Protein Primary Sequence Research Articles

A conserved WW domain-like motif regulates invariant chain-dependent cell-surface transport of the NKG2D ligand ULBP2

Malignant cells expressing NKG2D ligands on their cell surface can be directly sensed and killed by NKG2D-bearing lymphocytes. To ensure this immune recognition, accumulating evidence suggests that NKG2D ligands are trafficed via alternative pathways to the cell surface. We have previously shown that the NKG2D ligand ULBP2 traffics over an invariant chain (Ii)-dependent pathway to the cell surface. This study set out to elucidate how Ii regulates ULBP2 cell-surface transport: We discovered conserved tryptophan (Trp) residues in the primary protein sequence of ULBP1-6 but not in the related MICA/B. Substitution of Trp to alanine resulted in cell-surface inhibition of ULBP2 in different cancer cell lines. Moreover, the mutated ULBP2 constructs were retained and not degraded inside the cell, indicating a crucial role of this conserved Trp-motif in trafficking. Finally, overexpression of Ii increased surface expression of wt ULBP2 while Trp-mutants could not be expressed, proposing that this Trp-motif is required for an Ii-dependent cell-surface transport of ULBP2.Aberrant soluble ULBP2 is immunosuppressive. Thus, targeting a distinct protein module on the ULBP2 sequence could counteract this abnormal expression of ULBP2.

Comparative analysis of protein primary sequences with graph energy

We propose in this paper, the graph energy and Laplacian energy of 20 amino acids based on the codons coding the amino acids and apply them to put forward a novel 2-D graphical representation of proteins. The novel graphical representation has no circuit or degeneracy, uniquely represents proteins and allows one to easily and quickly visually observe and inspect similarity/dissimilarity between them. It also leads to two novel protein descriptors, the graph energy of a protein sequence, and the increment of graph energy between two protein sequences. We develop similarities/dissimilarities model and successfully analyze the similarities/dissimilarities of ND5, 36PDs, 24TFs and 27AFPs with good results consistent with ClustalW even better ones.

Predicting Protein-Protein Interactions from Primary Protein Sequences Using a Novel Multi-Scale Local Feature Representation Scheme and the Random Forest

The study of protein-protein interactions (PPIs) can be very important for the understanding of biological cellular functions. However, detecting PPIs in the laboratories are both time-consuming and expensive. For this reason, there has been much recent effort to develop techniques for computational prediction of PPIs as this can complement laboratory procedures and provide an inexpensive way of predicting the most likely set of interactions at the entire proteome scale. Although much progress has already been achieved in this direction, the problem is still far from being solved. More effective approaches are still required to overcome the limitations of the current ones. In this study, a novel Multi-scale Local Descriptor (MLD) feature representation scheme is proposed to extract features from a protein sequence. This scheme can capture multi-scale local information by varying the length of protein-sequence segments. Based on the MLD, an ensemble learning method, the Random Forest (RF) method, is used as classifier. The MLD feature representation scheme facilitates the mining of interaction information from multi-scale continuous amino acid segments, making it easier to capture multiple overlapping continuous binding patterns within a protein sequence. When the proposed method is tested with the PPI data of Saccharomyces cerevisiae, it achieves a prediction accuracy of 94.72% with 94.34% sensitivity at the precision of 98.91%. Extensive experiments are performed to compare our method with existing sequence-based method. Experimental results show that the performance of our predictor is better than several other state-of-the-art predictors also with the H. pylori dataset. The reason why such good results are achieved can largely be credited to the learning capabilities of the RF model and the novel MLD feature representation scheme. The experiment results show that the proposed approach can be very promising for predicting PPIs and can be a useful tool for future proteomic studies.

Fuzzy support vector machine model to predict human death domain protein–protein interactions

