Understanding the functional difference between growth arrest-specific protein 6 and protein S: an evolutionary approach

Although protein S (PROS1) and growth arrest-specific protein 6 (GAS6) proteins are homologous with a high degree of structural similarity, they are functionally different. The objectives of this study were to identify the evolutionary origins from which these functional differences arose. Bioinformatics methods were used to estimate the evolutionary divergence time and to detect the amino acid residues under functional divergence between GAS6 and PROS1. The properties of these residues were analysed in the light of their three-dimensional structures, such as their stability effects, the identification of electrostatic patches and the identification potential protein–protein interaction. The divergence between GAS6 and PROS1 probably occurred during the whole-genome duplications in vertebrates. A total of 78 amino acid sites were identified to be under functional divergence. One of these sites, Asn463, is involved in N-glycosylation in GAS6, but is mutated in PROS1, preventing this post-translational modification. Sites experiencing functional divergence tend to express a greater diversity of stabilizing/destabilizing effects than sites that do not experience such functional divergence. Three electrostatic patches in the LG1/LG2 domains were found to differ between GAS6 and PROS1. Finally, a surface responsible for protein–protein interactions was identified. These results may help researchers to analyse disease-causing mutations in the light of evolutionary and structural constraints, and link genetic pathology to clinical phenotypes.     

Contact Density Affects Protein Evolutionary Rate from Bacteria to Animals

The density of contacts or the fraction of buried sites in a protein structure is thought to be related to a protein’s designability, and genes encoding more designable proteins should evolve faster than other genes. Several recent studies have tested this hypothesis but have found conflicting results. Here, we investigate how a gene’s evolutionary rate is affected by its protein’s contact density, considering the four species Escherichia coli, Saccharomyces cerevisiae, Drosophila melanogaster, and Homo sapiens. We find for all four species that contact density correlates positively with evolutionary rate, and that these correlations do not seem to be confounded by gene expression level. The strength of this signal, however, varies widely among species. We also study the effect of contact density on domain evolution in multidomain proteins and find that a domain’s contact density influences the domain’s evolutionary rate. Within the same protein, a domain with higher contact density tends to evolve faster than a domain with lower contact density. Our study provides evidence that contact density can increase evolutionary rates, and that it acts similarly on the level of entire proteins and of individual protein domains. Electronic supplementary material The online version of this article (doi:) contains supplementary material, which is available to authorized users.     

Discarding functional residues from the substitution table improves predictions of active sites within three-dimensional structures

Substitutions of individual amino acids in proteins may be under very different evolutionary restraints depending on their structural and functional roles. The Environment Specific Substitution Table (ESST) describes the pattern of substitutions in terms of amino acid location within elements of secondary structure, solvent accessibility, and the existence of hydrogen bonds between side chains and neighbouring amino acid residues. Clearly amino acids that have very different local environments in their functional state compared to those in the protein analysed will give rise to inconsistencies in the calculation of amino acid substitution tables. Here, we describe how the calculation of ESSTs can be improved by discarding the functional residues from the calculation of substitution tables. Four categories of functions are examined in this study: protein–protein interactions, protein–nucleic acid interactions, protein–ligand interactions, and catalytic activity of enzymes. Their contributions to residue conservation are measured and investigated. We test our new ESSTs using the program CRESCENDO, designed to predict functional residues by exploiting knowledge of amino acid substitutions, and compare the benchmark results with proteins whose functions have been defined experimentally. The new methodology increases the Z-score by 98% at the active site residues and finds 16% more active sites compared with the old ESST. We also find that discarding amino acids responsible for protein–protein interactions helps in the prediction of those residues although they are not as conserved as the residues of active sites. Our methodology can make the substitution tables better reflect and describe the substitution patterns of amino acids that are under structural restraints only.     

Identification of domains and domain interface residues in multidomain proteins from graph spectral method

We present a novel method for the identification of structural domains and domain interface residues in proteins by graph spectral method. This method converts the three-dimensional structure of the protein into a graph by using atomic coordinates from the PDB file. Domain definitions are obtained by constructing either a protein backbone graph or a protein side-chain graph. The graph is constructed based on the interactions between amino acid residues in the three-dimensional structure of the proteins. The spectral parameters of such a graph contain information regarding the domains and subdomains in the protein structure. This is based on the fact that the interactions among amino acids are higher within a domain than across domains. This is evident in the spectra of the protein backbone and the side-chain graphs, thus differentiating the structural domains from one another. Further, residues that occur at the interface of two domains can also be easily identified from the spectra. This method is simple, elegant, and robust. Moreover, a single numeric computation yields both the domain definitions and the interface residues.     

