Random sampling of the Protein Data Bank: RaSPDB

A novel and simple procedure (RaSPDB) for Protein Data Bank mining is described. 10 PDB subsets, each containing 7000 randomly selected protein chains, are built and used to make 10 estimations of the average value of a generic feature F—the length of the protein chain, the amino acid composition, the crystallographic resolution, and the secondary structure composition. These 10 estimations are then used to compute an average estimation of F together with its standard error. It is heuristically verified that the dimension of these 10 subsets—7000 protein chains—is sufficiently small to avoid redundancy within each subset and sufficiently large to guarantee stable estimations amongst different subsets. RaSPDB has two major advantages over classical procedures aimed to build a single, non-redundant PDB subset: a larger fraction of the information stored in the PDB is used and an estimation of the standard error of F is possible.

Random Sampling of the Protein Data Bank - RaSPDB

A novel and simple procedure (RaSPDB) for Protein Data Bank mining is described. 10 PDB subsets, each containing 7000 randomly selected protein chains, are built and used to make 10 estimations of the average value of a generic feature F – the length of the protein chain, the amino acid composition, the crystallographic resolution, and the secondary structure composition. These 10 estimations are then used to compute an average estimation of F together with its standard error. It is heuristically verified that the dimension of these 10 subsets –7000 protein chains – is sufficiently small to avoid redundancy within each subset and sufficiently large to guarantee stable estimations amongst different subsets. RaSPDB has two major advantages over classical procedures aimed to build a single, non-redundant PDB subset: a larger fraction of the information stored in the PDB is used and an estimation of the standard error of F is possible.

Implementation of a Bayesian Secondary Structure Estimation Method for the SESCA Circular Dichroism Analysis Package

Circular dichroism spectroscopy is a structural biology technique frequently applied to determine the secondary structure composition of soluble proteins. Our recently introduced computational analysis package SESCA aids the interpretation of protein circular dichroism spectra and enables the validation of proposed corresponding structural models. To further these aims, we present the implementation and characterization of a new Bayesian secondary structure estimation method in SESCA, termed SESCA_bayes. SESCA_bayes samples possible secondary structures using a Monte Carlo scheme, driven by the likelihood of estimated scaling errors and non-secondary-structure contributions of the measured spectrum. SESCA_bayes provides an estimated secondary structure composition and separate uncertainties on the fraction of residues in each secondary structure class. It also assists efficient model validation by providing a posterior secondary structure probability distribution based on the measured spectrum. Our presented study indicates that SESCA_bayes estimates the secondary structure composition with a significantly smaller uncertainty than its predecessor, SESCA_deconv, which is based on spectrum deconvolution. Further, the mean accuracy of the two methods in our analysis is comparable, but SESCA_bayes provides more accurate estimates for circular dichroism spectra that contain considerable non-SS contributions.

Relationship of Secondary Structures and Wear Resistance of Antifriction Aluminum Alloys for Journal Bearings from the Point of View of Self-Organization During Friction

The paper investigates the relationship between the tribological properties/compositions of new aluminum antifriction alloys and compositions of the secondary structures formed on their friction surfaces. Eight alloys with various compositions have been analyzed. The elemental compositions of the secondary structures on their friction surfaces have been determined. The relationship between the alloy secondary structure compositions with wear rate has been found. An attempt has been made to determine the secondary structure composition patterns based on the non-equilibrium thermodynamics and self-organization theory.

Assessment of Antibiotic Influence on Structural Modifications of Amniotic Membrane by FTIR Spectroscopy

The amniotic membrane is a readily available biomaterial with an important potential for tissue regeneration in dermatology and ophtalmology, with anti-inflammatory and anti-microbial properties. The extracellular matrix of the amniotic membrane is composed mainly of collagen, fibronectin and laminin. The purpose of our study was to investigate the structural modifications of collagen extracellular matrix of amniotic membrane upon interaction with two different antibiotics, frequently used in surgical and post- surgical procedure, respectively ciprofloxacin and gentamicin. SEM micrographs evidenced the ultrastructure features of dried amniotic membrane, with laminar structure, flexible, transparent, with no blood vesels or nerves. FTIR spectroscopy combined with deconvolution techniques was applied with the aim to determine the extent of denaturation upon treatment with different antibiotics. By spectral analysis, we concluded that gentamicin treatment is more favorable compared to ciprofloxacin, as the denaturation process is reflected by the lower sheet/turns ratio of the secondary structure composition.

A Constant Proportion in Protein Structure

The principles governing protein structure are largely unknown. Here, a structural proportion universal (R2 = 0.978) among proteins is reported. The model variance is shown to be independent from protein size, secondary structure composition, compactness or relative surface area. The structural characteristic under study --named here QUILLO-- quantifies residue-type spatial clustering. In this way, polar, hydrophobic, acidic and basic residues are evaluated individually and their values added up. For the analysis, all X-Ray currently determined structures deposited in the Protein Data Bank were studied. The QUILLO proportion offers for the first time an a priori protein prediction quality-check. Indeed, predictions with unexpected proportion values correspond to low ranks in the CASP12 experiment. The reason behind a specific, constant rule for protein folding remains unknown.

SESCA: Predicting Circular Dichroism Spectra from Protein Molecular Structures

Circular dichroism spectroscopy is a highly sensitive, but low-resolution technique to study the structure of proteins. Combined with molecular modelling or other complementary techniques, CD spectroscopy can provide essential information at higher resolution. To this end, we introduce a new computational method to calculate the electronic circular dichroism spectra of proteins from a structural model or ensemble using the average secondary structure composition and a pre-calculated set of basis spectra. We compared the predictive power of our method to existing algorithms – namely DichroCalc and PDB2CD – and found that it predicts CD spectra more accurately, with a 50% smaller average deviation from the measured CD spectra. Our results indicate that the derived basis sets are robust to experimental errors in the reference spectra and to the choice of the secondary structure classification algorithm. For over 80% of the globular reference proteins, our basis sets accurately predict the experimental spectrum solely from their secondary structure composition. For the remaining 20%, correcting for intensity normalization considerably improves the prediction power. Additionally, we show that the predictions for short peptides and intrinsically disordered proteins strongly benefit from accounting for side-chain contributions and structural flexibility.Table Of Content Graphics: