Characterizing Hydropathy of Amino Acid Side Chain in a Protein Environment by Investigating the Structural Changes of Water Molecules Network

A machine learning framework to identify complex physicochemical features of B cell epitopes

During infection with Plasmodium falciparum in pregnancy, parasites express a unique virulence factor, VAR2CSA, that mediates binding of infected red blood cells to the placenta. A major goal in designing vaccines to protect pregnant women from malaria is to elicit antibodies to VAR2CSA. The challenge is that VAR2CSA is highly polymorphic and identifying conserved epitopes is essential to elicit strain-transcending immunity. Unexpectedly, a mouse monoclonal antibody, 3D10, raised against the unrelated Duffy binding protein from P. vivax (DBPII) cross-reacts with diverse alleles of VAR2CSA in vitro. To identify these potentially conserved epitopes in VAR2CSA, we designed a machine learning framework to analyse 3D10 reactivity to peptides derived from two alleles of VAR2CSA, DBPII, and PvEBP2 (negative control). We used decision trees and a panel of 430 features to extract features correlated to 3D10 binding. We analysed patterns of these features in the dataset and designed mutant peptides to test complex sequence motifs. Features associated with 3D10 reactivity were mapped onto predicted 3D structures of Plasmodium proteins and validated based on 3D10 reactivity to the recombinant antigens. While the array data identified certain linear epitopes, the framework predicted other epitopes that are conformational. With this approach, peptide array data can be mined to extract physicochemical properties of epitopes recognized by polyreactive antibodies.

Compact Assessment of Molecular Surface Complementarities Enhances Neural Network-Aided Prediction of Key Binding Residues

Predicting interactions between proteins is fundamental for understanding the mechanisms underlying cellular processes, since protein-protein complexes are crucial in physiological conditions but also in many diseases, for example by seeding aggregates formation. Despite the many advancements made so far, the performance of docking protocols is deeply dependent on their capability to identify binding regions. From this, the importance of developing low-cost and computationally efficient methods in this field. We present an integrated novel protocol mainly based on compact modeling of protein surface patches via sets of orthogonal polynomials to identify regions of high shape/electrostatic complementarity. By incorporating both hydrophilic and hydrophobic contributions, we define new binding matrices, which serve as effective inputs for training a neural network. In this work, we propose a new Neural Network (NN)-based architecture, Core Interacting Residues Network (CIRNet), which achieves a performance in terms of Area Under the Receiver Operating Characteristic Curve (ROC AUC) of approximately 0.87 in identifying pairs of core interacting residues on a balanced data set. In a blind search for core interacting residues, CIRNet distinguishes them from random decoys with an ROC AUC of 0.72. We test this protocol to enhance docking algorithms by filtering the proposed poses, addressing one of the still open problems in computational biology. Notably, when applied to the top ten models from three widely used docking servers, CIRNet improves docking outcomes, significantly reducing the average RMSD between the selected poses and the native state. Compared to another state-of-the-art tool for rescaling docking poses, CIRNet more efficiently identified the worst poses generated by the three docking servers under consideration and achieved superior rescaling performance in two cases.

Context Dependency of Hydrophobicity in Intrinsically Disordered Proteins: Insights from a New Dewetting Free Energy-Based Hydrophobicity Scale

The interaction between amino acids (AAs) and hydration water is fundamental to protein folding and protein-protein interactions. Here, we proposed a hydrophobicity scale for AAs based on their computed free energetic cost of dewetting. This metric captures both entropic and enthalpic contributions of AA-water interactions and allows a systematic and intuitive classification of AAs. Using indirect umbrella sampling (INDUS), we rank individual AAs based on the relative magnitude of their dewetting free energies, from lowest (most hydrophobic) to highest (most hydrophilic). This new hydrophobicity scale is a starting point to evaluate different elements of water hydration behavior, and we focus here on the water structure and translational diffusivity of the hydration waters. While the latter is commonly used as a proxy for hydrophobicity, we show that its behavior is in fact nonmonotonic: hydrophobic residues show slow water diffusion due to highly structured hydration water networks, while highly hydrophilic residues have slow water diffusion due to strong hydrogen bonds with water despite less structured hydration networks. We extend our analysis of hydration properties to intrinsically disordered peptides with varied sequence patterning (sequences of proline/leucine and arginine/glutamic acid residues). We find that the hydration behavior of these peptides is highly context-dependent, with hydrophobic (hydrophilic) patches cooperatively enhancing hydrophobicity (hydrophilicity). These molecular insights of sequence-dependent hydration behaviors may be particularly impactful for the study of intrinsically disordered proteins implicated in liquid-liquid phase separation and aggregation, processes where AAs' hydration environments are complex and changing.

