50 years of amino acid hydrophobicity scales: revisiting the capacity for peptide classification

AAontology: An Ontology of Amino Acid Scales for Interpretable Machine Learning

Amino acid scales are crucial for protein prediction tasks, many of them being curated in the AAindex database. Despite various clustering attempts to organize them and to better understand their relationships, these approaches lack the fine-grained classification necessary for satisfactory interpretability in many protein prediction problems. To address this issue, we developed AAontology-a two-level classification for 586 amino acid scales (mainly from AAindex) together with an in-depth analysis of their relations-using bag-of-word-based classification, clustering, and manual refinement over multiple iterations. AAontology organizes physicochemical scales into 8 categories and 67 subcategories, enhancing the interpretability of scale-based machine learning methods in protein bioinformatics. Thereby it enables researchers to gain a deeper biological insight. We anticipate that AAontology will be a building block to link amino acid properties with protein function and dysfunctions as well as aid informed decision-making in mutation analysis or protein drug design.

Mutations and Differential Transcription of Mating-Type and Pheromone Receptor Genes in Hirsutella sinensis and the Natural Cordyceps sinensis Insect-Fungi Complex

Sexual reproduction in ascomycetes is controlled by the mating-type (MAT) locus. (Pseudo)homothallic reproduction has been hypothesized on the basis of genetic data from Hirsutella sinensis (Genotype #1 of Ophiocordyceps sinensis). However, the differential occurrence and differential transcription of mating-type genes in the MAT1-1 and MAT1-2 idiomorphs were found in the genome and transcriptome assemblies of H. sinensis, and the introns of the MAT1-2-1 transcript were alternatively spliced with an unspliced intron I that contains stop codons. These findings reveal that O. sinensis reproduction is controlled at the genetic, transcriptional, and coupled transcriptional-translational levels. This study revealed that mutant mating proteins could potentially have various secondary structures. Differential occurrence and transcription of the a-/α-pheromone receptor genes were also found in H. sinensis. The data were inconsistent with self-fertilization under (pseudo)homothallism but suggest the self-sterility of H. sinensis and the requirement of mating partners to achieve O. sinensis sexual outcrossing under heterothallism or hybridization. Although consistent occurrence and transcription of the mating-type genes of both the MAT1-1 and MAT1-2 idiomorphs have been reported in natural and cultivated Cordyceps sinensis insect-fungi complexes, the mutant MAT1-1-1 and α-pheromone receptor transcripts in natural C. sinensis result in N-terminal or middle-truncated proteins with significantly altered overall hydrophobicity and secondary structures of the proteins, suggesting heterogeneous fungal source(s) of the proteins and hybridization reproduction because of the co-occurrence of multiple genomically independent genotypes of O. sinensis and >90 fungal species in natural C. sinensis.

Microphase Separation Produces Interfacial Environment within Diblock Biomolecular Condensates

The phase separation of intrinsically disordered proteins is emerging as an important mechanism for cellular organization. However, efforts to connect protein sequences to the physical properties of condensates, i.e., the molecular grammar, are hampered by a lack of effective approaches for probing high-resolution structural details. Using a combination of multiscale simulations and fluorescence lifetime imaging microscopy experiments, we systematically explored a series of systems consisting of diblock elastin-like polypeptides (ELP). The simulations succeeded in reproducing the variation of condensate stability upon amino acid substitution and revealed different microenvironments within a single condensate, which we verified with environmentally sensitive fluorophores. The interspersion of hydrophilic and hydrophobic residues and a lack of secondary structure formation result in an interfacial environment, which explains both the strong correlation between ELP condensate stability and interfacial hydrophobicity scales, as well as the prevalence of protein-water hydrogen bonds. Our study uncovers new mechanisms for condensate stability and organization that may be broadly applicable.

Experimental and computational approaches for membrane protein insertion and topology determination

Membrane proteins play pivotal roles in a wide array of cellular processes and constitute approximately a quarter of the protein-coding genes across all organisms. Despite their ubiquity and biological significance, our understanding of these proteins remains notably less comprehensive compared to their soluble counterparts. This disparity in knowledge can be attributed, in part, to the inherent challenges associated with employing specialized techniques for the investigation of membrane protein insertion and topology. This review will center on a discussion of molecular biology methodologies and computational prediction tools designed to elucidate the insertion and topology of helical membrane proteins.

Phase separation of multicomponent peptide mixtures into dehydrated clusters with hydrophilic cores

Phase separation of biomolecules underlies the formation and regulation of various membraneless condensates in cells. How condensates function reliably while surrounded by heterogeneous and dynamic mixtures of biomolecular components with specific and nonspecific interactions is yet to be understood. Studying multicomponent biomolecular mixtures with designer peptides has recently become an attractive avenue for learning about physicochemical principles governing cellular condensates. In this work, we employed long-timescale atomistic simulations of multicomponent tripeptide mixtures with all residue substitutions to illuminate the nature of direct and water-mediated interactions in a prototypical cellular condensate environment. We find that peptide mixtures form clusters with inverse hydrophobic order. Most multivalent and charged residues are localized in the cluster's core, with a large fraction of nonaromatic hydrophobic residues remaining on the surface. This inverse hydrophobic order in peptide clusters is partly driven by the expulsion of nonspecifically bound water molecules following peptide cluster growth. The growth of clusters is also accompanied by the formation of increasing numbers of specific water-mediated interactions between polar and charged residues. While the present study focused on the condensation of short peptide motifs, the general findings and analysis techniques should be helpful for future studies on larger peptides and protein condensation.

Mechanism of hydrophobic gating in the acetylcholine receptor channel pore

Neuromuscular acetylcholine receptors (AChRs) are hetero-pentameric, ligand-gated ion channels. The binding of the neurotransmitter acetylcholine (ACh) to two target sites promotes a global conformational change of the receptor that opens the channel and allows ion conduction through the channel pore. Here, by measuring free-energy changes from single-channel current recordings and using molecular dynamics simulations, we elucidate how a constricted hydrophobic region acts as a "gate" to regulate the channel opening in the pore of AChRs. Mutations of gate residues, including those implicated in congenital myasthenia syndrome, lower the permeation barrier of the channel substantially and increase the unliganded gating equilibrium constant (constitutive channel openings). Correlations between hydrophobicity and the observed free-energy changes, supported by calculations of water densities in the wild-type versus mutant channel pores, provide evidence for hydrophobic wetting-dewetting transition at the gate. The analysis of a coupled interaction network provides insight into the molecular mechanism of closed- versus open-state conformational changes at the gate. Studies of the transition state by "phi"(φ)-value analysis indicate that agonist binding serves to stabilize both the transition and the open state. Intersubunit interaction energy measurements and molecular dynamics simulations suggest that channel opening involves tilting of the pore-lining M2 helices, asymmetric outward rotation of amino acid side chains, and wetting transition of the gate region that lowers the barrier to ion permeation and stabilizes the channel open conformation. Our work provides new insight into the hydrophobic gate opening and shows why the gate mutations result in constitutive AChR channel activity.

