Comparative protein structure modeling of genes and genomes

Computer-Aided Drug Design towards New Psychotropic and Neurological Drugs

Central nervous system (CNS) disorders are a therapeutic area in drug discovery where demand for new treatments greatly exceeds approved treatment options. This is complicated by the high failure rate in late-stage clinical trials, resulting in exorbitant costs associated with bringing new CNS drugs to market. Computer-aided drug design (CADD) techniques minimise the time and cost burdens associated with drug research and development by ensuring an advantageous starting point for pre-clinical and clinical assessments. The key elements of CADD are divided into ligand-based and structure-based methods. Ligand-based methods encompass techniques including pharmacophore modelling and quantitative structure activity relationships (QSARs), which use the relationship between biological activity and chemical structure to ascertain suitable lead molecules. In contrast, structure-based methods use information about the binding site architecture from an established protein structure to select suitable molecules for further investigation. In recent years, deep learning techniques have been applied in drug design and present an exciting addition to CADD workflows. Despite the difficulties associated with CNS drug discovery, advances towards new pharmaceutical treatments continue to be made, and CADD has supported these findings. This review explores various CADD techniques and discusses applications in CNS drug discovery from 2018 to November 2022.

Comparative Surface Electrostatics and Normal Mode Analysis of High and Low Pathogenic H7N7 Avian Influenza Viruses

Influenza A viruses are rarely symptomatic in wild birds, while representing a higher threat to poultry and mammals, where they can cause a variety of symptoms, including death. H5 and H7 subtypes of influenza viruses are of particular interest because of their pathogenic potential and reported capacity to spread from poultry to mammals, including humans. The identification of molecular fingerprints for pathogenicity can help surveillance and early warning systems, which are crucial to prevention and protection from such potentially pandemic agents. In the past decade, comparative analysis of the surface features of hemagglutinin, the main protein antigen in influenza viruses, identified electrostatic fingerprints in the evolution and spreading of H5 and H9 subtypes. Electrostatic variation among viruses from avian or mammalian hosts was also associated with host jump. Recent findings of fingerprints associated with low and highly pathogenic H5N1 viruses, obtained by means of comparative electrostatics and normal modes analysis, prompted us to check whether such fingerprints can also be found in the H7 subtype. Indeed, evidence presented in this work showed that also in H7N7, hemagglutinin proteins from low and highly pathogenic strains present differences in surface electrostatics, while no meaningful variation was found in normal modes.

Steps of actin filament branch formation by Arp2/3 complex investigated with coarse-grained molecular dynamics

The nucleation of actin filament branches by the Arp2/3 complex involves activation through nucleation promotion factors (NPFs), recruitment of actin monomers, and binding of the complex to the side of actin filaments. Because of the large system size and processes that involve flexible regions and diffuse components, simulations of branch formation using all-atom molecular dynamics are challenging. We applied a coarse-grained model that retains amino-acid level information and allows molecular dynamics simulations in implicit solvent, with globular domains represented as rigid bodies and flexible regions allowed to fluctuate. We used recent electron microscopy structures of the inactive Arp2/3 complex bound to NPF domains and to mother actin filament for the activated Arp2/3 complex. We studied interactions of Arp2/3 complex with the activating VCA domain of the NPF Wiskott-Aldrich syndrome protein, actin monomers, and actin filament. We found stable configurations with one or two actin monomers bound along the branch filament direction and with CA domain of VCA associated to the strong and weak binding sites of the Arp2/3 complex, supporting prior structural studies and validating our approach. We reproduced delivery of actin monomers and CA to the Arp2/3 complex under different conditions, providing insight into mechanisms proposed in previous studies. Simulations of active Arp2/3 complex bound to a mother actin filament indicate the contribution of each subunit to the binding. Addition of the C-terminal tail of Arp2/3 complex subunit ArpC2, which is missing in the cryo-EM structure, increased binding affinity, indicating a possible stabilizing role of this tail.

In Silico Mining of Natural Products Atlas (NPAtlas) Database for Identifying Effective Bcl-2 Inhibitors: Molecular Docking, Molecular Dynamics, and Pharmacokinetics Characteristics

The Bcl-2 protein has a vital function in controlling the programmed cell doom of mitochondria. If programmed cell death signals are obstructed, an imbalance between cell survival and death will occur, which is a significant reason for cancer. Therefore, the Bcl-2 protein was identified as a possible therapeutic target for carcinoma treatment. Herein, the Natural Products Atlas (NPAtlas) compounds were virtually screened, seeking potent inhibitors towards the Bcl-2 protein. The performance of AutoDock Vina software to predict the docking score and pose of the investigated compounds was first validated according to the available experimental data. Based on the validated AutoDock Vina parameters, the NPAtlas database was filtered against the Bcl-2 protein. The natural compounds with docking scores less than that of the venetoclax (calc. -10.6 kcal/mol) were submitted to MD simulations, followed by MM-GBSA binding energy calculations. According to MM-GBSA//200 ns MD simulations, saquayamycin F (NPA002200) demonstrated promising binding affinity with a ΔG_binding value of -53.9 kcal/mol towards the Bcl-2 protein when compared to venetoclax (ΔG_binding = -50.6 kcal/mol). The energetical and structural analyses showed a great constancy of the saquayamycin F inside the Bcl-2 protein active site. Moreover, the ADMET and drug-likeness features of the saquayamycin F were anticipated, indicating its good oral bioavailability. According to in silico computations, saquayamycin F is proposed to be used as a therapeutic agent against the wild-type Bcl-2 protein and warrants further experimental assays.

Expression of KID syndromic mutation Cx26S17F produces hyperactive hemichannels in supporting cells of the organ of Corti

Some mutations in gap junction protein Connexin 26 (Cx26) lead to syndromic deafness, where hearing impairment is associated with skin disease, like in Keratitis Ichthyosis Deafness (KID) syndrome. This condition has been linked to hyperactivity of connexin hemichannels but this has never been demonstrated in cochlear tissue. Moreover, some KID mutants, like Cx26S17F, form hyperactive HCs only when co-expressed with other wild-type connexins. In this work, we evaluated the functional consequences of expressing a KID syndromic mutation, Cx26S17F, in the transgenic mouse cochlea and whether co-expression of Cx26S17F and Cx30 leads to the formation of hyperactive HCs. Indeed, we found that cochlear explants from a constitutive knock-in Cx26S17F mouse or conditional in vitro cochlear expression of Cx26S17F produces hyperactive HCs in supporting cells of the organ of Corti. These conditions also produce loss of hair cells stereocilia. In supporting cells, we found high co-localization between Cx26S17F and Cx30. The functional properties of HCs formed in cells co-expressing Cx26S17F and Cx30 were also studied in oocytes and HeLa cells. Under the recording conditions used in this study Cx26S17F did not form functional HCs and GJCs, but cells co-expressing Cx26S17F and Cx30 present hyperactive HCs insensitive to HCs blockers, Ca²⁺ and La³⁺, resulting in more Ca²⁺ influx and cellular damage. Molecular dynamic analysis of putative heteromeric HC formed by Cx26S17F and Cx30 presents alterations in extracellular Ca²⁺ binding sites. These results support that in KID syndrome, hyperactive HCs are formed by the interaction between Cx26S17F and Cx30 in supporting cells probably causing damage to hair cells associated to deafness.