Nanoscale Topography Dictates Residue Hydropathy in Proteins

Proteins exhibit diverse structures, including pockets, cavities, channels, and bumps, which are crucial in determining their functions. This diversity in topography also introduces significant chemical heterogeneity, with polar and charged domains often juxtaposed with nonpolar domains in proximity. Consequently, accurately assessing the hydropathy of amino acid residues within the intricate nanoscale topology of proteins is essential. This study presents quantitative hydropathy data for 277,877 amino acid residues, computed using the Protocol for Assigning a Residue's Character on a Hydropathy (PARCH) scale. Leveraging this data set comprising 1000 structurally diverse proteins sourced from the Protein Data Bank, we examined residues situated in various nanoscale environments and analyzed hydropathy in relation to protein topography. Our findings indicate that the hydropathy of a residue is intricately linked to both its individual characteristics and the geometric features of its neighboring residues in response to water. Changes in the number and chemical identity of the neighbors, as well as the nanoscale topography surrounding a residue, are mirrored in its hydropathy profile. Our calculations reveal the intricate interplay of hydrophilic, hydroneutral, and hydrophobic residues distributed across the surface and core of proteins. Notably, we observe that protein surfaces can be ten times more hydrophilic than their cores.

Computational evidences of a misfolding event in an aggregation-prone light chain preceding the formation of the non-native pathogenic dimer

Antibody light chain amyloidosis is a disorder in which protein aggregates, mainly composed of immunoglobulin light chains, deposit in diverse tissues impairing the correct functioning of organs. Interestingly, due to the high susceptibility of antibodies to mutations, AL amyloidosis appears to be strongly patient-specific. Indeed, every patient will display their own mutations that will make the proteins involved prone to aggregation thus hindering the study of this disease on a wide scale. In this framework, determining the molecular mechanisms that drive the aggregation could pave the way to the development of patient-specific therapeutics. Here, we focus on a particular patient-derived light chain, which has been experimentally characterized. We investigated the early phases of the aggregation pathway through extensive full-atom molecular dynamics simulations, highlighting a structural rearrangement and the exposure of two hydrophobic regions in the aggregation-prone species. Next, we moved to consider the pathological dimerization process through docking and molecular dynamics simulations, proposing a dimeric structure as a candidate pathological first assembly. Overall, our results shed light on the first phases of the aggregation pathway for a light chain at an atomic level detail, offering new structural insights into the corresponding aggregation process.

Computational Approaches to Predict Protein-Protein Interactions in Crowded Cellular Environments

Investigating protein-protein interactions is crucial for understanding cellular biological processes because proteins often function within molecular complexes rather than in isolation. While experimental and computational methods have provided valuable insights into these interactions, they often overlook a critical factor: the crowded cellular environment. This environment significantly impacts protein behavior, including structural stability, diffusion, and ultimately the nature of binding. In this review, we discuss theoretical and computational approaches that allow the modeling of biological systems to guide and complement experiments and can thus significantly advance the investigation, and possibly the predictions, of protein-protein interactions in the crowded environment of cell cytoplasm. We explore topics such as statistical mechanics for lattice simulations, hydrodynamic interactions, diffusion processes in high-viscosity environments, and several methods based on molecular dynamics simulations. By synergistically leveraging methods from biophysics and computational biology, we review the state of the art of computational methods to study the impact of molecular crowding on protein-protein interactions and discuss its potential revolutionizing effects on the characterization of the human interactome.

High-Throughput Interactome Determination via Sulfur Anomalous Scattering

We propose a novel approach for detecting the binding between proteins making use of the anomalous diffraction of natively present heavy elements, e.g., sulfurs, inside molecular three-dimensional structures. In particular, we analytically and numerically show that the diffraction patterns produced by the anomalous scattering of the sulfur atoms in a given direction depend additively on the relative distances between all couples of sulfur atoms. Thus, the differences in the patterns produced by bound proteins with respect to their nonbonded states can be exploited to rapidly assess protein complex formation. On the basis of our results, we suggest a possible experimental procedure for detecting protein-protein binding. Overall, the completely label-free and rapid method we propose may be readily extended to probe interactions on a large scale, thus paving the way for the development of a novel field of research based on a synchrotron light source.

