Protein structure modeling with MODELLER

Steered molecular dynamics simulation as a post-process to optimize the iBRAB-designed Fab model

Therapeutic monoclonal antibodies are an effective method of treating acute infectious diseases. However, knowing which of the produced antibodies in the vast number of human antibodies can cure the disease requires a long time and advanced technology. The previously introduced iBRAB method relies on studied antibodies to design a broad-spectrum antibody capable of neutralizing antigens of many different Influenza A viral strains. To evaluate the antigen-binding fragment as an applicable drug, the therapeutic antibody profiles providing guidelines collected from clinically staged therapeutic antibodies were used to access different measurements. Although the evaluated values were within an accepted range, the modification in the amino acid sequence is required for better properties. Thus, using the steered molecular dynamics (SMD) simulation to determine the binding capacity of amino acids in the functional region, the profile of interacted amino acids of Fab with the antigen was established for modified reference. As a result, the model was modified with amino acids elimination at positions 96-97 in the heavy chain and 26-27, 91, 96-97, and 102-103 in the light chain, which has better Therapeutic Antibody Profiler evaluations than the original designation. Thus again, SMD simulation is a promising computational approach for post-modification in rational drug design.

Unlocking the Door for Precision Medicine in Rare Conditions: Structural and Functional Consequences of Missense ACVR1 Variants

Rare diseases and conditions have thus far received relatively less attention in the field of precision/personalized medicine than common chronic diseases. There is a dire need for orphan drug discovery and therapeutics in ways that are informed by the precision/personalized medicine scholarship. Moreover, people with rare conditions, when considered collectively across diseases worldwide, impact many communities. In this overarching context, Activin A Receptor Type 1 (ACVR1) is a transmembrane kinase from the transforming growth factor-β superfamily and plays a critical role in modulating the bone morphogenetic protein signaling. Missense variants of the ACVR1 gene result in modifications in structure and function and, by extension, abnormalities and have been predominantly linked with two rare conditions: fibrodysplasia ossificans progressiva and diffuse intrinsic pontine glioma. We report here an extensive bioinformatic analyses assessing the pool of 50,951 variants and forecast seven highly destabilizing mutations (R206H, G356D, R258S, G328W, G328E, R375P, and R202I) that can significantly alter the structure and function of the native protein. Protein-protein interaction and ConSurf analyses revealed the crucial interactions and localization of highly deleterious mutations in highly conserved domains that may impact the binding and functioning of the protein. cBioPortal, CanSAR Black, and existing literature affirmed the association of these destabilizing mutations with posterior fossa ependymoma, uterine corpus carcinoma, and pediatric brain cancer. The current findings suggest these deleterious nonsynonymous single nucleotide polymorphisms as potential candidates for future functional annotations and validations associated with rare conditions, further aiding the development of precision medicine in rare diseases.

PNBACE: an ensemble algorithm to predict the effects of mutations on protein-nucleic acid binding affinity

BACKGROUND

Mutations occurring in nucleic acids or proteins may affect the binding affinities of protein-nucleic acid interactions. Although many efforts have been devoted to the impact of protein mutations, few computational studies have addressed the effect of nucleic acid mutations and explored whether the identical methodology could be applied to the prediction of binding affinity changes caused by these two mutation types.

RESULTS

Here, we developed a generalized algorithm named PNBACE for both DNA and protein mutations. We first demonstrated that DNA mutations could induce varying degrees of changes in binding affinity from multiple perspectives. We then designed a group of energy-based topological features based on different energy networks, which were combined with our previous partition-based energy features to construct individual prediction models through feature selections. Furthermore, we created an ensemble model by integrating the outputs of individual models using a differential evolution algorithm. In addition to predicting the impact of single-point mutations, PNBACE could predict the influence of multiple-point mutations and identify mutations significantly reducing binding affinities. Extensive comparisons indicated that PNBACE largely performed better than existing methods on both regression and classification tasks.

CONCLUSIONS

PNBACE is an effective method for estimating the binding affinity changes of protein-nucleic acid complexes induced by DNA or protein mutations, therefore improving our understanding of the interactions between proteins and DNA/RNA.

Advancing rational pesticide development against Drosophila suzukii: bioinformatics tools and applications-a systematic review

CONTEXT

Drosophila suzukii (Matsumura, 1931) is a widespread agricultural pest responsible for significant damage to various soft-skinned fruit hosts. The revolutionary potential of bioinformatics in agriculture emerges from its ability to provide extensive information on pests, fungi, chemical resistance, implications of non-target species, and other critical aspects. This wealth of information allows researchers to engage in projects and applied research in diverse agricultural domains that face these challenges. In this context, bioinformatics tools play a fundamental role. The negative impact of pests on crops, resulting in substantial economic losses, has highlighted the importance of in silico methods.

METHODS

To achieve this, we conducted a systematic search in scientific databases using as keywords "Drosophila suzukii," "biopesticides," "simulations computational," and "in-silico." After applying the filters of relevance and publication date, we organized the articles and prioritized those that directly addressed that matched the keywords and the use of bioinformatics tools. Additionally, we included studies focusing on in silico assays of biopesticides, such as molecular docking. Our review aimed to present a collection of recent literature on biopesticides against Drosophila suzukii, emphasizing bioinformatics methods. Through this work, we strive to contribute to the literature of new perspectives on the development and efficiency of biopesticides, along with to advance research that may improve pest control strategies.

RESULTS

In the results of the systematic review, we found 2734 articles related to the selected keywords. Six of these articles directly address Drosophila suzukii and the use of bioinformatics tools in the search for alternatives in pest control. In the selected studies, we observed that two articles tend to focus on phylogenetic approaches, searching for gene sequences, amino acids, and constructing phylogenetic trees. The other three articles used molecular modeling and docking of receptors such as GABA and TRP with plant-derived and synthetic compounds to study intermolecular interactions. However, we identified gaps in these studies that could lead to further research in the biorational development of biopesticides using bioinformatics tools.

Insights into the specific feature of the electrostatic recognition binding mechanism between BM2 and BM1: a molecular dynamics simulation study

Matrix protein 2 (M2) and matrix protein 1 (M1) of the influenza B virus are two important proteins, and the interactions between BM2 and BM1 play an important role in the process of virus assembly and replication. However, the interaction details between BM2 and BM1 are still unclear at the atomic level. Here, we constructed the BM2-BM1 complex system using homology modelling and molecular docking methods. Molecular dynamics (MD) simulations were used to illustrate the binding mechanism between BM2 and BM1. The results identify that the eight polar residues (E88B, E89B, H119BM1, E94B, R101BM1, K102BM1, R105BM1, and E104B) play an important role in stabilizing the binding through the formation of hydrogen bond networks and salt-bridge interactions at the binding interface. Furthermore, based on the simulation results and the experimental facts, the mutation experiments were designed to verify the influence of the mutation of residues both within and outside the effector domain. The mutations directly or indirectly disrupt interactions between polar residues, thus affecting viral assembly and replication. The results could help us understand the details of the interactions between BM2 and BM1 and provide useful information for the anti-influenza drug design.

Computational analysis of human gut microbial prolyl oligopeptidases (POPs) reveal candidate genes as therapeutics for celiac disease

Celiac disease (CD) is a common autoimmune disorder in which the patients are unable to digest gluten, which is present in foods made up of wheat, barley and rye. Whilst diagnosis happens late in 80% of the cases, avoidance of such foods appears to be the common solution. Alternative management strategies are required for the patients and their families since CD is also genetically carried over. Probiotic therapeutics and the consumption of appropriate enzymes, such as prolyloligopeptidases (POPs), from gut-friendly bacteria could reduce the disease burden and provide a better lifestyle for CD patients. We have examined around 5000 gut bacterial genomes and identified nearly 4000 non-redundant putative POPs. A select set of 10 gut bacterial POP sequences were subject to three-dimensional modelling, ligand docking and molecular dynamics simulations where stable interactions were observed between the POPs and gluten peptides. Our study provides sequence and structural analysis of potential POP enzymes in gut bacterial genomes, which form a strong basis to offer probiotic solutions to CD patients. In particular, these enzymes could be lead future therapeutics for this disease.

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.

