AmyloGraph: a comprehensive database of amyloid-amyloid interactions.

Information about the impact of interactions between amyloid proteins on their fibrillization propensity is scattered among many experimental articles and presented in unstructured form. We manually curated information located in almost 200 publications (selected out of 562 initially considered), obtaining details of 883 experimentally studied interactions between 46 amyloid proteins or peptides. We also proposed a novel standardized terminology for the description of amyloid-amyloid interactions, which is included in our database, covering all currently known types of such a cross-talk, including inhibition of fibrillization, cross-seeding and other phenomena. The new approach allows for more specific studies on amyloids and their interactions, by providing very well-defined data. AmyloGraph, an online database presenting information on amyloid-amyloid interactions, is available at (http://AmyloGraph.com/). Its functionalities are also accessible as the R package (https://github.com/KotulskaLab/AmyloGraph). AmyloGraph is the only publicly available repository for experimentally determined amyloid-amyloid interactions.

Cytotoxic Staphylococcus aureus PSMα3 inhibits the aggregation of human insulin in vitro.

Phenol-soluble modulins (PSMs) are extracellular short amphipathic peptides secreted by the bacteria Staphylococcus aureus (S. aureus). They play an essential role in the bacterial lifecycle, biofilm formation, and stabilisation. From the PSM family, PSMα3 has been of special interest recently due to its cytotoxicity and highly stable α-helical conformation, which also remains in its amyloid fibrils. In particular, PSMα3 fibrils were shown to be composed of self-associating "sheets" of α-helices oriented perpendicular to the fibril axis, mimicking the architecture of canonical cross-ß fibrils. Therefore, they were called cross-α-fibrils. PSMα3 was synthesised and verified for identity with wild-type sequences (S. aureus). Then, using several experimental techniques, we evaluated its propensity for in vitro aggregation. According to our findings, synthetic PSMα3 (which lacks the N-terminal formyl groups found in bacteria) does not form amyloid fibrils and maintains α-helical conformation in a soluble monomeric form for several days of incubation. We also evaluated the influence of PSMα3 on human insulin fibrillation in vitro, using a variety of experimental approaches in combination with computational molecular studies. First, it was shown that PSMα3 drastically inhibits the fibrillation of human insulin. The anti-fibrillation effect of PSMα3 was concentration-dependent and required a concentration ratio of PSMα3: insulin equal to or above 1 : 100. Molecular modelling revealed that PSMα3 most likely inhibits the production of insulin primary nuclei by competing for residues involved in its dimerization.

The role of tandem repeats in bacterial functional amyloids.

Repetitivity and modularity of proteins are two related notions incorporated into multiple evolutionary concepts. We discuss whether they may also be essential for functional amyloids. Amyloids are proteins that create very regular and usually highly insoluble fibrils, which are often associated with neurodegeneration. However, recent discoveries showed that amyloid structure of a protein could also be beneficial and desired, e.g., to promote cell adhesion. Functional amyloids are proteins which differ in their characteristics from pathological amyloids, so that the fibril formation could be more under control of an organism. We propose that repeats in the sequence could regulate the aggregation propensity of these proteins. The inclusion of multiple symmetric interactions, due to the presence of the repeats, could be supporting and strengthening the desirable structural properties of functional amyloids. Our results show that tandem repeats in bacterial functional amyloids have a distinct characteristic. The pattern of repeats supports the appropriate level of fibril formation and better controllability of fibril stability. The repeats tend to be more imperfect, which attenuates excessive aggregation propensity. Their desired structure and function are also reinforced by their amino acid profile. Although in the study we focused on bacterial functional amyloids, due to their importance in biofilm formation, we propose that similar mechanisms could be employed in other functional amyloids which are designed by evolution to aggregate in a desirable manner, but not necessarily in pathological amyloids.

Topological analysis as a tool for detection of abnormalities in protein-protein interaction data.