Occurrence and evolutionary conservation analysis of α-helical cationic amphiphilic segments in the human proteome

The existence of encrypted fragments with antimicrobial activity in human proteins has been thoroughly demonstrated in the literature. Recently, algorithms for the large-scale identification of these segments in whole proteomes were developed, and the pervasiveness of this phenomenon was stated. These algorithms typically mine encrypted cationic and amphiphilic segments of proteins, which, when synthesized as individual polypeptide sequences, exert antimicrobial activity by membrane disruption. In the present report, the human reference proteome was submitted to the software kamal for the uncovering of protein segments that correspond to putative intragenic antimicrobial peptides (IAPs). The assessment of the identity of these segments, frequency, functional classes of parent proteins, structural relevance, and evolutionary conservation of amino acid residues within their corresponding proteins was conducted in silico. Additionally, the antimicrobial and anticancer activity of six selected synthetic peptides was evaluated. Our results indicate that cationic and amphiphilic segments can be found in 2% of all human proteins, but are more common in transmembrane and peripheral membrane proteins. These segments are surface-exposed basic patches whose amino acid residues present similar conservation scores to other residues with similar solvent accessibility. Moreover, the antimicrobial and anticancer activity of the synthetic putative IAP sequences was irrespective to whether these are associated to membranes in the cellular setting. Our study discusses these findings in light of the current understanding of encrypted peptide sequences, offering some insights into the relevance of these segments to the organism in the context of their harboring proteins or as separate polypeptide sequences.

From Fundamental Amyloid Protein Self-Assembly to Development of Bioplastics

Proteins can self-assemble into a range of nanostructures as a result of molecular interactions. Amyloid nanofibrils, as one of them, were first discovered with regard to the relevance of neurodegenerative diseases but now have been exploited as building blocks to generate multiscale materials with designed functions for versatile applications. This review interconnects the mechanism of amyloid fibrillation, the current approaches to synthesizing amyloid protein-based materials, and the application in bioplastic development. We focus on the fundamental structures of self-assembled amyloid fibrils and how external factors can affect protein aggregation to optimize the process. Protein self-assembly is essentially the autonomous congregation of smaller protein units into larger, organized structures. Since the properties of the self-assembly can be manipulated by changing intrinsic factors and external conditions, protein self-assembly serves as an excellent building block for bioplastic development. Building on these principles, general processing methods and pathways from raw protein sources to mature state materials are proposed, providing a guide for the development of large-scale production. Additionally, this review discusses the diverse properties of protein-based amyloid nanofibrils and how they can be utilized as bioplastics. The economic feasibility of the protein bioplastics is also compared to conventional plastics in large-scale production scenarios, supporting their potential as sustainable bioplastics for future applications.

Visualization of Self-Assembly and Hydration of a β-Hairpin through Integrated Small and Wide-Angle Neutron Scattering

Fundamental understanding of the structure and assembly of nanoscale building blocks is crucial for the development of novel biomaterials with defined architectures and function. However, accessing self-consistent structural information across multiple length scales is challenging. This limits opportunities to exploit atomic scale interactions to achieve emergent macroscale properties. In this work we present an integrative small- and wide-angle neutron scattering approach coupled with computational modeling to reveal the multiscale structure of hierarchically self-assembled β hairpins in aqueous solution across 4 orders of magnitude in length scale from 0.1 Å to 300 nm. Our results demonstrate the power of this self-consistent cross-length scale approach and allows us to model both the large-scale self-assembly and small-scale hairpin hydration of the model β hairpin CLN025. Using this combination of techniques, we map the hydrophobic/hydrophilic character of this model self-assembled biomolecular surface with atomic resolution. These results have important implications for the multiscale investigation of aqueous peptides and proteins, for the prediction of ligand binding and molecular associations for drug design, and for understanding the self-assembly of peptides and proteins for functional biomaterials.

Pathologic polyglutamine aggregation begins with a self-poisoning polymer crystal

A long-standing goal of amyloid research has been to characterize the structural basis of the rate-determining nucleating event. However, the ephemeral nature of nucleation has made this goal unachievable with existing biochemistry, structural biology, and computational approaches. Here, we addressed that limitation for polyglutamine (polyQ), a polypeptide sequence that causes Huntington's and other amyloid-associated neurodegenerative diseases when its length exceeds a characteristic threshold. To identify essential features of the polyQ amyloid nucleus, we used a direct intracellular reporter of self-association to quantify frequencies of amyloid appearance as a function of concentration, conformational templates, and rational polyQ sequence permutations. We found that nucleation of pathologically expanded polyQ involves segments of three glutamine (Q) residues at every other position. We demonstrate using molecular simulations that this pattern encodes a four-stranded steric zipper with interdigitated Q side chains. Once formed, the zipper poisoned its own growth by engaging naive polypeptides on orthogonal faces, in a fashion characteristic of polymer crystals with intramolecular nuclei. We further show that self-poisoning can be exploited to block amyloid formation, by genetically oligomerizing polyQ prior to nucleation. By uncovering the physical nature of the rate-limiting event for polyQ aggregation in cells, our findings elucidate the molecular etiology of polyQ diseases.

Diffusive intracellular interactions: On the role of protein net charge and functional adaptation

A striking feature of nucleic acids and lipid membranes is that they all carry net negative charge and so is true for the majority of intracellular proteins. It is suggested that the role of this negative charge is to assure a basal intermolecular repulsion that keeps the cytosolic content suitably 'fluid' for function. We focus in this review on the experimental, theoretical and genetic findings which serve to underpin this idea and the new questions they raise. Unlike the situation in test tubes, any functional protein-protein interaction in the cytosol is subject to competition from the densely crowded background, i.e. surrounding stickiness. At the nonspecific limit of this stickiness is the 'random' protein-protein association, maintaining profuse populations of transient and constantly interconverting complexes at physiological protein concentrations. The phenomenon is readily quantified in studies of the protein rotational diffusion, showing that the more net negatively charged a protein is the less it is retarded by clustering. It is further evident that this dynamic protein-protein interplay is under evolutionary control and finely tuned across organisms to maintain optimal physicochemical conditions for the cellular processes. The emerging picture is then that specific cellular function relies on close competition between numerous weak and strong interactions, and where all parts of the protein surfaces are involved. The outstanding challenge is now to decipher the very basics of this many-body system: how the detailed patterns of charged, polar and hydrophobic side chains not only control protein-protein interactions at close- and long-range but also the collective properties of the cellular interior as a whole.

Solvation thermodynamics from cavity shapes of amino acids

According to common physical chemistry wisdom, the solvent cavities hosting a solute are tightly sewn around it, practically coinciding with its van der Waals surface. Solvation entropy is primarily determined by the surface and the volume of the cavity while enthalpy is determined by the solute-solvent interaction. In this work, we challenge this picture, demonstrating by molecular dynamics simulations that the cavities surrounding the 20 amino acids deviate significantly from the molecular surface. Strikingly, the shape of the cavity alone can be used to predict the solvation free energy, entropy, enthalpy, and hydrophobicity. Solute-solvent interactions involving the different chemical moieties of the amino acid, determine indirectly the cavity shape, and the properties of the branches but do not have to be taken explicitly into account in the prediction model.

Pathologic polyglutamine aggregation begins with a self-poisoning polymer crystal

A long-standing goal of amyloid research has been to characterize the structural basis of the rate-determining nucleating event. However, the ephemeral nature of nucleation has made this goal unachievable with existing biochemistry, structural biology, and computational approaches. Here, we addressed that limitation for polyglutamine (polyQ), a polypeptide sequence that causes Huntington's and other amyloid-associated neurodegenerative diseases when its length exceeds a characteristic threshold. To identify essential features of the polyQ amyloid nucleus, we used a direct intracellular reporter of self-association to quantify frequencies of amyloid appearance as a function of concentration, conformational templates, and rational polyQ sequence permutations. We found that nucleation of pathologically expanded polyQ involves segments of three glutamine (Q) residues at every other position. We demonstrate using molecular simulations that this pattern encodes a four-stranded steric zipper with interdigitated Q side chains. Once formed, the zipper poisoned its own growth by engaging naive polypeptides on orthogonal faces, in a fashion characteristic of polymer crystals with intramolecular nuclei. We further show that self-poisoning can be exploited to block amyloid formation, by genetically oligomerizing polyQ prior to nucleation. By uncovering the physical nature of the rate-limiting event for polyQ aggregation in cells, our findings elucidate the molecular etiology of polyQ diseases.

Multiway Analysis Reveals Hydrophobicity as the Sole Determinant of Dynamic Peptide-Acetonitrile-Water Association Behavior

Multiway analysis, a class of techniques developed for the purpose of studying multi-dimensional multivariate data, has been applied to study the dynamical structure of the first solvation layer of Ace-Gly-X-Gly-Nme peptides (where X is any amino acid) perturbed with the increase in concentrations of acetonitrile. Separate MD simulations of each peptide were carried out in five different concentrations of acetonitrile. Association of peptide, water, and acetonitrile atoms was quantified in terms of the relative abundance of Delaunay tetrahedra whose vertices could be centered on either the peptide, acetonitrile, or water atoms. A three-way data set comprising nine types of Delaunay tetrahedra in the first dimension, five concentrations of acetonitrile in the second dimension, and 26 different peptides in the third dimension was subjected to two different multiway methods viz., the constrained PARAFAC and the unconstrained Tucker3 analysis. The results unequivocally show that the dynamic peptide-acetonitrile-water association behavior could be solely explained by the hydrophobicity of the central amino acid. The study also demonstrates the utility of multiway analysis for the integration and interpretation of large number of separate MD simulations.

3D interaction homology: The hydrophobic residues alanine, isoleucine, leucine, proline and valine play different structural roles in soluble and membrane proteins

The aliphatic hydrophobic amino acid residues—alanine, isoleucine, leucine, proline and valine—are among the most common found in proteins. Their structural role in proteins is seemingly obvious: engage in hydrophobic interactions to stabilize secondary, and to a lesser extent, tertiary and quaternary structure. However, favorable hydrophobic interactions involving the sidechains of these residue types are generally less significant than the unfavorable set arising from interactions with polar atoms. Importantly, the constellation of interactions between residue sidechains and their environments can be recorded as three-dimensional maps that, in turn, can be clustered. The clustered average map sets compose a library of interaction profiles encoding interaction strengths, interaction types and the optimal 3D position for the interacting partners. This library is backbone angle-dependent and suggests solvent and lipid accessibility for each unique interaction profile. In this work, in addition to analysis of soluble proteins, a large set of membrane proteins that contained optimized artificial lipids were evaluated by parsing the structures into three distinct components: soluble extramembrane domain, lipid facing transmembrane domain, core transmembrane domain. The aliphatic residues were extracted from each of these sets and passed through our calculation protocol. Notable observations include: the roles of aliphatic residues in soluble proteins and in the membrane protein’s soluble domains are nearly identical, although the latter are slightly more solvent accessible; by comparing maps calculated with sidechain-lipid interactions to maps ignoring those interactions, the potential extent of residue-lipid and residue-interactions can be assessed and likely exploited in structure prediction and modeling; amongst these residue types, the levels of lipid engagement show isoleucine as the most engaged, while the other residues are largely interacting with neighboring helical residues.

Development of a novel ultrasound- and biocrosslinking-enhanced immobilization strategy with application to food enzymes

The increasing demand for greener food production makes biocatalysts more desirable than traditional production approaches. One limiting factor for biocatalyst efficiency is the immobilization strategy. In this work, a novel immobilization method was developed with the tyrosine-tag crosslinking mechanism. The immobilization efficiency was further enhanced with ultrasound treatment. Such a strategy was proven to be efficient with food enzyme lipase, d-amino acid oxidase and glucose dehydrogenase when they were immobilized on macroporous resins, amino resins, epoxy resins, and multiwalled carbon nanotubes. For lipase, glucose dehydrogenase and d-amino acid oxidase, the immobilization yield on macroporous resins increased by 20.4%, 21.1% and 24.1%, respectively. In addition, the immobilized enzymes had enhanced reusability, with a high degree of activity (more than 85%) detected after six cycles. Furthermore, the enzyme electrochemical sensors constructed by enzyme crosslinking have higher sensitivity, with peak currents 4-8 times those of sensors with uncrosslinked enzymes. The enzyme immobilization strategy developed in this study paves the way for better application of biocatalysts in the food industry.

Improved predictions of phase behaviour of intrinsically disordered proteins by tuning the interaction range

The formation and viscoelastic properties of condensates of intrinsically disordered proteins (IDPs) is dictated by amino acid sequence and solution conditions. Because of the involvement of biomolecular condensates in cell physiology and disease, advancing our understanding of the relationship between protein sequence and phase separation (PS) may have important implications in the formulation of new therapeutic hypotheses. Here, we present CALVADOS 2, a coarse-grained model of IDPs that accurately predicts conformational properties and propensities to undergo PS for diverse sequences and solution conditions. In particular, we systematically study the effect of varying the range of the nonionic interactions and use our findings to improve the temperature scale of the model. We further optimize the residue-specific model parameters against experimental data on the conformational properties of 55 proteins, while also leveraging 70 hydrophobicity scales from the literature to avoid overfitting the training data. Extensive testing shows that the model accurately predicts chain compaction and PS propensity for sequences of diverse length and charge patterning, as well as at different temperatures and salt concentrations.

