho cause photoreceptor death, leading to retinitis pigmentosa (RP) and consequently blindness. With no cure currently available, the development of efficient therapy for RP is an urgent need. Pharmacological supplementation with molecular chaperones, including flavonoids, improves stability, folding, and membrane targeting of the RP Rho mutants in vitro. Thus, we hypothesized that flavonoids by binding to P23H Rho and enhancing its conformational stability could mitigate detrimental effects of this mutation on retinal health. In this work, we evaluated the pharmacological potential of two model flavonoids, quercetin and myricetin, by using in silico, in vitro, and in vivo models of P23H Rho. Our computational analysis showed that quercetin could interact within the orthosteric binding pocket of P23H Rho and shift the conformation of its N-terminal loop toward the wild type (WT)-like state. Quercetin added to the NIH-3T3 cells stably expressing P23H Rho increased the stability of this receptor and improved its function. Systemic administration of quercetin to P23H Rho knock-in mice substantially improved retinal morphology and function, which was associated with an increase in levels of Rho and cone opsins. In addition, treatment with quercetin resulted in downregulation of the UPR signaling and oxidative stress-related markers. This study unravels the pharmacological potential of quercetin to slow down the progression of photoreceptor death in Rho-related RP and highlights its prospective as a lead compound to develop a novel therapeutic remedy to counter RP pathology.

Structural communication between the GTPase Sec4p and its activator Sec2p: Determinants of GEF activity and early deformations to nucleotide release

Ras GTPases are molecular switches that cycle between OFF and ON states depending on the bound nucleotide (i.e. GDP-bound and GTP-bound, respectively).

The Rab GTPase, Sec4p, plays regulatory roles in multiple steps of intracellular vesicle trafficking. Nucleotide release is catalyzed by the Guanine Nucleotide Exchange Factor (GEF) Sec2p.

Here, the integration of structural information with molecular dynamics (MD) simulations addressed a number of questions concerning the intrinsic and stimulated dynamics of Sec2p and Sec4p as well as the chain of structural deformations leading to GEF-assisted activation of the Rab GTPase.

Sec2p holds an intrinsic ability to adopt the conformation found in the crystallographic complexes with Sec4p, thus suggesting that the latter selects and shifts the conformational equilibrium towards a pre-existing bound-like conformation of Sec2p.

The anchoring of Sec4p to a suitable conformation of Sec2p favors the Sec2p-assisted pulling on itself of the α1/switch 1 (SWI) loop and of SWI, which loose any contact with GDP. Those deformations of Sec4p would occur earlier. Formation of the final Sec2p-Sec4p hydrophobic interface, accomplishes later. Disruption of the nucleotide cage would cause firstly loss of interactions with the guanine ring and secondly loss of interactions with the phosphates.

The ease in sampling the energy landscape and adopting a bound-like conformation likely favors the catalyzing ability of GEFs for Ras GTPases.

PSNtools for standalone and web-based structure network analyses of conformational ensembles

Structure graphs, in which interacting amino acids/nucleotides correspond to linked nodes, represent cutting-edge tools to investigate macromolecular function.

The graph-based approach defined as Protein Structure Network (PSN) was initially implemented in the Wordom software and subsequently in the webPSN server. PSNs are computed either on a molecular dynamics (MD) trajectory (PSN-MD) or on a single structure. In the latter case, information on atomic fluctuations is inferred from the Elastic Network Model-Normal Mode Analysis (ENM-NMA) (PSN-ENM). While Wordom performs both PSN-ENM and PSN-MD analyses but without output post-processing, the webPSN server performs only single-structure PSN-EMN but assisting the user in input setup and output analysis.

Here we release for the first time the standalone software PSNtools, which allows calculation and post-processing of PSN analyses carried out either on single structures or on conformational ensembles. Relevant unique and novel features of PSNtools are either comparisons of two networks or computations of consensus networks on sets of homologous/analogous macromolecular structures or conformational ensembles. Network comparisons and consensus serve to infer differences in functionally different states of the same system or network-based signatures in groups of bio-macromolecules sharing either the same functionality or the same fold.

In addition to the new software, here we release also an updated version of the webPSN server, which allows performing an interactive graphical analysis of PSN-MD, following the upload of the PSNtools output.