Characterization and chemical modulation of p62/SQSTM1/Sequestosome-1 as an autophagic N-recognin

In the Arg/N-degron pathway, single N-terminal (Nt) residues function as N-degrons recognized by UBR box-containing N-recognins that induce substrate ubiquitination and proteasomal degradation. Recent studies led to the discovery of the autophagic Arg/N-degron pathway, in which the autophagic receptor p62/SQSTM1/Sequestosome-1 acts as an N-recognin that binds the Nt-Arg and other destabilizing residues as N-degrons. Upon binding to Nt-Arg, p62 undergoes self-polymerization associated with its cargoes, accelerating the macroautophagic delivery of p62-cargo complexes to autophagosomes leading to degradation by lysosomal hydrolases. This autophagic mechanism is emerging as an important pathway that modulates the lysosomal degradation of various biomaterial ranging from protein aggregates and subcellular organelles to invading pathogens. Chemical mimics of the physiological N-degrons were developed to exert therapeutic efficacy in pathophysiological processes associated with neurodegeneration and other related diseases. Here, we describe the methods to monitor the activities of p62 in a dual role as an N-recognin and an autophagic receptor. The topic includes self-polymerization (for cargo condensation), its interaction with LC3 on autophagic membranes (for cargo targeting), and the degradation of p62-cargo complexes by lysosomal hydrolases. We also discuss the development and use of small molecule mimics of N-degrons that modulate p62-dependent macroautophagy in biological and pathophysiological processes.

Structural models of viral insulin-like peptides and their analogs

The insulin receptor (IR), the insulin-like growth factor-1 receptor (IGF1R), and the insulin/IGF1 hybrid receptors (hybR) are homologous transmembrane receptors. The peptide ligands, insulin and IGF1, exhibit significant structural homology and can bind to each receptor via site-1 and site-2 residues with distinct affinities. The variants of the Iridoviridae virus family show capability in expressing single-chain insulin/IGF1 like proteins, termed viral insulin-like peptides (VILPs), which can stimulate receptors from the insulin family. The sequences of VILPs lacking the central C-domain (dcVILPs) are known, but their structures in unbound and receptor-bound states have not been resolved to date. We report all-atom structural models of three dcVILPs (dcGIV, dcSGIV, and dcLCDV1) and their complexes with the receptors (μIR, μIGF1R, and μhybR), and probed the peptide/receptor interactions in each system using all-atom molecular dynamics (MD) simulations. Based on the nonbonded interaction energies computed between each residue of peptides (insulin and dcVILPs) and the receptors, we provide details on residues establishing significant interactions. The observed site-1 insulin/μIR interactions are consistent with previous experimental studies, and a residue-level comparison of interactions of peptides (insulin and dcVILPs) with the receptors revealed that, due to sequence differences, dcVILPs also establish some interactions distinct from those between insulin and IR. We also designed insulin analogs and report enhanced interactions between some analogs and the receptors.

Structural analysis and molecular dynamics simulation studies of HIV-1 antisense protein predict its potential role in HIV replication and pathogenesis

The functional significance of the HIV-1 Antisense Protein (ASP) has been a paradox since its discovery. The expression of this protein in HIV-1-infected cells and its involvement in autophagy, transcriptional regulation, and viral latency have sporadically been reported in various studies. Yet, the definite role of this protein in HIV-1 infection remains unclear. Deciphering the 3D structure of HIV-1 ASP would throw light on its potential role in HIV lifecycle and host-virus interaction. Hence, using extensive molecular modeling and dynamics simulation for 200 ns, we predicted the plausible 3D-structures of ASP from two reference strains of HIV-1 namely, Indie-C1 (subtype-C) and NL4-3 (subtype-B) so as to derive its functional implication through structural domain analysis. In spite of sequence and structural differences in subtype B and C ASP, both structures appear to share common domains like the Von Willebrand Factor Domain-A (VWFA), Integrin subunit alpha-X (ITGSX), and ETV6-Transcriptional repressor, thereby reiterating the potential role of HIV-1 ASP in transcriptional repression and autophagy, as reported in earlier studies. Gromos-based cluster analysis of the centroid structures also reassured the accuracy of the prediction. This is the first study to elucidate a highly plausible structure for HIV-1 ASP which could serve as a feeder for further experimental validation studies.

In Silico Structure-Based Vaccine Design

Structure-based vaccine design (SBVD) is an important technique in computational vaccine design that uses structural information on a targeted protein to design novel vaccine candidates. This increasing ability to rapidly model structural information on proteins and antibodies has provided the scientific community with many new vaccine targets and novel opportunities for future vaccine discovery. This chapter provides a comprehensive overview of the status of in silico SBVD and discusses the current challenges and limitations. Key strategies in the field of SBVD are exemplified by a case study on design of COVID-19 vaccines targeting SARS-CoV-2 spike protein.

A scoring function for the prediction of protein complex interfaces based on the neighborhood preferences of amino acids

Proteins often assemble into functional complexes, the structures of which are more difficult to obtain than those of the individual protein molecules. Given the structures of the subunits, it is possible to predict plausible complex models via computational methods such as molecular docking. Assessing the quality of the predicted models is crucial to obtain correct complex structures. Here, an energy-scoring function was developed based on the interfacial residues of structures in the Protein Data Bank. The statistically derived energy function (Nepre) imitates the neighborhood preferences of amino acids, including the types and relative positions of neighboring residues. Based on the preference statistics, a program iNepre was implemented and its performance was evaluated with several benchmarking decoy data sets. The results show that iNepre scores are powerful in model ranking to select the best protein complex structures.

Omics and Remote Homology Integration to Decipher Protein Functionality

In the recent years, several "omics" technologies based on specific biomolecules (from DNA, RNA, proteins, or metabolites) have won growing importance in the scientific field. Despite each omics possess their own laboratorial protocols, they share a background of bioinformatic tools for data integration and analysis. A recent subset of bioinformatic tools, based on available templates or remote homology protocols, allow computational fast and high-accuracy prediction of protein structures. The quickly predict of actually unsolved protein structures, together with late omics findings allow a boost of scientific advances in multiple fields such as cancer, longevity, immunity, mitochondrial function, toxicology, drug design, biosensors, and recombinant protein engineering. In this chapter, we assessed methodological approaches for the integration of omics and remote homology inferences to decipher protein functionality, opening the door to the next era of biological knowledge.

Label-free proteomic analysis reveals an estrous cycle transition between the follicular phase and the luteal phase in yak ovary

In order to further study the physiological mechanism about yak reproduction for the improvement of its fertility, differentially expressed proteins (DEGs) of the ovary were screened during the follicular and luteal phases of yak based on label-free quantitative proteomics. The main research results were as follows: it discovered 2867 proteins during the follicular phase and 2180 proteins during the luteal phase, among which there were 2011 proteins of expression during both phases, 856 during the follicular phase, and 169 during the luteal phase. According to the bioinformatics and Gene Ontology analysis, the screened differentially expressed proteins were mostly located in the cell membrane and extracellular region and primarily acted on the activity of laminin. Kyoto Encyclopedia of Genes and Genomes analysis indicated that the main involvement of differentially expressed proteins included steroid biosynthesis, chemokine signaling pathway, protein processing in the endoplasmic reticulum, glycerol phospholipid metabolism, carbon metabolism, PI3K-Akt signaling pathways, PPAR signaling pathways, nucleotide oligomerization domain (NOD) receptor signaling pathways, and purine metabolic pathways. Screened proteins related to reproductive function, so as to lay a theoretical foundation for the in-depth study of the regulation mechanism of yak reproduction and improve its fertility.

Quality Estimates for 3D Protein Models

Protein structure modeling is one of the most advanced and complex processes in computational biology. One of the major problems for the protein structure prediction field has been how to estimate the accuracy of the predicted 3D models, on both a local and global level, in the absence of known structures. We must be able to accurately measure the confidence that we have in the quality predicted 3D models of proteins for them to become widely adopted by the general bioscience community. To address this major issue, it was necessary to develop new model quality assessment (MQA) methods and integrate them into our pipelines for building 3D protein models. Our MQA method, called ModFOLD, has been ranked as one of the most accurate MQA tools in independent blind evaluations. This chapter discusses model quality assessment in the protein modeling field, demonstrating both its strengths and limitations. We also present some of the best methods according to independent benchmarking data, which has been gathered in recent years.

Informed Use of 3D-QSAR for the Rational Design of Coumarin Derivatives as Potent and Selective MAO B Inhibitors

The quantitative structure-activity relationship method based on the three-dimensional structure of the target molecules (3D-QSAR) represents a valuable predictive tool for the design of new bioactive agents. Herewith, a detailed procedure is described which uses a pool comprising 67 derivatives substituted at position 4 and 7 of the common coumarin scaffold as a benchmark for deriving a predictive 3D-QSAR model employed for guiding the rational design of 10 new potent and selective MAO B inhibitors.

