Protein secondary structure classification revisited: processing DSSP information with PSSC

PS-GO parametric protein search engine

With the explosive growth of protein-related data, we are confronted with a critical scientific inquiry: How can we effectively retrieve, compare, and profoundly comprehend these protein structures to maximize the utilization of such data resources? PS-GO, a parametric protein search engine, has been specifically designed and developed to maximize the utilization of the rapidly growing volume of protein-related data. This innovative tool addresses the critical need for effective retrieval, comparison, and deep understanding of protein structures. By integrating computational biology, bioinformatics, and data science, PS-GO is capable of managing large-scale data and accurately predicting and comparing protein structures and functions. The engine is built upon the concept of parametric protein design, a computer-aided method that adjusts and optimizes protein structures and sequences to achieve desired biological functions and structural stability. PS-GO utilizes key parameters such as amino acid sequence, side chain angle, and solvent accessibility, which have a significant influence on protein structure and function. Additionally, PS-GO leverages computable parameters, derived computationally, which are crucial for understanding and predicting protein behavior. The development of PS-GO underscores the potential of parametric protein design in a variety of applications, including enhancing enzyme activity, improving antibody affinity, and designing novel functional proteins. This advancement not only provides a robust theoretical foundation for the field of protein engineering and biotechnology but also offers practical guidelines for future progress in this domain.

Molecular dynamics simulation of the effect of temperature on the conformation of ubiquitin protein

CONTENT

Ubiquitin, a ubiquitous small protein found in all living organisms, is crucial for tagging proteins earmarked for degradation and holds pivotal importance in biomedicine. Protein functionality is intricately linked to its structure. To comprehend the impact of diverse temperatures on ubiquitin protein structure, our study delved into the energy landscape, hydrogen bonding, and overall structural stability of ubiquitin protein at varying temperatures. Through meticulous analysis of root mean square deviation and root mean square fluctuation, we validated the robustness of the simulation conditions employed. Within our simulated system, the bonding energy and electrostatic potential energy exhibited linear augmentation, while the van der Waals energy demonstrated a linear decline. Additionally, our findings highlighted that the α-Helix secondary structure of the ubiquitin protein gradually transitions toward helix destabilization under high-temperature conditions. The secondary structure of ubiquitin protein experiences distinct changes under varying temperatures. The outcomes of our molecular simulations offer a theoretical framework that enhances our comprehension of how temperature impacts the structural stability of ubiquitin protein. These insights contribute not only to a deeper understanding of iniquity's behavior but also hold broader implications in the realm of biomedicine and beyond.

METHODS

All the MD simulations were performed using the GROMACS software with GROMOS96 force field and SPC for water. The ubiquitin protein was put in the center of a cubic box with a length of 8 nm, a setting that allowed > 0.8 nm in the minimal distance between the protein surface and the box wall. To remove the possible coordinate collision of the configurations, in the beginning, the steepest descent method was used until the maximum force between atoms was under 100 kJ/mol·nm with a 0.01 nm step size. Minimization was followed by 30 ps of position-restrained MD simulation. The protein was restrained to its initial position, and the solvent was freely equilibrated. The product phase was obtained with the whole system simulated for 10 ns without any restraint using an integral time step of 1 fs with different temperatures. The cutoff for short-range electronic interaction was set to 1.5 nm. The long-range interactions were treated with a particle-mesh Ewald (PME) method with a grid width of 1.2 nm.

