Local Protein Structure Refinement via Molecular Dynamics Simulations with locPREFMD

In silico design of a novel hybrid epitope-based antigen harboring highly exposed immunogenic peptides of BamA, OmpA, and Omp34 against Acinetobacter baumannii

Acinetobacter baumannii, is among the highest priority bacteria according to the WHO categorization which necessitate the exploration of alternative strategies such as vaccination. OmpA, BamA, and Omp34 are assigned as appropriate antigens to serve in vaccine development against this pathogen. Experimentally validated exposed epitopes of OmpA and Omp34 along with selected exposed epitopes predicted by an integrative in silico approach were represented by the barrel domain of BamA as a scaffold. Among the 8 external loops of BamA, 5 loops were replaced with selected loops of OmpA and Omp34. The designed antigen was analyzed regarding the physicochemical properties, antigenicity, epitope retrieval, topology, structure, and safety. BamA is a two-domain OMP with a 16-stranded barrel in which L4, L6, and L7 were the longest loops of BamA in order. The designed antigen consisted of 478 amino acids with antigen probability of 0.7793. The novel antigen was a 16-stranded barrel. No identical 8-meric peptides were found in the human proteome against the designed antigen sequence. The designed construct was safe regarding the allergenicity, toxicity, and human proteome reactivity. The designed antigen could develop higher protection against A. baumannii in comparison to either OmpA, BamA, or Omp34 alone.

Decoding of Salty/Saltiness-Enhancing Peptides Derived from Goose Hemoglobin and the Interaction Mechanism with TMC4 Receptor

Amid the growing concern for health-oriented food choices, salt reduction has received widespread attention, particularly in the exploitation of salt alternatives. Peptides with a saltiness-enhancing effect may provide an alternative method for salt reduction. The objective of this study was to isolate and extract novel peptides with salt-reducing effects by fermenting goose blood using a Lactobacillus plantarum strain. Five potential target peptides were screened by a virtual database prediction and molecular docking. Sensory evaluation and E-tongue analysis showed that five peptides (NEALQRM, GDAVKNLD, HAYNLRVD, PEMHAAFDK, and AEEKQLITGL) were identified as target peptides. Particularly, the results of E-tongue showed that GDAVKNLD can increase the saltiness intensity (2.87 ± 0.02) in the complex system. The sensory evaluation results also indicated an increase in saltiness intensity (46.67 ± 4.67 mmol/L NaCl) after adding GDAVKNLD. The results of molecular dynamics simulation indicated that five peptides have good ability to bind tightly to TMC4 receptor, thereby stimulating it to exert an active effect. And these peptides interacted with the TMC4 receptor via hydrogen bonding, hydrophobic interactions, and electrostatic interactions. This research lays a theoretical foundation for discovering novel salty/saltiness-enhancing peptides and provides meaningful contributions to efforts in salt reduction.

Emergence of two distinct spatial folds in a pair of plant virus proteins encoded by nested genes

Virus genomes may encode overlapping or nested open reading frames that increase their coding capacity. It is not known whether the constraints on spatial structures of the two encoded proteins limit the evolvability of nested genes. We examine the evolution of a pair of proteins, p22 and p19, encoded by nested genes in plant viruses from the genus Tombusvirus. The known structure of p19, a suppressor of RNA silencing, belongs to the RAGNYA fold from the alpha+beta class. The structure of p22, the cell-to-cell movement protein from the 30K family widespread in plant viruses, is predicted with the AlphaFold approach, suggesting a single jelly-roll fold core from the all-beta class, structurally similar to capsid proteins from plant and animal viruses. The nucleotide and codon preferences impose modest constraints on the types of secondary structures encoded in the alternative reading frames, nonetheless allowing for compact, well-ordered folds from different structural classes in two similarly-sized nested proteins. Tombusvirus p22 emerged through radiation of the widespread 30K family, which evolved by duplication of a virus capsid protein early in the evolution of plant viruses, whereas lineage-specific p19 may have emerged by a stepwise increase in the length of the overprinted gene and incremental acquisition of functionally active secondary structure elements by the protein product. This evolution of p19 toward the RAGNYA fold represents one of the first documented examples of protein structure convergence in naturally occurring proteins.

Study on the saltiness-enhancing mechanism of chicken-derived umami peptides by sensory evaluation and molecular docking to transmembrane channel-like protein 4 (TMC4)

The previously obtained chicken-derived umami peptides in the laboratory were evaluated for their saltiness-enhancing effect by sensory evaluation and S-curve, and the results revealed that peptides TPPKID, PKESEKPN, TEDWGR, LPLQDAH, NEFGYSNR, and LPLQD had significant saltiness-enhancing effects. In the binary solution system with salt, the ratio of the experimental detection threshold (129.17 mg/L) to the theoretical detection threshold (274.43 mg/L) of NEFGYSNR was 0.47, which had a synergistic saltiness-enhancing effect with salt. The model of transmembrane channel-like protein 4 (TMC4) channel protein was constructed by homology modeling, which had a 10-fold transmembrane structure and was well evaluated. Molecular docking and frontier molecular orbitals showed that the main active sites of TMC4 were Lys 471, Met 379, Cys 475, Gln 377, and Pro 380, and the main active sites of NEFGYSNR were Tyr, Ser and Asn. This study may provide a theoretical reference for low-sodium diets.

In silico protein function prediction: the rise of machine learning-based approaches

Proteins function as integral actors in essential life processes, rendering the realm of protein research a fundamental domain that possesses the potential to propel advancements in pharmaceuticals and disease investigation. Within the context of protein research, an imperious demand arises to uncover protein functionalities and untangle intricate mechanistic underpinnings. Due to the exorbitant costs and limited throughput inherent in experimental investigations, computational models offer a promising alternative to accelerate protein function annotation. In recent years, protein pre-training models have exhibited noteworthy advancement across multiple prediction tasks. This advancement highlights a notable prospect for effectively tackling the intricate downstream task associated with protein function prediction. In this review, we elucidate the historical evolution and research paradigms of computational methods for predicting protein function. Subsequently, we summarize the progress in protein and molecule representation as well as feature extraction techniques. Furthermore, we assess the performance of machine learning-based algorithms across various objectives in protein function prediction, thereby offering a comprehensive perspective on the progress within this field.

Integrating deep learning, threading alignments, and a multi-MSA strategy for high-quality protein monomer and complex structure prediction in CASP15

We report the results of the "UM-TBM" and "Zheng" groups in CASP15 for protein monomer and complex structure prediction. These prediction sets were obtained using the D-I-TASSER and DMFold-Multimer algorithms, respectively. For monomer structure prediction, D-I-TASSER introduced four new features during CASP15: (i) a multiple sequence alignment (MSA) generation protocol that combines multi-source MSA searching and a structural modeling-based MSA ranker; (ii) attention-network based spatial restraints; (iii) a multi-domain module containing domain partition and arrangement for domain-level templates and spatial restraints; (iv) an optimized I-TASSER-based folding simulation system for full-length model creation guided by a combination of deep learning restraints, threading alignments, and knowledge-based potentials. For 47 free modeling targets in CASP15, the final models predicted by D-I-TASSER showed average TM-score 19% higher than the standard AlphaFold2 program. We thus showed that traditional Monte Carlo-based folding simulations, when appropriately coupled with deep learning algorithms, can generate models with improved accuracy over end-to-end deep learning methods alone. For protein complex structure prediction, DMFold-Multimer generated models by integrating a new MSA generation algorithm (DeepMSA2) with the end-to-end modeling module from AlphaFold2-Multimer. For the 38 complex targets, DMFold-Multimer generated models with an average TM-score of 0.83 and Interface Contact Score of 0.60, both significantly higher than those of competing complex prediction tools. Our analyses on complexes highlighted the critical role played by MSA generating, ranking, and pairing in protein complex structure prediction. We also discuss future room for improvement in the areas of viral protein modeling and complex model ranking.