Exploring microbial functional biodiversity at the protein family level-From metagenomic sequence reads to annotated protein clusters

Metagenomics has enabled accessing the genetic repertoire of natural microbial communities. Metagenome shotgun sequencing has become the method of choice for studying and classifying microorganisms from various environments. To this end, several methods have been developed to process and analyze the sequence data from raw reads to end-products such as predicted protein sequences or families. In this article, we provide a thorough review to simplify such processes and discuss the alternative methodologies that can be followed in order to explore biodiversity at the protein family level. We provide details for analysis tools and we comment on their scalability as well as their advantages and disadvantages. Finally, we report the available data repositories and recommend various approaches for protein family annotation related to phylogenetic distribution, structure prediction and metadata enrichment.

Tetramerization of GH2 β-Glucuronidases is Essential for Catalyzing the Hydrolysis of the Large Substrate Glycyrrhizin

In this study, structural analysis was employed to identify three hotspot residues that contribute most to the tetramer formation of a glycoside hydrolase family 2 (GH2) β-glucuronidase (GUS) from Aspergillus oryzae Li-3. Single-point mutation at these sites completely disrupted the tetramer structure and abolished the glycyrrhizin (GL)-hydrolyzing activity. Then, the W522A dimer was refactored into a tetramer by disulfide bonding, and partial GL activity was restored. Further saturated mutation showed a strong correlation between the GL activity of the mutants and their tetramer ratios. Molecular simulations were employed to illustrate the critical role of the tetramer interface in maintaining a functional active-site structure. The three highly conserved tetramer-forming residues were finally applied to two other GH2 GUSs for tetramer dissociation and demonstrated the significance of the homotetramerization for GL-hydrolyzing activity of GH2 GUSs. This study lays foundation for engineering GL-hydrolyzing GUSs at the quaternary structure level for function regulations.

Synthesis and Evaluation of Novel S-alkyl Phthalimide- and S-benzyl-oxadiazole-quinoline Hybrids as Inhibitors of Monoamine Oxidase and Acetylcholinesterase

New S-alkyl phthalimide 5a-f and S-benzyl 6a-d analogs of 5-(2-phenylquinolin-4-yl)-1,3,4-oxadiazole-2-thiol (4) were prepared by reacting 4 with N-bromoalkylphthalimide and CF₃-substituted benzyl bromides in excellent yields. Spectroscopic techniques were employed to elucidate the structures of the synthesized molecules. The inhibition activity of newly synthesized molecules toward MAO-A, MAO-B, and AChE enzymes, was also assessed. All these compounds showed activity in the submicromolar range against all enzymes. Compounds 5a and 5f were found to be the most potent compounds against MAO-A (IC₅₀ = 0.91 ± 0.15 nM) and MAO-B (IC₅₀ = 0.84 ± 0.06 nM), while compound 5c showed the most efficient acetylcholinesterase inhibition (IC₅₀ = 1.02± 0.65 μM). Docking predictions disclosed the docking poses of the synthesized molecules with all enzymes and demonstrated the outstanding potency of compounds 5a, 5f, and 5c (docking scores = -11.6, -15.3, and -14.0 kcal/mol against MAO-A, MAO-B, and AChE, respectively). These newly synthesized analogs act as up-and-coming candidates for the creation of safer curative use against Alzheimer's illness.

A rational in silico approach to identify inhibitors of Batroxrhagin from Bothrops atrox

Bothrops atrox venom comprises several types of bioactive molecules, enzymatic and non-enzymatic, among those, Batroxrhagin is the most predominant SVMP P-III enzyme, which are responsible for induction of local and systemic hemorrhage and muscle fibers damage, impairing regeneration. Due to great difficulties in establishing an antibothropic drug, new strategies must be addressed to achieve a more effective and efficient treatment. There are no studies of specific catalytic inhibitors of Batroxrhagin. However, there are in vitro studies that have described similar metalloprotease inhibitors. The inhibitor batimastat was used as a leading compound for the search and selection of similar candidates. This molecule is widely cited as a metalloprotease inhibitor and as an antimetastatic. In addition to batimastat-like molecules, four other reported metalloprotease inhibitors were included to compose the study's positive control group. Hence, 580 molecules were tested. The three-dimensional structure of B. atrox Batroxrhagin was predicted based on homologous structures using Modeller 9.20. Molecular docking calculation was performed using Autodock 4.2 and molecular surfaces and interactions were analyzed using Biovia/Discovery Studio 2017. Among 576 molecules, 42 similar to batismast resulted in a better energy of interaction than all positive controls, including batimastat itself. The batimastat-like molecules with lowest energy and positive controls were subjected to molecular dynamics for 30 ns in Gromacs 2019.4. This batimastat-like molecule produced better stability among all the Batroxrhagin-ligand complexes analyzed. Overall, the proposed compounds present justifiable evidence for future in vitro tests aiming to inhibit Batroxrhagin. Communicated by Ramaswamy H. Sarma.

Interactions of Apigenin and Safranal with the 5HT1A and 5HT2A Receptors and Behavioral Effects in Depression and Anxiety: A Molecular Docking, Lipid-Mediated Molecular Dynamics, and In Vivo Analysis

BACKGROUND

The current study utilizes in silico molecular docking/molecular dynamics to evaluate the binding affinity of apigenin and safranal with 5HT1AR/5HT2AR, followed by assessment of in vivo effects of these compounds on depressive and anxious behavior.

METHODS

The docking between apigenin and safranal and the 5HT1A and 5HT2A receptors was performed utilizing AutoDock Vina software, while MD and protein-lipid molecular dynamics simulations were executed by AMBER16 software. For in vivo analysis, healthy control (HC), disease control (DC), fluoxetine-, and apigenin-safranal-treated rats were tested for changes in depression and anxiety using the forced swim test (FST) and the elevated plus-maze test (EPMT), respectively.

RESULTS

The binding affinity estimations identified the superior interacting capacity of apigenin over safranal for 5HT1A/5HT2A receptors over 200 ns MD simulations. Both compounds exhibit oral bioavailability and absorbance. In the rodent model, there was a significant increase in the overall mobility time in the FST, while in the EPMT, there was a decrease in latency and an increase in the number of entries for the treated and HC rats compared with the DC rats, suggesting a reduction in depressive/anxiety symptoms after treatment.

CONCLUSIONS

Our analyses suggest apigenin and safranal as prospective medication options to treat depression and anxiety.

Structural Analysis of the Spike Protein of SARS-CoV-2 Variants and Other Betacoronaviruses Using Molecular Dynamics

A structural analysis over various spike proteins from three highly pathogenic Betacoronavirus was done to understand their structural differences. The proteins were modeled using crystal structures from SARS-CoV, MERS-CoV, and other Betacoronavirus that infect bats and pangolins. The group was split in two sets; the first set corresponds to the non-mutated spike proteins, while the second set corresponds to mutated spike variants alpha, beta, gamma, delta, omicron and mu; five of them classified as variants of concern and the last one as variant of interest. A conformational space exploration was carried out for every protein by using molecular dynamic simulations. Root mean square fluctuations, principal component and cross-correlation analysis were carried out over the dynamics to analyze the flexibility and rigidity of every protein in comparison to the wild type Spike protein from the SARS-CoV-2. The obtained results indicate that the proteins, which are not spread among humans, have smooth movements compared to those of SARS-CoV-2 and its variants. In addition, a relationship between the speed of the virulence and the movement of the protein can explain the behavior of delta and omicron variants.