PSNtools, the auxiliary binary version of Wordom software, and the WebPSN server are freely available at http://webpsn.hpc.unimo.it/wpsn3.php.

Structure network-based landscape of rhodopsin misfolding by mutations and algorithmic prediction of small chaperone action

Failure of a protein to achieve its functional structural state and normal cellular location contributes to the etiology and pathology of heritable human conformational diseases. The autosomal dominant form of retinitis pigmentosa (adRP) is an incurable blindness largely linked to mutations of the membrane protein rod opsin. While the mechanisms underlying the noxious effects of the mutated protein are not completely understood, a common feature is the functional protein conformational loss. Here, the wild type and 39 adRP rod opsin mutants were subjected to mechanical unfolding simulations coupled to the graph theory-based protein structure network analysis.

A robust computational model was inferred and in vitro validated in its ability to predict endoplasmic reticulum retention of adRP mutants, a feature linked to the mutation-caused misfolding. The structure-based approach could also infer the structural determinants of small chaperone action on misfolded protein mutants with therapeutic implications.

The approach is exportable to conformational diseases linked to missense mutations in any membrane protein.

Insights into the structure and function of the rate-limiting enzyme of chlorophyll degradation through analysis of a bacterial Mg-dechelatase homolog

The Mg-dechelatase enzyme encoded by the Stay-Green (SGR) gene catalyzes Mg²⁺ dechelation from chlorophyll a. This reaction is the first committed step of chlorophyll degradation pathway in plants and is thus indispensable for the process of leaf senescence. There is no structural information available for this or its related enzymes. This study aims to provide insights into the structure and reaction mechanism of the enzyme through biochemical and computational analysis of an SGR homolog from the Chloroflexi Anaerolineae (AbSGR-h). Recombinant AbSGR-h with its intact sequence and those with mutations were overexpressed in Escherichia coli and their Mg-dechelatase activity were compared. Two aspartates - D34 and D62 were found to be essential for catalysis, while R26, Y28, T29 and D114 were responsible for structural maintenance. Gel filtration analysis of the recombinant AbSGR-h indicates that it forms a homo-oligomer. The three-dimensional structure of AbSGR-h was predicted by a deep learning-based method, which was evaluated by protein structure quality evaluation programs while structural stability of wild-type and mutant forms were investigated through molecular dynamics simulations. Furthermore, in concordance with the results of enzyme assay, molecular docking concluded the significance of D34 in ligand interaction. By combining biochemical analysis and computational prediction, this study unveils the detailed structural characteristics of the enzyme, including the probable pocket of interaction and the residues of structural and functional importance. It also serves as a basis for further studies on Mg-dechelatase such as elucidation of its reaction mechanism or inhibitor screening.

Comparative study of the interaction of ivermectin with proteins of interest associated with SARS-CoV-2: A computational and biophysical approach

The SARS-CoV-2 pandemic has accelerated the study of existing drugs. The mixture of homologs called ivermectin (avermectin-B1a [HB1a] + avermectin-B1b [HB1b]) has shown antiviral activity against SARS-CoV-2 in vitro. However, there are few reports on the behavior of each homolog. We investigated the interaction of each homolog with promising targets of interest associated with SARS-CoV-2 infection from a biophysical and computational-chemistry perspective using docking and molecular dynamics. We observed a differential behavior for each homolog, with an affinity of HB1b for viral structures, and of HB1a for host structures considered. The induced disturbances were differential and influenced by the hydrophobicity of each homolog and of the binding pockets. We present the first comparative analysis of the potential theoretical inhibitory effect of both avermectins on biomolecules associated with COVID-19, and suggest that ivermectin through its homologs, has a multiobjective behavior.