Improved predictions of phase behaviour of intrinsically disordered proteins by tuning the interaction range

The formation and viscoelastic properties of condensates of intrinsically disordered proteins (IDPs) is dictated by amino acid sequence and solution conditions. Because of the involvement of biomolecular condensates in cell physiology and disease, advancing our understanding of the relationship between protein sequence and phase separation (PS) may have important implications in the formulation of new therapeutic hypotheses. Here, we present CALVADOS 2, a coarse-grained model of IDPs that accurately predicts conformational properties and propensities to undergo PS for diverse sequences and solution conditions. In particular, we systematically study the effect of varying the range of the nonionic interactions and use our findings to improve the temperature scale of the model. We further optimize the residue-specific model parameters against experimental data on the conformational properties of 55 proteins, while also leveraging 70 hydrophobicity scales from the literature to avoid overfitting the training data. Extensive testing shows that the model accurately predicts chain compaction and PS propensity for sequences of diverse length and charge patterning, as well as at different temperatures and salt concentrations.

Computational and comparative investigation of hydrophobic profile of spike protein of SARS-CoV-2 and SARS-CoV

The hydrophobic force is one of the most dominant factors in protein folding. A protein becomes functional only when it achieves its three-dimensional structure and stability upon folding. For a better understanding of the hydrophobic effects and their function in protein folding, quantitative measurement of the hydrophobicity of amino acid side chains is crucial. Spike protein is the primary structural protein in SARS-CoV-2 and SARS-CoV. This study explores how protein sequences in SARS-CoV-2 and SARS-CoV spike proteins encode hydrophobic interactions. Computational tools/techniques have been utilized to investigate the protein sequences of the spike proteins of SARS-CoV-2 and SARS-CoV. Investigations provided an estimate of hydrophobic distribution and its relative strength, indicating a hydrophobic pattern. Analysis of the spike protein's hydrophobic profile may help identify and treat the virus-caused disease; additionally, it can give an insight into the transmissibility and pathogenicity of the virus.

Electronic properties of amino acids and nucleobases: similarity classes and pairing principles from chemical reactivity indices

We present a new classification scheme for amino acids and nucleobases based on the electronic properties of the individual molecules. Using chemical reactivity indices such as electronegativity, electrophilicity, and chemical hardness, we can identify similarities and differences between each class of amino acids and nucleobases. Notable differences emerge in particular with regard to high, neutral or low electronegativity as well as different combinations of chemical hardness. Our approach allows us to relate these insights to the properties of the side groups in terms of a unique reference scheme. We further show that hydrophobic differences between amino acids are rather negligible in the context of electronic properties. Our classification scheme also rationalizes the occurrence of distinct stable nucleobase pairs and clearly emphasizes certain differences between individual molecules. The stability and abundant occurrence of Watson-Crick nucleobase pairs is further discussed in the context of the minimum electrophilicity principle.

Comparison of hydrophobicity scales for predicting biophysical properties of antibodies

While antibody-based therapeutics have grown to be one of the major classes of novel medicines, some antibody development candidates face significant challenges regarding expression levels, solubility, as well as stability and aggregation, under physiological and storage conditions. A major determinant of those properties is surface hydrophobicity, which promotes unspecific interactions and has repeatedly proven problematic in the development of novel antibody-based drugs. Multiple computational methods have been devised for in-silico prediction of antibody hydrophobicity, often using hydrophobicity scales to assign values to each amino acid. Those approaches are usually validated by their ability to rank potential therapeutic antibodies in terms of their experimental hydrophobicity. However, there is significant diversity both in the hydrophobicity scales and in the experimental methods, and consequently in the performance of in-silico methods to predict experimental results. In this work, we investigate hydrophobicity of monoclonal antibodies using hydrophobicity scales. We implement several scoring schemes based on the solvent-accessibility and the assigned hydrophobicity values, and compare the different scores and scales based on their ability to predict retention times from hydrophobic interaction chromatography. We provide an overview of the strengths and weaknesses of several commonly employed hydrophobicity scales, thereby improving the understanding of hydrophobicity in antibody development. Furthermore, we test several datasets, both publicly available and proprietary, and find that the diversity of the dataset affects the performance of hydrophobicity scores. We expect that this work will provide valuable guidelines for the optimization of biophysical properties in future drug discovery campaigns.

Characterization and Structural Prediction of Proteins in SARS-CoV-2 Bangladeshi Variant Through Bioinformatics

The renowned respiratory disease induced by the severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2) has become a global epidemic in just less than a year by the first half of 2020. The subsequent efficient human-to-human transmission of this virus eventually affected millions of people worldwide. The most devastating thing is that the infection rate is continuously uprising and resulting in significant mortality especially among the older age population and those with health co-morbidities. This enveloped, positive-sense RNA virus is chiefly responsible for the infection of the upper respiratory system. The virulence of the SARS-CoV-2 is mostly regulated by its proteins such as entry to the host cell through fusion mechanism, fusion of infected cells with neighboring uninfected cells to spread virus, inhibition of host gene expression, cellular differentiation, apoptosis, mitochondrial biogenesis, etc. But very little is known about the protein structures and functionalities. Therefore, the main purpose of this study is to learn more about these proteins through bioinformatics approaches. In this study, ORF10, ORF7b, ORF7a, ORF6, membrane glycoprotein, and envelope protein have been selected from a Bangladeshi Corona-virus strain G039392 and a number of bioinformatics tools (MEGA-X-V10.1.7, PONDR, ProtScale, ProtParam, SCRIBER, NetSurfP v2.0, IntFOLD, UCSF Chimera, and PyMol) and strategies were implemented for multiple sequence alignment and phylogeny analysis with 9 different variants, predicting hydropathicity, amino acid compositions, protein-binding propensity, protein disorders, and 2D and 3D protein modeling. Selected proteins were characterized as highly flexible, structurally and electrostatically extremely stable, ordered, biologically active, hydrophobic, and closely related to proteins of different variants. This detailed information regarding the characterization and structure of proteins of SARS-CoV-2 Bangladeshi variant was performed for the first time ever to unveil the deep mechanism behind the virulence features. And this robust appraisal also paves the future way for molecular docking, vaccine development targeting these characterized proteins.

A unified statistical potential reveals that amino acid stickiness governs nonspecific recruitment of client proteins into condensates

Membraneless organelles are cellular compartments that form by liquid-liquid phase separation of one or more components. Other molecules, such as proteins and nucleic acids, will distribute between the cytoplasm and the liquid compartment in accordance with the thermodynamic drive to lower the free energy of the system. The resulting distribution colocalizes molecular species to carry out a diversity of functions. Two factors could drive this partitioning: the difference in solvation between the dilute versus dense phase and intermolecular interactions between the client and scaffold proteins. Here, we develop a set of knowledge-based potentials that allow for the direct comparison between stickiness, which is dominated by desolvation energy, and pairwise residue contact propensity terms. We use these scales to examine experimental data from two systems: protein cargo dissolving within phase-separated droplets made from FG repeat proteins of the nuclear pore complex and client proteins dissolving within phase-separated FUS droplets. These analyses reveal a close agreement between the stickiness of the client proteins and the experimentally determined values of the partition coefficients (R > 0.9), while pairwise residue contact propensities between client and scaffold show weaker correlations. Hence, the stickiness of client proteins is sufficient to explain their differential partitioning within these two phase-separated systems without taking into account the composition of the condensate. This result implies that selective trafficking of client proteins to distinct membraneless organelles requires recognition elements beyond the client sequence composition. STATEMENT: Empirical potentials for amino acid stickiness and pairwise residue contact propensities are derived. These scales are unique in that they enable direct comparison of desolvation versus contact terms. We find that partitioning of a client protein to a condensate is best explained by amino acid stickiness.