Naturally occurring plant-based anticancerous candidates as prospective ABCG2 inhibitors: an in silico drug discovery study

ATP-binding cassette transporter G2 (ABCG2) is an efflux transporter related to the clinical multidrug resistance (MDR) phenomenon. Identifying ABCG2 inhibitors could help discover extraordinary curative strategies for carcinoma remediation. Hitherto, there is no medication drug inhibiting ABCG2 transporter, notwithstanding that a considerable number of drugs have been submitted to clinical-trial and investigational phases. In the search for unprecedented chemical compounds that could inhibit the ABCG2 transporter, an in silico screening was conducted on the Naturally Occurring Plant-based Anticancer Compound-Activity-Target (NPACT) database containing 1574 compounds. Inhibitor-ABCG2 binding affinities were estimated based on molecular docking and molecular minimization (MM) calculations and compared to a co-crystallized inhibitor (BWQ) acting as a reference inhibitor. Molecular dynamics (MD) simulations pursued by molecular mechanics-generalized Born surface area (MM-GBSA) binding energy estimations were further executed for compounds with MM-GBSA//MM binding energies lower than BWQ (calc. - 60.5 kcal/mol). NPACT00968 and NPACT01545 demonstrated auspicious inhibitory activities according to binding affinities (ΔGbinding) over the 100 ns MD simulations that were nearly one and a half folds compared to BWQ (- 100.4, - 94.7, and - 62.9 kcal/mol, respectively). Throughout the 100 ns MD simulations, structural and energetical analyses unveiled outstanding stability of the ABCG2 transporter when bound with NPACT00968 and NPACT01545. In silico calculations hold a promise for those two inhibitors as drug candidates of ABCG2 transporter and emphasize that further in vitro and in vivo experiments are guaranteed.

Collagen a1(XI) structure prediction by Alphafold 2

Collagen α1(XI) is a minor fibrillar collagen involved in the critical regulation of collagen fibrils such as nucleation, assembly, and regulation of fibril diameter. The amino propeptide domain of the collagen α1(XI) is retained on the surface of the collagen fibril for an extended period of time and may play a crucial role in the interaction with extracellular matrix glycosaminoglycans and other proteins during the process of fibrillogenesis. Understanding the mechanism of action of this protein will ultimately help us understand the organization and assembly of the extracellular matrix that underlies the structural integrity of connective tissues.

Novel computational pipelines in antiviral structure‑based drug design (Review)

Viral infections constitute a fundamental and continuous challenge for the global scientific and medical community, as highlighted by the ongoing COVID-19 pandemic. In combination with prophylactic vaccines, the development of safe and effective antiviral drugs remains a pressing need for the effective management of rare and common pathogenic viruses. The design of potent antivirals can be informed by the study of the three-dimensional structure of viral protein targets. Structure-based design of antivirals in silico provides a solution to the arduous and costly process of conventional drug development pipelines. Furthermore, rapid advances in high-throughput computing, along with the growth of available biomolecular and biochemical data, enable the development of novel computational pipelines in the hunt of antivirals. The incorporation of modern methods, such as deep-learning and artificial intelligence, has the potential to revolutionize the structure-based design and repurposing of antiviral compounds, with minimal side effects and high efficacy. The present review aims to provide an outline of both traditional computational drug design and emerging, high-level computing strategies.

BiP inactivation due to loss of the deAMPylation function of FICD causes a motor neuron disease

PURPOSE

The chaperone protein BiP is the master regulator of the unfolded protein response in the endoplasmic reticulum. BiP chaperone activity is regulated by the post-translational modification AMPylation, exclusively provided by FICD. We investigated whether FICD variants identified in patients with motor neuron disease could interfere with BiP activity regulation.

METHODS

Exome sequencing was performed to identify causative pathogenic variants associated with motor neuron diseases. Functional studies were conducted on fibroblasts from patients to explore the molecular mechanism of the disease.

RESULTS

We identified biallelic variants in FICD causing a neurodegenerative disease of upper and lower motor neurons. Affected individuals harbor a specific missense variant, Arg374His, positioned in the catalytic motif of the enzyme and important for adenosine triphosphate binding. The mutated residue abolishes intramolecular interaction with the regulatory residue Glu234, essential to inhibit AMPylation and to promote de-AMPylation by FICD. Consequently, fibroblasts from patients with FICD variants have abnormally increased levels of AMPylated and thus inactivated BiP.

CONCLUSION

Loss of BiP chaperone activity in patients likely results in a chronic impairment of the protein quality control system in the endoplasmic reticulum. These findings will guide the development of therapeutic strategies for motoneuron and related diseases linked to proteotoxic stress.

Accurate protein stability predictions from homology models

Calculating changes in protein stability (ΔΔG) has been shown to be central for predicting the consequences of single amino acid substitutions in protein engineering as well as interpretation of genomic variants for disease risk. Structure-based calculations are considered most accurate, however the tools used to calculate ΔΔGs have been developed on experimentally resolved structures. Extending those calculations to homology models based on related proteins would greatly extend their applicability as large parts of e.g. the human proteome are not structurally resolved. In this study we aim to investigate the accuracy of ΔΔG values predicted on homology models compared to crystal structures. Specifically, we identified four proteins with a large number of experimentally tested ΔΔGs and templates for homology modeling across a broad range of sequence identities, and selected three methods for ΔΔG calculations to test. We find that ΔΔG-values predicted from homology models compare equally well to experimental ΔΔGs as those predicted on experimentally established crystal structures, as long as the sequence identity of the model template to the target protein is at least 40%. In particular, the Rosetta cartesian_ddg protocol is robust against the small perturbations in the structure which homology modeling introduces. In an independent assessment, we observe a similar trend when using ΔΔGs to categorize variants as low or wild-type-like abundance. Overall, our results show that stability calculations performed on homology models can substitute for those on crystal structures with acceptable accuracy as long as the model is built on a template with sequence identity of at least 40% to the target protein.

Establishment of anti-DKK3 peptide for the cancer control in head and neck squamous cell carcinoma (HNSCC)

Background

Head and neck squamous cell carcinoma (HNSCC) is the most common malignant tumor of the head and neck. We identified cancer-specific genes in HNSCC and focused on DKK3 expression. DKK3 gene codes two isoforms of proteins (secreted and non-secreted) with two distinct cysteine rich domains (CRDs). It is reported that DKK3 functions as a negative regulator of oncogenic Wnt signaling and, is therefore, considered to be a tumor suppressor gene. However, our series of studies have demonstrated that DKK3 expression is specifically high in HNSCC tissues and cells, and that DKK3 might determine the malignant potentials of HNSCC cells via the activation of Akt. Further analyses strongly suggested that both secreted DKK3 and non-secreted DKK3 could activate Akt signaling in discrete ways, and consequently exert tumor promoting effects. We hypothesized that DKK3 might be a specific druggable target, and it is necessary to establish a DKK3 inhibitor that can inhibit both secreted and non-secreted isoforms of DKK3.

Methods

Using inverse polymerase chain reaction, we generated mutant expression plasmids that express DKK3 without CRD1, CRD2, or both CRD1 and CRD2 (DKK3ΔC1, DKK3ΔC2, and DKK3ΔC1ΔC2, respectively). These plasmids were then transfected into HNSCC-derived cells to determine the domain responsible for DKK3-mediated Akt activation. We designed antisense peptides using the MIMETEC program, targeting DKK3-specific amino acid sequences within CRD1 and CRD2. The structural models for peptides and DKK3 were generated using Raptor X, and then a docking simulation was performed using CluPro2. Afterward, the best set of the peptides was applied into HNSCC-derived cells, and the effects on Akt phosphorylation, cellular proliferation, invasion, and migration were assessed. We also investigated the therapeutic effects of the peptides in the xenograft models.

Results

Transfection of mutant expression plasmids and subsequent functional analyses revealed that it is necessary to delete both CRD1 and CRD2 to inhibit Akt activation and inhibition of proliferation, migration, and invasion. The inhibitory peptides for CRD1 and CRD2 of DKK3 significantly reduced the phosphorylation of Akt, and consequently suppressed cellular proliferation, migration, invasion and in vivo tumor growth at very low doses.

Conclusions

This inhibitory peptide represents a promising new therapeutic strategy for HNSCC treatment.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12935-022-02783-9.