Predicting protein crystallization propensity from protein sequence

The high-throughput structure determination pipelines developed by structural genomics programs offer a unique opportunity for data mining. One important question is how protein properties derived from a primary sequence correlate with the protein’s propensity to yield X-ray quality crystals (crystallizability) and 3D X-ray structures. A set of protein properties were computed for over 1,300 proteins that expressed well but were insoluble, and for ~720 unique proteins that resulted in X-ray structures. The correlation of the protein’s iso-electric point and grand average hydropathy (GRAVY) with crystallizability was analyzed for full length and domain constructs of protein targets. In a second step, several additional properties that can be calculated from the protein sequence were added and evaluated. Using statistical analyses we have identified a set of the attributes correlating with a protein’s propensity to crystallize and implemented a Support Vector Machine (SVM) classifier based on these. We have created applications to analyze and provide optimal boundary information for query sequences and to visualize the data. These tools are available via the web site .     

氨基酸在蛋白质空间结构中的深度倾向性因子

用统计和几何方法给出了氨基酸在蛋白质空间结构中的深度计算,并利用PDB数据库得到了不同氨基酸在蛋白质中的深度倾向性因子,并得到了这些倾向性因子与氨基酸的物理、化学综合特性的相关性质.     

Differences in amino acids composition and coupling patterns between mesophilic and thermophilic proteins

Summary. Thermophilic proteins show substantially higher intrinsic thermal stability than their mesophilic counterparts. Amino acid composition is believed to alter the intrinsic stability of proteins. Several investigations and mutagenesis experiment have been carried out to understand the amino acid composition for the thermostability of proteins. This review presents some generalized features of amino acid composition found in thermophilic proteins, including an increase in residue hydrophobicity, a decrease in uncharged polar residues, an increase in charged residues, an increase in aromatic residues, certain amino acid coupling patterns and amino acid preferences for thermophilic proteins. The differences of amino acids composition between thermophilic and mesophilic proteins are related to some properties of amino acids. These features provide guidelines for engineering mesophilic protein to thermophilic protein. Authors’ addresses: Yuan-Jiang Pan, Institute of Chemical Biology and Pharmaceutical Chemistry, Zhejiang University, Zhejiang University Road 38, Hangzhou 310027, China; Wei-Fen Li, Microbiology Division, College of Animal Science, Zhejiang University, Hangzhou 310029, China     

A machine learning based method for the prediction of G protein-coupled receptor-binding PDZ domain proteins

G protein-coupled receptors (GPCRs) are part of multi-protein networks called ‘receptosomes’. These GPCR interacting proteins (GIPs) in the receptosomes control the targeting, trafficking and signaling of GPCRs. PDZ domain proteins constitute the largest protein family among the GIPs, and the predominant function of the PDZ domain proteins is to assemble signaling pathway components into close proximity by recognition of the last four C-terminal amino acids of GPCRs. We present here a machine learning based approach for the identification of GPCR-binding PDZ domain proteins. In order to characterize the network of interactions between amino acid residues that contribute to the stability of the PDZ domain-ligand complex and to encode the complex into a feature vector, amino acid contact matrices and physicochemical distance matrix were constructed and adopted. This novel machine learning based method displayed high performance for the identification of PDZ domain-ligand interactions and allowed the identification of novel GPCR-PDZ domain protein interactions.     

The Myofibrillar Protein,Projectin, is Highly Conserved Across Insect Evolution Except for Its PEVK Domain

