Protein Functional Regions Research Articles

Elucidating the various multi-phosphorylation statuses of protein functional regions by 193-nm ultraviolet photodissociation

Ultraviolet photodissociation is a high-energy fast excitation method in mass spectrometry and has been successfully applied for the elucidation of sequences and structures of biomolecules. However, its ability to distinguish the phosphorylation sites isomers of multi-phosphopeptides has been not systematically investigated until now. A 193-nm ultraviolet laser dissociation mass spectrometry system was established in this study and applied to elucidate the complex multi-phosphorylation statuses mimicking the functional regions of Sic1, Gli3 and Tau. The numbers of matched fragment ions and phosphorylation site-determining ions were improved on average 123% and 104%, respectively, by utilizing the ultraviolet photodissociation strategy, comparing to the typically utilized collision induced dissociation strategy. Finally, 94% phosphorylation sites within various statuses were unambiguously elucidated.

GASS-WEB: a web server for identifying enzyme active sites based on genetic algorithms.

Enzyme active sites are important and conserved functional regions of proteins whose identification can be an invaluable step toward protein function prediction. Most of the existing methods for this task are based on active site similarity and present limitations including performing only exact matches on template residues, template size restraints, despite not being capable of finding inter-domain active sites. To fill this gap, we proposed GASS-WEB, a user-friendly web server that uses GASS (Genetic Active Site Search), a method based on an evolutionary algorithm to search for similar active sites in proteins. GASS-WEB can be used under two different scenarios: (i) given a protein of interest, to match a set of specific active site templates; or (ii) given an active site template, looking for it in a database of protein structures. The method has shown to be very effective on a range of experiments and was able to correctly identify >90% of the catalogued active sites from the Catalytic Site Atlas. It also managed to achieve a Matthew correlation coefficient of 0.63 using the Critical Assessment of protein Structure Prediction (CASP 10) dataset. In our analysis, GASS was ranking fourth among 18 methods. GASS-WEB is freely available at http://gass.unifei.edu.br/.

YMap: an automated method to map yeast variants to protein modifications and functional regions.

SummaryRecent advances in sequence technology result in large datasets of sequence variants. For the human genome, several tools are available to predict the impact of these variants on gene and protein functions. However, for model organisms such as yeast such tools are lacking, specifically to predict the effect of protein sequence altering variants on the protein level. We present a python framework that enables users to map in a fully automated fashion large set of variants to protein functional regions and post-translationally modified residues. Furthermore, we provide the user with the possibility to retrieve predicted functional information on modified residues from other resources for example that are predicted to play a role in protein-protein interactions. The results are complemented by statistical tests to highlight the significance of underlying functions and pathways affected by mutations. We show the application of this package on a yeast dataset derived from a recent evolutionary experiment on adaptation to ethanol.Availability and ImplementationThe package is available from https://github.com/CSB-KUL/yMap and is implemented in Python.Supplementary information Supplementary data are available at Bioinformatics online.

Systematic analysis of somatic mutations driving cancer: Uncovering functional protein regions in disease development

Recent advances in sequencing technologies enable the large-scale identification of genes that are affected by various genetic alterations in cancer. However, understanding tumor development requires insights into how these changes cause altered protein function and impaired network regulation in general and/or in specific cancer types. In this work we present a novel method called iSiMPRe that identifies regions that are significantly enriched in somatic mutations and short in-frame insertions or deletions (indels). Applying this unbiased method to the complete human proteome, by using data enriched through various cancer genome projects, we identified around 500 protein regions which could be linked to one or more of 27 distinct cancer types. These regions covered the majority of known cancer genes, surprisingly even tumor suppressors. Additionally, iSiMPRe also identified novel genes and regions that have not yet been associated with cancer. While local somatic mutations correspond to only a subset of genetic variations that can lead to cancer, our systematic analyses revealed that they represent an accompanying feature of most cancer driver genes regardless of the primary mechanism by which they are perturbed during tumorigenesis. These results indicate that the accumulation of local somatic mutations can be used to pinpoint genes responsible for cancer formation and can also help to understand the effect of cancer mutations at the level of functional modules in a broad range of cancer driver genes. This article was reviewed by Sandor Pongor, Michael Gromiha and Zoltan Gaspari.