Machine learning based personalized drug response prediction for lung cancer patients

Lung cancers with a mutated epidermal growth factor receptor (EGFR) are a major contributor to cancer fatalities globally. Targeted tyrosine kinase inhibitors (TKIs) have been developed against EGFR and show encouraging results for survival rate and quality of life. However, drug resistance may affect treatment plans and treatment efficacy may be lost after about a year. Predicting the response to EGFR-TKIs for EGFR-mutated lung cancer patients is a key research area. In this study, we propose a personalized drug response prediction model (PDRP), based on molecular dynamics simulations and machine learning, to predict the response of first generation FDA-approved small molecule EGFR-TKIs, Gefitinib/Erlotinib, in lung cancer patients. The patient's mutation status is taken into consideration in molecular dynamics (MD) simulation. Each patient's unique mutation status was modeled considering MD simulation to extract molecular-level geometric features. Moreover, additional clinical features were incorporated into machine learning model for drug response prediction. The complete feature set includes demographic and clinical information (DCI), geometrical properties of the drug-target binding site, and the binding free energy of the drug-target complex from the MD simulation. PDRP incorporates an XGBoost classifier, which achieves state-of-the-art performance with 97.5% accuracy, 93% recall, 96.5% precision, and 94% F1-score, for a 4-class drug response prediction task. We found that modeling the geometry of the binding pocket combined with binding free energy is a good predictor for drug response. However, we observed that clinical information had a little impact on the performance of the model. The proposed model could be tested on other types of cancers. We believe PDRP will support the planning of effective treatment regimes based on clinical-genomic information. The source code and related files are available on GitHub at:   https://github.com/rizwanqureshi123/PDRP/ .

Multi-state modeling of G-protein coupled receptors at experimental accuracy

The family of G-protein coupled receptors (GPCRs) is one of the largest protein families in the human genome. GPCRs transduct chemical signals from extracellular to intracellular regions via a conformational switch between active and inactive states upon ligand binding. While experimental structures of GPCRs remain limited, high-accuracy computational predictions are now possible with AlphaFold2. However, AlphaFold2 only predicts one state and is biased toward either the active or inactive conformation depending on the GPCR class. Here, a multi-state prediction protocol is introduced that extends AlphaFold2 to predict either active or inactive states at very high accuracy using state-annotated templated GPCR databases. The predicted models accurately capture the main structural changes upon activation of the GPCR at the atomic level. For most of the benchmarked GPCRs (10 out of 15), models in the active and inactive states were closer to their corresponding activation state structures. Median RMSDs of the transmembrane regions were 1.12 Å and 1.41 Å for the active and inactive state models, respectively. The models were more suitable for protein-ligand docking than the original AlphaFold2 models and template-based models. Finally, our prediction protocol predicted accurate GPCR structures and GPCR-peptide complex structures in GPCR Dock 2021, a blind GPCR-ligand complex modeling competition. We expect that high accuracy GPCR models in both activation states will promote understanding in GPCR activation mechanisms and drug discovery for GPCRs. At the time, the new protocol paves the way towards capturing the dynamics of proteins at high-accuracy via machine-learning methods.

HDOCK update for modeling protein-RNA/DNA complex structures

Protein-nucleic acid interactions are involved in various cellular processes. Therefore, determining the structures of protein-nucleic acid complexes can provide insights into the mechanisms of the interactions and thus guide the rational drug design to modulate these interactions. Due to the high cost and technical difficulties of solving complex structures experimentally, computational modeling such as molecular docking has been playing an important role in the study of molecular interactions. In order to make it easier for researchers to obtain biomolecular complex structures through molecular docking, we developed the HDOCK server for protein-protein and protein-RNA/DNA docking (accessed at http://hdock.phys.hust.edu.cn/). Since its first release in 2017, HDOCK has been widely used in the scientific community. As nucleic acids may include single-stranded (ss) RNA/DNA and double-stranded (ds) RNA/DNA, we now present an updated version of HDOCK, which offers new options for structural modeling of ssRNA, ssDNA, dsRNA, and dsDNA. We hope this update will better help the scientific community solve important biological problems, thereby advancing the field. In this article, we describe the general protocol of HDOCK with emphasis on the new functions on RNA/DNA modeling. Several application examples are also given to illustrate the usage of the new functions.

In Silico Characterization of African Swine Fever Virus Nucleoprotein p10 Interaction with DNA

African swine fever virus (ASFV) is the etiological agent of a highly contagious, hemorrhagic infectious swine disease, with a tremendous sanitary and economic impact on a global scale. Currently, there are no globally available vaccines or treatments. The p10 protein, a structural nucleoprotein encoded by ASFV, has been previously described as capable of binding double-stranded DNA (dsDNA), which may have implications for viral replication. However, the molecular mechanism that governs this interaction is still unknown, mostly due to the lack of a structural model for this protein. In this work, we have generated an ab initio model of the p10 protein and performed extensive structural characterization, using molecular dynamics simulations to identify the motifs and residues regulating DNA recognition. The helix-turn-helix motif identified at the C-terminal region of the protein was shown to be crucial to the dsDNA-binding efficiency. As with other DNA-binding proteins, two distinct serine and lysine-rich regions found in the two helices were identified as key players in the binding to DNA, whose importance was later validated using experimental binding assays. Altogether, these findings may contribute to a better understanding of the p10 function in ASFV replication.

Molecular insights into the binding of carnosine and anserine to human serum carnosinase 1 (CN1)

Carnosine (CAR) and anserine (ANS) are histidine-containing dipeptides that show the therapeutic properties and protective abilities against diabetes and cognitive deficit. Both dipeptides are rich in meat products and have been used as a supplement. However, in humans, both compounds have a short half-life due to the rapid degradation by dizinc carnosinase 1 (CN1) which is a hurdle for its therapeutic application. To date, a comparative study of carnosine- and anserine-CN1 complexes is limited. Thus, in this work, molecular dynamics (MD) simulations were performed to explore the binding of carnosine and anserine to CN1. CN1 comprises 2 chains (Chains A and B). Both monomers are found to work independently and alternatingly. The displacement of Zn2+ pair is found to disrupt the substrate binding. CN1 employs residues from the neighbour chain (H235, T335, and T337) to form the active site. This highlights the importance of a dimer for enzymatic activity. Anserine is more resistant to CN 1 than carnosine because of its bulky and dehydrated imidazole moiety. Although both dipeptides can direct the peptide oxygen to the active Zn2+ which can facilitate the catalytic reaction, the bulky methylated imidazole on anserine promotes various poses that can retard the hydrolytic activity in contrast to carnosine. Anserine is likely to be the temporary competitive inhibitor by retarding the carnosine catabolism.

Rab7 Investigation Insights into the Existence of White Spot Syndrome Virus in Crustaceans: An In Silico Approach

In this study, previously published Rab7 sequences from National Center for Biotechnology Information (NCBI) have been investigated from chordates, mollusks, annelids, cnidarians, amphibians, priapulids, brachiopods, and arthropods including decapods and other groups. Among decapod crustacean isolates, amino acid variations were found in 13 locations. Penaeid shrimps had variations in positions 13 (I ⟶ J), 22 (T ⟶ A), 124 (G ⟶ X), and 149 (V ⟶ X) while interestingly the freshwater prawn and mitten crab both had amino acid substitutions in positions 87 (V ⟶ C) and 95 (T ⟶ S) along with the other disagreements in amino acid positions 178 (S ⟶ N), 201 (D ⟶ E), 181 (E ⟶ D), 182 (L ⟶ I), 183 (Y ⟶ G), 184 (N ⟶ H), and 198 (A ⟶ T). Among 100 isolates of Rab7 from organisms of various phyla, mutations were observed in several positions. These mutations caused variations in hydrophobicity and isoelectric point which impact the ligand-protein binding affinity. Some common mutations were found in the organisms of the same phylum and among different phyla. Homology modeling of Rab7 proteins from different organisms was done using SWISS-MODEL and validated further by developing Ramachandran plots. Protein-protein docking showed that active residues were there in the binding interfaces of Rab7 from organisms of seven different phyla and VP28 of WSSV. Similarities were observed in the Rab7-VP28 complexes in those selected organisms which differed from the Rab7-VP28 complex in the case of Penaeid shrimp. The findings of this study suggest that WSSV may exist in different marine organisms that have Rab7 protein and transmit to crustaceans like shrimps and crabs which are of commercial importance.

