A coarse-grained protein force field for folding and structure prediction

De Novo Rational Design of Peptide-Based Protein-Protein Inhibitors (Pep-PPIs) Approach by Mapping the Interaction Motifs of the PP Interface and Physicochemical Filtration: A Case on p25-Cdk5-Mediated Neurodegenerative Diseases

Protein-protein interactions, or PPIs, are a part of every biological activity and have been linked to a number of diseases, including cancer, infectious diseases, and neurological disorders. As such, targeting PPIs is considered a strategic and vital approach in the development of new medications. Nonetheless, the wide and flat contact interface makes it difficult to find small-molecule PP inhibitors. An alternative strategy would be to use the PPI interaction motifs as building blocks for the design of peptide-based inhibitors. Herein, we designed 12-mer peptide inhibitors to target p25-inducing-cyclin-dependent kinase (Cdk5) hyperregulation, a PPI that has been shown to perpetuate neuroinflammation, which is one of the major causal implications of neurodegenerative disorders such as Alzheimer's disease, Parkinson's disease, and frontotemporal dementia. We generated a library of 5 062 500 peptide combination sequences (PCS) derived from the interaction motif of Cdk5/p25 PP interface. The 20 amino acids were differentiated into six groups, namely, hydrophobic (aliphatic), aromatic, basic, acidic, unique, and polar uncharged, on the basis of their physiochemical properties. To preserve the interaction motif necessary for ideal binding, de novo modeling of all possible peptide sequence substitutions was considered. A set of filters, backed by the Support Vector Machine (SVM) algorithm, was then used to create a shortlisted custom peptide library that met specific bioavailability, toxicity, and therapeutic relevance, leading to a refined library of 15 PCS. A greedy algorithm and coarse-grained force field were used to predict peptide structure and folding before subsequent modeling studies. Molecular docking was performed to estimate the relative binding affinities, and out of the top hits, Pep15 was subjected to molecular dynamics simulations and binding free-energy calculations in comparison to a known peptide inhibitor with experimental data (template peptide). Interestingly, the identified peptide through our protocol, Pep15, was found to show a significantly higher binding affinity than the reference template peptide (-48.10 ± 0.23 kcal/mol and -17.53 ± 0.27 kcal/mol, respectively). In comparison to the template peptide, Pep15 was found to possess a more compact and buried surface area, tighter binding landscape, and reduced conformational variability, leading to enhanced structural and kinetic stability of the Cdk5/p25 complex. Notably, both peptide inhibitors were found to have a minimal impact on the architectural integrity of the Cdk5/p25 secondary structure. Herein, we propose Pep15 as a novel and potentially disruptive peptide drug for Cdk5/p25-mediated neurodegenerative phenotypes that require further clinical investigation. The systematic protocol and findings of this report would serve as a valuable tool in the identification of critical PPI interface reactive residues, designing of analogs, and identification of more potent peptide-based PPI inhibitors.

In silico prediction of CD8+ and CD4+ T cell epitopes in Leishmania major proteome: Using immunoinformatics

The leishmaniases are NDTs (neglected tropical diseases) that affect people all over the world. They are brought on by protozoans from the genus Leishmania and disseminated by phlebotomine flies that are afflicted with the disease. The best option to manage and lower the incidence of these diseases has been thought by the creation of a safe and effective vaccination. This research used an in silico based mining approach to look for high potential epitopes that might bind to MHC Class I and MHC Class II molecules (mainly; HLA-A*02:01 & HLA-DRB1*03:01) from human population in order to promote vaccine development. Based on the presence of signal peptides, GPI anchors, antigenicity predictions, and a subtractive proteomic technique, we have screened 17 putative antigenic proteins from the 8083 total proteins of L. major. After that thorough immunogenic epitope prediction were done using IEDB-AR tools. We isolated five immunogenic epitopes (three 9-mer & two 15-mer) from five antigenic proteins through docking and MD simulation analysis. Finally, these five anticipated epitopes, viz., TLPEIPVNV, ELMAPVFGL, TLAAAVALL, NSINIRLDGVTSAGF and NVPLVVDASSLFRVA have considerably stronger binding potential with their respective alleles and may trigger immunological responses. The goal of this work was to identify MHC restricted epitopes for CD8+ and CD4+ T cells activation using immunoinformatics in order to identify potential vaccine candidates against L. major parasites.

Immunoinformatic based designing of potential immunogenic novel mRNA and peptide-based prophylactic vaccines against H5N1 and H7N9 avian influenza viruses

Influenza viruses are the most common cause of serious respiratory illnesses worldwide and are responsible for a significant number of annual fatalities. Therefore, it is crucial to look for new immunogenic sites that might trigger an effective immune response. In the present study, bioinformatics tools were used to design mRNA and multiepitope-based vaccines against H5N1 and H7N9 subtypes of avian influenza viruses. Several Immunoinformatic tools were employed to extrapolate T and B lymphocyte epitopes of HA and NA proteins of both subtypes. The molecular docking approach was used to dock the selected HTL and CTL epitopes with the corresponding MHC molecules. Eight (8) CTL, four (4) HTL, and Six (6) linear B cell epitopes were chosen for the structural arrangement of mRNA and of peptide-based prophylactic vaccine designs. Different physicochemical characteristics of the selected epitopes fitted with suitable linkers were analyzed. High antigenic, non-toxic, and non-allergenic features of the designed vaccines were noted at a neutral physiological pH. Codon optimization tool was used to check the GC content and CAI value of constructed MEVC-Flu vaccine, which were recorded to be 50.42% and 0.97 respectively. the GC content and CAI value verify the stable expression of vaccine in pET28a + vector. In-silico immunological simulation the MEVC-Flu vaccine construct revealed a high level of immune responses. The molecular dynamics simulation and docking results confirmed the stable interaction of TLR-8 and MEVC-Flu vaccine. Based on these parameters, vaccine constructs can be regarded as an optimistic choice against H5N1 and H7N9 strains of the influenza virus. Further experimental testing of these prophylactic vaccine designs against pathogenic avian influenza strains may clarify their safety and efficacy.Communicated by Ramaswamy H. Sarma.

Bioinformatics analysis of multi-epitope peptide vaccines against Hepatitis C virus: a molecular docking study

BACKGROUND

Hepatitis C Virus (HCV) infection is one of the causal agents of liver disease burden. Six multiple antigenic peptides were synthesized including (P315, P412, and P517) plus (P1771, P2121, and P2941) to induce humoral and cellular responses, respectively against HCV infection.

AIM

This paper aimed to employ computational tools to evaluate the efficacy of each peptide individually and to determine the most effective one for better vaccine development and/or immunotherapy.

METHODS

VaxiJen web and AllerTOP servers were used for antigenicity and allergenicity prediction, respectively. The ToxinPred web server was used to investigate the peptide toxicity. Each peptide was docked with its corresponding receptors.

RESULTS

No peptides were expected to be toxic. P315 and P2941 are predicted to have robust antigenic properties, lowest allergenicity, and minimal sOPEP energies. In turn, P315 (derived from gpE1) formed the highest hydrophobic bonds with the BCR and CD81 receptors that will elicit B cell function. P2941 (derived from NS5B) was shown to strongly bind to both CD4 and CD8 receptors that will elicit T cell function.

CONCLUSION

P315 successfully bound to B cell (BCR and CD81) receptors. Also, P2941 is strongly bound to T cell (CD4 and CD8) receptors.