Systematic analysis of somatic mutations driving cancer: uncovering functional protein regions in disease development.

BackgroundRecent advances in sequencing technologies enable the large-scale identification of genes that are affected by various genetic alterations in cancer. However, understanding tumor development requires insights into how these changes cause altered protein function and impaired network regulation in general and/or in specific cancer types.ResultsIn this work we present a novel method called iSiMPRe that identifies regions that are significantly enriched in somatic mutations and short in-frame insertions or deletions (indels). Applying this unbiased method to the complete human proteome, by using data enriched through various cancer genome projects, we identified around 500 protein regions which could be linked to one or more of 27 distinct cancer types. These regions covered the majority of known cancer genes, surprisingly even tumor suppressors. Additionally, iSiMPRe also identified novel genes and regions that have not yet been associated with cancer.ConclusionsWhile local somatic mutations correspond to only a subset of genetic variations that can lead to cancer, our systematic analyses revealed that they represent an accompanying feature of most cancer driver genes regardless of the primary mechanism by which they are perturbed during tumorigenesis. These results indicate that the accumulation of local somatic mutations can be used to pinpoint genes responsible for cancer formation and can also help to understand the effect of cancer mutations at the level of functional modules in a broad range of cancer driver genes.ReviewersThis article was reviewed by Sándor Pongor, Michael Gromiha and Zoltán Gáspári.Electronic supplementary materialThe online version of this article (doi:10.1186/s13062-016-0125-6) contains supplementary material, which is available to authorized users.

A Pan-Cancer Catalogue of Cancer Driver Protein Interaction Interfaces.

Despite their importance in maintaining the integrity of all cellular pathways, the role of mutations on protein-protein interaction (PPI) interfaces as cancer drivers has not been systematically studied. Here we analyzed the mutation patterns of the PPI interfaces from 10,028 proteins in a pan-cancer cohort of 5,989 tumors from 23 projects of The Cancer Genome Atlas (TCGA) to find interfaces enriched in somatic missense mutations. To that end we use e-Driver, an algorithm to analyze the mutation distribution of specific protein functional regions. We identified 103 PPI interfaces enriched in somatic cancer mutations. 32 of these interfaces are found in proteins coded by known cancer driver genes. The remaining 71 interfaces are found in proteins that have not been previously identified as cancer drivers even that, in most cases, there is an extensive literature suggesting they play an important role in cancer. Finally, we integrate these findings with clinical information to show how tumors apparently driven by the same gene have different behaviors, including patient outcomes, depending on which specific interfaces are mutated.

Natural Selection at the Brush-Border: Adaptations to Carbohydrate Diets in Humans and Other Mammals.

Dietary shifts can drive molecular evolution in mammals and a major transition in human history, the agricultural revolution, favored carbohydrate consumption. We investigated the evolutionary history of nine genes encoding brush-border proteins involved in carbohydrate digestion/absorption. Results indicated widespread adaptive evolution in mammals, with several branches experiencing episodic selection, particularly strong in bats. Many positively selected sites map to functional protein regions (e.g., within glucosidase catalytic crevices), with parallel evolution at SI (sucrase-isomaltase) and MGAM (maltase-glucoamylase). In human populations, five genes were targeted by positive selection acting on noncoding variants within regulatory elements. Analysis of ancient DNA samples indicated that most derived alleles were already present in the Paleolithic. Positively selected variants at SLC2A5 (fructose transporter) were an exception and possibly spread following the domestication of specific fruit crops. We conclude that agriculture determined no major selective event at carbohydrate metabolism genes in humans, with implications for susceptibility to metabolic disorders.

Analysis of individual protein regions provides novel insights on cancer pharmacogenomics.