Structural insights into modeling of hepatitis B virus reverse transcriptase and identification of its inhibitors from potential medicinal plants of Western Ghats: an in silico and in vitro study

The present study was proposed to model full-length HBV-RT and investigate the intermolecular interactions of known inhibitor and libraries of phytocompounds to probe the potential natural leads by in silico and in vitro studies. Homology modeling of RT was performed by Phyre2 and Modeller and virtual screening of ligands implemented through POAP pipeline. Molecular dynamics (MD) simulation (100 ns) and MM-GBSA calculations were performed using Schrodinger Desmond and Prime, respectively. Phytocompounds probable host protein targets gene set pathway enrichment and network analysis were executed by KEGG database and Cytoscape software. Prioritized plant extracts/enriched fraction LC-MS analysis was performed and along with pure compound, RT inhibitory activity, time-dependent HBsAg and HBeAg secretion, and intracellular HBV DNA, and pgRNA by qRT-PCR was performed in HepG2.2.15 cell line. Among the screened chemical library of 268 phytocompounds from 18 medicinal plants, 15 molecules from Terminalia chebula (6), Bidens pilosa (5), and Centella asiatica (4)) were identified as potential inhibitors of YMDD and RT1 motif of HBV-RT. MD simulation demonstrated stable interactions of 15 phytocompounds with HBV-RT, of which 1,2,3,4,6-Pentagalloyl Glucose (PGG) was identified as lead molecule. Out of 15 compounds, 11 were predicted to modulate 39 proteins and 15 molecular pathways associated with HBV infection. TCN and TCW (500 µg/mL) showed potent RT inhibition, decreased intracellular HBV DNA, and pgRNA, and time-dependent inhibition of HBsAg and HBeAg levels compared to PGG and Tenofovir Disoproxil Fumarate. We propose that the identified lead molecules from T. chebula as promising and cost-effective moieties for the management of HBV infection.Communicated by Ramaswamy H. Sarma.

Structural insights into redox signal transduction mechanisms in the control of nitrogen fixation by the NifLA system

NifL is a conformationally dynamic flavoprotein responsible for regulating the activity of the σ54-dependent activator NifA to control the transcription of nitrogen fixation (nif) genes in response to intracellular oxygen, cellular energy, or nitrogen availability. The NifL-NifA two-component system is the master regulatory system for nitrogen fixation. NifL serves as a sensory protein, undergoing signal-dependent conformational changes that modulate its interaction with NifA, forming the NifL-NifA complex, which inhibits NifA activity in conditions unsuitable for nitrogen fixation. While NifL-NifA regulation is well understood, these conformationally flexible proteins have eluded previous attempts at structure determination. In work described here, we advance a structural model of the NifL dimer supported by a combination of scattering techniques and mass spectrometry (MS)-coupled structural analyses that report on the average structure in solution. Using a combination of small angle X-ray scattering-derived electron density maps and MS-coupled surface labeling, we investigate the conformational dynamics responsible for NifL oxygen and energy responses. Our results reveal conformational differences in the structure of NifL under reduced and oxidized conditions that provide the basis for a model for modulating NifLA complex formation in the regulation of nitrogen fixation in response to oxygen in the model diazotroph, Azotobacter vinelandii.

Structure of a superoxide dismutase from a tardigrade: Ramazzottius varieornatus strain YOKOZUNA-1

Superoxide dismutase (SOD) is an essential and ubiquitous antioxidant protein that is widely present in biological systems. The anhydrobiotic tardigrades are some of the toughest micro-animals. They have an expanded set of genes for antioxidant proteins such as SODs. These proteins are thought to play an essential role in oxidative stress resistance in critical situations such as desiccation, although their functions at the molecular level have yet to be explored. Here, crystal structures of a copper/zinc-containing SOD (RvSOD15) from an anhydrobiotic tardigrade, Ramazzottius varieornatus strain YOKOZUNA-1, are reported. In RvSOD15, one of the histidine ligands of the catalytic copper center is replaced by a valine (Val87). The crystal structures of the wild type and the V87H mutant show that even though a histidine is placed at position 87, a nearby flexible loop can destabilize the coordination of His87 to the Cu atom. Model structures of other RvSODs were investigated and it was found that some of them are also unusual SODs, with features such as deletion of the electrostatic loop or β3 sheet and unusual metal-binding residues. These studies show that RvSOD15 and some other RvSODs may have evolved to lose the SOD function, suggesting that gene duplications of antioxidant proteins do not solely explain the high stress tolerance of anhydrobiotic tardigrades.

Orthogonal threading-through-β-sheet design of lung cancer EGFR extracellular domain-derived peptidic mimotopes binding to anti-EGFR antibody

Human epidermal growth factor receptor (EGFR) has been established as a therapeutic target of lung cancer and other diverse tumors. The antibody drug Cetuximab has been developed to target the third subdomain III (TSDIII) of EGFR extracellular domain (ECD) by competitively inhibiting epidermal growth factor binding. In this study, we performed systematic investigation on the crystal complex structure of EGFR ECD domain with Cetuximab to create a residue importance profile for the TSDIII subdomain, based on which a number of U-shaped, double-stranded linear peptides were derived and cyclized to orthogonally thread through most hotspot residues and many responsible residues within the TSDIII β-sheet plane; they represent mimotopes of the key antibody-recognition site of TSDIII subdomain. Computational analyses revealed that these linear peptides cannot spontaneously fold to the desired conformation in free state due to their intrinsic flexibility. Cell-free assays confirmed that the stapling can considerably improve the binding affinity of linear peptides to Cetuximab by up to 18-fold. The cOrt1 [3-18] cyclic peptide was measured to have the highest affinity in all designed linear and cyclic peptides.

Facilitating CG Simulations with MAD: The MArtini Database Server

The MArtini Database (MAD - https://mad.ibcp.fr) is a web server designed for the sharing of structures and topologies of molecules parametrized with the Martini coarse-grained (CG) force field. MAD can also convert atomistic structures into CG structures and prepare complex systems (including proteins, lipids, etc.) for molecular dynamics (MD) simulations at the CG level. It is dedicated to the generation of input files for Martini 3, the most recent version of this popular CG force field. Specifically, the MAD server currently includes tools to submit or retrieve CG models of a wide range of molecules (lipids, carbohydrates, nanoparticles, etc.), transform atomistic protein structures into CG structures and topologies, with fine control on the process and assemble biomolecules into large systems, and deliver all files necessary to start simulations in the GROMACS MD engine.

