Structure-based conformational preferences of amino acids

The Iconic α-Helix: From Pauling to the Present

The protein folding problem dates back to Pauling's insights almost a century ago, but the first venture into actual protein structure was the Pauling-Corey-Brandson α-helix in 1951, a proposed model that was confirmed almost immediately using X-ray crystallography. Many subsequent efforts to predict protein helices from the amino acid sequence met with only partial success, as discussed here. Surprisingly, in 2021, these efforts were superseded by deep-learning artificial intelligence, especially AlphaFold2, a machine learning program based on neural nets. This approach can predict most protein structures successfully at or near atomic resolution. Deservedly, deep-learning artificial intelligence was named Science magazine's 2021 "breakthrough of the year." Today, ~200 million predicted protein structures can be downloaded from the AlphaFold2 Protein Structure Database. Deep learning represents a deep conundrum because these successfully predicted macromolecular structures are based on methods that are completely devoid of a hypothesis or of any physical chemistry. Perhaps we are now poised to transcend five centuries of reductive science.

Amino acid propensities for secondary structures and its variation across protein structures using exhaustive PDB data

Amino acid propensities for protein secondary structures are vital for protein structure prediction, understanding folding, and design, and have been studied using various theoretical and experimental methods. Traditional assessments of average propensities using statistical methods have been done on relatively smaller dataset for only a few secondary structures. They also involve averaging out the environmental factors and lack insights into consistency of preferences across diverse protein structures. While a few studies have explored variations in propensities across protein structural classes and folds, exploration of such variations across protein structures remains to be carried out. In this work, we have revised the average propensities for all six different secondary structures, namely α-helix, β-strand, 310-helix, π-helix, turn and coil, analyzing the most exhaustive dataset available till date using two robust secondary structure assignment algorithms, DSSP and STRIDE. The propensities evaluated here can serve as a standard reference. Moreover, we present here, for the first time, the propensities within individual protein structures and investigated how the preferences of residues and more interestingly, of their groups formed based on their structural features, vary across different unique structures. We devised a novel approach- the minimal set analysis, based on the propensity distribution of residues, which along with the group propensities led us to the conclusion that a residue's preference for a specific secondary structure is primarily dictated by its side chain's structural features. The findings in this study provide a more insightful picture of residues propensities and can be useful in protein folding and design studies.

A unified analysis of evolutionary and population constraint in protein domains highlights structural features and pathogenic sites

Protein evolution is constrained by structure and function, creating patterns in residue conservation that are routinely exploited to predict structure and other features. Similar constraints should affect variation across individuals, but it is only with the growth of human population sequencing that this has been tested at scale. Now, human population constraint has established applications in pathogenicity prediction, but it has not yet been explored for structural inference. Here, we map 2.4 million population variants to 5885 protein families and quantify residue-level constraint with a new Missense Enrichment Score (MES). Analysis of 61,214 structures from the PDB spanning 3661 families shows that missense depleted sites are enriched in buried residues or those involved in small-molecule or protein binding. MES is complementary to evolutionary conservation and a combined analysis allows a new classification of residues according to a conservation plane. This approach finds functional residues that are evolutionarily diverse, which can be related to specificity, as well as family-wide conserved sites that are critical for folding or function. We also find a possible contrast between lethal and non-lethal pathogenic sites, and a surprising clinical variant hot spot at a subset of missense enriched positions.

Amyloid Aggregation Is Potently Slowed Down by Osmolytes Due to Compaction of Partially Folded State

Amyloid aggregation is a key process in amyloidoses and neurodegenerative diseases. Hydrophobicity is one of the major driving forces for this type of aggregation, as an increase in hydrophobicity generally correlates with aggregation susceptibility and rate. However, most experimental systems in vitro and prediction tools in silico neglect the contribution of protective osmolytes present in the cellular environment. Here, we assessed the role of hydrophobic mutations in amyloid aggregation in the presence of osmolytes. To achieve this goal, we used the model protein human muscle acylphosphatase (mAcP) and mutations to leucine that increased its hydrophobicity without affecting its thermodynamic stability. Osmolytes significantly slowed down the aggregation kinetics of the hydrophobic mutants, with an effect larger than that observed on the wild-type protein. The effect increased as the mutation site was closer to the middle of the protein sequence. We propose that the preferential exclusion of osmolytes from mutation-introduced hydrophobic side-chains quenches the aggregation potential of the ensemble of partially unfolded states of the protein by inducing its compaction and inhibiting its self-assembly with other proteins. Our results suggest that including the effect of the cellular environment in experimental setups and predictive softwares, for both mechanistic studies and drug design, is essential in order to obtain a more complete combination of the driving forces of amyloid aggregation.

Impaired activity and membrane association of most calpain-5 mutants causal for neovascular inflammatory vitreoretinopathy

Neovascular inflammatory vitreoretinopathy (NIV) is a rare eye disease that ultimately leads to complete blindness and is caused by mutations in the gene encoding calpain-5 (CAPN5), with six pathogenic mutations identified. In transfected SH-SY5Y cells, five of the mutations resulted in decreased membrane association, diminished S-acylation, and reduced calcium-induced autoproteolysis of CAPN5. CAPN5 proteolysis of the autoimmune regulator AIRE was impacted by several NIV mutations. R243, L244, K250 and the adjacent V249 are on β-strands in the protease core 2 domain. Conformational changes induced by Ca2+binding result in these β-strands forming a β-sheet and a hydrophobic pocket which docks W286 side chain away from the catalytic cleft, enabling calpain activation based on comparison with the Ca2+-bound CAPN1 protease core. The pathologic variants R243L, L244P, K250N, and R289W are predicted to disrupt the β-strands, β-sheet, and hydrophobic pocket, impairing calpain activation. The mechanism by which these variants impair membrane association is unclear. G376S impacts a conserved residue in the CBSW domain and is predicted to disrupt a loop containing acidic residues which may contribute to membrane binding. G267S did not impair membrane association and resulted in a slight but significant increase in autoproteolytic and proteolytic activity. However, G267S is also identified in individuals without NIV. Combined with the autosomal dominant pattern of NIV inheritance and evidence that CAPN5 may dimerize, the results are consistent with a dominant negative mechanism for the five pathogenic variants which resulted in impaired CAPN5 activity and membrane association and a gain-of-function for the G267S variant.

An immunoinformatics approach to study the epitopes of SARS-CoV-2 helicase, Nsp13

Introduction and objective.

Vaccines are administered worldwide to control on-going coronavirus disease-19 (COVID-19) pandemic caused by SARS-CoV-2. Vaccine efficacy is largely contributed by the epitopes present on the viral proteins and their alteration might help emerging variants to escape host immune surveillance. Therefore, this study was designed to study SARS-CoV-2 Nsp13 protein, its epitopes and evolution.

Methods

Clustal Omega was used to identify mutations in Nsp13 protein. Secondary structure and disorder score was predicted by CFSSP and PONDR-VSL2 webservers. Protein stability was predicted by DynaMut webserver. B cell epitopes were predicted by IEDB DiscoTope 2.0 tools and their 3D structures were represented by discovery studio. Antigenicity and allergenicity of epitopes were predicted by Vaxijen2.0 and AllergenFPv.1.0. Physiochemical properties of epitopes were predicted by Toxinpred, HLP webserver tool.