Functional mimicry of sea urchin biomineralization proteins with CaCO3-binding peptides selected by phage display

The intricate process of biomineralization, e.g. in sea urchins, involves the precise interplay of highly regulated mineralization proteins and the spatiotemporal coordination achieved through compartmentalization. However, the investigation of biomineralization effector molecules, e.g. proteins, is challenging, due to their very low abundance. Therefore, we investigate the functional mimicry in the bioinspired precipitation of calcium carbonate (CaCO3) with artificial peptides selected from a peptide library by phage display based on peptide-binding to calcite and aragonite, respectively. The structure-directing effects of the identified peptides were compared to those of natural protein mixes isolated from skeletal (test) structures of two sea urchin species (Arbacia lixula and Paracentrotus lividus). The calcium carbonate samples deposited in the absence or presence of peptides were analyzed with a set of complementary techniques with regard to morphology, polymorph, and nanostructural motifs. Remarkably, some of the CaCO3-binding peptides induced morphological features in calcite that appeared similar to those obtained in the presence of the natural protein mixes. Many of the peptides identified as most effective in exerting a structure-directing effect on calcium carbonate crystallization were rich in basic amino acid residues. Hence, our in vitro mineralization study further highlights the important, but often neglected, role of positively charged soluble organic matrices associated with biological and bioinspired CaCO3 deposition.

Pragmatic Coarse-Graining of Proteins: Models and Applications

The molecular details involved in the folding, dynamics, organization, and interaction of proteins with other molecules are often difficult to assess by experimental techniques. Consequently, computational models play an ever-increasing role in the field. However, biological processes involving large-scale protein assemblies or long time scale dynamics are still computationally expensive to study in atomistic detail. For these applications, employing coarse-grained (CG) modeling approaches has become a key strategy. In this Review, we provide an overview of what we call pragmatic CG protein models, which are strategies combining, at least in part, a physics-based implementation and a top-down experimental approach to their parametrization. In particular, we focus on CG models in which most protein residues are represented by at least two beads, allowing these models to retain some degree of chemical specificity. A description of the main modern pragmatic protein CG models is provided, including a review of the most recent applications and an outlook on future perspectives in the field.

Comparative Performance of Computer Simulation Models of Intrinsically Disordered Proteins at Different Levels of Coarse-Graining

Coarse-graining is commonly used to decrease the computational cost of simulations. However, coarse-grained models are also considered to have lower transferability, with lower accuracy for systems outside the original scope of parametrization. Here, we benchmark a bead-necklace model and a modified Martini 2 model, both coarse-grained models, for a set of intrinsically disordered proteins, with the different models having different degrees of coarse-graining. The SOP-IDP model has earlier been used for this set of proteins; thus, those results are included in this study to compare how models with different levels of coarse-graining compare. The sometimes naive expectation of the least coarse-grained model performing best does not hold true for the experimental pool of proteins used here. Instead, it showed the least good agreement, indicating that one should not necessarily trust the otherwise intuitive notion of a more advanced model inherently being better in model choice.

Elucidation of novel compounds and epitope-based peptide vaccine design against C30 endopeptidase regions of SARS-CoV-2 using immunoinformatics approaches

There has been progressive improvement in immunoinformatics approaches for epitope-based peptide design. Computational-based immune-informatics approaches were applied to identify the epitopes of SARS-CoV-2 to develop vaccines. The accessibility of the SARS-CoV-2 protein surface was analyzed, and hexa-peptide sequences (KTPKYK) were observed having a maximum score of 8.254, located between amino acids 97 and 102, whereas the FSVLAC at amino acids 112 to 117 showed the lowest score of 0.114. The surface flexibility of the target protein ranged from 0.864 to 1.099 having amino acid ranges of 159 to 165 and 118 to 124, respectively, harboring the FCYMHHM and YNGSPSG hepta-peptide sequences. The surface flexibility was predicted, and a 0.864 score was observed from amino acids 159 to 165 with the hepta-peptide (FCYMHHM) sequence. Moreover, the highest score of 1.099 was observed between amino acids 118 and 124 against YNGSPSG. B-cell epitopes and cytotoxic T-lymphocyte (CTL) epitopes were also identified against SARS-CoV-2. In molecular docking analyses, -0.54 to -26.21 kcal/mol global energy was observed against the selected CTL epitopes, exhibiting binding solid energies of -3.33 to -26.36 kcal/mol. Based on optimization, eight epitopes (SEDMLNPNY, GSVGFNIDY, LLEDEFTPF, DYDCVSFCY, GTDLEGNFY, QTFSVLACY, TVNVLAWLY, and TANPKTPKY) showed reliable findings. The study calculated the associated HLA alleles with MHC-I and MHC-II and found that MHC-I epitopes had higher population coverage (0.9019% and 0.5639%) than MHC-II epitopes, which ranged from 58.49% to 34.71% in Italy and China, respectively. The CTL epitopes were docked with antigenic sites and analyzed with MHC-I HLA protein. In addition, virtual screening was conducted using the ZINC database library, which contained 3,447 compounds. The 10 top-ranked scrutinized molecules (ZINC222731806, ZINC077293241, ZINC014880001, ZINC003830427, ZINC030731133, ZINC003932831, ZINC003816514, ZINC004245650, ZINC000057255, and ZINC011592639) exhibited the least binding energy (-8.8 to -7.5 kcal/mol). The molecular dynamics (MD) and immune simulation data suggest that these epitopes could be used to design an effective SARS-CoV-2 vaccine in the form of a peptide-based vaccine. Our identified CTL epitopes have the potential to inhibit SARS-CoV-2 replication.

GB1 Dimerization in Crowders: A Multiple Resolution Approach

In-cell protein-protein association, which is crucial in enzyme catalysis and polymerization, occurs in an environment that is highly heterogeneous and crowded. The crowder molecules exclude the reactant molecules from occupying certain regions of the cell, resulting in changes in the reaction thermodynamics and kinetics. Recent studies, both experiment and simulations, revealed that the nature of the interaction between crowder and protein species, in particular the soft interactions, plays an important role in crowder induced effects on protein association. To this end, from a simulation perspective, it is important to decipher the level of structural resolution in a protein-crowder model that can faithfully capture the influence of crowding on protein association. Here, we investigate the dimerization of model system GB1 in the presence of lysozyme crowders at two structural resolutions. The lower resolution model assumes both protein and crowder species as spherical beads, similar to the analytical scaled particle theory model, whereas the higher resolution model retains residue specific structural details for protein and crowder species. From the higher resolution model, it is found that GB1 dimer formation is destabilized in the presence of lysozyme crowders, and the destabilization is more for the side-by-side dimer compared to the domain-swapped dimer, in qualitative agreement with experimental findings. However, the low resolution CG model predicts stabilization of the dimers in the presence of the lysozyme crowder, similar to the SPT model. Our results indicate a nontrivial role of the choice of model resolution in computer simulation studies investigating crowder induced effects.

Chitosan and HPMCAS double-coating as protective systems for alginate microparticles loaded with Ctx(Ile21)-Ha antimicrobial peptide to prevent intestinal infections

The incorrect use of conventional drugs for both prevention and control of intestinal infections has contributed to a significant spread of bacterial resistance. In this way, studies that promote their replacement are a priority. In the last decade, the use of antimicrobial peptides (AMP), especially Ctx(Ile²¹)-Ha AMP, has gained strength, demonstrating efficient antimicrobial activity (AA) against pathogens, including multidrug-resistant bacteria. However, gastrointestinal degradation does not allow its direct oral application. In this research, double-coating systems using alginate microparticles loaded with Ctx(Ile²¹)-Ha peptide were designed, and in vitro release assays simulating the gastrointestinal tract were evaluated. Also, the AA against Salmonella spp. and Escherichia coli was examined. The results showed the physicochemical stability of Ctx(Ile²¹)-Ha peptide in the system and its potent antimicrobial activity. In addition, the combination of HPMCAS and chitosan as a gastric protection system can be promising for peptide carriers or other low pH-sensitive molecules, adequately released in the intestine. In conclusion, the coated systems employed in this study can improve the formulation of new foods or biopharmaceutical products for specific application against intestinal pathogens in animal production or, possibly, in the near future, in human health.