MOTIVATION: Protein-protein interaction datasets, which can be modeled as networks, constitute an essential layer in multi-omics approach to biomedical knowledge. This representation gives insight into molecular pathways, help to uncover novel potential drug targets or predict a therapy outcome. Nevertheless, the data that constitute such systems are frequently incomplete, error-prone and biased by scientific trends. Implementation of methods for detection of such shortcomings could improve protein-protein interaction data analysis. RESULTS: We performed topological analysis of three protein-protein interaction networks (PPINs) from IntAct Molecular Database, regarding cancer, Parkinson's disease (two most common subjects in PPINs analysis) and Human Reference Interactome. The data collections were shown to be often biased by scientific interests, which highly impact the networks structure. This may obscure correct systematic biological interpretation of the protein-protein interactions and limit their application potential. As a solution to this problem, we propose a set of topological methods for the bias detection, which performed in the first step provides more objective biological conclusions regarding protein-protein interactions and their multi-omics consequences. AVAILABILITY AND IMPLEMENTATION: A user-friendly tool Extensive Tool for Network Analysis (ETNA) is available on https://github.com/AlicjaNowakowska/ETNA. The software includes a graphical Colab notebook: https://githubtocolab.com/AlicjaNowakowska/ETNA/blob/main/ETNAColab.ipynb. CONTACT: alicja.nowakowska@pwr.edu.pl or malgorzata.kotulska@pwr.edu.pl. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Variability of Amyloid Propensity in Imperfect Repeats of CsgA Protein of Salmonella enterica and Escherichia coli.

CsgA is an aggregating protein from bacterial biofilms, representing a class of functional amyloids. Its amyloid propensity is defined by five fragments (R1-R5) of the sequence, representing non-perfect repeats. Gate-keeper amino acid residues, specific to each fragment, define the fragment's propensity for self-aggregation and aggregating characteristics of the whole protein. We study the self-aggregation and secondary structures of the repeat fragments of Salmonella enterica and Escherichia coli and comparatively analyze their potential effects on these proteins in a bacterial biofilm. Using bioinformatics predictors, ATR-FTIR and FT-Raman spectroscopy techniques, circular dichroism, and transmission electron microscopy, we confirmed self-aggregation of R1, R3, R5 fragments, as previously reported for Escherichia coli, however, with different temporal characteristics for each species. We also observed aggregation propensities of R4 fragment of Salmonella enterica that is different than that of Escherichia coli. Our studies showed that amyloid structures of CsgA repeats are more easily formed and more durable in Salmonella enterica than those in Escherichia coli.

Role of Bioinformatics in the Study of Ionic Channels.

Ionic channels belong to the group of the most important proteins. Not only do they enable transmembrane transport but they are also the key factors for proper cell function. Mutations changing their structure and functionality often lead to severe diseases called channelopathies. On the other hand, transmembrane channels are very difficult objects for experimental studies. Only 2% of experimentally identified structures are transmembrane proteins, while genomic studies show that transmembrane proteins make up 30% of all coded proteins. This gap could be diminished by bioinformatical methods which enable modeling unknown protein structures, functions, transmembrane location, and ligand binding. Several in silico methods dedicated to transmembrane proteins have been developed; some general methods could also be used. They provide the information unavailable from experiments. Current modeling tools use a variety of computational methods, which provide results of surprisingly high quality.

Fast assessment of structural models of ion channels based on their predicted current-voltage characteristics.

Computational prediction of protein structures is a difficult task, which involves fast and accurate evaluation of candidate model structures. We propose to enhance single-model quality assessment with a functionality evaluation phase for proteins whose quantitative functional characteristics are known. In particular, this idea can be applied to evaluation of structural models of ion channels, whose main function - conducting ions - can be quantitatively measured with the patch-clamp technique providing the current-voltage characteristics. The study was performed on a set of KcsA channel models obtained from complete and incomplete contact maps. A fast continuous electrodiffusion model was used for calculating the current-voltage characteristics of structural models. We found that the computed charge selectivity and total current were sensitive to structural and electrostatic quality of models. In practical terms, we show that evaluating predicted conductance values is an appropriate method to eliminate models with an occluded pore or with multiple erroneously created pores. Moreover, filtering models on the basis of their predicted charge selectivity results in a substantial enrichment of the candidate set in highly accurate models. Tests on three other ion channels indicate that, in addition to being a proof of the concept, our function-oriented single-model quality assessment method can be directly applied to evaluation of structural models of some classes of protein channels. Finally, our work raises an important question whether a computational validation of functionality should be included in the evaluation process of structural models, whenever possible.