Impact of Excipient Extraction and Buffer Exchange on Recombinant Monoclonal Antibody Stability

The foundation of a biosimilar manufacturer's regulatory filing is the demonstration of analytical and functional similarity between the biosimilar product and the pertinent originator product. The excipients in the formulation may interfere with characterization using typical analytical and functional techniques during this biosimilarity exercise. Consequently, the producers of biosimilar products resort to buffer exchange to isolate the biotherapeutic protein from the drug product formulation. However, the impact that this isolation has on the product stability is not completely known. This study aims to elucidate the extent to which mAb isolation via ultrafiltration-diafiltration-based buffer exchange impacts mAb stability. It has been demonstrated that repeated extraction cycles do result in significant changes in higher-order structure (red-shift of 5.0 nm in fluorescence maxima of buffer exchanged samples) of the mAb and also an increase in formation of basic variants from 19.1 to 26.7% and from 32.3 to 36.9% in extracted innovator and biosimilar Tmab samples, respectively. It was also observed that under certain conditions of tertiary structure disruptions, Tmab could be restabilized depending on formulation composition. Thus, mAb isolation through extraction with buffer exchange impacts the product stability. Based on the observations reported in this paper, we recommend that biosimilar manufacturers take into consideration these effects of excipients on protein stability when performing biosimilarity assessments.

Unveiling the affinity-stability relationship in anti-measles virus antibodies: a computational approach for hotspots prediction

Recent years have seen an uptick in the use of computational applications in antibody engineering. These tools have enhanced our ability to predict interactions with antigens and immunogenicity, facilitate humanization, and serve other critical functions. However, several studies highlight the concern of potential trade-offs between antibody affinity and stability in antibody engineering. In this study, we analyzed anti-measles virus antibodies as a case study, to examine the relationship between binding affinity and stability, upon identifying the binding hotspots. We leverage in silico tools like Rosetta and FoldX, along with molecular dynamics (MD) simulations, offering a cost-effective alternative to traditional in vitro mutagenesis. We introduced a pattern in identifying key residues in pairs, shedding light on hotspots identification. Experimental physicochemical analysis validated the predicted key residues by confirming significant decrease in binding affinity for the high-affinity antibodies to measles virus hemagglutinin. Through the nature of the identified pairs, which represented the relative hydropathy of amino acid side chain, a connection was proposed between affinity and stability. The findings of the study enhance our understanding of the interactions between antibody and measles virus hemagglutinin. Moreover, the implications of the observed correlation between binding affinity and stability extend beyond the field of anti-measles virus antibodies, thereby opening doors for advancements in antibody research.

An Affordable Topography-Based Protocol for Assigning a Residue's Character on a Hydropathy (PARCH) Scale

The hydropathy of proteins or quantitative assessment of protein-water interactions has been a topic of interest for decades. Most hydropathy scales use a residue-based or atom-based approach to assign fixed numerical values to the 20 amino acids and categorize them as hydrophilic, hydroneutral, or hydrophobic. These scales overlook the protein's nanoscale topography, such as bumps, crevices, cavities, clefts, pockets, and channels, in calculating the hydropathy of the residues. Some recent studies have included protein topography in determining hydrophobic patches on protein surfaces, but these methods do not provide a hydropathy scale. To overcome the limitations in the existing methods, we have developed a Protocol for Assigning a Residue's Character on the Hydropathy (PARCH) scale that adopts a holistic approach to assigning the hydropathy of a residue. The parch scale evaluates the collective response of the water molecules in the protein's first hydration shell to increasing temperatures. We performed the parch analysis of a set of well-studied proteins that include the following─enzymes, immune proteins, and integral membrane proteins, as well as fungal and virus capsid proteins. Since the parch scale evaluates every residue based on its location, a residue may have very different parch values inside a crevice versus a surface bump. Thus, a residue can have a range of parch values (or hydropathies) dictated by the local geometry. The parch scale calculations are computationally inexpensive and can compare hydropathies of different proteins. The parch analysis can affordably and reliably aid in designing nanostructured surfaces, identifying hydrophilic and hydrophobic patches, and drug discovery.

PepAnalyzer: predicting peptide properties using its sequence

Peptides are short linear molecules consisting of amino acids that play an essential role in most biological processes. They can treat diseases by working as a vaccine or antimicrobial agent and serves as a cancer molecule to deliver the drug to the target site for the treatment of cancer. They have the potential to solve the drawbacks of current medications and can be industrially produced in large quantities at low cost. However, poor chemical and physical stability, short circulating plasma half-life, and solubility are some issues that need solutions before they can be used as therapeutics. PepAnalyzer tool is a user-friendly tool that predicts 15 different properties such as binding potential, half-life, transmembrane patterns, test tube stability, charge, isoelectric point, molecular weights, and molar extinction coefficients only using the sequence. The tool is designed using BioPython utility and has even results with standard tools, such as Expasy, EBI, Genecorner, and Geneinfinity. The tool assists students, researchers, and the pharmaceutical sector. The PepAnalyzer tool's online platform is accessible at the link: http://www.iksmbrlabdu.in/peptool .

N-terminal alanine-rich (NTAR) sequences drive precise start codon selection resulting in elevated translation of multiple proteins including ERK1/2

We report the discovery of N-terminal alanine-rich sequences, which we term NTARs, that act in concert with their native 5'-untranslated regions to promote selection of the proper start codon. NTARs also facilitate efficient translation initiation while limiting the production of non-functional polypeptides through leaky scanning. We first identified NTARs in the ERK1/2 kinases, which are among the most important signaling molecules in mammals. Analysis of the human proteome reveals that hundreds of proteins possess NTARs, with housekeeping proteins showing a particularly high prevalence. Our data indicate that several of these NTARs act in a manner similar to those found in the ERKs and suggest a mechanism involving some or all of the following features: alanine richness, codon rarity, a repeated amino acid stretch and a nearby second AUG. These features may help slow down the leading ribosome, causing trailing pre-initiation complexes (PICs) to pause near the native AUG, thereby facilitating accurate translation initiation. Amplification of erk genes is frequently observed in cancer, and we show that NTAR-dependent ERK protein levels are a rate-limiting step for signal output. Thus, NTAR-mediated control of translation may reflect a cellular need to precisely control translation of key transcripts such as potential oncogenes. By preventing translation in alternative reading frames, NTAR sequences may be useful in synthetic biology applications, e.g. translation from RNA vaccines.

Differences in the organization of interface residues tunes the stability of the SARS-CoV-2 spike-ACE2 complex

The continuous emergence of novel variants represents one of the major problems in dealing with the SARS-CoV-2 virus. Indeed, also due to its prolonged circulation, more than ten variants of concern emerged, each time rapidly overgrowing the current viral version due to improved spreading features. As, up to now, all variants carry at least one mutation on the spike Receptor Binding Domain, the stability of the binding between the SARS-CoV-2 spike protein and the human ACE2 receptor seems one of the molecular determinants behind the viral spreading potential. In this framework, a better understanding of the interplay between spike mutations and complex stability can help to assess the impact of novel variants. Here, we characterize the peculiarities of the most representative variants of concern in terms of the molecular interactions taking place between the residues of the spike RBD and those of the ACE2 receptor. To do so, we performed molecular dynamics simulations of the RBD-ACE2 complexes of the seven variants of concern in comparison with a large set of complexes with different single mutations taking place on the RBD solvent-exposed residues and for which the experimental binding affinity was available. Analyzing the strength and spatial organization of the intermolecular interactions of the binding region residues, we found that (i) mutations producing an increase of the complex stability mainly rely on instaurating more favorable van der Waals optimization at the cost of Coulombic ones. In particular, (ii) an anti-correlation is observed between the shape and electrostatic complementarities of the binding regions. Finally, (iii) we showed that combining a set of dynamical descriptors is possible to estimate the outcome of point mutations on the complex binding region with a performance of 0.7. Overall, our results introduce a set of dynamical observables that can be rapidly evaluated to probe the effects of novel isolated variants or different molecular systems.

Electrostatic complementarity at the interface drives transient protein-protein interactions

