Predicting protein stability changes upon single-point mutation: a thorough comparison of the available tools on a new dataset.

Predicting the difference in thermodynamic stability between protein variants is crucial for protein design and understanding the genotype-phenotype relationships. So far, several computational tools have been created to address this task. Nevertheless, most of them have been trained or optimized on the same and 'all' available data, making a fair comparison unfeasible. Here, we introduce a novel dataset, collected and manually cleaned from the latest version of the ThermoMutDB database, consisting of 669 variants not included in the most widely used training datasets. The prediction performance and the ability to satisfy the antisymmetry property by considering both direct and reverse variants were evaluated across 21 different tools. The Pearson correlations of the tested tools were in the ranges of 0.21-0.5 and 0-0.45 for the direct and reverse variants, respectively. When both direct and reverse variants are considered, the antisymmetric methods perform better achieving a Pearson correlation in the range of 0.51-0.62. The tested methods seem relatively insensitive to the physiological conditions, performing well also on the variants measured with more extreme pH and temperature values. A common issue with all the tested methods is the compression of the $\Delta \Delta G$ predictions toward zero. Furthermore, the thermodynamic stability of the most significantly stabilizing variants was found to be more challenging to predict. This study is the most extensive comparisons of prediction methods using an entirely novel set of variants never tested before.

Revealing the Mechanism of NLRP3 Inflammatory Pathway Activation through K+ Efflux Induced by PLO via Signal Point Mutations.

Trueperella pyogenes is an important opportunistic pathogenic bacterium widely distributed in the environment. Pyolysin (PLO) is a primary virulence factor of T. pyogenes and capable of lysing many different cells. PLO is a member of the cholesterol-dependent cytolysin (CDC) family of which the primary structure only presents a low level of homology with other members from 31% to 45%. By deeply studying PLO, we can understand the overall pathogenic mechanism of CDC family proteins. This study established a mouse muscle tissue model infected with recombinant PLO (rPLO) and its single-point mutations, rPLO N139K and rPLO F240A, and explored its mechanism of causing inflammatory damage. The inflammatory injury abilities of rPLO N139K and rPLO F240A are significantly reduced compared to rPLO. This study elaborated on the inflammatory mechanism of PLO by examining its unit point mutations in detail. Our data also provide a theoretical basis and practical significance for future research on toxins and bacteria.

Analysis of amyloidogenic transthyretin mutations using continuum solvent free energy calculations.

Many proteins can undergo pathological conformational changes that result in the formation of amyloidogenic fibril structures. Various neurodegenerative diseases are associated with such pathological fibril formation of specific proteins. Transthyretin (TTR) is a tetrameric globular transport protein in the blood plasma that can dissociate, unfold, and form long and stable fibrils. Many TTR mutations are known that promote (TTR) amyloidosis and cause severe diseases. TTR amyloidosis has been studied extensively using biochemical methods and structures of various mutations in the globular form have been characterized. Recently, also the structure of a TTR fibril has been determined. In an effort to better understand why some mutations increase or decrease the tendency of amyloid formation, we have applied a combined molecular dynamics and continuum solvent approach to calculate the energetic influence of residue changes in the globular versus fibril form. For 29 out of 36 tested TTR single residue mutations, the approach correctly predicts the increased or decreased tendency for amyloidosis allowing us also to elucidate the origins of the tendency. We find that indeed the destabilization of the globular monomer or changes in dimer and tetramer stability due to mutation has a dominant influence on the amyloidogenic tendency. The continuum solvent model predicts a significantly more favorable mean energy per residue of the fibril form compared to the globular form. This effect is only slightly modulated by single-point mutations preserving the energetic preference for fibril formation upon protein unfolding. It explains why no correlation between experimental amyloidosis and calculated change in fibril stability was observed.

Droplet Digital PCR for Non-Invasive Prenatal Detection of Fetal Single-Gene Point Mutations in Maternal Plasma.