Predicting the functional effects of voltage-gated potassium channel missense variants with multi-task learning

BACKGROUND

Variants in genes encoding voltage-gated potassium channels are associated with a broad spectrum of neurological diseases including epilepsy, ataxia, and intellectual disability. Knowledge of the resulting functional changes, characterized as overall ion channel gain- or loss-of-function, is essential to guide clinical management including precision medicine therapies. However, for an increasing number of variants, little to no experimental data is available. New tools are needed to evaluate variant functional effects.

METHODS

We catalogued a comprehensive dataset of 959 functional experiments across 19 voltage-gated potassium channels, leveraging data from 782 unique disease-associated and synthetic variants. We used these data to train a taxonomy-based multi-task learning support vector machine (MTL-SVM), and compared performance to several baseline methods.

FINDINGS

MTL-SVM maintains channel family structure during model training, improving overall predictive performance (mean balanced accuracy 0·718 ± 0·041, AU-ROC 0·761 ± 0·063) over baseline (mean balanced accuracy 0·620 ± 0·045, AU-ROC 0·711 ± 0·022). We can obtain meaningful predictions even for channels with few known variants (KCNC1, KCNQ5).

INTERPRETATION

Our model enables functional variant prediction for voltage-gated potassium channels. It may assist in tailoring current and future precision therapies for the increasing number of patients with ion channel disorders.

FUNDING

This work was supported by intramural funding of the Medical Faculty, University of Tuebingen (PATE F.1315137.1), the Federal Ministry for Education and Research (Treat-ION, 01GM1907A/B/G/H) and the German Research Foundation (FOR-2715, Le1030/16-2, He8155/1-2).

Sense the moment: A highly sensitive antimicrobial activity predictor based on hydrophobic moment

BACKGROUND

Computer-aided identification and design tools are indispensable for developing antimicrobial agents for controlling antibiotic-resistant bacteria. Antimicrobial peptides (AMPs) have aroused intense interest, since they have a broad spectrum of activity, and therefore, several systems for predicting antimicrobial peptides have been developed, using scalar physicochemical properties; however, regardless of the machine learning algorithm, these systems often fail in discriminating AMPs from their shuffled versions, leading to the need for new training methods to overcome this bias. Aiming to solve this bias, here we present "Sense the Moment", a prediction system capable of discriminating AMPs and shuffled versions.

METHODS

The system was trained using 776 entries: 388 from known AMPs and another 388 based on shuffled versions of known AMPs. Each entry contained the geometric average of three hydrophobic moments measured with different scales.

RESULTS

The model showed good accuracy (>80%) and excellent sensitivity (>90%) for AMP prediction, exceeding deep-learning-based methods.

CONCLUSION

Our results demonstrate the system's applicability, aiding in identifying and discarding non-AMPs, since the number of false negatives is lower than false positives.

GENERAL SIGNIFICANCE

The application of this model in virtual screening protocols for identifying and/or creating antimicrobial agents could aid in the identification of potential drugs to control pathogenic microorganisms and in solving the antibiotic resistance crisis.

AVAILABILITY

The system was implemented as a web application, available at .

Regulatory spine RS3 residue of protein kinases: a lipophilic bystander or a decisive element in the small-molecule kinase inhibitor binding?

In recent years, protein kinases have been one of the most pursued drug targets. These determined efforts have resulted in ever increasing numbers of small-molecule kinase inhibitors reaching to the market, offering novel treatment options for patients with distinct diseases. One essential component related to the activation and normal functionality of a protein kinase is the regulatory spine (R-spine). The R-spine is formed of four conserved residues named as RS1–RS4. One of these residues, RS3, located in the C-terminal part of αC-helix, is usually accessible for the inhibitors from the ATP-binding cavity as its side chain is lining the hydrophobic back pocket in many protein kinases. Although the role of RS3 has been well acknowledged in protein kinase function, this residue has not been actively considered in inhibitor design, even though many small-molecule kinase inhibitors display interactions to this residue. In this minireview, we will cover the current knowledge of RS3, its relationship with the gatekeeper, and the role of RS3 in kinase inhibitor interactions. Finally, we comment on the future perspectives how this residue could be utilized in the kinase inhibitor design.

Information quantity for secondary structure propensities of protein subsequences in the Protein Data Bank

Elucidating the principles of sequence-structure relationships of proteins is a long-standing issue in biology. The nature of a short segment of a protein is determined by both the subsequence of the segment itself and its environment. For example, a type of subsequence, the so-called chameleon sequences, can form different secondary structures depending on its environments. Chameleon sequences are considered to have a weak tendency to form a specific structure. Although many chameleon sequences have been identified, they are only a small part of all possible subsequences in the proteome. The strength of the tendency to take a specific structure for each subsequence has not been fully quantified. In this study, we comprehensively analyzed subsequences consisting of four to nine amino acid residues, or N-gram (4≤N≤9), observed in non-redundant sequences in the Protein Data Bank (PDB). Tendencies to form a specific structure in terms of the secondary structure and accessible surface area are quantified as information quantities for each N-gram. Although the majority of observed subsequences have low information quantity due to lack of samples in the current PDB, thousands of N-grams with strong tendencies, including known structural motifs, were found. In addition, machine learning partially predicted the tendency of unknown N-grams, and thus, this technique helps to extract knowledge from the limited number of samples in the PDB.

A computational method for predicting the aggregation propensity of IgG1 and IgG4(P) mAbs in common storage buffers

The propensity for some monoclonal antibodies (mAbs) to aggregate at physiological and manufacturing pH values can prevent their use as therapeutic molecules or delay time to market. Consequently, developability assessments are essential to select optimum candidates, or inform on mitigation strategies to avoid potential late-stage failures. These studies are typically performed in a range of buffer solutions because factors such as pH can dramatically alter the aggregation propensity of the test mAbs (up to 100-fold in extreme cases). A computational method capable of robustly predicting the aggregation propensity at the pH values of common storage buffers would have substantial value. Here, we describe a mAb aggregation prediction tool (MAPT) that builds on our previously published isotype-dependent, charge-based model of aggregation. We show that the addition of a homology model-derived hydrophobicity descriptor to our electrostatic aggregation model enabled the generation of a robust mAb developability indicator. To contextualize our aggregation scoring system, we analyzed 97 clinical-stage therapeutic mAbs. To further validate our approach, we focused on six mAbs (infliximab, tocilizumab, rituximab, CNTO607, MEDI1912 and MEDI1912_STT) which have been reported to cover a large range of aggregation propensities. The different aggregation propensities of the case study molecules at neutral and slightly acidic pH were correctly predicted, verifying the utility of our computational method.