Understanding the mechanisms driving bio-molecules binding and determining the resulting complexes' stability is fundamental for the prediction of binding regions, which is the starting point for drug-ability and design. Characteristics like the preferentially hydrophobic composition of the binding interfaces, the role of van der Waals interactions, and the consequent shape complementarity between the interacting molecular surfaces are well established. However, no consensus has yet been reached on the role of electrostatic. Here, we perform extensive analyses on a large dataset of protein complexes for which both experimental binding affinity and pH data were available. Probing the amino acid composition, the disposition of the charges, and the electrostatic potential they generated on the protein molecular surfaces, we found that (i) although different classes of dimers do not present marked differences in the amino acid composition and charges disposition in the binding region, (ii) homodimers with identical binding region show higher electrostatic compatibility with respect to both homodimers with non-identical binding region and heterodimers. Interestingly, (iii) shape and electrostatic complementarity, for patches defined on short-range interactions, behave oppositely when one stratifies the complexes by their binding affinity: complexes with higher binding affinity present high values of shape complementarity (the role of the Lennard-Jones potential predominates) while electrostatic tends to be randomly distributed. Conversely, complexes with low values of binding affinity exploit Coulombic complementarity to acquire specificity, suggesting that electrostatic complementarity may play a greater role in transient (or less stable) complexes. In light of these results, (iv) we provide a novel, fast, and efficient method, based on the 2D Zernike polynomial formalism, to measure electrostatic complementarity without the need of knowing the complex structure. Expanding the electrostatic potential on a basis of 2D orthogonal polynomials, we can discriminate between transient and permanent protein complexes with an AUC of the ROC of [Formula: see text] 0.8. Ultimately, our work helps shedding light on the non-trivial relationship between the hydrophobic and electrostatic contributions in the binding interfaces, thus favoring the development of new predictive methods for binding affinity characterization.

Investigating the competition between ACE2 natural molecular interactors and SARS-CoV-2 candidate inhibitors

The SARS-CoV-2 pandemic still poses a threat to the global health as the virus continues spreading in most countries. Therefore, the identification of molecules capable of inhibiting the binding between the ACE2 receptor and the SARS-CoV-2 spike protein is of paramount importance. Recently, two DNA aptamers were designed with the aim to inhibit the interaction between the ACE2 receptor and the spike protein of SARS-CoV-2. Indeed, the two molecules interact with the ACE2 receptor in the region around the K353 residue, preventing its binding of the spike protein. If on the one hand this inhibition process hinders the entry of the virus into the host cell, it could lead to a series of side effects, both in physiological and pathological conditions, preventing the correct functioning of the ACE2 receptor. Here, we discuss through a computational study the possible effect of these two very promising DNA aptamers, investigating all possible interactions between ACE2 and its experimentally known molecular partners. Our in silico predictions show that some of the 10 known molecular partners of ACE2 could interact, physiologically or pathologically, in a region adjacent to the K353 residue. Thus, the curative action of the proposed DNA aptamers could recruit ACE2 from its biological functions.

Exploring the Potential of Black Soldier Fly Larval Proteins as Bioactive Peptide Sources through in Silico Gastrointestinal Proteolysis: A Cheminformatic Investigation

Despite their potential as a protein source for human consumption, the health benefits of black soldier fly larvae (BSFL) proteins following human gastrointestinal (GI) digestion are poorly understood. This computational study explored the potential of BSFL proteins to release health-promoting peptides after human GI digestion. Twenty-six proteins were virtually proteolyzed with GI proteases. The resultant peptides were screened for high GI absorption and non-toxicity. Shortlisted peptides were searched against the BIOPEP-UWM and Scopus databases to identify their bioactivities. The potential of the peptides as inhibitors of myeloperoxidase (MPO), NADPH oxidase (NOX), and xanthine oxidase (XO), as well as a disruptor of Keap1–Nrf2 protein–protein interaction, were predicted using molecular docking and dynamics simulation. Our results revealed that about 95% of the 5218 fragments generated from the proteolysis of BSFL proteins came from muscle proteins. Dipeptides comprised the largest group (about 25%) of fragments arising from each muscular protein. Screening of 1994 di- and tripeptides using SwissADME and STopTox tools revealed 65 unique sequences with high GI absorption and non-toxicity. A search of the databases identified 16 antioxidant peptides, 14 anti-angiotensin-converting enzyme peptides, and 17 anti-dipeptidyl peptidase IV peptides among these sequences. Results from molecular docking and dynamic simulation suggest that the dipeptide DF has the potential to inhibit Keap1–Nrf2 interaction and interact with MPO within a short time frame, whereas the dipeptide TF shows promise as an XO inhibitor. BSFL peptides were likely weak NOX inhibitors. Our in silico results suggest that upon GI digestion, BSFL proteins may yield high-GI-absorbed and non-toxic peptides with potential health benefits. This study is the first to investigate the bioactivity of peptides liberated from BSFL proteins following human GI digestion. Our findings provide a basis for further investigations into the potential use of BSFL proteins as a functional food ingredient with significant health benefits.

