An automated program to screen databases for members of protein families

We have developed a program, ScreenTransporter (ST), to screen for potential members of recognized transporter families. This program uses Blastpgp as the engine to search a nonredundant database, NRDB90, based on an adjustable E-value cut-off as well as adjustable protein size criteria. Additional parameters can be integrated in later versions. ST is convenient for easily obtaining nonredundant members of transporter families starting from several homologous query sequences. The program can be applied to any protein family.     

An EEG-based machine learning method to screen alcohol use disorder

Screening alcohol use disorder (AUD) patients has been challenging due to the subjectivity involved in the process. Hence, robust and objective methods are needed to automate the screening of AUD patients. In this paper, a machine learning method is proposed that utilized resting-state electroencephalography (EEG)-derived features as input data to classify the AUD patients and healthy controls and to perform automatic screening of AUD patients. In this context, the EEG data were recorded during 5 min of eyes closed and 5 min of eyes open conditions. For this purpose, 30 AUD patients and 15 aged-matched healthy controls were recruited. After preprocessing the EEG data, EEG features such as inter-hemispheric coherences and spectral power for EEG delta, theta, alpha, beta and gamma bands were computed involving 19 scalp locations. The selection of most discriminant features was performed with a rank-based feature selection method assigning a weight value to each feature according to a criterion, i.e., receiver operating characteristics curve. For example, a feature with large weight was considered more relevant to the target labels than a feature with less weight. Therefore, a reduced set of most discriminant features was identified and further be utilized during classification of AUD patients and healthy controls. As results, the inter-hemispheric coherences between the brain regions were found significantly different between the study groups and provided high classification efficiency (Accuracy = 80.8, sensitivity = 82.5, and specificity = 80, F-Measure = 0.78). In addition, the power computed in different EEG bands were found significant and provided an overall classification efficiency as (Accuracy = 86.6, sensitivity = 95, specificity = 82.5, and F-Measure = 0.88). Further, the integration of these EEG feature resulted into even higher results (Accuracy = 89.3 %, sensitivity = 88.5 %, specificity = 91 %, and F-Measure = 0.90). Based on the results, it is concluded that the EEG data (integration of the theta, beta, and gamma power and inter-hemispheric coherence) could be utilized as objective markers to screen the AUD patients and healthy controls.     

An integrated machine learning approach for predicting DosR-regulated genes in Mycobacterium tuberculosis

Background

Most cellular signal transduction mechanisms depend on a few molecular partners whose roles depend on their position and movement in relation to the input signal. This movement can follow various rules and take place in different compartments. Additionally, the molecules can form transient complexes. Complexation and signal transduction depend on the specific states partners and complexes adopt. Several spatial simulator have been developed to date, but none are able to model reaction-diffusion of realistic multi-state transient complexes.

Results

Meredys allows for the simulation of multi-component, multi-feature state molecular species in two and three dimensions. Several compartments can be defined with different diffusion and boundary properties. The software employs a Brownian dynamics engine to simulate reaction-diffusion systems at the reactive particle level, based on compartment properties, complex structure, and hydro-dynamic radii. Zeroth-, first-, and second order reactions are supported. The molecular complexes have realistic geometries. Reactive species can contain user-defined feature states which can modify reaction rates and outcome. Models are defined in a versatile NeuroML input file. The simulation volume can be split in subvolumes to speed up run-time.

Conclusions

Meredys provides a powerful and versatile way to run accurate simulations of molecular and sub-cellular systems, that complement existing multi-agent simulation systems. Meredys is a Free Software and the source code is available at http://meredys.sourceforge.net/.     

Application of high-throughput isothermal denaturation to assess protein stability and screen for ligands

Many diseases in humans are caused by mutations that decrease the stability of specific proteins or increase their susceptibility to aggregation. Consequently, the availability of high-throughput methods for assessing protein stability and aggregation properties under physiological conditions (e.g., 37 degrees C) is necessary to analyze physicochemical properties under conditions that are closer to in vivo models. Therefore, the authors have explored the use of isothermal denaturation (ITD) in a 384-well format to evaluate the reproducibility of the method in assessing the stability of proteins at temperatures below the melting temperature and detecting the binding of ligands. Under the conditions tested, the authors were able to assess the stability of citrate synthase and malate dehydrogenase at different constant temperatures and detect the binding of oxaloacetate and nicotinamide adenine dinucleotide to these 2 enzymes, respectively, using the 384-well format. The ITD experiments detected ligand binding to these proteins at about 4 times lower concentration compared with techniques that measure changes in melting temperature. The data show that ITD can be applied to screen libraries of a relatively large number of compounds or detect small stability differences between protein variants.     

