Measuring and comparing structural fluctuation patterns in large protein datasets

Role of death-associated protein kinase 1 (DAPK1) in retinal degenerative diseases: an in-silico approach towards therapeutic intervention

The Death-associated protein kinase 1 (DAPK1) has emerged as a crucial player in the pathogenesis of degenerative diseases. As a serine/threonine kinase family member, DAPK1 regulates critical signaling pathways, such as apoptosis and autophagy. In this study, we comprehensively analyzed DAPK1 interactors and enriched molecular functions, biological processes, phenotypic expression, disease associations, and aging signatures to elucidate the molecular networks of DAPK1. Furthermore, we employed a structure-based virtual screening approach using the PubChem database, which enabled the identification of potential bioactive compounds capable of inhibiting DAPK1, including caspase inhibitors and synthetic analogs. Three selected compounds, CID24602687, CID8843795, and CID110869998, exhibited high docking affinity and selectivity towards DAPK1, which were further investigated using molecular dynamics simulations to understand their binding patterns. Our findings establish a connection between DAPK1 and retinal degenerative diseases and highlight the potential of these selected compounds for the development of novel therapeutic strategies. This study provides valuable insights into the molecular mechanisms underlying DAPK1-related diseases, and offers new opportunities for the discovery of effective treatments for retinal degeneration.Communicated by Ramaswamy H. Sarma.

Discovering allatostatin type-C receptor specific agonists

Currently, there is no pesticide available for the selective control of the pine processionary moth (Thaumetopoea pityocampa-specific), and conventional methods typically rely on mechanical techniques such as pheromone traps or broad-spectrum larvicidal chemicals. As climate change increases the range and dispersion capacity of crop and forest pests, outbreaks of the pine processionary occur with greater frequency and significantly impact forestry and public health. Our study is carried out to provide a T. pityocampa-specific pesticide targeting the Allatostatin Type-C Receptor (AlstR-C). We use a combination of computational biology methods, a cell-based screening assay, and in vivo toxicity and side effect assays to identify, for the first time, a series of AlstR-C ligands suitable for use as T. pityocampa-specific insecticides. We further demonstrate that the novel AlstR-C targeted agonists are specific to lepidopteran larvae, with no harmful effects on coleopteran larvae or adults. Overall, our study represents an important initial advance toward an insect GPCR-targeted next-generation pesticide design. Our approach may apply to other invertebrate GPCRs involved in vital metabolic pathways.

Novel thiazolidin-4-one benzenesulfonamide hybrids as PPARγ agonists: Design, synthesis and in vivo anti-diabetic evaluation

In the current study, two series of novel thiazolidin-4-one benzenesulfonamide arylidene hybrids 9a-l and 10a-f were designed, synthesized and tested in vitro for their PPARɣ agonistic activity. The phenethyl thiazolidin-4-one sulphonamide 9l showed the highest PPARɣ activation % by 41.7%. Whereas, the 3-methoxy- and 4-methyl-4-benzyloxy thiazolidin-4-one sulphonamides 9i, and 9k revealed moderate PPARɣ activation % of 31.7, and 32.8%, respectively, in addition, the 3-methoxy-3-benzyloxy thiazolidin-4-one sulphonamide 10d showed PPARɣ activation % of 33.7% compared to pioglitazone. Compounds 9b, 9i, 9k, 9l, and 10d revealed higher selectivity to PPARɣ over the PPARδ, and PPARα isoforms. An immunohistochemical study was performed in HepG-2 cells to confirm the PPARɣ protein expression for the most active compounds. Compounds 9i, 9k, and 10d showed higher PPARɣ expression than that of pioglitazone. Pharmacological studies were also performed to determine the anti-diabetic activity in rats at a dose of 36 mg/kg, and it was revealed that compounds 9i and 10d improved insulin secretion as well as anti-diabetic effects. The 3-methoxy-4-benzyloxy thiazolidin-4-one sulphonamide 9i showed a better anti-diabetic activity than pioglitazone. Moreover, it showed a rise in blood insulin by 4-folds and C-peptide levels by 48.8%, as well as improved insulin sensitivity. Moreover, compound 9i improved diabetic complications as evidenced by decreasing liver serum enzymes, restoration of total protein and kidney functions. Besides, it combated oxidative stress status and exerted anti-hyperlipidemic effect. Compound 9i showed a superior activity by normalizing some parameters and amelioration of pancreatic, hepatic, and renal histopathological alterations caused by STZ-induction of diabetes. Molecular docking studies, molecular dynamic simulations, and protein ligand interaction analysis were also performed for the newly synthesized compounds to investigate their predicted binding pattern and energies in PPARɣ binding site.

Predicting Plasmodium falciparum kinase inhibitors from antimalarial medicinal herbs using computational modeling approach

Malaria remains a significant public health challenge, with resistance to available drugs necessitating the development of novel therapies targeting invasion-dependent proteins. Plasmodium falciparum calcium-dependent protein kinase 1 (PfCDPK-1) is essential for host erythrocyte invasion and parasite asexual development. This study screened a library of 490 compounds using computational methods to identify potential PfCDPK-1 inhibitors. Three compounds; 17-hydroxyazadiradione, Picracin, and Epicatechin-gallate derived from known antimalarial botanicals, showed potent inhibitory effects on PfCDPK-1. These compounds exhibited better binding affinities (-8.8, -9.1, -9.3 kCal/mol respectively), pharmacokinetics, and physicochemical properties than the purported inhibitory standard of PfCDPK-1, Purfalcamine. Molecular dynamics simulations (50 ns) and molecular mechanics analyses confirmed the stability and binding rigidity of these compounds at the active pocket of PfCDPK-1. The results suggest that these compounds are promising pharmacological targets with potential therapeutic effects for malaria treatment/management without undesirable side effects. Therefore, this study provides new insights into the development of effective antimalarial agents targeting invasion-dependent proteins, which could help combat the global malaria burden.

Supplementary Information

The online version contains supplementary material available at 10.1007/s40203-023-00175-z.

Normal mode analysis and comparative study of intrinsic dynamics of alcohol oxidase enzymes from GMC protein family

Glucose-Methanol-Choline (GMC) family enzymes are very important in catalyzing the oxidation of a wide range of structurally diverse substrates. Enzymes that constitute the GMC family, share a common tertiary fold but < 25% sequence identity. Cofactor FAD, FAD binding signature motif, and similar structural scaffold of the active site are common features of oxidoreductase enzymes of the GMC family. Protein functionality mainly depends on protein three-dimensional structures and dynamics. In this study, we used the normal mode analysis method to search the intrinsic dynamics of GMC family enzymes. We have explored the dynamical behavior of enzymes with unique substrate catabolism and active site characteristics from different classes of the GMC family. Analysis of individual enzymes and comparative ensemble analysis of enzymes from different classes has shown conserved dynamic motion at FAD binding sites. The present study revealed that GMC enzymes share a strong dynamic similarity (Bhattacharyya coefficient >90% and root mean squared inner product >52%) despite low sequence identity across the GMC family enzymes. The study predicts that local deformation energy between atoms of the enzyme may be responsible for the catalysis of different substrates. This study may help that intrinsic dynamics can be used to make meaningful classifications of proteins or enzymes from different organisms.Communicated by Ramaswamy H. Sarma.