Bioinformatics and computational analyses of kidney stone modulatory proteins lead to solid experimental evidence and therapeutic potential

In recent biomedical research, bioinformatics and computational analyses have played essential roles for examining experimental findings and database information. Several bioinformatic tools have been developed and made publicly available for analyzing protein sequence, structure, functional motif/domain, and interactions network. Such properties are very helpful to define biochemical and functional roles of the protein(s) of interest. During the past few decades, bioinformatics and computational biotechnology have been widely applied to kidney stone research. This review summarizes commonly used tools and evidence of bioinformatics and computational biotechnology applied to kidney stone disease (KSD) with special emphasis on analyses of the stone modulatory proteins that play critical roles in kidney stone formation. Such analyses lead to solid experimental evidence to demonstrate mechanisms underlying their stone modulatory activities. The findings obtained from such analyses may also lead to better understanding of KSD pathogenesis and to further development of new therapeutic and preventive strategies.

Caseins: Versatility of Their Micellar Organization in Relation to the Functional and Nutritional Properties of Milk

The milk of mammals is a complex fluid mixture of various proteins, minerals, lipids, and other micronutrients that play a critical role in providing nutrition and immunity to newborns. Casein proteins together with calcium phosphate form large colloidal particles, called casein micelles. Caseins and their micelles have received great scientific interest, but their versatility and role in the functional and nutritional properties of milk from different animal species are not fully understood. Caseins belong to a class of proteins that exhibit open and flexible conformations. Here, we discuss the key features that maintain the structures of the protein sequences in four selected animal species: cow, camel, human, and African elephant. The primary sequences of these proteins and their posttranslational modifications (phosphorylation and glycosylation) that determine their secondary structures have distinctively evolved in these different animal species, leading to differences in their structural, functional, and nutritional properties. The variability in the structures of milk caseins influence the properties of their dairy products, such as cheese and yogurt, as well as their digestibility and allergic properties. Such differences are beneficial to the development of different functionally improved casein molecules with variable biological and industrial utilities.

Effect of Structural Targeted Modifications on the Potential Allergenicity of Peanut Allergen Ara h 2

Protein structure affects allergenicity, and critical structural elements, especially conformational epitopes that determine allergenicity, have attracted a great deal of interest. In this study, we aimed to identify the localized structure that affects the potential allergenicity of protein by making targeted modifications of Ara h 2 and comparing the structure and allergenicity of mutants with those of the wide-type allergen. The structures of the allergen and its mutants were characterized by circular dichroism and ultraviolet absorption spectroscopy and simulated by molecular dynamics. The allergenicity was assessed by Western blotting, an indirect competitive enzyme-linked immunosorbent assay, a cell model, and a mouse model. Then, the structures that affect allergenicity were analyzed and screened. Our results showed that mutations in amino acids changed the nearby localized structure and the overall structures. The structural changes affected the IgE binding capacity of the allergen and reduced its potential allergenicity. The solvent accessible surface area (SASA) of aromatic residues was positively correlated with the IgE binding capacity. The integrity of the disulfide bond is also critical for the binding of IgE to allergens. Interestingly, different mutations induced similar electrostatic potential and allergenicity changes, such as localized structure R⁶²DPYSPSQDPYSPS⁷⁵. In conclusion, the disulfide bond and the SASA of aromatic residues are important for the allergenicity of Ara h 2. The localized structure R⁶²DPYSPSQDPYSPS⁷⁵ is also crucial for the allergenicity of Ara h 2.

Defining the characteristics of interferon-alpha-stimulated human genes: insight from expression data and machine learning

BACKGROUND

A virus-infected cell triggers a signalling cascade, resulting in the secretion of interferons (IFNs), which in turn induces the upregulation of the IFN-stimulated genes (ISGs) that play a role in antipathogen host defence. Here, we conducted analyses on large-scale data relating to evolutionary gene expression, sequence composition, and network properties to elucidate factors associated with the stimulation of human genes in response to IFN-α.

RESULTS

We find that ISGs are less evolutionary conserved than genes that are not significantly stimulated in IFN experiments (non-ISGs). ISGs show obvious depletion of GC content in the coding region. This influences the representation of some compositions following the translation process. IFN-repressed human genes (IRGs), downregulated genes in IFN experiments, can have similar properties to the ISGs. Additionally, we design a machine learning framework integrating the support vector machine and novel feature selection algorithm that achieves an area under the receiver operating characteristic curve (AUC) of 0.7455 for ISG prediction. Its application in other IFN systems suggests the similarity between the ISGs triggered by type I and III IFNs.