A computational workflow for analysis of missense mutations in precision oncology

Every year, more than 19 million cancer cases are diagnosed, and this number continues to increase annually. Since standard treatment options have varying success rates for different types of cancer, understanding the biology of an individual's tumour becomes crucial, especially for cases that are difficult to treat. Personalised high-throughput profiling, using next-generation sequencing, allows for a comprehensive examination of biopsy specimens. Furthermore, the widespread use of this technology has generated a wealth of information on cancer-specific gene alterations. However, there exists a significant gap between identified alterations and their proven impact on protein function. Here, we present a bioinformatics pipeline that enables fast analysis of a missense mutation's effect on stability and function in known oncogenic proteins. This pipeline is coupled with a predictor that summarises the outputs of different tools used throughout the pipeline, providing a single probability score, achieving a balanced accuracy above 86%. The pipeline incorporates a virtual screening method to suggest potential FDA/EMA-approved drugs to be considered for treatment. We showcase three case studies to demonstrate the timely utility of this pipeline. To facilitate access and analysis of cancer-related mutations, we have packaged the pipeline as a web server, which is freely available at https://loschmidt.chemi.muni.cz/predictonco/ .Scientific contributionThis work presents a novel bioinformatics pipeline that integrates multiple computational tools to predict the effects of missense mutations on proteins of oncological interest. The pipeline uniquely combines fast protein modelling, stability prediction, and evolutionary analysis with virtual drug screening, while offering actionable insights for precision oncology. This comprehensive approach surpasses existing tools by automating the interpretation of mutations and suggesting potential treatments, thereby striving to bridge the gap between sequencing data and clinical application.

Anhydroparthenin as a dual-target inhibitor against Sterol C-24 methyltransferase and Sterol 14-α demethylase of Leishmania donovani: A comprehensive in vitro and in silico study

Parthenium hysterophorus plant has a diverse chemical profile and immense bioactive potential. It exhibits excellent pharmacological properties such as anti-cancer, anti-inflammatory, anti-malarial, microbicidal, and anti-trypanosomal. The present study aims to evaluate the anti-leishmanial potential and toxicological safety of anhydroparthenin isolated from P. hysterophorus. Anydroparthenin was extracted from the leaves of P. hysterophorus and characterized through detailed analysis of 1H, 13C NMR, and HRMS. Dye-based in vitro and ex vivo assays confirmed that anhydroparthenin significantly inhibited both promastigote and amastigote forms of the Leishmania donovani parasites. Both the cytotoxicity experiment and hemolytic assay revealed its non-toxic nature and safety index in the range of 10 to 15. Further, various mechanistic assays suggested that anhydroparthenin led to the generation of oxidative stress, intracellular ATP depletion, alterations in morphology and mitochondrial membrane potential, formation of intracellular lipid bodies, and acidic vesicles, ultimately leading to parasite death. As a dual targeting approach, computational studies and sterol quantification assays confirmed that anhydroparthenin inhibits the Sterol C-24 methyl transferase and Sterol 14-α demethylase proteins involved in the ergosterol biosynthesis in Leishmania parasites. These results suggest that anhydroparthenin could be a promising anti-leishmanial molecule and can be developed as a novel therapeutic stratagem against leishmaniasis.

The unusual kinetics of lactate dehydrogenase of Schistosoma mansoni and their role in the rapid metabolic switch after penetration of the mammalian host

Lactate dehydrogenase (LDH) from Schistosoma mansoni has peculiar properties for a eukaryotic LDH. Schistosomal LDH (SmLDH) isolated from schistosomes, and the recombinantly expressed protein, are strongly inhibited by ATP, which is neutralized by fructose-1,6-bisphosphate (FBP). In the conserved FBP/anion binding site we identified two residues in SmLDH (Val187 and Tyr190) that differ from the conserved residues in LDHs of other eukaryotes, but are identical to conserved residues in FBP-sensitive prokaryotic LDHs. Three-dimensional (3D) models were generated to compare the structure of SmLDH with other LDHs. These models indicated that residues Val187, and especially Tyr190, play a crucial role in the interaction of FBP with the anion pocket of SmLDH. These 3D models of SmLDH are also consistent with a competitive model of SmLDH inhibition in which ATP (inhibitor) and FBP (activator) compete for binding in a well-defined anion pocket. The model of bound ATP predicts a distortion of the nearby key catalytic residue His195, resulting in enzyme inhibition. To investigate a possible physiological role of this allosteric regulation of LDH in schistosomes we made a kinetic model in which the allosteric regulation of the glycolytic enzymes can be varied. The model showed that inhibition of LDH by ATP prevents fermentation to lactate in the free-living stages in water and ensures complete oxidation via the Krebs cycle of the endogenous glycogen reserves. This mechanism of allosteric inhibition by ATP prevents the untimely depletion of these glycogen reserves, the only fuel of the free-living cercariae. Neutralization by FBP of this ATP inhibition of LDH prevents accumulation of glycolytic intermediates when S. mansoni schistosomula are confronted with the sudden large increase in glucose availability upon penetration of the final host. It appears that the LDH of S. mansoni is special and well suited to deal with the variations in glucose availability the parasite encounters during its life cycle.

Computational analysis of non-synonymous single nucleotide polymorphism in the bovine PKLR geneComputational analysis of bovine PKLR gene

Pyruvate kinase (PKLR) is a potential candidate gene for milk production traits in cows. The main aim of this work is to investigate the potentially deleterious non-synonymous single nucleotide polymorphisms (nsSNPs) in the PKLR gene by using several computational tools. In silico tools including SIFT, Polyphen-2, SNAP2 and Panther indicated only 18 nsSNPs out of 170 were considered deleterious. The analysis of proteins' stability change due to amino acid substitution performed by the use of the I-mutant, MUpro, CUPSTAT, SDM and Dynamut confirmed that 9 nsSNPs decreased protein stability. ConSurf analysis predicted that all 18 nsSNPs were evolutionary moderately or highly conserved. Two different domains of PKLR protein were revealed by the InterPro tool with 12 nsSNPs positioned in the Pyruvate Kinase barrel domain and 6 nsSNP present in the Pyruvate Kinase C Terminal. The PKLR 3D model was predicted by MODELLER software and validated via Ramachandran plot and Prosa which indicated a good quality model. The analysis of energy minimizations for the native and mutated structures was performed by SWISS PDB viewer with GROMOS 96 program and showed that 3 structural and 4 functional residues had total energy higher than the native model. These findings indicate that these mutant structures (rs441424814, rs449326723, rs476805413, rs472263384, rs474320860, rs475521477, rs441633284) were less stable than the native model. Molecular Dynamics simulations were performed to confirm the impact of nsSNPs on the protein structure and function. The present study provides useful information about functional SNPs that have an impact on PKLR protein in cattle.Communicated by Ramaswamy H. Sarma.

State-of-the-art in engineering small molecule biosensors and their applications in metabolic engineering

Genetically encoded biosensors are crucial for enhancing our understanding of how molecules regulate biological systems. Small molecule biosensors, in particular, help us understand the interaction between chemicals and biological processes. They also accelerate metabolic engineering by increasing screening throughput and eliminating the need for sample preparation through traditional chemical analysis. Additionally, they offer significantly higher spatial and temporal resolution in cellular analyte measurements. In this review, we discuss recent progress in in vivo biosensors and control systems-biosensor-based controllers-for metabolic engineering. We also specifically explore protein-based biosensors that utilize less commonly exploited signaling mechanisms, such as protein stability and induced degradation, compared to more prevalent transcription factor and allosteric regulation mechanism. We propose that these lesser-used mechanisms will be significant for engineering eukaryotic systems and slower-growing prokaryotic systems where protein turnover may facilitate more rapid and reliable measurement and regulation of the current cellular state. Lastly, we emphasize the utilization of cutting-edge and state-of-the-art techniques in the development of protein-based biosensors, achieved through rational design, directed evolution, and collaborative approaches.

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.

Role of phosphatidic acid lipids on plasma membrane association of the Ebola virus matrix protein VP40

The Ebola virus matrix protein VP40 is responsible for the formation of the viral matrix by localizing at the inner leaflet of the human plasma membrane (PM). Various lipid types, including PI(4,5)P2 (i.e. PIP2) and phosphatidylserine (PS), play active roles in this process. Specifically, the negatively charged headgroups of both PIP2 and PS interact with the basic residues of VP40 and stabilize it at the membrane surface, allowing for eventual egress. Phosphatidic acid (PA), resulting from the enzyme phospholipase D (PLD), is also known to play an active role in viral development. In this work, we performed a biophysical and computational analysis to investigate the effects of the presence of PA on the membrane localization and association of VP40. We used coarse-grained molecular dynamics simulations to quantify VP40 hexamer interactions with the inner leaflet of the PM. Analysis of the local distribution of lipids shows enhanced lipid clustering when PA is abundant in the membrane. We observed that PA lipids have a similar role to that of PS lipids in VP40 association due to the geometry and charge. Complementary experiments performed in cell culture demonstrate competition between VP40 and a canonical PA-binding protein for the PM. Also, inhibition of PA synthesis reduced the detectable budding of virus-like particles. These computational and experimental results provide new insights into the early stages of Ebola virus budding and the role that PA lipids have on the VP40-PM association.

