Reconsideration of Bertillonage in the age of digitalisation: Digital anthropometric patterns as a promising method for establishing identity.

The idea of using measurements of the human body for identity matching is deeply associated with Bertillonage, a historic biometric system that was briefly applied until it was superseded by fingerprinting in the early 20th century. The apparent failure then commonly causes doubt with regard to the suitability of a set of measurements as a biometric identifier in the present. Hence, the aim of this paper is to explore the potentials of using an anthropometric pattern, comprising of a set of body measurements, for identity matching. For this purpose, it will begin with a thorough examination of Bertillon's system and move on to conduct a comprehensive inquiry of the current possibilities of using digital anthropometric patterns in image or video-based evidence.

A multiscale model of the regulation of aquaporin 2 recycling.

The response of cells to their environment is driven by a variety of proteins and messenger molecules. In eukaryotes, their distribution and location in the cell are regulated by the vesicular transport system. The transport of aquaporin 2 between membrane and storage region is a crucial part of the water reabsorption in renal principal cells, and its malfunction can lead to Diabetes insipidus. To understand the regulation of this system, we aggregated pathways and mechanisms from literature and derived three models in a hypothesis-driven approach. Furthermore, we combined the models to a single system to gain insight into key regulatory mechanisms of Aquaporin 2 recycling. To achieve this, we developed a multiscale computational framework for the modeling and simulation of cellular systems. The analysis of the system rationalizes that the compartmentalization of cAMP in renal principal cells is a result of the protein kinase A signalosome and can only occur if specific cellular components are observed in conjunction. Endocytotic and exocytotic processes are inherently connected and can be regulated by the same protein kinase A signal.

Analysis of the influence of EDTA-treated reference samples on forensic bloodstain age estimation.

The age estimation of blood traces provides important leads for the chronological assessment of criminal events and their reconstruction. To determine bloodstain age, experimental comparative data from a laboratory environment are used. Under these conditions the utilization of anticoagulants such as EDTA helps to suppress the blood clotting mechanism to allow the examination over a longer time period. This unnatural prevention of blood coagulation is highly questionable when estimating bloodstain age, since the blood's physical and chemical properties are altered. For this reason, the authors determined actual influence of EDTA on blood spectra over time in order to formulate a statement as to whether this effect can be measured. Human and porcine blood samples were aged under controlled conditions. The resulting UV/VIS spectra were separated into their individual components using signal separation techniques, allowing the changes in the ratios of the individual hemoglobin derivatives to be observed over time. The results show a significant influence of EDTA on the conversion of oxyhemoglobin to methemoglobin and a minor influence on the conversion of methemoglobin to hemichrome within the relevant time range of 5-100 h. The use of EDTA thus slows down the aging process of blood spots. To illustrate the great influence of EDTA, spectra of untreated pig blood samples were included as comparison data. These show that the difference between EDTA-treated and untreated blood samples is as great as the difference between human blood and pig blood. As a consequence of our findings experimental comparative data for the age estimation of bloodstains should never result from EDTA-treated blood.

The structural basis of the genetic code: amino acid recognition by aminoacyl-tRNA synthetases.

Storage and directed transfer of information is the key requirement for the development of life. Yet any information stored on our genes is useless without its correct interpretation. The genetic code defines the rule set to decode this information. Aminoacyl-tRNA synthetases are at the heart of this process. We extensively characterize how these enzymes distinguish all natural amino acids based on the computational analysis of crystallographic structure data. The results of this meta-analysis show that the correct read-out of genetic information is a delicate interplay between the composition of the binding site, non-covalent interactions, error correction mechanisms, and steric effects.

StructureDistiller: Structural relevance scoring identifies the most informative entries of a contact map.