Non-invasive prenatal testing (NIPT) is based on the detection and characterization of circulating cell-free fetal DNA (ccffDNA) in maternal plasma and aims to identify genetic abnormalities. At present, commercial NIPT kits can detect only aneuploidies, small deletions and insertions and some paternally inherited single-gene point mutations causing genetic diseases, but not maternally inherited ones. In this work, we have developed two NIPT assays, based on the innovative and sensitive droplet digital PCR (ddPCR) technology, to identify the two most common ß thalassemia mutations in the Mediterranean area (ß+IVSI-110 and ß039), maternally and/or paternally inherited, by fetal genotyping. The assays were optimized in terms of amplification efficiency and hybridization specificity, using mixtures of two genomic DNAs with different genotypes and percentages to simulate fetal and maternal circulating cell-free DNA (ccfDNA) at various gestational weeks. The two ddPCR assays were then applied to determine the fetal genotype from 52 maternal plasma samples at different gestational ages. The diagnostic outcomes were confirmed for all the samples by DNA sequencing. In the case of mutations inherited from the mother or from both parents, a precise dosage of normal and mutated alleles was required to determine the fetal genotype. In particular, we identified two diagnostic ranges for allelic ratio values statistically distinct and not overlapping, allowing correct fetal genotype determinations for almost all the analyzed samples. In conclusion, we have developed a simple and sensitive diagnostic tool, based on ddPCR, for the NIPT of ß+IVSI-110 and ß039 mutations paternally and, for the first time, maternally inherited, a tool, which may be applied to other single point mutations causing monogenic diseases.

SAAFEC-SEQ: A Sequence-Based Method for Predicting the Effect of Single Point Mutations on Protein Thermodynamic Stability.

Modeling the effect of mutations on protein thermodynamics stability is useful for protein engineering and understanding molecular mechanisms of disease-causing variants. Here, we report a new development of the SAAFEC method, the SAAFEC-SEQ, which is a gradient boosting decision tree machine learning method to predict the change of the folding free energy caused by amino acid substitutions. The method does not require the 3D structure of the corresponding protein, but only its sequence and, thus, can be applied on genome-scale investigations where structural information is very sparse. SAAFEC-SEQ uses physicochemical properties, sequence features, and evolutionary information features to make the predictions. It is shown to consistently outperform all existing state-of-the-art sequence-based methods in both the Pearson correlation coefficient and root-mean-squared-error parameters as benchmarked on several independent datasets. The SAAFEC-SEQ has been implemented into a web server and is available as stand-alone code that can be downloaded and embedded into other researchers' code.

Inheritance and Molecular Characterization of a Novel Mutated AHAS Gene Responsible for the Resistance of AHAS-Inhibiting Herbicides in Rapeseed (Brassica napus L.).

The use of herbicides is an effective and economic way to control weeds, but their availability for rapeseed is limited due to the shortage of herbicide-resistant cultivars in China. The single-point mutation in the acetohydroxyacid synthase (AHAS) gene can lead to AHAS-inhibiting herbicide resistance. In this study, the inheritance and molecular characterization of the tribenuron-methyl (TBM)-resistant rapeseed (Brassica napus L.) mutant, K5, are performed. Results indicated that TBM-resistance of K5 was controlled by one dominant allele at a single nuclear gene locus. The novel substitution of cytosine with thymine at position 544 in BnAHAS1 was identified in K5, leading to the alteration of proline with serine at position 182 in BnAHAS1. The TBM-resistance of K5 was approximately 100 times that of its wild-type ZS9, and K5 also showed cross-resistance to bensufuron-methyl and monosulfuron-ester sodium. The BnAHAS1544T transgenic Arabidopsis exhibited higher TBM-resistance than that of its wild-type, which confirmed that BnAHAS1544T was responsible for the herbicide resistance of K5. Simultaneously, an allele-specific marker was developed to quickly distinguish the heterozygous and homozygous mutated alleles BnAHAS1544T. In addition, a method for the fast screening of TBM-resistant plants at the cotyledon stage was developed. Our research identified and molecularly characterized one novel mutative AHAS allele in B. napus and laid a foundation for developing herbicide-resistant rapeseed cultivars.

iSEE: Interface structure, evolution, and energy-based machine learning predictor of binding affinity changes upon mutations.