Structural assessment of OsNIP2;1 highlighted critical residues defining solute specificity and functionality of NIP class aquaporins

INTRODUCTION

Nodulin-26-like intrinsic proteins (NIPs) are integral membrane proteins belonging to the aquaporin family, that facilitate the transport of neutral solutes across the bilayer. The OsNIP2;1 a member of NIP-III class of aquaporins is permeable to beneficial elements like silicon and hazardous arsenic. However, the atomistic cross-talk of these molecules traversing the OsNIP2;1 channel is not well understood.

OBJECTIVE

Due to the lack of genomic variation but the availability of high confidence crystal structure, this study aims to highlight structural determinants of metalloid permeation through OsNIP2;1.

METHODS

The molecular simulations, combined with site-directed mutagenesis were used to probe the role of specific residues in the metalloid transport activity of OsNIP2;1.

RESULTS

We drew energetic landscape of OsNIP2;1, for silicic and arsenous acid transport. Potential Mean Force (PMF) construction illuminate three prominent energetic barriers for metalloid passage through the pore. One corresponds to the extracellular molecular entry in the channel, the second located on ar/R filter, and the third size constriction in the cytoplasmic half. Comparative PMF for silicic acid and arsenous acid elucidate a higher barrier for silicic acid at the cytoplasmic constrict resulting in longer residence time for silicon. Furthermore, our simulation studies explained the importance of conserved residues in loop-C and loop-D with a direct effect on pore dynamics and metalloid transport. Next we assessed contribution of predicted key residues for arsenic uptake, by functional complementation in yeast. With the aim of reducing arsenic uptake while maintaining beneficial elements uptake, we identified novel OsNIP2;1 mutants with substantial reduction in arsenic uptake in yeast.

CONCLUSION

We provide a comprehensive assessment of pore lining residues of OsNIP2;1 with respect to metalloid uptake. The findings will expand mechanistic understanding of aquaporin's metalloid selectivity and facilitate variant interpretation to develop novel alleles with preference for beneficial metalloid species and reducing hazardous ones.

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.

Single-molecule FRET probes allosteric effects on protein-translocating pore loops of a AAA+ machine

AAA+ proteins (ATPases associated with various cellular activities) comprise a family of powerful ring-shaped ATP-dependent translocases that carry out numerous vital substrate-remodeling functions. ClpB is a AAA+ protein disaggregation machine that forms a two-tiered hexameric ring, with flexible pore loops protruding into its center and binding to substrate proteins. It remains unknown whether these pore loops contribute only passively to substrate-protein threading or have a more active role. Recently, we have applied single-molecule FRET spectroscopy to directly measure the dynamics of substrate-binding pore loops in ClpB. We have reported that the three pore loops of ClpB (PL1-3) undergo large-scale fluctuations on the microsecond timescale that are likely to be mechanistically important for disaggregation. Here, using single-molecule FRET, we study the allosteric coupling between the pore loops and the two nucleotide-binding domains of ClpB (NBD1-2). By mutating the conserved Walker B motifs within the NBDs to abolish ATP hydrolysis, we demonstrate how the nucleotide state of each NBD tunes pore-loop dynamics. This effect is surprisingly long-ranged; in particular, PL2 and PL3 respond differentially to a Walker B mutation in either NBD1 or NBD2, as well as to mutations in both. We characterize the conformational dynamics of pore loops and the allosteric paths connecting NBDs to pore loops by molecular dynamics simulations and find that both principal motions and allosteric paths can be altered by changing the ATPase state of ClpB. Remarkably, PL3, which is highly conserved in AAA+ machines, is found to favor an upward conformation when only NBD1 undergoes ATP hydrolysis but a downward conformation when NBD2 is active. These results explicitly demonstrate a significant long-range allosteric effect of ATP hydrolysis sites on pore-loop dynamics. Pore loops are therefore established as active participants that undergo ATP-dependent conformational changes to translocate substrate proteins through the central pores of AAA+ machines.

Decoding the structural integrity and multifunctional role of Era protein in the survival of Mycobacterium tuberculosis H37Rv

Era, a widely known GTP binding protein found in many organisms including prokaryotes and eukaryotes and plays a significant role in many fundamental cellular processes like cell growth, differentiation and signaling. In Mycobacterium tuberculosis (Mtb) H37Rv, Era protein had been proved as a GTPase protein but its structural and functional insights are still lacking. Through comparative analysis, structural modeling, docking and using various bioinformatic tools, a detailed investigation of Era was carried out to deduce the structure, function and residues involved in the activity of the protein. Intriguingly, docking results revealed high binding affinity of Era not only with GTP but also with ATP. Myristoylation modifications and phosphorylations on Era were predicted to possibly aid in regulating Era activity and localization; and also the role of Era in translation regulation was foreseen by showing its association with 16s rRNA. Moreover, point mutation of Era residues revealed the effect of W288G and K19G in highly destabilizing the protein structure and activity. Additionally, Era protein was docked with 25 GTPase/ATPase inhibitors, where, Dynasore inhibitor showed the highest affinity for the protein's GTP binding sites and can be used for further drug trials to inhibit growth of mycobacteria.Communicated by Ramaswamy H. Sarma.

Engineering the next generation of theranostic biomaterials with synthetic biology

Biomaterials have evolved from inert materials to responsive entities, playing a crucial role in disease diagnosis, treatment, and modeling. However, their advancement is hindered by limitations in chemical and mechanical approaches. Synthetic biology enabling the genetically reprograming of biological systems offers a new paradigm. It has achieved remarkable progresses in cell reprogramming, engineering designer cells for diverse applications. Synthetic biology also encompasses cell-free systems and rational design of biological molecules. This review focuses on the application of synthetic biology in theranostics, which boost rapid development of advanced biomaterials. We introduce key fundamental concepts of synthetic biology and highlight frontier applications thereof, aiming to explore the intersection of synthetic biology and biomaterials. This integration holds tremendous promise for advancing biomaterial engineering with programable complex functions.

Combining molecular docking and molecular dynamics simulation to discover four novel umami peptides from tuna skeletal myosin with sensory evaluation validation

Umami peptides are an important component of food flavoring agents and have high nutritional value. This work aimed to identify umami peptides from tuna skeletal myosin using a new model method of computer simulation, explore their umami mechanism, and further validate the umami tastes with sensory evaluation. Umami peptides LADW, MEIDD, VAEQE, and EEAEGT were discovered, and all of them bound to taste type 1 receptor 1 and receptor 3 via hydrogen bonds and van der Waals forces to form stable complexes. LADW exhibited the best affinity energy and binding capability. Sensory evaluation and electronic tongue confirmed that all peptides possessed an umami taste, and LADW exhibited the strongest umami intensity. This study not only explored four novel umami peptides to improve the value of tuna skeletal myosin but also provided a new method for the rapid discovery of umami peptides.

Searching for Structure: Characterizing the Protein Conformational Landscape with Clustering-Based Algorithms

The identification and characterization of the main conformations from a protein population are a challenging and inherently high-dimensional problem. Here, we evaluate the performance of the Secondary sTructural Ensembles with machine LeArning (StELa) double-clustering method, which clusters protein structures based on the relationship between the φ and ψ dihedral angles in a protein backbone and the secondary structure of the protein, thus focusing on the local properties of protein structures. The classification of states as vectors composed of the clusters' indices arising naturally from the Ramachandran plot is followed by the hierarchical clustering of the vectors to allow for the identification of the main features of the corresponding free energy landscape (FEL). We compare the performance of StELa with the established root-mean-squared-deviation (RMSD)-based clustering algorithm, which focuses on global properties of protein structures and with Combinatorial Averaged Transient Structure (CATS), the combinatorial averaged transient structure clustering method based on distributions of the φ and ψ dihedral angle coordinates. Using ensembles of conformations from molecular dynamics simulations of intrinsically disordered proteins (IDPs) of various lengths (tau protein fragments) or short fragments from a globular protein, we show that StELa is the clustering method that identifies many of the minima and relevant energy states around the minima from the corresponding FELs. In contrast, the RMSD-based algorithm yields a large number of clusters that usually cover most of the FEL, thus being unable to distinguish between states, while CATS does not sample well the FELs for long IDPs and fragments from globular proteins.

E2EATP: Fast and High-Accuracy Protein-ATP Binding Residue Prediction via Protein Language Model Embedding

Identifying the ATP-binding sites of proteins is fundamentally important to uncover the mechanisms of protein functions and explore drug discovery. Many computational methods are proposed to predict ATP-binding sites. However, due to the limitation of the quality of feature representation, the prediction performance still has a big room for improvement. In this study, we propose an end-to-end deep learning model, E2EATP, to dig out more discriminative information from a protein sequence for improving the ATP-binding site prediction performance. Concretely, we employ a pretrained deep learning-based protein language model (ESM2) to automatically extract high-latent discriminative representations of protein sequences relevant for protein functions. Based on ESM2, we design a residual convolutional neural network to train a protein-ATP binding site prediction model. Furthermore, a weighted focal loss function is used to reduce the negative impact of imbalanced data on the model training stage. Experimental results on the two independent testing data sets demonstrate that E2EATP could achieve higher Matthew's correlation coefficient and AUC values than most existing state-of-the-art prediction methods. The speed (about 0.05 s per protein) of E2EATP is much faster than the other existing prediction methods. Detailed data analyses show that the major advantage of E2EATP lies at the utilization of the pretrained protein language model that extracts more discriminative information from the protein sequence only. The standalone package of E2EATP is freely available for academic at https://github.com/jun-csbio/e2eatp/.

Using deep learning and molecular dynamics simulations to unravel the regulation mechanism of peptides as noncompetitive inhibitor of xanthine oxidase

Xanthine oxidase (XO) is a crucial enzyme in the development of hyperuricemia and gout. This study focuses on LWM and ALPM, two food-derived inhibitors of XO. We used molecular docking to obtain three systems and then conducted 200 ns molecular dynamics simulations for the Apo, LWM, and ALPM systems. The results reveal a stronger binding affinity of the LWM peptide to XO, potentially due to increased hydrogen bond formation. Notable changes were observed in the XO tunnel upon inhibitor binding, particularly with LWM, which showed a thinner, longer, and more twisted configuration compared to ALPM. The study highlights the importance of residue F914 in the allosteric pathway. Methodologically, we utilized the perturbed response scan (PRS) based on Python, enhancing tools for MD analysis. These findings deepen our understanding of food-derived anti-XO inhibitors and could inform the development of food-based therapeutics for reducing uric acid levels with minimal side effects.

Pharmacophore-guided drug design using LdNMT as a model drug target for leishmaniasis

Leishmaniasis is caused by Leishmania genus parasites and has a high mortality rate. The available drugs to treat leishmaniasis fail due to acquired resistance in parasites. Several enzymes of the Leishmania parasite have been used to design new therapeutic molecules against leishmaniasis. This study uses a pharmacophore-guided approach to design the drug candidate by targeting Leishmania N-Myristoyl transferase (LdNMT). From the initial sequence analysis of LdNMT, we have identified a unique 20 amino acid stretch exploited for screening and designing the small molecules. The pharmacophore for the myristate binding site on LdNMT was elucidated, and a heatmap was constructed. The leishmanial NMT pharmacophore has similarities with other pathogenic microorganisms. Moreover, substituting alanine in pharmacophoric residues elevates the affinity of myristate with NMT. Furthermore, a molecular dynamics (MD) simulation study was conducted to ascertain the stability of the mutants and or wild type. The wild-type NMT has a comparatively low affinity to myristate compared to alanine mutants, indicating that hydrophobic residues favor the myristate binding. The molecules were initially designed by using pharmacophore as a sieving mechanism. In subsequent steps, the selected molecules screened against leishmanial unique amino acid stretch and subsequently with human, leishmanial full-size NMTs. The compounds BP5, TYI, DMU, 3PE and 4UL were the top hits and chemical features similar to the myristate. The molecule 4UL was found to be highly specific towards leishmanial NMT over human NMT, suggesting the molecule is a strong leishmanial NMT inhibitor. The molecule can be taken further to assess it in in-vitro conditions.

Design of Inhibitors That Target the Menin-Mixed-Lineage Leukemia Interaction

The prognosis of mixed-lineage leukemia (MLL) has remained a significant health concern, especially for infants. The minimal treatments available for this aggressive type of leukemia has been an ongoing problem. Chromosomal translocations of the KMT2A gene are known as MLL, which expresses MLL fusion proteins. A protein called menin is an important oncogenic cofactor for these MLL fusion proteins, thus providing a new avenue for treatments against this subset of acute leukemias. In this study, we report results using the structure-based drug design (SBDD) approach to discover potential novel MLL-mediated leukemia inhibitors from natural products against menin. The three-dimensional (3D) protein model was derived from Protein Databank (Protein ID: 4GQ4), and EasyModeller 4.0 and I-TASSER were used to fix missing residues during rebuilding. Out of the ten protein models generated (five from EasyModeller and I-TASSER each), one model was selected. The selected model demonstrated the most reasonable quality and had 75.5% of residues in the most favored regions, 18.3% of residues in additionally allowed regions, 3.3% of residues in generously allowed regions, and 2.9% of residues in disallowed regions. A ligand library containing 25,131 ligands from a Chinese database was virtually screened using AutoDock Vina, in addition to three known menin inhibitors. The top 10 compounds including ZINC000103526876, ZINC000095913861, ZINC000095912705, ZINC000085530497, ZINC000095912718, ZINC000070451048, ZINC000085530488, ZINC000095912706, ZINC000103580868, and ZINC000103584057 had binding energies of -11.0, -10.7, -10.6, -10.2, -10.2, -9.9, -9.9, -9.9, -9.9, and -9.9 kcal/mol, respectively. To confirm the stability of the menin-ligand complexes and the binding mechanisms, molecular dynamics simulations including molecular mechanics Poisson-Boltzmann surface area (MM/PBSA) computations were performed. The amino acid residues that were found to be potentially crucial in ligand binding included Phe243, Met283, Cys246, Tyr281, Ala247, Ser160, Asn287, Asp185, Ser183, Tyr328, Asn249, His186, Leu182, Ile248, and Pro250. MI-2-2 and PubChem CIDs 71777742 and 36294 were shown to possess anti-menin properties; thus, this justifies a need to experimentally determine the activity of the identified compounds. The compounds identified herein were found to have good pharmacological profiles and had negligible toxicity. Additionally, these compounds were predicted as antileukemic, antineoplastic, chemopreventive, and apoptotic agents. The 10 natural compounds can be further explored as potential novel agents for the effective treatment of MLL-mediated leukemia.

Molecular Dynamics as a Tool for Virtual Ligand Screening

Rational drug design is essential for new drugs to emerge, especially when the structure of a target protein or nucleic acid is known. To that purpose, high-throughput virtual ligand screening campaigns aim at discovering computationally new binding molecules or fragments to modulate particular biomolecular interactions or biological activities, related to a disease process. The structure-based virtual ligand screening process primarily relies on docking methods which allow predicting the binding of a molecule to a biological target structure with a correct conformation and the best possible affinity. The docking method itself is not sufficient as it suffers from several and crucial limitations (lack of full protein flexibility information, no solvation and ion effects, poor scoring functions, and unreliable molecular affinity estimation).At the interface of computer techniques and drug discovery, molecular dynamics (MD) allows introducing protein flexibility before or after a docking protocol, refining the structure of protein-drug complexes in the presence of water, ions, and even in membrane-like environments, describing more precisely the temporal evolution of the biological complex and ranking these complexes with more accurate binding energy calculations. In this chapter, we describe the up-to-date MD, which plays the role of supporting tools in the virtual ligand screening (VS) process.Without a doubt, using docking in combination with MD is an attractive approach in structure-based drug discovery protocols nowadays. It has proved its efficiency through many examples in the literature and is a powerful method to significantly reduce the amount of required wet experimentations (Tarcsay et al, J Chem Inf Model 53:2990-2999, 2013; Barakat et al, PLoS One 7:e51329, 2012; De Vivo et al, J Med Chem 59:4035-4061, 2016; Durrant, McCammon, BMC Biol 9:71-79, 2011; Galeazzi, Curr Comput Aided Drug Des 5:225-240, 2009; Hospital et al, Adv Appl Bioinforma Chem 8:37-47, 2015; Jiang et al, Molecules 20:12769-12786, 2015; Kundu et al, J Mol Graph Model 61:160-174, 2015; Mirza et al, J Mol Graph Model 66:99-107, 2016; Moroy et al, Future Med Chem 7:2317-2331, 2015; Naresh et al, J Mol Graph Model 61:272-280, 2015; Nichols et al, J Chem Inf Model 51:1439-1446, 2011; Nichols et al, Methods Mol Biol 819:93-103, 2012; Okimoto et al, PLoS Comput Biol 5:e1000528, 2009; Rodriguez-Bussey et al, Biopolymers 105:35-42, 2016; Sliwoski et al, Pharmacol Rev 66:334-395, 2014).