Nonpeptidic ligands for peptide and protein receptors.

The first potent nonpeptidic ligands for somatostatin, luteinizing hormone-releasing hormone, glucagon and bradykinin receptors have been reported. Nonpeptidic clinical candidates have been identified or are currently under study for substance P, bradykinin, endothelin, growth hormone secretagogue, angiotensin, vasopressin, motilin and cholecystokinin. Design, screening, combinatorial chemistry and classical medicinal chemistry all played important roles in these advances.     

An in vitro system to screen for diarrheagenic chemicals

We examined an in vitro system to screen for diarrheagenic chemicals using an established intestinal cell line (T₈₄ human colonic carcinoma). The cells were grown on Millicell-PCF (polycarbonate membrane) wells. The cells were seeded at approximately 5 × 10₆ cells/30mm well and incubated for 9–11 days in a 5% C0₂ incubator saturated with water at 37°C. The culture medium was a 1:1 mixture of Ham's F12 and Dulbecco's MEM with 5% fetal bovine serum and 25 pglml gentamicin sulfate. The well containing cells was removed from the incubator and mounted in a modified Ussing chamber for measurement of shortcircuit current (I_sc). Chemical-induced increases in Psc are usually indicative of electrogenic epithelial Cl^– secretion, which is associated with diarrheagenic effects in animals and humans. T₈₄ cells grown on Millicell-PCF membrane responded with an increase in Isc after basolateral addition of the cholinergic (muscarznic) agonist carbachol, prostaglandin E₂, 16,16-dimethylprostaglandin E₂, and forskolin, while non-diarrheagenic prostaglandin D₂ did not affect I_sc. Based on our results, this in vitro system has the potential to be adapted as a rapid screen for detecting diarrheagenic chemicals.Abbreviations dmPGE₂ 16,16-dimethylPGE₂ - EC₅₀ 50% of maximum effective concentration - EDTA ethylenediaminetetraacetate - I_SC short-circuit current - PGD₂ prostaglandin D₂ - PGE₂ prostaglandin E₂ - PD potential difference - R_T transepithelial resistance     

An information-based machine learning approach to elasticity imaging

An information-based technique is described for applications in mechanical property imaging of soft biological media under quasi-static loads. We adapted the Autoprogressive method that was originally developed for civil engineering applications for this purpose. The Autoprogressive method is a computational technique that combines knowledge of object shape and a sparse distribution of force and displacement measurements with finite-element analyses and artificial neural networks to estimate a complete set of stress and strain vectors. Elasticity imaging parameters are then computed from estimated stresses and strains. We introduce the technique using ultrasonic pulse-echo measurements in simple gelatin imaging phantoms having linear-elastic properties so that conventional finite-element modeling can be used to validate results. The Autoprogressive algorithm does not require any assumptions about the material properties and can, in principle, be used to image media with arbitrary properties. We show that by selecting a few well-chosen force–displacement measurements that are appropriately applied during training and establish convergence, we can estimate all nontrivial stress and strain vectors throughout an object and accurately estimate an elastic modulus at high spatial resolution. This new method of modeling the mechanical properties of tissue-like materials introduces a unique method of solving the inverse problem and is the first technique for imaging stress without assuming the underlying constitutive model.     

Selection of peptide ligands binding to fibroblast growth factor receptor 1

Inappropriate expression of fibroblast growth factors (FGFs) or activation of FGF receptors (FGFRs) could contribute to several human angiogenic pathologies. In an attempt to design antagonists of FGF, we developed a screening procedure for identifying peptide ligands binding to FGFR1. To retain the natural conformation of FGFR1 during screening, we expressed recombinant FGFR1 on the surface of Sf9 insect cells. A 6-mer phage display peptide library was then screened on the cell surface and a group of hydrophobic peptide sequences were identified. Further experiments demonstrated that the phages displaying these sequences can specifically bind to FGFR1. The docking analysis suggests that the peptide ValTyrMetSerProPhe can specifically bind to the hydrophobic surface of FGFR1. The synthetic peptide Ac-ValTyrMetSerProPhe-NH2 can inhibit mitogenic activity of aFGF and has the potential to become a therapeutic agent as an aFGF antagonist.     

Designing peptide and protein ligands for biological receptors.

A clearer understanding of structure-function relationships of protein hormone-receptor systems is emerging from the increased use of molecular biology approaches. On the other hand, the introduction of rationally designed conformation constraints into peptide hormones and neurotransmitters is leading to the development of highly receptor-selective ligands that allow further investigations into the topographic modulation of their bioactivities and rational design principles for peptide antagonists.     