In silico study to identify novel NEK7 inhibitors from natural sources by a combination strategy

Cancer poses a significant global health challenge and significantly contributes to mortality. NEK7, related to the NIMA protein kinase family, plays a crucial role in spindle assembly and cell division. The dysregulation of NEK7 is closely linked to the onset and progression of various cancers, especially colon and breast cancer, making it a promising target for cancer therapy. Nevertheless, the shortage of high-quality NEK7 inhibitors highlights the need for new therapeutic strategies. In this study, we utilized a multidisciplinary approach, including virtual screening, molecular docking, pharmacokinetics, molecular dynamics simulations (MDs), and MM/PBSA calculations, to evaluate natural compounds as NEK7 inhibitors comprehensively. Through various docking strategies, we identified three natural compounds: (-)-balanol, digallic acid, and scutellarin. Molecular docking revealed significant interactions at residues such as GLU112 and ALA114, with docking scores of -15.054, -13.059, and -11.547 kcal/mol, respectively, highlighting their potential as NEK7 inhibitors. MDs confirmed the stability of these compounds at the NEK7-binding site. Hydrogen bond analysis during simulations revealed consistent interactions, supporting their strong binding capacity. MM/PBSA analysis identified other crucial amino acids contributing to binding affinity, including ILE20, VAL28, ILE75, LEU93, ALA94, LYS143, PHE148, LEU160, and THR161, crucial for stabilizing the complex. This research demonstrated that these compounds exceeded dabrafenib in binding energy, according to MM/PBSA calculations, underscoring their effectiveness as NEK7 inhibitors. ADME/T predictions showed lower oral toxicity for these compounds, suggesting their potential for further development. This study highlights the promise of these natural compounds as bases for creating more potent derivatives with significant biological activities, paving the way for future experimental validation.

Structural Basis of Nucleic Acid Recognition and 6mA Demethylation by Caenorhabditis elegans NMAD-1A

N6-methyladenine (6mA) of DNA is an emerging epigenetic mark in the genomes of Chlamydomonas, Caenorhabditis elegans, and mammals recently. Levels of 6mA undergo drastic fluctuation and thus affect fertility during meiosis and early embryogenesis. Here, we showed three complex structures of 6mA demethylase C. elegans NMAD-1A, a canonical isoform of NMAD-1 (F09F7.7). Biochemical results revealed that NMAD-1A prefers 6mA Bubble or Bulge DNAs. Structural studies of NMAD-1A revealed an unexpected "stretch-out" conformation of its Flip2 region, a conserved element that is usually bent over the catalytic center to facilitate substrate base flipping in other DNA demethylases. Moreover, the wide channel between the Flip1 and Flip2 of the NMAD-1A explained the observed preference of NMAD-1A for unpairing substrates, of which the flipped 6mA was primed for catalysis. Structural analysis and mutagenesis studies confirmed that key elements such as carboxy-terminal domain (CTD) and hypothetical zinc finger domain (ZFD) critically contributed to structural integrity, catalytic activity, and nucleosome binding. Collectively, our biochemical and structural studies suggest that NMAD-1A prefers to regulate 6mA in the unpairing regions and is thus possibly associated with dynamic chromosome regulation and meiosis regulation.

Conotoxin Prediction: New Features to Increase Prediction Accuracy

Conotoxins are toxic, disulfide-bond-rich peptides from cone snail venom that target a wide range of receptors and ion channels with multiple pathophysiological effects. Conotoxins have extraordinary potential for medical therapeutics that include cancer, microbial infections, epilepsy, autoimmune diseases, neurological conditions, and cardiovascular disorders. Despite the potential for these compounds in novel therapeutic treatment development, the process of identifying and characterizing the toxicities of conotoxins is difficult, costly, and time-consuming. This challenge requires a series of diverse, complex, and labor-intensive biological, toxicological, and analytical techniques for effective characterization. While recent attempts, using machine learning based solely on primary amino acid sequences to predict biological toxins (e.g., conotoxins and animal venoms), have improved toxin identification, these methods are limited due to peptide conformational flexibility and the high frequency of cysteines present in toxin sequences. This results in an enumerable set of disulfide-bridged foldamers with different conformations of the same primary amino acid sequence that affect function and toxicity levels. Consequently, a given peptide may be toxic when its cysteine residues form a particular disulfide-bond pattern, while alternative bonding patterns (isoforms) or its reduced form (free cysteines with no disulfide bridges) may have little or no toxicological effects. Similarly, the same disulfide-bond pattern may be possible for other peptide sequences and result in different conformations that all exhibit varying toxicities to the same receptor or to different receptors. We present here new features, when combined with primary sequence features to train machine learning algorithms to predict conotoxins, that significantly increase prediction accuracy.