Quantitative evaluation of binding affinity changes upon mutations is crucial for protein engineering and drug design. Machine learning-based methods are gaining increasing momentum in this field. Due to the limited number of experimental data, using a small number of sensitive predictive features is vital to the generalization and robustness of such machine learning methods. Here we introduce a fast and reliable predictor of binding affinity changes upon single point mutation, based on a random forest approach. Our method, iSEE, uses a limited number of interface Structure, Evolution, and Energy-based features for the prediction. iSEE achieves, using only 31 features, a high prediction performance with a Pearson correlation coefficient (PCC) of 0.80 and a root mean square error of 1.41 kcal/mol on a diverse training dataset consisting of 1102 mutations in 57 protein-protein complexes. It competes with existing state-of-the-art methods on two blind test datasets. Predictions for a new dataset of 487 mutations in 56 protein complexes from the recently published SKEMPI 2.0 database reveals that none of the current methods perform well (PCC < 0.42), although their combination does improve the predictions. Feature analysis for iSEE underlines the significance of evolutionary conservations for quantitative prediction of mutation effects. As an application example, we perform a full mutation scanning of the interface residues in the MDM2-p53 complex.

UPF1 silenced cellular model systems for screening of read-through agents active on ß039 thalassemia point mutation.

BACKGROUND: Nonsense mutations promote premature translational termination, introducing stop codons within the coding region of mRNAs and causing inherited diseases, including thalassemia. For instance, in ß039 thalassemia the CAG (glutamine) codon is mutated to the UAG stop codon, leading to premature translation termination and to mRNA destabilization through the well described NMD (nonsense-mediated mRNA decay). In order to develop an approach facilitating translation and, therefore, protection from NMD, ribosomal read-through molecules, such as aminoglycoside antibiotics, have been tested on mRNAs carrying premature stop codons. These findings have introduced new hopes for the development of a pharmacological approach to the ß039 thalassemia therapy. While several strategies, designed to enhance translational read-through, have been reported to inhibit NMD efficiency concomitantly, experimental tools for systematic analysis of mammalian NMD inhibition by translational read-through are lacking. RESULTS: We developed a human cellular model of the ß039 thalassemia mutation with UPF-1 suppressed and showing a partial NMD suppression. CONCLUSIONS: This novel cellular model could be used for the screening of molecules exhibiting preferential read-through activity allowing a great rescue of the mutated transcripts.

The maturation mechanism of γ-glutamyl transpeptidases: Insights from the crystal structure of a precursor mimic of the enzyme from Bacillus licheniformis and from site-directed mutagenesis studies.

γ-Glutamyl transpeptidases (γ-GTs) are members of N-terminal nucleophile hydrolase superfamily. They are synthetized as single-chain precursors, which are then cleaved to form mature enzymes. Basic aspects of autocatalytic processing of these pro-enzymes are still unknown. Here we describe the X-ray structure of the precursor mimic of Bacillus licheniformis γ-GT (BlGT), obtained by mutating catalytically important threonine to alanine (T399A-BlGT), and report results of autoprocessing of mutants of His401, Thr415, Thr417, Glu419 and Arg571. Data suggest that Thr417 is in a competent position to activate the catalytic threonine (Thr399) for nucleophilic attack of the scissile peptide bond and that Thr415 plays a major role in assisting the process. On the basis of these new structural results, a possible mechanism of autoprocessing is proposed. This mechanism, which guesses the existence of a six-membered transition state involving one carbonyl and two hydroxyl groups, is in agreement with all the available experimental data collected on γ-GTs from different species and with our new Ala-scanning mutagenesis data.