Results

Our data revealed 182 mutations in Nsp13 among Indian SARS-CoV-2 isolates, which were characterised by secondary structure and per-residue disorderness, stability and dynamicity predictions. To correlate the functional impact of these mutations, we characterised the most prominent B cell and T cell epitopes contributed by Nsp13. Our data revealed twenty-one epitopes, which exhibited antigenicity, stability and interactions with MHC class-I and class-II molecules. Subsequently, the physiochemical properties of these epitopes were analysed. Furthermore, eighteen mutations reside in these Nsp13 epitopes.

Conclusions

We report appearance of eighteen mutations in the predicted twenty-one epitopes of Nsp13. Among these, at least seven epitopes closely matches with the functionally validated epitopes. Altogether, our study shows the pattern of evolution of Nsp13 epitopes and their probable implications.

Non-Invasive, Reliable, and Fast Quantification of DNA Loading on Gold Nanoparticles by a One-Step Optical Measurement

An exquisite, versatile, and reproducible quantification of DNA loading on gold nanoparticles (Au NPs) has long been pursued because this loading influences the analytical, therapeutic, and self-assembly behaviors of DNA-Au NPs. Nevertheless, the existing methods used thus far rely solely on the invasive detachment and subsequent spectroscopic quantification of DNA, which are error-prone and highly dependent on trained personnel. Here, we present a non-invasive optical framework that can determine the number of DNA strands on Au NPs by versatile one-step measurement of the visible absorption spectra of DNA-Au NP solutions without any invasive modifications or downstream processes. Using effective medium theory in conjunction with electromagnetic numerical calculation, the change in DNA loading density, resulting from varying the ion concentration, Au NP size, DNA strand length, and surrounding temperature, can be tracked in situ merely by the one-step measurement of visible absorption spectra, which is otherwise impossible to achieve. Moreover, the simplicity and robustness of this method promote reproducible DNA loading quantification regardless of experimental adeptness, which is in stark contrast with existing invasive and multistep methods. Overall, the optical framework outlined in this work can contribute to democratizing research on DNA-Au NPs and facilitating their rapid adoption in transformative applications.

Local Backbone Geometry Plays a Critical Role in Determining Conformational Preferences of Amino Acid Residues in Proteins

The definition of the structural basis of the conformational preferences of the genetically encoded amino acid residues is an important yet unresolved issue of structural biology. In order to gain insights into this intricate topic, we here determined and compared the amino acid propensity scales for different (φ, ψ) regions of the Ramachandran plot and for different secondary structure elements. These propensities were calculated using the Chou–Fasman approach on a database of non-redundant protein chains retrieved from the Protein Data Bank. Similarities between propensity scales were evaluated by linear regression analyses. One of the most striking and unexpected findings is that distant regions of the Ramachandran plot may exhibit significantly similar propensity scales. On the other hand, contiguous regions of the Ramachandran plot may present anticorrelated propensities. In order to provide an interpretative background to these results, we evaluated the role that the local variability of protein backbone geometry plays in this context. Our analysis indicates that (dis)similarities of propensity scales between different regions of the Ramachandran plot are coupled with (dis)similarities in the local geometry. The concept that similarities of the propensity scales are dictated by the similarity of the NCαC angle and not necessarily by the similarity of the (φ, ψ) conformation may have far-reaching implications in the field.

Engineered Spider Silk Proteins for Biomimetic Spinning of Fibers with Toughness Equal to Dragline Silks

Spider silk is the toughest fiber found in nature, and bulk production of artificial spider silk that matches its mechanical properties remains elusive. Development of miniature spider silk proteins (mini-spidroins) has made large-scale fiber production economically feasible, but the fibers' mechanical properties are inferior to native silk. The spider silk fiber's tensile strength is conferred by poly-alanine stretches that are zipped together by tight side chain packing in β-sheet crystals. Spidroins are secreted so they must be void of long stretches of hydrophobic residues, since such segments get inserted into the endoplasmic reticulum membrane. At the same time, hydrophobic residues have high β-strand propensity and can mediate tight inter-β-sheet interactions, features that are attractive for generation of strong artificial silks. Protein production in prokaryotes can circumvent biological laws that spiders, being eukaryotic organisms, must obey, and the authors thus design mini-spidroins that are predicted to more avidly form stronger β-sheets than the wildtype protein. Biomimetic spinning of the engineered mini-spidroins indeed results in fibers with increased tensile strength and two fiber types display toughness equal to native dragline silks. Bioreactor expression and purification result in a protein yield of ≈9 g L-1 which is in line with requirements for economically feasible bulk scale production.

In vitro evolution reveals non-cationic protein-RNA interaction mediated by metal ions

RNA–peptide/protein interactions have been of utmost importance to life since its earliest forms, reaching even before the last universal common ancestor (LUCA). However, the ancient molecular mechanisms behind this key biological interaction remain enigmatic because extant RNA–protein interactions rely heavily on positively charged and aromatic amino acids that were absent (or heavily under-represented) in the early pre-LUCA evolutionary period. Here, an RNA-binding variant of the ribosomal uL11 C-terminal domain was selected from an approximately 10¹⁰ library of partially randomized sequences, all composed of ten prebiotically plausible canonical amino acids. The selected variant binds to the cognate RNA with a similar overall affinity although it is less structured in the unbound form than the wild-type protein domain. The variant complex association and dissociation are both slower than for the wild-type, implying different mechanistic processes involved. The profile of the wild-type and mutant complex stabilities along with molecular dynamics simulations uncovers qualitative differences in the interaction modes. In the absence of positively charged and aromatic residues, the mutant uL11 domain uses ion bridging (K⁺/Mg²⁺) interactions between the RNA sugar-phosphate backbone and glutamic acid residues as an alternative source of stabilization. This study presents experimental support to provide a new perspective on how early protein–RNA interactions evolved, where the lack of aromatic/basic residues may have been compensated by acidic residues plus metal ions.

Antiviral Strategies Against SARS-CoV-2: A Systems Biology Approach

The unprecedented scientific achievements in combating the COVID-19 pandemic reflect a global response informed by unprecedented access to data. We now have the ability to rapidly generate a diversity of information on an emerging pathogen and, by using high-performance computing and a systems biology approach, we can mine this wealth of information to understand the complexities of viral pathogenesis and contagion like never before. These efforts will aid in the development of vaccines, antiviral medications, and inform policymakers and clinicians. Here we detail computational protocols developed as SARS-CoV-2 began to spread across the globe. They include pathogen detection, comparative structural proteomics, evolutionary adaptation analysis via network and artificial intelligence methodologies, and multiomic integration. These protocols constitute a core framework on which to build a systems-level infrastructure that can be quickly brought to bear on future pathogens before they evolve into pandemic proportions.

In silico prediction of Severe Acute Respiratory Syndrome Coronavirus 2 main protease cleavage sites

One of the emerging subjects to combat the SARS-CoV-2 virus is to design accurate and efficient drug such as inhibitors against the viral protease to stop the viral spread. In addition to laboratory investigation of the viral protease, which is fundamental, the in silico research of viral protease such as the protease cleavage site prediction is critically important and urgent. However, this problem has yet to be addressed. This article has, for the first time, investigated this problem using the pattern recognition approaches. The article has shown that the pattern recognition approaches incorporating a specially tailored kernel function for dealing with amino acids has the outstanding performance in the accuracy of cleavage site prediction and the discovery of the prototype cleavage peptides.