A Transformer-Based Ensemble Framework for the Prediction of Protein-Protein Interaction Sites

The identification of protein-protein interaction (PPI) sites is essential in the research of protein function and the discovery of new drugs. So far, a variety of computational tools based on machine learning have been developed to accelerate the identification of PPI sites. However, existing methods suffer from the low predictive accuracy or the limited scope of application. Specifically, some methods learned only global or local sequential features, leading to low predictive accuracy, while others achieved improved performance by extracting residue interactions from structures but were limited in their application scope for the serious dependence on precise structure information. There is an urgent need to develop a method that integrates comprehensive information to realize proteome-wide accurate profiling of PPI sites. Herein, a novel ensemble framework for PPI sites prediction, EnsemPPIS, was therefore proposed based on transformer and gated convolutional networks. EnsemPPIS can effectively capture not only global and local patterns but also residue interactions. Specifically, EnsemPPIS was unique in (a) extracting residue interactions from protein sequences with transformer and (b) further integrating global and local sequential features with the ensemble learning strategy. Compared with various existing methods, EnsemPPIS exhibited either superior performance or broader applicability on multiple PPI sites prediction tasks. Moreover, pattern analysis based on the interpretability of EnsemPPIS demonstrated that EnsemPPIS was fully capable of learning residue interactions within the local structure of PPI sites using only sequence information. The web server of EnsemPPIS is freely available at http://idrblab.org/ensemppis.

Parasite Metalo-aminopeptidases as Targets in Human Infectious Diseases

BACKGROUND

Parasitic human infectious diseases are a worldwide health problem due to the increased resistance to conventional drugs. For this reason, the identification of novel molecular targets and the discovery of new chemotherapeutic agents are urgently required. Metalo- aminopeptidases are promising targets in parasitic infections. They participate in crucial processes for parasite growth and pathogenesis.

OBJECTIVE

In this review, we describe the structural, functional and kinetic properties, and inhibitors, of several parasite metalo-aminopeptidases, for their use as targets in parasitic diseases.

CONCLUSION

Plasmodium falciparum M1 and M17 aminopeptidases are essential enzymes for parasite development, and M18 aminopeptidase could be involved in hemoglobin digestion and erythrocyte invasion and egression. Trypanosoma cruzi, T. brucei and Leishmania major acidic M17 aminopeptidases can play a nutritional role. T. brucei basic M17 aminopeptidase down-regulation delays the cytokinesis. The inhibition of Leishmania basic M17 aminopeptidase could affect parasite viability. L. donovani methionyl aminopeptidase inhibition prevents apoptosis but not the parasite death. Decrease in Acanthamoeba castellanii M17 aminopeptidase activity produces cell wall structural modifications and encystation inhibition. Inhibition of Babesia bovis growth is probably related to the inhibition of the parasite M17 aminopeptidase, probably involved in host hemoglobin degradation. Schistosoma mansoni M17 aminopeptidases inhibition may affect parasite development, since they could participate in hemoglobin degradation, surface membrane remodeling and eggs hatching. Toxoplasma gondii M17 aminopeptidase inhibition could attenuate parasite virulence, since it is apparently involved in the hydrolysis of cathepsin Cs- or proteasome-produced dipeptides and/or cell attachment/invasion processes. These data are relevant to validate these enzymes as targets.