Proteins have crucial importance in every living system, and Protein–Protein Interactions (PPIs) play a pivotal role in regulation of virtually all biological processes such as DNA transcription, replication, metabolic cycles and signaling cascades. The PPIs play an important role in the complex process of cell death which mainly occurs via apoptosis and necrosis in eukaryotic cells. Apoptosis is an orderly cellular suicide program critical for the development and homeostasis of multicellular organism. Failure to control apoptosis can have catastrophic consequences. The cascades of amazing reactions carried out by proteins such as Caspase, CARD, NLRP, NOD, FADD, DEDD, POP, Myd88 etc. play important role in the process of cell death. The high throughput experimental methods for determining PPIs are time consuming, expensive and are generating huge amount of PPIs data. Therefore, there is need to develop computational methods to efficiently and accurately predict PPIs. In this work, an attempt has been made to develop a fuzzy support vector machine (F-SVM) model for predicting death domain (DD) PPIs based on sixteen physicochemical, biochemical and structural features of amino acids which are monomers of proteins. First, the protein primary sequences are encoded into sequential features represented by descriptors. Then, the Support Vector Machine and Sequential Minimal Optimization of WEKA software are employed to classify interacting and non-interacting protein pairs. The performance of SVM and F-SVM with various kernel functions has been evaluated and it was observed that libSVM with Polynomial kernel was found to be best with accuracy of 77.94 % via F-SVM which is optimum model in predicting human DD-PPIs. Validation is performed by tenfold cross-validation technique. The F-SVM performance measure is 2.94 % higher than SVM in terms of accuracy with the use of custom designed fuzzy membership function. The results obtained are in agreement with available experimental data. Such models can be useful in providing PPI information of DD proteins which can be useful in understanding the molecular mechanisms involved in death of cells taking place due to aging, programmed cell death and various diseases. It may through some light on the study of cancerous cell and gerontology.

RBRIdent: An algorithm for improved identification of RNA-binding residues in proteins from primary sequences.

Rapid and correct identification of RNA-binding residues based on the protein primary sequences is of great importance. In most prevalent machine-learning-based identification methods; however, either some features are inefficiently represented, or the redundancy between features is not effectively removed. Both problems may weaken the performance of a classifier system and raise its computational complexity. Here, we addressed the above problems and developed a better classifier (RBRIdent) to identify the RNA-binding residues. In an independent benchmark test, RBRIdent achieved an accuracy of 76.79%, Matthews correlation coefficient of 0.3819 and F-measure of 75.58%, remarkably outperforming all prevalent methods. These results suggest the necessity of proper feature description and the essential role of feature selection in this project. All source data and codes are freely available at http://166.111.152.91/RBRIdent.

Incorporating Research in the Biochemistry Laboratory

Many of the undergraduates enrolled in natural science courses will become scientists and technicians in their future careers. As such, it is critical that these students learn necessary skills such as making careful measurements, designing experiments, managing their time, and troubleshooting of experimental difficulties. To provide this training, we have designed and implemented a biochemistry laboratory that teaches many of these skills through the contribution to publishable research projects. Our first set of projects has involved investigating the relationship between protein primary sequence and enzymatic function. Each student is responsible for production and kinetic characterization of a unique point mutant of an enzyme. Students learn all the techniques taught in a traditional biochemistry laboratory while benefiting from an environment that encourages them to become responsible learners through participation in research. The mechanics of running this laboratory course will be discussed along with notable successes and lessons learned.

Chemical synthesis of a polypeptide backbone derived from the primary sequence of the cancer protein NY-ESO-1 enabled by kinetically controlled ligation and pseudoprolines.

The cancer protein NY-ESO-1 has been shown to be one of the most promising vaccine candidates although little is known about its cellular function. Using a chemical protein strategy, the 180 amino acid polypeptide, tagged with an arginine solubilizing tail, was assembled in a convergent manner from four unprotected peptide α-thioester peptide building blocks and one cysteinyl polypeptide, which were in turn prepared by Boc and Fmoc solid phase peptide synthesis (SPPS) respectively. To facilitate the assembly by ligation chemistries, non-native cysteines were introduced as chemical handles into the polypeptide fragments; pseudoproline dipeptides and microwave assisted Fmoc SPPS were crucial techniques to prepare the challenging hydrophobic C-terminal fragment. Three sequential kinetically controlled ligations, which exploited the reactivity between peptide arylthioesters and peptide alkylthioesters, were then used in order to assemble the more tractable N-terminal region of NY-ESO-1. The ensuing 147 residue polypeptide thioester then underwent successful final native chemical ligation with the very hydrophobic C-terminal polypeptide bearing an N-terminal cysteine affording the 186 residue polypeptide as an advanced intermediate en route to the native NY-ESO-1 protein.

Solid-State mAbs and ADCs Subjected to Heat-Stress Stability Conditions can be Covalently Modified with Buffer and Excipient Molecules

We report that a unique type of chemical modification occurs on lyophilized proteins. Freeze-dried mAbs and antibody-drug conjugates (ADCs) can be covalently modified with buffer and excipient molecules on the side chains of Glu, Asp, Thr, and Ser amino acids when subjected to temperature stress. The reaction occurs primarily via condensation of common buffers and excipients such as histidine, tris, trehalose and sucrose, with Glu and Asp carboxylates in the primary sequence of proteins. The reaction was also found to proceed through condensation of carboxylate containing buffers such as citrate, with Thr and Ser hydroxyls in the primary sequence of proteins. Based on the mass of the covalent adducts observed on mAbs and ADCs, it is apparent that the reaction produces water as a product and is thus favored in a low moisture environments such as a lyophilized protein cake. Herein, we present the evidence for the covalent modification of proteins drawn from case studies of in-depth characterization of heat-stressed mAbs and ADCs in the solid state. We also demonstrate how common charge variant assays such as imaged capillary isoelectric focusing and mass spectrometry can be used to monitor this specific class of protein modification.