The Effect of Millisecond Pulsed Electric Fields (msPEF) on Intracellular Drug Transport with Negatively Charged Large Nanocarriers Made of Solid Lipid Nanoparticles (SLN): In Vitro Study.

Drug delivery technology is still a dynamically developing field of medicine. The main direction in nanotechnology research (nanocarriers, nanovehicles, etc.) is efficient drug delivery to target cells with simultaneous drug reduction concentration. However, nanotechnology trends in reducing the carrier sizes to several nanometers limit the volume of the loaded substance and may pose a danger of uncontrolled access into the cells. On the other hand, nanoparticles larger than 200 nm in diameter have difficulties to undergo rapid diffusional transport through cell membranes. The main advantage of large nanoparticles is higher drug encapsulation efficiency and the ability to deliver a wider array of drugs. Our present study contributes a new approach with large Tween 80 solid lipid nanoparticles SLN (i.e., hydrodynamic GM-SLN-glycerol monostearate, GM, as the lipid and ATO5-SLNs-glyceryl palmitostearate, ATO5, as the lipid) with diameters DH of 379.4 nm and 547 nm, respectively. They are used as drug carriers alone and in combination with electroporation (EP) induced by millisecond pulsed electric fields. We evaluate if EP can support the transport of large nanocarriers into cells. The study was performed with two cell lines: human colon adenocarcinoma LoVo and hamster ovarian fibroblastoid CHO-K1 with coumarin 6 (C6) as a fluorescent marker for encapsulation. The biological safety of the potential treatment procedure was evaluated with cell viability after their exposure to nanoparticles and EP. The EP efficacy was evaluated by FACS method. The impact on intracellular structure organization of cytoskeleton was visualized by CLSM method with alpha-actin and beta-tubulin. The obtained results indicate low cytotoxicity of both carrier types, free and loaded with C6. The evaluation of cytoskeleton proteins indicated no intracellular structure damage. The intracellular uptake and accumulation show that SLNs do not support transport of C6 coumarin. Only application of electroporation improved the transport of encapsulated and free C6 into both treated cell lines.

AmyLoad: website dedicated to amyloidogenic protein fragments.

UNLABELLED: Analyses of amyloidogenic sequence fragments are essential in studies of neurodegenerative diseases. However, there is no one internet dataset that collects all the sequences that have been investigated for their amyloidogenicity. Therefore, we have created the AmyLoad website which collects the amyloidogenic sequences from all major sources. The website allows for filtration of the fragments and provides detailed information about each of them. Registered users can both personalize their work with the website and submit their own sequences into the database. To maintain database reliability, submitted sequences are reviewed before making them available to the public. Finally, we re-implemented several amyloidogenic sequence predictors, thus the AmyLoad website can be used as a sequence analysis tool. We encourage researchers working on amyloid proteins to contribute to our service. AVAILABILITY AND IMPLEMENTATION: The AmyLoad website is freely available at http://comprec-lin.iiar.pwr.edu.pl/amyload/. CONTACT: malgorzata.kotulska@pwr.edu.pl.

FISH Amyloid - a new method for finding amyloidogenic segments in proteins based on site specific co-occurrence of aminoacids.

BACKGROUND: Amyloids are proteins capable of forming fibrils whose intramolecular contact sites assume densely packed zipper pattern. Their oligomers can underlie serious diseases, e.g. Alzheimer's and Parkinson's diseases. Recent studies show that short segments of aminoacids can be responsible for amyloidogenic properties of a protein. A few hundreds of such peptides have been experimentally found but experimental testing of all candidates is currently not feasible. Here we propose an original machine learning method for classification of aminoacid sequences, based on discovering a segment with a discriminative pattern of site-specific co-occurrences between sequence elements. The pattern is based on the positions of residues with correlated occurrence over a sliding window of a specified length. The algorithm first recognizes the most relevant training segment in each positive training instance. Then the classification is based on maximal distances between co-occurrence matrix of the relevant segments in positive training sequences and the matrix from negative training segments. The method was applied for studying sequences of aminoacids with regard to their amyloidogenic properties. RESULTS: Our method was first trained on available datasets of hexapeptides with the amyloidogenic classification, using 5 or 6-residue sliding windows. Depending on the choice of training and testing datasets, the area under ROC curve obtained the value up to 0.80 for experimental, and 0.95 for computationally generated (with 3D profile method) datasets. Importantly, the results on 5-residue segments were not significantly worse, although the classification required that algorithm first recognized the most relevant training segments. The dataset of long sequences, such as sup35 prion and a few other amyloid proteins, were applied to test the method and gave encouraging results. Our web tool FISH Amyloid was trained on all available experimental data 4-10 residues long, offers prediction of amyloidogenic segments in protein sequences. CONCLUSIONS: We proposed a new original classification method which recognizes co-occurrence patterns in sequences. The method reveals characteristic classification pattern of the data and finds the segments where its scoring is the strongest, also in long training sequences. Applied to the problem of amyloidogenic segments recognition, it showed a good potential for classification problems in bioinformatics.