RGN: Residue-Based Graph Attention and Convolutional Network for Protein-Protein Interaction Site Prediction

The prediction of a protein-protein interaction site (PPI site) plays a very important role in the biochemical process, and lots of computational methods have been proposed in the past. However, the majority of the past methods are time consuming and lack accuracy. Hence, coming up with an effective computational method is necessary. In this article, we present a novel computational model called RGN (residue-based graph attention and convolutional network) to predict PPI sites. In our paper, the protein is treated as a graph. The amino acid can be seen as the node in the graph structure. The position-specific scoring matrix, hidden Markov model, hydrogen bond estimation algorithm, and ProtBert are applied as node features. The edges are decided by the spatial distance between the amino acids. Then, we utilize a residue-based graph convolutional network and graph attention network to further extract the deeper feature. Finally, the processed node feature is fed into the prediction layer. We show the superiority of our model by comparing it with the other four protein structure-based methods and five protein sequence-based methods. Our model obtains the best performance on all the evaluation metrics (accuracy, precision, recall, F₁ score, Matthews correlation coefficient, area under the receiver operating characteristic curve, and area under the precision recall curve). We also conduct a case study to demonstrate that extracting the protein information from the protein structure perspective is effective and points out the difficult aspect of PPI site prediction.

A Perspective on the (Rise and Fall of) Protein β-Turns

The β-turn is the third defined secondary structure after the α-helix and the β-sheet. The β-turns were described more than 50 years ago and account for more than 20% of protein residues. Nonetheless, they are often overlooked or even misunderstood. This poor knowledge of these local protein conformations is due to various factors, causes that I discuss here. For example, confusion still exists about the assignment of these local protein structures, their overlaps with other structures, the potential absence of a stabilizing hydrogen bond, the numerous types of β-turns and the software's difficulty in assigning or visualizing them. I also propose some ideas to potentially/partially remedy this and present why β-turns can still be helpful, even in the AlphaFold 2 era.

Targeted Mass Spectrometry-Based Proteomics Method to Quantify Placental Extracellular Vesicles

Extracellular vesicles (EVs) carry a wide range of molecules, such as proteins, RNAs, and DNA. EVs are secreted from a wide range of cells, including placental cells. Interestingly, EVs secreted from placental cells have been identified in maternal circulation as early as 6 weeks of gestation, and their concentration increases with the gestational age. While there is growing interest in elucidating the role of exosomes during normal and complicated pregnancies, progress in the field has been delayed because of the inability to quantify placental EVs from the maternal circulation. Recent reports have demonstrated the presence of placental-type alkaline phosphatase (PLAP) EVs only in the blood of pregnant women, indicating that PLAP is a marker to identify EVs secreted from the placenta. Therefore, here we describe a workflow to quantify placental EVs from maternal circulation using a targeted proteomics approach based on selecting specific peptides identified in the PLAP protein.

Residue Folding Degree-Relationship to Secondary Structure Categories and Use as Collective Variable

Recently, we have shown that the residue folding degree, a network-based measure of folded content in proteins, is able to capture backbone conformational transitions related to the formation of secondary structures in molecular dynamics (MD) simulations. In this work, we focus primarily on developing a collective variable (CV) for MD based on this residue-bound parameter to be able to trace the evolution of secondary structure in segments of the protein. We show that this CV can do just that and that the related energy profiles (potentials of mean force, PMF) and transition barriers are comparable to those found by others for particular events in the folding process of the model mini protein Trp-cage. Hence, we conclude that the relative segment folding degree (the newly proposed CV) is a computationally viable option to gain insight into the formation of secondary structures in protein dynamics. We also show that this CV can be directly used as a measure of the amount of α-helical content in a selected segment.

PIK3CA hotspot mutations p. H1047R and p. H1047L sensitize breast cancer cells to thymoquinone treatment by regulating the PI3K/Akt1 pathway

BACKGROUND

Nigella sativa (N. sativa) exhibits anti-inflammatory, antioxidant, antidiabetic, antimetastatic and antinociceptive effects and has been used to treat dozens of diseases. Thymoquinone (TQ) is an important and active component isolated from N. sativa seeds. Inhibition of cancer-associated activating PIK3CA mutations is a new prospective targeted therapy in personalized metastatic breast cancer (MBC). TQ is reported to be an effective inhibitor of the PI3K/Akt1 pathway in MBC. This study aimed to evaluate the in vitro antitumor effect of TQ in the context of two PIK3CA hotspot mutations, p. H1047R and p. H1047L.