Protein folding and structure prediction are two sides of the same coin. Contact maps and the related techniques of constraint-based structure reconstruction can be considered as unifying aspects of both processes. We present the Structural Relevance (SR) score which quantifies the information content of individual contacts and residues in the context of the whole native structure. The physical process of protein folding is commonly characterized with spatial and temporal resolution: some residues are Early Folding while others are Highly Stable with respect to unfolding events. We employ the proposed SR score to demonstrate that folding initiation and structure stabilization are subprocesses realized by distinct sets of residues. The example of cytochrome c is used to demonstrate how StructureDistiller identifies the most important contacts needed for correct protein folding. This shows that entries of a contact map are not equally relevant for structural integrity. The proposed StructureDistiller algorithm identifies contacts with the highest information content; these entries convey unique constraints not captured by other contacts. Identification of the most informative contacts effectively doubles resilience toward contacts which are not observed in the native contact map. Furthermore, this knowledge increases reconstruction fidelity on sparse contact maps significantly by 0.4 Å.

Structural change in GadD2 of Listeria monocytogenes field isolates supports nisin resistance.

The lantibiotic nisin is used as a food additive to effectively inactivate a broad spectrum of Gram-positive bacteria such as Listeria monocytogenes. In total, 282 L. monocytogenes field isolates from German ready-to-eat food products, food-processing environments and patient samples and 39 Listeria reference strains were evaluated for their susceptibility to nisin. The MIC90 value was <1500 IU ml-1. Whole genome sequences (WGS) of four nisin susceptible (NS; growth <200 IU ml-1) and two nisin resistant L. monocytogenes field isolates (NR; growth >1500 IU ml-1) of serotype IIa were analyzed for DNA sequence variants (DSVs) in genes putatively associated with NR and its regulation. WGS of NR differed from NS in the gadD2 gene encoding for the glutamate decarboxylase system (GAD). Moreover, homology modeling predicted a protein structure of GadD2 in NR that promoted a less pH dependent GAD activity and may therefore be beneficial for nisin resistance. Likewise NR had a significant faster growth rate compared to NS in presence of nisin at pH 7. In conclusion, results contributed to ongoing debate that a genetic shift in GAD supports NR state.

Application of an interpretable classification model on Early Folding Residues during protein folding.

BACKGROUND: Machine learning strategies are prominent tools for data analysis. Especially in life sciences, they have become increasingly important to handle the growing datasets collected by the scientific community. Meanwhile, algorithms improve in performance, but also gain complexity, and tend to neglect interpretability and comprehensiveness of the resulting models. RESULTS: Generalized Matrix Learning Vector Quantization (GMLVQ) is a supervised, prototype-based machine learning method and provides comprehensive visualization capabilities not present in other classifiers which allow for a fine-grained interpretation of the data. In contrast to commonly used machine learning strategies, GMLVQ is well-suited for imbalanced classification problems which are frequent in life sciences. We present a Weka plug-in implementing GMLVQ. The feasibility of GMLVQ is demonstrated on a dataset of Early Folding Residues (EFR) that have been shown to initiate and guide the protein folding process. Using 27 features, an area under the receiver operating characteristic of 76.6% was achieved which is comparable to other state-of-the-art classifiers. The obtained model is accessible at https://biosciences.hs-mittweida.de/efpred/. CONCLUSIONS: The application on EFR prediction demonstrates how an easy interpretation of classification models can promote the comprehension of biological mechanisms. The results shed light on the special features of EFR which were reported as most influential for the classification: EFR are embedded in ordered secondary structure elements and they participate in networks of hydrophobic residues. Visualization capabilities of GMLVQ are presented as we demonstrate how to interpret the results.

Unsupervised Discovery of Geometrically Common Structural Motifs and Long-Range Contacts in Protein 3D Structures.

The essential role of small evolutionarily conserved structural units in proteins has been extensively researched and validated. A popular example are serine proteases, where the peptide cleavage reaction is realized by a configuration of only three residues. Brought to spatial proximity during the protein folding process, such structural motifs are often long-range contacts and usually hard to detect at sequence level. Due to the constantly increasing resource of protein 3D structure data, the computational identification of structural motifs can contribute significantly to the understanding of protein fold and function. Thus, we propose a method to discover structural motifs of high geometrical similarity and desired sequence separation in protein 3D structure data. By utilizing methods originated from data mining, no a priori knowledge is required. The applicability of the method is demonstrated by the identification of the catalytic unit of serine proteases and the ion-coordination center of cupredoxins. Furthermore, large-scale analysis of the entire Protein Data Bank points towards the presence of ubiquitous structural motifs, independent of any specific fold or function. We envision that our method is suitable to uncover functional mechanisms and to derive fingerprint libraries of structural motifs, which could be used to assess protein family association.