CONCLUSIONS

ISGs have some unique properties that make them different from the non-ISGs. The representation of some properties has a strong correlation with gene expression following IFN-α stimulation, which can be used as a predictive feature in machine learning. Our model predicts several genes as putative ISGs that so far have shown no significant differential expression when stimulated with IFN-α in the cell/tissue types in the available databases. A web server implementing our method is accessible at http://isgpre.cvr.gla.ac.uk/. The docker image at https://hub.docker.com/r/hchai01/isgpre can be downloaded to reproduce the prediction.

Spatial organization of hydrophobic and charged residues affects protein thermal stability and binding affinity

What are the molecular determinants of protein–protein binding affinity and whether they are similar to those regulating fold stability are two major questions of molecular biology, whose answers bring important implications both from a theoretical and applicative point of view. Here, we analyze chemical and physical features on a large dataset of protein–protein complexes with reliable experimental binding affinity data and compare them with a set of monomeric proteins for which melting temperature data was available. In particular, we probed the spatial organization of protein (1) intramolecular and intermolecular interaction energies among residues, (2) amino acidic composition, and (3) their hydropathy features. Analyzing the interaction energies, we found that strong Coulombic interactions are preferentially associated with a high protein thermal stability, while strong intermolecular van der Waals energies correlate with stronger protein–protein binding affinity. Statistical analysis of amino acids abundances, exposed to the molecular surface and/or in interaction with the molecular partner, confirmed that hydrophobic residues present on the protein surfaces are preferentially located in the binding regions, while charged residues behave oppositely. Leveraging on the important role of van der Waals interface interactions in binding affinity, we focused on the molecular surfaces in the binding regions and evaluated their shape complementarity, decomposing the molecular patches in the 2D Zernike basis. For the first time, we quantified the correlation between local shape complementarity and binding affinity via the Zernike formalism. In addition, considering the solvent interactions via the residue hydropathy, we found that the hydrophobicity of the binding regions dictates their shape complementary as much as the correlation between van der Waals energy and binding affinity. In turn, these relationships pave the way to the fast and accurate prediction and design of optimal binding regions as the 2D Zernike formalism allows a rapid and superposition-free comparison between possible binding surfaces.

Molecular and computational analysis of spike protein of newly emerged omicron variant in comparison to the delta variant of SARS-CoV-2 in Iraq

Background

The Severe Acute Respiratory Syndrome Coronavirus (SARS-CoV-2) has had a major impact on world health over the last 2 years. The emergence of SARS-CoV-2 variants, particularly concerning variants, may affect the virus’s pathogenicity, transmissibility, and vaccines potency. Both delta and the omicron variants have been designated by WHO as variants of concern.

Methods and results

In this study, molecular techniques such as qPCR, conventional PCR, and sequencing were used to identify the first SARS-CoV-2 omicron variant that circulated in Iraq in January 2022. Bioinformatics and computational tools like phylogenetic analysis, predicted physical and chemical properties, stability, and molecular docking of the spike protein were used to compare the omicron with the delta variants. We found the receptor binding domain (RBD) and spike protein in omicron contain a greater number of hydrophobic amino acids compared to delta variant. We discovered a disorder–order conversion in RBD regions of the omicron variant, and this change may be important in terms of the effect of disordered residues/regions on spike protein stability and interaction with human angiotensin converting enzyme 2 (ACE2). Docking studies show that the omicron variant requires less energy to engage with ACE2, contributing to its higher binding affinity with human ACE2, consistent with more contagious transmission.

Conclusion

This is the first molecular study of the circulated omicron and delta variants in Iraq, showing that the omicron variant in Iraq had a higher affinity for ACE2 than the delta variant, which may lead to higher transmissibility.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11033-022-07545-4.

Large-Size Subunit Catalases Are Chimeric Proteins: A H2O2 Selecting Domain with Catalase Activity Fused to a Hsp31-Derived Domain Conferring Protein Stability and Chaperone Activity

Bacterial and fungal large-size subunit catalases (LSCs) are like small-size subunit catalases (SSCs) but have an additional C-terminal domain (CT). The catalytic domain is conserved at both primary sequence and structural levels and its amino acid composition is optimized to select H₂O₂ over water. The CT is structurally conserved, has an amino acid composition similar to very stable proteins, confers high stability to LSCs, and has independent molecular chaperone activity. While heat and denaturing agents increased Neurospora crassa catalase-1 (CAT-1) activity, a CAT-1 version lacking the CT (C63) was no longer activated by these agents. The addition of catalase-3 (CAT-3) CT to the CAT-1 or CAT-3 catalase domains prevented their heat denaturation in vitro. Protein structural alignments indicated CT similarity with members of the DJ-1/PfpI superfamily and the CT dimers present in LSCs constitute a new type of symmetric dimer within this superfamily. However, only the bacterial Hsp31 proteins show sequence similarity to the bacterial and fungal catalase mobile coil (MC) and are phylogenetically related to MC_CT sequences. LSCs might have originated by fusion of SSC and Hsp31 encoding genes during early bacterial diversification, conferring at the same time great stability and molecular chaperone activity to the novel catalases.