Common Features in lncRNA Annotation and Classification: A Survey

Long non-coding RNAs (lncRNAs) are widely recognized as important regulators of gene expression. Their molecular functions range from miRNA sponging to chromatin-associated mechanisms, leading to effects in disease progression and establishing them as diagnostic and therapeutic targets. Still, only a few representatives of this diverse class of RNAs are well studied, while the vast majority is poorly described beyond the existence of their transcripts. In this review we survey common in silico approaches for lncRNA annotation. We focus on the well-established sets of features used for classification and discuss their specific advantages and weaknesses. While the available tools perform very well for the task of distinguishing coding sequence from other RNAs, we find that current methods are not well suited to distinguish lncRNAs or parts thereof from other non-protein-coding input sequences. We conclude that the distinction of lncRNAs from intronic sequences and untranslated regions of coding mRNAs remains a pressing research gap.

Accurate model of liquid-liquid phase behavior of intrinsically disordered proteins from optimization of single-chain properties

Many intrinsically disordered proteins (IDPs) may undergo liquid-liquid phase separation (LLPS) and participate in the formation of membraneless organelles in the cell, thereby contributing to the regulation and compartmentalization of intracellular biochemical reactions. The phase behavior of IDPs is sequence dependent, and its investigation through molecular simulations requires protein models that combine computational efficiency with an accurate description of intramolecular and intermolecular interactions. We developed a general coarse-grained model of IDPs, with residue-level detail, based on an extensive set of experimental data on single-chain properties. Ensemble-averaged experimental observables are predicted from molecular simulations, and a data-driven parameter-learning procedure is used to identify the residue-specific model parameters that minimize the discrepancy between predictions and experiments. The model accurately reproduces the experimentally observed conformational propensities of a set of IDPs. Through two-body as well as large-scale molecular simulations, we show that the optimization of the intramolecular interactions results in improved predictions of protein self-association and LLPS.

Accurate model of liquid-liquid phase behavior of intrinsically disordered proteins from optimization of single-chain properties

Many intrinsically disordered proteins (IDPs) may undergo liquid-liquid phase separation (LLPS) and participate in the formation of membraneless organelles in the cell, thereby contributing to the regulation and compartmentalization of intracellular biochemical reactions. The phase behavior of IDPs is sequence dependent, and its investigation through molecular simulations requires protein models that combine computational efficiency with an accurate description of intramolecular and intermolecular interactions. We developed a general coarse-grained model of IDPs, with residue-level detail, based on an extensive set of experimental data on single-chain properties. Ensemble-averaged experimental observables are predicted from molecular simulations, and a data-driven parameter-learning procedure is used to identify the residue-specific model parameters that minimize the discrepancy between predictions and experiments. The model accurately reproduces the experimentally observed conformational propensities of a set of IDPs. Through two-body as well as large-scale molecular simulations, we show that the optimization of the intramolecular interactions results in improved predictions of protein self-association and LLPS.

Modeling lipid-protein interactions for coarse-grained lipid and Cα protein models

Biological membranes that play major roles in diverse functions are composed of numerous lipids and proteins, making them an important target for coarse-grained (CG) molecular dynamics (MD) simulations. Recently, we have developed the CG implicit solvent lipid force field (iSoLF) that has a resolution compatible with the widely used Cα protein representation [D. Ugarte La Torre and S. Takada, J. Chem. Phys. 153, 205101 (2020)]. In this study, we extended it and developed a lipid-protein interaction model that allows the combination of the iSoLF and the Cα protein force field, AICG2+. The hydrophobic-hydrophilic interaction is modeled as a modified Lennard-Jones potential in which parameters were tuned partly to reproduce the experimental transfer free energy and partly based on the free energy profile normal to the membrane surface from previous all-atom MD simulations. Then, the obtained lipid-protein interaction is tested for the configuration and placement of transmembrane proteins, water-soluble proteins, and peripheral proteins, showing good agreement with prior knowledge. The interaction is generally applicable and is implemented in the publicly available software, CafeMol.

Experimental lipophilicity scale for coded and noncoded amino acid residues

Among other features, the polarity of amino acid residues is the key parameter for understanding their role in proteins. The wide occurrence of protein modifications in nature and the advent of genetic code engineering techniques created a need for an experimental polarity value integrating both coded (canonical) and noncoded (noncanonical) residues on one universal scale. To address this issue, this work reports on a polarity scale based on the experimental lipophilicity of methyl esters of N-acetylamino acids. The derivatization of amino acids was performed in two steps under mild conditions that allowed conversion of a wide array of amino acids into analytical derivatives. The partitioning/distribution between octan-1-ol and water/buffer was measured using the intensity of the NMR signal as a characteristic for the concentration. The reference set of twenty coded amino acids generated log P values spanning 5.1 units: from tryptophan being the most hydrophobic to aspartate being the most hydrophilic. Furthermore, lipophilicity was measured for a set of analogues of phenylalanine, tyrosine, tryptophan, methionine, proline, and lysine that are typical in nature and/or laboratory practice. The polarity scale reported here will aid the rationalization of amino acid replacements in proteins, and will guide further efforts in experimental genetic code engineering.

Toward Guided Mutagenesis: Gaussian Process Regression Predicts MHC Class II Antigen Mutant Binding

Antigen-specific immunotherapies (ASI) require successful loading and presentation of antigen peptides into the major histocompatibility complex (MHC) binding cleft. One route of ASI design is to mutate native antigens for either stronger or weaker binding interaction to MHC. Exploring all possible mutations is costly both experimentally and computationally. To reduce experimental and computational expense, here we investigate the minimal amount of prior data required to accurately predict the relative binding affinity of point mutations for peptide-MHC class II (pMHCII) binding. Using data from different residue subsets, we interpolate pMHCII mutant binding affinities by Gaussian process (GP) regression of residue volume and hydrophobicity. We apply GP regression to an experimental data set from the Immune Epitope Database, and theoretical data sets from NetMHCIIpan and Free Energy Perturbation calculations. We find that GP regression can predict binding affinities of nine neutral residues from a six-residue subset with an average R² coefficient of determination value of 0.62 ± 0.04 (±95% CI), average error of 0.09 ± 0.01 kcal/mol (±95% CI), and with an receiver operating characteristic (ROC) AUC value of 0.92 for binary classification of enhanced or diminished binding affinity. Similarly, metrics increase to an R² value of 0.69 ± 0.04, average error of 0.07 ± 0.01 kcal/mol, and an ROC AUC value of 0.94 for predicting seven neutral residues from an eight-residue subset. Our work finds that prediction is most accurate for neutral residues at anchor residue sites without register shift. This work holds relevance to predicting pMHCII binding and accelerating ASI design.