Human ACE2 orthologous peptide sequences show better binding affinity to SARS-CoV-2 RBD domain: Implications for drug design

Computational methods coupled with experimental validation play a critical role in the identification of novel inhibitory peptides that interact with viral antigenic determinants. The interaction between the receptor binding domain (RBD) of SARS-CoV-2 spike protein and the helical peptide of human angiotensin-converting enzyme-2 (ACE2) is a necessity for the initiation of viral infection. Herein, natural orthologs of human ACE2 helical peptide were evaluated for competitive inhibitory binding to the viral RBD by use of a computational approach, which was experimentally validated. A total of 624 natural ACE2 orthologous 32-amino acid long peptides were identified through a similarity search. Molecular docking was used to virtually screen and rank the peptides based on binding affinity metrics, benchmarked against human ACE2 peptide docked to the RBD. Molecular dynamics (MD) simulations were done for the human reference and the Nipponia nippon peptide as it exhibited the highest binding affinity (Gibbs free energy; -14 kcal/mol) predicted from the docking results. The MD simulation confirmed the stability of the assessed peptide in the complex (-12.3 kcal/mol). The top three docked-peptides (from Chitinophaga sancti, Nipponia nippon, and Mus musculus) and the human reference were experimentally validated by use of surface plasmon resonance technology. The human reference exhibited the weakest binding affinity (Kd of 318-441 pM) among the peptides tested, in agreement with the docking prediction, while the peptide from Nipponia nippon was the best, with 267-538-fold higher affinity than the reference. The validated peptides merit further investigation. This work showcases that the approach herein can aid in the identification of inhibitory biosimilar peptides for other viruses.

Evaluating Biofilm Inhibitory Potential in Fish Pathogen, Aeromonas hydrophila by Agricultural Waste Extracts and Assessment of Aerolysin Inhibitors Using In Silico Approach

Aeromonas hydrophila, an opportunistic bacteria, causes several devastating diseases in humans and animals, particularly aquatic species. Antibiotics have been constrained by the rise of antibiotic resistance caused by drug overuse. Therefore, new strategies are required to prevent appropriate antibiotic inability from antibiotic-resistant strains. Aerolysin is essential for A. hydrophila pathogenesis and has been proposed as a potential target for inventing drugs with anti-virulence properties. It is a unique method of disease prevention in fish to block the quorum-sensing mechanism of A. hydrophila. In SEM analysis, the crude solvent extracts of both groundnut shells and black gram pods exhibited a reduction of aerolysin formation and biofilm matrix formation by blocking the QS in A. hydrophila. Morphological changes were identified in the extracts treated bacterial cells. Furthermore, in previous studies, 34 ligands were identified with potential antibacterial metabolites from agricultural wastes, groundnut shells, and black gram pods using a literature survey. Twelve potent metabolites showed interactions between aerolysin and metabolites during molecular docking analysis, in that H-Pyran-4-one-2,3 dihydro-3,5 dihydroxy-6-methyl (-5.3 kcal/mol) and 2-Hexyldecanoic acid (-5.2 kcal/mol) showed promising results with potential hydrogen bond interactions with aerolysin. These metabolites showed a better binding affinity with aerolysin for 100 ns in molecular simulation dynamics. These findings point to a novel strategy for developing drugs using metabolites from agricultural wastes that may be feasible pharmacological solutions for treating A. hydrophila infections for the betterment of aquaculture.

Selectivity and Ranking of Tight-Binding JAK-STAT Inhibitors Using Markovian Milestoning with Voronoi Tessellations

Janus kinases (JAK), a group of proteins in the nonreceptor tyrosine kinase (NRTKs) family, play a crucial role in growth, survival, and angiogenesis. They are activated by cytokines through the Janus kinase-signal transducer and activator of a transcription (JAK-STAT) signaling pathway. JAK-STAT signaling pathways have significant roles in the regulation of cell division, apoptosis, and immunity. Identification of the V617F mutation in the Janus homology 2 (JH2) domain of JAK2 leading to myeloproliferative disorders has stimulated great interest in the drug discovery community to develop JAK2-specific inhibitors. However, such inhibitors should be selective toward JAK2 over other JAKs and display an extended residence time. Recently, novel JAK2/STAT5 axis inhibitors (N-(1H-pyrazol-3-yl)pyrimidin-2-amino derivatives) have displayed extended residence times (hours or longer) on target and adequate selectivity excluding JAK3. To facilitate a deeper understanding of the kinase-inhibitor interactions and advance the development of such inhibitors, we utilize a multiscale Markovian milestoning with Voronoi tessellations (MMVT) approach within the Simulation-Enabled Estimation of Kinetic Rates v.2 (SEEKR2) program to rank order these inhibitors based on their kinetic properties and further explain the selectivity of JAK2 inhibitors over JAK3. Our approach investigates the kinetic and thermodynamic properties of JAK-inhibitor complexes in a user-friendly, fast, efficient, and accurate manner compared to other brute force and hybrid-enhanced sampling approaches.

Internal Normal Mode Analysis Applied to RNA Flexibility and Conformational Changes

We investigated the capability of internal normal modes to reproduce RNA flexibility and predict observed RNA conformational changes and, notably, those induced by the formation of RNA-protein and RNA-ligand complexes. Here, we extended our iNMA approach developed for proteins to study RNA molecules using a simplified representation of the RNA structure and its potential energy. Three data sets were also created to investigate different aspects. Despite all the approximations, our study shows that iNMA is a suitable method to take into account RNA flexibility and describe its conformational changes opening the route to its applicability in any integrative approach where these properties are crucial.

Statistical Learning of Protein Elastic Network from Positional Covariance Matrix

Positional fluctuation and covariance during protein dynamics are key observables for understanding the molecular origin of biological functions. A frequently employed potential energy function for describing protein structural variation at the coarse-gained level is elastic network model (ENM). A long-standing issue in biomolecular simulation is thus the parametrization of ENM spring constants from the components of positional covariance matrix (PCM). Based on sensitivity analysis of PCM, the direct-coupling statistics of each spring, which is a specific combination of position fluctuation and covariance, is found to exhibit prominent signal of parameter dependence. This finding provides the basis for devising the objective function and the scheme of running through the effective one-dimensional optimization of every spring by self-consistent iteration. Formal derivation of the positional covariance statistical learning (PCSL) method also motivates the necessary data regularization for stable calculations. Robust convergence of PCSL is achieved in taking an all-atom molecular dynamics trajectory or an ensemble of homologous structures as input data. The PCSL framework can also be generalized with mixed objective functions to capture specific property such as the residue flexibility profile. Such physical chemistry-based statistical learning thus provides a useful platform for integrating the mechanical information encoded in various experimental or computational data.

In silico analysis of substitution mutations in the β-globin gene in Turkish population of β-thalassemia

Beta-thalassemia is a genetic blood disorder represented by anomalies in hemoglobin's beta chain production. Most hemoglobin defects are a result of mutations of the structural β-globin gene. Many diseases, including β-thalassemia, benefit from computational studies that aid researchers in investigating the association of genotype and phenotype. In this study, the alanine substitution mutations of the β-globin protein sub-units in the Turkish population (Hb Ankara, Hb Siirt and Hb Izmir) and the effects of those mutations on the β-globin protein structure and performance are examined using molecular dynamics simulation. While Hb Ankara variant showed a non-conservative mutation, Hb Siirt and Hb Izmir showed a semi-conservative mutation. RMSF values of Hb Siirt, between residues 95 and 99, were higher than wild-type and the other mutant proteins. The residues of Hb Ankara showed lower fluctuation compared to the other structures. The mean ROG values were 1.47 nm, 1.46 nm, 1.49 nm and 1.48 and the average number of the hydrogen bonds were 92, 100, 99, and 89 for Hb Ankara, Hb Siirt and Hb Izmir, respectively. Moreover, a significant increase in overall motion in Hb Siirt was observed based on PCA analysis. Hb Siirt substitution mutation might cause an effect in β-globin proteins which could impact the protein function. This indicates a major role on beta globin subunit's stability for alanine on 27th position. However, Hb Ankara and Hb Izmir variants may act as a silent mutation, since these two mutations did not show a large change in the dynamics of the protein.Communicated by Ramaswamy H. Sarma.