Structural deformability induced in proteins of potential interest associated with COVID-19 by binding of homologues present in ivermectin: Comparative study based in elastic networks models

The COVID-19 pandemic has accelerated the study of the potential of multi-target drugs (MTDs). The mixture of homologues called ivermectin (avermectin-B1a + avermectin-B1b) has been shown to be a MTD with potential antiviral activity against SARS-CoV-2 in vitro. However, there are few reports on the effect of each homologue on the flexibility and stiffness of proteins associated with COVID-19, described as ivermectin targets. We observed that each homologue was stably bound to the proteins studied and was able to induce detectable changes with Elastic Network Models (ENM). The perturbations induced by each homologue were characteristic of each compound and, in turn, were represented by a disruption of native intramolecular networks (interactions between residues). The homologues were able to slightly modify the conformation and stability of the connection points between the Cα atoms of the residues that make up the structural network of proteins (nodes), compared to free proteins. Each homologue was able to modified differently the distribution of quasi-rigid regions of the proteins, which could theoretically alter their biological activities. These results could provide a biophysical-computational view of the potential MTD mechanism that has been reported for ivermectin.

pyProGA-A PyMOL plugin for protein residue network analysis

The field of protein residue network (PRN) research has brought several useful methods and techniques for structural analysis of proteins and protein complexes. Many of these are ripe and ready to be used by the proteomics community outside of the PRN specialists. In this paper we present software which collects an ensemble of (network) methods tailored towards the analysis of protein-protein interactions (PPI) and/or interactions of proteins with ligands of other type, e.g. nucleic acids, oligosaccharides etc. In parallel, we propose the use of the network differential analysis as a method to identify residues mediating key interactions between proteins. We use a model system, to show that in combination with other, already published methods, also included in pyProGA, it can be used to make such predictions. Such extended repertoire of methods allows to cross-check predictions with other methods as well, as we show here. In addition, the possibility to construct PRN models from various kinds of input is so far a unique asset of our code. One can use structural data as defined in PDB files and/or from data on residue pair interaction energies, either from force-field parameters or fragment molecular orbital (FMO) calculations. pyProGA is a free open-source software available from https://gitlab.com/Vlado_S/pyproga.

Mechanistic Insights into the Allosteric Inhibition of Androgen Receptors by Binding Function 3 Antagonists from an Integrated Molecular Modeling Study

An androgen receptor (AR) is an intensively studied treatment target for castration-resistant prostate cancer that is irresponsive to conventional antiandrogen therapeutics. Binding function 3 (BF3) inhibitors with alternative modes of action have emerged as a promising approach to overcoming antiandrogen resistance. However, how these BF3 inhibitors modulate AR function remains elusive, hindering the development of BF3-targeting agents. Here, we performed an integrated computational study to interrogate the binding mechanism of several known BF3 inhibitors with ARs. Our results show that the inhibitory effect of the BF3 antagonists arises from their allosteric modulation of the activation function (AF2) site, which alters the dynamic coupling between the BF3 and AF2 sites as well as the AF2-coactivator (SRC2-3) interaction. Moreover, the per-residue binding energy analyses reveal the "anchor" role of the linker connecting the phenyl ring and benzimidazole/indole in these BF3 inhibitors. Furthermore, the allosteric driver-interacting residues are found to include both "positive", e.g., Phe673 and Asn833, and "negative" ones, e.g., Phe826, and the differential interactions with these residues provide an explanation why stronger binding does not necessarily result in higher inhibitory activities. Finally, our allosteric communication pathway analyses delineate how the allosteric signals triggered by BF3 binding are propagated to the AF2 pocket through multiple short- and/or long-ranged transmission pathways. Collectively, our combined computational study provides a comprehensive structural mechanism underlying how the selected set of BF3 inhibitors modulate AR function, which will help guide future development of BF3 antagonists.

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.

Rewards of divergence in sequences, 3-D structures and dynamics of yeast and human spliceosome SF3b complexes

