A method for protein accessibility prediction based on residue types and conformational states

Prediction of protein accessibility from sequence, as prediction of protein secondary structure is an intermediate step for predicting structures and consequently functions of proteins. Most of the currently used methods are based on single residue prediction, either by statistical means or evolutionary information, and accessibility state of central residue in a window predicted. By expansion of databases of proteins with known 3D structures, we extracted information of pairwise residue types and conformational states of pairs simultaneously. For solving the problem of ambiguity in state prediction by one residue window sliding, we used dynamic programming algorithm to find the path with maximum score. The three state overall per-residue accuracy, Q₃, of this method in a Jackknife test with dataset of known proteins is more than 65% which is an improvement on results of methods based on evolutionary information.     

Protein secondary structure prediction using neural networks and deep learning: A review

Literature contains over fifty years of accumulated methods proposed by researchers for predicting the secondary structures of proteins in silico. A large part of this collection is comprised of artificial neural network-based approaches, a field of artificial intelligence and machine learning that is gaining increasing popularity in various application areas. The primary objective of this paper is to put together the summary of works that are important but sparse in time, to help new researchers have a clear view of the domain in a single place. An informative introduction to protein secondary structure and artificial neural networks is also included for context. This review will be valuable in designing future methods to improve protein secondary structure prediction accuracy. The various neural network methods found in this problem domain employ varying architectures and feature spaces, and a handful stand out due to significant improvements in prediction. Neural networks with larger feature scope and higher architecture complexity have been found to produce better protein secondary structure prediction. The current prediction accuracy lies around the 84% marks, leaving much room for further improvement in the prediction of secondary structures in silico. It was found that the estimated limit of 88% prediction accuracy has not been reached yet, hence further research is a timely demand.     

SPINE X: improving protein secondary structure prediction by multistep learning coupled with prediction of solvent accessible surface area and backbone torsion angles

Accurate prediction of protein secondary structure is essential for accurate sequence alignment, three-dimensional structure modeling, and function prediction. The accuracy of ab initio secondary structure prediction from sequence, however, has only increased from around 77 to 80% over the past decade. Here, we developed a multistep neural-network algorithm by coupling secondary structure prediction with prediction of solvent accessibility and backbone torsion angles in an iterative manner. Our method called SPINE X was applied to a dataset of 2640 proteins (25% sequence identity cutoff) previously built for the first version of SPINE and achieved a 82.0% accuracy based on 10-fold cross validation (Q(3)). Surpassing 81% accuracy by SPINE X is further confirmed by employing an independently built test dataset of 1833 protein chains, a recently built dataset of 1975 proteins and 117 CASP 9 targets (critical assessment of structure prediction techniques) with an accuracy of 81.3%, 82.3% and 81.8%, respectively. The prediction accuracy is further improved to 83.8% for the dataset of 2640 proteins if the DSSP assignment used above is replaced by a more consistent consensus secondary structure assignment method. Comparison to the popular PSIPRED and CASP-winning structure-prediction techniques is made. SPINE X predicts number of helices and sheets correctly for 21.0% of 1833 proteins, compared to 17.6% by PSIPRED. It further shows that SPINE X consistently makes more accurate prediction in helical residues (6%) without over prediction while PSIPRED makes more accurate prediction in coil residues (3-5%) and over predicts them by 7%. SPINE X Server and its training/test datasets are available at http://sparks.informatics.iupui.edu/     

PreSSAPro: a software for the prediction of secondary structure by amino acid properties

PreSSAPro is a software, available to the scientific community as a free web service designed to provide predictions of secondary structures starting from the amino acid sequence of a given protein. Predictions are based on our recently published work on the amino acid propensities for secondary structures in either large but not homogeneous protein data sets, as well as in smaller but homogeneous data sets corresponding to protein structural classes, i.e. all-alpha, all-beta, or alpha–beta proteins. Predictions result improved by the use of propensities evaluated for the right protein class. PreSSAPro predicts the secondary structure according to the right protein class, if known, or gives a multiple prediction with reference to the different structural classes. The comparison of these predictions represents a novel tool to evaluate what sequence regions can assume different secondary structures depending on the structural class assignment, in the perspective of identifying proteins able to fold in different conformations. The service is available at the URL http://bioinformatica.isa.cnr.it/PRESSAPRO/.     