Covalent and Noncovalent Targeting of the Tcf4/β-Catenin Strand Interface with β-Hairpin Mimics

β-Strands are a fundamental component of protein structure, and these extended peptide regions serve as binding epitopes for numerous protein-protein complexes. However, synthetic mimics that capture the conformation of these epitopes and inhibit selected protein-protein interactions are rare. Here we describe covalent and noncovalent β-hairpin mimics of an extended strand region mediating the Tcf4/β-catenin interaction. Our efforts afford a rationally designed lead for an underexplored region of β-catenin, which has been the subject of numerous ligand discovery campaigns.

An experimental approach probing the conformational transitions and energy landscape of antibodies: a glimmer of hope for reviving lost therapeutic candidates using ionic liquid

Understanding protein folding in different environmental conditions is fundamentally important for predicting protein structures and developing innovative antibody formulations. While the thermodynamics and kinetics of folding and unfolding have been extensively studied by computational methods, experimental methods for determining antibody conformational transition pathways are lacking. Motivated to fill this gap, we prepared a series of unique formulations containing a high concentration of a chimeric immunoglobin G4 (IgG4) antibody with different excipients in the presence and absence of the ionic liquid (IL) choline dihydrogen phosphate. We determined the effects of different excipients and IL on protein thermal and structural stability by performing variable temperature circular dichroism and bio-layer interferometry analyses. To further rationalise the observations of conformational changes with temperature, we carried out molecular dynamics simulations on a single antibody binding fragment from IgG4 in the different formulations, at low and high temperatures. We developed a methodology to study the conformational transitions and associated thermodynamics of biomolecules, and we showed IL-induced conformational transitions. We showed that the increased propensity for conformational change was driven by preferential binding of the dihydrogen phosphate anion to the antibody fragment. Finally, we found that a formulation containing IL with sugar, amino acids and surfactant is a promising candidate for stabilising proteins against conformational destabilisation and aggregation. We hope that ultimately, we can help in the quest to understand the molecular basis of the stability of antibodies and protein misfolding phenomena and offer new candidate formulations with the potential to revive lost therapeutic candidates.

Longer charged amino acids favor β-strand formation in hairpin peptides

Interactions between charged amino acids significantly influence the structure and function of proteins. The encoded charged amino acids Asp, Glu, Arg, and Lys have different number of hydrophobic methylenes linking the backbone to the charged functionality. It remains to be fully understood how does this difference in the number of methylenes affect protein structure stability. Protein secondary structures are the fundamental three-dimensional building blocks of protein structures. β-Sheet structures are particularly interesting, because these structures have been associated with a number of protein misfolding diseases. Herein, we report the effect of charged amino acid side chain length at two β-strand positions individually on the stability of a β-hairpin. The charged amino acids include side chains with a carboxylate, an ammonium, or a guanidinium group. The experimental peptides, fully folded reference peptides, and fully unfolded reference peptides were synthesized by solid phase peptide synthesis and analyzed by 2D NMR methods including TOCSY, DQF-COSY, and ROESY. Sequence specific assignments were performed for all peptides. The chemical shift data were used to derive the fraction folded population and the folding free energy for the experimental peptides. Results showed that the fraction folded population increased with increasing charged amino acid side chain length. These results should be useful for developing functional peptides that adopt the β-conformation.

Comparison of Immunological Profiles of SARS-CoV-2 Variants in the COVID-19 Pandemic Trends: An Immunoinformatics Approach

The current dynamics of the COVID-19 pandemic have become a serious concern with the emergence of a series of mutant variants of the SARS-CoV-2 virus. Unlike the previous strain, it is reported that the descendants are associated with increased risk of transmission yet causing less impact in terms of hospital admission, the severity of illness, or mortality. Moreover, the vaccine efficacy is also not believed to vary among the population depending on the variants of the virus and ethnicity. It has been determined that the mutations recorded in the spike gene and protein of the newly evolved viruses are specificallyresponsible for this transformation in the behavior of the virus and its disease condition. Hence, this study aimed to compare the immunogenic profiles of the spike protein from the latest variants of the SARS-CoV-2 virus concerning the probability of COVID-19 severity. Genome sequences of the latest SARS-CoV-2 variants were obtained from GISAID and NCBI repositories. The translated protein sequences were run against T-cell and B-cell epitope prediction tools. Subsequently, antigenicity, immunogenicity, allergenicity, toxicity, and conservancy of the identified epitopes were ascertained using various prediction servers. Only the non-allergic and non-toxic potential epitopes were matched for population relevance by using the Human Leucocyte Antigen population registry in IEDB. Finally, the selected epitopes were validated by docking and simulation studies. The evaluated immunological parameters would concurrently reveal the severity of COVID-19, determining the infection rate of the pathogen. Our immunoinformatics approach disclosed that spike protein of the five variants was capable of forming potential T and B-cell epitopes with varying immune responses. Although the Wuhan strain showed a high number of epitope/HLA combinations, relatively less antigenicity and higher immunogenicity results in poor neutralizing capacity, which could be associated with increased disease severity. Our data demonstrate that increased viral antigenicity with moderate to high immunogenicity, and several potential epitope/HLA combinations in England strain, the USA, India, and South Africa variants, could possess a high neutralizing ability. Therefore, our findings reinforce that the newly circulating variants of SARS-CoV-2 might be associated with more infectiousness and less severe disease condition despite their greater viremia, as reported in the recent COVID-19 cases, whichconsequently determine their increased epidemiological fitness.

A systematic analysis of the beta hairpin motif in the Protein Data Bank

The beta hairpin motif is a ubiquitous protein structural motif that can be found in molecules across the tree of life. This motif, which is also popular in synthetically designed proteins and peptides, is known for its stability and adaptability to broad functions. Here, we systematically probe all 49,000 unique beta hairpin substructures contained within the Protein Data Bank (PDB) to uncover key characteristics correlated with stable beta hairpin structure, including amino acid biases and enriched interstrand contacts. We find that position specific amino acid preferences, while seen throughout the beta hairpin structure, are most evident within the turn region, where they depend on subtle turn dynamics associated with turn length and secondary structure. We also establish a set of broad design principles, such as the inclusion of aspartic acid residues at a specific position and the careful consideration of desired secondary structure when selecting residues for the turn region, that can be applied to the generation of libraries encoding proteins or peptides containing beta hairpin structures.

T and B cell Epitope analysis of SARS-CoV-2 S protein based on immunoinformatics and experimental research

COVID-19 caused by SARS-CoV-2 is pandemic with a severe morbidity and mortality rate across the world. Despite the race for effective vaccine and drug against further expansion and fatality rate of this novel coronavirus, there is still lack of effective antiviral therapy. To this effect, we deemed it necessary to identify potential B and T cell epitopes from the envelope S protein. This can be used as potential targets to develop anti-SARS-CoV-2 vaccine preparations. In this study, we used immunoinformatics to identify conservative B and T cell epitopes for S proteins of SARS-CoV-2, which might play roles in the initiation of SARS-CoV-2 infection. We identified the B cell and T cell peptide epitopes of S protein and their antigenicity, as well as the interaction between the peptide epitopes and human leucocyte antigen (HLA). Among the B cell epitopes, 'EILDITPCSFGGVS' has the highest score of antigenicity and great immunogenicity. In T cell epitopes, MHC-I peptide 'KIADYNYKL' and MHC-II peptide 'LEILDITPC' were identified as high antigens. Besides, docking analysis showed that the predicted peptide 'KIADYNYKL' was closely bound to the HLA-A*0201. The results of molecular dynamics simulation through GROMACS software showed that 'HLA-A*0201~peptide' complex was very stable. And the peptide we selected could induce the T cell response similar to that of SARS-CoV-2 infection. Moreover, the predicted peptides were highly conserved in different isolates from different countries. The antigenic epitopes presumed in this study were effective new vaccine targets to prevent SARS-CoV-2 infection.