The promise of personalized cancer medicine cannot be fulfilled until we gain better understanding of the connections between the genomic makeup of a patient's tumor and its response to anticancer drugs. Several datasets that include both pharmacologic profiles of cancer cell lines as well as their genomic alterations have been recently developed and extensively analyzed. However, most analyses of these datasets assume that mutations in a gene will have the same consequences regardless of their location. While this assumption might be correct in some cases, such analyses may miss subtler, yet still relevant, effects mediated by mutations in specific protein regions. Here we study such perturbations by separating effects of mutations in different protein functional regions (PFRs), including protein domains and intrinsically disordered regions. Using this approach, we have been able to identify 171 novel associations between mutations in specific PFRs and changes in the activity of 24 drugs that couldn't be recovered by traditional gene-centric analyses. Our results demonstrate how focusing on individual protein regions can provide novel insights into the mechanisms underlying the drug sensitivity of cancer cell lines. Moreover, while these new correlations are identified using only data from cancer cell lines, we have been able to validate some of our predictions using data from actual cancer patients. Our findings highlight how gene-centric experiments (such as systematic knock-out or silencing of individual genes) are missing relevant effects mediated by perturbations of specific protein regions. All the associations described here are available from http://www.cancer3d.org.

Aligning and Clustering Patterns to Reveal the Protein Functionality of Sequences.

Discovering sequence patterns with variations unveils significant functions of a protein family. Existing combinatorial methods of discovering patterns with variations are computationally expensive, and probabilistic methods require more elaborate probabilistic representation of the amino acid associations. To overcome these shortcomings, this paper presents a new computationally efficient method for representing patterns with variations in a compact representation called Aligned Pattern Cluster (AP Cluster). To tackle the runtime, our method discovers a shortened list of non-redundant statistically significant sequence associations based on our previous work. To address the representation of protein functional regions, our pattern alignment and clustering step, presented in this paper captures the conservations and variations of the aligned patterns. We further refine our solution to allow more coverage of sequences via extending the AP Clusters containing only statistically significant patterns to Weak and Conserved AP Clusters. When applied to the cytochrome c, the ubiquitin, and the triosephosphate isomerase protein families, our algorithm identifies the binding segments as well as the binding residues. When compared to other methods, ours discovers all binding sites in the AP Clusters with superior entropy and coverage. The identification of patterns with variations help biologists to avoid time-consuming simulations and experimentations. (Software available upon request).

Malaria adhesins: structure and function

The malaria parasite Plasmodium utilizes specialized proteins for adherence to cellular receptors in its mosquito vector and human host. Adherence is critical for parasite development, host cell traversal and invasion, and protection from vector and host immune mechanisms. These vital roles have identified several adhesins as vaccine candidates. A deficiency in current adhesin-based vaccines is induction of antibodies targeting non-conserved, non-functional and decoy epitopes due to the use of full length proteins or binding domains. To alleviate the elicitation of non-inhibitory antibodies, conserved functional regions of proteins must be identified and exploited. Structural biology provides the tools necessary to achieve this goal, and has succeeded in defining biologically functional receptor binding and oligomerization interfaces for a number of promising malaria vaccine candidates. We describe here the current knowledge of Plasmodium adhesin structure and function, and how it has illuminated elements of parasite biology and defined interactions at the host/vector and parasite interface.

Latent Effects of Hsp90 Mutants Revealed at Reduced Expression Levels

In natural systems, selection acts on both protein sequence and expression level, but it is unclear how selection integrates over these two dimensions. We recently developed the EMPIRIC approach to systematically determine the fitness effects of all possible point mutants for important regions of essential genes in yeast. Here, we systematically investigated the fitness effects of point mutations in a putative substrate binding loop of yeast Hsp90 (Hsp82) over a broad range of expression strengths. Negative epistasis between reduced expression strength and amino acid substitutions was common, and the endogenous expression strength frequently obscured mutant defects. By analyzing fitness effects at varied expression strengths, we were able to uncover all mutant effects on function. The majority of mutants caused partial functional defects, consistent with this region of Hsp90 contributing to a mutation sensitive and critical process. These results demonstrate that important functional regions of proteins can tolerate mutational defects without experimentally observable impacts on fitness.