Uncovering the Structural and Binding Insights of Dual Inhibitors Simultaneously Targeting Two Distinct Sites on EGFR Kinase

Epidermal growth factor receptor (EGFR) is the first growth factor receptor identified in normal cells that is related to the receptor tyrosine kinase, which causes regular cell division. A point mutation in EGFR intracellular kinase domain forces the abnormal cell divisions throughout time, leading to non-small cell lung cancer (NSCLC) transformation. Thus, competitive inhibitors that bind to the ATP binding pocket have been developed as a targeted therapy for NSCLC. The third-generation kinase inhibitor Osimertinib is currently playing a very vital role in the treatment of NSCLC. However, it is not effective toward the C797S kinase domain mutation. For this reason, fourth-generation kinase noncompetitive inhibitors are introduced which work through binding to an allosteric pocket near the ATP binding region and act as a better binding agent for this mutated kinase domain. However, the problem is that these single fourth-generation kinase inhibitors may not be as effective as a single agent. The aim of this work was to apply combinations of these two inhibitors together in different binding regions of EGFR without overlapping the resistance mechanism to obtain the key direct and indirect interactions occurring between them. Moreover, the free energy of dissociation of an inhibitor from its binding sites in the presence of a second inhibitor immobilized in another binding site was also the focus of the study. To realize this aim, we performed conventional molecular dynamics simulations and principal component analysis and dynamic cross-correlation matrices along with umbrella sampling. Our results demonstrated that binding of dual inhibitors triggered conformational changes of the protein more toward the inactive state. Furthermore, allosteric inhibitors bound more strongly to protein kinase EGFR than the orthosteric inhibitors in the presence of dual inhibitors. Finally, the binding mechanism and important hydrogen-bonding residues during unbinding of the inhibitors were fully elucidated. This study provides insight into the binding of the receptor-orthosteric inhibitor-allosteric inhibitor, which can be helpful for further design of novel inhibitors that have a better inhibitory action.

Identification of potential inhibitor molecule against MabA protein of Mycobacterium leprae by integrated in silico approach

Leprosy is one of the chronic diseases with which humanity has struggled globally for millennia. The potent anti-leprosy medications rifampicin, clofazimine and dapsone, among others, are used to treat leprosy. Nevertheless, even in regions of the world where these drugs have been successfully implemented, resistance continues to be observed. Due to the problems with the current treatments, this disease should be fought at every level of society with new drugs. The purpose of this research was to identify natural candidates with the ability to inhibit MabA (gene-fabG1) with fewer negative effects. The work was accomplished through molecular docking, followed by a dynamic investigation of protein-ligand, which play a significant role in the design of pharmaceuticals. After modelling the protein structure with MODELLER 9.21v, AutoDock Vina was used to perform molecular docking with 13 3 D anti-leprosy medicines and a zinc library to determine the optimal protein-ligand interaction. In addition, the docking result was filtered based on binding energy, ADMET characteristics, PASS analysis and the most crucial binding residues. The ZINC08101051 chemical compound was prioritized for further study. Using an all-atom 100 ns MD simulation, the binding pattern and conformational changes in protein upon ligand binding were studied. Recommendation for subsequent validation based on deviation, fluctuation, gyration and hydrogen bond analysis, followed by main component and free energy landscape.Communicated by Ramaswamy H. Sarma.

Exploring the Deoxy-D-xylulose-5-phosphate Synthase Gene Family in Tomato (Solanum lycopersicum)

Isoprenoids are a wide family of metabolites including high-value chemicals, flavors, pigments, and drugs. Isoprenoids are particularly abundant and diverse in plants. The methyl-D-erythritol 4-phosphate (MEP) pathway produces the universal isoprenoid precursors isopentenyl diphosphate and dimethylallyl diphosphate in plant plastids for the downstream production of monoterpenes, diterpenes, and photosynthesis-related isoprenoids such as carotenoids, chlorophylls, tocopherols, phylloquinone, and plastoquinone. The enzyme deoxy-D-xylulose 5-phosphate synthase (DXS) is the first and main rate-determining enzyme of the MEP pathway. In tomato (Solanum lycopersicum), a plant with an active isoprenoid metabolism in several tissues, three genes encode DXS-like proteins (SlDXS1 to 3). Here, we show that the expression patterns of the three genes suggest distinct physiological roles without excluding that they might function together in some tissues. We also confirm that SlDXS1 and 2 are true DXS enzymes, whereas SlDXS3 lacks DXS activity. We further show that SlDXS1 and 2 co-localize in plastidial speckles and that they can be immunoprecipitated together, suggesting that they might form heterodimers in vivo in at least some tissues. These results provide novel insights for the biotechnological use of DXS isoforms in metabolic engineering strategies to up-regulate the MEP pathway flux.

Integrated Molecular Modeling and Machine Learning for Drug Design

Modern therapeutic development often involves several stages that are interconnected, and multiple iterations are usually required to bring a new drug to the market. Computational approaches have increasingly become an indispensable part of helping reduce the time and cost of the research and development of new drugs. In this Perspective, we summarize our recent efforts on integrating molecular modeling and machine learning to develop computational tools for modulator design, including a pocket-guided rational design approach based on AlphaSpace to target protein-protein interactions, delta machine learning scoring functions for protein-ligand docking as well as virtual screening, and state-of-the-art deep learning models to predict calculated and experimental molecular properties based on molecular mechanics optimized geometries. Meanwhile, we discuss remaining challenges and promising directions for further development and use a retrospective example of FDA approved kinase inhibitor Erlotinib to demonstrate the use of these newly developed computational tools.

A virtual screening investigation to identify bioactive natural compounds as potential inhibitors of cyclin-dependent kinase 9

Cyclin-dependent kinase 9 (CDK9) is a transcription-associated protein involved in controlling the cell cycle and is often deregulated in stress conditions. CDK9 is being studied as a well-known druggable target for developing effective therapeutics against a wide range of cancer, cardiac dysfunction and inflammatory diseases. Owing to the significance of CDK9 in the etiology of hematological and solid malignancies, its structure, biological activity, regulation and its pharmacological inhibition are being explored for therapeutic management of cancer. We employed a structure-based virtual high-throughput screening of bioactive compounds from the IMPPAT database to discover potential bioactive inhibitors of CDK9. The preliminary results were obtained from the Lipinski criteria, ADMET parameters and sorting compounds without any PAINS patterns. Subsequently, binding affinity and selectivity analyses were used to find effective CDK9 hits. This screening resulted in the identification of two natural compounds, Glabrene and Guggulsterone with high affinity and specificity for the CDK9 binding site. Both compounds exhibit drug-like characteristics, as projected by ADMET analysis, physicochemical data and PASS evaluation. Both compounds preferentially bind to the ATP-binding pocket of CDK9 and interact with functionally important residues. Further, the dynamics and consistency of CDK9 interaction with Glabrene and Guggulsteron were evaluated through all-atom molecular dynamic (MD) simulations which suggested the stability of both complexes. The results might be deployed to introduce novel CDK9 inhibitors that may treat life-threatening diseases, including cancer.Communicated by Ramaswamy H. Sarma.

In Vitro and In Silico Analysis of the Anticancer Effects of Eurycomanone and Eurycomalactone from Eurycoma longifolia

Eurycomanone and eurycomalactone are known quassinoids present in the roots and stems of Eurycoma longifolia. These compounds had been reported to have cytotoxic effects, however, their mechanism of action in a few cancer cell lines have yet to be elucidated. This study was aimed at investigating the anticancer effects and mechanisms of action of eurycomanone and eurycomalactone in cervical (HeLa), colorectal (HT29) and ovarian (A2780) cancer cell lines via Sulforhodamine B assay. Their mechanism of cell death was evaluated based on Hoechst 33342 assay and in silico molecular docking toward DHFR and TNF-α as putative protein targets. Eurycomanone and eurycomalactone exhibited in vitro anticancer effects manifesting IC50 values of 4.58 ± 0.090 µM and 1.60 ± 0.12 µM (HeLa), 1.22 ± 0.11 µM and 2.21 ± 0.049 µM (HT-29), and 1.37 ± 0.13 µM and 2.46 ± 0.081 µM (A2780), respectively. They induced apoptotic cancer cell death in dose- and time-dependent manners. Both eurycomanone and eurycomalactone were also predicted to have good inhibitory potential as demonstrated by the docking into TNF-α with binding affinity of -8.83 and -7.51 kcal/mol, respectively, as well as into DHFR with binding affinity results of -8.05 and -8.87 kcal/mol, respectively. These results support the evidence of eurycomanone and eurycomalactone as anticancer agents via apoptotic cell death mechanism that could be associated with TNF-α and DHFR inhibition as among possible protein targets.

Protein Sizing with 15 nm Conical Biological Nanopore YaxAB

Nanopores are promising single-molecule tools for the electrical identification and sequencing of biomolecules. However, the characterization of proteins, especially in real-time and in complex biological samples, is complicated by the sheer variety of sizes and shapes in the proteome. Here, we introduce a large biological nanopore, YaxAB for folded protein analysis. The 15 nm cis-opening and a 3.5 nm trans-constriction describe a conical shape that allows the characterization of a wide range of proteins. Molecular dynamics showed proteins are captured by the electroosmotic flow, and the overall resistance is largely dominated by the narrow trans constriction region of the nanopore. Conveniently, proteins in the 35-125 kDa range remain trapped within the conical lumen of the nanopore for a time that can be tuned by the external bias. Contrary to cylindrical nanopores, in YaxAB, the current blockade decreases with the size of the trapped protein, as smaller proteins penetrate deeper into the constriction region than larger proteins do. These characteristics are especially useful for characterizing large proteins, as shown for pentameric C-reactive protein (125 kDa), a widely used health indicator, which showed a signal that could be identified in the background of other serum proteins.