Structural investigation of human cystine/glutamate antiporter system xc− (Sxc−) using homology modeling and molecular dynamics

The cystine/glutamate antiporter system xc− (Sxc−) belongs to the SLC7 family of plasma membrane transporters. It exports intracellular glutamate along the latter’s concentration gradient as a driving force for cellular uptake of cystine. Once imported, cystine is mainly used for the production of glutathione, a tripeptide thiol crucial in maintenance of redox homeostasis and protection of cells against oxidative stress. Overexpression of Sxc− has been found in several cancer cells, where it is thought to counteract the increased oxidative stress. In addition, Sxc− is important in the central nervous system, playing a complex role in regulating glutamatergic neurotransmission and glutamate toxicity. Accordingly, this transporter is considered a potential target for the treatment of cancer as well as neurodegenerative diseases. Till now, no specific inhibitors are available. We herein present four conformations of Sxc− along its transport pathway, obtained using multi-template homology modeling and refined by means of Molecular Dynamics. Comparison with a very recently released cryo-EM structure revealed an excellent agreement with our inward-open conformation. Intriguingly, our models contain a structured N-terminal domain that is unresolved in the experimental structures and is thought to play a gating role in the transport mechanism of other SLC7 family members. In contrast to the inward-open model, there is no direct experimental counterpart for the other three conformations we obtained, although they are in fair agreement with the other stages of the transport mechanism seen in other SLC7 transporters. Therefore, our models open the prospect for targeting alternative Sxc− conformations in structure-based drug design efforts.

Functional characterization of the p.(Gln195His) or Tainan and novel p.(Ser184Cys) or Toluca glucose-6-phosphate dehydrogenase (G6PD) gene natural variants identified through Mexican newborn screening for glucose-6-phosphate dehydrogenase deficiency

BACKGROUND

Newborn screening for glucose-6-phosphate dehydrogenase deficiency (G6PDd) was implemented in Mexico beginning in 2017. In a Mexican population, genotyping analysis of G6PD as a second-tier method identified a previously unreported missense variant, p.(Ser184Cys), which we propose to call "Toluca", and the extremely rare p.(Gln195His) or "Tainan" variant, which was previously described in the Taiwanese population as a Class II allele through in silico evaluations. Here, we sought to perform in vitro biochemical characterizations of the Toluca and Tainan G6PD natural variants and describe their associated phenotypes.

METHODS

The "Toluca" and "Tainan" variants were identified in three unrelated G6PDd newborn males, two of whom lacked evidence of acute hemolytic anemia (AHA) or neonatal hyperbilirubinemia (NHB). We constructed wild-type (WT), Tainan, and Toluca G6PD recombinant enzymes and performed in vitro assessments.

RESULTS

Both variants had diminished G6PD expression, decreased affinities for glucose-6-phosphate and NADP⁺ substrates, significant decreases in catalytic efficiency (∼97 % with respect to WT-G6PD), and diminished thermostabilities that were partially rescued by NADP⁺. In silico protein modeling predicted that the variants would have destabilizing effects on the protein tertiary structure, potentially reducing the enzyme half-lives and/or catalytic efficiencies.

CONCLUSION

Our data suggest that G6PD "Tainan" and "Toluca" are potential Class II natural variants, which agrees with the absence of chronic nonspherocytic hemolytic anemia (CNSHA) in our patients. It remains to be determined whether these variants represent high-risk genetic factors for developing CNSHA, AHA, and/or NHB.

Tp0684, Tp0750, and Tp0792 Recombinant Proteins as Antigens for the Serodiagnosis of Syphilis

The incidence of syphilis has increased alarmingly over the years. Its diagnosis continues to be a challenge, leading to the search for new alternative and effective methods. The objective of this study was to select and evaluate three Treponema pallidum recombinant proteins for potential use in syphilis serodiagnosis. Bioinformatics analysis was performed with three T. pallidum antigens (Tp0684, Tp0750, and Tp0792) to assess their physical, antigenic, and structural characteristics. The antigens were chemically synthesized, recombinant plasmids were expressed in Escherichia coli BL21 Star™ (DE3), and the recombinant proteins were purified by nickel affinity chromatography. The antigenicity of the recombinant proteins was evaluated by western blotting and enzyme-linked immunosorbent assay (ELISA), using the sera from patients with primary and latent syphilis. In silico analysis indicated the antigenic potential once the exposed B cell epitopes were detected in the evaluated proteins. Sera from patients with primary and latent syphilis specifically recognized rTp0684, rTp0750, and rTp0792 recombinant antigens. Moreover, the rTp0684-ELISA receiver operating characteristic (ROC) analysis showed an area under the ROC curve of 0.99, indicating high diagnostic efficacy with 97.62% specificity and 95% sensitivity. In conclusion, rTp0684 showed better potential as an antigen for the development of syphilis serodiagnosis. Thus, bioinformatic analysis can be an important tool to guide the selection of antigens for serological diagnosis.

Supplementary Information

The online version contains supplementary material available at 10.1007/s12088-022-01017-w.

Structure-Activity Relationship of the Dimeric and Oligomeric Forms of a Cytotoxic Biotherapeutic Based on Diphtheria Toxin

Protein aggregation is a well-recognized problem in industrial preparation, including biotherapeutics. These low-energy states constantly compete with a native-like conformation, which is more pronounced in the case of macromolecules of low stability in the solution. A better understanding of the structure and function of such aggregates is generally required for the more rational development of therapeutic proteins, including single-chain fusion cytotoxins to target specific receptors on cancer cells. Here, we identified and purified such particles as side products of the renaturation process of the single-chain fusion cytotoxin, composed of two diphtheria toxin (DT) domains and interleukin 13 (IL-13), and applied various experimental techniques to comprehensively understand their molecular architecture and function. Importantly, we distinguished soluble purified dimeric and fractionated oligomeric particles from aggregates. The oligomers are polydisperse and multimodal, with a distribution favoring lower and even stoichiometries, suggesting they are composed of dimeric building units. Importantly, all these oligomeric particles and the monomer are cystine-dependent as their innate disulfide bonds have structural and functional roles. Their reduction triggers aggregation. Presumably the dimer and lower oligomers represent the metastable state, retaining the native disulfide bond. Although significantly reduced in contrast to the monomer, they preserve some fraction of bioactivity, manifested by their IL-13RA2 receptor affinity and selective cytotoxic potency towards the U-251 glioblastoma cell line. These molecular assemblies probably preserve structural integrity and native-like fold, at least to some extent. As our study demonstrated, the dimeric and oligomeric cytotoxin may be an exciting model protein, introducing a new understanding of its monomeric counterpart’s molecular characteristics.

Differences in Charge Distribution in Leishmania tarentolae Leishmanolysin Result in a Reduced Enzymatic Activity

Leishmania tarentolae is a non-pathogenic trypanosomatid isolated from lizards widely used for heterologous protein expression and extensively studied to understand the pathogenic mechanisms of leishmaniasis. The repertoire of leishmanolysin genes was reported to be expanded in L. tarentolae genome, but no proteolytic activity was detected. Here, we analyzed L. tarentolae leishmanolysin proteins from the genome to the structural levels and evaluated the enzymatic activity of the wild-type and overexpressing mutants of leishmanolysin. A total of 61 leishmanolysin sequences were retrieved from the L. tarentolae genome. Five of them were selected for phylogenetic analysis, and for three of them, we built 3D models based on the crystallographic structure of L. major ortholog. Molecular dynamics simulations of these models disclosed a less negative electrostatic potential compared to the template. Subsequently, L. major LmjF.10.0460 and L. tarentolae LtaP10.0650 leishmanolysins were cloned in a pLEXSY expression system into L. tarentolae. Proteins from the wild-type and the overexpressing parasites were submitted to enzymatic analysis. Our results revealed that L. tarentolae leishmanolysins harbor a weak enzymatic activity about three times less abundant than L. major leishmanolysin. Our findings strongly suggest that the less negative electrostatic potential of L. tarentolae leishmanolysin can be the reason for the reduced proteolytic activity detected in this parasite.

