Protein Secondary Structure Information Research Articles

EMNUSS: a deep learning framework for secondary structure annotation in cryo-EM maps.

Cryo-electron microscopy (cryo-EM) has become one of important experimental methods in structure determination. However, despite the rapid growth in the number of deposited cryo-EM maps motivated by advances in microscopy instruments and image processing algorithms, building accurate structure models for cryo-EM maps remains a challenge. Protein secondary structure information, which can be extracted from EM maps, is beneficial for cryo-EM structure modeling. Here, we present a novel secondary structure annotation framework for cryo-EM maps at both intermediate and high resolutions, named EMNUSS. EMNUSS adopts a three-dimensional (3D) nested U-net architecture to assign secondary structures for EM maps. Tested on three diverse datasets including simulated maps, middle resolution experimental maps, and high-resolution experimental maps, EMNUSS demonstrated its accuracy and robustness in identifying the secondary structures for cyro-EM maps of various resolutions. The EMNUSS program is freely available at http://huanglab.phys.hust.edu.cn/EMNUSS.

Fourier-Transform Infrared (FTIR) Spectroscopy Transmission Technique for Analysis of Characteristic Infrared Bands of Peptide Linkage using Dried Proteins

Protein activity is well dependent on its conformational structure and thus secondary structure prediction and determination of proteins are vital requirements. FTIR is rapidly coming up for protein structural studies, as an alternative to circular dichroism. However, FTIR spectra are primarily recorded using protein samples prepared in water, organic solvents, deuterium oxide, and membranes. FTIR spectra are thus dominated by the solvent spectrum and subtraction of the solvent spectrum introduces severe spectral distortions. In aqueous protein solutions, a strong water signal overlaps amide I bands (C=O stretching; 1700-1600 cm−1), thus masking most of the protein secondary structure information. Further, most of the purified proteins including protein drug formulations are more stable in the solid state than in the liquid state. Hence there is a need to explore the use of FTIR for various physical states of proteins. In the present study, we showed that protein secondary structure can be analyzed in a dried state; using potassium bromide (KBr) pellet method for dried known proteins (bovine serum albumin, proteinase K, fibrinogen etc). KBr pellet method gave excellent spectra for all the used dried proteins; shown by the appearance of characteristic infrared bands of peptide linkage (Amide A, B, I-VI) without much interference from water, buffers, stabilizing factors, co-factors etc. We also found that KBr pellet method can analyze a range of protein types; globular to fibrous. Thus KBr method is a great tool for prediction/determination of the secondary structure of unknown proteins and quality control of solid-state protein drug formulations.

Prediction of Protein Phosphorylation Sites by Integrating Secondary Structure Information and Other One-Dimensional Structural Properties.

Studies on phosphorylation are important but challenging for both wet-bench experiments and computational studies, and accurate non-kinase-specific prediction tools are highly desirable for whole-genome annotation in a wide variety of species. Here, we describe a phosphorylation site prediction webserver, PhosphoSVM, that employs Support Vector Machine to combine protein secondary structure information and seven other one-dimensional structural properties, including Shannon entropy, relative entropy, predicted protein disorder information, predicted solvent accessible area, amino acid overlapping properties, averaged cumulative hydrophobicity, and subsequence k-nearest neighbor profiles. This method achieved AUC values of 0.8405/0.8183/0.7383 for serine (S), threonine (T), and tyrosine (Y) phosphorylation sites, respectively, in animals with a tenfold cross-validation. The model trained by the animal phosphorylation sites was also applied to a plant phosphorylation site dataset as an independent test. The AUC values for the independent test data set were 0.7761/0.6652/0.5958 for S/T/Y phosphorylation sites, respectively. This algorithm with the optimally trained model was implemented as a webserver. The webserver, trained model, and all datasets used in the current study are available at http://sysbio.unl.edu/PhosphoSVM .

Intelligent Consensus Modeling for Proline Cis-Trans Isomerization Prediction.

Proline cis-trans isomerization (CTI) plays a key role in the rate-determining steps of protein folding. Accurate prediction of proline CTI is of great importance for the understanding of protein folding, splicing, cell signaling, and transmembrane active transport in both the human body and animals. Our goal is to develop a state-of-the-art proline CTI predictor based on a biophysically motivated intelligent consensus modeling through the use of sequence information only (i.e., position specific scores generated by PSI-BLAST). The current computational proline CTI predictors reach about 70-73 percent Q2 accuracies and about 0.40 Matthew correlation coefficient (Mcc) through the use of sequence-based evolutionary information as well as predicted protein secondary structure information. However, our approach that utilizes a novel decision tree-based consensus model with a powerful randomized-metal earning technique has achieved 86.58 percent Q2 accuracy and 0.74 Mcc, on the same proline CTI data set, which is a better result than those of any existing computational proline CTI predictors reported in the literature.