A refined pH-dependent coarse-grained model for peptide structure prediction in aqueous solution

Introduction: Peptides carry out diverse biological functions and the knowledge of the conformational ensemble of polypeptides in various experimental conditions is important for biological applications. All fast dedicated softwares perform well in aqueous solution at neutral pH. Methods: In this study, we go one step beyond by combining the Debye-Hückel formalism for charged-charged amino acid interactions and a coarse-grained potential of the amino acids to treat pH and salt variations. Results: Using the PEP-FOLD framework, we show that our approach performs as well as the machine-leaning AlphaFold2 and TrRosetta methods for 15 well-structured sequences, but shows significant improvement in structure prediction of six poly-charged amino acids and two sequences that have no homologous in the Protein Data Bank, expanding the range of possibilities for the understanding of peptide biological roles and the design of candidate therapeutic peptides.

Immunoinformatics based design and prediction of proteome-wide killer cell epitopes of Leishmania donovani: Potential application in vaccine development

Despite several extensive and exhaustive efforts, search for potential therapy against leishmaniasis has not made much progress. In the present work, we have employed mining strategy to screen Leishmania donovani proteome for identification of promising vaccine candidate. We have screened 21 potential antigenic proteins from 7960 total protein of L. donovani, based on the presence of signal peptide, GPI anchor, antigenicity prediction and substractive proteomic approach. Secondly, we have also performed comprehensive immunogenic epitope prediction from the screened 21 proteins, using IEDB-AR tools. Out of the 21 antigenic proteins, we obtained 11 immunogenic epitopes from 9 proteins. The final results revealed that four predicted epitopes namely; YPAFAALVF, VAVAATVAY, AAAPTEAAL and MYPLVAVVF, have significantly better binding potential with respective alleles and could elicits immune responses. Docking analysis using PATCHDOCK server and molecular dynamic simulation using GROMACS revealed the potential of the sequences as immunogenic epitopes. In silico studies also suggested that the epitopes occupied almost same binding cleft with the respective alleles, when compared with the reference peptides. It is also suggested from the molecular dynamic simulation data that the peptides were intact in the pocket for longer periods of time. Our study was designed to select MHC class I restricted epitopes for the activation of CD8 T cells using immunoinformatics for the prediction of probable vaccine candidate against L. donovani parasites. Communicated by Ramaswamy H. Sarma.

Toxicological and Safety Pharmacological Profiling of the Anti-Infective and Anti-Inflammatory Peptide Pep19-2.5

Aspidasept (Pep19-2.5) and its derivative Pep19-4LF ("Aspidasept II") are anti-infective and anti-inflammatory synthetic polypeptides currently in development for application against a variety of moderate to severe bacterial infections that could lead to systemic inflammation, as in the case of severe sepsis and septic shock, as well as application to non-systemic diseases in the case of skin and soft tissue infections (SSTI). In the present study, Aspidasept and Aspidasept II and their part structures were analysed with respect to their toxic behavior in different established models against a variety of relevant cells, and in electrophysiological experiments targeting the hERG channel according to ICH S7B. Furthermore, the effects in mouse models of neurobiological behavior and the local lymph node according to OECD test guideline 429 were investigated, as well as a rat model of repeated dose toxicology according to ICH M3. The data provide conclusive information about potential toxic effects, thus specifying a therapeutic window for the application of the peptides. Therefore, these data allow us to define Aspidasept concentrations for their use in clinical studies as parenteral application.

Identification of Novel Antifreeze Peptides from Takifugu obscurus Skin and Molecular Mechanism in Inhibiting Ice Crystal Growth

Foodborne hydrolyzed antifreeze peptides have been widely used in the food industry and the biomedical field. However, the components of hydrolyzed peptides are complex and the molecular mechanism remains unclear. This study focused on identification and mechanism analysis of novel antifreeze peptides from Takifugu obscurus skin by traditional methods and computer-assisted techniques. Results showed that three peptides (EGPRAGGAPG, GDAGPSGPAGPTG, and GEAGPAGPAG) possessed cryoprotection via reducing the freezing point and inhibiting ice crystal growth. Molecular docking confirmed that the cryoprotective property was related to peptide structure, especially α-helix, and hydrogen bond sites. Moreover, the antifreeze peptides were double-faces, which controlled ice crystals while affecting the arrangement of surrounding water molecules, thus exhibiting a strong antifreeze activity. This investigation deepens the comprehension of the mechanism of antifreeze peptides at molecular scale, and the novel efficient antifreeze peptides can be developed in antifreeze materials design and applied in food industry.

Bottom-up Coarse-Graining: Principles and Perspectives

Large-scale computational molecular models provide scientists a means to investigate the effect of microscopic details on emergent mesoscopic behavior. Elucidating the relationship between variations on the molecular scale and macroscopic observable properties facilitates an understanding of the molecular interactions driving the properties of real world materials and complex systems (e.g., those found in biology, chemistry, and materials science). As a result, discovering an explicit, systematic connection between microscopic nature and emergent mesoscopic behavior is a fundamental goal for this type of investigation. The molecular forces critical to driving the behavior of complex heterogeneous systems are often unclear. More problematically, simulations of representative model systems are often prohibitively expensive from both spatial and temporal perspectives, impeding straightforward investigations over possible hypotheses characterizing molecular behavior. While the reduction in resolution of a study, such as moving from an atomistic simulation to that of the resolution of large coarse-grained (CG) groups of atoms, can partially ameliorate the cost of individual simulations, the relationship between the proposed microscopic details and this intermediate resolution is nontrivial and presents new obstacles to study. Small portions of these complex systems can be realistically simulated. Alone, these smaller simulations likely do not provide insight into collectively emergent behavior. However, by proposing that the driving forces in both smaller and larger systems (containing many related copies of the smaller system) have an explicit connection, systematic bottom-up CG techniques can be used to transfer CG hypotheses discovered using a smaller scale system to a larger system of primary interest. The proposed connection between different CG systems is prescribed by (i) the CG representation (mapping) and (ii) the functional form and parameters used to represent the CG energetics, which approximate potentials of mean force (PMFs). As a result, the design of CG methods that facilitate a variety of physically relevant representations, approximations, and force fields is critical to moving the frontier of systematic CG forward. Crucially, the proposed connection between the system used for parametrization and the system of interest is orthogonal to the optimization used to approximate the potential of mean force present in all systematic CG methods. The empirical efficacy of machine learning techniques on a variety of tasks provides strong motivation to consider these approaches for approximating the PMF and analyzing these approximations.

In silico identification of two peptides with antibacterial activity against multidrug-resistant Staphylococcus aureus

Here we report two antimicrobial peptides (AMPs), HG2 and HG4 identified from a rumen microbiome metagenomic dataset, with activity against multidrug-resistant (MDR) bacteria, especially methicillin-resistant Staphylococcus aureus (MRSA) strains, a major hospital and community-acquired pathogen. We employed the classifier model design to analyse, visualise, and interpret AMP activities. This approach allowed in silico discrimination of promising lead AMP candidates for experimental evaluation. The lead AMPs, HG2 and HG4, are fast-acting and show anti-biofilm and anti-inflammatory activities in vitro and demonstrated little toxicity to human primary cell lines. The peptides were effective in vivo within a Galleria mellonella model of MRSA USA300 infection. In terms of mechanism of action, HG2 and HG4 appear to interact with the cytoplasmic membrane of target cells and may inhibit other cellular processes, whilst preferentially binding to bacterial lipids over human cell lipids. Therefore, these AMPs may offer additional therapeutic templates for MDR bacterial infections.