Investigation of the Inhibition Mechanism of Xanthine Oxidoreductase by Oxipurinol: A Computational Study

Xanthine oxidoreductase (XOR) is an enzyme found in various organisms. It converts hypoxanthine to xanthine and urate, which are crucial steps in purine elimination in humans. Elevated uric acid levels can lead to conditions like gout and hyperuricemia. Therefore, there is significant interest in developing drugs that target XOR for treating these conditions and other diseases. Oxipurinol, an analogue of xanthine, is a well-known inhibitor of XOR. Crystallographic studies have revealed that oxipurinol directly binds to the molybdenum cofactor (MoCo) in XOR. However, the precise details of the inhibition mechanism are still unclear, which would be valuable for designing more effective drugs with similar inhibitory functions. In this study, molecular dynamics and quantum mechanics/molecular mechanics calculations are employed to investigate the inhibition mechanism of XOR by oxipurinol. The study examines the structural and dynamic effects of oxipurinol on the pre-catalytic structure of the metabolite-bound system. Our results provide insights on the reaction mechanism catalyzed by the MoCo center in the active site, which aligns well with experimental findings. Furthermore, the results provide insights into the residues surrounding the active site and propose an alternative mechanism for developing alternative covalent inhibitors.

In Silico Screening and Identification of Antidiabetic Inhibitors Sourced from Phytochemicals of Philippine Plants against Four Protein Targets of Diabetes (PTP1B, DPP-4, SGLT-2, and FBPase)

Current oral medications for type 2 diabetes target a single main physiological mechanism. They either activate or inhibit receptors to enhance insulin sensitivity, increase insulin secretion, inhibit glucose absorption, or inhibit glucose production. In advanced stages, combination therapy may be required because of the limited efficacy of single-target drugs; however, medications are becoming more costly, and there is also the risk of developing the combined side effects of each drug. Thus, identifying a multi-target drug may be the best strategy to improve treatment efficacy. This study sees the potential of 2657 Filipino phytochemicals as a source of natural inhibitors against four targets of diabetes: PTP1B, DPP-4, SGLT-2, and FBPase. Different computer-aided drug discovery techniques, including ADMET profiling, DFT optimization, molecular docking, MD simulations, and MM/PBSA energy calculations, were employed to elucidate the stability and determine the binding affinity of the candidate ligands. Through in silico methods, we have identified seven potential natural inhibitors against PTP1B, DPP-4, and FBPase, and ten against SGLT-2. Eight plants containing at least one natural inhibitor of each protein target were also identified. It is recommended to further investigate the plants' potential to be transformed into a safe and scientifically validated multi-target drug for diabetes therapies.

The dynamics of agonist-β2-adrenergic receptor activation induced by binding of GDP-bound Gs protein

There is considerable uncertainty about the mechanism by which the β₂-adrenergic receptor (β₂AR) is activated. Here we use molecular metadynamics computations to predict the mechanism by which an agonist induces the activation of the β₂AR and its cognate Gs protein. We found that binding agonist alone to the inactive β₂AR does not break the ionic lock and hence does not drive the β₂AR towards the activated conformation. However, we found that attaching the inactive Gs protein to the agonist-bound inactive β₂AR (containing the ionic lock) leads to partial insertion of Gαs-α5 into the core of β₂AR, which breaks the ionic lock, leading to activation of the Gs protein coupled to β₂AR. Upon activation, the Gαs protein undergoes a remarkable opening of the GDP binding pocket, making the GDP available for exchange or release. Concomitantly, Gαs-α5 undergoes a remarkable expansion in the β₂AR cytoplasmic region after the ionic lock is broken, inducing TM6 to displace outward by ~5 Å from TM3.

The XBB.1.5 slightly increase the binding affinity for host receptor ACE2 and exhibit strongest immune escaping features: molecular modeling and free energy calculation

Introduction: The current XBB variant of SARS-CoV-2 with the strongest immune escaping properties is currently the most dominant variant circulating around the world. With the emergence of XBB global morbidities and mortalities have raised again. In the current scenario, it was highly required to delineate the binding capabilities of NTD of XBB subvariant towards human neutralizing antibodies and to dig out the binding affinity of RBD of XBB subvariant with ACE2 receptor. Materials and Methods: The current study uses molecular interaction and simulation-based approaches to decipher the binding mechanism of RBD with ACE2 and mAb interaction with NTD of the spike protein. Results: Molecular docking of the Wild type NTD with mAb revealed a docking score of -113.2 ± 0.7 kcal/mol while XBB NTD docking with mAb reported -76.2 ± 2.3 kcal/mol. On the other hand, wild-type RBD and XBB RBD with ACE2 receptor demonstrated docking scores of -115.0 ± 1.5 kcal/mol and -120.8 ± 3.4 kcal/mol respectively. Moreover, the interaction network analysis also revealed significant variations in the number of hydrogen bonds, salt-bridges, and non-bonded contacts. These findings were further validated by computing the dissociation constant (KD). Molecular simulation analysis such as RMSD, RMSF, Rg and hydrogen bonding analysis revealed variation in the dynamics features of the RBD and NTD complexes due to the acquired mutations. Furthermore, the total binding energy for the wild-type RBD in complex with ACE2 reported -50.10 kcal/mol while XBB-RBD coupled with ACE2 reported -52.66 kcal/mol respectively. This shows though the binding of XBB is slightly increased but due to the variation in the bonding network and other factors makes the XBB variant to enter into the host cell efficiently than the wild type. On the other hand, the total binding free energy for the wildtype NTD-mAb was calculated to be -65.94 kcal/mol while for XBB NTD-mAb was reported to be -35.06 kcal/mol respectively. The significant difference in the total binding energy factors explains that the XBB variant possess stronger immune evasion properties than the others variants and wild type. Conclusions: The current study provides structural features for the XBB variant binding and immune evasion which can be used to design novel therapeutics.

Nicotinamide N-methyltransferase (NNMT) regulates the glucocorticoid signaling pathway during the early phase of adipogenesis

Obesity is associated with adipose tissue dysfunction through the differentiation and expansion of pre-adipocytes to adipocytes (hyperplasia) and/or increases in size of pre-existing adipocytes (hypertrophy). A cascade of transcriptional events coordinates the differentiation of pre-adipocytes into fully differentiated adipocytes; the process of adipogenesis. Although nicotinamide N-methyltransferase (NNMT) has been associated with obesity, how NNMT is regulated during adipogenesis, and the underlying regulatory mechanisms, remain undefined. In present study we used genetic and pharmacological approaches to elucidate the molecular signals driving NNMT activation and its role during adipogenesis. Firstly, we demonstrated that during the early phase of adipocyte differentiation NNMT is transactivated by CCAAT/Enhancer Binding Protein beta (CEBPB) in response to glucocorticoid (GC) induction. We found that Nnmt knockout, using CRISPR/Cas9 approach, impaired terminal adipogenesis by influencing the timing of cellular commitment and cell cycle exit during mitotic clonal expansion, as demonstrated by cell cycle analysis and RNA sequencing experiments. Biochemical and computational methods showed that a novel small molecule, called CC-410, stably binds to and highly specifically inhibits NNMT. CC-410 was, therefore, used to modulate protein activity during pre-adipocyte differentiation stages, demonstrating that, in line with the genetic approach, chemical inhibition of NNMT at the early stages of adipogenesis impairs terminal differentiation by deregulating the GC network. These congruent results conclusively demonstrate that NNMT is a key component of the GC-CEBP axis during the early stages of adipogenesis and could be a potential therapeutic target for both early-onset obesity and glucocorticoid-induced obesity.

