Computational analysis of the amino acid interactions that promote or decrease protein solubility

Rational design approach to improve the solubility of the β-sandwich domain 1 of a thermophilic protein

The β-sandwich domain 1 (SD1) of islandisin is a stable thermophilic protein with surface loops that can be redesigned for specific target binding, architecturally comparable to the variable domain of immunoglobulin (IgG). SD1's propensity to aggregate due to incorrect folding and subsequent accumulation in Escherichia coli inclusion bodies limits its use in biotechnological applications. We rationally designed SD1 for improved variants that were expressed in soluble forms in E. coli while maintaining the intrinsic thermal stability of the protein (melting temperature (Tm) = 73). We used FoldX's ΔΔG predictions to find beneficial mutations and aggregation-prone regions (APRs) using Tango. The S26K substitution within protein core residues did not affect protein stability. Among the soluble mutants studied, the S26K/Q91P combination significantly improved the expression and solubility of SD1. We also examined the effects of the surface residue, pH, and concentration on the solubility of SD1. We showed that the surface polarity of proteins had little or no effect on solubility, whereas surface charges played a substantial role. The storage stability of several SD1 variants was impaired at pH values near their isoelectric point, and pH levels resulting in highly charged groups. We observed that mutations that create an uneven distribution of charged groups on the SD1 surface could enhance protein solubility by eliminating favorable protein-protein surface charge interactions. Our findings suggest that SD1 is mutationally tolerant to new functionalities, thus providing a novel perspective for the application of rational design to improve the solubility of targeted proteins.

Chagasin from Trypanosoma cruzi as a molecular scaffold to express epitopes of TSA-1 as soluble recombinant chimeras

Trypanosoma cruzi is the causative agent of Chagas disease, a global public health problem. New therapeutic drugs and biologics are needed. The TSA-1 recombinant protein of T. cruzi is one such promising antigen for developing a therapeutic vaccine. However, it is overexpressed in E. coli as inclusion bodies, requiring an additional refolding step. As an alternative, in this study, we propose the endogenous cysteine protease inhibitor chagasin as a molecular scaffold to generate chimeric proteins. These proteins will contain combinations of two of the five conserved epitopes (E1 to E5) of TSA-1 in the L4 and L6 chagasin loops. Twenty chimeras (Q1-Q20) were designed, and their solubility was predicted using bioinformatics tools. Nine chimeras with different degrees of solubility were selected and expressed in E. coli BL21 (DE3). Western blot assays with anti-6x-His and anti-chagasin antibodies confirmed the expression of soluble recombinant chimeras. Both theoretically and experimentally, the Q12 (E5-E3) chimera was the most soluble, and the Q20 (E4-E5) the most insoluble protein. Q4 (E5-E1) and Q8 (E5-E2) chimeras were classified as proteins with medium solubility that exhibited the highest yield in the soluble fraction. Notably, Q4 has a yield of 239 mg/L, well above the yield of recombinant chagasin (16.5 mg/L) expressed in a soluble form. The expression of the Q4 chimera was scaled up to a 7 L fermenter obtaining a yield of 490 mg/L. These data show that chagasin can serve as a molecular scaffold for the expression of TSA-1 epitopes in the form of soluble chimeras.

Ionome mapping and amino acid metabolome profiling of Phaseolus vulgaris L. seeds imbibed with computationally informed phytoengineered copper sulphide nanoparticles

This study reports the effects of a computationally informed and avocado-seed mediated Phyto engineered CuS nanoparticles as fertilizing agent on the ionome and amino acid metabolome of Pinto bean seeds using both bench top and ion beam analytical techniques. Physico-chemical analysis of the Phyto engineered nanoparticles with scanning-electron microscopy, transmission electron microscopy, X-ray diffraction, and Fourier Transform Infrared Spectroscopy confirmed the presence of CuS nanoparticles. Molecular dynamics simulations to investigate the interaction of some active phytocompounds in avocado seeds that act as reducing agents with the nano-digenite further showed that 4-hydroxybenzoic acid had a higher affinity for interacting with the nanoparticle's surface than other active compounds. Seeds treated with the digenite nanoparticles exhibited a unique ionome distribution pattern as determined with external beam proton-induced X-ray emission, with hotspots of Cu and S appearing in the hilum and micropyle area that indicated a possible uptake mechanism via the seed coat. The nano-digenite also triggered a plant stress response by slightly altering seed amino acid metabolism. Ultimately, the nano-digenite may have important implications as a seed protective or nutritive agent as advised by its unique distribution pattern and effect on amino acid metabolism.