PlantLoc: an accurate web server for predicting plant protein subcellular localization by substantiality motif

Knowledge of subcellular localizations (SCLs) of plant proteins relates to their functions and aids in understanding the regulation of biological processes at the cellular level. We present PlantLoc, a highly accurate and fast webserver for predicting the multi-label SCLs of plant proteins. The PlantLoc server has two innovative characters: building localization motif libraries by a recursive method without alignment and Gene Ontology information; and establishing simple architecture for rapidly and accurately identifying plant protein SCLs without a machine learning algorithm. PlantLoc provides predicted SCLs results, confidence estimates and which is the substantiality motif and where it is located on the sequence. PlantLoc achieved the highest accuracy (overall accuracy of 80.8%) of identification of plant protein SCLs as benchmarked by using a new test dataset compared other plant SCL prediction webservers. The ability of PlantLoc to predict multiple sites was also significantly higher than for any other webserver. The predicted substantiality motifs of queries also have great potential for analysis of relationships with protein functional regions. The PlantLoc server is available at http://cal.tongji.edu.cn/PlantLoc/.

Mutational properties of amino acid residues: implications for evolvability of phosphorylatable residues.

As François Jacob pointed out over 30 years ago, evolution is a tinkering process, and, as such, relies on the genetic diversity produced by mutation subsequently shaped by Darwinian selection. However, there is one implicit assumption that is made when studying this tinkering process; it is typically assumed that all amino acid residues are equally likely to mutate or to result from a mutation. Here, by reconstructing ancestral sequences and computing mutational probabilities for all the amino acid residues, we refute this assumption and show extensive inequalities between different residues in terms of their mutational activity. Moreover, we highlight the importance of the genetic code and physico-chemical properties of the amino acid residues as likely causes of these inequalities and uncover serine as a mutational hot spot. Finally, we explore the consequences that these different mutational properties have on phosphorylation site evolution, showing that a higher degree of evolvability exists for phosphorylated threonine and, to a lesser extent, serine in comparison with tyrosine residues. As exemplified by the suppression of serine's mutational activity in phosphorylation sites, our results suggest that the cell can fine-tune the mutational activities of amino acid residues when they reside in functional protein regions.

Functional region prediction with a set of appropriate homologous sequences-an index for sequence selection by integrating structure and sequence information with spatial statistics

BackgroundThe detection of conserved residue clusters on a protein structure is one of the effective strategies for the prediction of functional protein regions. Various methods, such as Evolutionary Trace, have been developed based on this strategy. In such approaches, the conserved residues are identified through comparisons of homologous amino acid sequences. Therefore, the selection of homologous sequences is a critical step. It is empirically known that a certain degree of sequence divergence in the set of homologous sequences is required for the identification of conserved residues. However, the development of a method to select homologous sequences appropriate for the identification of conserved residues has not been sufficiently addressed. An objective and general method to select appropriate homologous sequences is desired for the efficient prediction of functional regions.ResultsWe have developed a novel index to select the sequences appropriate for the identification of conserved residues, and implemented the index within our method to predict the functional regions of a protein. The implementation of the index improved the performance of the functional region prediction. The index represents the degree of conserved residue clustering on the tertiary structure of the protein. For this purpose, the structure and sequence information were integrated within the index by the application of spatial statistics. Spatial statistics is a field of statistics in which not only the attributes but also the geometrical coordinates of the data are considered simultaneously. Higher degrees of clustering generate larger index scores. We adopted the set of homologous sequences with the highest index score, under the assumption that the best prediction accuracy is obtained when the degree of clustering is the maximum. The set of sequences selected by the index led to higher functional region prediction performance than the sets of sequences selected by other sequence-based methods.ConclusionsAppropriate homologous sequences are selected automatically and objectively by the index. Such sequence selection improved the performance of functional region prediction. As far as we know, this is the first approach in which spatial statistics have been applied to protein analyses. Such integration of structure and sequence information would be useful for other bioinformatics problems.