Isofunctional Clustering and Conformational Analysis of the Arsenate Reductase Superfamily Reveals Nine Distinct Clusters

Arsenate reductase (ArsC) is a superfamily of enzymes that reduce arsenate. Due to active site similarities, some ArsC can function as low-molecular weight protein tyrosine phosphatases (LMW-PTPs). Broad superfamily classifications align with redox partners (Trx- or Grx-linked). To understand this superfamily's mechanistic diversity, the ArsC superfamily is classified on the basis of active site features utilizing the tools TuLIP (two-level iterative clustering process) and autoMISST (automated multilevel iterative sequence searching technique). This approach identified nine functionally relevant (perhaps isofunctional) protein groups. Five groups exhibit distinct ArsC mechanisms. Three are Grx-linked: group 4AA (classical ArsC), group 3AAA (YffB-like), and group 5BAA. Two are Trx-linked: groups 6AAAAA and 7AAAAAAAA. One is an Spx-like transcriptional regulatory group, group 5AAA. Three are potential LMW-PTP groups: groups 7BAAAA, and 7AAAABAA, which have not been previously identified, and the well-studied LMW-PTP family group 8AAA. Molecular dynamics simulations were utilized to explore functional site details. In several families, we confirm and add detail to literature-based mechanistic information. Mechanistic roles are hypothesized for conserved active site residues in several families. In three families, simulations of the unliganded structure sample specific conformational ensembles, which are proposed to represent either a more ligand-binding-competent conformation or a pathway toward a more binding-competent state; these active sites may be designed to traverse high-energy barriers to the lower-energy conformations necessary to more readily bind ligands. This more detailed biochemical understanding of ArsC and ArsC-like PTP mechanisms opens possibilities for further understanding of arsenate bioremediation and the LMW-PTP mechanism.

Exploring conformational preferences of proteins: ionic liquid effects on the energy landscape of avidin

In this work we experimentally investigate solvent and temperature induced conformational transitions of proteins and examine the role of ion–protein interactions in determining the conformational preferences of avidin, a homotetrameric glycoprotein, in choline-based ionic liquid (IL) solutions. Avidin was modified by surface cationisation and the addition of anionic surfactants, and the structural, thermal, and conformational stabilities of native and modified avidin were examined using dynamic light scattering, differential scanning calorimetry, and thermogravimetric analysis experiments. The protein-surfactant nanoconjugates showed higher thermostability behaviour compared to unmodified avidin, demonstrating distinct conformational ensembles. Small-angle X-ray scattering data showed that with increasing IL concentration, avidin became more compact, interpreted in the context of molecular confinement. To experimentally determine the detailed effects of IL on the energy landscape of avidin, differential scanning fluorimetry and variable temperature circular dichroism spectroscopy were performed. We show that different IL solutions can influence avidin conformation and thermal stability, and we provide insight into the effects of ILs on the folding pathways and thermodynamics of proteins. To further study the effects of ILs on avidin binding and correlate thermostability with conformational heterogeneity, we conducted a binding study. We found the ILs examined inhibited ligand binding in native avidin while enhancing binding in the modified protein, indicating ILs can influence the conformational stability of the distinct proteins differently. Significantly, this work presents a systematic strategy to explore protein conformational space and experimentally detect and characterise ‘invisible’ rare conformations using ILs.

Revealing solvent and temperature induced conformational transitions of proteins and the role of ion–protein interactions in determining the conformational preferences of avidin in ionic liquids.

Assessment of Force Field Accuracy Using Cryogenic Electron Microscopy Data of Hyper-thermostable Glutamate Dehydrogenase

Molecular dynamics (MD) simulations in biophysically relevant time scales of microseconds is a powerful tool for studying biomolecular processes, but results often display force field dependency. Therefore, assessment of force field accuracy using experimental data of biomolecules in solution is essential for simulation studies. Here, we propose the use of structural models obtained via cryo-electron microscopy (cryoEM), which provides biomolecular structures in vitreous ice mimicking the environment in solution. The accuracy of the AMBER (ff99SB-ILDN-NMR, ff14SB, ff15ipq, and ff15FB) and CHARMM (CHARMM22 and CHARMM36m) force fields was assessed by comparing their MD trajectories with the cryoEM data of thermostable hexameric glutamate dehydrogenase (GDH), which included a cryoEM map at a resolution of approximately 3 Å and structure models of subunits reflecting metastable conformations in domain motion occurring in GDH. In the assessment, we validated the force fields with respect to the reproducibility and stability of secondary structures and intersubunit interactions in the cryoEM data. Furthermore, we evaluated the force fields regarding the reproducibility of the energy landscape in the domain motion expected from the cryoEM data. As a result, among the six force fields, ff15FB and ff99SB-ILDN-NMR displayed good agreement with the experiment. The present study demonstrated the advantages of the high-resolution cryoEM map and suggested the optimal force field to reproduce experimentally observed protein structures.

An immunoinformatics study on the spike protein of SARS-CoV-2 revealing potential epitopes as vaccine candidates

Background

The pandemic situation of SARS-CoV-2 infection has sparked global concern due to the disease COVID-19 caused by it. Since the first cluster of confirmed cases in China in December 2019, the infection has been reported across the continents and inflicted upon a substantial number of populations.

Method

This study is focused on immunoinformatics analyses of the SARS-CoV-2 spike glycoprotein (S protein) which is key for the viral attachment to human host cells. Computational analyses were carried out for the prediction of B-cell and T-cell (MHC class I and II) epitopes of S protein and the analyses were extended further for the prediction of their immunogenic properties. The interaction and binding affinity of T-cell epitopes with HLA-B7 were also investigated by molecular docking.

Result

Three distinct epitopes for vaccine design were predicted from the sequence of S protein. The potential B-cell epitope was KNHTSPDVDLG possessing the highest antigenicity score of 1.4039 among other B-cell epitopes. T-cell epitope for human MHC class I was VVVLSFELL with an antigenicity score of 1.0909 and binding ability to 29 MHC-I alleles. The predicted T-cell epitope for human MHC class II molecule was VVIGIVNNT with a corresponding 1.3063 antigenicity score, less digesting enzymes, and 7 MHC-II alleles binding ability. All these three peptides were predicted to be highly antigenic, non-allergenic, and non-toxic. Analyses of the physiochemical properties of these predicted epitopes indicate their stable nature for plausible vaccine design. Furthermore, molecular docking investigation between the MHC class-I epitopes and human HLA-B7 reflects the stable interaction with high affinity among them.

Conclusion

The present study posits three potential epitopes of S protein of SARS-CoV-2 predicted by immunoinformatic methods based on their immunogenic properties and interactions with the host counterpart that can facilitate the development of vaccine against SARS-CoV-2. This study can act as the springboard for the future development of the COVID-19 vaccine.