METHODS AND RESULTS

Molecular dynamics, free energy landscapes and principal component analyses were also used to survey the mechanistic effects of the p. H1047R and p. H1047L mutations on the PI3K/Akt1 pathway. Our findings clearly confirmed that the p. H1047R and p. H1047L mutants could reduce the inhibitory effect of ΔNp63α on the kinase domain of PIK3CA, resulting in increased activity of PI3K downstream signals. Structurally, the partial disruption of the interaction between the ΔNp63α DNA binding domain and the PIK3CA kinase domain at residues 114-359 and 797-1068 destabilizes the conformation of the activation loop and modifies the PIK3CA/ΔNp63α complex. Alongside these structural changes, we found that TQ treatment resulted in high PI3K/Akt1 pathway inhibition in p. H1047R and p. H1047L-expressing cells versus wild-type cells.

CONCLUSIONS

These two PIK3CA hotspot mutations therefore not only contribute to tumor progression in patients with MBC but may also serve as targets for the development of novel small molecule therapeutic strategies.

Assigning secondary structure in proteins using AI

Knowledge about protein structure assignment enriches the structural and functional understanding of proteins. Accurate and reliable structure assignment data is crucial for secondary structure prediction systems. Since the 1980s, various methods based on hydrogen bond analysis and atomic coordinate geometry, followed by machine learning, have been employed in protein structure assignment. However, the assignment process becomes challenging when missing atoms are present in the protein files. Our method proposed a multi-class classifier program named DLFSA for assigning protein secondary structure elements (SSE) using convolutional neural networks (CNNs). A fast and efficient GPU-based parallel procedure extracts fragments from protein files. The model implemented in this work is trained with a subset of the protein fragments and achieves 88.1% and 82.5% train and test accuracy, respectively. The model uses only C_α coordinates for secondary structure assignments. The model has been successfully tested on a few full-length proteins also. Results from the fragment-based studies demonstrate the feasibility of applying deep learning solutions for structure assignment problems.

TFG binds LC3C to regulate ULK1 localization and autophagosome formation

The early secretory pathway and autophagy are two essential and evolutionarily conserved endomembrane processes that are finely interlinked. Although growing evidence suggests that intracellular trafficking is important for autophagosome biogenesis, the molecular regulatory network involved is still not fully defined. In this study, we demonstrate a crucial effect of the COPII vesicle-related protein TFG (Trk-fused gene) on ULK1 puncta number and localization during autophagy induction. This, in turn, affects formation of the isolation membrane, as well as the correct dynamics of association between LC3B and early ATG proteins, leading to the proper formation of both omegasomes and autophagosomes. Consistently, fibroblasts derived from a hereditary spastic paraparesis (HSP) patient carrying mutated TFG (R106C) show defects in both autophagy and ULK1 puncta accumulation. In addition, we demonstrate that TFG activity in autophagy depends on its interaction with the ATG8 protein LC3C through a canonical LIR motif, thereby favouring LC3C-ULK1 binding. Altogether, our results uncover a link between TFG and autophagy and identify TFG as a molecular scaffold linking the early secretion pathway to autophagy.

PSIQUE: Protein Secondary Structure Identification on the Basis of Quaternions and Electronic Structure Calculations

The secondary structure is important in protein structure analysis, classification, and modeling. We have developed a novel method for secondary structure assignment, termed PSIQUE, based on the potential energy surface (PES) of polyalanine obtained using an infinitely long chain model and density functional theory calculations. First, uniform protein segments are determined in terms of a difference of quaternions between neighboring amino acids along the protein backbone. Then, the identification of the secondary structure motifs is carried out based on the minima found in the PES. PSIQUE shows good agreement with other secondary structure assignment methods. However, it provides better discrimination of subtle secondary structures (e.g., helix types) and termini and produces more uniform segments while also accounting for local distortions. Overall, PSIQUE provides a precise and reliable assignment of secondary structures, so it should be helpful for the detailed characterization of the protein structure.