Characterizing the relation of functional and Early Folding Residues in protein structures using the example of aminoacyl-tRNA synthetases.

Proteins are chains of amino acids which adopt a three-dimensional structure and are then able to catalyze chemical reactions or propagate signals in organisms. Without external influence, many proteins fold into their native structure, and a small number of Early Folding Residues (EFR) have previously been shown to initiate the formation of secondary structure elements and guide their respective assembly. Using the two diverse superfamilies of aminoacyl-tRNA synthetases (aaRS), it is shown that the position of EFR is preserved over the course of evolution even when the corresponding sequence conservation is small. Folding initiation sites are positioned in the center of secondary structure elements, independent of aaRS class. In class I, the predicted position of EFR resembles an ancient structural packing motif present in many seemingly unrelated proteins. Furthermore, it is shown that EFR and functionally relevant residues in aaRS are almost entirely disjoint sets of residues. The Start2Fold database is used to investigate whether this separation of EFR and functional residues can be observed for other proteins. EFR are found to constitute crucial connectors of protein regions which are distant at sequence level. Especially, these residues exhibit a high number of non-covalent residue-residue contacts such as hydrogen bonds and hydrophobic interactions. This tendency also manifests as energetically stable local regions, as substantiated by a knowledge-based potential. Despite profound differences regarding how EFR and functional residues are embedded in protein structures, a strict separation of structurally and functionally relevant residues cannot be observed for a more general collection of proteins.

Detailed Analysis of 17ß-Estradiol-Aptamer Interactions: A Molecular Dynamics Simulation Study.

Micro-pollutants such as 17ß-Estradiol (E2) have been detected in different water resources and their negative effects on the environment and organisms have been observed. Aptamers are established as a possible detection tool, but the underlying ligand binding is largely unexplored. In this study, a previously described 35-mer E2-specific aptamer was used to analyse the binding characteristics between E2 and the aptamer with a MD simulation in an aqueous medium. Because there is no 3D structure information available for this aptamer, it was modeled using coarse-grained modeling method. The E2 ligand was positioned inside a potential binding area of the predicted aptamer structure, the complex was used for an 25 ns MD simulation, and the interactions were examined for each time step. We identified E2-specific bases within the interior loop of the aptamer and also demonstrated the influence of frequently underestimated water-mediated hydrogen bonds. The study contributes to the understanding of the behavior of ligands binding with aptamer structure in an aqueous solution. The developed workflow allows generating and examining further appealing ligand-aptamer complexes.

Backbone Brackets and Arginine Tweezers delineate Class I and Class II aminoacyl tRNA synthetases.

The origin of the machinery that realizes protein biosynthesis in all organisms is still unclear. One key component of this machinery are aminoacyl tRNA synthetases (aaRS), which ligate tRNAs to amino acids while consuming ATP. Sequence analyses revealed that these enzymes can be divided into two complementary classes. Both classes differ significantly on a sequence and structural level, feature different reaction mechanisms, and occur in diverse oligomerization states. The one unifying aspect of both classes is their function of binding ATP. We identified Backbone Brackets and Arginine Tweezers as most compact ATP binding motifs characteristic for each Class. Geometric analysis shows a structural rearrangement of the Backbone Brackets upon ATP binding, indicating a general mechanism of all Class I structures. Regarding the origin of aaRS, the Rodin-Ohno hypothesis states that the peculiar nature of the two aaRS classes is the result of their primordial forms, called Protozymes, being encoded on opposite strands of the same gene. Backbone Brackets and Arginine Tweezers were traced back to the proposed Protozymes and their more efficient successors, the Urzymes. Both structural motifs can be observed as pairs of residues in contemporary structures and it seems that the time of their addition, indicated by their placement in the ancient aaRS, coincides with the evolutionary trace of Proto- and Urzymes.

Pyridine Nucleotide Coenzyme Specificity of p-Hydroxybenzoate Hydroxylase and Related Flavoprotein Monooxygenases.

p-Hydroxybenzoate hydroxylase (PHBH; EC 1.14.13.2) is a microbial group A flavoprotein monooxygenase that catalyzes the ortho-hydroxylation of 4-hydroxybenzoate to 3,4-dihydroxybenzoate with the stoichiometric consumption of NAD(P)H and oxygen. PHBH and related enzymes lack a canonical NAD(P)H-binding domain and the way they interact with the pyridine nucleotide coenzyme has remained a conundrum. Previously, we identified a surface exposed protein segment of PHBH from Pseudomonas fluorescens involved in NADPH binding. Here, we report the first amino acid sequences of NADH-preferring PHBHs and a phylogenetic analysis of putative PHBHs identified in currently available bacterial genomes. It was found that PHBHs group into three clades consisting of NADPH-specific, NAD(P)H-dependent and NADH-preferring enzymes. The latter proteins frequently occur in Actinobacteria. To validate the results, we produced several putative PHBHs in Escherichia coli and confirmed their predicted coenzyme preferences. Based on phylogeny, protein energy profiling and lifestyle of PHBH harboring bacteria we propose that the pyridine nucleotide coenzyme specificity of PHBH emerged through adaptive evolution and that the NADH-preferring enzymes are the older versions of PHBH. Structural comparison and distance tree analysis of group A flavoprotein monooxygenases indicated that a similar protein segment as being responsible for the pyridine nucleotide coenzyme specificity of PHBH is involved in determining the pyridine nucleotide coenzyme specificity of the other group A members.

Towards substrate-independent age estimation of blood stains based on dimensionality reduction and k-nearest neighbor classification of absorbance spectroscopic data.

The age determination of blood traces provides important hints for the chronological assessment of criminal events and their reconstruction. Current methods are often expensive, involve significant experimental complexity and often fail to perform when being applied to aged blood samples taken from different substrates. In this work an absorption spectroscopy-based blood stain age estimation method is presented, which utilizes 400-640nm absorption spectra in computation. Spectral data from 72 differently aged pig blood stains (2h to three weeks) dried on three different substrate surfaces (cotton, polyester and glass) were acquired and the turnover-time correlations were utilized to develop a straightforward age estimation scheme. More precisely, data processing includes data dimensionality reduction, upon which classic k-nearest neighbor classifiers are employed. This strategy shows good agreement between observed and predicted blood stain age (r>0.9) in cross-validation. The presented estimation strategy utilizes spectral data from dissolved blood samples to bypass spectral artifacts which are well known to interfere with other spectral methods such as reflection spectroscopy. Results indicate that age estimations can be drawn from such absorbance spectroscopic data independent from substrate the blood dried on. Since data in this study was acquired under laboratory conditions, future work has to consider perturbing environmental conditions in order to assess real-life applicability.

Simulation of diffusion using a modular cell dynamic simulation system.

A variety of mathematical models is used to describe and simulate the multitude of natural processes examined in life sciences. In this paper we present a scalable and adjustable foundation for the simulation of natural systems. Based on neighborhood relations in graphs and the complex interactions in cellular automata, the model uses recurrence relations to simulate changes on a mesoscopic scale. This implicit definition allows for the manipulation of every aspect of the model even during simulation. The definition of value rules ω facilitates the accumulation of change during time steps. Those changes may result from different physical, chemical or biological phenomena. Value rules can be combined into modules, which in turn can be used to create baseline models. Exemplarily, a value rule for the diffusion of chemical substances was designed and its applicability is demonstrated. Finally, the stability and accuracy of the solutions is analyzed.

Loss of heterozygosity in FANCG, FANCF and BRIP1 from head and neck squamous cell carcinoma of the oral cavity.

Recent advances have been made in the understanding of Fanconi anemia (FA), a hereditary disease that increases the risk for head and neck squamous cell carcinomas (HNSCC) by 500- to 700-fold. FA patients harbour germline mutations in genes of cellular DNA repair pathways that are assumed to facilitate the accumulation of mutations during HNSCC development. Mutations in these FA genes may also contribute to HNSCC in general. In the present study, we analysed three FA genes; FANCF, FANCG and BRIP1, that are involved in the repair of DNA inter strand cross-links, in HNSCC and their potential role for patient survival. We measured loss of heterozygosity (LOH) mutations at eight microsatellite loci flanking three FA genes in 54 HNSCC of the oral cavity and corresponding blood samples. Survival analyses were carried out using mutational data and clinical variables. LOH was present in 17% (FANCF region), 41% (FANCG region) and 11% (BRIP1 region) of the patients. Kaplan-Meier survival curves and log-rank tests indicated strong clinical predictors (lymph node stages with decreased survival: p=2.69e-12; surgery with improved survival: p=0.0005). LOH in the FANCF region showed a weaker association with decreased overall survival (p=0.006), which however, did not hold in multivariate analyses. LOH may predominantly indicate copy number gains in FANCF and losses in FANCG and BRIP1. Integration of copy number data and gene expression proved difficult as the available sample sets did not overlap. In conclusion, LOH in FA genes appears to be a common feature of HNSCC development seen here in 57% of patients and other mutation types may increase this mutation frequency. We suggest larger patient cohorts would be needed to test the observed association of LOH in FANCF and patient survival comprehensively.

PIPINO: A Software Package to Facilitate the Identification of Protein-Protein Interactions from Affinity Purification Mass Spectrometry Data.

The functionality of most proteins is regulated by protein-protein interactions. Hence, the comprehensive characterization of the interactome is the next milestone on the path to understand the biochemistry of the cell. A powerful method to detect protein-protein interactions is a combination of coimmunoprecipitation or affinity purification with quantitative mass spectrometry. Nevertheless, both methods tend to precipitate a high number of background proteins due to nonspecific interactions. To address this challenge the software Protein-Protein-Interaction-Optimizer (PIPINO) was developed to perform an automated data analysis, to facilitate the selection of bona fide binding partners, and to compare the dynamic of interaction networks. In this study we investigated the STAT1 interaction network and its activation dependent dynamics. Stable isotope labeling by amino acids in cell culture (SILAC) was applied to analyze the STAT1 interactome after streptavidin pull-down of biotagged STAT1 from human embryonic kidney 293T cells with and without activation. Starting from more than 2,000 captured proteins 30 potential STAT1 interaction partners were extracted. Interestingly, more than 50% of these were already reported or predicted to bind STAT1. Furthermore, 16 proteins were found to affect the binding behavior depending on STAT1 phosphorylation such as STAT3 or the importin subunits alpha 1 and alpha 6.

SequenceCEROSENE: a computational method and web server to visualize spatial residue neighborhoods at the sequence level.

BACKGROUND: To understand the molecular function of biopolymers, studying their structural characteristics is of central importance. Graphics programs are often utilized to conceive these properties, but with the increasing number of available structures in databases or structure models produced by automated modeling frameworks this process requires assistance from tools that allow automated structure visualization. In this paper a web server and its underlying method for generating graphical sequence representations of molecular structures is presented. RESULTS: The method, called SequenceCEROSENE (color encoding of residues obtained by spatial neighborhood embedding), retrieves the sequence of each amino acid or nucleotide chain in a given structure and produces a color coding for each residue based on three-dimensional structure information. From this, color-highlighted sequences are obtained, where residue coloring represent three-dimensional residue locations in the structure. This color encoding thus provides a one-dimensional representation, from which spatial interactions, proximity and relations between residues or entire chains can be deduced quickly and solely from color similarity. Furthermore, additional heteroatoms and chemical compounds bound to the structure, like ligands or coenzymes, are processed and reported as well. To provide free access to SequenceCEROSENE, a web server has been implemented that allows generating color codings for structures deposited in the Protein Data Bank or structure models uploaded by the user. Besides retrieving visualizations in popular graphic formats, underlying raw data can be downloaded as well. In addition, the server provides user interactivity with generated visualizations and the three-dimensional structure in question. CONCLUSIONS: Color encoded sequences generated by SequenceCEROSENE can aid to quickly perceive the general characteristics of a structure of interest (or entire sets of complexes), thus supporting the researcher in the initial phase of structure-based studies. In this respect, the web server can be a valuable tool, as users are allowed to process multiple structures, quickly switch between results, and interact with generated visualizations in an intuitive manner. The SequenceCEROSENE web server is available at https://biosciences.hs-mittweida.de/seqcerosene.