All striated muscles respond to stretch by a delayed increase in tension. This physiological response, known as stretch activation, is, however, predominantly found in vertebrate cardiac muscle and insect asynchronous flight muscles. Stretch activation relies on an elastic third filament system composed of giant proteins known as titin in vertebrates or kettin and projectin in insects. The projectin insect protein functions jointly as a “scaffold and ruler” system during myofibril assembly and as an elastic protein during stretch activation. An evolutionary analysis of the projectin molecule could potentially provide insight into how distinct protein regions may have evolved in response to different evolutionary constraints. We mined candidate genes in representative insect species from Hemiptera to Diptera, from published and novel genome sequence data, and carried out a detailed molecular and phylogenetic analysis. The general domain organization of projectin is highly conserved, as are the protein sequences of its two repeated regions—the immunoglobulin type C and fibronectin type III domains. The conservation in structure and sequence is consistent with the proposed function of projectin as a scaffold and ruler. In contrast, the amino acid sequences of the elastic PEVK domains are noticeably divergent, although their length and overall unusual amino acid makeup are conserved. These patterns suggest that the PEVK region working as an unstructured domain can still maintain its dynamic, and even its three-dimensional, properties, without the need for strict amino acid conservation. Phylogenetic analysis of the projectin proteins also supports a reclassification of the Hymenoptera in relation to Diptera and Coleoptera. Electronic supplementary material  The online version of this article (doi:) contains supplementary material, which is available to authorized users.     

Defective Protein Folding and Aggregation as the Basis of Neurodegenerative Diseases: The Darker Aspect of Proteins

Abstract  

The ability of a polypeptide to fold into a unique, functional, and three-dimensional structure depends on the intrinsic properties of the amino acid sequence, function of the molecular chaperones, proteins, and enzymes. Every polypeptide has a finite tendency to misfold and this forms the darker side of the protein world. Partially folded and misfolded proteins that escape the cellular quality control mechanism have the high tendency to form inter-molecular hydrogen bonding between the same protein molecules resulting in aggregation. This review summarizes the underlying and universal mechanism of protein folding. It also deals with the factors responsible for protein misfolding and aggregation. This article describes some of the consequences of such behavior particularly in the context of neurodegenerative conformational diseases such as Alzheimer’s, Parkinson’s, Huntington’s, amyotrophic lateral sclerosis and other non-neurodegenerative conformational diseases such as cancer and cystic fibrosis etc. This will encourage a more proactive approach to the early diagnosis of conformational diseases and nutritional counseling for patients.     

Distinguishing structural and functional restraints in evolution in order to identify interaction sites

Structural genomics projects are producing many three-dimensional structures of proteins that have been identified only from their gene sequences. It is therefore important to develop computational methods that will predict sites involved in productive intermolecular interactions that might give clues about functions. Techniques based on evolutionary conservation of amino acids have the advantage over physiochemical methods in that they are more general. However, the majority of techniques neither use all available structural and sequence information, nor are able to distinguish between evolutionary restraints that arise from the need to maintain structure and those that arise from function. Three methods to identify evolutionary restraints on protein sequence and structure are described here. The first identifies those residues that have a higher degree of conservation than expected: this is achieved by comparing for each amino acid position the sequence conservation observed in the homologous family of proteins with the degree of conservation predicted on the basis of amino acid type and local environment. The second uses information theory to identify those positions where environment-specific substitution tables make poor predictions of the overall amino acid substitution pattern. The third method identifies those residues that have highly conserved positions when three-dimensional structures of proteins in a homologous family are superposed. The scores derived from these methods are mapped onto the protein three-dimensional structures and contoured, allowing identification clusters of residues with strong evolutionary restraints that are sites of interaction in proteins involved in a variety of functions. Our method differs from other published techniques by making use of structural information to identify restraints that arise from the structure of the protein and differentiating these restraints from others that derive from intermolecular interactions that mediate functions in the whole organism.     

Recognizing protein–protein interfaces with empirical potentials and reduced amino acid alphabets

Background  

In structural genomics, an important goal is the detection and classification of protein–protein interactions, given the structures of the interacting partners. We have developed empirical energy functions to identify native structures of protein–protein complexes among sets of decoy structures. To understand the role of amino acid diversity, we parameterized a series of functions, using a hierarchy of amino acid alphabets of increasing complexity, with 2, 3, 4, 6, and 20 amino acid groups. Compared to previous work, we used the simplest possible functional form, with residue–residue interactions and a stepwise distance-dependence. We used increased computational ressources, however, constructing 290,000 decoys for 219 protein–protein complexes, with a realistic docking protocol where the protein partners are flexible and interact through a molecular mechanics energy function. The energy parameters were optimized to correctly assign as many native complexes as possible. To resolve the multiple minimum problem in parameter space, over 64000 starting parameter guesses were tried for each energy function. The optimized functions were tested by cross validation on subsets of our native and decoy structures, by blind tests on series of native and decoy structures available on the Web, and on models for 13 complexes submitted to the CAPRI structure prediction experiment.     