The Non-Fibrillating N-Terminal of α-Synuclein Binds and Co-Fibrillates with Heparin

The intrinsically disordered protein α-synuclein (aSN) is, in its fibrillated state, the main component of Lewy bodies-hallmarks of Parkinson's disease. Additional Lewy body components include glycosaminoglycans, including heparan sulfate proteoglycans. In humans, heparan sulfate has, in an age-dependent manner, shown increased levels of sulfation. Heparin, a highly sulfated glycosaminoglycan, is a relevant mimic for mature heparan sulfate and has been shown to influence aSN fibrillation. Here, we decompose the underlying properties of the interaction between heparin and aSN and the effect of heparin on fibrillation. Via the isolation of the first 61 residues of aSN, which lacked intrinsic fibrillation propensity, fibrillation could be induced by heparin, and access to the initial steps in fibrillation was possible. Here, structural changes with shifts from disorder via type I β-turns to β-sheets were revealed, correlating with an increase in the aSN1-61/heparin molar ratio. Fluorescence microscopy revealed that heparin and aSN1-61 co-exist in the final fibrils. We conclude that heparin can induce the fibrillation of aSN1-61, through binding to the N-terminal with an affinity that is higher in the truncated form of aSN. It does so by specifically modulating the structure of aSN via the formation of type I β-turn structures likely critical for triggering aSN fibrillation.

Identifying Antifreeze Proteins Based on Key Evolutionary Information

Antifreeze proteins are important antifreeze materials that have been widely used in industry, including in cryopreservation, de-icing, and food storage applications. However, the quantity of some commercially produced antifreeze proteins is insufficient for large-scale industrial applications. Further, many antifreeze proteins have properties such as cytotoxicity, severely hindering their applications. Understanding the mechanisms underlying the protein-ice interactions and identifying novel antifreeze proteins are, therefore, urgently needed. In this study, to uncover the mechanisms underlying protein-ice interactions and provide an efficient and accurate tool for identifying antifreeze proteins, we assessed various evolutionary features based on position-specific scoring matrices (PSSMs) and evaluated their importance for discriminating of antifreeze and non-antifreeze proteins. We then parsimoniously selected seven key features with the highest importance. We found that the selected features showed opposite tendencies (regarding the conservation of certain amino acids) between antifreeze and non-antifreeze proteins. Five out of the seven features had relatively high contributions to the discrimination of antifreeze and non-antifreeze proteins, as revealed by a principal component analysis, i.e., the conservation of the replacement of Cys, Trp, and Gly in antifreeze proteins by Ala, Met, and Ala, respectively, in the related proteins, and the conservation of the replacement of Arg in non-antifreeze proteins by Ser and Arg in the related proteins. Based on the seven parsimoniously selected key features, we established a classifier using support vector machine, which outperformed the state-of-the-art tools. These results suggest that understanding evolutionary information is crucial to designing accurate automated methods for discriminating antifreeze and non-antifreeze proteins. Our classifier, therefore, is an efficient tool for annotating new proteins with antifreeze functions based on sequence information and can facilitate their application in industry.

PVPred-SCM: Improved Prediction and Analysis of Phage Virion Proteins Using a Scoring Card Method

Although, existing methods have been successful in predicting phage (or bacteriophage) virion proteins (PVPs) using various types of protein features and complex classifiers, such as support vector machine and naïve Bayes, these two methods do not allow interpretability. However, the characterization and analysis of PVPs might be of great significance to understanding the molecular mechanisms of bacteriophage genetics and the development of antibacterial drugs. Hence, we herein proposed a novel method (PVPred-SCM) based on the scoring card method (SCM) in conjunction with dipeptide composition to identify and characterize PVPs. In PVPred-SCM, the propensity scores of 400 dipeptides were calculated using the statistical discrimination approach. Rigorous independent validation test showed that PVPred-SCM utilizing only dipeptide composition yielded an accuracy of 77.56%, indicating that PVPred-SCM performed well relative to the state-of-the-art method utilizing a number of protein features. Furthermore, the propensity scores of dipeptides were used to provide insights into the biochemical and biophysical properties of PVPs. Upon comparison, it was found that PVPred-SCM was superior to the existing methods considering its simplicity, interpretability, and implementation. Finally, in an effort to facilitate high-throughput prediction of PVPs, we provided a user-friendly web-server for identifying the likelihood of whether or not these sequences are PVPs. It is anticipated that PVPred-SCM will become a useful tool or at least a complementary existing method for predicting and analyzing PVPs.

Atypical structural tendencies among low-complexity domains in the Protein Data Bank proteome

A variety of studies have suggested that low-complexity domains (LCDs) tend to be intrinsically disordered and are relatively rare within structured proteins in the Protein Data Bank (PDB). Although LCDs are often treated as a single class, we previously found that LCDs enriched in different amino acids can exhibit substantial differences in protein metabolism and function. Therefore, we wondered whether the structural conformations of LCDs are likewise dependent on which specific amino acids are enriched within each LCD. Here, we directly examined relationships between enrichment of individual amino acids and secondary structure tendencies across the entire PDB proteome. Secondary structure tendencies varied as a function of the identity of the amino acid enriched and its degree of enrichment. Furthermore, divergence in secondary structure profiles often occurred for LCDs enriched in physicochemically similar amino acids (e.g. valine vs. leucine), indicating that LCDs composed of related amino acids can have distinct secondary structure tendencies. Comparison of LCD secondary structure tendencies with numerous pre-existing secondary structure propensity scales resulted in relatively poor correlations for certain types of LCDs, indicating that these scales may not capture secondary structure tendencies as sequence complexity decreases. Collectively, these observations provide a highly resolved view of structural tendencies among LCDs parsed by the nature and magnitude of single amino acid enrichment.

The structures that proteins adopt are directly related to their amino acid sequences. Low-complexity domains (LCDs) in protein sequences are unusual regions made up of only a few different types of amino acids. Although this is the key feature that classifies sequences as LCDs, the physical properties of LCDs will differ based on the types of amino acids that are found in each domain. For example, the sequences “AAAAAAAAAA”, “EEEEEEEEEE”, and “EEKRKEEEKE” will have very different properties, even though they would all be classified as LCDs by traditional methods. In a previous study, we developed a new method to further divide LCDs into categories that more closely reflect the differences in their physical properties. In this study, we apply that approach to examine the structures of LCDs when sorted into different categories based on their amino acids. This allowed us to define relationships between the types of amino acids in the LCDs and their corresponding structures. Since protein structure is closely related to protein function, this has important implications for understanding the basic functions and properties of LCDs in a variety of proteins.

Novel Amino Acid Assembly in the Silk Tubes of Arid-Adapted Segestriid Spiders

We investigated in different sites inside or outside the Namib Desert the amino acids composition of the protein material forming the tube silk of Ariadna spiders. These spiders belong to the primitive Segestriidae family and spend their life inside vertical silk burrows dug within the sandy and gravelly soil of arid areas. The silks, previously purified by solubilization in hexafluoroisopropanol, were subjected to partial or total acid hydrolysis. Partial hydrolyzed samples, analyzed by mass spectrometry (matrix assisted laser desorption/ionization and electrospray), led to relevant information on the amino acid sequences in the proteins. The free amino acids formed by complete hydrolysis were derivatized with the Marfey's reagent and characterized by electrospray mass spectrometry. The reconstruction of the amino acids highlights a homogeneous plan in the chemical structure of all the analyzed silks. Eight amino acids constituting the primary structure of the proteins were identified. Alanine and glycine are the most abundant ones, with a prevalence of alanine, constituting together at least 61% of the chemical composition of the protein material, differently from what occurs in known spidroins. High percentages of proline, serine and threonine and low percentages of leucine complete the peculiarity of these proteins. The purified silks were also characterized by Fourier-transform Infrared Spectroscopy and their thermal properties were investigated by differential scanning calorimetry. The comparison of the silk tubes among the various Namibian populations, carried out through a multivariate statistical analysis, shows significant differences in their amino acid assembly possibly due to habitat features.