Membrane partitioning and lipid selectivity of the N-terminal amphipathic H0 helices of endophilin isoforms

Endophilin is an N-BAR protein, which is characterized by a crescent-shaped BAR domain and an amphipathic helix that contributes to the membrane binding of these proteins. The exact function of that H0 helix has been a topic of debate. In mammals, there are five different endophilin isoforms, grouped into A (three members) and B (two members) subclasses, which have been described to differ in their subcellular localization and function. We asked to what extent molecular properties of the H0 helices of these members affect their membrane targeting behavior. We found that all H0 helices of the endophilin isoforms display a two-state equilibrium between disordered and α-helical states in which the helical secondary structure can be stabilized through trifluoroethanol. The helicities in high TFE were strikingly different among the H0 peptides. We investigated H0-membrane partitioning by the monitoring of secondary structure changes via CD spectroscopy. We found that the presence of anionic phospholipids is critical for all H0 helices partitioning into membranes. Membrane partitioning is found to be sensitive to variations in membrane complexity. Overall, the H0 B subfamily displays stronger membrane partitioning than the H0 A subfamily. The H0 A peptide-membrane binding occurs predominantly through electrostatic interactions. Variation among the H0 A subfamily may be attributed to slight alterations in the amino acid sequence. Meanwhile, the H0 B subfamily displays greater specificity for certain membrane compositions, and this may link H0 B peptide binding to endophilin B's cellular function.

A Data-Driven Hydrophobicity Scale for Predicting Liquid-Liquid Phase Separation of Proteins

An accurate model for macroscale disordered assemblies of biological macromolecules such as those formed in so-called membraneless organelles would greatly assist in studying their structure, function, and dynamics. Recent evidence has suggested that liquid-liquid phase separation (LLPS) underlies the formation of membraneless organelles. While the general mechanism of exchange of macromolecule/water for macromolecule/macromolecule interactions is known to be the driving force for LLPS, the specific interactions involved are not well understood. One way that protein-water and protein-protein interactions have been understood historically is via hydrophobicity scales. However, these scales are typically optimized for describing these relative interactions in certain cases, such as protein folding or insertion of proteins into membranes. To better describe the relative interactions of proteins that undergo LLPS, we have developed a new, data-driven hydrophobicity scale. To determine the new scale, we used coarse-grained molecular dynamics simulations using the hydrophobicity scale coarse-grained model, which relates the interactions between amino acids to their hydrophobicity. Hydrophobicity values were determined via the force-balance method on a library of proteins that includes unfolded, intrinsically disordered, and phase-separating proteins (PSP). The resulting hydrophobicity scale can better predict whether a given protein will undergo LLPS at physiological conditions by using coarse-grained molecular dynamics simulations than existing hydrophobicity scales. This new scale confirms the importance of π-π interactions between amino acids as important drivers of LLPS. This new hydrophobicity scale provides a convenient and compact description of protein-protein interactions for proteins that undergo LLPS and could be used to develop new models to describe interactions between PSP and other components, such as nucleic acids.

Estimating the Influence of Physicochemical and Biochemical Property Indexes on Selection for Amino Acids Usage in Eukaryotic Cells

Proteins can evolve by accumulating changes on amino acid sequences. These changes are mainly caused by missense mutations on its DNA coding sequences. Mutations with neutral or positive effects on fitness can be maintained while deleterious mutations tend to be eliminated by natural selection. Amino acid changes are influenced by the biophysical, chemical, and biological properties of amino acids. There is a multiplicity of amino acid properties that can influence the function and expression of proteins. Amino acid properties can be expressed into numerical indexes, which can help to predict functional and structural aspects of proteins and allow statistical inferences of selection pressure on amino acid usage. The accuracy of these analyses may be compromised by the existence of several numerical indexes that measure the same amino acid property, and the lack of objective parameters to determine the most accurate and biologically relevant index. In the present study, the gradient consistency test was used in order to estimate the magnitude of directional selection imparted by amino acid biochemical and biophysical properties on protein evolution.

Conformational Ensembles of Antibodies Determine Their Hydrophobicity

A major challenge in the development of antibody biotherapeutics is their tendency to aggregate. One root cause for aggregation is exposure of hydrophobic surface regions to the solvent. Many current techniques predict the relative aggregation propensity of antibodies via precalculated scales for the hydrophobicity or aggregation propensity of single amino acids. However, those scales cannot describe the nonadditive effects of a residue’s surrounding on its hydrophobicity. Therefore, they are inherently limited in their ability to describe the impact of subtle differences in molecular structure on the overall hydrophobicity. Here, we introduce a physics-based approach to describe hydrophobicity in terms of the hydration free energy using grid inhomogeneous solvation theory (GIST). We apply this method to assess the effects of starting structures, conformational sampling, and protonation states on the hydrophobicity of antibodies. Our results reveal that high-quality starting structures, i.e., crystal structures, are crucial for the prediction of hydrophobicity and that conformational sampling can compensate errors introduced by the starting structure. On the other hand, sampling of protonation states only leads to good results when combined with high-quality structures, whereas it can even be detrimental otherwise. We conclude by pointing out that a single static homology model may not be adequate for predicting hydrophobicity.

3D Printed Microfluidic Devices for Solid-Phase Extraction and On-Chip Fluorescent Labeling of Preterm Birth Risk Biomarkers

Solid-phase extraction (SPE) is a general preconcentration method for sample preparation that can be performed on a variety of specimens. The miniaturization of SPE within a 3D printed microfluidic device further allows for fast and simple extraction of analytes while also enabling integration of SPE with other sample preparation and separation methods. Here, we present the development and application of a reversed-phase lauryl methacrylate-based monolith, formed in 3D printed microfluidic devices, which can selectively retain peptides and proteins. The effectiveness of these SPE monoliths and 3D printed microfluidic devices was tested using a panel of nine preterm birth biomarkers of varying hydrophobicities and ranging in mass from 2 to 470 kDa. The biomarkers were selectively retained, fluorescently labeled, and eluted separately from the excess fluorescent label in 3D printed microfluidic systems. These are the first results demonstrating microfluidic analysis processes on a complete panel of preterm birth biomarkers, an important step toward developing a miniaturized, fully integrated analysis system.

Optimization of a Soft Ensemble Vote Classifier for the Prediction of Chimeric Virus-Like Particle Solubility and Other Biophysical Properties

Chimeric virus-like particles (cVLPs) are protein-based nanostructures applied as investigational vaccines against infectious diseases, cancer, and immunological disorders. Low solubility of cVLP vaccine candidates is a challenge that can prevent development of these very substances. Solubility of cVLPs is typically assessed empirically, leading to high time and material requirements. Prediction of cVLP solubility in silico can aid in reducing this effort. Protein aggregation by hydrophobic interaction is an important factor driving protein insolubility. In this article, a recently developed soft ensemble vote classifier (sEVC) for the prediction of cVLP solubility was used based on 91 literature amino acid hydrophobicity scales. Optimization algorithms were developed to boost model performance, and the model was redesigned as a regression tool for ammonium sulfate concentration required for cVLP precipitation. The present dataset consists of 568 cVLPs, created by insertion of 71 different peptide sequences using eight different insertion strategies. Two optimization algorithms were developed that (I) modified the sEVC with regard to systematic misclassification based on the different insertion strategies, and (II) modified the amino acid hydrophobicity scale tables to improve classification. The second algorithm was additionally used to synthesize scales from random vectors. Compared to the unmodified model, Matthew’s Correlation Coefficient (MCC), and accuracy of the test set predictions could be elevated from 0.63 and 0.81 to 0.77 and 0.88, respectively, for the best models. This improved performance compared to literature scales was suggested to be due to a decreased correlation between synthesized scales. In these, tryptophan was identified as the most hydrophobic amino acid, i.e., the amino acid most problematic for cVLP solubility, supported by previous literature findings. As a case study, the sEVC was redesigned as a regression tool and applied to determine ammonium sulfate concentrations for the precipitation of cVLPs. This was evaluated with a small dataset of ten cVLPs resulting in an R² of 0.69. In summary, we propose optimization algorithms that improve sEVC model performance for the prediction of cVLP solubility, allow for the synthesis of amino acid scale tables, and further evaluate the sEVC as regression tool to predict cVLP-precipitating ammonium sulfate concentrations.

Mutations in transmembrane proteins: diseases, evolutionary insights, prediction and comparison with globular proteins

Membrane proteins are unique in that they interact with lipid bilayers, making them indispensable for transporting molecules and relaying signals between and across cells. Due to the significance of the protein's functions, mutations often have profound effects on the fitness of the host. This is apparent both from experimental studies, which implicated numerous missense variants in diseases, as well as from evolutionary signals that allow elucidating the physicochemical constraints that intermembrane and aqueous environments bring. In this review, we report on the current state of knowledge acquired on missense variants (referred to as to single amino acid variants) affecting membrane proteins as well as the insights that can be extrapolated from data already available. This includes an overview of the annotations for membrane protein variants that have been collated within databases dedicated to the topic, bioinformatics approaches that leverage evolutionary information in order to shed light on previously uncharacterized membrane protein structures or interaction interfaces, tools for predicting the effects of mutations tailored specifically towards the characteristics of membrane proteins as well as two clinically relevant case studies explaining the implications of mutated membrane proteins in cancer and cardiomyopathy.

Computational prediction of protein aggregation: Advances in proteomics, conformation-specific algorithms and biotechnological applications

Protein aggregation is a widespread phenomenon that stems from the establishment of non-native intermolecular contacts resulting in protein precipitation. Despite its deleterious impact on fitness, protein aggregation is a generic property of polypeptide chains, indissociable from protein structure and function. Protein aggregation is behind the onset of neurodegenerative disorders and one of the serious obstacles in the production of protein-based therapeutics. The development of computational tools opened a new avenue to rationalize this phenomenon, enabling prediction of the aggregation propensity of individual proteins as well as proteome-wide analysis. These studies spotted aggregation as a major force driving protein evolution. Actual algorithms work on both protein sequences and structures, some of them accounting also for conformational fluctuations around the native state and the protein microenvironment. This toolbox allows to delineate conformation-specific routines to assist in the identification of aggregation-prone regions and to guide the optimization of more soluble and stable biotherapeutics. Here we review how the advent of predictive tools has change the way we think and address protein aggregation.

Ensembles of Hydrophobicity Scales as Potent Classifiers for Chimeric Virus-Like Particle Solubility - An Amino Acid Sequence-Based Machine Learning Approach

Virus-like particles (VLPs) are protein-based nanoscale structures that show high potential as immunotherapeutics or cargo delivery vehicles. Chimeric VLPs are decorated with foreign peptides resulting in structures that confer immune responses against the displayed epitope. However, insertion of foreign sequences often results in insoluble proteins, calling for methods capable of assessing a VLP candidate's solubility in silico. The prediction of VLP solubility requires a model that can identify critical hydrophobicity-related parameters, distinguishing between VLP-forming aggregation and aggregation leading to insoluble virus protein clusters. Therefore, we developed and implemented a soft ensemble vote classifier (sEVC) framework based on chimeric hepatitis B core antigen (HBcAg) amino acid sequences and 91 publicly available hydrophobicity scales. Based on each hydrophobicity scale, an individual decision tree was induced as classifier in the sEVC. An embedded feature selection algorithm and stratified sampling proved beneficial for model construction. With a learning experiment, model performance in the space of model training set size and number of included classifiers in the sEVC was explored. Additionally, seven models were created from training data of 24-384 chimeric HBcAg constructs, which were validated by 100-fold Monte Carlo cross-validation. The models predicted external test sets of 184-544 chimeric HBcAg constructs. Best models showed a Matthew's correlation coefficient of >0.6 on the validation and the external test set. Feature selection was evaluated for classifiers with best and worst performance in the chimeric HBcAg VLP solubility scenario. Analysis of the associated hydrophobicity scales allowed for retrieval of biological information related to the mechanistic backgrounds of VLP solubility, suggesting a special role of arginine for VLP assembly and solubility. In the future, the developed sEVC could further be applied to hydrophobicity-related problems in other domains, such as monoclonal antibodies.

Unbinding events of amino acids and peptides from water-pyrite interfaces: A case study of life's origin on mineral surfaces

Selective binding of aqueous-phase amino acids to mineral surfaces is regarded as a plausible first step in oligopeptide formation on early Earth. To clarify the strength and underlying mechanism of amino acid binding to pyrite surfaces, we measured the unbinding (pull-off) force of ten amino acids and two oligo-peptides from water-pyrite interfaces using atomic force microscopy (AFM). The most probable unbinding force could be described by a linearly increasing function with the size of the amino acid and a characteristic offset. A good correlation was obtained between the most probable unbinding force and the residue volume, surface area and polarizability of samples suggesting at least a partial contribution of van der Waals (vdW) forces, especially the London dispersion force. These results are useful in analysis of adhesion phenomena of amino acids in the given environmental settings such as in this work.

pH-Dependent Aggregation in Intrinsically Disordered Proteins Is Determined by Charge and Lipophilicity

Protein aggregation is associated with an increasing number of human disorders and premature aging. Moreover, it is a central concern in the manufacturing of recombinant proteins for biotechnological and therapeutic applications. Nevertheless, the unique architecture of protein aggregates is also exploited by nature for functional purposes, from bacteria to humans. The relevance of this process in health and disease has boosted the interest in understanding and controlling aggregation, with the concomitant development of a myriad of algorithms aimed to predict aggregation propensities. However, most of these programs are blind to the protein environment and, in particular, to the influence of the pH. Here, we developed an empirical equation to model the pH-dependent aggregation of intrinsically disordered proteins (IDPs) based on the assumption that both the global protein charge and lipophilicity depend on the solution pH. Upon its parametrization with a model IDP, this simple phenomenological approach showed unprecedented accuracy in predicting the dependence of the aggregation of both pathogenic and functional amyloidogenic IDPs on the pH. The algorithm might be useful for diverse applications, from large-scale analysis of IDPs aggregation properties to the design of novel reversible nanofibrillar materials.