SSThread: Template‐free protein structure prediction by threading pairs of contacting secondary structures followed by assembly of overlapping pairs

Acquiring the three‐dimensional structure of a protein from its amino acid sequence alone, despite a great deal of work and significant progress on the subject, is still an unsolved problem. SSThread, a new template‐free algorithm is described here that consists of making several predictions of contacting pairs of α‐helices and β‐strands derived from a database of experimental structures using a knowledge‐based potential, secondary structure prediction, and contact map prediction followed by assembly of overlapping pair predictions to create an ensemble of core structure predictions whose loops are then predicted. In a set of seven CASP10 targets SSThread outperformed the two leading methods for two targets each. The targets were all β‐strand containing structures and most of them have a high relative contact order which demonstrates the advantages of SSThread. The primary bottlenecks based on sets of 74 and 21 test cases are the pair prediction and loop prediction stages. © 2014 Wiley Periodicals, Inc.     

Refinement of modelled structures by knowledge-based energy profiles and secondary structure prediction: Application to the human procarboxypeptidase A2

Knowledge-based energy profiles combined with secondary structure prediction have been applied to molecular modelling refinement. To check the procedure, three different models of human procarboxypeptidase A2 (hPCPA2) have been built using the 3D structures of procarboxypeptidase A1 (pPCPA1) and bovine procarboxypeptidase A (bPCPA) as templates. The results of the refinement can be tested against the X-ray structure of hPCPA2 which has been recently determined. Regions miss-modelled in the activation segment of hPCPA2 were detected by means of pseudo-energies using Prosa II and modified afterwards according to the secondary structure prediction. Moreover, models obtained by automated methods as COMPOSER, MODELLER and distance restraints have also been compared, where it was found possible to find out the best model by means of pseudo-energies. Two general conclusions can be elicited from this work: (1) on a given set of putative models it is possible to distinguish among them the one closest to the crystallographic structure, and (2) within a given structure it is possible to find by means of pseudo-energies those regions that have been defectively modelled.     

Prediction of protein structure using a knowledge-based off-lattice united-residue force field and global optimization methods

A united-residue model of polypeptide chains developed in our laboratories with united side-chains and united peptide groups as interaction sites is presented. The model is designed to work in continuous space; hence efficient global-optimization methods can be applied. In this work, we adopted the distance-scaling method that is based on continuous deformation of the original rugged energy hypersurface to obtain a smoothed surface. The method has been applied successfully to predict the structures of simple motifs, such as the three-helix bundle structure of the 10-58 fragment of staphylococcal protein A in de novo folding simulations and more complicated motifs in inverse-folding simulations. Received: 24 April 1998 / Accepted: 4 August 1998 / Published online: 2 November 1998     

Ab initio prediction of helical segments in polypeptides

An ab initio method has been developed to predict helix formation for polypeptides. The approach relies on the systematic analysis of overlapping oligopeptides to determine the helical propensity for individual residues. Detailed atomistic level modeling, including entropic contributions, and solvation/ionization energies calculated through the solution of the Poisson-Boltzmann equation, is utilized. The calculation of probabilities for helix formation is based on the generation of ensembles of low energy conformers. The approach, which is easily amenable to parallelization, is shown to perform very well for several benchmark polypeptide systems, including the bovine pancreatic trypsin inhibitor, the immunoglobulin binding domain of protein G, the chymotrypsin inhibitor 2, the R69 N-terminal domain of phage 434 repressor, and the wheat germ agglutinin.     

Predicting structural class for protein sequences of 40% identity based on features of primary and secondary structure using Random Forest algorithm

At present, tertiary structure discovery growth rate is lagging far behind discovery of primary structure. The prediction of protein structural class using Machine Learning techniques can help reduce this gap. The Structural Classification of Protein – Extended (SCOPe 2.07) is latest and largest dataset available at present. The protein sequences with less than 40% identity to each other are used for predicting α, β, α/β and α + β SCOPe classes. The sensitive features are extracted from primary and secondary structure representations of Proteins. Features are extracted experimentally from secondary structure with respect to its frequency, pitch and spatial arrangements. Primary structure based features contain species information for a protein sequence. The species parameters are further validated with uniref100 dataset using TaxId. As it is known, protein tertiary structure is manifestation of function. Functional differences are observed in species. Hence, the species are expected to have strong correlations with structural class, which is discovered in current work. It enhances prediction accuracy by 7%–10%. The subset of SCOPe 2.07 is trained using 65 dimensional feature vector using Random Forest classifier. The test result for the rest of the set gives consistent accuracy of better than 95%. The accuracy achieved on benchmark datasets ASTRAL 1.73, 25PDB and FC699 is better than 86%, 91% and 97% respectively, which is best reported to our knowledge.     