The hazardous effects of the environmental toxic gases on amyloid beta-peptide aggregation: A theoretical perspective

Alzheimer's disease (AD) is one of the leading causes of dementia in elderly people. It has been well documented that the exposure to environmental toxins such as CO, CO₂, SO₂ and NO₂ that are present in the air is considered as a hallmark for the progression of Alzheimer's disease. However, their actual mechanism by which environmental toxin triggers the aggregation of Aβ42 peptide at the molecular and atomic levels remain unknown. In this study, molecular dynamics simulation was carried out to study the aggregation mechanism of the Aβ₄₂ peptide due to its interaction of toxic gas (CO, CO₂, SO₂ and NO₂). During the 400 ns simulation, all the Aβ₄₂ interacted toxic gas (CO, CO₂, SO_2, and NO₂) complexes have smaller Root Mean Square Deviation values when compared to the Aβ₄₂ peptide, which shows that the interaction of toxic gases (CO, CO₂, SO_2, and NO₂) would increase the Aβ₄₂ peptide structural stability. The radius of gyration analysis also supports that Aβ₄₂ interacted CO₂ and SO₂ complexes have the minimum value in the range of 0.95 nm and 1.5 nm. It is accounted that the Aβ₄₂ interacted CO₂ and SO₂ complexes have a greater compact structure in comparison to Aβ₄₂ interacted CO and NO₂ complexes. Furthermore, all the Aβ₄₂ interacted toxic gas (CO, CO₂, SO_2, and NO₂) complexes exhibited an enhanced secondary structural probability for coil and turn regions with a reduced α-helix probability, which indicates that the interaction of toxic gases may enhance the toxicity and aggregation of Aβ₄₂.

In silico studies of the inhibition mechanism of dengue with papain

Dengue virus is becoming a major global disease; the envelope protein is the major target for vaccine development against Dengue. Nowadays, the attention has focused on developing inhibitors based on Papain is a promising target for treating Dengue. In the present work, the theoretical studies of E-protein(Cys74-Glu79;Lys110)…Papain(Cys25, Asn175 and His159) complexes are analysed by Density Functional Theory (M06-2X/cc-pVDZ) method. Among the E-protein(Cys74-Glu79;Lys110)…Papain(Cys25, Asn175 and Hys159) complexes, E-protein(Glu76)…Papain(Cys25) complex has the highest interaction value of -352.22 kcal/mol. Moreover, the natural bond orbital analysis also supports the above results. The 100 ns Molecular Dynamics simulation reveals that, E-protein(Ala54-Ile129)…Papain(Cys25) complex had the lowest root mean square deviation value of 1 Å compared to the E-protein(Ala54-Ile129)… Papain(Asn175 & His159) complexes. The salt bridge formation between the Asp103 and Lys110 residues are the important stabilizing factor in E-protein(Ala54-Ile129)…Papain(Cys25) complex. This result can extend our knowledge of the functional behaviour of Papain and provides structural insight to target Envelope protein as forthcoming drug targets in Dengue.

Computational Prediction of Secondary and Supersecondary Structures from Protein Sequences

Many new methods for the sequence-based prediction of the secondary and supersecondary structures have been developed over the last several years. These and older sequence-based predictors are widely applied for the characterization and prediction of protein structure and function. These efforts have produced countless accurate predictors, many of which rely on state-of-the-art machine learning models and evolutionary information generated from multiple sequence alignments. We describe and motivate both types of predictions. We introduce concepts related to the annotation and computational prediction of the three-state and eight-state secondary structure as well as several types of supersecondary structures, such as β hairpins, coiled coils, and α-turn-α motifs. We review 34 predictors focusing on recent tools and provide detailed information for a selected set of 14 secondary structure and 3 supersecondary structure predictors. We conclude with several practical notes for the end users of these predictive methods.

Dimerization of Coronavirus nsp9 with Diverse Modes Enhances Its Nucleic Acid Binding Affinity

Coronaviruses pose serious health threats to humans and other animals. Understanding the mechanisms of their replication has important implications for global health and economic stability. Nonstructural protein 9 (nsp9) is an essential RNA binding protein for coronavirus replication. However, the mechanisms of the dimerization and nucleic acid binding of nsp9 remain elusive. Here, we report four crystal structures, including wild-type porcine delta coronavirus (PDCoV) nsp9, PDCoV nsp9-ΔN7 (N-terminal 7 amino acids deleted), wild-type porcine epidemic diarrhea virus (PEDV) nsp9, and PEDV nsp9-C59A mutant. These structures reveal the diverse dimerization forms of coronavirus nsp9. We first found that the N-finger of nsp9 from PDCoV plays a critical role in dimerization. Meanwhile, PEDV nsp9 is distinguished by the presence of a disulfide bond in the dimer interface. Interestingly, size exclusion chromatography and analytical ultracentrifugation analyses indicate that the PDCoV nsp9-ΔN7 and PEDV nsp9-C59A mutants are monomeric in solution. In addition, electrophoretic mobility shift assays and microscale thermophoresis analysis indicate that the monomeric forms of PDCoV nsp9 and PEDV nsp9 still have nucleic acid binding affinity, although it is lower than that of the wild type. Our results show that the diverse dimerization forms of coronavirus nsp9 proteins enhance their nucleic acid binding affinity.IMPORTANCE Coronaviruses cause widespread respiratory, gastrointestinal, and central nervous system diseases in humans and other animals, threatening human health and causing economic loss. Coronavirus nsp9, a member of the replication complex, is an important RNA binding subunit in the RNA-synthesizing machinery of all coronaviruses. However, the mechanisms of the dimerization and nucleic acid binding of nsp9 remain elusive. In this study we determined the nsp9 crystal structures of PDCoV and PEDV. We first found that the N-finger of nsp9 from PDCoV plays a critical role in dimerization. Meanwhile, PEDV nsp9 is distinguished by the presence of a disulfide bond in the dimer interface. This study provides a structural and functional basis for understanding the mechanism of dimerization and shows that the diverse dimerization modes of coronavirus nsp9 proteins enhance their nucleic acid binding affinity. Importantly, these findings may provide a new insight for antiviral drug development.

How does binding of agonist ligands control intrinsic molecular dynamics in human NMDA receptors?

NMDA-type glutamate receptors (NMDAR) are ligand-gated ion channels that contribute to excitatory neurotransmission in the central nervous system. NMDAR dysfunction has been found to be involved in various neurological disorders. Recent crystallographic and EM studies have shown the static structure of different states of the non-human NMDARs. Here we describe a model of a human NMDA receptor (hNMDAR) and its molecular dynamics (MD) before and after the binding of agonist ligands, glutamate and glycine. It is shown that the binding of ligands promotes a global reduction in molecular flexibility that produces a more tightly packed conformation than the unbound hNMDAR, and a higher cooperative regularity of moving. The ligand-induced synchronization of motion, identified on all structural levels of the modular hNMDA receptor is apparently a fundamental factor in channel gating. Although the time scale of the MD simulations (300 ns) was not sufficient to observe the complete gating event, the obtained data has shown the ligand-induced stabilization of hNMDAR that conforms the “going to be open state”. We propose a mechanistic dynamic model of the ligand-dependent gating mechanism in the hNMDA receptor. At the binding of the ligands, the differently twisted conformations of the highly flexible receptor are stabilized in unique conformation with a linear molecular axis, which is a condition that is optimal for pore development. By searching the receptor surface, we have identified three new pockets, which are different from the pockets described in the literature as the potential and known positive allosteric modulator binding sites. A successful docking of two NMDAR modulators to their binding sites validates the model of a human NMDA receptor as a biological relevant target.