Reduction of stress responses in honey bees by synthetic ligands targeting an allatostatin receptor

Honey bees are of great economic and ecological importance, but are facing multiple stressors that can jeopardize their pollination efficiency and survival. Therefore, understanding the physiological bases of their stress response may help defining treatments to improve their resilience. We took an original approach to design molecules with this objective. We took advantage of the previous identified neuropeptide allatostatin A (ASTA) and its receptor (ASTA-R) as likely mediators of the honey bee response to a biologically relevant stressor, exposure to an alarm pheromone compound. A first series of ASTA-R ligands were identified through in silico screening using a homology 3D model of the receptor and in vitro binding experiments. One of these (A8) proved also efficient in vivo, as it could counteract two behavioral effects of pheromone exposure, albeit only in the millimolar range. This putative antagonist was used as a template for the chemical synthesis of a second generation of potential ligands. Among these, two compounds showed improved efficiency in vivo (in the micromolar range) as compared to A8 despite no major improvement in their affinity for the receptor in vitro. These new ligands are thus promising candidates for alleviating stress in honey bees.

Species Differences in Response to Binding Interactions of Bisphenol A and its Analogs with the Modeled Estrogen Receptor 1 and In Vitro Reporter Gene Assay in Human and Zebrafish

Adverse impacts associated with the interactions of numerous endocrine-disruptor chemicals (EDCs) with estrogen receptor 1 play a pivotal role in reproductive dysfunction. The predictive studies on these interactions thus are crucial in the risk assessment of EDCs but rely heavily on the accuracy of specific protein structure in three dimensions. As the three-dimensional (3D) structure of zebrafish estrogen receptor 1 (zEsr1) is not available, the 3D structure of zEsr1 ligand-binding domain (zEsr1-LBD) was generated using MODELLER and its quality was assessed by the PROCHECK, ERRAT, ProSA, and Verify-3D tools. After the generated model was verified as reliable, bisphenol A and its analogs were docked on the zEsr1-LBD and human estrogen receptor 1 ligand-binding domain (hESR1-LBD) using the Discovery Studio and Autodock Vina programs. The molecular dynamics followed by molecular docking were simulated using the Nanoscale Molecular Dynamics program and compared to those of the in vitro reporter gene assays. Some chemicals were bound with an orientation similar to that of 17β-estradiol in both models and in silico binding energies showed moderate or high correlations with in vitro results (0.33 ≤ r²  ≤ 0.71). Notably, hydrogen bond occupancy during molecular dynamics simulations exhibited a high correlation with in vitro results (r²  ≥ 0.81) in both complexes. These results show that the combined in silico and in vitro approaches is a valuable tool for identifying EDCs in different species, facilitating the assessment of EDC-induced reproductive toxicity. Environ Toxicol Chem 2022;41:2431-2443. © 2022 SETAC.

Redβ177 annealase structure reveals details of oligomerization and λ Red-mediated homologous DNA recombination

The Redβ protein of the bacteriophage λ red recombination system is a model annealase which catalyzes single-strand annealing homologous DNA recombination. Here we present the structure of a helical oligomeric annealing intermediate of Redβ, consisting of N-terminal residues 1-177 bound to two complementary 27mer oligonucleotides, determined via cryogenic electron microscopy (cryo-EM) to a final resolution of 3.3 Å. The structure reveals a continuous binding groove which positions and stabilizes complementary DNA strands in a planar orientation to facilitate base pairing via a network of hydrogen bonding. Definition of the inter-subunit interface provides a structural basis for the propensity of Redβ to oligomerize into functionally significant long helical filaments, a trait shared by most annealases. Our cryo-EM structure and molecular dynamics simulations suggest that residues 133-138 form a flexible loop which modulates access to the binding groove. More than half a century after its discovery, this combination of structural and computational observations has allowed us to propose molecular mechanisms for the actions of the model annealase Redβ, a defining member of the Redβ/RecT protein family.

Redβ annealase catalyses single-strand annealing homologous DNA recombination. Here, the authors present a cryo-EM structure of a Redβ annealing intermediate bound to two complementary 27mer oligonucleotides.

In silico design of refined ferritin-SARS-CoV-2 glyco-RBD nanoparticle vaccine

With the onset of Coronavirus disease 2019 (COVID-19) pandemic, all attention was drawn to finding solutions to cure the coronavirus disease. Among all vaccination strategies, the nanoparticle vaccine has been shown to stimulate the immune system and provide optimal immunity to the virus in a single dose. Ferritin is a reliable self-assembled nanoparticle platform for vaccine production that has already been used in experimental studies. Furthermore, glycosylation plays a crucial role in the design of antibodies and vaccines and is an essential element in developing effective subunit vaccines. In this computational study, ferritin nanoparticles and glycosylation, which are two unique facets of vaccine design, were used to model improved nanoparticle vaccines for the first time. In this regard, molecular modeling and molecular dynamics simulation were carried out to construct three atomistic models of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) receptor binding domain (RBD)-ferritin nanoparticle vaccine, including unglycosylated, glycosylated, and modified with additional O-glycans at the ferritin–RBD interface. It was shown that the ferritin–RBD complex becomes more stable when glycans are added to the ferritin–RBD interface and optimal performance of this nanoparticle can be achieved. If validated experimentally, these findings could improve the design of nanoparticles against all microbial infections.

Site directed mutagenesis of Catharanthus roseus (+)-vincadifformine 19-hydroxylase (CYP71BY3) results in two distinct enzymatic functions

The most abundant monoterpenoid indole alkaloids (MIAs) in Catharanthus roseus roots include lochnericine and (+)-echitovenine. The formation of (+)-echitovenine involves a 3-step pathway including (+)-vincadifformine-19-hydroxylase (V19H) that differentiates it from a parallel pathway involved in the formation of lochnericine, hörhammericine and its O-acetylated derivative. Homology based modeling and docking experiments in the present study show that (+) and (-) vincadifformine can occupy the V19H active site and is proven experimentally by showing that (-)-vincadifformine is a competitive inhibitor of V19H. Comparative modeling of V19H with tabersonine 3-oxidase (T3O) and tabersonine 19-hydroxylase (T19H) that accept (-)-aspidosperma MIAs identified four conserved amino acid residues in T3O and T19H that were different in the V19H binding site and were used to generate a series of single-, double-, or four-point mutations in V19H. While all mutants retained their ability to convert (+)-vincadifformine to (+)-minovincinine only the four-point mutant gained T3O activity enabling it to convert (-)-tabersonine to tabersonine 2,3-epoxide. The gain of T3O-like activity following mutagenesis without the loss of V19H activity supports the hypothesis that V19H shares a common ancestor to T3O which is involved in vindoline biosynthesis in C. roseus leaves.

5 ALA Is a Potent Lactate Dehydrogenase Inhibitor But Not a Substrate: Implications for Cell Glycolysis and New Avenues in 5 ALA-Mediated Anticancer Action

Simple Summary

In the present work, we found that 5-ALA, a natural precursor of heme, can hinder cell glycolysis, which is the main path of energy production for most cancer cells. More specifically, we found that 5-ALA can block an enzyme involved in glycolysis, called lactate dehydrogenase (LDH). We found that 5-ALA has a potency of LDH inhibition comparable to other established LDH inhibitors, such as oxamate or tartronic acid. Nevertheless, 5-ALA has a high accumulation rate in cancers and specifically in the incurable brain cancer glioblastoma multiforme (GBM), which is an important advantage. In fact, because of its high specificity to GBM, 5-ALA is used in the clinic to accurately guide the resection of the tumours, through the light emission of its photoactive product protoporphyrin IX (PpIX). PpIX is the penultimate step in the heme production. Importantly, we show here that continuous administration of 5-ALA killed GBM cells according to their dependence on glycolysis. We additionally found that 20% of externally administered 5-ALA is engaged in the inhibition of LDH, as when LDH was pre-loaded by another inhibitor, tartronic acid, then the cell production of PpIX from 5-ALA was increased by 20%. Since PpIX is an important drug for photodynamic therapy of cancer (excitation by light of PpIX produces oxygen by-products that can kill cancer cells), we additionally discovered that preloading LDH with its inhibitor tartronic acid before performing 5-ALA PDT increases the cancer cell death by 15%.