Cholesterol Biases the Conformational Landscape of the Chemokine Receptor CCR3: A MAS SSNMR-Filtered Molecular Dynamics Study

Cholesterol directs the pathway of ligand-induced G protein-coupled receptor (GPCR) signal transduction. The GPCR C-C motif chemokine receptor 3 (CCR3) is the principal chemotactic receptor for eosinophils, with roles in cancer metastasis and autoinflammatory conditions. Recently, we discovered a direct correlation between bilayer cholesterol and increased agonist-triggered CCR3 signal transduction. However, the allosteric molecular mechanism escalating ligand affinity and G protein coupling is unknown. To study cholesterol-guided CCR3 conformational selection, we implement comparative, objective measurement of protein architectures by scoring shifts (COMPASS) to grade model structures from molecular dynamics simulations. In this workflow, we scored predicted chemical shifts against 2-dimensional solid-state NMR 13C-13C correlation spectra of U-15N,13C-CCR3 samples prepared with and without cholesterol. Our analysis of trajectory model structures uncovers that cholesterol induces site-specific conformational restraint of extracellular loop (ECL) 2 and conserved motion in transmembrane helices and ECL3 not observed in simulations of bilayers with only phosphatidylcholine lipids. PyLipID analysis implicates direct cholesterol agency in CCR3 conformational selection and dynamics. Residue-residue contact scoring shows that cholesterol biases the conformational selection of the orthosteric pocket involving Y411.39, Y1133.32, and E2877.39. Lastly, we observe contact remodeling in activation pathway residues centered on the initial transmission switch, Na+ pocket, and R3.50 in the DRY motif. Our observations have unique implications for understanding of CCR3 ligand recognition and specificity and provide mechanistic insight into how cholesterol functions as an allosteric regulator of CCR3 signal transduction.

Computational Characterization of the Inhibition Mechanism of Xanthine Oxidoreductase by Topiroxostat

Xanthine oxidase (XO) is a member of the molybdopterin-containing enzyme family. It interconverts xanthine to uric acid as the last step of purine catabolism in the human body. The high uric acid concentration in the blood directly leads to human diseases like gout and hyperuricemia. Therefore, drugs that inhibit the biosynthesis of uric acid by human XO have been clinically used for many years to decrease the concentration of uric acid in the blood. In this study, the inhibition mechanism of XO and a new promising drug, topiroxostat (code: FYX-051), is investigated by employing molecular dynamics (MD) and quantum mechanics/molecular mechanics (QM/MM) calculations. This drug has been reported to act as both a noncovalent and covalent inhibitor and undergoes a stepwise inhibition by all its hydroxylated metabolites, which include 2-hydroxy-FYX-051, dihydroxy-FYX-051, and trihydroxy-FYX-051. However, the detailed mechanism of inhibition of each metabolite remains elusive and can be useful for designing more effective drugs with similar inhibition functions. Hence, herein we present the computational investigation of the structural and dynamical effects of FYX-051 and the calculated reaction mechanism for all of the oxidation steps catalyzed by the molybdopterin center in the active site. Calculated results for the proposed reaction mechanisms for each metabolite's inhibition reaction in the enzyme's active site, binding affinities, and the noncovalent interactions with the surrounding amino acid residues are consistent with previously reported experimental findings. Analysis of the noncovalent interactions via energy decomposition analysis (EDA) and noncovalent interaction (NCI) techniques suggests that residues L648, K771, E802, R839, L873, R880, R912, F914, F1009, L1014, and A1079 can be used as key interacting residues for further hybrid-type inhibitor development.

Best Practices on QM/MM Simulations of Biological Systems

During the second half of the 20th century, following structural biology hallmark works on DNA and proteins, biochemists shifted their questions from "what does this molecule look like?" to "how does this process work?". Prompted by the theoretical and practical developments in computational chemistry, this led to the emergence of biomolecular simulations and, along with the 2013 Nobel Prize in Chemistry, to the development of hybrid QM/MM methods. QM/MM methods are necessary whenever the problem we want to address involves chemical reactivity and/or a change in the system's electronic structure, with archetypal examples being the studies of an enzyme's reaction mechanism and a metalloprotein's active site. In the last decades QM/MM methods have seen an increasing adoption driven by their incorporation in widely used biomolecular simulation software. However, properly setting up a QM/MM simulation is not an easy task, and several issues need to be properly addressed to obtain meaningful results. In the present work, we describe both the theoretical concepts and practical issues that need to be considered when performing QM/MM simulations. We start with a brief historical perspective on the development of these methods and describe when and why QM/MM methods are mandatory. Then we show how to properly select and analyze the performance of the QM level of theory, the QM system size, and the position and type of the boundaries. We show the relevance of performing prior QM model system (or QM cluster) calculations in a vacuum and how to use the corresponding results to adequately calibrate those derived from QM/MM. We also discuss how to prepare the starting structure and how to select an adequate simulation strategy, including those based on geometry optimizations as well as free energy methods. In particular, we focus on the determination of free energy profiles using multiple steered molecular dynamics (MSMD) combined with Jarzynski's equation. Finally, we describe the results for two illustrative and complementary examples: the reaction performed by chorismate mutase and the study of ligand binding to hemoglobins. Overall, we provide many practical recommendations (or shortcuts) together with important conceptualizations that we hope will encourage more and more researchers to incorporate QM/MM studies into their research projects.

Fast, accurate antibody structure prediction from deep learning on massive set of natural antibodies

Antibodies have the capacity to bind a diverse set of antigens, and they have become critical therapeutics and diagnostic molecules. The binding of antibodies is facilitated by a set of six hypervariable loops that are diversified through genetic recombination and mutation. Even with recent advances, accurate structural prediction of these loops remains a challenge. Here, we present IgFold, a fast deep learning method for antibody structure prediction. IgFold consists of a pre-trained language model trained on 558 million natural antibody sequences followed by graph networks that directly predict backbone atom coordinates. IgFold predicts structures of similar or better quality than alternative methods (including AlphaFold) in significantly less time (under 25 s). Accurate structure prediction on this timescale makes possible avenues of investigation that were previously infeasible. As a demonstration of IgFold's capabilities, we predicted structures for 1.4 million paired antibody sequences, providing structural insights to 500-fold more antibodies than have experimentally determined structures.

Inhibitory effect of natural compounds on Dihydropteroate synthase of Mycobacterium leprae: Molecular dynamic study

Leprosy is a chronic infectious disease caused by a bacillus, Mycobacterium leprae. According to official data from 139 countries in the 6 WHO Regions, there were 127558 new leprosy cases worldwide in 2020. Leprosy mainly affects the skin, the peripheral nerves, mucosa of the upper respiratory tract, and the eyes. If this disease is left untreated, can harm the skin, nerves, limbs, eyes, and skin permanently. The disease is curable with multidrug therapy. Over a period of time Mycobacterium leprae has become resistant to these drugs. Therefore, new therapeutic molecules are warranted. This study was aimed to carry out the in-silico analysis to determine the inhibitory effect of natural compounds on Dihydropteroate synthase (DHPS) of Mycobacterium leprae. The DHPS is a key enzyme in the folate biosynthesis pathway in M. leprae and acts as a competitive inhibitor of PABA. The 3D structure of DHPS protein was modeled using homology modeling and was validated. Molecular docking and simulation along with other in-silico methods were employed to determine the inhibitory effect of ligand molecules towards DHPS target protein. Results revealed ZINC03830554 molecule as a potential inhibitor of DHPS. Binding experiments and bioassays utilizing this strong inhibitor molecule against purified DHPS protein are necessary to validate these early findings.Communicated by Ramaswamy H. Sarma.

Cheminformatics-Based Study Identifies Potential Ebola VP40 Inhibitors

The Ebola virus (EBOV) is still highly infectious and causes severe hemorrhagic fevers in primates. However, there are no regulatorily approved drugs against the Ebola virus disease (EVD). The highly virulent and lethal nature of EVD highlights the need to develop therapeutic agents. Viral protein 40 kDa (VP40), the most abundantly expressed protein during infection, coordinates the assembly, budding, and release of viral particles into the host cell. It also regulates viral transcription and RNA replication. This study sought to identify small molecules that could potentially inhibit the VP40 protein by targeting the N-terminal domain using an in silico approach. The statistical quality of AutoDock Vina’s capacity to discriminate between inhibitors and decoys was determined, and an area under the curve of the receiver operating characteristic (AUC-ROC) curve of 0.791 was obtained. A total of 29,519 natural-product-derived compounds from Chinese and African sources as well as 2738 approved drugs were successfully screened against VP40. Using a threshold of −8 kcal/mol, a total of 7, 11, 163, and 30 compounds from the AfroDb, Northern African Natural Products Database (NANPDB), traditional Chinese medicine (TCM), and approved drugs libraries, respectively, were obtained after molecular docking. A biological activity prediction of the lead compounds suggested their potential antiviral properties. In addition, random-forest- and support-vector-machine-based algorithms predicted the compounds to be anti-Ebola with IC50 values in the micromolar range (less than 25 μM). A total of 42 natural-product-derived compounds were identified as potential EBOV inhibitors with desirable ADMET profiles, comprising 1, 2, and 39 compounds from NANPDB (2-hydroxyseneganolide), AfroDb (ZINC000034518176 and ZINC000095485942), and TCM, respectively. A total of 23 approved drugs, including doramectin, glecaprevir, velpatasvir, ledipasvir, avermectin B1, nafarelin acetate, danoprevir, eltrombopag, lanatoside C, and glycyrrhizin, among others, were also predicted to have potential anti-EBOV activity and can be further explored so that they may be repurposed for EVD treatment. Molecular dynamics simulations coupled with molecular mechanics Poisson–Boltzmann surface area calculations corroborated the stability and good binding affinities of the complexes (−46.97 to −118.9 kJ/mol). The potential lead compounds may have the potential to be developed as anti-EBOV drugs after experimental testing.