Increasing the intracellular isoprenoid pool in Saccharomyces cerevisiae by structural fine-tuning of a bifunctional farnesyl diphosphate synthase.

Farnesyl diphosphate synthase (FPPS) is a key enzyme responsible for the supply of isoprenoid precursors for several essential metabolites, including sterols, dolichols and ubiquinone. In Saccharomyces cerevisiae, FPPS catalyzes the sequential condensation of two molecules of isopentenyl diphosphate (IPP) with dimethylallyl diphosphate (DMAPP), producing geranyl diphosphate (GPP) and farnesyl diphosphate (FPP). Critical amino acid residues that determine product chain length were determined by a comparative study of strict GPP synthases versus strict FPPS. In silico ΔΔG, i.e. differential binding energy between a protein and two different ligands-of yeast FPPS mutants was evaluated, and F96, A99 and E165 residues were identified as key determinants for product selectivity. A99X variants were evaluated in vivo, S. cerevisiae strains carrying A99R and A99H variants showed significant differences on GPP concentrations and specific growth rates. The FPPS A99T variant produced unquantifiable amounts of FPP and no effect on GPP production was observed. Strains carrying A99Q, A99Y and A99K FPPS accumulated high amounts of DMAPP-IPP, with a decrease in GPP and FPP. Our results demonstrated the relevance of the first residue before FARM (First Aspartate Rich Motif) over substrate consumption and product specificity of S. cerevisiae FPPS in vivo. The presence of A99H significantly modified product selectivity and appeared to be relevant for GPP synthesis.

Label-free plasmonic spectral profiling of serum DNA.

Genetic and epigenetic modifications are linked to the activation of oncogenes and inactivation of tumor suppressor genes. Likewise, the associated molecular alternations can best inform precision medicine for personalized tumor treatment. Therefore, performing characterization of genetic and epigenetic alternations at the molecular level represents a crucial step in early diagnosis and/or therapeutics of cancer. However, the prevailing methods for DNA analysis involve a series of tedious and complicated steps, in which important genetic and epigenetic information could be lost or altered. To provide a potential approach for non-invasive, direct, and efficient DNA analysis, herein, we present a promising strategy for label-free molecular profiling of serum DNA in its pristine form by fusing surface-enhanced Raman spectroscopy with machine learning on a superior plasmonic nanostructured platform. Using DNA methylation and single-point mutation as two case studies, the presented strategy allows a well-balanced sensitive and specific detection of epigenetic and genetic changes at the single-nucleotide level in serum. We envision the presented label-free strategy could serve as a versatile tool for direct molecular profiling in pristine forms of a wide range of biological markers and aid biomedical diagnostics as well as therapeutics.

Engineering polysaccharide hydrolases in the product-releasing cleft to alter their product profiles.

Polysaccharide hydrolases are enzymes capable of hydrolyzing polysaccharides to generate oligosaccharides that have diverse applications in the food, feed and pharmaceutical industries. However, the detailed mechanisms governing the compositions of their hydrolysates remain poorly understood. Previously, we identified a novel neopullulase Amy117, which exclusively converts pullulan to panose by specifically cleaving α-1,4-glycosidic bonds. Yet, several enzymes with high homology to Amy117 produce a mixture of glucose, maltose and panose during pullulan hydrolysis. To explore this particular phenomenon, we compared the sequences and structures between Amy117 and the maltose amylase ThMA, and identified a specific residue Thr299 in Amy117 (equivalent to His294 in ThMA) within the product-releasing cleft of Amy117, which might be responsible for this characteristic feature. Using structure-based rational design, we have successfully converted the product profiles of pullulan hydrolysates between Amy117 and ThMA by simply altering this key residue. Molecular docking analysis indicated that the key residue at the product-releasing outlet altered the product profile by affecting the panose release rate. Moreover, we modeled the long-chain pullulan substrate G8 to examine its potential conformations and found that G8 might undergo a conformational change in the narrow cleft that allows the Amy117 variant to specifically recognize α-1,6-glycosidic bonds.