Randomized sequence databases for tandem mass spectrometry peptide and protein identification

Tandem mass spectrometry (MS/MS) combined with database searching is currently the most widely used method for high-throughput peptide and protein identification. Many different algorithms, scoring criteria, and statistical models have been used to identify peptides and proteins in complex biological samples, and many studies, including our own, describe the accuracy of these identifications, using at best generic terms such as "high confidence." False positive identification rates for these criteria can vary substantially with changing organisms under study, growth conditions, sequence databases, experimental protocols, and instrumentation; therefore, study-specific methods are needed to estimate the accuracy (false positive rates) of these peptide and protein identifications. We present and evaluate methods for estimating false positive identification rates based on searches of randomized databases (reversed and reshuffled). We examine the use of separate searches of a forward then a randomized database and combined searches of a randomized database appended to a forward sequence database. Estimated error rates from randomized database searches are first compared against actual error rates from MS/MS runs of known protein standards. These methods are then applied to biological samples of the model microorganism Shewanella oneidensis strain MR-1. Based on the results obtained in this study, we recommend the use of use of combined searches of a reshuffled database appended to a forward sequence database as a means providing quantitative estimates of false positive identification rates of peptides and proteins. This will allow researchers to set criteria and thresholds to achieve a desired error rate and provide the scientific community with direct and quantifiable measures of peptide and protein identification accuracy as opposed to vague assessments such as "high confidence."     

A machine learning information retrieval approach to protein fold recognition

MOTIVATION: Recognizing proteins that have similar tertiary structure is the key step of template-based protein structure prediction methods. Traditionally, a variety of alignment methods are used to identify similar folds, based on sequence similarity and sequence-structure compatibility. Although these methods are complementary, their integration has not been thoroughly exploited. Statistical machine learning methods provide tools for integrating multiple features, but so far these methods have been used primarily for protein and fold classification, rather than addressing the retrieval problem of fold recognition-finding a proper template for a given query protein. RESULTS: Here we present a two-stage machine learning, information retrieval, approach to fold recognition. First, we use alignment methods to derive pairwise similarity features for query-template protein pairs. We also use global profile-profile alignments in combination with predicted secondary structure, relative solvent accessibility, contact map and beta-strand pairing to extract pairwise structural compatibility features. Second, we apply support vector machines to these features to predict the structural relevance (i.e. in the same fold or not) of the query-template pairs. For each query, the continuous relevance scores are used to rank the templates. The FOLDpro approach is modular, scalable and effective. Compared with 11 other fold recognition methods, FOLDpro yields the best results in almost all standard categories on a comprehensive benchmark dataset. Using predictions of the top-ranked template, the sensitivity is approximately 85, 56, and 27% at the family, superfamily and fold levels respectively. Using the 5 top-ranked templates, the sensitivity increases to 90, 70, and 48%.     

gamma-Aminobutyric acid/benzodiazepine receptor protein carries binding sites for both ligands on both two major peptide subunits

Affinity column-purified GABA-benzodiazepine receptor proteins from human, cow, and rat brain were photoaffinity labeled with both [3H]flunitrazepam and [3H]muscimol and examined by gel electrophoresis in sodium dodecyl sulfate. Using high receptor protein concentrations (1 microM), the benzodiazepine ligand [3H]flunitrazepam was incorporated covalently primarily into the expected 52 kiloDalton major subunit but also significantly into a second 57 kiloDalton peptide. Likewise the GABA ligand [3H]muscimol photolabeled primarily the 57 kiloDalton peptide but also to some extent the 52 kiloDalton peptide. This cross-labeling suggests strongly that both major subunits carry binding sites for both GABA and benzodiazepine.     

Combinatorial chemistry defines general properties of linkers for the optimal display of peptide ligands for binding soluble protein targets to TentaGel microscopic beads

Affinity chromatography and the binding of soluble target proteins to novel or known ligands attached to solid supports are important phenomena to basic and applied research. Satisfactory display of a ligand for the acceptor protein is critical for successful binding to occur. Here we describe the application of combinatorial chemistry to systematically explore the properties of linkers used to present peptide ligands to various protein targets. Our main interest is in drug discovery, and our results probably explain, in large part, the disappointing efficiency of an early drug discovery method known as the "Selectide Process" (Lam, K. S., et al. (1991) Nature 358, 82-84). Interestingly, for all seven protein targets studied, a cationic feature was found to be a common theme for optimal linkers displaying peptide ligands on TentaGel beads, and this is not likely to be caused by ionic exchange mechanisms.     