Reducing dimensionality in remote homology detection using predicted contact maps

In this paper, a new method for remote protein homology detection is presented. Most discriminative methods concatenate the values extracted from physicochemical properties to build a model that separates homolog and non-homolog examples. Each discriminative method uses a specific strategy to represent the information extracted from the protein sequence and a different number of indices. After the vector representation is achieved, support vector machines (SVM) are usually used. Most classification techniques are not suitable in remote homology detection because they do not address high dimensional datasets. In this paper, we propose a method that reduces the high dimensionality of the vector representation using models that are defined at the 3D level. Next, the models are mapped from the protein primary sequence. The new method, called remote-C3D, is presented and tested on the SCOP 1.53 and SCOP 1.55 datasets. The remote-C3D method achieves a higher accuracy than the composition-based methods and a comparable performance with profile-based methods.

Prediction of Structural Patterns of Interest from Protein Primary Sequence through Structural Alphabet: Illustration to ATP/GTP Binding Site Prediction

The prediction of particular structural motifs associated to biological functions or to structure is of utmost importance. Given the increasing availability of primary sequences without any structure information, predictions from amino-acid (AA) sequences are essential. The proposed prediction method of structural motifs is a two-step approach based on a structural alphabet. This alphabet allows encoding any 3D structure into a 1D sequence of structural letters (SL). First, basic correspondence rules between AA and SL are learnt through genetic programming. Then, a Hidden Markov Model is learnt for each beforehand identified motif of interest. Finally, a probability to correspond to a given 3D motif for any given amino-acid sequence is provided. The method is applied on ATP binding sites to compare the efficiency of our method to other ones for a classical function. Then, the method ability to learn motifs corresponding to more rarely predicted functions or to other types of motifs is illustrated.

Prediction of Protein Structural Classes for Low-Similarity Sequences Based on Consensus Sequence and Segmented PSSM.

Prediction of protein structural classes for low-similarity sequences is useful for understanding fold patterns, regulation, functions, and interactions of proteins. It is well known that feature extraction is significant to prediction of protein structural class and it mainly uses protein primary sequence, predicted secondary structure sequence, and position-specific scoring matrix (PSSM). Currently, prediction solely based on the PSSM has played a key role in improving the prediction accuracy. In this paper, we propose a novel method called CSP-SegPseP-SegACP by fusing consensus sequence (CS), segmented PsePSSM, and segmented autocovariance transformation (ACT) based on PSSM. Three widely used low-similarity datasets (1189, 25PDB, and 640) are adopted in this paper. Then a 700-dimensional (700D) feature vector is constructed and the dimension is decreased to 224D by using principal component analysis (PCA). To verify the performance of our method, rigorous jackknife cross-validation tests are performed on 1189, 25PDB, and 640 datasets. Comparison of our results with the existing PSSM-based methods demonstrates that our method achieves the favorable and competitive performance. This will offer an important complementary to other PSSM-based methods for prediction of protein structural classes for low-similarity sequences.

Interaction of the Intrinsically Disordered c-Myc Oncoprotein with Racemic and Enantiopure Small Molecules

The prevalence of intrinsically disordered proteins (IDPs) in cell signaling and disease makes them significant targets. Despite the absence of defined tertiary structure, small molecules can bind IDPs at sites determined by a short, linear segment of the protein's primary sequence. The oncoprotein c-Myc, a transcription factor that must undergo coupled folding and binding to its obligate partner Max in order to interact with DNA, is an ideal system for understanding specificity in small-molecule binding to IDPs. Three small molecule interaction sites exist in the bHLHZip region of c-Myc, the segment necessary for coupled folding and binding to Max. The chiral small molecule 10074-A4 interacts with one of these sites (Myc372-389). The presence of Myc372-389 induces small molecule circular dichroism of racemic 10074-A4, indicating an enantiospecific interaction. We have synthesized the pure R and S enantiomers of 10074-A4 (as well as pure enantiomers of derivatives) and found that at 10 uM and above the compound can undergo a transition upon addition to water from an aggregate, to a dispersed molecule, to an assembled chiral complex with a strong CD signature. SPR measurements indicate interaction between Myc and 10074-A4. These data suggest multiple possible binding modes. We also report the hydrodynamic radius of the bHLHZip region of c-Myc, as determined by fluorescence correlation spectroscopy and dynamic light scattering, under different conditions including in the presence of various small molecules and c-Myc's obligate partner Max.