Epitope-based peptide vaccine design and target site depiction against Middle East Respiratory Syndrome Coronavirus: an immune-informatics study

Background

Middle East Respiratory Syndrome Coronavirus (MERS-COV) is the main cause of lung and kidney infections in developing countries such as Saudi Arabia and South Korea. This infectious single-stranded, positive (+) sense RNA virus enters the host by binding to dipeptidyl-peptide receptors. Since no vaccine is yet available for MERS-COV, rapid case identification, isolation, and infection prevention strategies must be used to combat the spreading of MERS-COV infection. Additionally, there is a desperate need for vaccines and antiviral strategies.

Methods

The present study used immuno-informatics and computational approaches to identify conserved B- and T cell epitopes for the MERS-COV spike (S) protein that may perform a significant role in eliciting the resistance response to MERS-COV infection.

Results

Many conserved cytotoxic T-lymphocyte epitopes and discontinuous and linear B-cell epitopes were predicted for the MERS-COV S protein, and their antigenicity and interactions with the human leukocyte antigen (HLA) B7 allele were estimated. Among B-cell epitopes, QLQMGFGITVQYGT displayed the highest antigenicity-score, and was immensely immunogenic. Among T-cell epitopes, MHC class-I peptide YKLQPLTFL and MHC class-II peptide YCILEPRSG were identified as highly antigenic. Furthermore, docking analyses revealed that the predicted peptides engaged in strong bonding with the HLA-B7 allele.

Conclusion

The present study identified several MERS-COV S protein epitopes that are conserved among various isolates from different countries. The putative antigenic epitopes may prove effective as novel vaccines for eradication and combating of MERS-COV infection.

Conserved B and T cell epitopes prediction of ebola virus glycoprotein for vaccine development: An immuno-informatics approach

Ebola virus (EBOV), a non-segmented single-stranded RNA virus, is often-most transmitted through body fluids like sweat, tears, saliva, and nasal secretions. Till date, there is no licensed vaccine of EBOV is available in the market; however, the world is increasingly vulnerable to this emerging threat. Hence, it is the need of time to develop a vaccine for EBOV to hinder its dissemination. The current study has been designed for identification and characterization of the potential B and T-cell epitopes using the Immuno-informatics tools, and it helped in finding the potent vaccine candidates against EBOV. Prediction, antigenicity and allergenicity testing of predicted B and T cells' epitopes was done as well to identify their potential as a vaccine candidate and to measure their safety level respectively. Among B-cell epitopes "WIPAGIGVTGVIIA" showed a high antigenicity score and it would play an important role in evoking the immune response. In T-cell epitopes, peptides "AIGLAWIPY" and "IRGFPRCRY" presented high antigenicity score, which binds to MHC class-I and MHC class-II alleles respectively. All predicted epitopes were analyzed and compared with already reported peptides carefully. Comparatively, Peptides predicted in the present study showed more immunogenicity score than already reported peptides, used as positive control, and are more immunogenic as compared to them. Peptides reported in the present study do not target only Zaire EBOV (ZEBOV), as in previous studies, but also other species, i.e. Tai Forest EBOV (TAFV), Sudan EBOV (SUDV), Bundibugyo EBOV (BDBV), and Reston EBOV (RESTV) and would bring the promising results as potent vaccine candidates.

An aromatic amino acid and associated helix in the C-terminus of the potato leafroll virus minor capsid protein regulate systemic infection and symptom expression

The C-terminal region of the minor structural protein of potato leafroll virus (PLRV), known as the readthrough protein (RTP), is involved in efficient virus movement, tissue tropism and symptom development. Analysis of numerous C-terminal deletions identified a five-amino acid motif that is required for RTP function. A PLRV mutant expressing RTP with these five amino acids deleted (Δ5aa-RTP) was compromised in systemic infection and symptom expression. Although the Δ5aa-RTP mutant was able to move long distance, limited infection foci were observed in systemically infected leaves suggesting that these five amino acids regulate virus phloem loading in the inoculated leaves and/or unloading into the systemically infected tissues. The 5aa deletion did not alter the efficiency of RTP translation, nor impair RTP self-interaction or its interaction with P17, the virus movement protein. However, the deletion did alter the subcellular localization of RTP. When co-expressed with a PLRV infectious clone, a GFP tagged wild-type RTP was localized to discontinuous punctate spots along the cell periphery and was associated with plasmodesmata, although localization was dependent upon the developmental stage of the plant tissue. In contrast, the Δ5aa-RTP-GFP aggregated in the cytoplasm. Structural modeling indicated that the 5aa deletion would be expected to perturb an α-helix motif. Two of 30 plants infected with Δ5aa-RTP developed a wild-type virus infection phenotype ten weeks post-inoculation. Analysis of the virus population in these plants by deep sequencing identified a duplication of sequences adjacent to the deletion that were predicted to restore the α-helix motif. The subcellular distribution of the RTP is regulated by the 5-aa motif which is under strong selection pressure and in turn contributes to the efficient long distance movement of the virus and the induction of systemic symptoms.

Peptide vaccine against chikungunya virus: immuno-informatics combined with molecular docking approach

BACKGROUND

Chikungunya virus (CHIKV), causes massive outbreaks of chikungunya infection in several regions of Asia, Africa and Central/South America. Being positive sense RNA virus, CHIKV replication within the host resulting in its genome mutation and led to difficulties in creation of vaccine, drugs and treatment strategies. Vector control strategy has been a gold standard to combat spreading of CHIKV infection, but to eradicate a species from the face of earth is not an easy task. Therefore, alongside vector control, there is a dire need to prevent the infection through vaccine as well as through antiviral strategies.

METHODS

This study was designed to find out conserved B cell and T cell epitopes of CHIKV structural proteins through immuno-informatics and computational approaches, which may play an important role in evoking the immune responses against CHIKV.

RESULTS

Several conserved cytotoxic T-lymphocyte epitopes, linear and conformational B cell epitopes were predicted for CHIKV structural polyprotein and their antigenicity was calculated. Among B-cell epitopes "PPFGAGRPGQFGDI" showed a high antigenicity score and it may be highly immunogenic. In case of T cell epitopes, MHC class I peptides 'TAECKDKNL' and MHC class II peptides 'VRYKCNCGG' were found extremely antigenic.

CONCLUSION

The study led to the discovery of various epitopes, conserved among various strains belonging to different countries. The potential antigenic epitopes can be successfully utilized in designing novel vaccines for combating and eradication of CHIKV disease.