Extraction, identification and application of gliadin from gluten: Impact of pH on physicochemical properties of unloaded- and lutein-loaded gliadin nanoparticles

In the present study, high purity gliadin was extracted from gluten by the marginally modified Osborne method and the effect of different pHs in the aqueous ethanol on the physicochemical properties of unloaded gliadin nanoparticles (UGNs) and lutein-loaded gliadin nanoparticles (LGNs) was investigated. The results revealed that the formation of UGNs and LGNs at diverse pHs was driven by a conjunction of hydrogen bonding, electrostatic interactions and hydrophobic effects, but their dominant roles varied at different pHs. pH also significantly impacted the surface hydrophobicity, secondary structure and aromatic amino acid microenvironment of UGNs and LGNs. LGNs at pH 5.0 and at pH 9.0 exhibited better loading capacity and could reach 9.7884 ± 0.0006 % and 9.7360 ± 0.0017 %, respectively. These two samples also had greater photostability and thermal stability. Half-lives of LGNs at pH 5.0 were 2.185 h and 54.579 h, respectively. Half-lives of LGNs at pH 9.0 were 2.937 h and 49.159 h, respectively. LGNs at pH 5.0 and LGNs at pH 9.0 also had higher bioaccessibility of lutein, with 15.98 ± 0.04 % and 15.27 ± 0.03 %, respectively. These findings yielded precious inspirations for designing innovative lutein delivery system.

Affinity Peptide-Tethered Suspension Hydrogel Sensor for Selective and Sensitive Detection of Influenza Virus

Influenza viruses are known to cause pandemic flu outbreaks through both inter-human and animal-to-human transmissions. Therefore, the rapid and accurate detection of such pathogenic viruses is crucial for effective pandemic control. Here, we introduce a novel sensor based on affinity peptide-immobilized hydrogel microspheres for the selective detection of influenza A virus (IAV) H3N2. To enhance the binding affinity performance, we identified novel affinity peptides using phage display and further optimized their design. The functional hydrogel microspheres were constructed using the drop microfluidic technique, employing a structure composed of natural (chitosan) and synthetic (poly(ethylene glycol) diacrylate and PEG 6 kDa) polymers with the activation of azadibenzocyclooctyne for the subsequent click chemistry reaction. The binding peptide-immobilized hydrogel microsphere (BP-Hyd) was characterized by field emission scanning electron microscopy, X-ray photoelectron spectroscopy, and Fourier transform infrared spectroscopy and exhibited selective detection capability for the IAV H3N2. To capture the detected IAV H3N2, a Cy3-labeled IAV hemagglutinin antibody was utilized. By incorporating the affinity peptide with hydrogel microspheres, we achieved quantitative and selective detection of IAV H3N2 with a detection limit of 1.887 PFU mL-1. Furthermore, the developed suspension sensor exhibited excellent reproducibility and showed reusability potential. Our results revealed that the BP-Hyd-based fluorescence sensor platform could be feasibly employed to detect other pathogens because the virus-binding peptides can be easily replaced with other peptides through phage display, enabling selective and sensitive binding to different targets.

Ostrinia furnacalis PBP2 solution NMR structure: Insight into ligand binding and release mechanisms

Ostrinia furnacalis is an invasive lepidopteran agricultural pest that relies on olfaction for mating and reproduction. Male moths have an extremely sensitive olfactory system that can detect the sex pheromones emitted by females over a great distance. Pheromone-binding proteins present in the male moth antenna play a key role in the pheromone uptake, transport, and release at the dendritic membrane of the olfactory neuron. Here, we report the first high-resolution NMR structure of a pheromone-binding protein from an Ostrinia species at pH 6.5. The core of the Ostrinia furnacalis PBP2 (OfurPBP2) consists of six helices, α1a (2-14), α1b (16-22), α2 (27-37), α3 (46-60), α4 (70-80), α5 (84-100), and α6 (107-124) surrounding a large hydrophobic pocket. The structure is stabilized by three disulfide bridges, 19-54, 50-108, and 97-117. In contrast to the unstructured C-terminus of other lepidopteran PBPs, the C-terminus of OfurPBP2 folds into an α-helix (α7) at pH 6.5. The protein has nanomolar affinity towards both pheromone isomers. Molecular docking of both pheromones, E-12 and Z-12-tetradecenyl acetate, to OfurPBP2 revealed that the residues Met5, Lys6, Met8, Thr9, Phe12, Phe36, Trp37, Phe76, Ser115, Phe118, Lys119, Ile122, His123, and Ala128 interact with both isomers, while Thr9 formed a hydrogen bond with the acetate head group. NMR structure and thermal unfolding studies with CD suggest that ligand release at pH 4.5 is likely due to the partial unfolding of the protein.