Developing Leave One Out GFP Based Biosensors

The green fluorescent protein (GFP) has seen its utility expand far beyond that of a fluorophore in Aqueroia victoria. Some of the main drivers of this have been work done to increase the spectroscopic range of the protein as well as to develop GFP as a biosensor. In our study we have generated leave-one-out variants of GFP (LOO-GFPs) that have been circularly permuted with a secondary structural element omitted. Co-expression of the truncated GFP (sensor) with wt peptides (target) reconstitutes fluorescence to varying degrees depending on which strand has been omitted. We have observed that the efficiency in chromophore (CRO) maturation correlates with the strength of the CD signal and the thermodynamic stability of the sensor-target complex.Additionally, we envision that these insights can prove useful for computationally designing LOO-GFP biosensors with the ability to detect exogenous targets. Given the structure of a desired target protein, the primary sequence of the target protein is scanned to find fragments whose chemical composition are similar to that of one of the late folding β strands of GFP. These fragments can replace the corresponding β strand of GFP and the region around the new strand can be redesigned to find a set of biosensor primary sequences that best accommodates the exogenous target sequence. Initial screening of a library of biosensor sequences, even with covalent linkage of the LOO-GFP to the target, failed to produce a functional protein; suggesting that additional considerations are necessary. In particular, success depends on understanding the sequence determinants of the CRO maturation process. To this end, we are testing sequence tolerance of CRO-proximal residues using saturation mutagenesis, time lapse analysis of in vivo fluorescence and subsequent DNA sequence analysis.

Detecting protein-protein interactions with a novel matrix-based protein sequence representation and support vector machines.

Proteins and their interactions lie at the heart of most underlying biological processes. Consequently, correct detection of protein-protein interactions (PPIs) is of fundamental importance to understand the molecular mechanisms in biological systems. Although the convenience brought by high-throughput experiment in technological advances makes it possible to detect a large amount of PPIs, the data generated through these methods is unreliable and may not be completely inclusive of all possible PPIs. Targeting at this problem, this study develops a novel computational approach to effectively detect the protein interactions. This approach is proposed based on a novel matrix-based representation of protein sequence combined with the algorithm of support vector machine (SVM), which fully considers the sequence order and dipeptide information of the protein primary sequence. When performed on yeast PPIs datasets, the proposed method can reach 90.06% prediction accuracy with 94.37% specificity at the sensitivity of 85.74%, indicating that this predictor is a useful tool to predict PPIs. Achieved results also demonstrate that our approach can be a helpful supplement for the interactions that have been detected experimentally.

Ensemble Calculation for Intrinsically Disordered Proteins Using NMR Parameters.

Intrinsically disordered proteins (IDPs) perform their function despite their lack of well-defined tertiary structure. Residual structure has been observed in IDPs, commonly described as transient/dynamic or expressed in terms of fractional populations. In order to understand how the protein primary sequence dictates the dynamic and structural properties of IDPs and in general to understand how IDPs function, atomic-level descriptions are needed. Nuclear magnetic resonance spectroscopy provides information about local and long-range structure in IDPs at amino acid specific resolution and can be used in combination with ensemble descriptions to represent the dynamic nature of IDPs. In this chapter we describe sample-and-select approaches for ensemble modelling of local structural propensities in IDPs with specific emphasis on validation of these ensembles.

Measles Virus: Identification in the M Protein Primary Sequence of a Potential Molecular Marker for Subacute Sclerosing Panencephalitis

Subacute Sclerosing Panencephalitis (SSPE), a rare lethal disease of children and young adults due to persistence of measles virus (MeV) in the brain, is caused by wild type (wt) MeV. Why MeV vaccine strains never cause SSPE is completely unknown. Hypothesizing that this phenotypic difference could potentially be represented by a molecular marker, we compared glycoprotein and matrix (M) genes from SSPE cases with those from the Moraten vaccine strain, searching for differential structural motifs. We observed that all known SSPE viruses have residues P64, E89, and A209 (PEA) in their M proteins whereas the equivalent residues for vaccine strains are either S64, K89, and T209 (SKT) as in Moraten or PKT. Through the construction of MeV recombinants, we have obtained evidence that the wt MeV-M protein PEA motif, in particular A209, is linked to increased viral spread. Importantly, for the 10 wt genotypes (of 23) that have had their M proteins sequenced, 9 have the PEA motif, the exception being B3, which has PET. Interestingly, cases of SSPE caused by genotype B3 have yet to be reported. In conclusion, our results strongly suggest that the PEA motif is a molecular marker for wt MeV at risk to cause SSPE.