Influence of Model Structures on Predictors of Protein Stability Changes from Single-Point Mutations.

Missense variation in genomes can affect protein structure stability and, in turn, the cell physiology behavior. Predicting the impact of those variations is relevant, and the best-performing computational tools exploit the protein structure information. However, most of the current protein sequence variants are unresolved, and comparative or ab initio tools can provide a structure. Here, we evaluate the impact of model structures, compared to experimental structures, on the predictors of protein stability changes upon single-point mutations, where no significant changes are expected between the original and the mutated structures. We show that there are substantial differences among the computational tools. Methods that rely on coarse-grained representation are less sensitive to the underlying protein structures. In contrast, tools that exploit more detailed molecular representations are sensible to structures generated from comparative modeling, even on single-residue substitutions.

Challenges in predicting stabilizing variations: An exploration.

An open challenge of computational and experimental biology is understanding the impact of non-synonymous DNA variations on protein function and, subsequently, human health. The effects of these variants on protein stability can be measured as the difference in the free energy of unfolding (ΔΔG) between the mutated structure of the protein and its wild-type form. Throughout the years, bioinformaticians have developed a wide variety of tools and approaches to predict the ΔΔG. Although the performance of these tools is highly variable, overall they are less accurate in predicting ΔΔG stabilizing variations rather than the destabilizing ones. Here, we analyze the possible reasons for this difference by focusing on the relationship between experimentally-measured ΔΔG and seven protein properties on three widely-used datasets (S2648, VariBench, Ssym) and a recently introduced one (S669). These properties include protein structural information, different physical properties and statistical potentials. We found that two highly used input features, i.e., hydrophobicity and the Blosum62 substitution matrix, show a performance close to random choice when trying to separate stabilizing variants from either neutral or destabilizing ones. We then speculate that, since destabilizing variations are the most abundant class in the available datasets, the overall performance of the methods is higher when including features that improve the prediction for the destabilizing variants at the expense of the stabilizing ones. These findings highlight the need of designing predictive methods able to exploit also input features highly correlated with the stabilizing variants. New tools should also be tested on a not-artificially balanced dataset, reporting the performance on all the three classes (i.e., stabilizing, neutral and destabilizing variants) and not only the overall results.

Can a Micronutrient Mixture Delay the Onset and Progression of Symptoms of Single-Point Mutation Diseases?

Single-point mutation diseases in which substitution of one nucleotide with another in a gene occurs include familial Alzheimer's disease (fAD), familial Parkinson's disease (fPD), and familial Creutzfeldt-Jacob disease (fCJD) as well as Huntington's disease (HD), sickle cell anemia, and hemophilia. Inevitability of occurrence of these diseases is certain. However, the time of appearance of symptoms could be influenced by the diet, environment, and possibly other genetic factors. There are no effective approaches to delay the onset or progression of symptoms of these diseases. The fact that increased oxidative stress and inflammation significantly contribute to the initiation and progression of these point mutation diseases shows that antioxidants could be useful. The major objectives are (a) to present evidence that increased oxidative stress and chronic inflammation are associated with selected single-point mutation diseases, such as fAD, fPD, and fCJD, HD, sickle cell anemia, and hemophilia; (b) to describe limited studies on the role of individual antioxidants in experimental models of some of these diseases; and (c) to discuss a rationale for utilizing a comprehensive mixture of micronutrients, which may delay the development and progression of symptoms of above diseases by simultaneously reducing oxidative and inflammatory damages.Key teaching pointsSelected single-point mutation diseases and their pattern of inheritanceCharacteristics of each selected single-point mutation diseaseEvidence for increased oxidative stress and inflammation in each diseasePotential reasons for failure of single antioxidants in human studiesRationale for using a comprehensive mixture of micronutrients in delaying the onset and progression of single-point mutation diseases.