Development of a Hybrid-Resolution Force Field for Peptide Self-Assembly Simulations: Optimizing Peptide-Peptide and Peptide-Solvent Interactions

Atomic descriptions of peptide self-assembly are crucial to an understanding of disease-related peptide aggregation and the design of peptide-assembled materials. Obtaining these descriptions through computer simulation is challenging because current force fields, which were not designed for this process and are often unable to describe correctly peptide self-assembly behavior and the sequence dependence. Here, we developed a framework using dipeptide aggregation as a model system to improve force fields for simulations of self-assembly. Aggregation-related structural properties were designed and used to guide the optimization of peptide-peptide and peptide-solvent interactions. With this framework, we developed a self-assembly force field, termed PACE-ASM, by reoptimizing a hybrid-resolution force field that was originally developed for folding simulation. With its applicability in folding simulations, the new PACE was used to simulate the self-assembly of two disease-related short peptides, Aβ_16-21 and PHF6, into β-sheet-rich cross-β amyloids. These simulations reproduced the crystal structures of Aβ_16-21 and PHF6 amyloids at near-atomic resolution and captured the difference in packing orientations between the two sequences, a task which is challenging even with all-atom force fields. Apart from cross-β amyloids, the self-assembly of emerging helix-rich cross-α amyloids by another peptide PSMα3 can also be correctly described with the new PACE, manifesting the versatility of the force field. We demonstrated that the ability of the PACE-ASM to model peptide self-assembly is based largely on its improved description of peptide-peptide and peptide-solvent interactions. This was achieved with our optimization framework that can readily identify and address the deficiency in describing these interactions.

Epitope-based peptide vaccine design and elucidation of novel compounds against 3C like protein of SARS-CoV-2

Coronaviruses (CoVs) are positive-stranded RNA viruses with short clubs on their edges. CoVs are pathogenic viruses that infect several animals and plant organisms, as well as humans (lethal respiratory dysfunctions). A noval strain of CoV has been reported and named as SARS-CoV-2. Numerous COVID-19 cases were being reported all over the World. COVID-19 and has a high mortality rate. In the present study, immunoinformatics techniques were utilized to predict the antigenic epitopes against 3C like protein. B-cell epitopes and Cytotoxic T-lymphocyte (CTL) were designed computationally against SARS-CoV-2. Multiple Sequence Alignment (MSA) of seven complete strains (HCoV-229E, HCoV-NL63, HCoV-OC43, HCoV-HKU1, SARS-CoV, MERS-CoV, and SARS-CoV-2) was performed to elucidate the binding domain and interacting residues. MHC-I binding epitopes were evaluated by analyzing the binding affinity of the top-ranked peptides having HLA molecule. By utilizing the docked complexes of CTL epitopes with antigenic sites, the binding relationship and affinity of top-ranked predicted peptides with the MHC-I HLA protein were investigated. The molecular docking analyses were conducted on the ZINC database library and twelve compounds having least binding energy were scrutinized. In conclusion, twelve CTL epitopes (GTDLEGNFY, TVNVLAWLY, GSVGFNIDY, SEDMLNPNY, LSQTGIAV, VLDMCASLK, LTQDHVDIL, TTLNDFNLV, CTSEDMLNP, TTITVNVLA, YNGSPSGVY, and SMQNCVLKL) were identified against SARS-CoV-2.

On the need to introduce environmental characteristics in ab initio protein structure prediction using a coarse-grained UNRES force field

During the protein folding process in computer simulations involving the use of a United RESidue (UNRES) force field, an additional module was introduced to represent directly the presence of a polar solvent in water form. This module implements the fuzzy oil drop model (FOD) where the 3D Gauss function expresses the presence of a polar environment which directs the polypeptide chain folding process towards the generation of a centric hydrophobic core. Sample test polypeptide chains of 8 proteins with chain lengths ranging from 37 to 75 aa were simulated in silico using the UNRES (U) package with an implicit solvent model and a built-in module expressing the FOD model (UNRES-FOD-UNRES (U + F) interleaved simulation). The protein structure obtained by both *** simulation schemes, i.e., accordingly***U and U + F, for all the analyzed protein models shows the presence of a hydrophobic core including where it is absent in the native structure. The proposed FOD-M model (M-modified) explaining the source of this phenomenon reveals the need to modify the external field expressing the role of a folding environment. The modification takes into account the influence of other than polar factors present in the folding environment.

A Generalized Attraction-Repulsion Potential and Revisited Fragment Library Improves PEP-FOLD Peptide Structure Prediction

Fast and accurate structure prediction is essential to the study of peptide function, molecular targets, and interactions and has been the subject of considerable efforts in the past decade. In this work, we present improvements to the popular simplified PEP-FOLD technique for small peptide structure prediction. PEP-FOLD originality is threefold: (i) it uses a predetermined structural alphabet, (ii) it uses a sequential algorithm to reconstruct the tridimensional structures of these peptides in a discrete space using a fragment library, and (iii) it assesses the energy of these structures using a coarse-grained representation in which all of the backbone atoms but the α-hydrogen are present, and the side chain corresponds to a unique bead. In former versions of PEP-FOLD, a van der Waals formulation was used for non-bonded interactions, with each side chain being associated with a fixed radius. Here, we explore the relevance of using instead a generalized formulation in which not only the optimal distance of interaction and the energy at this distance are parameters but also the distance at which the potential is zero. This allows each side chain to be associated with a different radius and potential energy shape, depending on its interaction partner, and in principle to make more effective the coarse-grained representation. In addition, the new PEP-FOLD version is associated with an updated library of fragments. We show that these modifications lead to important improvements for many of the problematic targets identified with the former PEP-FOLD version while maintaining already correct predictions. The improvement is in terms of both model ranking and model accuracy. We also compare the PEP-FOLD enhanced version to state-of-the-art techniques for both peptide and structure predictions: APPTest, RaptorX, and AlphaFold2. We find that the new predictions are superior, in particular with respect to the prediction of small β-targets, to those of APPTest and RaptorX and bring, with its original approach, additional understanding on folded structures, even when less precise than AlphaFold2. With their strong physical influence, the revised structural library and coarse-grained potential offer, however, the means for a deeper understanding of the nature of folding and open a solid basis for studying flexibility and other dynamical properties not accessible to IA structure prediction approaches.

Effect of Cholesterol Molecules on Aβ1-42 Wild-Type and Mutants Trimers

Alzheimer’s disease displays aggregates of the amyloid-beta (Aβ) peptide in the brain, and there is increasing evidence that cholesterol may contribute to the pathogenesis of the disease. Though many experimental and theoretical studies have focused on the interactions of Aβ oligomers with membrane models containing cholesterol, an understanding of the effect of free cholesterol on small Aβ42 oligomers is not fully established. To address this question, we report on replica exchange with a solute tempering simulation of an Aβ42 trimer with cholesterol and compare it with a previous replica exchange molecular dynamics simulation. We show that the binding hot spots of cholesterol are rather complex, involving hydrophobic residues L17–F20 and L30–M35 with a non-negligible contribution of loop residues D22–K28 and N-terminus residues. We also examine the effects of cholesterol on the trimers of the disease-causing A21G and disease-protective A2T mutations by molecular dynamics simulations. We show that these two mutations moderately impact cholesterol-binding modes. In our REST2 simulations, we find that cholesterol is rarely inserted into aggregates but rather attached as dimers and trimers at the surface of Aβ42 oligomers. We propose that cholesterol acts as a glue to speed up the formation of larger aggregates; this provides a mechanistic link between cholesterol and Alzheimer’s disease.