Mutational dynamics across VOCs in International travellers and Community transmission underscores importance of Spike-ACE2 interaction

Background

Emergence of SARS-CoV-2 VOCs at different time points through COVID-19 pandemic raised concern for increased transmissibility, infectivity and vaccination breakthroughs.

Methods

1567 international travellers plus community transmission COVID-19 cases were analysed for mutational profile of VOCS, that led to notable waves in India, namely Alpha, Delta, and Omicron. Spike mutations in Linkage Disequilibrium were investigated for potential impact on structural and functional changes of Spike-ACE2.

Results

ORF1ab and spike harboured diverse mutational signatures for each lineage. B.1.617.2 and AY. * demonstrated comparable profile, yet non-clade defining mutations were majorly unique between international vs community samples. Contrarily, Omicron lineages showed substantial overlap in non-clade defining mutations, signifying early phase of transmission and evolution within Indian community. Mutations in LD for Alpha [N501Y, A570D, D1118H, S982A], Delta [P681R, L452R, EFR:156–158 G, D950N, G142D] and Omicron [P681H, D796Y, N764K, N969K, N501Y, S375F] resulted in decreased binding affinity of Spike-ACE2 for Alpha and BA.1 whereas Delta, Omicron and BA.2 demonstrated strong binding.

Conclusion

Genomic surveillance tracked spread of VOCs in international travellers’ vs community transmission. Behavioural transmission patterns of variants, based on selective advantage incurred by spike mutations, led us to predict sudden takeover of Delta over Alpha and BA.2 over BA.1 in India.

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The mutational landscape of 1567 international travellers and community transmission were characterized across VOCs in India

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Mutations in LD for VOCs demonstrated differentially altered binding affinity and electrostatic interactions of Spike-ACE2.

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Altered Spike-ACE2 affinity among VOCs predicted sudden takeover of Delta over Alpha and BA.2 over BA.1 in India.

Maximizing the recovery of the native p28 bacterial peptide with improved activity and maintained solubility and stability in Escherichia coli BL21 (DE3)

p28 is a natural bacterial product, which recently has attracted much attention as an efficient cell penetrating peptide (CPP) and a promising anticancer agent. Considering the interesting biological qualities of p28, maximizing its expression appears to be a prominent priority. The optimization of such bioprocesses might be facilitated by utilizing statistical approaches such as Design of Experiment (DoE). In this study, we aimed to maximize the expression of "biologically active" p28 in Escherichia coli BL21 (DE3) host by harnessing statistical tools and experimental methods. Using Minitab, Plackett-Burman and Box-Behnken Response Surface Methodology (RSM) designs were generated to optimize the conditions for the expression of p28. Each condition was experimentally investigated by assessing the biological activity of the purified p28 in the MCF-7 breast cancer cell line. Seven independent variables were investigated, and three of them including ethanol concentration, OD₆₀₀ of the culture at the time of induction, and the post-induction temperature were demonstrated to significantly affect the p28 expression in E. coli. The cytotoxicity, penetration efficiency, and total process time were measured as dependent variables. The optimized expression conditions were validated experimentally, and the final products were investigated in terms of expression yield, solubility, and stability in vitro. Following the optimization, an 8-fold increase of the concentration of p28 expression was observed. In this study, we suggest an optimized combination of effective factors to produce soluble p28 in the E. coli host, a protocol that results in the production of a significantly high amount of the biologically active peptide with retained solubility and stability.

Side Group of Hydrophobic Amino Acids Controls Chiral Discrimination among Chiral Counterions and Metal-Organic Cages

The self-assembly of chiral Pd₁₂L₂₄ metal-organic cages (MOCs) based on hydrophobic amino acids, including alanine (Ala), valine (Val), and leucine (Leu), into single-layered hollow spherical blackberry-type structures is triggered by nitrates through counterion-mediated attraction. In addition to nitrates, anionic N-(tert-butoxycarbonyl) (Boc)-protected Ala, Val, and Leu were used as chiral counterions during the self-assembly of d-MOCs. Previously, we showed that l-Ala suppresses the self-assembly process of d-Pd₁₂Ala₂₄ but has no effect on l-Pd₁₂Ala₂₄, i.e., chiral discrimination. Here, we indicate when the amino acid used as the chiral counterion has a bulkier side group than the amino acid in the MOC structure, no chiral discrimination exists; otherwise, chiral discrimination exists. For example, Ala can induce chiral discrimination in all chiral MOCs, whereas Leu can induce chiral discrimination only in Pd₁₂Leu₂₄. Moreover, chiral anionic d- and l-alanine-based surfactants have no chiral discrimination, indicating that bulkier chiral counterions with more hydropohobic side groups can erase chiral discrimination.

BRANEart: Identify Stability Strength and Weakness Regions in Membrane Proteins

Understanding the role of stability strengths and weaknesses in proteins is a key objective for rationalizing their dynamical and functional properties such as conformational changes, catalytic activity, and protein-protein and protein-ligand interactions. We present BRANEart, a new, fast and accurate method to evaluate the per-residue contributions to the overall stability of membrane proteins. It is based on an extended set of recently introduced statistical potentials derived from membrane protein structures, which better describe the stability properties of this class of proteins than standard potentials derived from globular proteins. We defined a per-residue membrane propensity index from combinations of these potentials, which can be used to identify residues which strongly contribute to the stability of the transmembrane region or which would, on the contrary, be more stable in extramembrane regions, or vice versa. Large-scale application to membrane and globular proteins sets and application to tests cases show excellent agreement with experimental data. BRANEart thus appears as a useful instrument to analyze in detail the overall stability properties of a target membrane protein, to position it relative to the lipid bilayer, and to rationally modify its biophysical characteristics and function. BRANEart can be freely accessed from http://babylone.3bio.ulb.ac.be/BRANEart.

Characterization of gliadin, secalin and hordein fractions using analytical techniques

Prolamins, alcohol soluble storage proteins of the Triticeae tribe of Gramineae family, are known as gliadin, secalin and hordein in wheat, rye and barley respectively. Prolamins were extracted from fifteen cultivars using DuPont protocol to study their physiochemical, morphological and structural characteristics. SDS-PAGE of prolamins showed well resolved low molecular weight proteins with significant amount of albumin and globulin as cross-contaminant. The β-sheet (32.72–37.41%) and β-turn (30.36–37.91%) were found higher in gliadins, while α-helix (20.32–28.95%) and random coil (9.05–10.28%) in hordeins. The high colloidal stability as depicted by zeta-potential was observed in gliadins (23.5–27.0 mV) followed secalins (11.2–16.6 mV) and hordeins (4.1–7.8 mV). Surface morphology by SEM illustrated the globular particle arrangement in gliadins, sheet like arrangement in secalins and stacked flaky particle arrangement in hordeins fraction. TEM studies showed that secalin and hordein fractions were globular in shape while gliadins in addition to globular structure also possessed rod-shaped particle arrangement. XRD pattern of prolamin fractions showed the ordered crystalline domain at 2θ values of 44.1°, 37.8° and 10.4°. The extracted prolamins fractions showed amorphous as well as crystalline structures as revealed by XRD and TEM analysis. Space saving hexagonal molecular symmetry was also observed in TEM molecular arrangement of prolamins which has profound application in development of plant-based polymers and fibres.

Computational-Designed Enzyme for β-Tyrosine Production in Lignin Valorization

Lignin is an underutilized sustainable source of aromatic compounds. To valorize the low-value lignin monomers, we proposed an efficient strategy, involving enzymatic conversion from trans-p-hydroxycinnamic acids to generate valued-added canonical and non-canonical aromatic amino acids. Among them, β-amino acids are recognized as building blocks for bioactive natural products and pharmaceutical ingredients due to their attractive antitumor properties. Using computational enzyme design, the (R)-β-selective phenylalanine aminomutase from Taxus chinensis (TchPAM) was successfully mutated to accept β-tyrosine as the substrate, as well as to generate the (R)-β-tyrosine with excellent enantiopurity (ee > 99%) as the unique product from trans-p-hydroxycinnamic acid. Moreover, the kinetic parameters were determined for the reaction of four Y424 enzyme variants with the synthesis of different phenylalanine and tyrosine enantiomers. In the ammonia elimination reaction of (R)-β-tyrosine, the variants Y424N and Y424C displayed a two-fold increased catalytic efficiency of the wild type. In this work, a binding pocket in the active site, including Y424, K427, I431, and E455, was examined for its influence on the β-enantioselectivity of this enzyme family. Combining the upstream lignin depolymerization and downstream production, a sustainable value chain based on lignin is enabled. In summary, we report a β-tyrosine synthesis process from a monolignol component, offering a new way for lignin valorization by biocatalyst modification.