Fit3D: a web application for highly accurate screening of spatial residue patterns in protein structure data.

UNLABELLED: The clarification of linkage between protein structure and function is still a demanding process and can be supported by comparison of spatial residue patterns, so-called structural motifs. However, versatile up-to-date resources to search for local structure similarities are rare. We present Fit3D, an easily accessible web application for highly accurate screening of structural motifs in 3D protein data. AVAILABILITY AND IMPLEMENTATION: The web application is accessible at https://biosciences.hs-mittweida.de/fit3d and program sources of the command line version were released under the terms of GNU GPLv3. Platform-independent binaries and documentations for offline usage are available at https://bitbucket.org/fkaiser/fit3d CONTACT: florian.kaiser@hs-mittweida.de SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Numeric promoter description - A comparative view on concepts and general application.

Nucleic acid molecules play a key role in a variety of biological processes. Starting from storage and transfer tasks, this also comprises the triggering of biological processes, regulatory effects and the active influence gained by target binding. Based on the experimental output (in this case promoter sequences), further in silico analyses aid in gaining new insights into these processes and interactions. The numerical description of nucleic acids thereby constitutes a bridge between the concrete biological issues and the analytical methods. Hence, this study compares 26 descriptor sets obtained by applying well-known numerical description concepts to an established dataset of 38 DNA promoter sequences. The suitability of the description sets was evaluated by computing partial least squares regression models and assessing the model accuracy. We conclude that the major importance regarding the descriptive power is attached to positional information rather than to explicitly incorporated physico-chemical information, since a sufficient amount of implicit physico-chemical information is already encoded in the nucleobase classification. The regression models especially benefited from employing the information that is encoded in the sequential and structural neighborhood of the nucleobases. Thus, the analyses of n-grams (short fragments of length n) suggested that they are valuable descriptors for DNA target interactions. A mixed n-gram descriptor set thereby yielded the best description of the promoter sequences. The corresponding regression model was checked and found to be plausible as it was able to reproduce the characteristic binding motifs of promoter sequences in a reasonable degree. As most functional nucleic acids are based on the principle of molecular recognition, the findings are not restricted to promoter sequences, but can rather be transferred to other kinds of functional nucleic acids. Thus, the concepts presented in this study could provide advantages for future nucleic acid-based technologies, like biosensoring, therapeutics and molecular imaging.

Evolutionary Influenced Interaction Pattern as Indicator for the Investigation of Natural Variants Causing Nephrogenic Diabetes Insipidus.

The importance of short membrane sequence motifs has been shown in many works and emphasizes the related sequence motif analysis. Together with specific transmembrane helix-helix interactions, the analysis of interacting sequence parts is helpful for understanding the process during membrane protein folding and in retaining the three-dimensional fold. Here we present a simple high-throughput analysis method for deriving mutational information of interacting sequence parts. Applied on aquaporin water channel proteins, our approach supports the analysis of mutational variants within different interacting subsequences and finally the investigation of natural variants which cause diseases like, for example, nephrogenic diabetes insipidus. In this work we demonstrate a simple method for massive membrane protein data analysis. As shown, the presented in silico analyses provide information about interacting sequence parts which are constrained by protein evolution. We present a simple graphical visualization medium for the representation of evolutionary influenced interaction pattern pairs (EIPPs) adapted to mutagen investigations of aquaporin-2, a protein whose mutants are involved in the rare endocrine disorder known as nephrogenic diabetes insipidus, and membrane proteins in general. Furthermore, we present a new method to derive new evolutionary variations within EIPPs which can be used for further mutagen laboratory investigations.