The point mutation of the cholesterol trafficking membrane protein NPC1 may affect its proper function in more than a single step: Molecular dynamics simulation study.

The Niemann-Pick type C1 (NPC1) protein is one of the key players of cholesterol trafficking from the lysosome and its function is closely coupled with the Niemann-Pick type C2 (NPC2) protein. The dysfunction of one of these proteins can cause problems in the overall cholesterol homeostasis and leads to a disease, which is called the Niemann-Pick type C (NPC) disease. The parts of the cholesterol transport mechanism by NPC1 have begun to recently emerge, especially after the full-length NPC1 structure was determined from a cryo-EM study. However, many details about the overall cholesterol trafficking process by NPC1 still remain to be elucidated. Notably, the NPC1 could act as one of the target proteins for the control of infectious diseases due to its role as the virus entry point into the cells as well as for cancer treatment due to the inhibitory effect of tumor growth. A mutation of NPC1 can leads to dysfunctions and understanding this process can provide valuable insights into the mechanisms of the corresponding protein and the therapeutic strategies against the disease that are caused by the mutation. It has been found that patients with the point mutation R518W (or R518Q) on the NPC1 show the accumulation of lipids within the lysosomal lumen. In this paper, we report how the corresponding mutation can affect the cholesterol transport process by NPC1 in the different stages by the molecular dynamics simulations. The simulation results show that the point mutation intervenes at least at two different steps during the cholesterol transport by NPC1 and NPC2 in combination, which includes the association step of NPC2 with the NPC1, the cholesterol transfer step from NPC2 to NPC1-NTD while the cholesterol passage within the NPC1 via a channel is relatively unaffected by R518W mutation. The detailed analysis of the resulting simulation trajectories reveals the important structural features that are essential for the proper functioning of the NPC1 for the cholesterol transport, and it shows how the overall structure, which thereby includes the function, can be affected by a single mutation.

Ultrasensitive Electrochemical Detection of Mutated Viral RNAs with Single-Nucleotide Resolution Using a Nanoporous Electrode Array (NPEA).

The detection of nucleic acids and their mutation derivatives is vital for biomedical science and applications. Although many nucleic acid biosensors have been developed, they often require pretreatment processes, such as target amplification and tagging probes to nucleic acids. Moreover, current biosensors typically cannot detect sequence-specific mutations in the targeted nucleic acids. To address the above problems, herein, we developed an electrochemical nanobiosensing system using a phenomenon comprising metal ion intercalation into the targeted mismatched double-stranded nucleic acids and a homogeneous Au nanoporous electrode array (Au NPEA) to obtain (i) sensitive detection of viral RNA without conventional tagging and amplifying processes, (ii) determination of viral mutation occurrence in a simple detection manner, and (iii) multiplexed detection of several RNA targets simultaneously. As a proof-of-concept demonstration, a SARS-CoV-2 viral RNA and its mutation derivative were used in this study. Our developed nanobiosensor exhibited highly sensitive detection of SARS-CoV-2 RNA (∼1 fM detection limit) without tagging and amplifying steps. In addition, a single point mutation of SARS-CoV-2 RNA was detected in a one-step analysis. Furthermore, multiplexed detection of several SARS-CoV-2 RNAs was successfully demonstrated using a single chip with four combinatorial NPEAs generated by a 3D printing technique. Collectively, our developed nanobiosensor provides a promising platform technology capable of detecting various nucleic acids and their mutation derivatives in highly sensitive, simple, and time-effective manners for point-of-care biosensing.

Impact of Structural Observables From Simulations to Predict the Effect of Single-Point Mutations in MHC Class II Peptide Binders.