An integrated system for genetic analysis

Background  

Large-scale genetic mapping projects require data management systems that can handle complex phenotypes and detect and correct high-throughput genotyping errors, yet are easy to use.     

An analogue of the binding polynomial for the case of ligands binding to an aggregating macromolecule

It is known that when a macromolecule exists as a mixture of forms with different molecular weights in equilibrium in dilute solution with ligands then the saturation functions cannot be derived by differentiating a binding polynomial. However, we demonstrate that in such circumstances a saturation function can be expressed in terms of the binding polynomials for each of the polymeric forms of macromolecule and integration of this using the conservation equation affords a function, Q, which is the analogue of the binding polynomial for a non-aggregating system. The existence of Q leads to heterotropic linkage relationships.     

A peptide core motif for binding to heterotrimeric G protein alpha subunits

Recently, in vitro selection using mRNA display was used to identify a novel peptide sequence that binds with high affinity to Galpha(i1). The peptide was minimized to a 9-residue sequence (R6A-1) that retains high affinity and specificity for the GDP-bound state of Galpha(i1) and acts as a guanine nucleotide dissociation inhibitor (GDI). Here we demonstrate that the R6A-1 peptide interacts with Galpha subunits representing all four G protein classes, acting as a core motif for Galpha interaction. This contrasts with the consensus G protein regulatory(GPR) sequence, a 28-mer peptide GDI derived from the GoLoco (Galpha(i/0)-Loco interaction)/GPR motif that shares no homology with R6A-1 and binds only to Galpha(i1-3) in this assay. Binding of R6A-1 is generally specific to the GDP-bound state of the Galpha subunits and excludes association with Gbetagamma. R6A-Galpha(i1) complexes are resistant to trypsin digestion and exhibit distinct stability in the presence of Mg(2+), suggesting that the R6A and GPR peptides exert their activities using different mechanisms. Studies using Galpha(i1)/Galpha(s) chimeras identify two regions of Galpha(i1) (residues 1-35 and 57-88) as determinants for strong R6A-G(ialpha1) interaction. Residues flanking the R6A-1 peptide confer unique binding properties, indicating that the core motif could be used as a starting point for the development of peptides exhibiting novel activities and/or specificity for particular G protein subclasses or nucleotide-bound states.     

Fluorescence-based duplex-quadruplex competition test to screen for telomerase RNA quadruplex ligands

RNA and DNA guanine-rich sequences can adopt unusual structures called Guanine quadruplexes (G4). A quadruplex-prone RNA sequence is present at the 5'-end of the 451-nt-long RNA component of telomerase, hTERC. As this quadruplex may interfere with P1 helix formation, a key structural element for this RNA, we are seeking molecules that would alter this RNA duplex-quadruplex equilibrium. In this work, we present a fluorescence-based test designed to identify G4 ligands specific for the hTERC G-rich motif and that can prevent P1 helix formation. From an initial panel of 169 different molecules, 11 were found to be excellent P1 duplex inhibitors. Interestingly, some of the compounds not only exhibit a strong selectivity for quadruplexes over duplexes, but also demonstrated a preference for G4-RNA over all other quadruplexes. This test may easily be adapted to almost any quadruplex-forming sequence and converted into HTS format.     

An automated pipeline to screen membrane protein 2D crystallization

Electron crystallography relies on electron cryomicroscopy of two-dimensional (2D) crystals and is particularly well suited for studying the structure of membrane proteins in their native lipid bilayer environment. To obtain 2D crystals from purified membrane proteins, the detergent in a protein–lipid–detergent ternary mixture must be removed, generally by dialysis, under conditions favoring reconstitution into proteoliposomes and formation of well-ordered lattices. To identify these conditions a wide range of parameters such as pH, lipid composition, lipid-to-protein ratio, ionic strength and ligands must be screened in a procedure involving four steps: crystallization, specimen preparation for electron microscopy, image acquisition, and evaluation. Traditionally, these steps have been carried out manually and, as a result, the scope of 2D crystallization trials has been limited. We have therefore developed an automated pipeline to screen the formation of 2D crystals. We employed a 96-well dialysis block for reconstitution of the target protein over a wide range of conditions designed to promote crystallization. A 96-position magnetic platform and a liquid handling robot were used to prepare negatively stained specimens in parallel. Robotic grid insertion into the electron microscope and computerized image acquisition ensures rapid evaluation of the crystallization screen. To date, 38 2D crystallization screens have been conducted for 15 different membrane proteins, totaling over 3000 individual crystallization experiments. Three of these proteins have yielded diffracting 2D crystals. Our automated pipeline outperforms traditional 2D crystallization methods in terms of throughput and reproducibility.