Abstract

In a course of metabolic experiments, we determined that the addition of δ-aminolevulinic acid (5-ALA) to a panel of glioblastoma multiforme (GBM) cells caused a steep reduction in their glycolytic activity. This reduction was accompanied by a decrease in adenosine triphosphate (ATP) production from glycolysis. These results suggested that 5-ALA is an inhibitor of glycolysis; due to the structural similarity of 5-ALA to the established lactate dehydrogenase (LDH) inhibitors oxamate (OXM) and tartronate (TART), we initially investigated LDH inhibition by 5-ALA in silico. The modelling revealed that 5-ALA could indeed be a competitive inhibitor of LDH but not a substrate. These theoretical findings were corroborated by enzymatic and cell lysate assays in which 5-ALA was found to confer a potent LDH inhibition comparable to that of OXM and TART. We subsequently evaluated the effect of 5-ALA-induced glycolysis inhibition on the viability of GBM cells with diverse metabolic phenotypes. In the Warburg-type cell lines Ln18 and U87, incubation with 5-ALA elicited profound and irreversible cell death (90–98%) at 10 mM after merely 24 h. In T98G, however, which exhibited both high respiratory and glycolytic rates, LD95 was achieved after 72 h of incubation with 20 mM 5-ALA. We additionally examined the production of the 5-ALA photosensitive metadrug protoporphyrin IX (PpIX), with and without prior LDH inhibition by TART. These studies revealed that ~20% of the 5-ALA taken up by the cells was engaged in LDH inhibition. We subsequently performed 5-ALA photodynamic therapy (PDT) on Ln18 GBM cells, again with and without prior LDH inhibition with TART, and found a PDT outcome enhancement of ~15% upon LDH pre-inhibition. We expect our findings to have a profound impact on contemporary oncology, particularly for the treatment of otherwise incurable brain cancers such as GBM, where the specific accumulation of 5-ALA is very high compared to the surrounding normal tissue.

ABCA1 is an extracellular phospholipid translocase

Production of high density lipoprotein (HDL) requires ATP-binding cassette transporter A1 (ABCA1) to drive phospholipid (PL) from the plasma membrane into extracellular apolipoprotein A-I. Here, we use simulations to show that domains of ABCA1 within the plasma membrane remove PL from the membrane’s outer leaflet. In our simulations, after the lipid diffuses into the interior of ABCA1’s outward-open cavity, PL extracted by the gateway passes through a ring-shaped domain, the annulus orifice, which forms the base of an elongated hydrophobic tunnel in the transporter’s extracellular domain. Engineered mutations in the gateway and annulus strongly inhibit lipid export by ABCA1 without affecting cell-surface expression levels. Our finding that ABCA1 extracts lipid from the outer face of the plasma membrane and forces it through its gateway and annulus into an elongated hydrophobic tunnel contrasts with the alternating access model, which proposes that ABCA1 flops PL substrate from the inner leaflet to the outer leaflet of the membrane. Consistent with our model, ABCA1 lacks the charged amino acid residues in the transmembrane domain found in the floppase members of the ABC transporter family.

ATP-binding cassette transporter A1 (ABCA1) drives phospholipid (PL) from the plasma membrane into extracellular apolipoprotein A-I, for the production of high density lipoprotein (HDL). Here, the authors use simulations to assess the mechanism of ABCA1 function and show that ABCA1 extracts lipid from the outer face of the plasma membrane.

Key Residues Affecting Binding Affinity of Sirex noctilio Fabricius Odorant-Binding Protein (SnocOBP9) to Aggregation Pheromone

Sirex noctilio Fabricius (Hymenoptera Siricidae) is a major quarantine pest responsible for substantial economic losses in the pine industry. To achieve better pest control, (Z)-3-decen-ol was identified as the male pheromone and used as a field chemical trapping agent. However, the interactions between odorant-binding proteins (OBPs) and pheromones are poorly described. In this study, SnocOBP9 had a higher binding affinity with Z3D (Ki = 1.53 ± 0.09 μM) than other chemical ligands. Molecular dynamics simulation and binding mode analysis revealed that several nonpolar residues were the main drivers for hydrophobic interactions between SnocOBP9 and Z3D. Additionally, computational alanine scanning results indicated that five amino acids (MET54, PHE57, PHE71, PHE74, LEU116) in SnocOBP9 could potentially alter the binding affinity to Z3D. Finally, we used single-site-directed mutagenesis to substitute these five residues with alanine. These results imply that the five residues play crucial roles in the SnocOBP9-Z3D complex. Our research confirmed the function of SnocOBP9, uncovered the key residues involved in SnocOBP9-Z3D interactions, and provides an inspiration to improve the effects of pheromone agent traps.

To what extent naringenin binding and membrane depolarization shape mitoBK channel gating—A machine learning approach

The large conductance voltage- and Ca2+-activated K+ channels from the inner mitochondrial membrane (mitoBK) are modulated by a number of factors. Among them flavanones, including naringenin (Nar), arise as a promising group of mitoBK channel regulators from a pharmacological point of view. It is well known that in the presence of Nar the open state probability (pop) of mitoBK channels significantly increases. Nevertheless, the molecular mechanism of the mitoBK-Nar interactions remains still unrevealed. It is also not known whether the effects of naringenin administration on conformational dynamics can resemble those which are exerted by the other channel-activating stimuli. In aim to answer this question, we examine whether the dwell-time series of mitoBK channels which were obtained at different voltages and Nar concentrations (yet allowing to reach comparable pops) are discernible by means of artificial intelligence methods, including k-NN and shapelet learning. The obtained results suggest that the structural complexity of the gating dynamics is shaped both by the interaction of channel gate with the voltage sensor (VSD) and the Nar-binding site. For a majority of data one can observe stimulus-specific patterns of channel gating. Shapelet algorithm allows to obtain better prediction accuracy in most cases. Probably, because it takes into account the complexity of local features of a given signal. About 30% of the analyzed time series do not sufficiently differ to unambiguously distinguish them from each other, which can be interpreted in terms of the existence of the common features of mitoBK channel gating regardless of the type of activating stimulus. There exist long-range mutual interactions between VSD and the Nar-coordination site that are responsible for higher levels of Nar-activation (Δpop) at deeply depolarized membranes. These intra-sensor interactions are anticipated to have an allosteric nature.

Structural and functional studies of serine acetyltransferase isoform from Entamoeba histolytica reveals novel role of the C-terminal tail in loss of regulation from feedback inhibition

Serine acetyltransferase (SAT) catalyzes the acetylation of l-serine in the first step of the two-step pathway to synthesize L-cysteine in bacteria, protozoans and plants. L-cysteine is known to be involved in feedback regulation of SAT. However, in E. histolytica, SAT exists in three isoforms where third isoform SAT3 is nearly insensitive to feedback inhibition. Here, we explored the previously unknown precise mechanism of the insensitivity of EhSAT3 to L-cysteine. The C-terminal deletion mutants of EhSAT3 were inhibited completely by L-cysteine in contrast to the wildtype EhSAT3. The crystal structure of EhSAT3ΔC22 in complex with cysteine revealed that C-terminal region swaps over the neighboring monomer in the trimer. This structure combined with the modeled C-terminal residues suggests that EhSAT3 C-terminal end interacts with the active site and play crucial role in feedback inhibition. The interacting distances between sulfur of cysteine and protein indicate cysteine is in deprotonated (S^-) state, thus making stronger interactions than serine. In the full length SAT3, C-terminal tail provides an acidic environment at the active site pocket, so that cysteine can't be deprotonated and bind strongly at the active site. These results conveyed a unique role of the C-terminal region of EhSAT3 in regulating the feedback inhibition.