A novel computational strategy for defining the minimal protein molecular surface representation

Most proteins perform their biological function by interacting with one or more molecular partners. In this respect, characterizing local features of the molecular surface, that can potentially be involved in the interaction with other molecules, represents a step forward in the investigation of the mechanisms of recognition and binding between molecules. Predictive methods often rely on extensive samplings of molecular patches with the aim to identify hot spots on the surface. In this framework, analysis of large proteins and/or many molecular dynamics frames is often unfeasible due to the high computational cost. Thus, finding optimal ways to reduce the number of points to be sampled maintaining the biological information (including the surface shape) carried by the molecular surface is pivotal. In this perspective, we here present a new theoretical and computational algorithm with the aim of defining a set of molecular surfaces composed of points not uniformly distributed in space, in such a way as to maximize the information of the overall shape of the molecule by minimizing the number of total points. We test our procedure’s ability in recognizing hot-spots by describing the local shape properties of portions of molecular surfaces through a recently developed method based on the formalism of 2D Zernike polynomials. The results of this work show the ability of the proposed algorithm to preserve the key information of the molecular surface using a reduced number of points compared to the complete surface, where all points of the surface are used for the description. In fact, the methodology shows a significant gain of the information stored in the sampling procedure compared to uniform random sampling.

Antifouling Aptasensor Based on Self-Assembled Loop-Closed Peptides with Enhanced Stability for CA125 Assay in Complex Biofluids

A brief and universal ultralow fouling sensing platform capable of assaying targets in complex biofluids was developed based on designed antifouling peptides that could form a loop-closed structure with enhanced stability. The newly designed peptide with thiol groups in its two terminals self-assembled onto a gold nanoparticle (AuNP)-modified electrode surface to form a stable loop structure, which displayed excellent antifouling performance, outstanding stability under enzymatic hydrolysis, and satisfactory long-term antifouling capability in complex biofluids (clinical human serum). The antifouling and highly sensitive electrochemical aptasensor was constructed via one-step co-immobilization of the designed peptides and aptamers onto the electrode surface modified with electrodeposited poly(3,4-ethylenedioxythiophene) (PEDOT) and AuNPs. The developed peptide-based aptasensor exhibited a decent response for the analysis of the cancer antigen 125 (CA125), with a relatively wide linear range (0.1-1000 U mL^-1) and a low limit of detection (0.027 U mL^-1), and was capable of detecting CA125 in clinical serum samples with acceptable accuracy. This antifouling strategy-based self-assembled peptide with a loop-closed structure provided a potential path for the development of various low-fouling biosensors for application in complex biological fluids.

Isoelectric Point of Proteins at Hydrophobic Interfaces

Structural and colloidal stability of proteins at different surfaces and interfaces is of great importance in many fields including medical, pharmaceutical, or material science. Due to their flexibility, proteins tend to respond to their environmental conditions and can undergo structural and conformational changes. For instance, alterations in physiological factors such as temperature, ions concentration, or pH as well as the adsorption to an interface can initiate protein aggregation. Therefore, at different surfaces and interfaces the characterization of the structural and colloidal stability of proteins, which is mainly influenced by their electrostatic and hydrophobic interactions, is of fundamental importance. In this study, we utilized sum frequency generation (SFG) spectroscopy to assess the role of solution pH on the polarity and magnitude of the electric field within the hydration shell of selected model proteins adsorbed to a hydrophobic surface. We used polystyrene (PS) as a model hydrophobic surface and determined the isoelectric point (IEP) of four structurally different model proteins. Comparing the measured IEP of proteins at the PS/solution or air/solution interface with that determined in the bulk solution via zeta potential measurement, we found significant similarities between the IEP of surface adsorbed proteins and those in the bulk aqueous phase. The pH dependence behavior of proteins was correlated to their amino acid composition and degree of hydrophobicity.