Automated procedure for contact-map-based protein structure reconstruction.

Knowledge of the three-dimensional structures of ion channels allows for modeling their conductivity characteristics using biophysical models and can lead to discovering their cellular functionality. Recent studies show that quality of structure predictions can be significantly improved using protein contact site information. Therefore, a number of procedures for protein structure prediction based on their contact-map have been proposed. Their comparison is difficult due to different methodologies used for validation. In this work, a Contact Map-to-Structure pipeline (C2S_pipeline) for contact-based protein structure reconstruction is designed and validated. The C2S_pipeline can be used to reconstruct monomeric and multimeric proteins. The median RMSD of structures obtained during validation on a representative set of protein structures, equaled 5.27 Å, and the best structure was reconstructed with RMSD of 1.59 Å. The validation is followed by a detailed case study on the KcsA ion channel. Models of KcsA are reconstructed based on different portions of contact site information. Structural feature analysis of acquired KcsA models is supported by a thorough analysis of electrostatic potential distributions inside the channels. The study shows that electrostatic parameters are correlated with structural quality of models. Therefore, they can be used to discriminate between high and low quality structures. We show that 30 % of contact information is needed to obtain accurate structures of KcsA, if contacts are selected randomly. This number increases to 70 % in case of erroneous maps in which the remaining contacts or non-contacts are changed to the opposite. Furthermore, the study reveals that local reconstruction accuracy is correlated with the number of contacts in which amino acid are involved. This results in higher reconstruction accuracy in the structure core than peripheral regions.

Structural effects of charge destabilization and amino acid substitutions in amyloid fragments of CsgA.

The most studied functional amyloid is the CsgA, major curli subunit protein, which is produced by numerous strains of Enterobacteriaceae. Although CsgA sequences are highly conserved, they exhibit species diversity, which reflects the specific evolutionary and functional adaptability of the major curli subunit. Herein, we performed bioinformatics analyses to uncover the differences in the amyloidogenic properties of the R4 fragments in Escherichia coli and Salmonella enterica and proposed four mutants for more detailed studies: M1, M2, M3, and M4. The mutated sequences were characterized by various experimental techniques, such as circular dichroism, ATR-FTIR, FT-Raman, thioflavin T, transmission electron microscopy and confocal microscopy. Additionally, molecular dynamics simulations were performed to determine the role of buffer ions in the aggregation process. Our results demonstrated that the aggregation kinetics, fibril morphology, and overall structure of the peptide were significantly affected by the positions of charged amino acids within the repeat sequences of CsgA. Notably, substituting glycine with lysine resulted in the formation of distinctive spherically packed globular aggregates. The differences in morphology observed are attributed to the influence of phosphate ions, which disrupt the local electrostatic interaction network of the polypeptide chains. This study provides knowledge on the preferential formation of amyloid fibrils based on charge states within the polypeptide chain.

On the amyloid datasets used for training PAFIG--how (not) to extend the experimental dataset of hexapeptides.

BACKGROUND: Amyloids are proteins capable of forming aberrant intramolecular contact sites, characteristic of beta zipper configuration. Amyloids can underlie serious health conditions, e.g. Alzheimer's or Parkinson's diseases. It has been proposed that short segments of amino acids can be responsible for protein amyloidogenicity, but no more than two hundred such hexapeptides have been experimentally found. The authors of the computational tool Pafig published in BMC Bioinformatics a method for extending the amyloid hexapeptide dataset that could be used for training and testing models. They assumed that all hexapeptides belonging to an amyloid protein can be regarded as amylopositive, while those from proteins never reported as amyloid are always amylonegative. Here we show why the above described method of extending datasets is wrong and discuss the reasons why the incorrect data could lead to falsely correct classification. RESULTS: The amyloid classification of hexapeptides by Pafig was confronted with the classification results from different state of the art computational methods and the outputs of all methods were studied by clustering analysis. The clustering methods show that Pafig is an outlier with regard to other approaches. Our study of the statistical patterns of its training and testing datasets showed a strong bias towards STVIIE hexapeptide in their positive part. Different statistical patterns of seemingly amylo-positive and -negative hexapeptides allow for a repeatable classification, which is not related to amyloid propensity of the hexapetides. CONCLUSIONS: Our study on recognition of amyloid hexapeptides showed that occurrence of incidental patterns in wrongly selected datasets can produce falsely correct results of classification. The assumption that all hexapeptides belonging to amyloid protein can be regarded as amylopositive and those from proteins never reported as amyloid are always amylonegative is not supported by any other computational method. This is in line with experimental observations that amyloid propensity of a full protein can result from only one amyloidogenic fragment in this protein, while the occurrence of amyliodogenic part that is well hidden inside the protein may never lead to fibril formation. This leads to the conclusion that Pafig does not provide correct classification with regard to amyloidogenicity.

Machine learning methods can replace 3D profile method in classification of amyloidogenic hexapeptides.

BACKGROUND: Amyloids are proteins capable of forming fibrils. Many of them underlie serious diseases, like Alzheimer disease. The number of amyloid-associated diseases is constantly increasing. Recent studies indicate that amyloidogenic properties can be associated with short segments of aminoacids, which transform the structure when exposed. A few hundreds of such peptides have been experimentally found. Experimental testing of all possible aminoacid combinations is currently not feasible. Instead, they can be predicted by computational methods. 3D profile is a physicochemical-based method that has generated the most numerous dataset - ZipperDB. However, it is computationally very demanding. Here, we show that dataset generation can be accelerated. Two methods to increase the classification efficiency of amyloidogenic candidates are presented and tested: simplified 3D profile generation and machine learning methods. RESULTS: We generated a new dataset of hexapeptides, using more economical 3D profile algorithm, which showed very good classification overlap with ZipperDB (93.5%). The new part of our dataset contains 1779 segments, with 204 classified as amyloidogenic. The dataset of 6-residue sequences with their binary classification, based on the energy of the segment, was applied for training machine learning methods. A separate set of sequences from ZipperDB was used as a test set. The most effective methods were Alternating Decision Tree and Multilayer Perceptron. Both methods obtained area under ROC curve of 0.96, accuracy 91%, true positive rate ca. 78%, and true negative rate 95%. A few other machine learning methods also achieved a good performance. The computational time was reduced from 18-20 CPU-hours (full 3D profile) to 0.5 CPU-hours (simplified 3D profile) to seconds (machine learning). CONCLUSIONS: We showed that the simplified profile generation method does not introduce an error with regard to the original method, while increasing the computational efficiency. Our new dataset proved representative enough to use simple statistical methods for testing the amylogenicity based only on six letter sequences. Statistical machine learning methods such as Alternating Decision Tree and Multilayer Perceptron can replace the energy based classifier, with advantage of very significantly reduced computational time and simplicity to perform the analysis. Additionally, a decision tree provides a set of very easily interpretable rules.

Optimization of 3D Poisson-Nernst-Planck model for fast evaluation of diverse protein channels.

We show the accuracy and applicability of our fast algorithmic implementation of a three-dimensional Poisson-Nernst-Planck (3D-PNP) flow model for characterizing different protein channels. Due to its high computational efficiency, our model can predict the full current-voltage characteristics of a channel within minutes, based on the experimental 3D structure of the channel or its computational model structure. Compared with other methods, such as Brownian dynamics, which currently needs a few weeks of the computational time, or even much more demanding molecular dynamics modeling, 3D-PNP is the only available method for a function-based evaluation of very numerous tentative structural channel models. Flow model tests of our algorithm and its optimal parametrization are provided for five native channels whose experimental structures are available in the protein data bank (PDB) in an open conductive state, and whose experimental current-voltage characteristics have been published. The channels represent very different geometric and structural properties, which makes it the widest test to date of the accuracy of 3D-PNP on real channels. We test whether the channel conductance, rectification, and charge selectivity obtained from the flow model, could be sufficiently sensitive to single-point mutations, related to unsignificant changes in the channel structure. Our results show that the classical 3D-PNP model, under proper parametrization, is able to achieve a qualitative agreement with experimental data for a majority of the tested characteristics and channels, including channels with narrow and irregular conductivity pores. We propose that although the standard PNP model cannot provide insight into complex physical phenomena due to its intrinsic limitations, its semiquantitative agreement is achievable for rectification and selectivity at a level sufficient for the bioinformatical purpose of selecting the best structural models with a great advantage of a very short computational time.

Electric field-assisted delivery of photofrin to human breast carcinoma cells.

The influence of electroporation on the Photofrin uptake and distribution was evaluated in the breast adenocarcinoma cells (MCF-7) and normal Chinese hamster ovary cells (CHO) lacking voltage-dependent channels in vitro. Photofrin was used at a concentration of 5 and 25 µM. The uptake of Photofrin was assessed using flow cytometry and fluorescence microscopy methods. Cells viability was evaluated with crystal violet assay. Our results indicated that electropermeabilization of cells, in the presence of Photofrin, increased the uptake of the photosensitizer. Even at the lowest electric field intensity (700 V/cm) Photofrin transport was enhanced. Flow cytometry results for MCF-7 cells revealed ~1.7 times stronger fluorescence emission intensity for cells exposed to Photofrin and electric field of 700 V/cm than cells treated with Photofrin alone. Photofrin was effective only when irradiated with blue light. Our studies on combination of photodynamic reaction with electroporation suggested improved effectiveness of the treatment and showed intracellular distribution of Photofrin. This approach may be attractive for cancer treatment as enhanced cellular uptake of Photofrin in MCF-7 cells can help to reduce effective dose of the photosensitizer and exposure time in this type of cancer, diminishing side effects of the therapy.

PACT - Prediction of amyloid cross-interaction by threading.

Amyloid proteins are often associated with the onset of diseases, including Alzheimer's, Parkinson's and many others. However, there is a wide class of functional amyloids that are involved in physiological functions, e.g., formation of microbial biofilms or storage of hormones. Recent studies showed that an amyloid fibril could affect the aggregation of another protein, even from a different species. This may result in amplification or attenuation of the aggregation process. Insight into amyloid cross-interactions may be crucial for better understanding of amyloid diseases and the potential influence of microbial amyloids on human proteins. However, due to the demanding nature of the needed experiments, knowledge of such interactions is still limited. Here, we present PACT (Prediction of Amyloid Cross-interaction by Threading) - the computational method for the prediction of amyloid cross-interactions. The method is based on modeling of a heterogeneous fibril formed by two amyloidogenic peptides. The resulting structure is assessed by the structural statistical potential that approximates its plausibility and energetic stability. PACT was developed and first evaluated mostly on data collected in the AmyloGraph database of interacting amyloids and achieved high values of Area Under ROC (AUC=0.88) and F1 (0.82). Then, we applied our method to study the interactions of CsgA - a bacterial biofilm protein that was not used in our in-reference datasets, which is expressed in several bacterial species that inhabit the human intestines - with two human proteins. The study included alpha-synuclein, a human protein that is involved in Parkinson's disease, and human islet amyloid polypeptide (hIAPP), which is involved in type 2 diabetes. In both cases, PACT predicted the appearance of cross-interactions. Importantly, the method indicated specific regions of the proteins, which were shown to play a central role in both interactions. We experimentally confirmed the novel results of the indicated CsgA fragments interacting with hIAPP based on the kinetic characteristics obtained with the ThT assay. PACT opens the possibility of high-throughput studies of amyloid interactions. Importantly, it can work with fairly long protein fragments, and as a purely physicochemical approach, it relies very little on scarce training data. The tool is available as a web server at https://pact.e-science.pl/pact/ . The local version can be downloaded from https://github.com/KubaWojciechowski/PACT .

Quality assessment of protein model-structures based on structural and functional similarities.

BACKGROUND: Experimental determination of protein 3D structures is expensive, time consuming and sometimes impossible. A gap between number of protein structures deposited in the World Wide Protein Data Bank and the number of sequenced proteins constantly broadens. Computational modeling is deemed to be one of the ways to deal with the problem. Although protein 3D structure prediction is a difficult task, many tools are available. These tools can model it from a sequence or partial structural information, e.g. contact maps. Consequently, biologists have the ability to generate automatically a putative 3D structure model of any protein. However, the main issue becomes evaluation of the model quality, which is one of the most important challenges of structural biology. RESULTS: GOBA--Gene Ontology-Based Assessment is a novel Protein Model Quality Assessment Program. It estimates the compatibility between a model-structure and its expected function. GOBA is based on the assumption that a high quality model is expected to be structurally similar to proteins functionally similar to the prediction target. Whereas DALI is used to measure structure similarity, protein functional similarity is quantified using standardized and hierarchical description of proteins provided by Gene Ontology combined with Wang's algorithm for calculating semantic similarity. Two approaches are proposed to express the quality of protein model-structures. One is a single model quality assessment method, the other is its modification, which provides a relative measure of model quality. Exhaustive evaluation is performed on data sets of model-structures submitted to the CASP8 and CASP9 contests. CONCLUSIONS: The validation shows that the method is able to discriminate between good and bad model-structures. The best of tested GOBA scores achieved 0.74 and 0.8 as a mean Pearson correlation to the observed quality of models in our CASP8 and CASP9-based validation sets. GOBA also obtained the best result for two targets of CASP8, and one of CASP9, compared to the contest participants. Consequently, GOBA offers a novel single model quality assessment program that addresses the practical needs of biologists. In conjunction with other Model Quality Assessment Programs (MQAPs), it would prove useful for the evaluation of single protein models.

Molecular-level characterization of lipid membrane electroporation using linearly rising current.

We present experimental and theoretical results of electroporation of small patches of planar lipid bilayers by means of linearly rising current. The experiments were conducted on ~120-µm-diameter patches of planar phospholipid bilayers. The steadily increasing voltage across the bilayer imposed by linearly increasing current led to electroporation of the membrane for voltages above a few hundred millivolts. This method shows new molecular mechanisms of electroporation. We recorded small voltage drops preceding the breakdown of the bilayer due to irreversible electroporation. These voltage drops were often followed by a voltage re-rise within a fraction of a second. Modeling the observed phenomenon by equivalent electric circuits showed that these events relate to opening and closing of conducting pores through the bilayer. Molecular dynamics simulations performed under similar conditions indicate that each event is likely to correspond to the opening and closing of a single pore of about 5 nm in diameter, the conductance of which ranges in the 100-nS scale. This combined experimental and theoretical investigation provides a better quantitative characterization of the size, conductance and lifetime of pores created during lipid bilayer electroporation. Such a molecular insight should enable better control and tuning of electroporation parameters for a wide range of biomedical and biotechnological applications.

Electroporation-induced changes in normal immature rat myoblasts (H9C2).

Application of a high electric field causes an electric shock to the heart. This is utilized in defibrillation to reestablish normal contraction rhythms during dangerous arrhythmias or in cardiac arrest. If shock-induced transmembrane potentials are large enough, they can cause tissue destruction due to irreversible electroporation (EP). Also electrochemotherapy of nearby tissues may have an adverse effect on the heart. Herein, we present experimental data on effects of electroporation in culture of cardiac cells (H9C2). The electric field was applied in short pulses of 25-3250 V/cm, 50 µs each. The viability of cells was tested by MTT assay after 24 hours. For detection of DNA fragmentation, associated with apoptosis, alkaline and neutral comet assays were performed after EP. Additionally phase contrast images of cells obtained directly after EP were analyzed. Although cell images indicated disruption of cell membranes after EP with high intensities, only a few percent of apoptotic cells and no necrotic effects in the cell nucleus could be observed in comet assay tests performed 2 hours post EP. MTT viability test showed that pulse intensities above 375 V/cm are destructive for myocytes viability.