Computational Models for the Study of Protein Aggregation

Protein aggregation has been studied by many groups around the world for many years because it can be the cause of a number of neurodegenerative diseases that have no effective treatment. Obtaining the structure of related fibrils and toxic oligomers, as well as describing the pathways and main factors that govern the self-organization process, is of paramount importance, but it is also very difficult. To solve this problem, experimental and computational methods are often combined to get the most out of each method. The effectiveness of the computational approach largely depends on the construction of a reasonable molecular model. Here we discussed different versions of the four most popular all-atom force fields AMBER, CHARMM, GROMOS, and OPLS, which have been developed for folded and intrinsically disordered proteins, or both. Continuous and discrete coarse-grained models, which were mainly used to study the kinetics of aggregation, are also summarized.

Dynamics of Amyloid Formation from Simplified Representation to Atomistic Simulations

Amyloid fibril formation is an intrinsic property of short peptides, non-disease proteins, and proteins associated with neurodegenerative diseases. Aggregates of the Aβ and tau proteins, the α-synuclein protein, and the prion protein are observed in the brain of Alzheimer's, Parkinson's, and prion disease patients, respectively. Due to the transient short-range and long-range interactions of all species and their high aggregation propensities, the conformational ensemble of these devastating proteins, the exception being for the monomeric prion protein, remains elusive by standard structural biology methods in bulk solution and in lipid membranes. To overcome these limitations, an increasing number of simulations using different sampling methods and protein models have been performed. In this chapter, we first review our main contributions to the field of amyloid protein simulations aimed at understanding the early aggregation steps of short linear amyloid peptides, the conformational ensemble of the Aβ40/42 dimers in bulk solution, and the stability of Aβ aggregates in lipid membrane models. Then we focus on our studies on the interactions of amyloid peptides/inhibitors to prevent aggregation, and long amyloid sequences, including new results on a monomeric tau construct.

Theory and Practice of Coarse-Grained Molecular Dynamics of Biologically Important Systems

Molecular dynamics with coarse-grained models is nowadays extensively used to simulate biomolecular systems at large time and size scales, compared to those accessible to all-atom molecular dynamics. In this review article, we describe the physical basis of coarse-grained molecular dynamics, the coarse-grained force fields, the equations of motion and the respective numerical integration algorithms, and selected practical applications of coarse-grained molecular dynamics. We demonstrate that the motion of coarse-grained sites is governed by the potential of mean force and the friction and stochastic forces, resulting from integrating out the secondary degrees of freedom. Consequently, Langevin dynamics is a natural means of describing the motion of a system at the coarse-grained level and the potential of mean force is the physical basis of the coarse-grained force fields. Moreover, the choice of coarse-grained variables and the fact that coarse-grained sites often do not have spherical symmetry implies a non-diagonal inertia tensor. We describe selected coarse-grained models used in molecular dynamics simulations, including the most popular MARTINI model developed by Marrink's group and the UNICORN model of biological macromolecules developed in our laboratory. We conclude by discussing examples of the application of coarse-grained molecular dynamics to study biologically important processes.

Differentiable molecular simulation can learn all the parameters in a coarse-grained force field for proteins

Finding optimal parameters for force fields used in molecular simulation is a challenging and time-consuming task, partly due to the difficulty of tuning multiple parameters at once. Automatic differentiation presents a general solution: run a simulation, obtain gradients of a loss function with respect to all the parameters, and use these to improve the force field. This approach takes advantage of the deep learning revolution whilst retaining the interpretability and efficiency of existing force fields. We demonstrate that this is possible by parameterising a simple coarse-grained force field for proteins, based on training simulations of up to 2,000 steps learning to keep the native structure stable. The learned potential matches chemical knowledge and PDB data, can fold and reproduce the dynamics of small proteins, and shows ability in protein design and model scoring applications. Problems in applying differentiable molecular simulation to all-atom models of proteins are discussed along with possible solutions and the variety of available loss functions. The learned potential, simulation scripts and training code are made available at https://github.com/psipred/cgdms.

From System Modeling to System Analysis: The Impact of Resolution Level and Resolution Distribution in the Computer-Aided Investigation of Biomolecules

The ever increasing computer power, together with the improved accuracy of atomistic force fields, enables researchers to investigate biological systems at the molecular level with remarkable detail. However, the relevant length and time scales of many processes of interest are still hardly within reach even for state-of-the-art hardware, thus leaving important questions often unanswered. The computer-aided investigation of many biological physics problems thus largely benefits from the usage of coarse-grained models, that is, simplified representations of a molecule at a level of resolution that is lower than atomistic. A plethora of coarse-grained models have been developed, which differ most notably in their granularity; this latter aspect determines one of the crucial open issues in the field, i.e. the identification of an optimal degree of coarsening, which enables the greatest simplification at the expenses of the smallest information loss. In this review, we present the problem of coarse-grained modeling in biophysics from the viewpoint of system representation and information content. In particular, we discuss two distinct yet complementary aspects of protein modeling: on the one hand, the relationship between the resolution of a model and its capacity of accurately reproducing the properties of interest; on the other hand, the possibility of employing a lower resolution description of a detailed model to extract simple, useful, and intelligible information from the latter.

Identification of naturally processed Zika virus peptides by mass spectrometry and validation of memory T cell recall responses in Zika convalescent subjects

Once an obscure pathogen, Zika virus (ZIKV) has emerged as a significant global public health concern. Several studies have linked ZIKV infection in pregnant women with the development of microcephaly and other neurological abnormalities, emphasizing the need for a safe and effective vaccine to combat the spread of this disease. Preclinical studies and vaccine development efforts have largely focused on the role of humoral immunity in disease protection. Consequently, relatively little is known in regard to cellular immunity against ZIKV, although an effective vaccine will likely need to engage both the humoral and cellular arms of the immune system. To that end, we utilized two-dimensional liquid chromatography coupled with tandem mass spectrometry to identify 90 ZIKV peptides that were naturally processed and presented on HLA class I and II molecules (HLA-A*02:01/HLA-DRB1*04:01) of an immortalized B cell line infected with ZIKV (strain PRVABC59). Sequence identity clustering was used to filter the number of candidate peptides prior to evaluating memory T cell recall responses in ZIKV convalescent subjects. Peptides that individually elicited broad (4 of 7 subjects) and narrow (1 of 7 subjects) T cell responses were further analyzed using a suite of predictive algorithms and in silico modeling to evaluate HLA binding and peptide structural properties. A subset of nine broadly reactive peptides was predicted to provide robust global population coverage (97.47% class I; 70.74% class II) and to possess stable structural properties amenable for vaccine formulation, highlighting the potential clinical benefit for including ZIKV T cell epitopes in experimental vaccine formulations.

Immunoinformatics prediction of potential B-cell and T-cell epitopes as effective vaccine candidates for eliciting immunogenic responses against Epstein-Barr virus

BACKGROUND

The ongoing search for viable treatment options to curtail Epstein Barr Virus (EBV) pathogenicity has necessitated a paradigmatic shift towards the design of peptide-based vaccines. Potential B-cell and T-cell epitopes were predicted for nine antigenic EBV proteins that mediate epithelial cell-attachment and spread, capsid self-assembly, DNA replication and processivity.

METHODS

Predictive algorithms incorporated in the Immune Epitope Database (IEDB) resources were used to determine potential B-cell epitopes based on their physicochemical attributes. These were combined with a string-kernel method and an antigenicity predictive AlgPred tool to enhance accuracy in the end-point selection of highly potential antigenic EBV B-cell epitopes. NetCTL 1.2 algorithms enabled the prediction of probable T-cell epitopes which were structurally modeled and subjected to blind peptide-protein docking with HLA-A*02:01. All-atom molecular dynamics (MD) simulation and Molecular Mechanics Generalized-Born Surface Area methods were used to investigate interaction dynamics and affinities of predicted T-cell peptide-protein complexes.