TISIGNER.com: web services for improving recombinant protein production

Experiments that are planned using accurate prediction algorithms will mitigate failures in recombinant protein production. We have developed TISIGNER (https://tisigner.com) with the aim of addressing technical challenges to recombinant protein production. We offer three web services, TIsigner (Translation Initiation coding region designer), SoDoPE (Soluble Domain for Protein Expression) and Razor, which are specialised in synonymous optimisation of recombinant protein expression, solubility and signal peptide analysis, respectively. Importantly, TIsigner, SoDoPE and Razor are linked, which allows users to switch between the tools when optimising genes of interest.

Solubility and Aggregation of Selected Proteins Interpreted on the Basis of Hydrophobicity Distribution

Protein solubility is based on the compatibility of the specific protein surface with the polar aquatic environment. The exposure of polar residues to the protein surface promotes the protein’s solubility in the polar environment. The aquatic environment also influences the folding process by favoring the centralization of hydrophobic residues with the simultaneous exposure to polar residues. The degree of compatibility of the residue distribution, with the model of the concentration of hydrophobic residues in the center of the molecule, with the simultaneous exposure of polar residues is determined by the sequence of amino acids in the chain. The fuzzy oil drop model enables the quantification of the degree of compatibility of the hydrophobicity distribution observed in the protein to a form fully consistent with the Gaussian 3D function, which expresses an idealized distribution that meets the preferences of the polar water environment. The varied degrees of compatibility of the distribution observed with the idealized one allow the prediction of preferences to interactions with molecules of different polarity, including water molecules in particular. This paper analyzes a set of proteins with different levels of hydrophobicity distribution in the context of the solubility of a given protein and the possibility of complex formation.

Solubility-Weighted Index: fast and accurate prediction of protein solubility

Motivation

Recombinant protein production is a widely used technique in the biotechnology and biomedical industries, yet only a quarter of target proteins are soluble and can therefore be purified.

Results

We have discovered that global structural flexibility, which can be modeled by normalized B-factors, accurately predicts the solubility of 12 216 recombinant proteins expressed in Escherichia coli. We have optimized these B-factors, and derived a new set of values for solubility scoring that further improves prediction accuracy. We call this new predictor the ‘Solubility-Weighted Index’ (SWI). Importantly, SWI outperforms many existing protein solubility prediction tools. Furthermore, we have developed ‘SoDoPE’ (Soluble Domain for Protein Expression), a web interface that allows users to choose a protein region of interest for predicting and maximizing both protein expression and solubility.

Availability and implementation

The SoDoPE web server and source code are freely available at https://tisigner.com/sodope and https://github.com/Gardner-BinfLab/TISIGNER-ReactJS, respectively. The code and data for reproducing our analysis can be found at https://github.com/Gardner-BinfLab/SoDoPE_paper_2020.

Supplementary information

Supplementary data are available at Bioinformatics online.

Toward complete rational control over protein structure and function through computational design

The grand challenge of protein design is a general method for producing a polypeptide with arbitrary functionality, conformation, and biochemical properties. To that end, a wide variety of methods have been developed for the improvement of native proteins, the design of ideal proteins de novo, and the redesign of suboptimal proteins with better-performing substructures. These methods employ informatic comparisons of function-structure-sequence relationships as well as knowledge-based evaluation of protein properties to narrow the immense protein sequence search space down to an enumerable and often manually evaluable set of structures that meet specified criteria. While arbitrary manipulation of protein-protein interfaces and molecular catalysis remains an unsolved problem, and no protein shape or behavior manipulation algorithm is universally applicable, the promising results thus far are a strong indicator that a general approach to the arbitrary manipulation of polypeptides is within reach.