Influence of depth on sex-specific energy allocation patterns in a tropical reef fish

The effect of depth on the distribution and sex-specific energy allocation patterns of a common coral reef fish, Chrysiptera rollandi (Pomacentridae), was investigated using depth-stratified collections over a broad depth range (5–39 m) and a translocation experiment. C. rollandi consistently selected rubble habitats at each depth, however abundance patterns did not reflect the availability of the preferred microhabitat suggesting a preference for depth as well as microhabitat. Reproductive investment (gonado-somatic index), energy stores (liver cell density and hepatocyte vacuolation), and overall body condition (hepato-somatic index and Fulton’s K) of female fish varied significantly among depths and among the three reefs sampled. Male conspecifics displayed no variation between depth or reef. Depth influenced growth dynamics, with faster initial growth rates and smaller mean asymptotic lengths with decreasing depth. In female fish, relative gonad weight and overall body condition (Fulton’s K and hepato-somatic index) were generally higher in shallower depths (≤10 m). Hepatic lipid storage was highest at the deepest sites sampled on each reef, whereas hepatic glycogen stores tended to decrease with depth. Depth was found to influence energy allocation dynamics in C. rollandi. While it is unclear what processes directly influenced the depth-related patterns in energy allocation, this study shows that individuals across a broad depth gradient are not all in the same physiological state and may contribute differentially to the population reproductive output. Electronic supplementary material The online version of this article (doi:) contains supplementary material, which is available to authorized users.     

Structural motifs in protein cores and at protein–protein interfaces are different

Structures of proteins and protein–protein complexes are determined by the same physical principles and thus share a number of similarities. At the same time, there could be differences because in order to function, proteins interact with other molecules, undergo conformations changes, and so forth, which might impose different restraints on the tertiary versus quaternary structures. This study focuses on structural properties of protein–protein interfaces in comparison with the protein core, based on the wealth of currently available structural data and new structure‐based approaches. The results showed that physicochemical characteristics, such as amino acid composition, residue–residue contact preferences, and hydrophilicity/hydrophobicity distributions, are similar in protein core and protein–protein interfaces. On the other hand, characteristics that reflect the evolutionary pressure, such as structural composition and packing, are largely different. The results provide important insight into fundamental properties of protein structure and function. At the same time, the results contribute to better understanding of the ways to dock proteins. Recent progress in predicting structures of individual proteins follows the advancement of deep learning techniques and new approaches to residue coevolution data. Protein core could potentially provide large amounts of data for application of the deep learning to docking. However, our results showed that the core motifs are significantly different from those at protein–protein interfaces, and thus may not be directly useful for docking. At the same time, such difference may help to overcome a major obstacle in application of the coevolutionary data to docking—discrimination of the intramolecular information not directly relevant to docking.     

Uncovering network systems within protein structures

Traditionally, proteins have been viewed as a construct based on elements of secondary structure and their arrangement in three-dimensional space. In a departure from this perspective we show that protein structures can be modelled as network systems that exhibit small-world, single-scale, and to some degree, scale-free properties. The phenomenological network concept of degrees of separation is applied to three-dimensional protein structure networks and reveals how amino acid residues can be connected to each other within six degrees of separation. This work also illuminates the unique features of protein networks in comparison to other networks currently studied. Recognising that proteins are networks provides a means of rationalising the robustness in the overall three-dimensional fold of a protein against random mutations and suggests an alternative avenue to investigate the determinants of protein structure, function and folding.     

Amino Acid Metabolic Origin as an Evolutionary Influence on Protein Sequence in Yeast