RESULTS

Computational predictions and sequence overlapping analysis yielded 18 linear (continuous) and discontinuous (conformational) subunit epitopes from the antigenic proteins with characteristic surface accessibility, flexibility and antigenicity, and predictive scores above the threshold value (1) set. A novel site was identified on HLA-A*02:01 with preferential affinity binding for modeled BMRF2, BXLF1 and BGLF4 T-cell epitopes. Interaction dynamics and energies were also computed in addition to crucial residues that mediated complex formation and stability.

CONCLUSION

This study implemented an integrative meta-analytical approach to model highly probable B-cell and T-cell epitopes as potential peptide-vaccine candidates for the treatment of EBV-related diseases.

The rumen eukaryotome is a source of novel antimicrobial peptides with therapeutic potential

BACKGROUND

The rise of microbial antibiotic resistance is a leading threat to the health of the human population. As such, finding new approaches to tackle these microbes, including development of novel antibiotics is vital.

RESULTS

In this study, we mined a rumen eukaryotic metatranscriptomic library for novel Antimicrobial peptides (AMPs) using computational approaches and thereafter characterised the therapeutic potential of the AMPs. We identified a total of 208 potentially novel AMPs from the ruminal eukaryotome, and characterised one of those, namely Lubelisin. Lubelisin (GIVAWFWRLAR) is an α-helical peptide, 11 amino acid long with theoretical molecular weight of 1373.76 D. In the presence of Lubelisin, strains of methicillin-resistant Staphylococcus aureus (MRSA) USA300 and EMRSA-15 were killed within 30 min of exposure with ≥103 and 104 CFU/mL reduction in viable cells respectively. Cytotoxicity of Lubelisin against both human and sheep erythrocytes was low resulting in a therapeutic index of 0.43. Membrane permeabilisation assays using propidium iodide alongside transmission electron microscopy revealed that cytoplasmic membrane damage may contribute to the antimicrobial activities of Lubelisin.

CONCLUSIONS

We demonstrate that the rumen eukaryotome is a viable source for the discovery of antimicrobial molecules for the treatment of bacterial infections and further development of these may provide part of the potential solution to the ongoing problem of antimicrobial resistance. The role of these AMPs in the ecological warfare within the rumen is also currently unknown.

Adaptively Iterative Multiscale Switching Simulation Strategy and Applications to Protein Folding and Structure Prediction

Structure prediction is an important means to quickly understand new protein functions. However, the prediction of effects of proteins that have no detectable templates is still to be improved. Molecular dynamics simulation is supposed to be the primary research tool for structure predictions, but it still has limitations of huge computational cost in all-atom (AA) models and rough accuracy in coarse-grained (CG) models. We propose a universal multiscale simulation strategy named AIMS in which simulations can iteratively switch among multiple resolutions in order to adaptively trade off AA accuracy and CG high-efficiency. AIMS follows the idea of CG-guided enhanced sampling so that final results always keep AA accuracy. We successfully achieve four ab initio and four data-assisted protein structure predictions using AIMS. The prediction result is an ensemble rather than a structure and provides special insights on folding metastable states. AIMS is estimated to achieve a computational speed about 40 times faster than that of conventional AA simulations.

Immunoinformatics and Molecular Docking Studies Predicted Potential Multiepitope-Based Peptide Vaccine and Novel Compounds against Novel SARS-CoV-2 through Virtual Screening

BACKGROUND

Coronaviruses (CoVs) are enveloped positive-strand RNA viruses which have club-like spikes at the surface with a unique replication process. Coronaviruses are categorized as major pathogenic viruses causing a variety of diseases in birds and mammals including humans (lethal respiratory dysfunctions). Nowadays, a new strain of coronaviruses is identified and named as SARS-CoV-2. Multiple cases of SARS-CoV-2 attacks are being reported all over the world. SARS-CoV-2 showed high death rate; however, no specific treatment is available against SARS-CoV-2.

METHODS

In the current study, immunoinformatics approaches were employed to predict the antigenic epitopes against SARS-CoV-2 for the development of the coronavirus vaccine. Cytotoxic T-lymphocyte and B-cell epitopes were predicted for SARS-CoV-2 coronavirus protein. Multiple sequence alignment of three genomes (SARS-CoV, MERS-CoV, and SARS-CoV-2) was used to conserved binding domain analysis.

RESULTS

The docking complexes of 4 CTL epitopes with antigenic sites were analyzed followed by binding affinity and binding interaction analyses of top-ranked predicted peptides with MHC-I HLA molecule. The molecular docking (Food and Drug Regulatory Authority library) was performed, and four compounds exhibiting least binding energy were identified. The designed epitopes lead to the molecular docking against MHC-I, and interactional analyses of the selected docked complexes were investigated. In conclusion, four CTL epitopes (GTDLEGNFY, TVNVLAWLY, GSVGFNIDY, and QTFSVLACY) and four FDA-scrutinized compounds exhibited potential targets as peptide vaccines and potential biomolecules against deadly SARS-CoV-2, respectively. A multiepitope vaccine was also designed from different epitopes of coronavirus proteins joined by linkers and led by an adjuvant.

CONCLUSION

Our investigations predicted epitopes and the reported molecules that may have the potential to inhibit the SARS-CoV-2 virus. These findings can be a step towards the development of a peptide-based vaccine or natural compound drug target against SARS-CoV-2.

Assessing SIRAH's Capability to Simulate Intrinsically Disordered Proteins and Peptides

The challenges posed by intrinsically disordered proteins (IDPs) to atomistic and coarse-grained (CG) simulations are boosting efforts to develop and reparametrize current force fields. An assessment of the dynamical behavior of IDPs' and unstructured peptides with the CG SIRAH force field suggests that the current version achieves a fair description of IDPs' conformational flexibility. Moreover, we found a remarkable capability to capture the effect of point mutations in loosely structured peptides.

Immunoinformatics designed T cell multi epitope dengue peptide vaccine derived from non structural proteome

Dengue viral disease has been reported as an Aedes aegypti mosquito-borne human disease and causing a severe global public health concern. In this study, immunoinformatics methods was deployed for crafting CTL T-cell epitopes as dengue vaccine candidates. The NS1 protein sequence of dengue serotype 1 strain retrieved from the protein database and T-cell epitopes (n = 85) were predicted by the artificial neural network. The conserved epitopes (n = 10) were predicted and selected for intensive computational analysis. The machine learning technique and quantitative matrix-based toxicity analysis assured nontoxic peptide selection. Hidden Markov Model derived Structural Alphabet (SA) based algorithm predicted the 3D molecular structure and all-atom structure of peptide ligand validated by Ramachandran-plot. Three-tier molecular docking approaches were used to predictthe peptide - HLA docking complex. Molecular dynamics (MD) simulation study confirmed the docking complex was stable in the time frame of 100ns. Population coverage analysis predicted the interaction epitope interaction with a particular population of HLA. These results concluded that the computationally designed HTLWSNGVL and FTTNIWLKL epitope peptides could be used as putative agents for the multi CTL T cell epitope vaccine. The vaccine protein sequence expression and translation were analyzed in the prokaryotic vector adapted by codon usage. Such in silico formulated CTL T-cell-based prophylactic vaccines could encourage the commercial development of dengue vaccines.

A journey through mapping space: characterising the statistical and metric properties of reduced representations of macromolecules

ABSTRACT

A mapping of a macromolecule is a prescription to construct a simplified representation of the system in which only a subset of its constituent atoms is retained. As the specific choice of the mapping affects the analysis of all-atom simulations as well as the construction of coarse-grained models, the characterisation of the mapping space has recently attracted increasing attention. We here introduce a notion of scalar product and distance between reduced representations, which allows the study of the metric and topological properties of their space in a quantitative manner. Making use of a Wang-Landau enhanced sampling algorithm, we exhaustively explore such space, and examine the qualitative features of mappings in terms of their squared norm. A one-to-one correspondence with an interacting lattice gas on a finite volume leads to the emergence of discontinuous phase transitions in mapping space, which mark the boundaries between qualitatively different reduced representations of the same molecule.