Molecular simulation unravels the amyloidogenic misfolding of nascent ApoA1 protein, driven by deleterious point mutations occurring in between 170-178 hotspot region

Protein ApoA1 is extensively studied for its role in lipid metabolism. Its seedy dark side of amyloid formulation remains relatively understudied yet. Due to genetic mutations, the protein pathologically misshapes into its amyloid form that gets accumulated in various organs, including the heart. To contrive effective therapeutics against this debilitating congenital disorder, it is imperative to comprehend the structural ramifications induced by mutations in APoA1's dynamic conformation. Till now, several point mutations have been implicated in ApoA1's amyloidosis, although only a handful has been examined considerably. Especially, the single nucleotide polymorphisms (SNPs) that occur in-between 170-178 mutation hotspot site of APoA1 needs to be investigated, since most of them are culpable of amyloid deposition in the heart. To that effect, in the present study, we have computationally quantified and studied the ApoA1's biomolecular modifications fostered by SNPs in the 170-178 mutation hotspot. Findings from discrete molecular dynamics simulation studies indicate that the SNPs have noticeably steered the ApoA1's behaviour from its native structural dynamics. Analysis of protein's secondary structural changes exhibits a considerable change upon mutations. Further, subjecting the protein structures to simulated thermal denaturation shows increased resistance to denaturation among mutants when compared to native. Further, normal mode analysis of protein's dynamic motion also shows discrepancy in its dynamic structural change upon SNP. These structural digressions induced by SNPs can very well be the biomolecular incendiary that drives ApoA1 into its amyloidogenesis. And, understanding these structural modifications initiates a better understanding of SNP's amyloidogenic pathology on APoA1.Communicated by Ramaswamy H. Sarma.

Explicit versus implicit consideration of binding partners in protein-protein complex to elucidate intrinsic dynamics

The binding of many proteins to their protein partners is tightly regulated via control of their relative intrinsic dynamics during the binding process, a phenomenon which can in turn be modulated. Therefore, investigating the intrinsic dynamics of proteins is necessary to understand function in a comprehensive way. By intrinsic dynamics herein, we principally refer to the vibrational signature of a protein molecule popularly obtained from normal modes or essential modes. For normal modes, one often considers that the molecule under investigation is a collection of springs in a solvent-free or implicit-solvent medium. In the context of a protein-binding partner, the analysis of vibration of the target protein is often complicated due to molecular interaction within the complex. Generally, it is assumed that the isolated bound conformation of the target protein captures the implicit effect of the binding partner on the intrinsic dynamics, therefore suggesting that any influence of the partner molecule is also already integrated. Such an assumption allows large-scale studies of the conservation of protein flexibility. However, in cases where a partner protein directly influences the vibration of the target via critical contacts at the protein-protein interface, the above assumption falls short of providing a detailed view. In this review article, we discuss the implications of considering the dynamics of a protein in a protein-protein complex, as modelled implicitly and explicitly with methods dependent on elastic network models. We further propose how such an explicit consideration can be applied to understand critical protein-protein contacts that can be targeted in future studies.

Isolation and In Silico Prediction of Potential Drug-like Compounds with a New Dimeric Prenylated Quinolone Alkaloid from Zanthoxylum rhetsa (Roxb.) Root Extracts Targeted against SARS-CoV-2 (Mpro)

A new dimeric prenylated quinolone alkaloid, named 2,11-didemethoxy-vepridimerine A, was isolated from the root bark of Zanthoxylum rhetsa, together with twelve known compounds. The structure of the new compound was elucidated on the basis of spectroscopic investigations (NMR and Mass). The interaction of the isolated compounds with the main protease of SARS-CoV-2 (Mpro) was evaluated using molecular docking followed by MD simulations. The result suggests that 2,11-didemethoxy-vepridimerine A, the new compound, has the highest negative binding affinity against the Mpro with a free energy of binding of -8.5 Kcal/mol, indicating interaction with the Mpro. This interaction was further validated by 100 ns MD simulation. This implies that the isolated new compound, which can be employed as a lead compound for an Mpro-targeting drug discovery program, may be able to block the action of Mpro.

Exploration of novel ligands to target C-C Motif Chemokine Receptor 2 (CCR2) as a promising pharmacological treatment against traumatic brain injury

It is widely reported that the overexpression of the C-C Motif Chemokine Receptor 2 (CCR2) has negative implications in neuroinflammatory diseases such as traumatic brain injury (TBI), although promising drugs to tackle this have been less forthcoming. As of 2016, only 2 drugs specifically targeting this receptor have made their way to market, with unsuccessful outcome unfortunately, suggesting that the search for more specific and precise ligands is utterly necessary. In this paper we hypothesized that by targeting Glu291, Met295, Trp98, Leu45 and Val189 amino acids, essential in the binding of CCR2 with C-C Motif Chemokine Ligand 2 (CCL2), the endogenous substrate, mitigates its activity in TBI. We used a pharmacophore model to screen for suitable ligands that may bind to CCR2, which returned 871 ligands. Docking and molecular dynamics results uncovered that two ligands (A102) and (A435) contained several of those important residues and showed a stability and compactness when in complex with CCR2, with these results confirmed by MMGBSA calculations with A102 recording a better interaction compared to A435. Finally, a PPI network was built to explore downstream signaling being regulated by both ligands in TBI, showing amyloid precursor protein (APP) as a key target and neuroactive-ligand receptor interaction (1.80E-27) the top functional annotated category. In conclusion, for the first time we report novel ligands A102 and A435 targeting CCR2 as a potential new pharmacological approach to target inflammation post-TBI.

In silico approach to probe the binding affinity between OMVs harboring the ZEGFR affibody and the EGF receptor

There is a growing interest in designing a nanocarrier containing an EGFR targeting affibody to direct toward cancer cells. Here, cytolysin A was cloned at the N-terminus of Z_EGFR:1907 affibody to guarantee its surface presentation on the OMVs while targeting the epidermal growth factor receptors (EGFRs). A separate construct including a fusogenic peptide (GALA) was also designed for the endosomal escape of the nanocarrier. Binding of the two constructs ClyA-affi_EGFR and ClyA-affi_EGFR-GALA to domain III of EGFR was investigated using molecular docking and molecular dynamic simulations. The higher stability of the ClyA-affi_EGFR-GALA/EGFR as compared to the ClyA-affi_EGFR/EGFR complex was evident. The ClyA-affi_EGFR-GALA structure showed a higher RMSD during the first half of the simulation time implying a much less stable behavior. Plateau state of the radius of gyration plot of ClyA-affi_EGFR-GALA confirmed a well-folded structure in the presence of the GALA sequence. Solvent accessible surface area for both proteins was in the same range. The data obtained from hydrogen bond analysis revealed a more equilibrated and stable form of the ClyA-affi_EGFR-GALA structure upon interaction with EGFR. The data provided here was a requisite for our biological evaluation of the synthesized constructs as a component of a novel drug delivery system.

Protein dynamics developments for the large scale and cryoEM: case study of ProDy 2.0

New computational biophysics pipelines for analysing the global dynamics of structural ensembles and large, dynamic complexes resolved by cryoEM are reviewed.

Cryo-electron microscopy (cryoEM) has become a well established technique with the potential to produce structures of large and dynamic supramolecular complexes that are not amenable to traditional approaches for studying structure and dynamics. The size and low resolution of such molecular systems often make structural modelling and molecular dynamics simulations challenging and computationally expensive. This, together with the growing wealth of structural data arising from cryoEM and other structural biology methods, has driven a trend in the computational biophysics community towards the development of new pipelines for analysing global dynamics using coarse-grained models and methods. At the centre of this trend has been a return to elastic network models, normal mode analysis (NMA) and ensemble analyses such as principal component analysis, and the growth of hybrid simulation methodologies that make use of them. Here, this field is reviewed with a focus on ProDy, the Python application programming interface for protein dynamics, which has been developed over the last decade. Two key developments in this area are highlighted: (i) ensemble NMA towards extracting and comparing the signature dynamics of homologous structures, aided by the recent SignDy pipeline, and (ii) pseudoatom fitting for more efficient global dynamics analyses of large and low-resolution supramolecular assemblies from cryoEM, revisited in the CryoDy pipeline. It is believed that such a renewal and extension of old models and methods in new pipelines will be critical for driving the field forward into the next cryoEM revolution.