The metabolic cycle of Saccharomyces cerevisiae consists of alternating oxidative (respiration) and reductive (glycolysis) energy-yielding reactions. The intracellular concentrations of amino acid precursors generated by these reactions oscillate accordingly, attaining maximal concentration during the middle of their respective yeast metabolic cycle phases. Typically, the amino acids themselves are most abundant at the end of their precursor’s phase. We show that this metabolic cycling has likely biased the amino acid composition of proteins across the S. cerevisiae genome. In particular, we observed that the metabolic source of amino acids is the single most important source of variation in the amino acid compositions of functionally related proteins and that this signal appears only in (facultative) organisms using both oxidative and reductive metabolism. Periodically expressed proteins are enriched for amino acids generated in the preceding phase of the metabolic cycle. Proteins expressed during the oxidative phase contain more glycolysis-derived amino acids, whereas proteins expressed during the reductive phase contain more respiration-derived amino acids. Rare amino acids (e.g., tryptophan) are greatly overrepresented or underrepresented, relative to the proteomic average, in periodically expressed proteins, whereas common amino acids vary by a few percent. Genome-wide, we infer that 20,000 to 60,000 residues have been modified by this previously unappreciated pressure. This trend is strongest in ancient proteins, suggesting that oscillating endogenous amino acid availability exerted genome-wide selective pressure on protein sequences across evolutionary time. Electronic supplementary material  The online version of this article (doi:) contains supplementary material, which is available to authorized users. Benjamin L. de Bivort and Ethan O. Perlstein have contributed equally to this work.     

Prediction of protein domain boundaries based on statistics of appearance of amino acid residues

We have created a database of two-domain proteins with homology less than 25% (452 proteins). Based on one half of this set of proteins statistics of appearance of amino acid residues on the domain boundaries of multiple domain proteins has been obtained. Small and hydrophilic amino acids (proline, glycine, asparagine, glutamic acid, arginine and others) appear on the domain boundaries more often than in the whole protein. Opposite, hydrophobic amino acid residues (tryptophane, methionine, phenylalanine and others) appear on the domain boundaries more rarely. The obtained scales of the appearance of amino acid residues on the boundary regions from the statistics have been used for calculation of domain boundaries in the proteins of the second half of the database. The probability scale obtained by averaging the appearance of amino acid residues on the domain boundary region including 8 residues (+/-4 residues from the real domain boundary) gives the best result: for 57% of proteins the predicted boundary was closer than 40 residues to the boundary assigned from three-dimensional structures, for 41% it was closer than 20 residues from the real boundary. The probability scale was used to predict domain boundaries for proteins with unknown three-dimensional structure (international competition CASP6).     

Conserved residue clustering and protein structure prediction

Protein residues that are critical for structure and function are expected to be conserved throughout evolution. Here, we investigate the extent to which these conserved residues are clustered in three-dimensional protein structures. In 92% of the proteins in a data set of 79 proteins, the most conserved positions in multiple sequence alignments are significantly more clustered than randomly selected sets of positions. The comparison to random subsets is not necessarily appropriate, however, because the signal could be the result of differences in the amino acid composition of sets of conserved residues compared to random subsets (hydrophobic residues tend to be close together in the protein core), or differences in sequence separation of the residues in the different sets. In order to overcome these limits, we compare the degree of clustering of the conserved positions on the native structure and on alternative conformations generated by the de novo structure prediction method Rosetta. For 65% of the 79 proteins, the conserved residues are significantly more clustered in the native structure than in the alternative conformations, indicating that the clustering of conserved residues in protein structures goes beyond that expected purely from sequence locality and composition effects. The differences in the spatial distribution of conserved residues can be utilized in de novo protein structure prediction: We find that for 79% of the proteins, selection of the Rosetta generated conformations with the greatest clustering of the conserved residues significantly enriches the fraction of close-to-native structures.     

Protein–protein interactions within peroxiredoxin systems

Peroxiredoxin systems in plants were demonstrated involved in crucial roles related to reactive oxygenated species (ROS) metabolism and the linked cell signalling to ROS. Peroxiredoxins function as peroxidasic systems that combine at least a reactivating reductant agent like thioredoxins, and sometimes glutaredoxins and glutathion. In the past three years a number of peroxiredoxin structures were solved by crystallography in different experimental crystallisation conditions. The structures have revealed a significant propensity of peroxiredoxins for oligomerism that was confirmed by biophysical studies in solution using NMR and other methods as analytical ultra-centrifugation. These studies showed that quaternary structures of peroxiredoxins involve specific protein–protein interaction interfaces that rely upon the peroxiredoxin types and/or their redox conditions. The protein–protein interactions with the reactivating redoxins essentially lead to transient unstable complexes. We review herein the different protein–protein interactions characterized or deduced from those reports.VNM is recipient of a PhD fellowship of the French Ministère de l’Enseignement Supérieur de la Recherche et des Nouvelles Technologies for the year 2003–2006 and the Research Doctorate School of Chemistry of Lyon.