The prediction of peptide binders to Major Histocompatibility Complex (MHC) class II receptors is of great interest to study autoimmune diseases and for vaccine development. Most approaches predict the affinities using sequence-based models trained on experimental data and multiple alignments from known peptide substrates. However, detecting activity differences caused by single-point mutations is a challenging task. In this work, we used interactions calculated from simulations to build scoring matrices for quickly estimating binding differences by single-point mutations. We modelled a set of 837 peptides bound to an MHC class II allele, and optimized the sampling of the conformations using the Rosetta backrub method by comparing the results to molecular dynamics simulations. From the dynamic trajectories of each complex, we averaged and compared structural observables for each amino acid at each position of the 9°mer peptide core region. With this information, we generated the scoring-matrices to predict the sign of the binding differences. We then compared the performance of the best scoring-matrix to different computational methodologies that range in computational costs. Overall, the prediction of the activity differences caused by single mutated peptides was lower than 60% for all the methods. However, the developed scoring-matrix in combination with existing methods reports an increase in the performance, up to 86% with a scoring method that uses molecular dynamics.

Electrochemical Detection of Single-Nucleotide Polymorphism Associated with Rifampicin Resistance in Mycobacterium tuberculosis Using Solid-Phase Primer Elongation with Ferrocene-Linked Redox-Labeled Nucleotides.

Here, we report the electrochemical detection of single-point mutations using solid-phase isothermal primer elongation with redox-labeled oligonucleotides. A single-base mutation associated with resistance to rifampicin, an antibiotic commonly used for the treatment of Mycobacterium tuberculosis, was used as a model system to demonstrate a proof-of-concept of the approach. Four 5'-thiolated primers, designed to be complementary with the same fragment of the target sequence and differing only in the last base, addressing the polymorphic site, were self-assembled via chemisorption on individual gold electrodes of an array. Following hybridization with single-stranded DNA, Klenow (exo-) DNA polymerase-mediated primer extension with ferrocene-labeled 2'-deoxyribonucleoside triphosphates (dNFcTPs) was only observed to proceed at the electrode where there was full complementarity between the surface-tethered probe and the target DNA being interrogated. We tested all four ferrocenylethynyl-linked dNTPs and optimized the ratio of labeled/natural nucleotides to achieve maximum sensitivity. Following a 20 min hybridization step, Klenow (exo-) DNA polymerase-mediated primer elongation at 37 °C for 5 min was optimal for the enzymatic incorporation of a ferrocene-labeled nucleotide, achieving unequivocal electrochemical detection of a single-point mutation in 14 samples of genomic DNA extracted from Mycobacterium tuberculosis strains. The approach is rapid, cost-effective, facile, and can be extended to multiplexed electrochemical single-point mutation genotyping.

Impact of a Single Point Mutation on the Antimicrobial and Fibrillogenic Properties of Cryptides from Human Apolipoprotein B.

Host defense peptides (HDPs) are gaining increasing interest, since they are endowed with multiple activities, are often effective on multidrug resistant bacteria and do not generally lead to the development of resistance phenotypes. Cryptic HDPs have been recently identified in human apolipoprotein B and found to be endowed with a broad-spectrum antimicrobial activity, with anti-biofilm, wound healing and immunomodulatory properties, and with the ability to synergistically act in combination with conventional antibiotics, while being not toxic for eukaryotic cells. Here, a multidisciplinary approach was used, including time killing curves, differential scanning calorimetry, circular dichroism, ThT binding assays, and transmission electron microscopy analyses. The effects of a single point mutation (Pro → Ala in position 7) on the biological properties of ApoB-derived peptide r(P)ApoBLPro have been evaluated. Although the two versions of the peptide share similar antimicrobial and anti-biofilm properties, only r(P)ApoBLAla peptide was found to exert bactericidal effects. Interestingly, antimicrobial activity of both peptide versions appears to be dependent from their interaction with specific components of bacterial surfaces, such as LPS or LTA, which induce peptides to form ß-sheet-rich amyloid-like structures. Altogether, obtained data indicate a correlation between ApoB-derived peptides self-assembling state and their antibacterial activity.