Molecular characterization of linker and loop-mediated structural modulation and hinge motion in the C4-C5 domains of cMyBPC

INTRODUCTION

The central C4 and C5 domains (C4C5) of cardiac myosin binding protein C (cMyBPC) contain a flexible interdomain linker and a cardiac-isoform specific loop. However, their importance in the functional regulation of cMyBPC has not been extensively studied.

METHODS AND RESULTS

We expressed recombinant C4C5 proteins with deleted linker and loop regions and performed biophysical experiments to determine each of their structural and dynamic roles. We show that the linker and C5 loop regions modulate the secondary structure and thermal stability of C4C5. Furthermore, we provide evidence through extended molecular dynamics simulations and principle component analyses that C4C5 can adopt a completely bent or latched conformation. The simulation trajectory and interaction network analyses reveal that the completely bent conformation of C4C5 exhibits a specific pattern of residue-level interactions. Therefore, we propose a "hinge-and-latch" mechanism where the linker allows a great degree of flexibility and bending, while the loop aids in achieving a completely bent and latched conformation. Although this may be one of many bent positions that C4C5 can adopt, we illustrate for the first time in molecular detail that this type of large scale conformational change can occur in the central domains of cMyBPC.

CONCLUSIONS

Our hinge-and-latch mechanism demonstrates that the linker and loop regions participate in dynamic modulation of cMyBPC's motion and global conformation. These structural and dynamic features may contribute to muscle isoform-specific regulation of actomyosin activity, and have potential implications regarding its ability to propagate or retract cMyBPC's regulatory N-terminal domains.

Identification of Phosphorylation and Other Post-Translational Modifications in the Central C4C5 Domains of Murine Cardiac Myosin Binding Protein C

Cardiac myosin binding protein C (cMyBPC) is a critical multidomain protein that modulates myosin cross bridge behavior and cardiac contractility. cMyBPC is principally regulated by phosphorylation of the residues within the M-domain of its N-terminus. However, not much is known about the phosphorylation or other post-translational modification (PTM) landscape of the central C4C5 domains. In this study, the presence of phosphorylation outside the M-domain was confirmed in vivo using mouse models expressing cMyBPC with nonphosphorylatable serine (S) to alanine substitutions. Purified recombinant mouse C4C5 domain constructs were incubated with 13 different kinases, and samples from the 6 strongest kinases were chosen for mass spectrometry analysis. A total of 26 unique phosphorylated peptides were found, representing 13 different phosphorylation sites including 10 novel sites. Parallel reaction monitoring and subsequent mutagenesis experiments revealed that the S690 site (UniProtKB O70468) was the predominant target of PKA and PKG1. We also report 6 acetylation and 7 ubiquitination sites not previously described in the literature. These PTMs demonstrate the possibility of additional layers of regulation and potential importance of the central domains of cMyBPC in cardiac health and disease. Data are available via ProteomeXchange with identifier PXD031262.

Identification of novel saltiness-enhancing peptides from yeast extract and their mechanism of action for transmembrane channel-like 4 (TMC4) protein through experimental and integrated computational modeling

Excessive consumption of sodium salt is one of the important inducers of cardiovascular and cerebrovascular diseases. The reduction of physical labor and attention to health make research on low-sodium salt imminent. Ultrafiltration, gel filtration, preparative high-performance liquid chromatography, and liquid chromatography with tandem mass spectrometry were employed for further purification and identification of the salty enhancing peptides in yeast extracts. Moreover, human transmembrane channel-like 4 (TMC4) was constructed and evaluated by computer-based methods, and salt-enhancing peptides were identified based on its allosteric sites. PN, NSE, NE and SPE were further determined to be salty enhancing peptides through sensory evaluation, and their taste mechanism was investigated. The results presented here suggest that silicon screening focused on TMC4 allosteric sites and sensory evaluation experiments can greatly increase the discoverability and identifiability of salty enhancer peptides, and this strategy is the first to be applied to the development of salty enhancer peptides.

Common synaptic phenotypes arising from diverse mutations in the human NMDA receptor subunit GluN2A

Dominant mutations in the human gene GRIN2A, encoding NMDA receptor (NMDAR) subunit GluN2A, make a significant and growing contribution to the catalogue of published single-gene epilepsies. Understanding the disease mechanism in these epilepsy patients is complicated by the surprising diversity of effects that the mutations have on NMDARs. Here we have examined the cell-autonomous effect of five GluN2A mutations, 3 loss-of-function and 2 gain-of-function, on evoked NMDAR-mediated synaptic currents (NMDA-EPSCs) in CA1 pyramidal neurons in cultured hippocampal slices. Despite the mutants differing in their functional incorporation at synapses, prolonged NMDA-EPSC current decays (with only marginal changes in charge transfer) were a common effect for both gain- and loss-of-function mutants. Modelling NMDA-EPSCs with mutant properties in a CA1 neuron revealed that the effect of GRIN2A mutations can lead to abnormal temporal integration and spine calcium dynamics during trains of concerted synaptic activity. Investigations beyond establishing the molecular defects of GluN2A mutants are much needed to understand their impact on synaptic transmission.

The cell-autonomous effect of five severe loss- or gain-of-function GluN2A (NMDA receptor) mutations is assessed on evoked NMDAR-mediated synaptic currents in CA1 pyramidal neurons in cultured mouse hippocampal slices. Data and modelling suggest that mutant-like NMDA-EPSCs can lead to abnormal temporal summation and spine calcium dynamics.

In Silico Research of New Therapeutics Rotenoids Derivatives against Leishmania amazonensis Infection

Yearly, 1,500,000 cases of leishmaniasis are diagnosed, causing thousands of deaths. To advance in its therapy, we present an interdisciplinary protocol that unifies ethnobotanical knowledge of natural compounds and the latest bioinformatics advances to respond to an orphan disease such as leishmaniasis and specifically the one caused by Leishmania amazonensis. The use of ethnobotanical information serves as a basis for the development of new drugs, a field in which computer-aided drug design (CADD) has been a revolution. Taking this information from Amazonian communities, located in the area with a high prevalence of this disease, a protocol has been designed to verify new leads. Moreover, a method has been developed that allows the evaluation of lead molecules, and the improvement of their affinity and specificity against therapeutic targets. Through this approach, deguelin has been identified as a good lead to treat the infection due to its potential as an ornithine decarboxylase (ODC) inhibitor, a key enzyme in Leishmania development. Using an in silico-generated combinatorial library followed by docking approaches, we have found deguelin derivatives with better affinity and specificity against ODC than the original compound, suggesting that this approach could be adapted for developing new drugs against leishmaniasis.

In silico analysis and characterization of medicinal mushroom cystathionine beta-synthase as an angiotensin converting enzyme (ACE) inhibitory protein

Angiotensin-converting enzyme (ACE) regulates blood pressure and has been implicated in several conditions including lung injury, fibrosis and Alzheimer's disease. Medicinal mushroom Ganordema lucidum (Reishi) cystathionine beta-synthase (GlCBS) was previously reported to possess ACE inhibitory activities. However, the inhibitory mechanism of CBS protein remains unreported. Therefore, this study integrates in silico sequencing, structural and functional based-analysis, protein modelling, molecular docking and binding affinity calculation to elucidate the inhibitory mechanism of GlCBS and Lignosus rhinocerus (Tiger milk mushroom) CBS protein (LrCBS) towards ACE. In silico analysis indicates that CBSs from both mushrooms share high similarities in terms of physical properties, structural properties and domain distribution. Protein-protein docking analysis revealed that both GlCBS and LrCBS potentially modulate the C-terminal domain of ACE (C-ACE) activity via regulation of chloride activation and/or prevention of substrate entry. GICBS and LrCBS were also shown to interact with ACE at the same region that presumably inhibits the function of ACE.

Homology modelling of Trypanosoma brucei major surface proteases and molecular docking of variant surface glycoproteins and inhibitor ligands for drug design

Trypanosomes, which cause animal African trypanosomiasis, escape host immune responses by renewing their variable surface glycoprotein (VSG) coat. Chemotherapy is currently the only form of external intervention available. However, the efficacy of current trypanocides is poor due to overuse leading to an increase in drug resistance. Major surface proteases (MSPs) of trypanosomes, which are zinc-dependent metalloproteases, are possible drug targets. A Trypanosoma brucei MSP-B (TbMSP-B) mediates parasite antigenic variation via cleavage of 60% of VSG molecules. Whilst TbMSP-A has no apparent role in VSG cleavage; it is not known if TbMSP-C is involved in VSG cleavage. In this study, three-dimensional structures of TbMSP-A, TbMSP-B and TbMSP-C were modelled. By comparing the docking poses of the C-terminal domains of VSG substrates into the models, TbMSP-C showed an affinity for similar VSG substrate sites as TbMSP-B, but these sites differed from those recognised by TbMSP-A. This observation suggests that TbMSP-C may be involved in VSG cleavage during antigenic variation. Furthermore, by docking small inhibitor ligands into the TbMSP-B and TbMSP-C homology models, followed by molecular dynamics simulations, ligands with potential anti-trypanosomal activity were identified. Docking studies also revealed the depth of the S₁' pockets of TbMSP-B and TbMSP-C, which is influential in ligand and substrate binding, thereby identifying the protease subsite pocket that should be targeted in drug design.

Physics-based protein structure refinement in the era of artificial intelligence

Protein structure refinement is the last step in protein structure prediction pipelines. Physics-based refinement via molecular dynamics (MD) simulations has made significant progress during recent years. During CASP14, we tested a new refinement protocol based on an improved sampling strategy via MD simulations. MD simulations were carried out at an elevated temperature (360 K). An optimized use of biasing restraints and the use of multiple starting models led to enhanced sampling. The new protocol generally improved the model quality. In comparison with our previous protocols, the CASP14 protocol showed clear improvements. Our approach was successful with most initial models, many based on deep learning methods. However, we found that our approach was not able to refine machine-learning models from the AlphaFold2 group, often decreasing already high initial qualities. To better understand the role of refinement given new types of models based on machine-learning, a detailed analysis via MD simulations and Markov state modeling is presented here. We continue to find that MD-based refinement has the potential to improve AI predictions. We also identified several practical issues that make it difficult to realize that potential. Increasingly important is the consideration of inter-domain and oligomeric contacts in simulations; the presence of large kinetic barriers in refinement pathways also continues to present challenges. Finally, we provide a perspective on how physics-based refinement could continue to play a role in the future for improving initial predictions based on machine learning-based methods.

The impact of phosphatidylserine exposure on cancer cell membranes on the activity of the anticancer peptide HB43

HB43 (FAKLLAKLAKKLL) is a synthetic peptide active against cell lines derived from breast, colon, melanoma, lung, prostate, and cervical cancers. Despite its remarkable spectrum of activity, the mechanism of action at the molecular level has never been investigated, preventing further optimization of its selectivity. The alternation of charged and hydrophobic residues suggests amphipathicity, but the formation of alpha-helical structure seems discouraged by its short length and the large number of positively charged residues. Using different biophysical and in silico approaches we show that HB43 is completely unstructured in solution but assumes alpha-helical conformation in the presence of DPC micelles and liposomes exposing phosphatidylserine (PS) used as mimics of cancer cell membranes. Membrane permeabilization assays demonstrate that the interaction leads to the preferential destabilization of PS-containing vesicles with respect to PC-containing ones, here used as noncancerous cell mimics. ssNMR reveals that HB43 is able to fluidify the internal structure of cancer-cell mimicking liposomes while MD simulations show its internalization in such bilayers. This is achieved by the formation of specific interactions between the lysine side chains and the carboxylate group of phosphatidylserine and/or the phosphate oxygen atoms of targeted phospholipids, which could catalyze the formation of the alpha helix required for internalization. With the aim of better understanding the peptide biocompatibility and the additional antibacterial activity, the interaction with noncancerous cell mimicking liposomes exposing phosphatidylcholine (PC) and bacterial mimicking bilayers exposing phosphatidylglycerol (PG) is also described.

Revisiting Jatropha curcas Monomeric Esterase: A Dienelactone Hydrolase Compatible with the Electrostatic Catapult Model

Jatropha curcas contains seeds with a high oil content, suitable for biodiesel production. After oil extraction, the remaining mass can be a rich source of enzymes. However, data from the literature describing physicochemical characteristics for a monomeric esterase from the J. curcas seed did not fit the electrostatic catapult model for esterases/lipases. We decided to reevaluate this J. curcas esterase and extend its characterization to check this apparent discrepancy and gain insights into the enzyme's potential as a biocatalyst. After anion exchange chromatography and two-dimensional gel electrophoresis, we identified the enzyme as belonging to the dienelactone hydrolase family, characterized by a cysteine as the nucleophile in the catalytic triad. The enzyme displayed a basic optimum hydrolysis pH of 9.0 and an acidic pI range, in contrast to literature data, making it well in line with the electrostatic catapult model. Furthermore, the enzyme showed low hydrolysis activity in an organic solvent-containing medium (isopropanol, acetonitrile, and ethanol), which reverted when recovering in an aqueous reaction mixture. This enzyme can be a valuable tool for hydrolysis reactions of short-chain esters, useful for pharmaceutical intermediates synthesis, due to both its high hydrolytic rate in basic pH and its stability in an organic solvent.

Cytidine nucleoside analog is an effective antiviral drug against Trichomonasvirus

BACKGROUND

Trichomonas vaginalis is the causative agent of a sexually transmitted disease in humans. The virulence of the parasite depends on multiple factors, including the presence of endosymbiotic dsRNA viruses. The presence of Trichomonasviruses (TVV) was associated with more severe genital symptoms, increased proinflammatory host reactions, and modulated parasite sensitivity to metronidazole. However, no efficient antiviral drugs are available against TVV to derive isogenic TVV-positive and TVV-negative cell lines that are essential for investigations of the TVV impact on T. vaginalis biology.

METHODS

7-Deaza-2'-C-methyladenosine (7d2CMA) and 2'-C-methylcytidine (2CMC) were used for TVV inhibitory assay. TVV replication was monitored using quantitative reverse transcription PCR (RT qPCR) and western blotting. Modeling of TVV1 RNA-dependent RNA polymerase (RdRp) was performed to visualize the inhibitor-RdRp interaction. Susceptibility to metronidazole was performed under aerobic and anaerobic conditions.

RESULTS

We demonstrated that 2CMC but not 7d2CMA is a potent inhibitor of TVV replication. Molecular modeling suggested that the RdRp active site can accommodate 2CMC in the active triphosphate nucleotide form. The effect of 2CMC was shown on strains infected with a single and multiple TVV species. The optimal 2CMC concentration (10 μM) demonstrated strong selectivity for TVVs over trichomonad growth. The presence of TVV has no effect on T. vaginalis metronidazole susceptibility in derived isogenic cell lines.

CONCLUSIONS

2CMC acts against TVVs and represents a new inhibitor against Totiviridae viruses. Our isogenic clones are now available for further studies of various aspects of T. vaginalis biology related to TVV infection.

Identification and In Silico Characterization of Novel Helicobacter pylori Glucose-6-Phosphate Dehydrogenase Inhibitors

Helicobacter pylori (H. pylori) is a pathogen that can remain in the stomach of an infected person for their entire life. As a result, this leads to the development of severe gastric diseases such as gastric cancer. In addition, current therapies have several problems including antibiotics resistance. Therefore, new practical options to eliminate this bacterium, and its induced affections, are required to avoid morbidity and mortality worldwide. One strategy in the search for new drugs is to detect compounds that inhibit a limiting step in a central metabolic pathway of the pathogen of interest. In this work, we tested 55 compounds to gain insights into their possible use as new inhibitory drugs of H. pylori glucose-6-phosphate dehydrogenase (HpG6PD) activity. The compounds YGC-1; MGD-1, MGD-2; TDA-1; and JMM-3 with their respective scaffold 1,3-thiazolidine-2,4-dione; 1H-benzimidazole; 1,3-benzoxazole, morpholine, and biphenylcarbonitrile showed the best inhibitory activity (IC₅₀ = 310, 465, 340, 204 and 304 μM, respectively). We then modeled the HpG6PD protein by homology modeling to conduct an in silico study of the chemical compounds and discovers its possible interactions with the HpG6PD enzyme. We found that compounds can be internalized at the NADP⁺ catalytic binding site. Hence, they probably exert a competitive inhibitory effect with NADP⁺ and a non-competitive or uncompetitive effect with G6P, that of the compounds binding far from the enzyme’s active site. Based on these findings, the tested compounds inhibiting HpG6PD represent promising novel drug candidates against H. pylori.

Pharmacophore-Based Screening & Modification of Amiloride Analogs for targeting the NhaP-type Cation-Proton Antiporter in Vibrio cholerae

Structural and mutational analysis of Vc-NhaP2 identified a putative cation binding pocket formed by antiparallel extended regions of two transmembrane segments (TMSs V/XII) along with TMS VI. Molecular Dynamics (MD) simulations suggested that the flexibility of TMS-V/XII is crucial for the intra-molecular conformational events in Vc-NhaP2. In this study, we developed some putative Vc-NhaP2 inhibitors from Amiloride analogs (AAs). Molecular docking of the modified AAs revealed promising binding. The four selected drugs potentially interacted with functionally important amino acid residues located on the cytoplasmic side of TMS VI, the extended chain region of TMS V and TMS XII and the loop region between TMSs VIIII and IX. Molecular dynamics simulations revealed that binding of the selected drugs can potentially destabilize the Vc-NhaP2 and alters the flexibility of the functionally important TMS VI. The work presents the utility of in silico approaches for the rational identification of potential targets and drugs that could target NhaP2 cation proton antiporter to control Vibrio cholerae. The goal is to identify potential drugs that can be validated in future experiments.

Cytotoxic T-lymphocyte elicited therapeutic vaccine candidate targeting cancer against MAGE-A11 carcinogenic protein

Immunotherapy is a breakthrough approach for cancer treatment and prevention. By exploiting the fact that cancer cells have overexpression of tumor antigens responsible for its growth and progression, which can be identified and removed by boosting the immune system. In silico techniques have provided efficient ways for developing preventive measures to ward off cancer. Herein, we have designed a potent cytotoxic T-lymphocyte epitope to elicit a desirable immune response against carcinogenic melanoma-associated antigen-A11. Potent epitope was predicted using reliable algorithms and characterized by advanced computational avenue CABS molecular dynamics simulation, for full flexible binding with HLA-A*0201 and androgen receptor to large-scale rearrangements of the complex system. Results showed the potent immunogenic construct (KIIDLVHLL), from top epitopes using five algorithms. Molecular docking analyses showed the strong binding of epitope with HLA-A*0201 and androgen receptor with docking score of -780.6 and -641.06 kcal/mol, respectively. Molecular dynamics simulation analysis revealed strong binding of lead epitope with androgen receptor by involvement of 127 elements through atomic-model study. Full flexibility study showed stable binding of epitope with an average root mean square deviation (RMSD) 2.21 Å and maximum RMSD value of 6.48 Å in optimal cluster density area. The epitope also showed remarkable results with radius of gyration 23.0777 Å, world population coverage of 39.08% by immune epitope database, and transporter associated with antigen processing (TAP) affinity IC50 value of 2039.65 nm. Moreover, in silico cloning approach confirmed the expression and translation capacity of the construct within a suitable expression vector. The present study paves way for a potential immunogenic construct for prevention of cancer.

Structure of the dihydrolipoamide succinyltransferase (E2) component of the human alpha-ketoglutarate dehydrogenase complex (hKGDHc) revealed by cryo-EM and cross-linking mass spectrometry: Implications for the overall hKGDHc structure

BACKGROUND

The human mitochondrial alpha-ketoglutarate dehydrogenase complex (hKGDHc) converts KG to succinyl-CoA and NADH. Malfunction of and reactive oxygen species generation by the hKGDHc as well as its E1-E2 subcomplex are implicated in neurodegenerative disorders, ischemia-reperfusion injury, E3-deficiency and cancers.

METHODS

We performed cryo-EM, cross-linking mass spectrometry (CL-MS) and molecular modeling analyses to determine the structure of the E2 component of the hKGDHc (hE2k); hE2k transfers a succinyl group to CoA and forms the structural core of hKGDHc. We also assessed the overall structure of the hKGDHc by negative-stain EM and modeling.

RESULTS

We report the 2.9 Å resolution cryo-EM structure of the hE2k component. The cryo-EM map comprises density for hE2k residues 151-386 - the entire (inner) core catalytic domain plus a few additional residues -, while residues 1-150 are not observed due to the inherent flexibility of the N-terminal region. The structure of the latter segment was also determined by CL-MS and homology modeling. Negative-stain EM on in vitro assembled hKGDHc and previous data were used to build a putative overall structural model of the hKGDHc.

CONCLUSIONS

The E2 core of the hKGDHc is composed of 24 hE2k chains organized in octahedral (8 × 3 type) assembly. Each lipoyl domain is oriented towards the core domain of an adjacent chain in the hE2k homotrimer. hE1k and hE3 are most likely tethered at the edges and faces, respectively, of the cubic hE2k assembly.

GENERAL SIGNIFICANCE

The revealed structural information will support the future pharmacologically targeting of the hKGDHc.

Structure-Function Relationship Study of a Secretory Amoebic Phosphatase: A Computational-Experimental Approach

Phosphatases are hydrolytic enzymes that cleave the phosphoester bond of numerous substrates containing phosphorylated residues. The typical classification divides them into acid or alkaline depending on the pH at which they have optimal activity. The histidine phosphatase (HP) superfamily is a large group of functionally diverse enzymes characterized by having an active-site His residue that becomes phosphorylated during catalysis. HP enzymes are relevant biomolecules due to their current and potential application in medicine and biotechnology. Entamoeba histolytica, the causative agent of human amoebiasis, contains a gene (EHI_146950) that encodes a putative secretory acid phosphatase (EhHAPp49), exhibiting sequence similarity to histidine acid phosphatase (HAP)/phytase enzymes, i.e., branch-2 of HP superfamily. To assess whether it has the potential as a biocatalyst in removing phosphate groups from natural substrates, we studied the EhHAPp49 structural and functional features using a computational-experimental approach. Although the combined outcome of computational analyses confirmed its structural similarity with HP branch-2 proteins, the experimental results showed that the recombinant enzyme (rEhHAPp49) has negligible HAP/phytase activity. Nonetheless, results from supplementary activity evaluations revealed that rEhHAPp49 exhibits Mg²⁺-dependent alkaline pyrophosphatase activity. To our knowledge, this study represents the first computational-experimental characterization of EhHAPp49, which offers further insights into the structure-function relationship and the basis for future research.