Structural characterization of the D290V mutation site in hnRNPA2 low-complexity-domain polymers

Human genetic studies have given evidence of familial, disease-causing mutations in the analogous amino acid residue shared by three related RNA binding proteins causative of three neurological diseases. Alteration of aspartic acid residue 290 of hnRNPA2 to valine is believed to predispose patients to multisystem proteinopathy. Mutation of aspartic acid 262 of hnRNPA1 to either valine or asparagine has been linked to either amyotrophic lateral sclerosis or multisystem proteinopathy. Mutation of aspartic acid 378 of hnRNPDL to either asparagine or histidine has been associated with limb girdle muscular dystrophy. All three of these aspartic acid residues map to evolutionarily conserved regions of low-complexity (LC) sequence that may function in states of either intrinsic disorder or labile self-association. Here, we present a combination of solid-state NMR spectroscopy with segmental isotope labeling and electron microscopy on the LC domain of the hnRNPA2 protein. We show that, for both the wild-type protein and the aspartic acid 290-to-valine mutant, labile polymers are formed in which the LC domain associates into an in-register cross-β conformation. Aspartic acid 290 is shown to be charged at physiological pH and immobilized within the polymer core. Polymers of the aspartic acid 290-to-valine mutant are thermodynamically more stable than wild-type polymers. These observations give evidence that removal of destabilizing electrostatic interactions may be responsible for the increased propensity of the mutated LC domains to self-associate in disease-causing conformations.

A Solid-State Conceptualization of Information Transfer from Gene to Message to Protein

In this review, we describe speculative ideas and early stage research concerning the flow of genetic information from the nuclear residence of genes to the disparate, cytoplasmic sites of protein synthesis. We propose that this process of information transfer is meticulously guided by transient structures formed from protein segments of low sequence complexity/intrinsic disorder. These low complexity domains are ubiquitously associated with regulatory proteins that control gene expression and RNA biogenesis, but they are also found in the central channel of nuclear pores, the nexus points of intermediate filament assembly, and the locations of action of other well-studied cellular proteins and pathways. Upon being organized into localized cellular positions via mechanisms utilizing properly folded protein domains, thereby facilitating elevated local concentration, certain low complexity domains adopt cross-β interactions that are both structurally specific and labile to disassembly. These weakly tethered assemblies, we propose, are built to relay the passage of genetic information from one site to another within a cell, ensuring that the process is of extreme fidelity.

Tom70 enhances mitochondrial preprotein import efficiency by binding to internal targeting sequences

N-terminal matrix-targeting signals (MTSs) are critical for mitochondrial protein import. Backes et al. identified additional internal MTS-like sequences scattered along the sequences of mitochondrial proteins. By binding to Tom70 on the mitochondrial surface, these sequences support the import process.

The biogenesis of mitochondria depends on the import of hundreds of preproteins. N-terminal matrix-targeting signals (MTSs) direct preproteins to the surface receptors Tom20, Tom22, and Tom70. In this study, we show that many preproteins contain additional internal MTS-like signals (iMTS-Ls) in their mature region that share the characteristic properties of presequences. These features allow the in silico prediction of iMTS-Ls. Using Atp1 as model substrate, we show that iMTS-Ls mediate the binding to Tom70 and have the potential to target the protein to mitochondria if they are presented at its N terminus. The import of preproteins with high iMTS-L content is significantly impaired in the absence of Tom70, whereas preproteins with low iMTS-L scores are less dependent on Tom70. We propose a stepping stone model according to which the Tom70-mediated interaction with internal binding sites improves the import competence of preproteins and increases the efficiency of their translocation into the mitochondrial matrix.

Substrates of the chloroplast small heat shock proteins 22E/F point to thermolability as a regulative switch for heat acclimation in Chlamydomonas reinhardtii

We have identified 39 proteins that interact directly or indirectly with high confidence with chloroplast HSP22E/F under heat stress thus revealing chloroplast processes affected by heat. Under conditions promoting protein unfolding, small heat shock proteins (sHsps) prevent the irreversible aggregation of unfolding proteins by integrating into forming aggregates. Aggregates containing sHsps facilitate the access of Hsp70 and ClpB/Hsp104 chaperones, which in ATP-dependent reactions disentangle individual proteins from the aggregates and assist in their refolding to the native state. Chlamydomonas reinhardtii encodes eight different sHsps (HSP22A to H). The goal of this work was to identify chloroplast-targeted sHsps in Chlamydomonas and to obtain a comprehensive list of the substrates with which they interact during heat stress in order to understand which chloroplast processes are disturbed under heat stress. We show that HSP22E and HSP22F are major chloroplast-targeted sHsps that have emerged from a recent gene duplication event resulting from the ongoing diversification of sHsps in the Volvocales. HSP22E/F strongly accumulate during heat stress and form high molecular mass complexes. Using differential immunoprecipitation, mass spectrometry and a stringent filtering algorithm we identified 39 proteins that with high-confidence interact directly or indirectly with HSP22E/F under heat stress. We propose that the apparent thermolability of several of these proteins might be a desired trait as part of a mechanism enabling Chlamydomonas chloroplasts to rapidly react to thermal stress.

Mass & secondary structure propensity of amino acids explain their mutability and evolutionary replacements

Why is an amino acid replacement in a protein accepted during evolution? The answer given by bioinformatics relies on the frequency of change of each amino acid by another one and the propensity of each to remain unchanged. We propose that these replacement rules are recoverable from the secondary structural trends of amino acids. A distance measure between high-resolution Ramachandran distributions reveals that structurally similar residues coincide with those found in substitution matrices such as BLOSUM: Asn ↔ Asp, Phe ↔ Tyr, Lys ↔ Arg, Gln ↔ Glu, Ile ↔ Val, Met → Leu; with Ala, Cys, His, Gly, Ser, Pro, and Thr, as structurally idiosyncratic residues. We also found a high average correlation (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\overline{R}$$\end{document}R¯ = 0.85) between thirty amino acid mutability scales and the mutational inertia (I_X), which measures the energetic cost weighted by the number of observations at the most probable amino acid conformation. These results indicate that amino acid substitutions follow two optimally-efficient principles: (a) amino acids interchangeability privileges their secondary structural similarity, and (b) the amino acid mutability depends directly on its biosynthetic energy cost, and inversely with its frequency. These two principles are the underlying rules governing the observed amino acid substitutions.

Human EGF-derived direct and reverse short linear motifs: conformational dynamics insight into the receptor-binding residues

Short linear motifs (SLiMs) have been recognized to perform diverse functions in a variety of regulatory proteins through the involvement in protein-protein interactions, signal transduction, cell cycle regulation, protein secretion, etc. However, detailed molecular mechanisms underlying their functions including roles of definite amino acid residues remain obscure. In our previous studies, we demonstrated that conformational dynamics of amino acid residues in oligopeptides derived from regulatory proteins such as alpha-fetoprotein (AFP), carcino-embryonic antigen (CEA), and pregnancy specific β1-glycoproteins (PSGs) contributes greatly to their biological activities. In the present work, we revealed the 22-member linear modules composed of direct and reverse AFP_14-20-like heptapeptide motifs linked by CxxGY/FxGx consensus motif within epidermal growth factor (EGF), growth factors of EGF family and numerous regulatory proteins containing EGF-like modules. We showed, first, the existence of similarity in amino acid signatures of both direct and reverse motifs in terms of their physicochemical properties. Second, molecular dynamics (MD) simulation study demonstrated that key receptor-binding residues in human EGF in the aligned positions of the direct and reverse motifs may have similar distribution of conformational probability densities and dynamic behavior despite their distinct physicochemical properties. Third, we found that the length of a polypeptide chain (from 7 to 53 residues) has no effect, while disulfide bridging and backbone direction significantly influence the conformational distribution and dynamics of the residues. Our data may contribute to the atomic level structure-function analysis and protein structure decoding; additionally, they may provide a basis for novel protein/peptide engineering and peptide-mimetic drug design.