PoPMuSiC 2.1: a web server for the estimation of protein stability changes upon mutation and sequence optimality

BackgroundThe rational design of modified proteins with controlled stability is of extreme importance in a whole range of applications, notably in the biotechnological and environmental areas, where proteins are used for their catalytic or other functional activities. Future breakthroughs in medical research may also be expected from an improved understanding of the effect of naturally occurring disease-causing mutations on the molecular level.ResultsPoPMuSiC-2.1 is a web server that predicts the thermodynamic stability changes caused by single site mutations in proteins, using a linear combination of statistical potentials whose coefficients depend on the solvent accessibility of the mutated residue. PoPMuSiC presents good prediction performances (correlation coefficient of 0.8 between predicted and measured stability changes, in cross validation, after exclusion of 10% outliers). It is moreover very fast, allowing the prediction of the stability changes resulting from all possible mutations in a medium size protein in less than a minute. This unique functionality is user-friendly implemented in PoPMuSiC and is particularly easy to exploit. Another new functionality of our server concerns the estimation of the optimality of each amino acid in the sequence, with respect to the stability of the structure. It may be used to detect structural weaknesses, i.e. clusters of non-optimal residues, which represent particularly interesting sites for introducing targeted mutations. This sequence optimality data is also expected to have significant implications in the prediction and the analysis of particular structural or functional protein regions. To illustrate the interest of this new functionality, we apply it to a dataset of known catalytic sites, and show that a much larger than average concentration of structural weaknesses is detected, quantifying how these sites have been optimized for function rather than stability.ConclusionThe freely available PoPMuSiC-2.1 web server is highly useful for identifying very rapidly a list of possibly relevant mutations with the desired stability properties, on which subsequent experimental studies can be focused. It can also be used to detect sequence regions corresponding to structural weaknesses, which could be functionally important or structurally delicate regions, with obvious applications in rational protein design.

Nbr1 Is a Novel Inhibitor of Ligand-Mediated Receptor Tyrosine Kinase Degradation

Neighbor of BRCA1 (Nbr1) is a highly conserved multidomain scaffold protein with proposed roles in endocytic trafficking and selective autophagy. However, the exact function of Nbr1 in these contexts has not been studied in detail. Here we investigated the role of Nbr1 in the trafficking of receptor tyrosine kinases (RTKs). We report that ectopic Nbr1 expression inhibits the ligand-mediated lysosomal degradation of RTKs, and this is probably done via the inhibition of receptor internalization. Conversely, the depletion of endogenous NBR1 enhances RTK degradation. Analyses of truncation mutations demonstrated that the C terminus of Nbr1 is essential but not sufficient for this activity. Moreover, the C terminus of Nbr1 is essential but not sufficient for the localization of the protein to late endosomes. We demonstrate that the C terminus of Nbr1 contains a novel membrane-interacting amphipathic α-helix, which is essential for the late endocytic localization of the protein but not for its effect on RTK degradation. Finally, autophagic and late endocytic localizations of Nbr1 are independent of one another, suggesting that the roles of Nbr1 in each context might be distinct. Our results define Nbr1 as a negative regulator of ligand-mediated RTK degradation and reveal the interplay between its various regions for protein localization and function.

In silico saturation mutagenesis and docking screening for the analysis of protein-ligand interaction: the Endothelial Protein C Receptor case study