PCI-SS: MISO dynamic nonlinear protein secondary structure prediction

BackgroundSince the function of a protein is largely dictated by its three dimensional configuration, determining a protein's structure is of fundamental importance to biology. Here we report on a novel approach to determining the one dimensional secondary structure of proteins (distinguishing α-helices, β-strands, and non-regular structures) from primary sequence data which makes use of Parallel Cascade Identification (PCI), a powerful technique from the field of nonlinear system identification.ResultsUsing PSI-BLAST divergent evolutionary profiles as input data, dynamic nonlinear systems are built through a black-box approach to model the process of protein folding. Genetic algorithms (GAs) are applied in order to optimize the architectural parameters of the PCI models. The three-state prediction problem is broken down into a combination of three binary sub-problems and protein structure classifiers are built using 2 layers of PCI classifiers. Careful construction of the optimization, training, and test datasets ensures that no homology exists between any training and testing data. A detailed comparison between PCI and 9 contemporary methods is provided over a set of 125 new protein chains guaranteed to be dissimilar to all training data. Unlike other secondary structure prediction methods, here a web service is developed to provide both human- and machine-readable interfaces to PCI-based protein secondary structure prediction. This server, called PCI-SS, is available at . In addition to a dynamic PHP-generated web interface for humans, a Simple Object Access Protocol (SOAP) interface is added to permit invocation of the PCI-SS service remotely. This machine-readable interface facilitates incorporation of PCI-SS into multi-faceted systems biology analysis pipelines requiring protein secondary structure information, and greatly simplifies high-throughput analyses. XML is used to represent the input protein sequence data and also to encode the resulting structure prediction in a machine-readable format. To our knowledge, this represents the only publicly available SOAP-interface for a protein secondary structure prediction service with published WSDL interface definition.ConclusionRelative to the 9 contemporary methods included in the comparison cascaded PCI classifiers perform well, however PCI finds greatest application as a consensus classifier. When PCI is used to combine a sequence-to-structure PCI-based classifier with the current leading ANN-based method, PSIPRED, the overall error rate (Q3) is maintained while the rate of occurrence of a particularly detrimental error is reduced by up to 25%. This improvement in BAD score, combined with the machine-readable SOAP web service interface makes PCI-SS particularly useful for inclusion in a tertiary structure prediction pipeline.

A 9-state hidden Markov model using protein secondary structure information for protein fold recognition

In protein fold recognition, the main disadvantage of hidden Markov models (HMMs) is the employment of large-scale model architectures which require large data sets and high computational resources for training. Also, HMMs must consider sequential information about secondary structures of proteins, to improve prediction performance and reduce model parameters. Therefore, we propose a novel method for protein fold recognition based on a hidden Markov model, called a 9-state HMM. The method can (i) reduce the number of states using secondary structure information about proteins for each fold and (ii) recognize protein folds more accurately than other HMMs.

Angle‐distance image matching techniques for protein structure comparison

Proteins that contain similar structural elements often have analogous functions regardless of the degree of sequence similarity or structure connectivity in space. In general, protein structure comparison (PSC) provides a straightforward methodology for biologists to determine critical aspects of structure and function. Here, we developed a novel PSC technique based on angle-distance image (A-D image) transformation and matching, which is independent of sequence similarity and connectivity of secondary structure elements (SSEs). An A-D image is constructed by utilizing protein secondary structure information. According to various types of SSEs, the mutual SSE pairs of the query protein are classified into three different types of sub-images. Subsequently, corresponding sub-images between query and target protein structures are compared using modified cross-correlation approaches to identify the similarity of various patterns. Structural relationships among proteins are displayed by hierarchical clustering trees, which facilitate the establishment of the evolutionary relationships between structure and function of various proteins.Four standard testing datasets and one newly created dataset were used to evaluate the proposed method. The results demonstrate that proteins from these five datasets can be categorized in conformity with their spatial distribution of SSEs. Moreover, for proteins with low sequence identity that share high structure similarity, the proposed algorithms are an efficient and effective method for structural comparison.

Multiple Alignment Through Protein Secondary-Structure Information

It is well known that protein secondary-structure information can help the process of performing multiple alignment, in particular when the amount of similarity among the involved sequences moves toward the "twilight zone" (less than 30% of pairwise similarity). In this paper, a multiple alignment algorithm is presented, explicitly designed for exploiting any available secondary-structure information. A layered architecture with two interacting levels has been defined for dealing with both primary- and secondary-structure information of target sequences. Secondary structure (either available or predicted by resorting to a technique based on multiple experts) is used to calculate an initial alignment at the secondary level, to be arranged by locally scoped operators devised to refine the alignment at the primary level. Aimed at evaluating the impact of secondary information on the quality of alignments, in particular alignments with a low degree of similarity, the technique has been implemented and assessed on relevant test cases.

Folding type specific secondary structure propensities of synonymous codons.

We have proposed new amino acid secondary structure propensities in proteins with different folding types based on synonymous codons. They have been derived from 200 all alpha, all beta, alpha/beta, and alpha + beta proteins of known structures and their coding genes. The secondary structure propensities of the same codon in gene coding for different folding type proteins are not the same. For instance, amino acid Ile coded by AUU is indifferent to form the alpha unit in the alpha + beta protein class, but it is a former and a breaker for the alpha unit in the all alpha protein class and the alpha/beta class, respectively. On the other hand, the secondary structure propensities of different synonymous codons in the coding genes with the same folding type are also not all the same. As an example, CGU, CGG, and AGA, which are synonymous codons of Arg, are preferential to form the alpha unit in all alpha proteins, while CGA is an alpha unit breaker and the other two synonymous codons, CGC and AGG, are indifferent to form or break the alpha unit. As a result, protein secondary structure information contained both in mRNA sequences and in amino acid sequences has been introduced in these codon-based amino acid secondary structure propensities. These codon-based amino acid secondary structure propensities are helpful to in vitro protein design and protein secondary structure prediction.

Prediction of Residues in Protein-RNA Interaction Sites by Neural Networks

Structural studies of protein-RNA interactions have been focused on analyzing intraand intermolecular interactions or recognizing binding patterns [3, 4, 5]. It has been examined that specific nucleotide-protein secondary structures are favored in protein-RNA interactions. The primary focus of our study is to investigate a predicting problem of the protein-RNA interaction sites upon a protein chain. A prediction system based on artificial neural networks was developed to identify accurate residues involved in protein-RNA interaction sites from combining the residue sequence and the protein secondary structure information.