Improved Sampling Strategies for Protein Model Refinement Based on Molecular Dynamics Simulation

Protein structures provide valuable information for understanding biological processes. Protein structures can be determined by experimental methods such as X-ray crystallography, nuclear magnetic resonance spectroscopy, or cryogenic electron microscopy. As an alternative, in silico methods can be used to predict protein structures. These methods utilize protein structure databases for structure prediction via template-based modeling or for training machine-learning models to generate predictions. Structure prediction for proteins distant from proteins with known structures often results in lower accuracy with respect to the true physiological structures. Physics-based protein model refinement methods can be applied to improve model accuracy in the predicted models. Refinement methods rely on conformational sampling around the predicted structures, and if structures closer to the native states are sampled, improvements in the model quality become possible. Molecular dynamics simulations have been especially successful for improving model qualities but although consistent refinement can be achieved, the improvements in model qualities are still moderate. To extend the refinement performance of a simulation-based protocol, we explored new schemes that focus on optimized use of biasing functions and the application of increased simulation temperatures. In addition, we tested the use of alternative initial models so that the simulations can explore the conformational space more broadly. Based on the insights of this analysis, we are proposing a new refinement protocol that significantly outperformed previous state-of-the-art molecular dynamics simulation-based protocols in the benchmark tests described here.

Computational Methods for the Elucidation of Protein Structure and Interactions

Biologists are increasingly aware of the importance of protein structure in revealing function. The computational tools now exist which allow researchers to model unknown proteins simply on the basis of their primary sequence. However, for the non-specialist bioinformatician, there is a dazzling array of terminology, acronyms, and competing computer software available for this process. This review is intended to highlight the key stages of computational protein structure prediction, as well as explain the reasons behind some of the procedures and list some established workarounds for common pitfalls. Thereafter follows a review of five one-stop servers for start-to-finish structure prediction.

CGMD Platform: Integrated Web Servers for the Preparation, Running, and Analysis of Coarse-Grained Molecular Dynamics Simulations

Advances in coarse-grained molecular dynamics (CGMD) simulations have extended the use of computational studies on biological macromolecules and their complexes, as well as the interactions of membrane protein and lipid complexes at a reduced level of representation, allowing longer and larger molecular dynamics simulations. Here, we present a computational platform dedicated to the preparation, running, and analysis of CGMD simulations. The platform is built on a completely revisited version of our Martini coarsE gRained MembrAne proteIn Dynamics (MERMAID) web server, and it integrates this with other three dedicated services. In its current version, the platform expands the existing implementation of the Martini force field for membrane proteins to also allow the simulation of soluble proteins using the Martini and the SIRAH force fields. Moreover, it offers an automated protocol for carrying out the backmapping of the coarse-grained description of the system into an atomistic one.

Discovery and Characterization of a Novel Thermostable β-Amino Acid Transaminase from a Meiothermus Strain Isolated in an Icelandic Hot Spring

A Meiothermus strain capable of using β-phenylalanine for growth is isolated by culture enrichment of samples collected in hot environments and the genome is sequenced showing the presence of 22 putative transaminase (TA) sequences. On the basis of phylogenetic and sequence analysis, a TA termed Ms-TA2 is selected for further studies. The enzyme is successfully produced in Escherichia coli Rosetta(DE3) cells, with 70 mg of pure protein obtained from 1 L culture after purification by affinity chromatography. Ms-TA2 shows high activity toward (S)-β-phenylalanine and other (S)-β-amino acids, as well as a preference for α-ketoglutarate and aromatic aldehydes as amino acceptors. Moreover, Ms-TA2 is shown to be a thermostable enzyme by maintaining about 60% of the starting activity after 3 h incubation at 50 °C and showing a melting temperature of about 73 °C. Finally, a homology-based structural model of Ms-TA2 is built and key active site interactions for substrate and cofactor binding are analyzed.

The endoplasmic reticulum acetyltransferases ATase1/NAT8B and ATase2/NAT8 are differentially regulated to adjust engagement of the secretory pathway

Nε-lysine acetylation of nascent glycoproteins within the endoplasmic reticulum (ER) lumen regulates the efficiency of the secretory pathway. The ER acetylation machinery consists of the membrane transporter, acetyl-CoA transporter 1 (AT-1/SLC33A1), and two acetyltransferases, ATase1/NAT8B and ATase2/NAT8. Dysfunctional ER acetylation is associated with severe neurological diseases with duplication of AT-1/SLC33A1 being associated with autism spectrum disorder, intellectual disability, and dysmorphism. Neuron-specific AT-1 over-expression in the mouse alters neuron morphology and function, causing an autism-like phenotype, indicating that ER acetylation plays a key role in neurophysiology. As such, characterizing the molecular mechanisms that regulate the acetylation machinery could reveal critical information about its biology. By using structure-biochemistry approaches, we discovered that ATase1 and ATase2 share enzymatic properties but differ in that ATase1 is post-translationally regulated via acetylation. Furthermore, gene expression studies revealed that the promoters of AT-1, ATase1, and ATase2 contain functional binding sites for the neuron-related transcription factors cAMP response element-binding protein and the immediate-early genes c-FOS and c-JUN, and that ATase1 and ATase2 exhibit additional modes of transcriptional regulation relevant to aging and Alzheimer's disease. In vivo rodent gene expression experiments revealed that Atase2 is specifically induced following activity-dependent events. Finally, over-expression of either ATase1 or ATase2 was sufficient to increase the engagement of the secretory pathway in PC12 cells. Our results indicate important regulatory roles for ATase1 and ATase2 in neuron function with induction of ATase2 expression potentially serving as a critical event that adjusts the efficiency of the secretory pathway for activity-dependent neuronal functions.

MERMAID: dedicated web server to prepare and run coarse-grained membrane protein dynamics

Atomistic molecular dynamics simulations of membrane proteins have been shown to be extremely useful for characterizing the molecular features underlying their function, but require high computational power, limiting the understanding of complex events in membrane proteins, e.g. ion channels gating, GPCRs activation. To overcome this issue, it has been shown that coarse-grained approaches, although requiring less computational power, are still capable of correctly describing molecular events underlying big conformational changes in biological systems. Here, we present the Martini coarse-grained membrane protein dynamics (MERMAID), a publicly available web interface that allows the user to prepare and run coarse-grained molecular dynamics (CGMD) simulations and to analyse the trajectories.

Comparative modelling studies of fruit bromelain using molecular dynamics simulation

Fruit bromelain is a cysteine protease accumulated in pineapple fruits. This proteolytic enzyme has received high demand for industrial and therapeutic applications. In this study, fruit bromelain sequences QIM61759, QIM61760 and QIM61761 were retrieved from the National Center for Biotechnology Information (NCBI) Genbank Database. The tertiary structure of fruit bromelain QIM61759, QIM61760 and QIM61761 was generated by using MODELLER. The result revealed that the local stereochemical quality of the generated models was improved by using multiple templates during modelling process. Moreover, by comparing with the available papain model, structural analysis provides an insight on how pro-peptide functions as a scaffold in fruit bromelain folding and contributing to inactivation of mature protein. The structural analysis also disclosed the similarities and differences between these models. Lastly, thermal stability of fruit bromelain was studied. Molecular dynamics simulation of fruit bromelain structures at several selected temperatures demonstrated how fruit bromelain responds to elevation of temperature.