Inhibiting Leishmania donovani Sterol Methyltransferase to Identify Lead Compounds Using Molecular Modelling

The recent outlook of leishmaniasis as a global public health concern coupled with the reportage of resistance and lack of efficacy of most antileishmanial drugs calls for a concerted effort to find new leads. The study combined In silico and in vitro approaches to identify novel potential synthetic small-molecule inhibitors targeting the Leishmania donovani sterol methyltransferase (LdSMT). The LdSMT enzyme in the ergosterol biosynthetic pathway is required for the parasite’s membrane fluidity, distribution of membrane proteins, and control of the cell cycle. The lack of LdSMT homologue in the human host and its conserved nature among all Leishmania parasites makes it a viable target for future antileishmanial drugs. Initially, six known inhibitors of LdSMT with IC50 < 10 μM were used to generate a pharmacophore model with a score of 0.9144 using LigandScout. The validated model was used to screen a synthetic library of 95,630 compounds obtained from InterBioScreen limited. Twenty compounds with pharmacophore fit scores above 50 were docked against the modelled three-dimensional structure of LdSMT using AutoDock Vina. Consequently, nine compounds with binding energies ranging from −7.5 to −8.7 kcal/mol were identified as potential hit molecules. Three compounds comprising STOCK6S-06707, STOCK6S-84928, and STOCK6S-65920 with respective binding energies of −8.7, −8.2, and −8.0 kcal/mol, lower than 22,26-azasterol (−7.6 kcal/mol), a known LdSMT inhibitor, were selected as plausible lead molecules. Molecular dynamics simulation studies and molecular mechanics Poisson–Boltzmann surface area calculations showed that the residues Asp25 and Trp208 were critical for ligand binding. The compounds were also predicted to have antileishmanial activity with reasonable pharmacological and toxicity profiles. When the antileishmanial activity of the three hits was evaluated in vitro against the promastigotes of L. donovani, mean half-maximal inhibitory concentrations (IC50) of 21.9 ± 1.5 μM (STOCK6S-06707), 23.5 ± 1.1 μM (STOCK6S-84928), and 118.3 ± 5.8 μM (STOCK6S-65920) were obtained. Furthermore, STOCK6S-84928 and STOCK6S-65920 inhibited the growth of Trypanosoma brucei, with IC50 of 14.3 ± 2.0 μM and 18.1 ± 1.4 μM, respectively. The identified compounds could be optimised to develop potent antileishmanial therapeutic agents.

Reversal of the unique Q493R mutation increases the affinity of Omicron S1-RBD for ACE2

The SARS-CoV-2 Omicron variant containing 15 mutations, including the unique Q493R, in the spike protein receptor binding domain (S1-RBD) is highly infectious. While comparison with previously reported mutations provide some insights, the mechanism underlying the increased infections and the impact of the reversal of the unique Q493R mutation seen in BA.4, BA.5, BA.2.75, BQ.1 and XBB lineages is not yet completely understood. Here, using structural modelling and molecular dynamics (MD) simulations, we show that the Omicron mutations increases the affinity of S1-RBD for ACE2, and a reversal of the unique Q493R mutation further increases the ACE2-S1-RBD affinity. Specifically, we performed all atom, explicit solvent MD simulations using a modelled structure of the Omicron S1-RBD-ACE2 and compared the trajectories with the WT complex revealing a substantial reduction in the Cα-atom fluctuation in the Omicron S1-RBD and increased hydrogen bond and other interactions. Residue level analysis revealed an alteration in the interaction between several residues including a switch in the interaction of ACE2 D38 from S1-RBD Y449 in the WT complex to the mutated R residue (Q493R) in Omicron complex. Importantly, simulations with Revertant (Omicron without the Q493R mutation) complex revealed further enhancement of the interaction between S1-RBD and ACE2. Thus, results presented here not only provide insights into the increased infectious potential of the Omicron variant but also a mechanistic basis for the reversal of the Q493R mutation seen in some Omicron lineages and will aid in understanding the impact of mutations in SARS-CoV-2 evolution.

Structural basis of constitutive c-Src kinase activity due to R175L and W118A mutations

Cellular Src (c-Src) belongs to a non-receptor membrane-associated tyrosine kinase family that plays essential roles in cellular processes. Growing evidence suggests that R175L and W118A mutations in SH2/SH3 domains of c-Src functionally inactivate these domains leading to constitutive activation of kinase domain (KD). Here we modeled c-Src^R175L, c-Src^W118A and c-Src^W118A+R175L structures by inducing phosphorylation at Y416 or Y527, respectively to characterize the comparative dynamics in the active versus inactive states through molecular dynamics simulation assay. We observed more conformational readjustments in c-Src^open than its close variants. In particular, C-terminal tail residues of c-Src^W118A-open and c-Src^{W118A+R175L-open} demonstrate significantly higher transitions. The cross-correlation analysis revealed an anticorrelation behavior in the motion of KD with respect to SH2, SH3 and the linker region of Src^{W118A+R175L-open}, while in c-Src^WT-open, SH2 and SH3 domains were anticorrelated, while KD and C-terminal tail motions were correlated. Due to these conformational differences, c-Src open forms exhibited lower interaction between pY527 and SH2 domain. Through detailed structural analysis, we observed a uniform myristate binding cavity in c-Src^WT-open, while the myristoyl pockets of mutant forms were deformed. We propose that constitutive activation of mutant Src forms may presumably be achieved by the prolonged membrane binding due to unusual conformations of C-terminal and myristoyl switch residues that may result in a higher dephosphorylation rate at pY527 in the myristoylated c-Src. Thus, our study establishes novel clues to decipher the constitutive activation status of c-Src in response to known mutations that may help in devising novel therapeutic strategies for cancer metastasis treatment.Communicated by Ramaswamy H. Sarma.

The pathogenic effect of SNPs on structure and function of human TLR4 using a computational approach

The human toll-like receptor (hTLR) 4 single nucleotide polymorphisms (SNPs) are interconnected with cancer, multiple genetic disorders and other immune-related diseases. The detrimental effect of SNPs in hTLR4 with respect to structure and function has not been explored in depth. The present study concatenates the biological consequences of the SNPs along with structural modifications predicted at the hTLR4 gene. A total of 7910 SNPs of hTLR4 were screened, and 21 damage-causing SNPs were identified. Out of 21, seven are present in the extracellular region, of which three were detected as deleterious and the fourth one as moderate. These three mutations are located in a highly conserved region and influence conformational change. The change leads to the widening of the Leucine-rich repeat (LRR) arc to a maximum of 16.9 Å and a minimum of 8.7 Å. Expansion/shortening of LRR arc, never discussed before, would cause loss of myeloid differentiation factor 2 (MD-2) interactions in the interior and diminish lipopolysaccharide (LPS) responses. Similarly, in all mutant structures, the binding region for HMGB1 and LPS is deflating or in an unsupportive conformation. Thus, SNPs affect the regular signaling cascade and might result in human sepsis, genetic disorders, cancer and other immunological related diseases.Communicated by Ramaswamy H. Sarma.

A Zn-dependent structural transition of SOD1 modulates its ability to undergo phase separation

The misfolding and mutation of Cu/Zn superoxide dismutase (SOD1) is commonly associated with amyotrophic lateral sclerosis (ALS). SOD1 can accumulate within stress granules (SGs), a type of membraneless organelle, which is believed to form via liquid-liquid phase separation (LLPS). Using wild-type, metal-deficient, and different ALS disease mutants of SOD1 and computer simulations, we report here that the absence of Zn leads to structural disorder within two loop regions of SOD1, triggering SOD1 LLPS and amyloid formation. The addition of exogenous Zn to either metal-free SOD1 or to the severe ALS mutation I113T leads to the stabilization of the loops and impairs SOD1 LLPS and aggregation. Moreover, partial Zn-mediated inhibition of LLPS was observed for another severe ALS mutant, G85R, which shows perturbed Zn-binding. By contrast, the ALS mutant G37R, which shows reduced Cu-binding, does not undergo LLPS. In addition, SOD1 condensates induced by Zn-depletion exhibit greater cellular toxicity than aggregates formed by prolonged incubation under aggregating conditions. Overall, our work establishes a role for Zn-dependent modulation of SOD1 conformation and LLPS properties that may contribute to amyloid formation.