SARS-CoV-2 Spike Protein Unlikely to Bind to Integrins via the Arg-Gly-Asp (RGD) Motif of the Receptor Binding Domain: Evidence From Structural Analysis and Microscale Accelerated Molecular Dynamics

The Receptor Binding Domain (RBD) of SARS-CoV-2 virus harbors a sequence of Arg-Gly-Asp tripeptide named RGD motif, which has also been identified in extracellular matrix proteins that bind integrins as well as other disintegrins and viruses. Accordingly, integrins have been proposed as host receptors for SARS-CoV-2. However, given that the microenvironment of the RGD motif imposes a structural hindrance to the protein-protein association, the validity of this hypothesis is still uncertain. Here, we used normal mode analysis, accelerated molecular dynamics microscale simulation, and protein-protein docking to investigate the putative role of RGD motif of SARS-CoV-2 RBD for interacting with integrins. We found, that neither RGD motif nor its microenvironment showed any significant conformational shift in the RBD structure. Highly populated clusters of RBD showed no capability to interact with the RGD binding site in integrins. The free energy landscape revealed that the RGD conformation within RBD could not acquire an optimal geometry to allow the interaction with integrins. In light of these results, and in the event where integrins are confirmed to be host receptors for SARS-CoV-2, we suggest a possible involvement of other residues to stabilize the interaction.

A Comparative Evaluation of the Structural and Dynamic Properties of Insect Odorant Binding Proteins

Insects devote a major part of their metabolic resources to the production of odorant binding proteins (OBPs). Although initially, these proteins were implicated in the solubilisation, binding and transport of semiochemicals to olfactory receptors, it is now recognised that they may play diverse, as yet uncharacterised, roles in insect physiology. The structures of these OBPs, the majority of which are known as “classical” OBPs, have shed some light on their potential functional roles. However, the dynamic properties of these proteins have received little attention despite their functional importance. Structural dynamics are encoded in the native protein fold and enable the adaptation of proteins to substrate binding. This paper provides a comparative review of the structural and dynamic properties of OBPs, making use of sequence/structure analysis, statistical and theoretical physics-based methods. It provides a new layer of information and additional methodological tools useful in unravelling the relationship between structure, dynamics and function of insect OBPs. The dynamic properties of OBPs, studied by means of elastic network models, reflect the similarities/dissimilarities observed in their respective structures and provides insights regarding protein motions that may have important implications for ligand recognition and binding. Furthermore, it was shown that the OBPs studied in this paper share conserved structural ‘core’ that may be of evolutionary and functional importance.

Mutational landscape of SARS-CoV-2 genome in Turkey and impact of mutations on spike protein structure

The Coronavirus Disease 2019 (COVID-19) was declared a pandemic in March 2020 by the World Health Organization (WHO). As of May 25th, 2021 there were 2.059.941 SARS-COV2 genome sequences that have been submitted to the GISAID database, with numerous variations. Here, we aim to analyze the SARS-CoV-2 genome data submitted to the GISAID database from Turkey and to determine the variant and clade distributions by the end of May 2021, in accordance with their appearance timeline. We compared these findings to USA, Europe, and Asia data as well. We have also evaluated the effects of spike protein variations, detected in a group of genome sequences of 13 patients who applied to our clinic, by using 3D modeling algorithms. For this purpose, we analyzed 4607 SARS-CoV-2 genome sequences submitted by different lab centers from Turkey to the GISAID database between March 2020 and May 2021. Described mutations were also introduced in silico to the spike protein structure to analyze their isolated impacts on the protein structure. The most abundant clade was GR followed by G, GH, and GRY and we did not detect any V clade. The most common variant was B.1, followed by B.1.1, and the UK variant, B.1.1.7. Our results clearly show a concordance between the variant distributions, the number of cases, and the timelines of different variant accumulations in Turkey. The 3D simulations indicate an increase in the surface hydrophilicity of the reference spike protein and the detected mutations. There was less surface hydrophilicity increase in the Asp614Gly mutation, which exhibits a more compact conformation around the ACE-2 receptor binding domain region, rendering the structure in a “down” conformation. Our genomic findings can help to model vaccination programs and protein modeling may lead to different approaches for COVID-19 treatment strategies.

A Conservative Replacement in the Transmembrane Domain of SARS-CoV-2 ORF7a as a Putative Risk Factor in COVID-19

Simple Summary

The pathogenicity and transmissibility of the COVID-19 pandemic causative agent, the SARS-CoV-2 virus, is related to the functions of the proteins synthesized intracellularly, as guided by viral RNA. It is vitally important to accurately pinpoint novel variants of concern of the SARS-CoV-2 virus, in order to understand the molecular features of novel mutations and manage the on-going battle against the COVID-19 pandemic. We focused on A105V mutation in the ORF7a accessory protein. Sequencing and clinical data showed that this mutation is associated with increased severity and lethality in a group of Romanian patients, despite a lower viral copy number and a lower number of associated comorbidities. This effect is primarily due to increased protein stability through allosteric effects as shown by molecular dynamics analyses. This behavior manifests especially among residues 39–56, and the ones adjacent to 26–30 loop, placed in direct contact with potential interaction partners. Together, the results provide novel insights into the role of ORF7a in the pathogenicity of SARS-CoV-2.

Abstract

The ongoing COVID-19 pandemic follows an unpredictable evolution, driven by both host-related factors such as mobility, vaccination status, and comorbidities and by pathogen-related ones. The pathogenicity of its causative agent, SARS-CoV-2 virus, relates to the functions of the proteins synthesized intracellularly, as guided by viral RNA. These functions are constantly altered through mutations resulting in increased virulence, infectivity, and antibody-evasion abilities. Well-characterized mutations in the spike protein, such as D614G, N439K, Δ69–70, E484K, or N501Y, are currently defining specific variants; however, some less studied mutations outside the spike region, such as p. 3691 in NSP6, p. 9659 in ORF-10, 8782C > T in ORF-1ab, or 28144T > C in ORF-8, have been proposed for altering SARS-CoV-2 virulence and pathogenicity. Therefore, in this study, we focused on A105V mutation of SARS-CoV-2 ORF7a accessory protein, which has been associated with severe COVID-19 clinical manifestation. Molecular dynamics and computational structural analyses revealed that this mutation differentially alters ORF7a dynamics, suggesting a gain-of-function role that may explain its role in the severe form of COVID-19 disease.

Evolution of dynamical networks enhances catalysis in a designer enzyme

Activation heat capacity is emerging as a crucial factor in enzyme thermoadaptation, as shown by the non-Arrhenius behaviour of many natural enzymes. However, its physical origin and relationship to the evolution of catalytic activity remain uncertain. Here we show that directed evolution of a computationally designed Kemp eliminase reshapes protein dynamics, which gives rise to an activation heat capacity absent in the original design. These changes buttress transition-state stabilization. Extensive molecular dynamics simulations show that evolution results in the closure of solvent-exposed loops and a better packing of the active site. Remarkably, this gives rise to a correlated dynamical network that involves the transition state and large parts of the protein. This network tightens the transition-state ensemble, which induces a negative activation heat capacity and non-linearity in the activity-temperature dependence. Our results have implications for understanding enzyme evolution and suggest that selectively targeting the conformational dynamics of the transition-state ensemble by design and evolution will expedite the creation of novel enzymes.