Determine the Potential Epitope Based Peptide Vaccine Against Novel SARS-CoV-2 Targeting Structural Proteins Using Immunoinformatics Approaches

Coronaviruses (CoVs) belong to the Coronaviridae-family. The genus Beta-coronaviruses, are enveloped positive strand RNA viruses with club-like spikes at the surface with a unique replication process and a large RNA genome (∼25 kb). CoVs are known as one of the major pathogenic viruses causing a variety of diseases in birds and mammals including humans (lethal respiratory dysfunctions). Recently, a new strain of coronavirus has been identified and named as SARS-CoV-2. A large number of COVID-19 (disease caused by SARS-CoV-2) cases are being diagnosed all over the World especially in China (Wuhan). COVID-19 showed high mortality rate exponentially, however, not even a single effective cure is being introduced yet against COVID-19. In the current study, immunoinformatics approaches were employed to predict the antigenic epitopes against COVID-19 for the development of a coronavirus peptide vaccine. Cytotoxic T-lymphocyte (CTL) and B-cell epitopes were predicted for SARS-CoV-2 coronavirus structural proteins (Spikes, Membrane, Envelope, and Nucleocapsid). The docking complexes of the top 10 epitopes having antigenic sites were analyzed led by binding affinity and binding interactional analyses of top ranked predicted peptides with the MHC-I HLA molecule. The predicted peptides may have potential to be used as peptide vaccine against COVID-19.

Proteome-wide mapping and reverse vaccinology-based B and T cell multi-epitope subunit vaccine designing for immune response reinforcement against Porphyromonas gingivalis

Porphyromonas gingivalis, a prominent pathogen responsible for acute periodontal diseases, is widely studied by the scientific community for its successful evasion of the host immune system. P. gingivalis is associated with rheumatoid arthritis, dementia, and Alzheimer's. The pathogen successfully survives itself against the heavy load of conventional antibiotics because of its ability to evade the host immune system. Subtractive proteomics and reverse vaccinology approaches were employed in order to prioritize the best proteins for vaccine designing. Three vaccine candidates with Uniprot ID: Q7MWZ2 (histidine Kinase), Q7MVL1 (Fe (2+) transporter), and Q7MWZ2 (Capsular polysaccharide transport protein) were identified for vaccine designing. These proteins are antigenic and essential for pathogen survival. A wide range of immunoinformatics tools was applied for the prediction of epitopes, B, and T cells, for the vaccine candidate proteins. Molecular docking of the predicted epitopes against the MHC molecules were carried out. In-silico vaccine was constructed using carefully evaluated epitopes and consequently modeled for docking with human Toll-like receptor 2. Chain C of Pam3CSK4 (PDB ID; 2Z7X) was linked to the vaccine as an adjuvant to boost immune response towards the vaccine. For stability evaluation of the vaccine-TLR-2 docked complex, Molecular Dynamics simulations were performed. The reverse-translated nucleotide sequence cloned in Eschericia coli to attain the maximal expression of the vaccine protein. The maximal expression was ensured by CAI score of 0.96. The current vaccine requires future experimental validation to confirm its effectiveness. The vaccine developed will be helpful to protect against P. gingivalis associated infections.Communicated by Ramaswamy H. Sarma.

Extending the Martini Coarse-Grained Force Field to N-Glycans

Glycans play a vital role in a large number of cellular processes. Their complex and flexible nature hampers structure-function studies using experimental techniques. Molecular dynamics (MD) simulations can help in understanding dynamic aspects of glycans if the force field parameters used can reproduce key experimentally observed properties. Here, we present optimized coarse-grained (CG) Martini force field parameters for N-glycans, calibrated against experimentally derived binding affinities for lectins. The CG bonded parameters were obtained from atomistic (ATM) simulations for different glycan topologies including high mannose and complex glycans with various branching patterns. In the CG model, additional elastic networks are shown to improve maintenance of the overall conformational distribution. Solvation free energies and octanol-water partition coefficients were also calculated for various N-glycan disaccharide combinations. When using standard Martini nonbonded parameters, we observed that glycans spontaneously aggregated in the solution and required down-scaling of their interactions for reproduction of ATM model radial distribution functions. We also optimized the nonbonded interactions for glycans interacting with seven lectin candidates and show that a relatively modest scaling down of the glycan-protein interactions can reproduce free energies obtained from experimental studies. These parameters should be of use in studying the role of glycans in various glycoproteins and carbohydrate binding proteins as well as their complexes, while benefiting from the efficiency of CG sampling.

Exploring Conformational Space with Thermal Fluctuations Obtained by Normal-Mode Analysis

Proteins in their native states can be represented as ensembles of conformers in dynamical equilibrium. Thermal fluctuations are responsible for transitions between these conformers. Normal-modes analysis (NMA) using elastic network models (ENMs) provides an efficient procedure to explore global dynamics of proteins commonly associated with conformational transitions. In the present work, we present an iterative approach to explore protein conformational spaces by introducing structural distortions according to their equilibrium dynamics at room temperature. The approach can be used either to perform unbiased explorations of conformational space or to explore guided pathways connecting two different conformations, e.g., apo and holo forms. In order to test its performance, four proteins with different magnitudes of structural distortions upon ligand binding have been tested. In all cases, the conformational selection model has been confirmed and the conformational space between apo and holo forms has been encompassed. Different strategies have been tested that impact on the efficiency either to achieve a desired conformational change or to achieve a balanced exploration of the protein conformational multiplicity.

Scale-consistent approach to the derivation of coarse-grained force fields for simulating structure, dynamics, and thermodynamics of biopolymers

In this chapter the scale-consistent approach to the derivation of coarse-grained force fields developed in our laboratory is presented, in which the effective energy function originates from the potential of mean force of the system under consideration and embeds atomistically detailed interactions in the resulting energy terms through use of Kubo's cluster-cumulant expansion, appropriate selection of the major degrees of freedom to be averaged out in the derivation of analytical approximations to the energy terms, and appropriate expression of the interaction energies at the all-atom level in these degrees of freedom. Our approach enables the developers to find correct functional forms of the effective coarse-grained energy terms, without having to import them from all-atom force fields or deriving them on a heuristic basis. In particular, the energy terms derived in such a way exhibit correct dependence on coarse-grained geometry, in particular on site orientation. Moreover, analytical formulas for the multibody (correlation) terms, which appear to be crucial for coarse-grained modeling of many of the regular structures such as, e.g., protein α-helices and β-sheets, can be derived in a systematic way. Implementation of the developed theory to the UNIfied COarse-gRaiNed (UNICORN) model of biological macromolecules, which consists of the UNRES (for proteins), NARES-2P (for nucleic acids), and SUGRES-1P (for polysaccharides) components, and is being developed in our laboratory is described. Successful applications of UNICORN to the prediction of protein structure, simulating the folding and stability of proteins and nucleic acids, and solving biological problems are discussed.