Evaluating the accuracy of the AMBER protein force fields in modeling dihydrofolate reductase structures: misbalance in the conformational arrangements of the flexible loop domains

Protein flexible loop regions were once thought to be simple linkers between other more functional secondary structural elements. However, as it becomes clearer that these loop domains are critical players in a plethora of biological processes, accurate conformational sampling of 3D loop structures is vital to the advancement of drug design techniques and the overall growth of knowledge surrounding molecular systems. While experimental techniques provide a wealth of structural information, the resolution of flexible loop domains is sometimes low or entirely absent due to their complex and dynamic nature. This highlights an opportunity for de novo structure prediction using in silico methods with molecular dynamics (MDs). This study evaluates some of the AMBER protein force field's (ffs) ability to accurately model dihydrofolate reductase (DHFR) conformations, a protein complex characterized by specific arrangements and interactions of multiple flexible loops whose conformations are determined by the presence or absence of bound ligands and cofactors. Although the AMBER ffs, including ff19SB, studied well model most protein structures with rich secondary structure, results obtained here suggest the inability to significantly sample the expected DHFR loop-loop conformations - of the six distinct protein-ligand systems simulated, a majority lacked consistent stabilization of experimentally derived metrics definitive the three enzyme conformations. Although under-sampling and the chosen ff parameter combinations could be the cause, given past successes with these MD approaches for many protein systems, this suggests a potential misbalance in available ff parameters required to accurately predict the structure of multiple flexible loop regions present in proteins.Communicated by Ramaswamy H. Sarma.

A plausible contributor to multiple sclerosis; presentation of antigenic myelin protein epitopes by major histocompatibility complexes

BACKGROUND

Multiple sclerosis (MS) can be induced upon successful presentation of myelin antigens by MHC I/II. Antigenic similarity between the myelin and viral proteins may worsen the immunological responses.

METHODOLOGY

Antigenic regions within myelin proteins; PLP1, MBP, MOG, and MAG were analyzed using SVMTrip and EMBOSS. Homology search identified sequence similarity between the predicted host epitopes and viral proteins. NetMHCpan predicted MHC I/II binding followed by peptide-protein docking through the HPEPDOCK server. Thereafter we analyzed conformational flexibility and stability of 15 protein-peptide complexes based on high docking scores. The binding free energy was calculated using conventional (MD) and Gaussian accelerated molecular dynamics simulation.

RESULTS

PLP1, MBP, MAG and MOG contained numerous antigenic epitopes. MBP and MOG epitopes had sequence similarity to HHV-6 BALF5; EBNA1 and CMV glycoprotein M (gM), and EBV LMP2B, gp350/220; HHV-8 ORFs respectively. Many herpes virus proteins like tegument, envelope glycoproteins, and ORFs of EBV, CMV, HHV-6, and HHV-8 demonstrated sequence similarity with MAG and PLP1. Some antigenic peptides were also linear B-cell epitopes and influenced cytokine production by T-cell. MHC I allele HLA-B*57:01 bound to PLP1 peptide and HLA-A*68:02 bound to a MAG peptide strongly. MHC II alleles HLA-DRB1*04:05 and HLA-DR1*01:01 associated with MAG- and MOG-derived peptides, respectively, demonstrating high HPEPDOCK scores. MD simulations established stable binding of certain peptides with the MHC namely HLA-B*51:01-MBP(DYKSAHKGFKGVDAQGTLSKIFKL), HLA-B*57:01-PLP1(PDKFVGITYALTVVWLLVFACSAVPVYIYF), HLA-DR1*01:01-MOG(VEDPFYWVSPGVLVLLAVLPVLLLQITVGLVFLCLQYR) and HLA-DRB1*04:05-MAG(TWVQVSLLHFVPTREA).

CONCLUSIONS

Cross-reactivity between self-antigens and pathogen derived immunodominant epitopes may induce MS. Our study supported the role of specific MHC alleles as a contributing MS risk factor.

Benchmarking the Accuracy of AlphaFold 2 in Loop Structure Prediction

The inhibition of protein-protein interactions is a growing strategy in drug development. In addition to structured regions, many protein loop regions are involved in protein-protein interactions and thus have been identified as potential drug targets. To effectively target such regions, protein structure is critical. Loop structure prediction is a challenging subgroup in the field of protein structure prediction because of the reduced level of conservation in protein sequences compared to the secondary structure elements. AlphaFold 2 has been suggested to be one of the greatest achievements in the field of protein structure prediction. The AlphaFold 2 predicted protein structures near the X-ray resolution in the Critical Assessment of protein Structure Prediction (CASP 14) competition in 2020. The purpose of this work is to survey the performance of AlphaFold 2 in specifically predicting protein loop regions. We have constructed an independent dataset of 31,650 loop regions from 2613 proteins (deposited after the AlphaFold 2 was trained) with both experimentally determined structures and AlphaFold 2 predicted structures. With extensive evaluation using our dataset, the results indicate that AlphaFold 2 is a good predictor of the structure of loop regions, especially for short loop regions. Loops less than 10 residues in length have an average Root Mean Square Deviation (RMSD) of 0.33 Å and an average the Template Modeling score (TM-score) of 0.82. However, we see that as the number of residues in a given loop increases, the accuracy of AlphaFold 2's prediction decreases. Loops more than 20 residues in length have an average RMSD of 2.04 Å and an average TM-score of 0.55. Such a correlation between accuracy and length of the loop is directly linked to the increase in flexibility. Moreover, AlphaFold 2 does slightly over-predict α-helices and β-strands in proteins.

Mechanism Study of Proteins under Membrane Environment

Membrane proteins play crucial roles in various physiological processes, including molecule transport across membranes, cell communication, and signal transduction. Approximately 60% of known drug targets are membrane proteins. There is a significant need to deeply understand the working mechanism of membrane proteins in detail, which is a challenging work due to the lack of available membrane structures and their large spatial scale. Membrane proteins carry out vital physiological functions through conformational changes. In the current study, we utilized a coarse-grained (CG) model to investigate three representative membrane protein systems: the TMEM16A channel, the family C GPCRs mGlu2 receptor, and the P4-ATPase phospholipid transporter. We constructed the reaction pathway of conformational changes between the two-end structures. Energy profiles and energy barriers were calculated. These data could provide reasonable explanations for TMEM16A activation, the mGlu2 receptor activation process, and P4-ATPase phospholipid transport. Although they all belong to the members of membrane proteins, they behave differently in terms of energy. Our work investigated the working mechanism of membrane proteins and could give novel insights into other membrane protein systems of interest.

Machine learning approaches and their applications in drug discovery and design

This review is focused on several machine learning approaches used in chemoinformatics. Machine learning approaches provide tools and algorithms to improve drug discovery. Many physicochemical properties of drugs like toxicity, absorption, drug-drug interaction, carcinogenesis, and distribution have been effectively modeled by QSAR techniques. Machine learning is a subset of artificial intelligence, and this technique has shown tremendous potential in the field of drug discovery. Techniques discussed in this review are capable of modeling non-linear datasets, as well as big data of increasing depth and complexity. Various machine learning-based approaches are being used for drug target prediction, modeling the structure of drug target, binding site prediction, ligand-based similarity searching, de novo designing of ligands with desired properties, developing scoring functions for molecular docking, building QSAR model for biological activity prediction, and prediction of pharmacokinetic and pharmacodynamic properties of ligands. In recent years, these predictive tools and models have achieved good accuracy. By the use of more related input data, relevant parameters, and appropriate algorithms, the accuracy of these predictions can be further improved.