The importance of short structural motifs in protein structure analysis

Summary Proteins tend to use recurrent structural motifs on all levels of organization. In this paper we first survey the topics of recurrent motifs on the local secondary structure level and on the global fold level. Then, we focus on the intermediate level which we call the short structural motifs. We were able to identify a set of structural building blocks that are very common in protein structure. We suggest that these building blocks can be used as an important link between the primary sequence and the tertiary structure. In this framework, we present our latest results on the structural variability of the extended strand motifs. We show that extended strands can be divided into three distinct structural classes, each with its own sequence specificity. Other approaches to the study of short structural motifs are reviewed.     

The effect of molecular structure on the secondary transitions and their influence on the decoloration kinetics of photochromic dyes in co‐polycarbonates

Photochromic dyes have restricted use in rigid polycarbonates because of slow coloration and decoloration kinetics. In this study, it is shown that the decoloration kinetics of two photochromic dyes can be controlled by tuning the chain stiffness and free volume of the host matrix. The introduction of flexible moieties in rigid BPA‐based polycarbonate chain accelerates the decoloration of these dyes whereas a rigid co‐monomer delays the decoloration kinetics. Although T_g might be used as a parameter to improve photochromism in polymer matrices, dynamic mechanical analysis demonstrates that the decoloration kinetics of the dyes in host polymer matrices having similar T_g depends primarily on the secondary relaxations and, thus, on the polymer architecture. The effect of the co‐monomer type on the characteristic ratio is also discussed underlining the potential relationship between the free volume and chain stiffness. These results open the possibility to develop transparent or semitransparent photochromic materials based on tailor‐made co‐polycarbonates. © 2016 Wiley Periodicals, Inc. J. Polym. Sci., Part B: Polym. Phys. 2016 , 54, 1593–1601     

The salt–cocrystal spectrum in salicylic acid–adenine: the influence of crystal structure on proton‐transfer balance

At one extreme of the proton‐transfer spectrum in cocrystals, proton transfer is absent, whilst at the opposite extreme, in salts, the proton‐transfer process is complete. However, for acid–base pairs with a small ΔpK_a (pK_a of base ? pK_a of acid), prediction of the extent of proton transfer is not possible as there is a continuum between the salt and cocrystal ends. In this context, we attempt to illustrate that in these systems, in addition to ΔpK_a, the crystalline environment could change the extent of proton transfer. To this end, two compounds of salicylic acid (SaH) and adenine (Ad) have been prepared. Despite the same small ΔpK_a value (≈1.2), different ionization states are found. Both crystals, namely adeninium salicylate monohydrate, C₅H₆N₅⁺·C₇H₅O₃^?·H₂O, I , and adeninium salicylate–adenine–salicylic acid–water (1/2/1/2), C₅H₆N₅⁺·C₇H₅O₃^?·2C₅H₅N₅·C₇H₆O₃·2H₂O, II , have been characterized by single‐crystal X‐ray diffraction, IR spectroscopy and elemental analysis (C, H and N) techniques. In addition, the intermolecular hydrogen‐bonding interactions of compounds I and II have been investigated and quantified in detail on the basis of Hirshfeld surface analysis and fingerprint plots. Throughout the study, we use crystal engineering, which is based on modifications of the intermolecular interactions, thus offering a more comprehensive screening of the salt–cocrystal continuum in comparison with pure pK_a analysis.     

The influence of organic agents on the resultant crystal structure in the reactions of the [Re4Te4(CN)12] tetrahedral cluster anion with Nd cations

The reaction of the cluster salt K₄[Re₄Te₄(CN)₁₂]·5H₂O with NdCl₃·6H₂O was studied in either an acidic medium (HCl) or in a water solution in the presence of the following organic agents: hexafluoroacetylacetonate, 2,2′-bipyridine or 1,10-phenanthroline (phen). The crystal structures of four new compounds have been solved by single crystal X-ray diffraction analysis: (H)[{Nd(H₂O)₅}{Re₄Te₄(CN)₁₂}]·5.5H₂O (1) (space group P2₁/c, framework structure), K₂[{Nd(H₂O)₇}₂{Re₄Te₄(CN)₁₂}₂]·8H₂O (2) (space group С2/c, isolated structure), K_0.5H_0.5[{Nd(H₂O)₅}{Re₄Te₄(CN)₁₂}]·3H₂O (3) (space group Сmcm, layered structure) and (phenH)[{Nd(H₂O)₂(phen)₂}{Re₄Te₄(CN)₁₂}]·11H₂O (4) (space group С2/c, chain structure). 1,10-Phenanthroline was found to have been incorporated into the structure of compound 4, whilst hexafluoroacetylacetonate and 2,2′-bipyridine did not enter the structures of 2 and 3. It was shown that the structures of compounds 2-4 differ dramatically from that found for compound 1, which was obtained in the absence of the organic agents.