Parameterizing elastic network models to capture the dynamics of proteins

Coarse-grained normal mode analyses of protein dynamics rely on the idea that the geometry of a protein structure contains enough information for computing its fluctuations around its equilibrium conformation. This geometry is captured in the form of an elastic network (EN), namely a network of edges between its residues. The normal modes of a protein are then identified with the normal modes of its EN. Different approaches have been proposed to construct ENs, focusing on the choice of the edges that they are comprised of, and on their parameterizations by the force constants associated with those edges. Here we propose new tools to guide choices on these two facets of EN. We study first different geometric models for ENs. We compare cutoff-based ENs, whose edges have lengths that are smaller than a cutoff distance, with Delaunay-based ENs and find that the latter provide better representations of the geometry of protein structures. We then derive an analytical method for the parameterization of the EN such that its dynamics leads to atomic fluctuations that agree with experimental B-factors. To limit overfitting, we attach a parameter referred to as flexibility constant to each atom instead of to each edge in the EN. The parameterization is expressed as a non-linear optimization problem whose parameters describe both rigid-body and internal motions. We show that this parameterization leads to improved ENs, whose dynamics mimic MD simulations better than ENs with uniform force constants, and reduces the number of normal modes needed to reproduce functional conformational changes.

Molecular modelling approaches predicted 1,2,3-triazolyl ester of ketorolac (15K) to be a novel allosteric modulator of the oncogenic kinase PAK1

P21-activated kinases (PAKs) are serine/threonine protein kinase which have six different isoforms (PAK1-6). Of those, PAK1 is overexpressed in many cancers and considered to be a major chemotherapeutic target. Most of the developed PAK1 inhibitor drugs work as pan-PAK inhibitors and show undesirable toxicity due to having untargeted kinase inhibition activities. Selective PAK1 inhibitors are therefore highly desired and oncogenic drug hunters are trying to develop allosteric PAK1 inhibitors. We previously synthesized 1,2,3-triazolyl ester of ketorolac (15K) through click chemistry technique, which exhibits significant anti-cancer effects via inhibiting PAK1. Based on the selective anticancer effects of 15K against PAK1-dependent cancer cells, we hypothesize that it may act as an allosteric PAK1 inhibitor. In this study, computational analysis was done with 15K to explore its quantum chemical and thermodynamic properties, molecular interactions and binding stability with PAK1, physicochemical properties, ADMET, bioactivities, and druglikeness features. Molecular docking analysis demonstrates 15K as a potent allosteric ligand that strongly binds to a novel allosteric site of PAK1 (binding energy ranges - 8.6 to - 9.2 kcal/mol) and does not target other PAK isoforms; even 15K shows better interactions than another synthesized PAK1 inhibitor. Molecular dynamics simulation clearly supports the stable binding properties of 15K with PAK1 crystal. Density functional theory-based calculations reveal that it can be an active drug with high softness and moderate polarity, and ADMET predictions categorize it as a non-toxic drug as evidenced by in vitro studies with brine shrimp and fibroblast cells. Structure-activity relationship clarifies the role of ester bond and triazol moiety of 15K in establishing novel allosteric interactions. Our results summarize that 15K selectively inhibits PAK1 as an allosteric inhibitor and in turn shows anticancer effects without toxicity.

Camptothecin shows better promise than Curcumin in the inhibition of the Human Telomerase: A computational study

OBJECTIVES

The Human Telomerase enzyme has become a drug target in the treatment of cancers and age-related disorders. This study aims to identify potential natural inhibitors of the Human Telomerase from compounds derived from edible African plants.

MATERIALS AND METHODS

A library of 1,126 natural compounds was molecularly docked against the Telomerase Reverse Transcriptase (PDB ID: 5ugw), the catalytic subunit of the target protein. Curcumin, a known Telomerase inhibitor was used as the standard. The front-runner compounds were screened for bioavailability, pharmacokinetic properties, and bioactivity using the SWISSADME, PKCSM, and Molinspiration webservers respectively. The molecular dynamic simulation and analyses of the apo and holo proteins were performed by the Galaxy supercomputing webserver.

RESULTS

The results of the molecular docking and virtual screening reveal Augustamine and Camptothecin as lead compounds. Augustamine has better drug-likeness and pharmacokinetic properties while Camptothecin showed better bioactivity and stronger binding affinity (-8.2 kcal/mol) with the target. The holo structure formed by Camptothecin showed greater inhibitory activity against the target with a total RMSF of 169.853, B-Factor of 20.164, and 108 anti-correlating residues.

CONCLUSION

Though they both act at the same binding site, Camptothecin induces greater Telomerase inhibition and better molecular stability than the standard, Curcumin. Further tests are required to investigate the inhibitory activities of the lead compounds.

Computational assessment of select antiviral phytochemicals as potential SARS-Cov-2 main protease inhibitors: molecular dynamics guided ensemble docking and extended molecular dynamics

Covid-19 caused by novel coronavirus, 2019-nCoV or SARS-CoV-2 has become most severe pandemic of this century. No specific therapies are available to treat Covid-19 so far. Recently, main protease (M^pro), a potential drug target from SARS-CoV-2 has been successfully crystallised. The present study is aimed at assessment of bioactive antiviral phytochemicals as potential SARS-COV-2 M^pro inhibitors, using ensemble docking, molecular dynamics and MM-PBSA calculations. Ensemble docking studies were performed with Autodock vina program. The top 5 compounds having highest binding free energy were subjected to 100 ns molecular dynamics simulations with Gromacs. The resulting trajectories of converged period of MD were further exploited in MM-PBSA calculations to derive accurate estimates of binding free energies. The MD results were analysed with respect to RMSD, RMSF and hydrogen bond formation and occupancy parameters. The drugs remdesivir and nelfinavir were used as standard drugs for comparative studies. In the docking studies five phytochemicals, dalpanitin, amentoflavone, naringin, hinokiflavone, and rutin were found having lowest binding free energies (< − 10 kcal mol⁻¹) which is lower than standard drugs. MD studies suggested that the complexes of these five phytochemicals with M^pro stabilize with well accepted RMSD. Amongst these phytochemicals, hinokiflavove, amentoflavone and naringin were found having better binding affinity with ΔG_binging than the standard drug remdesivir. Investigations and validation of these inhibitors against SARS-CoV-2 would be helpful in bring these molecules at the clinical settings.

Design and synthesis of novel quinazolinone-pyrazole derivatives as potential α-glucosidase inhibitors: Structure-activity relationship, molecular modeling and kinetic study

In this study, a new series of quinazolinone-pyrazole hybrids were designed, synthesized and screened for their α-glucosidase inhibitory activity. The results of the in vitro screening indicated that all the molecular hybrids exhibited more inhibitory activity (IC₅₀ values ranging from 60.5 ± 0.3 µM-186.6 ± 20 μM) in comparison to standard acarbose (IC₅₀ = 750.0 ± 10.0 µM). Limited structure-activity relationship suggested that the variation in the inhibitory activities of the compounds affected by different substitutions on phenyl rings of diphenyl pyrazole moiety. The enzyme kinetic studies of the most potent compound 9i revealed that it inhibited α-glucosidase in a competitive mode with a Ki of 56 μM. Molecular docking study was performed to predict the putative binding interaction. As expected, all pharmacophoric moieties used in the initial structure design playing a pivotal role in the interaction with the binding site of the enzyme. In addition, by performing molecular dynamic investigation and MM-GBSA calculation, we investigated the difference in structural perturbation and dynamic behavior that is observed over α-glycosidase in complex with the most active compound and acarbose relative to unbound α-glycosidase enzyme.

Phytochemical Moieties From Indian Traditional Medicine for Targeting Dual Hotspots on SARS-CoV-2 Spike Protein: An Integrative in-silico Approach

SARS-CoV-2 infection across the world has led to immense turbulence in the treatment modality, thus demanding a swift drug discovery process. Spike protein of SARS-CoV-2 binds to ACE2 receptor of human to initiate host invasion. Plethora of studies demonstrate the inhibition of Spike-ACE2 interactions to impair infection. The ancient Indian traditional medicine has been of great interest of Virologists worldwide to decipher potential antivirals. Hence, in this study, phytochemicals (1,952 compounds) from eight potential medicinal plants used in Indian traditional medicine were meticulously collated, based on their usage in respiratory disorders, along with immunomodulatory and anti-viral potential from contemporary literature. Further, these compounds were virtually screened against Receptor Binding Domain (RBD) of Spike protein. The potential compounds from each plant were prioritized based on the binding affinity, key hotspot interactions at ACE2 binding region and glycosylation sites. Finally, the potential hits in complex with spike protein were subjected to Molecular Dynamics simulation (450 ns), to infer the stability of complex formation. Among the compounds screened, Tellimagrandin-II (binding energy of −8.2 kcal/mol and binding free energy of −32.08 kcal/mol) from Syzygium aromaticum L. and O-Demethyl-demethoxy-curcumin (binding energy of −8.0 kcal/mol and binding free energy of −12.48 kcal/mol) from Curcuma longa L. were found to be highly potential due to their higher binding affinity and significant binding free energy (MM-PBSA), along with favorable ADMET properties and stable intermolecular interactions with hotspots (including the ASN343 glycosylation site). The proposed hits are highly promising, as these are resultant of stringent in silico checkpoints, traditionally used, and are documented through contemporary literature. Hence, could serve as promising leads for subsequent experimental validations.

Characterization of a novel mCH3 conjugated anti-PcrV scFv molecule

Pseudomonas aeruginosa (PA) is a leading cause of nosocomial infections and death in cystic fibrosis patients. The study was conducted to evaluate the physicochemical structure, biological activity and serum stability of a recombinant anti-PcrV single chain variable antibody fragment genetically attached to the mCH3cc domain. The stereochemical properties of scFv-mCH3 (YFL001) and scFv (YFL002) proteins as well as molecular interactions towards Pseudomonas aeruginosa PcrV were evaluated computationally. The subcloned fragments encoding YFL001 and YFL002 in pET28a were expressed within the E. coli BL21-DE3 strain. After Ni–NTA affinity chromatography, the biological activity of the proteins in inhibition of PA induced hemolysis as well as cellular cytotoxicity was assessed. In silico analysis revealed the satisfactory stereochemical quality of the models as well as common residues in their interface with PcrV. The structural differences of proteins through circular dichroism spectroscopy were confirmed by NMR analysis. Both proteins indicated inhibition of ExoU positive PA strains in hemolysis of red blood cells compared to ExoU negative strains as well as cytotoxicity effect on lung epithelial cells. The ELISA test showed the longer serum stability of the YFL001 molecule than YFL002. The results were encouraging to further evaluation of these two scFv molecules in animal models.

State-dependent sequential allostery exhibited by chaperonin TRiC/CCT revealed by network analysis of Cryo-EM maps

The eukaryotic chaperonin TRiC/CCT plays a major role in assisting the folding of many proteins through an ATP-driven allosteric cycle. Recent structures elucidated by cryo-electron microscopy provide a broad view of the conformations visited at various stages of the chaperonin cycle, including a sequential activation of its subunits in response to nucleotide binding. But we lack a thorough mechanistic understanding of the structure-based dynamics and communication properties that underlie the TRiC/CCT machinery. In this study, we present a computational methodology based on elastic network models adapted to cryo-EM density maps to gain a deeper understanding of the structure-encoded allosteric dynamics of this hexadecameric machine. We have analysed several structures of the chaperonin resolved in different states toward mapping its conformational landscape. Our study indicates that the overall architecture intrinsically favours cooperative movements that comply with the structural variabilities observed in experiments. Furthermore, the individual subunits CCT1-CCT8 exhibit state-dependent sequential events at different states of the allosteric cycle. For example, in the ATP-bound state, subunits CCT5 and CCT4 selectively initiate the lid closure motions favoured by the overall architecture; whereas in the apo form of the heteromer, the subunit CCT7 exhibits the highest predisposition to structural change. The changes then propagate through parallel fluxes of allosteric signals to neighbours on both rings. The predicted state-dependent mechanisms of sequential activation provide new insights into TRiC/CCT intra- and inter-ring signal transduction events.

Probing Protein-Protein Interactions and Druggable Site Identification: Mechanistic Binding Events between Ubiquitin and Zinc Finger with UFM1-Specific Peptidase Domain Protein (ZUFSP)

BACKGROUND

Deubiquitinating enzymes (DUBs) protein family have been implicated in some deregulated pathways involved in carcinogenesis such as cell cycle, gene expression, and DNA damage response (DDR). Zinc finger with UFM1-specific peptidase domain protein (ZUFSP) is one of the recently discovered members of the DUBs Objectives: To identify and cross validate the ZUFSP binding site using the bioinformatic tools including SiteMap & Metapocket respectively. To understand the molecular basis of complementary ZUFSP-Ub interaction and associated structural events using MD Simulation Methods: In this study, four binding pockets were predicted, characterized, and cross-validated based on physiochemical features such as site score, druggability score, site volume, and site size. Also, Molecular dynamics simulation technique was employed to determine the impact of ubiquitin-binding on ZUFSP Results: Site 1 with a site score 1.065, Size 102, D scores 1.00, and size volume 261 was predicted to be the most druggable site. Structural studies revealed that upon ubiquitin-binding, the motional movement of ZUFSP was reduced when compared to the unbound ZUFSP. Also, the ZUFSP helical arm (ZHA) domain orient in such a way that it moves closer to the Ub, this orientation enables the formation of a UBD which is very peculiar to ZUFSP.

CONCLUSION

The impact of ubiquitin on ZUFSP movement and the characterization of its predicted druggable site can be targeted in the development of therapeutics.

Using normal mode analysis on protein structural models. How far can we go on our predictions?

Normal mode analysis (NMA) is a fast and inexpensive approach that is largely used to gain insight into functional protein motions, and more recently to create conformations for further computational studies. However, when the protein structure is unknown, the use of computational models is necessary. Here, we analyze the capacity of NMA in internal coordinate space to predict protein motion, its intrinsic flexibility, and atomic displacements, using protein models instead of native structures, and the possibility to use it for model refinement. Our results show that NMA is quite insensitive to modeling errors, but that calculations are strictly reliable only for very accurate models. Our study also suggests that internal NMA is a more suitable tool for the improvement of structural models, and for integrating them with experimental data or in other computational techniques, such as protein docking or more refined molecular dynamics simulations.

Computational Approach for Rational Design of Fusion Uricase with PAS Sequences

Tumor lysis syndrome is a life-threatening condition for humans due to the lack of urate oxidase. In this study, several variants of PASylated uricase from the Aspergillus flavus species were analyzed computationally to find the appropriate fusions to solve short half-life and stability concern. The Ab initio method was performed using Rosetta software to structurally characterize the PAS sequences. The 3D structures of fusions were predicted for fused C- or N-terminally PAS sequences in different length to the uricase. The refinement and energy minimization steps revealed that physicochemical and conformational properties of fusions improved while the structures possessed prolonged PAS sequences. Molecular docking results showed that the highest binding affinity to uric acid belonged to uricase-PAS1-100 by the formation of six hydrogen and four non-hydrogen bonds. Altogether, the results indicated that the PASylation process would be promising upon the production of urate oxidase with improved solubility and stability.

Understanding the Binding Specificity of G-Protein Coupled Receptors toward G-Proteins and Arrestins: Application to the Dopamine Receptor Family

G-Protein coupled receptors (GPCRs) are involved in a myriad of pathways key for human physiology through the formation of complexes with intracellular partners such as G-proteins and arrestins (Arrs). However, the structural and dynamical determinants of these complexes are still largely unknown. Herein, we developed a computational big-data pipeline that enables the structural characterization of GPCR complexes with no available structure. This pipeline was used to study a well-known group of catecholamine receptors, the human dopamine receptor (DXR) family and its complexes, producing novel insights into the physiological properties of these important drug targets. A detailed description of the protein interfaces of all members of the DXR family (D1R, D2R, D3R, D4R, and D5R) and the corresponding protein interfaces of their binding partners (Arrs: Arr2 and Arr3; G-proteins: Gi1, Gi2, Gi3, Go, Gob, Gq, Gslo, Gssh, Gt2, and Gz) was generated. To produce reliable structures of the DXR family in complex with either G-proteins or Arrs, we performed homology modeling using as templates the structures of the β2-adrenergic receptor (β2AR) bound to Gs, the rhodopsin bound to Gi, and the recently acquired neurotensin receptor-1 (NTSR1) and muscarinic 2 receptor (M2R) bound to arrestin (Arr). Among others, the work demonstrated that the three partner groups, Arrs and Gs- and Gi-proteins, are all structurally and dynamically distinct. Additionally, it was revealed the involvement of different structural motifs in G-protein selective coupling between D1- and D2-like receptors. Having constructed and analyzed 50 models involving DXR, this work represents an unprecedented large-scale analysis of GPCR-intracellular partner interface determinants. All data is available at www.moreiralab.com/resources/dxr.