High GC content causes orphan proteins to be intrinsically disordered

De novo creation of protein coding genes involves the formation of short ORFs from noncoding regions; some of these ORFs might then become fixed in the population. These orphan proteins need to, at the bare minimum, not cause serious harm to the organism, meaning that they should for instance not aggregate. Therefore, although the creation of short ORFs could be truly random, the fixation should be subjected to some selective pressure. The selective forces acting on orphan proteins have been elusive, and contradictory results have been reported. In Drosophila young proteins are more disordered than ancient ones, while the opposite trend is present in yeast. To the best of our knowledge no valid explanation for this difference has been proposed. To solve this riddle we studied structural properties and age of proteins in 187 eukaryotic organisms. We find that, with the exception of length, there are only small differences in the properties between proteins of different ages. However, when we take the GC content into account we noted that it could explain the opposite trends observed for orphans in yeast (low GC) and Drosophila (high GC). GC content is correlated with codons coding for disorder promoting amino acids. This leads us to propose that intrinsic disorder is not a strong determining factor for fixation of orphan proteins. Instead these proteins largely resemble random proteins given a particular GC level. During evolution the properties of a protein change faster than the GC level causing the relationship between disorder and GC to gradually weaken.

We show that the GC content of a genome is of great importance for the properties of an orphan protein. GC content affects the frequency of the codons and this affects the probability for each amino acid to be included in a de novo created protein. The codons encoding for Ala, Pro and Gly contain 80% GC, while codons for Lys, Phe, Asn, Tyr and Ile contain 20% or less. The three high GC amino acids are all disorder promoting, while Phe, Tyr and Ile are order promoting. Therefore, random protein sequences at a high GC will be more disordered than the ones created at a low GC. The structural properties of the youngest proteins match to a large degree the properties of random proteins when the GC content is taken into account. In contrast, structural properties of ancient proteins only show a weak correlation with GC content. This suggests that even after fixation in the population, proteins largely resemble random proteins given a certain GC content. Thereafter, during evolution the correlation between structural properties and GC weakens.

Conformational study of melectin and antapin antimicrobial peptides in model membrane environments

Antimicrobial peptides have long been considered as promising compounds against drug-resistant pathogens. In this work, we studied the secondary structure of antimicrobial peptides melectin and antapin using electronic (ECD) and vibrational circular dichroism (VCD) spectroscopies that are sensitive to peptide secondary structures. The results from quantitative ECD spectral evaluation by Dichroweb and CDNN program and from the qualitative evaluation of the VCD spectra were compared. The antimicrobial activity of the selected peptides depends on their ability to adopt an amphipathic α-helical conformation on the surface of the bacterial membrane. Hence, solutions of different zwitterionic and negatively charged liposomes and micelles were used to mimic the eukaryotic and bacterial biological membranes. The results show a significant content of α-helical conformation in the solutions of negatively charged liposomes mimicking the bacterial membrane, thus correlating with the antimicrobial activity of the studied peptides. On the other hand in the solutions of zwitterionic liposomes used as models of the eukaryotic membranes, the fraction of α-helical conformation was lower, which corresponds with their moderate hemolytic activity.

CryoProtect: A Web Server for Classifying Antifreeze Proteins from Nonantifreeze Proteins

Antifreeze protein (AFP) is an ice-binding protein that protects organisms from freezing in extremely cold environments. AFPs are found across a diverse range of species and, therefore, significantly differ in their structures. As there are no consensus sequences available for determining the ice-binding domain of AFPs, thus the prediction and characterization of AFPs from their sequence is a challenging task. This study addresses this issue by predicting AFPs directly from sequence on a large set of 478 AFPs and 9,139 non-AFPs using machine learning (e.g., random forest) as a function of interpretable features (e.g., amino acid composition, dipeptide composition, and physicochemical properties). Furthermore, AFPs were characterized using propensity scores and important physicochemical properties via statistical and principal component analysis. The predictive model afforded high performance with an accuracy of 88.28% and results revealed that AFPs are likely to be composed of hydrophobic amino acids as well as amino acids with hydroxyl and sulfhydryl side chains. The predictive model is provided as a free publicly available web server called CryoProtect for classifying query protein sequence as being either AFP or non-AFP. The data set and source code are for reproducing the results which are provided on GitHub.

Tweaking of the supramolecular gelation properties of a dipeptide based ambidextrous organogelator through the cooperative influence of hydrophobicity, steric bulk and conformational flexibility of the side chain residue of a single hydrophobic α-amino acid encrypted on a designed molecular frame

Fine tuning of gelation behavior via singular alteration of hydrophobic C_α-amino acid on the backbone of a dipeptide based ambidextrous organogelator.

Supramolecular assembly of multifunctional maspin-mimetic nanostructures as a potent peptide-based angiogenesis inhibitor

Aberrant angiogenesis plays a large role in pathologies ranging from tumor growth to macular degeneration. Anti-angiogenic proteins have thus come under scrutiny as versatile, potent therapeutics but face problems with purification and tissue retention. We report here on the synthesis of supramolecular nanostructures that mimic the anti-angiogenic activity of maspin, a class II tumor suppressor protein. These maspin-mimetic nanostructures are formed via self-assembly of small peptide amphiphiles containing the g-helix motif of maspin. Using tubulogenesis assays with human umbilical vein endothelial cells, we demonstrate that maspin-mimetic nanostructures show anti-angiogenic activity at concentrations that are significantly lower than those necessary for the g-helix peptide. Furthermore, in vivo assays in the chick chorioallantoic membrane show maspin-mimetic nanostructures to be effective over controls at inhibiting angiogenesis. Thus, the nanostructures investigated here offer an attractive alternative to the use of anti-angiogenic recombinant proteins in the treatment of cancer or other diseases involving abnormal blood vessel formation.

Effect of charged amino acid side chain length at non-hydrogen bonded strand positions on β-hairpin stability

β-Sheets have been implicated in various neurological disorders, and ∼20% of protein residues adopt a sheet conformation. Therefore, studies on the structural origin of sheet stability can provide fundamental knowledge with potential biomedical applications. Oppositely charged amino acids are frequently observed across one another in antiparallel β-sheets. Interestingly, the side chains of natural charged amino acids Asp, Glu, Arg, Lys have different numbers of hydrophobic methylenes linking the backbone to the hydrophilic charged functionalities. To explore the inherent effect of charged amino acid side chain length on antiparallel sheets, the stability of a designed hairpin motif containing charged amino acids with varying side chain lengths at non-hydrogen bonded positions was studied. Peptides with the guest position on the N-terminal strand and the C-terminal strand were investigated by NMR methods. The charged amino acids (Xaa) included negatively charged residues with a carboxylate group (Asp, Glu, Aad in increasing length), positively charged residues with an ammonium group (Dap, Dab, Orn, Lys in increasing length), and positively charged residues with a guanidinium group (Agp, Agb, Arg, Agh in increasing length). The fraction folded and folding free energy for each peptide were derived from the chemical shift deviation data. The stability of the peptides with the charged residues at the N-terminal guest position followed the trends: Asp > Glu > Aad, Dap < Dab < Orn ∼ Lys, and Agb < Arg < Agh < Agp. The stability of the peptides with the charged residues at the C-terminal guest position followed the trends: Asp < Glu < Aad, Dap ∼ Dab < Orn ∼ Lys, and Agb < Arg ∼ Agp < Agh. These trends were rationalized by thermodynamic sheet propensity and cross-strand interactions.