Thermal Selection of Aqueous Molecular Conformations for Tailored Energetics of Peptide Assemblies at Solid Interfaces

Key to the development of functional bioinorganic soft interfaces is the predictive control over the micron-scale assembly structure and energetics of biomolecules at solid interfaces. While assembly of labile biomolecules, such as short peptides, at interfaces is a great deal affected by the shape of the molecule, biomolecular conformations are prompted by external solution conditions, involving temperature, pH, and salt concentration. In this light, one can expect that the environmental conformational selection of aqueous biomolecules could potentially allow for fine-tuning of the equilibrium assembly structure at interfaces, as well as, the binding strength and molecular mobility within these assemblies. Here, we demonstrate the energetic and structural tailoring of two-dimensional surface assemblies of graphite-binding dodecapeptides, through the thermal selection of aqueous peptide conformations. Our findings based on a scanning probe energetic analysis, supplemented by molecular dynamics modeling, show that peptide-graphite and peptide-peptide intermolecular interactions strongly depend on the thermally selected molecular conformation and that the extent of the conformational change is directly related to the observed assembled structure. Enabled by these results was the design of a peptide with predictable binding and assembled structure, thus, suggesting environmental preconditioning of peptides as a means for controlling self-assembling active bioinorganic interfaces for bioelectronic implementations such as biomolecular fuel cells and biosensors.

Aggregation of disease-related peptides

Protein misfolding and aggregation of amyloid proteins is the fundamental cause of more than 20 diseases. Molecular mechanisms of the self-assembly and the formation of the toxic aggregates are still elusive. Computer simulations have been intensively used to study the aggregation of amyloid peptides of various amino acid lengths related to neurodegenerative diseases. We review atomistic and coarse-grained simulations of short amyloid peptides aimed at determining their transient oligomeric structures and the early and late aggregation steps.

Prediction and validation of potent peptides against herpes simplex virus type 1 via immunoinformatic and systems biology approach

The human herpes simplex virus type 1 (HSV-1) is an extremely rampant human pathogen, and its infection could cause life-long diseases, including the central nervous system disorders. The glycoproteins of HSV-1 such as glycoprotein B, glycoprotein C, glycoprotein D, glycoprotein H, and glycoprotein L are highly involved in mediating the viral attachment and infection of the host cell. Therefore, immunoinformatic approaches followed by molecular dynamics simulation and systems biology has been used to analyze these glycoproteins in order to propose effective peptide-based vaccine candidates against the HSV-1 infection. The ElliPro and NetCTL.1.2 online tools were employed to forecast the B- and T-lymphocyte (CTL) epitopes for gB, gC, gD, gH, and gL. The 3D coordinates of these epitopes were modeled and docked against the human major histocompatibility complex molecule-1. The outcomes obtained from postdocking analysis along with TAP (Transporter associated with antigen processing), MHC binding, and C-terminal cleavage score assisted in the selection of potential epitopes. These epitopes were further subjected to molecular dynamics simulation and systems biology approach which showed significant results. On the basis of these substantial outcomes, peptides are proposed that could be used to provoke immunity against the HSV-1 infection.

Comparing the Membrane-Interaction Profiles of Two Antiviral Peptides: Insights into Structure-Function Relationship

In recent years, certain amphipathic, α-helical peptides have been discovered that inhibit medically important enveloped viruses by disrupting the lipid membrane surrounding individual virus particles. Interestingly, only a small subset of amphipathic, α-helical peptides demonstrate inhibitory activity, and there is broad interest in understanding how the structures of these peptides contribute to functional activity against lipid membranes. To address this question, herein, we employed multiple surface-sensitive measurement techniques along with computational simulations in order to investigate how AH and C5A peptides, two of the most biologically active peptides in this class, interact with model lipid membranes while gaining insight into membrane-induced peptide conformational changes. Circular dichroism spectroscopy experiments revealed that both AH and C5A peptides undergo pronounced coil-to-helix transitions in the presence of lipid membrane environments, and the C5A conformational change was the largest. Time-lapsed fluorescence microscopy measurements were conducted to monitor the interaction of peptides with arrays of tethered, individual lipid vesicles and showed that C5A potently lyses lipid vesicles indiscriminate of vesicle size at peptide concentrations as low as 10 nM whereas AH peptide preferentially lyses lipid vesicles with high membrane curvature and is less potent than C5A. These findings were complemented by electrochemical impedance spectroscopy measurements on a tethered lipid bilayer membrane platform, which indicated that C5A solubilizes lipid membranes in a manner that is distinct from how AH disrupts lipid membranes via pore formation. Computational simulations supported that the distinct membrane-interaction profiles arise from different helical folding patterns, whereby AH monomers predominantly exist as two shorter helices with a hinge in-between and C5A monomers form a single helix. Taken together, our findings demonstrate that membrane-active antiviral peptides can exhibit distinct membrane-interaction profiles that confer different degrees of targeting selectivity, and the corresponding structural insights will be useful for peptide engineering applications.

Molecular Dynamics Simulations Combined with Nuclear Magnetic Resonance and/or Small-Angle X-ray Scattering Data for Characterizing Intrinsically Disordered Protein Conformational Ensembles

The concept of intrinsically disordered proteins (IDPs) has emerged relatively slowly, but over the past 20 years, it has become an intense research area in structural biology. Indeed, because of their considerable flexibility and structural heterogeneity, the determination of IDP conformational ensemble is particularly challenging and often requires a combination of experimental measurements and computational approaches. With the improved accuracy of all-atom force fields and the increasing computing performances, molecular dynamics (MD) simulations have become more and more reliable to generate realistic conformational ensembles. And the combination of MD simulations with experimental approaches, such as nuclear magnetic resonance (NMR) and/or small-angle X-ray scattering (SAXS) allows one to converge toward a more accurate and exhaustive description of IDP structures. In this Review, we discuss the state of the art of MD simulations of IDP conformational ensembles, with a special focus on studies that back-calculated and directly compared theoretical and experimental NMR or SAXS observables, such as chemical shifts (CS), ³J-couplings (³Jc), residual dipolar couplings (RDC), or SAXS intensities. We organize the review in three parts. In the first section, we discuss the studies which used NMR and/or SAXS data to test and validate the development of force fields or enhanced sampling techniques. In the second part, we explore different methods for the refinement of MD-derived structural ensembles, such as NMR or SAXS data-restrained MD simulations or ensemble reweighting to better fit experiments. Finally, we survey some recent studies combining MD simulations with NMR and/or SAXS measurements to investigate the relationship between IDP conformational ensemble and biological activity, as well as their implication in human diseases. From this review, we noticed that quite a few studies compared MD-generated conformational ensembles with both NMR and SAXS measurements to validate IDP structures at both local and global levels. Yet, beside the IDP propensity to form local secondary structures, their dynamic extension or compactness also appears important for their activity. Thus, we believe that a close synergy between MD simulations, NMR, and SAXS experiments would be greatly appropriate to address the challenges of characterizing the disordered structures of proteins and their complexes, relative to their biological functions.

Immunoinformatic and systems biology approaches to predict and validate peptide vaccines against Epstein-Barr virus (EBV)

Epstein-Barr virus (EBV), also known as human herpesvirus 4 (HHV-4), is a member of the Herpesviridae family and causes infectious mononucleosis, Burkitt's lymphoma, and nasopharyngeal carcinoma. Even in the United States of America, the situation is alarming, as EBV affects 95% of the young population between 35 and 40 years of age. In this study, both linear and conformational B-cell epitopes as well as cytotoxic T-lymphocyte (CTL) epitopes were predicted by using the ElliPro and NetCTL.1.2 webservers for EBV proteins (GH, GL, GB, GN, GM, GP42 and GP350). Molecular modelling tools were used to predict the 3D coordinates of peptides, and these peptides were then docked against the MHC molecules to obtain peptide-MHC complexes. Studies of their post-docking interactions helped to select potential candidates for the development of peptide vaccines. Our results predicted a total of 58 T-cell epitopes of EBV;  where the most potential were selected based on their TAP, MHC binding and C-terminal Cleavage score. The top most peptides were subjected to MD simulation and stability analysis. Validation of our predicted epitopes using a 0.45 µM concentration was carried out by using a systems biology approach. Our results suggest a panel of epitopes that could be used to immunize populations to protect against multiple diseases caused by EBV.