Computational evaluation of a fusion protein consisted of pertussis toxin and filamentous hemagglutinin from Bordetella pertussis to target Claudin-4 using C-terminal fragment of Clostridium perfringens enterotoxin

Pertussis, caused by Bordetella pertussis is still one of the controversial diseases worldwide due to its high prevalence in both the developed and the developing countries, especially among young children. As currently approved vaccines are not protective enough and provide Th2-type immune responses, there is an urgent need to develop new vaccines. In the current study, we applied the C-terminal fragment of Clostridium perferingens enterotoxin (C-CPE) as a delivery system and F1S1 fragment (Filamentous hemagglutinin (F1) and subunit 1 of pertussis toxin (S1) of B. pertussis to design a novel chimeric protein in silico, to target Claudin-4 receptors in mice lung cells. To achieve this goal, the primary, secondary and tertiary structures of the fusion protein were evaluated and the interaction of this protein with Claudin-4 receptors was studied. Molecular dynamic (MD) simulation analysis was performed to investigate the physical movement of atoms in a fixed period. According to the results; the full-length fusion protein has consisted of 807 amino acid residues which could be classified as a stable protein. There was a convenient consistency between the 3D predicted structure and the secondary structure prediction. An acceptable percentage of the residues were also detected in the most favored and allowed regions for the model. Based on HADDOCK results, there were no considerable differences between the interactions and MD simulation analysis, indicating that the predicted structures were stable during the simulation. Altogether, the data reported in this study represents the first step toward developing a nasal vaccine candidate against B. pertussis infection. Communicated by Ramaswamy H. Sarma.

Developing robust protein analysis profiles to identify bacterial acid phosphatases in genomes and metagenomic libraries

Phylogenetic analysis of more than 4000 annotated bacterial acid phosphatases was carried out. Our analysis enabled us to sort these enzymes into the following three types: (1) class B acid phosphatases, which were distantly related to the other types, (2) class C acid phosphatases and (3) generic acid phosphatases (GAP). Although class B phosphatases are found in a limited number of bacterial families, which include known pathogens, class C acid phosphatases and GAP proteins are found in a variety of microbes that inhabit soil, fresh water and marine environments. As part of our analysis, we developed three profiles, named Pfr-B-Phos, Pfr-C-Phos and Pfr-GAP, to describe the three groups of acid phosphatases. These sequence-based profiles were then used to scan genomes and metagenomes to identify a large number of formerly unknown acid phosphatases. A number of proteins in databases annotated as hypothetical proteins were also identified by these profiles as putative acid phosphatases. To validate these in silico results, we cloned genes encoding candidate acid phosphatases from genomic DNA or recovered from metagenomic libraries or genes synthesized in vitro based on protein sequences recovered from metagenomic data. Expression of a number of these genes, followed by enzymatic analysis of the proteins, further confirmed that sequence similarity searches using our profiles could successfully identify previously unknown acid phosphatases.

Dynamics-function relationship in the catalytic domains of N-terminal acetyltransferases

N-terminal acetyltransferases (NATs) belong to the superfamily of acetyltransferases. They are enzymes catalysing the transfer of an acetyl group from acetyl coenzyme A to the N-terminus of polypeptide chains. N-terminal acetylation is one of the most common protein modifications. To date, not much is known on the molecular basis for the exclusive substrate specificity of NATs. All NATs share a common fold called GNAT. A characteristic of NATs is the β6β7 hairpin loop covering the active site and forming with the α1α2 loop a narrow tunnel surrounding the catalytic site in which cofactor and polypeptide meet and exchange an acetyl group. We investigated the dynamics-function relationships of all available structures of NATs covering the three domains of Life. Using an elastic network model and normal mode analysis, we found a common dynamics pattern conserved through the GNAT fold; a rigid V-shaped groove formed by the β4 and β5 strands and splitting the fold in two dynamical subdomains. Loops α1α2, β3β4 and β6β7 all show clear displacements in the low frequency normal modes. We characterized the mobility of the loops and show that even limited conformational changes of the loops along the low-frequency modes are able to significantly change the size and shape of the ligand binding sites. Based on the fact that these movements are present in most low-frequency modes, and common to all NATs, we suggest that the α1α2 and β6β7 loops may regulate ligand uptake and the release of the acetylated polypeptide.

The Role of Conformational Dynamics and Allostery in Modulating Protein Evolution

Advances in sequencing techniques and statistical methods have made it possible not only to predict sequences of ancestral proteins but also to identify thousands of mutations in the human exome, some of which are disease associated. These developments have motivated numerous theories and raised many questions regarding the fundamental principles behind protein evolution, which have been traditionally investigated horizontally using the tip of the phylogenetic tree through comparative studies of extant proteins within a family. In this article, we review a vertical comparison of the modern and resurrected ancestral proteins. We focus mainly on the dynamical properties responsible for a protein's ability to adapt new functions in response to environmental changes. Using the Dynamic Flexibility Index and the Dynamic Coupling Index to quantify the relative flexibility and dynamic coupling at a site-specific, single-amino-acid level, we provide evidence that the migration of hinges, which are often functionally critical rigid sites, is a mechanism through which proteins can rapidly evolve. Additionally, we show that disease-associated mutations in proteins often result in flexibility changes even at positions distal from mutational sites, particularly in the modulation of active site dynamics.

How good are comparative models in the understanding of protein dynamics?

The 3D structure of a protein is essential to understand protein dynamics. If experimentally determined structure is unavailable, comparative models could be used to infer dynamics. However, the effectiveness of comparative models, compared to experimental structures, in inferring dynamics is not clear. To address this, we compared dynamics features of ~800 comparative models with their crystal structures using normal mode analysis. Average similarity in magnitude, direction, and correlation of residue motions is >0.8 (where value 1 is identical) indicating that the dynamics of models and crystal structures are highly similar. Accuracy of 3D structure and dynamics is significantly higher for models built on multiple and/or high sequence identity templates (>40%). Three-dimensional (3D) structure and residue fluctuations of models are closer to that of crystal structures than to templates (TM score 0.9 vs 0.7 and square inner product 0.92 vs 0.88). Furthermore, long-range molecular dynamics simulations on comparative models of RNase 1 and Angiogenin showed significant differences in the conformational sampling of conserved active-site residues that characterize differences in their activity levels. Similar analyses on two EGFR kinase variant models highlight the effect of mutations on the functional state-specific αC helix motions and these results corroborate with the previous experimental observations. Thus, our study adds confidence to the use of comparative models in understanding protein dynamics.