A Systematic Analysis of the Structures of Heterologously Expressed Proteins and Those from Their Native Hosts in the RCSB PDB Archive

Recombinant expression of proteins has become an indispensable tool in modern day research. The large yields of recombinantly expressed proteins accelerate the structural and functional characterization of proteins. Nevertheless, there are literature reported that the recombinant proteins show some differences in structure and function as compared with the native ones. Now there have been more than 100,000 structures (from both recombinant and native sources) publicly available in the Protein Data Bank (PDB) archive, which makes it possible to investigate if there exist any proteins in the RCSB PDB archive that have identical sequence but have some difference in structures. In this paper, we present the results of a systematic comparative study of the 3D structures of identical naturally purified versus recombinantly expressed proteins. The structural data and sequence information of the proteins were mined from the RCSB PDB archive. The combinatorial extension (CE), FATCAT-flexible and TM-Align methods were employed to align the protein structures. The root-mean-square distance (RMSD), TM-score, P-value, Z-score, secondary structural elements and hydrogen bonds were used to assess the structure similarity. A thorough analysis of the PDB archive generated five-hundred-seventeen pairs of native and recombinant proteins that have identical sequence. There were no pairs of proteins that had the same sequence and significantly different structural fold, which support the hypothesis that expression in a heterologous host usually could fold correctly into their native forms.

A New Secondary Structure Assignment Algorithm Using Cα Backbone Fragments

The assignment of secondary structure elements in proteins is a key step in the analysis of their structures and functions. We have developed an algorithm, SACF (secondary structure assignment based on Cα fragments), for secondary structure element (SSE) assignment based on the alignment of Cα backbone fragments with central poses derived by clustering known SSE fragments. The assignment algorithm consists of three steps: First, the outlier fragments on known SSEs are detected. Next, the remaining fragments are clustered to obtain the central fragments for each cluster. Finally, the central fragments are used as a template to make assignments. Following a large-scale comparison of 11 secondary structure assignment methods, SACF, KAKSI and PROSS are found to have similar agreement with DSSP, while PCASSO agrees with DSSP best. SACF and PCASSO show preference to reducing residues in N and C cap regions, whereas KAKSI, P-SEA and SEGNO tend to add residues to the terminals when DSSP assignment is taken as standard. Moreover, our algorithm is able to assign subtle helices (310-helix, π-helix and left-handed helix) and make uniform assignments, as well as to detect rare SSEs in β-sheets or long helices as outlier fragments from other programs. The structural uniformity should be useful for protein structure classification and prediction, while outlier fragments underlie the structure-function relationship.

Molecular Dynamics Simulations of Intrinsically Disordered Proteins: Force Field Evaluation and Comparison with Experiment

An increasing number of studies using molecular dynamics (MD) simulations of unfolded and intrinsically disordered proteins (IDPs) suggest that current force fields sample conformations that are overly collapsed. Here, we study the applicability of several state-of-the-art MD force fields, of the AMBER and GROMOS variety, for the simulation of Histatin 5, a short (24 residues) cationic salivary IDP with antimicrobial and antifungal properties. The quality of the simulations is assessed in three complementary analyses: (i) protein shape and size comparison with recent experimental small-angle X-ray scattering data; (ii) secondary structure prediction; (iii) energy landscape exploration and conformational class analysis. Our results show that, indeed, standard force fields sample conformations that are too compact, being systematically unable to reproduce experimental evidence such as the scattering function, the shape of the protein as compared with the Kratky plot, and intrapeptide distances obtained through the pair distance distribution function, p(r). The consistency of this deviation suggests that the problem is not mainly due to protein-protein or water-water interactions, whose parametrization varies the most between force fields and water models. In fact, as originally proposed in [ Best et al. J. Chem. Theory Comput. 2014, 10, 5113 - 5124.], balanced protein-water interactions may be the key to solving this problem. Our simulations using this approach produce results in very good agreement with experiment.