Investigation of Supercharging as A Strategy to Enhance the Solubility and Plasminogen Cleavage Activity of Reteplase

Background

Reteplase, the recombinant form of tissue plasminogen activator, is a thrombolytic drug with outstanding characteristics, while demonstrating limited solubility and reduced plasminogen activation. Previously, we in silico designed a variant of Reteplase with positively supercharged surface, which showed promising stability, solubility and activity. This study was devoted to evaluation of the utility of supercharging technique for enhancing these characteristics in Reteplase.

Objective

To test the hypothesis that reinforced surface charge of a rationally-designed Reteplase variant will not compromise its stability, will increase its solubility, and will enhance its plasminogen cleavage activity.

Materials and Methods

Supercharged Reteplase coding sequence was cloned in pDest527 vector and expressed in E. coli BL21 (DE3). The expressed protein was extracted by cell disruption. Inclusion bodies were solubilized using guanidine hydrochloride, followed by dialysis for protein refolding. After confirmation with SDS-PAGE and western blotting, extracted proteins were assayed for solubility and tested for bioactivity.

Results

SDS-PAGE and western blot analysis confirmed the successful expression of Reteplase. Western blot experiments showed most of Reteplase expressed in the insoluble form. Plasminogen cleavage assay showed significantly higher activity of the supercharged variant than the wild type protein (P < 0.001). The stability of the supercharged variant was also comparable to the wild type.

Conclusion

Our findings, i.e. the contribution of the surface supercharging technique to retained stability, enhanced plasminogen cleavage activity, while inefficiently changed solubility of Reteplase, contain implications for future designs of soluble variants of this fibrinolytic protein drug.

SOLart: a structure-based method to predict protein solubility and aggregation

MOTIVATION

The solubility of a protein is often decisive for its proper functioning. Lack of solubility is a major bottleneck in high-throughput structural genomic studies and in high-concentration protein production, and the formation of protein aggregates causes a wide variety of diseases. Since solubility measurements are time-consuming and expensive, there is a strong need for solubility prediction tools.

RESULTS

We have recently introduced solubility-dependent distance potentials that are able to unravel the role of residue-residue interactions in promoting or decreasing protein solubility. Here, we extended their construction by defining solubility-dependent potentials based on backbone torsion angles and solvent accessibility, and integrated them, together with other structure- and sequence-based features, into a random forest model trained on a set of Escherichia coli proteins with experimental structures and solubility values. We thus obtained the SOLart protein solubility predictor, whose most informative features turned out to be folding free energy differences computed from our solubility-dependent statistical potentials. SOLart performances are very good, with a Pearson correlation coefficient between experimental and predicted solubility values of almost 0.7 both in cross-validation on the training dataset and in an independent set of Saccharomyces cerevisiae proteins. On test sets of modeled structures, only a limited drop in performance is observed. SOLart can thus be used with both high-resolution and low-resolution structures, and clearly outperforms state-of-art solubility predictors. It is available through a user-friendly webserver, which is easy to use by non-expert scientists.

AVAILABILITY AND IMPLEMENTATION

The SOLart webserver is freely available at http://babylone.ulb.ac.be/SOLART/.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Characterization and Quantitative Determination of a Diverse Group of Bacillus subtilis subsp. subtilis NCIB 3610 Antibacterial Peptides

Five antibacterial peptides produced by Bacillus subtilis NCIB 3610 were purified, quantified, characterized, and identified in the present study. Cell-free extracts were subjected to three purification protocols employing ammonium sulfate or organic solvent precipitation and their combination, followed by ion-exchange chromatography, solid-phase extraction, and preparative high-performance liquid chromatography (HPLC). The combined ammonium sulfate and organic solvent precipitation extraction protocol presented the best results for peptide purification. In the five fractions that presented antimicrobial activity, antibacterial peptides were quantified by the turbidometric method and by HPLC using nisin for external calibration, with the second providing more accurate results. All peptides were pH- and temperature-resistant and their sensitivity to proteases treatment indicated their proteinic nature. The five peptides were subjected to microwave-assisted acid hydrolysis (MAAH) and following derivatization were analyzed using norleucine as the internal standard, to determine their amino acid content. The identification of the isolated peptides using the UniProt and PubChem databases indicated that the four peptides correspond to UniProt entries of the bacteriocins Subtilosin-A (Q1W152) Subtilosin-SbOX (H6D9P4), Ericin B (Q93GH3), Subtilin (P10946), and the fifth to the non-ribosomal antibacterial lipopeptide surfactin (CID:443592). The amino acid content determination and computational analyses, applied in the present work on the antimicrobial peptides of B. subtilis, proved an efficient screening and quantification method of bacteriocins that could potentially be applied in other bacterial strains. The constructed phylogenetic trees heterogeneity observed across the five peptides investigated might be indicative of competitive advantage of the strain.