Shared Signature Dynamics Tempered by Local Fluctuations Enables Fold Adaptability and Specificity

Recent studies have drawn attention to the evolution of protein dynamics, in addition to sequence and structure, based on the premise structure-encodes-dynamics-encodes-function. Of interest is to understand how functional differentiation is accomplished while maintaining the fold, or how intrinsic dynamics plays out in the evolution of structural variations and functional specificity. We performed a systematic computational analysis of 26,899 proteins belonging to 116 CATH superfamilies. Characterizing cooperative mechanisms and convergent/divergent features that underlie the shared/differentiated dynamics of family members required a methodology that lends itself to efficient analyses of large ensembles of proteins. We therefore introduced, SignDy, an integrated pipeline for evaluating the signature dynamics of families based on elastic network models. Our analysis confirmed that family members share conserved, highly cooperative (global) modes of motion. Importantly, our analysis discloses a subset of motions that sharply distinguishes subfamilies, which lie in a low-to-intermediate frequency regime of the mode spectrum. This regime has maximal impact on functional differentiation of families into subfamilies, while being evolutionarily conserved among subfamily members. Notably, the high-frequency end of the spectrum also reveals evolutionary conserved features across and within subfamilies; but in sharp contrast to global motions, high-frequency modes are minimally collective. Modulation of robust/conserved global dynamics by low-to-intermediate frequency fluctuations thus emerges as a versatile mechanism ensuring the adaptability of selected folds and the specificity of their subfamilies. SignDy further allows for dynamics-based categorization as a new layer of information relevant to distinctive mechanisms of action of subfamilies, beyond sequence or structural classifications.

Can Conformational Changes of Proteins Be Represented in Torsion Angle Space? A Study with Rescaled Ridge Regression

Torsion angles are the natural degrees of freedom of protein structures. The ability to determine torsional variations corresponding to observed changes in Cartesian coordinates is highly valuable, notably to investigate the mechanisms of functional conformational changes or to develop computational models of protein dynamics. This issue is far from trivial in practice since the impact of modifying one torsion angle strongly depends on all other angles, and the compounding effects of small variations in bond lengths and valence angles can completely disrupt a protein fold. We demonstrate that naive strategies, such as directly comparing torsion angles between structures without correcting for variations in bond lengths and valence angles or fitting torsional variations without a proper regularization scheme, fail at producing an adequate representation of conformational changes in internal coordinates. In contrast, rescaled ridge regression, a method recently introduced to regularize multidimensional regressions with correlated explanatory variables, is shown to consistently identify a minimal set of torsion angles variations that closely reproduce changes in Cartesian coordinates. This torsional representation of conformational changes is shown to be robust to the choice of experimental structures. It also provides a better agreement with theoretical models of protein dynamics than the Cartesian representation, regarding notably the predominance of low-frequency normal modes in functional motions and the presence, in predicted equilibrium dynamics, of hints of natural selection for specific functional motions. The software is available at https://github.com/ugobas/tnm .

Analyzing Fluctuation Properties in Protein Elastic Networks with Sequence-Specific and Distance-Dependent Interactions

Simple protein elastic networks which neglect amino-acid information often yield reasonable predictions of conformational dynamics and are broadly used. Recently, model variants which incorporate sequence-specific and distance-dependent interactions of residue pairs have been constructed and demonstrated to improve agreement with experimental data. We have applied the new variants in a systematic study of protein fluctuation properties and compared their predictions with those of conventional anisotropic network models. We find that the quality of predictions is frequently linked to poor estimations in highly flexible protein regions. An analysis of a large set of protein structures shows that fluctuations of very weakly connected network residues are intrinsically prone to be significantly overestimated by all models. This problem persists in the new models and is not resolved by taking into account sequence information. The effect becomes even enhanced in the model variant which takes into account very soft long-ranged residue interactions. Beyond these shortcomings, we find that model predictions are largely insensitive to the integration of chemical information, at least regarding the fluctuation properties of individual residues. One can furthermore conclude that the inherent drawbacks may present a serious hindrance when improvement of elastic network models are attempted.

Insight into the binding of a synthetic nitro-flavone derivative with human poly (ADP-ribose) polymerase 1

Flavones are important bioactive compounds, many of which are effective in cancer therapy for their ability to target enzymes related to DNA repair and cell proliferation. In this report, the interaction of a synthetic nitroflavone, 2,4-nitrophenylchromen-4-one (4NCO) with human poly (ADP-ribose) polymerase 1 (hPARP1) was investigated to explore its inhibitory action. Its interaction with hPARP1 was compared with that of other inhibitors through molecular docking studies. Further insight into the 4NCO-hPARP1 interaction was obtained from competitive docking and molecular dynamic simulation studies. In silico mutagenesis studies and per-residue interaction energy calculations were carried out. Quantitative Structure Activity Relationship analysis was also performed to calculate its predictive percent inhibitory activity. Our results indicated that 4NCO exhibited competitive mode of binding to hPARP1. It formed a stable interaction with the protein thereby hindering any further molecular interaction to render it inactive with a predictive inhibition of 96%. It also had good ADMET properties and showed best Autodock binding free energy values compared to other known inhibitors. 4NCO showed good hPARP1 inhibitory properties with higher bioavailability and lower probability of getting effluxed. Development of inhibitors against hPARP1 is important for cell proliferative disorders, where 4NCO can be predicted as a potential new drug.

Drug promiscuity: Exploring the polypharmacology potential of 1, 3, 6-trisubstituted 1, 4-diazepane-7-ones as an inhibitor of the 'god father' of immune checkpoint

High production cost, instability, low tumor penetration are some of the shortcomings that have characterized and undermined the use of antibodies as a target for Cytotoxic T-lymphocytes associated protein 4 (CTLA-4). Design and discovery of small molecule inhibitors have therefore become a sine qua non in targeting immune proteins implicated in immune disorders. In this study, we utilized a drug repositioning approach to explore the characteristic feature of unrelated proteins to have similar binding sites and the promiscuity of drugs to repurpose an existing drug to target CTLA-4. CTLA-4 and Kallikrein-7 were found to have similar binding sites, we therefore used 1, 3, 6-trisubstituted 1, 4-diazepane-7-ones (TDSO) which is an inhibitor of Kallikrein-7 as our lead compound. High throughput screening using TDSO as a lead compound resulted in 9 hits with ZINC04515726 and ZINC08985213 having the highest binding score. We went ahead to investigate the interaction of these compounds with CTLA-4 by conducting a molecular dynamic simulation. Molecular Mechanics/Poisson-Boltzmann Surface Area (MM/PBSA) estimations revealed that TDSO had the highest binding energy value of -28.51Kcal/mol, with ZINC04515726 and ZINC08985213 having -23.76Kcal/mol and -21.03Kcal/mol respectively. The per-residue decomposition highlighted Tyr24, Ala25, Gly28, Ala30, Tyr53 and Asn72 as having significantly high electrostatic energy contributions and the main contributing residues to the binding of TDSO, ZINC04515726 and ZINC08985213 to Cytotoxic T lymphocytes CTLA-4. Summarily, from the results gathered, we proposed that TDSO can be an effective immune check point small molecule inhibitor against the suppression of T-cell activation, proliferation, and tumor cell eradication.

Identification of new DNA gyrase inhibitors based on bioactive compounds from streptomyces: structure-based virtual screening and molecular dynamics simulations approaches

DNA gyrase enzyme has vital role in bacterial survival and can be considered as a potential drug target. Owing to the appearance of resistance to gyrase-targeted drugs, especially fluoroquinolone, screening new compounds which bind more efficiently to the mutant binding pocket is essential. Hence, in this work, using Smina Autodock and through structure-based virtual screening of StreptomeDB, several natural products were discovered based on the SimocyclinoneD8 (SD8) binding pocket of GyrA subunit of DNA gyrase. After evaluation of binding affinity, binding modes, critical interactions and physicochemical and pharmaceutical properties, three lead compounds were selected for further analysis. Afterward 60 ns molecular dynamics simulations were performed and binding free energies were calculated by the molecular mechanics/Poisson-Boltzmann surface area method. Also, interaction of the selected lead compounds with the mutated GyrA protein was evaluated. Results indicated that all of the selected compounds could bind to the both wild-type and mutated GyrA with the binding affinities remarkably higher than SimocyclinoneD8. Interestingly, we noticed that the selected compounds comprised angucycline moiety in their structure which could sufficiently interact with GyrA and block the DNA binding pocket of DNA gyrase, in silico. In conclusion, three DNA gyrase inhibitors were identified successfully which were highly capable of impeding DNA gyrase and can be considered as potential drug candidates for treatment of fluoroquinolone-resistant strains.Communicated by Ramaswamy H. Sarma.