Silencing the lettuce homologs of small rubber particle protein does not influence natural rubber biosynthesis in lettuce (Lactuca sativa)

Natural rubber, cis-1,4-polyisoprene, is an important raw material in chemical industries, but its biosynthetic mechanism remains elusive. Natural rubber is known to be synthesized in rubber particles suspended in laticifer cells in the Brazilian rubber tree (Hevea brasiliensis). In the rubber tree, rubber elongation factor (REF) and its homolog, small rubber particle protein (SRPP), were found to be the most abundant proteins in rubber particles, and they have been implicated in natural rubber biosynthesis. As lettuce (Lactuca sativa) can synthesize natural rubber, we utilized this annual, transformable plant to examine in planta roles of the lettuce REF/SRPP homologs by RNA interference. Among eight lettuce REF/SRPP homologs identified, transcripts of two genes (LsSRPP4 and LsSRPP8) accounted for more than 90% of total transcripts of REF/SRPP homologs in lettuce latex. LsSRPP4 displays a typical primary protein sequence as other REF/SRPP, while LsSRPP8 is twice as long as LsSRPP4. These two major LsSRPP transcripts were individually and simultaneously silenced by RNA interference, and relative abundance, polymer molecular weight, and polydispersity of natural rubber were analyzed from the LsSRPP4- and LsSRPP8-silenced transgenic lettuce. Despite previous data suggesting the implications of REF/SRPP in natural rubber biosynthesis, qualitative and quantitative alterations of natural rubber could not be observed in transgenic lettuce lines. It is concluded that lettuce REF/SRPP homologs are not critically important proteins in natural rubber biosynthesis in lettuce.

Bayesian model of protein primary sequence for secondary structure prediction.

Determining the primary structure (i.e., amino acid sequence) of a protein has become cheaper, faster, and more accurate. Higher order protein structure provides insight into a protein’s function in the cell. Understanding a protein’s secondary structure is a first step towards this goal. Therefore, a number of computational prediction methods have been developed to predict secondary structure from just the primary amino acid sequence. The most successful methods use machine learning approaches that are quite accurate, but do not directly incorporate structural information. As a step towards improving secondary structure reduction given the primary structure, we propose a Bayesian model based on the knob-socket model of protein packing in secondary structure. The method considers the packing influence of residues on the secondary structure determination, including those packed close in space but distant in sequence. By performing an assessment of our method on 2 test sets we show how incorporation of multiple sequence alignment data, similarly to PSIPRED, provides balance and improves the accuracy of the predictions. Software implementing the methods is provided as a web application and a stand-alone implementation.

Modes of MAPK substrate recognition and control.

Mitogen-activated protein kinase (MAPK) cascades are universal, evolutionary conserved signalling modules, which translate environmental information into appropriate responses via phosphorylation of their substrate proteins. In Arabidopsis, the MAPK MPK3 regulates numerous cellular processes, including the adaptation to abiotic and biotic stresses. The molecular steps immediately downstream of MPK3 induction have, therefore, received abundant attention, and a respectable number of MPK3 targets are known by now. These proteins illustrate the substrate promiscuity of MPK3. They also are evidence for how manifold phosphorylation-regulated functions can be. This review presents the current knowledge about the function and regulation of MPK3-targeted proteins, takes a close look at their primary protein sequences, and proposes a model of how MPK3 recognises, binds, and phosphorylates its targets.

Predicting recombinant protein expression experiments using molecular dynamics simulation

Soluble expression of de novo-designed proteins in Escherichia coli (E. coli) remains empirical. For given experimental conditions expression success is determined in part by protein primary sequence. This has been previously explored with varying success using a variety of statistical solubility prediction tools though without taking fold stability into account. In the present study, the three-dimensional structure of proteins in molecular dynamics (MD) simulations is used to predict expression as a new approach with a set of four-helix bundles. Stability-related parameters for ten structures were determined in a thermal unfolding MD simulation and used to build statistical models with a support vector machine (SVM) classifier. The most accurate models were identified by their performance on five independent four-helix bundle sequences. The final model provided accurate classification prediction for this test set and was successfully applied in a model challenge with two newly designed sequences.The combination of simulation-derived parameters and an SVM classifier has potential to predict recombinant expression outcome for this set of four-helix bundles. With further development, this approach of utilizing higher-dimensional protein structural information to predict expression may have potential to advance recombinant biotechnology through modern computational and statistical science.