Melting properties of amino acids and their solubility in water

The state-of-the-art unit operation for separation and purification of amino acids is still crystallization, which requires solubility data and melting properties of pure compounds. Since measuring solubility is time-consuming, prediction tools are desired. Further, melting properties are not yet available due to decomposition of amino acids upon slow heating. In this work, melting properties of twenty amino acids (except Met) were measured by Fast Scanning Calorimetry (FSC) with heating rates up to 20 000 K s⁻¹. PC-SAFT was used to predict interactions in amino acid + water systems. Additionally, solubility, pH, and PXRD was measured. By combining FSC and PC-SAFT, the solubility of 15 amino acids was successfully predicted in a wide temperature range in good agreement with the experimental data. Thus, this work provides melting properties of amino acids for the first time and highlights the usefulness of such data to predict material properties such as aqueous solubility of amino acids.

We report the melting properties of amino acids for the first time and highlight the usefulness of such data to predict material properties such as aqueous solubility of amino acids.

Strong stacking interactions at large horizontal displacements of tropylium and cyclooctatetraenide ligands of transition metal complexes: crystallographic and DFT study

Large offset stacking of tropylium and COT ligands, which is dominant in crystal structures, surpasses an energy of −3.0 kcal mol⁻¹.

Charge and hydrophobicity are key features in sequence-trained machine learning models for predicting the biophysical properties of clinical-stage antibodies

Improved understanding of properties that mediate protein solubility and resistance to aggregation are important for developing biopharmaceuticals, and more generally in biotechnology and synthetic biology. Recent acquisition of large datasets for antibody biophysical properties enables the search for predictive models. In this report, machine learning methods are used to derive models for 12 biophysical properties. A physicochemical perspective is maintained in analysing the models, leading to the observation that models cluster largely according to charge (cross-interaction measurements) and hydrophobicity (self-interaction methods). These two properties also overlap in some cases, for example in a new interpretation of variation in hydrophobic interaction chromatography. Since the models are developed from differences of antibody variable loops, the next stage is to extend models to more diverse protein sets.

AVAILABILITY

The web application for the sequence-based algorithms are available on the protein-sol webserver, at https://protein-sol.manchester.ac.uk/abpred, with models and virtualisation software available at https://protein-sol.manchester.ac.uk/software.

A comprehensive computational study of amino acid interactions in membrane proteins

Transmembrane proteins play a fundamental role in a wide series of biological processes but, despite their importance, they are less studied than globular proteins, essentially because their embedding in lipid membranes hampers their experimental characterization. In this paper, we improved our understanding of their structural stability through the development of new knowledge-based energy functions describing amino acid pair interactions that prevail in the transmembrane and extramembrane regions of membrane proteins. The comparison of these potentials and those derived from globular proteins yields an objective view of the relative strength of amino acid interactions in the different protein environments, and their role in protein stabilization. Separate potentials were also derived from α-helical and β-barrel transmembrane regions to investigate possible dissimilarities. We found that, in extramembrane regions, hydrophobic residues are less frequent but interactions between aromatic and aliphatic amino acids as well as aromatic-sulfur interactions contribute more to stability. In transmembrane regions, polar residues are less abundant but interactions between residues of equal or opposite charges or non-charged polar residues as well as anion-π interactions appear stronger. This shows indirectly the preference of the water and lipid molecules to interact with polar and hydrophobic residues, respectively. We applied these new energy functions to predict whether a residue is located in the trans- or extramembrane region, and obtained an AUC score of 83% in cross validation, which demonstrates their accuracy. As their application is, moreover, extremely fast, they are optimal instruments for membrane protein design and large-scale investigations of membrane protein stability.

Stacking interactions between ruthenium p-cymene complexes: combined crystallographic and density functional study

Stacking interactions between ruthenium p-cymene complexes are significantly strengthened by additional simultaneous C–H/π interactions of aromatic rings and their substituents.

Strong stacking interactions of metal–chelate rings are caused by substantial electrostatic component

Stacking interactions of metal–chelate rings are strong due to very strong electrostatic energy component.