High-accuracy protein structures by combining machine-learning with physics-based refinement

Protein structure prediction has long been available as an alternative to experimental structure determination, especially via homology modeling based on templates from related sequences. Recently, models based on distance restraints from coevolutionary analysis via machine learning to have significantly expanded the ability to predict structures for sequences without templates. One such method, AlphaFold, also performs well on sequences where templates are available but without using such information directly. Here we show that combining machine-learning based models from AlphaFold with state-of-the-art physics-based refinement via molecular dynamics simulations further improves predictions to outperform any other prediction method tested during the latest round of CASP. The resulting models have highly accurate global and local structures, including high accuracy at functionally important interface residues, and they are highly suitable as initial models for crystal structure determination via molecular replacement.

Evaluation of model refinement in CASP13

Performance in the model refinement category of the 13th round of Critical Assessment of Structure Prediction (CASP13) is assessed, showing that some groups consistently improve most starting models whereas the majority of participants continue to degrade the starting model on average. Using the ranking formula developed for CASP12, it is shown that only 7 of 32 groups perform better than a “naïve predictor” who just submits the starting model. Common features in their approaches include a dependence on physics‐based force fields to judge alternative conformations and the use of molecular dynamics to relax models to local minima, usually with some restraints to prevent excessively large movements. In addition to the traditional CASP metrics that focus largely on the quality of the overall fold, alternative metrics are evaluated, including comparisons of the main‐chain and side‐chain torsion angles, and the utility of the models for solving crystal structures by the molecular replacement method. It is proposed that the introduction of these metrics, as well as consideration of the accuracy of coordinate error estimates, would improve the discrimination between good and very good models.

Driven to near-experimental accuracy by refinement via molecular dynamics simulations

Protein model refinement has been an essential part of successful protein structure prediction. Molecular dynamics simulation-based refinement methods have shown consistent improvement of protein models. There had been progress in the extent of refinement for a few years since the idea of ensemble averaging of sampled conformations emerged. There was little progress in CASP12 because conformational sampling was not sufficiently diverse due to harmonic restraints. During CASP13, a new refinement method was tested that achieved significant improvements over CASP12. The new method intended to address previous bottlenecks in the refinement problem by introducing new features. Flat-bottom harmonic restraints replaced harmonic restraints, sampling was performed iteratively, and a new scoring function and selection criteria were used. The new protocol expanded conformational sampling at reduced computational costs. In addition to overall improvements, some models were refined significantly to near-experimental accuracy.

Methods for the Refinement of Protein Structure 3D Models

The refinement of predicted 3D protein models is crucial in bringing them closer towards experimental accuracy for further computational studies. Refinement approaches can be divided into two main stages: The sampling and scoring stages. Sampling strategies, such as the popular Molecular Dynamics (MD)-based protocols, aim to generate improved 3D models. However, generating 3D models that are closer to the native structure than the initial model remains challenging, as structural deviations from the native basin can be encountered due to force-field inaccuracies. Therefore, different restraint strategies have been applied in order to avoid deviations away from the native structure. For example, the accurate prediction of local errors and/or contacts in the initial models can be used to guide restraints. MD-based protocols, using physics-based force fields and smart restraints, have made significant progress towards a more consistent refinement of 3D models. The scoring stage, including energy functions and Model Quality Assessment Programs (MQAPs) are also used to discriminate near-native conformations from non-native conformations. Nevertheless, there are often very small differences among generated 3D models in refinement pipelines, which makes model discrimination and selection problematic. For this reason, the identification of the most native-like conformations remains a major challenge.

Predicting improved protein conformations with a temporal deep recurrent neural network

Accurate protein structure prediction from amino acid sequence is still an unsolved problem. The most reliable methods centre on template based modelling. However, the accuracy of these models entirely depends on the availability of experimentally resolved homologous template structures. In order to generate more accurate models, extensive physics based molecular dynamics (MD) refinement simulations are performed to sample many different conformations to find improved conformational states. In this study, we propose a deep recurrent network model, called DeepTrajectory, that is able to identify these improved conformational states, with high precision, from a variety of different MD based sampling protocols. The proposed model learns the temporal patterns of features computed from MD trajectory data in order to classify whether each recorded simulation snapshot is an improved quality conformational state, decreased quality conformational state or whether there is no perceivable change in state with respect to the starting conformation. The model was trained and tested on 904 trajectories from 42 different protein systems with a cumulative number of more than 1.7 million snapshots. We show that our model outperforms other state of the art machine-learning algorithms that do not consider temporal dependencies. To our knowledge, DeepTrajectory is the first implementation of a time-dependent deep-learning protocol that is re-trainable and able to adapt to any new MD based sampling procedure, thereby demonstrating how a neural network can be used to learn the latter part of the protein folding funnel.

Structure refinement of membrane proteins via molecular dynamics simulations

A refinement protocol based on physics-based techniques established for water soluble proteins is tested for membrane protein structures. Initial structures were generated by homology modeling and sampled via molecular dynamics simulations in explicit lipid bilayer and aqueous solvent systems. Snapshots from the simulations were selected based on scoring with either knowledge-based or implicit membrane-based scoring functions and averaged to obtain refined models. The protocol resulted in consistent and significant refinement of the membrane protein structures similar to the performance of refinement methods for soluble proteins. Refinement success was similar between sampling in the presence of lipid bilayers and aqueous solvent but the presence of lipid bilayers may benefit the improvement of lipid-facing residues. Scoring with knowledge-based functions (DFIRE and RWplus) was found to be as good as scoring using implicit membrane-based scoring functions suggesting that differences in internal packing is more important than orientations relative to the membrane during the refinement of membrane protein homology models.

Structural insights into suppressor of cytokine signaling 1 protein- identification of new leads for type 2 diabetes mellitus

The study considers the Suppressor of cytokine signaling 1 (SOCS1) protein as a novel Type 2 diabetes mellitus (T2DM) drug target. T2DM in human beings is also triggered by the over expression of SOCS proteins. The SOCS1 acts as a ubiquitin ligase (E3), degrades Insulin Receptor Substrate 1 and 2 (IRS1 and IRS2) proteins, and causes insulin resistance. Therefore, the structure of the SOCS1 protein was evaluated using homology-modeling and molecular dynamics methods and validated using standard computational protocols. The Protein-Protein docking study of SOCS1 with its natural substrates, IRS1 and IRS2, and subsequent solvent accessible surface area analysis gave insight into the binding region of the SOCS1 protein. The in silico active site prediction tools highlight the residues Val155 to Ile211 in SOCS1 being implicated in the ubiquitin mediated protein degradation of the proteins IRS1 and IRS2. Virtual screening in the active site region, using large structural databases, results in selective lead structures with 3-Pyridinol, Xanthine, and Alanine moieties as Pharmacophore. The virtual screening study shows that the residues Glu149, Gly187, Arg188, Leu191, and Ser205 of the SOCS1 are important for binding. The docking study with current anti-diabetic therapeutics shows that the drugs Glibenclamide and Glyclopyramide have a partial affinity towards SOCS1. The predicted ADMET and IC₅₀ properties for the identified ligands are within the acceptable range with drug-like properties. The structural data of SOCS1, its active site, and the identified lead structures are expedient in the development of new T2DM therapeutics.