The evolution of homologous and functionally equivalent multiprotein assemblies is intriguing considering sequence divergence of constituent proteins. Here, we studied the implications of protein sequence divergence on the structure, dynamics and function of homologous yeast and human SF3b spliceosomal subcomplexes. Human and yeast SF3b comprise of 7 and 6 proteins respectively, with all yeast proteins homologous to their human counterparts at moderate sequence identity. SF3b6, an additional component in the human SF3b, interacts with the N-terminal extension of SF3b1 while the yeast homologue Hsh155 lacks the equivalent region. Through detailed homology studies, we show that SF3b6 is absent not only in yeast but in multiple lineages of eukaryotes implying that it is critical in specific organisms. We probed for the potential role of SF3b6 in the spliceosome assembled form through structural and flexibility analyses. By analysing normal modes derived from anisotropic network models of SF3b1, we demonstrate that when SF3b1 is bound to SF3b6, similarities in the magnitude of residue motions (0.86) and inter-residue correlated motions (0.94) with Hsh155 are significantly higher than when SF3b1 is considered in isolation (0.21 and 0.89 respectively). We observed that SF3b6 promotes functionally relevant 'open-to-close' transition in SF3b1 by enhancing concerted residue motions. Such motions are found to occur in the Hsh155 without SF3b6. The presence of SF3b6 influences motions of 16 residues that interact with U2 snRNA/branchpoint duplex and supports the participation of its interface residues in long-range communication in the SF3b1. These results advocate that SF3b6 potentially acts as an allosteric regulator of SF3b1 for BPS selection and might play a role in alternative splicing. Furthermore, we observe variability in the relative orientation of SF3b4 and in the local structure of three β-propeller domains of SF3b3 with reference to their yeast counterparts. Such differences influence the inter-protein interactions of SF3b between these two organisms. Together, our findings highlight features of SF3b evolution and suggests that the human SF3b may have evolved sophisticated mechanisms to fine tune its molecular function.

Binding mode characterization of 13b in the monomeric and dimeric states of SARS-CoV-2 main protease using molecular dynamics simulations

The main protease, M^pro/3CL^pro, plays an essential role in processing polyproteins translated from viral RNA to produce functional viral proteins and therefore serve as an attractive target for discovering COVID-19 therapeutics. The availability of both monomer and dimer crystal bound with a common ligand, '13b' (α-ketoamide inhibitor), opened up opportunities to understand the M^pro mechanism of action. A comparative analysis of both forms of M^pro was carried out to elucidate the binding site architectural differences in the presence and absence of '13b'. Molecular dynamics simulations suggest that the presence of '13b' enhances the stability of M^pro than the unbound APO form. The N- and C- terminals of both the protomers stabilize each other, and making it's interface essential for the active form of M^pro. In comparison to monomer, the relatively high affinity of '13b' is gained in dimer pocket due to the high stability of the pocket by the interaction of S1 residue of chain B with residues F140, E166 and H172 of chain A, which is absent in monomer. The comprehensive essential dynamics, protein structure network analysis and thermodynamic profiling highlight the hot-spots, pivotal in molecular recognition process at protein-ligand and protein-protein interaction levels, cross-validated through computational alanine scanning study. A comparative description of '13b' binding mechanism in both forms illustrates valuable insights into the inhibition mechanism and the selection of critical residues suitable for the structure-based approaches for the identification of more potent M^pro inhibitors.Communicated by Ramaswamy H. Sarma.

Comparative protein structure network analysis on 3CLpro from SARS-CoV-1 and SARS-CoV-2

The main protease M^pro, 3CL^pro is an important target from coronaviruses. In spite of having 96% sequence identity among M^pros from SARS‐CoV‐1 and SARS‐CoV‐2; the inhibitors used to block the activity of SARS‐CoV‐1 M^pro so far, were found to have differential inhibitory effect on M^pro of SARS‐CoV‐2. The possible reason could be due to the difference of few amino acids among the peptidases. Since, overall 3‐D crystallographic structure of M^pro from SARS‐CoV‐1 and SARS‐CoV‐2 is quite similar and mapping a subtle structural variation is seemingly impossible. Hence, we have attempted to study a structural comparison of SARS‐CoV‐1 and SARS‐CoV‐2 M^pro in apo and inhibitor bound states using protein structure network (PSN) based approach at contacts level. The comparative PSNs analysis of apo M^pros from SARS‐CoV‐1 and SARS‐CoV‐2 uncovers small but significant local changes occurring near the active site region and distributed throughout the structure. Additionally, we have shown how inhibitor binding perturbs the PSG and the communication pathways in M^pros. Moreover, we have also investigated the network connectivity on the quaternary structure of M^pro and identified critical residue pairs for complex formation using three centrality measurement parameters along with the modularity analysis. Taken together, these results on the comparative PSN provide an insight into conformational changes that may be used as an additional guidance towards specific drug development.