BackgroundThe design of mutants in protein functional regions, such as the ligand binding sites, is a powerful approach to recognize the determinants of specific protein activities in cellular pathways. For an exhaustive analysis of selected positions of protein structure large scale mutagenesis techniques are often employed, with laborious and time consuming experimental set-up. 'In silico' mutagenesis and screening simulation represents a valid alternative to laboratory methods to drive the 'in vivo' testing toward more focused objectives.ResultsWe present here a high performance computational procedure for large-scale mutant modelling and subsequent evaluation of the effect on ligand binding affinity. The mutagenesis was performed with a 'saturation' approach, where all 20 natural amino acids were tested in positions involved in ligand binding sites. Each modelled mutant was subjected to molecular docking simulation and stability evaluation. The simulated protein-ligand complexes were screened for their impairment of binding ability based on change of calculated Ki compared to the wild-type.An example of application to the Endothelial Protein C Receptor residues involved in lipid binding is reported.ConclusionThe computational pipeline presented in this work is a useful tool for the design of structurally stable mutants with altered affinity for ligand binding, considerably reducing the number of mutants to be experimentally tested. The saturation mutagenesis procedure does not require previous knowledge of functional role of the residues involved and allows extensive exploration of all possible substitutions and their pairwise combinations. Mutants are screened by docking simulation and stability evaluation followed by a rationally driven selection of those presenting the required characteristics. The method can be employed in molecular recognition studies and as a preliminary approach to select models for experimental testing.

Phosphorylated Intrinsically Disordered Region of FACT Masks Its Nucleosomal DNA Binding Elements

FACT is a heterodimer of SPT16 and SSRP1, which each contain several conserved regions in the primary structure. The interaction of FACT with nucleosomes induces chromatin remodeling through the combinatorial action of its distinct functional protein regions. However, there is little mechanistic insight into how these regions cooperatively contribute to FACT functions, particularly regarding the recognition of nucleosomal DNA. Here, we report the identification of novel phosphorylation sites of Drosophila melanogaster FACT (dFACT) expressed in Sf9 cells. These sites are densely concentrated in the acidic intrinsically disordered (ID) region of the SSRP1 subunit and control nucleosomal DNA binding by dFACT. This region and the adjacent segment of the HMG domain form weak electrostatic intramolecular interactions, which is reinforced by the phosphorylation, thereby blocking DNA binding competitively. Importantly, this control mechanism appears to support rapid chromatin transactions during early embryogenesis through the dephosphorylation of some sites in the maternally transmitted dSSRP1.

Cancer-associated mutations are preferentially distributed in protein kinase functional sites

Protein kinases are a superfamily involved in many crucial cellular processes, including signal transmission and regulation of cell cycle. As a consequence of this role, kinases have been reported to be associated with many types of cancer and are considered as potential therapeutic targets. We analyzed the distribution of pathogenic somatic point mutations (drivers) in the protein kinase superfamily with respect to their location in the protein, such as in structural, evolutionary, and functionally relevant regions. We find these driver mutations are more clearly associated with key protein features than other somatic mutations (passengers) that have not been directly linked to tumor progression. This observation fits well with the expected implication of the alterations in protein kinase function in cancer pathogenicity. To explain the relevance of the detected association of cancer driver mutations at the molecular level in the human kinome, we compare these with genetically inherited mutations (SNPs). We find that the subset of nonsynonymous SNPs that are associated to disease, but sufficiently mild to the point of being widespread in the population, tend to avoid those key protein regions, where they could be more detrimental for protein function. This tendency contrasts with the one detected for cancer associated-driver-mutations, which seems to be more directly implicated in the alteration of protein function. The detailed analysis of protein kinase groups and a number of relevant examples, confirm the relation between cancer associated-driver-mutations and key regions for protein kinase structure and function.

Homology Modelling of Hypoxanthin–Guanine Phosphoribosyltransferase, Enzyme Involved in Salvage Pathway of Purine Metabolism

Hypoxanthin-guanine phosphoribosyltransferase (HPRT) is responsible for catalyzing a reaction which breaks down uric acid in the body. The lack of this enzyme creates a build-up of uric-acid, leading to a neurological problem, joint pain and kidney problem. The condition is called Lesch-Nyhan Syndrome. A comparative modelling of the protein Hypoxanthin-guanine phosphoribosyltransferase is performed using the modeling program modeller9v3.The following structure validation programs PROCHECK, PROSA, VERIFY3D and WHATIF are used. Descret Optimized Protein Energy (DOPE), is a statistical potential used to asses homology model in protein structure prediction. Predicted model can be useful to develop new inhibitor against Lesch-Nyhan Syndrome. CASTp can be used to study surface features and functional regions of proteins.