Protein‐spanning water networks and implications for prediction of protein–protein interactions mediated through hydrophobic effects

Hydrophobic effects, often conflated with hydrophobic forces, are implicated as major determinants in biological association and self‐assembly processes. Protein–protein interactions involved in signaling pathways in living systems are a prime example where hydrophobic effects have profound implications. In the context of protein–protein interactions, a priori knowledge of relevant binding interfaces (i.e., clusters of residues involved directly with binding interactions) is difficult. In the case of hydrophobically mediated interactions, use of hydropathy‐based methods relying on single residue hydrophobicity properties are routinely and widely used to predict propensities for such residues to be present in hydrophobic interfaces. However, recent studies suggest that consideration of hydrophobicity for single residues on a protein surface require accounting of the local environment dictated by neighboring residues and local water. In this study, we use a method derived from percolation theory to evaluate spanning water networks in the first hydration shells of a series of small proteins. We use residue‐based water density and single‐linkage clustering methods to predict hydrophobic regions of proteins; these regions are putatively involved in binding interactions. We find that this simple method is able to predict with sufficient accuracy and coverage the binding interface residues of a series of proteins. The approach is competitive with automated servers. The results of this study highlight the importance of accounting of local environment in determining the hydrophobic nature of individual residues on protein surfaces. Proteins 2014; 82:3312–3326. © 2014 Wiley Periodicals, Inc.     

Concomitant disorder and high‐affinity zinc binding in the human zinc‐ and iron‐regulated transport protein 4 intracellular loop

The human zinc‐ and iron‐regulated transport protein 4 (hZIP4) protein is the major plasma membrane protein responsible for the uptake of zinc in the body, and as such it plays a key role in cellular zinc homeostasis. hZIP4 plasma membrane levels are regulated through post‐translational modification of its large, disordered, histidine‐rich cytosolic loop (ICL2) in response to intracellular zinc concentrations. Here, structural characteristics of the isolated disordered loop region, both in the absence and presence of zinc, were investigated using nuclear magnetic resonance (NMR) spectroscopy. NMR chemical shifts, coupling constants and temperature coefficients of the apoprotein, are consistent with a random coil with minor propensities for transient polyproline Type II helices and β‐strand in regions implicated in post‐translational modifications. The ICL2 protein remains disordered upon zinc binding, which induces exchange broadening. Paramagnetic relaxation enhancement experiments reveal that the histidine‐rich region in the apoprotein makes transient tertiary contacts with predicted post‐translational modification sites. The residue‐specific data presented here strengthen the relationship between hZIP4 post‐translational modifications, which impact its role in cellular zinc homeostasis, and zinc sensing by the intracellular loop. Furthermore, the zinc sensing mechanism employed by the ICL2 protein demonstrates that high‐affinity interactions can occur in the presence of conformational disorder.     

Metapredict: a fast,accurate, and easy-to-use predictor of consensus disorder and structure

Intrinsically disordered proteins and protein regions make up a substantial fraction of many proteomes in which they play a wide variety of essential roles. A critical first step in understanding the role of disordered protein regions in biological function is to identify those disordered regions correctly. Computational methods for disorder prediction have emerged as a core set of tools to guide experiments, interpret results, and develop hypotheses. Given the multiple different predictors available, consensus scores have emerged as a popular approach to mitigate biases or limitations of any single method. Consensus scores integrate the outcome of multiple independent disorder predictors and provide a per-residue value that reflects the number of tools that predict a residue to be disordered. Although consensus scores help mitigate the inherent problems of using any single disorder predictor, they are computationally expensive to generate. They also necessitate the installation of multiple different software tools, which can be prohibitively difficult. To address this challenge, we developed a deep-learning-based predictor of consensus disorder scores. Our predictor, metapredict, utilizes a bidirectional recurrent neural network trained on the consensus disorder scores from 12 proteomes. By benchmarking metapredict using two orthogonal approaches, we found that metapredict is among the most accurate disorder predictors currently available. Metapredict is also remarkably fast, enabling proteome-scale disorder prediction in minutes. Importantly, metapredict is a fully open source and is distributed as a Python package, a collection of command-line tools, and a web server, maximizing the potential practical utility of the predictor. We believe metapredict offers a convenient, accessible, accurate, and high-performance predictor for single-proteins and proteomes alike.     

Protein flexibility and intrinsic disorder

Comparisons were made among four categories of protein flexibility: (1) low-B-factor ordered regions, (2) high-B-factor ordered regions, (3) short disordered regions, and (4) long disordered regions. Amino acid compositions of the four categories were found to be significantly different from each other, with high-B-factor ordered and short disordered regions being the most similar pair. The high-B-factor (flexible) ordered regions are characterized by a higher average flexibility index, higher average hydrophilicity, higher average absolute net charge, and higher total charge than disordered regions. The low-B-factor regions are significantly enriched in hydrophobic residues and depleted in the total number of charged residues compared to the other three categories. We examined the predictability of the high-B-factor regions and developed a predictor that discriminates between regions of low and high B-factors. This predictor achieved an accuracy of 70% and a correlation of 0.43 with experimental data, outperforming the 64% accuracy and 0.32 correlation of predictors based solely on flexibility indices. To further clarify the differences between short disordered regions and ordered regions, a predictor of short disordered regions was developed. Its relatively high accuracy of 81% indicates considerable differences between ordered and disordered regions. The distinctive amino acid biases of high-B-factor ordered regions, short disordered regions, and long disordered regions indicate that the sequence determinants for these flexibility categories differ from one another, whereas the significantly-greater-than-chance predictability of these categories from sequence suggest that flexible ordered regions, short disorder, and long disorder are, to a significant degree, encoded at the primary structure level.     

Large‐scale analysis of intrinsic disorder flavors and associated functions in the protein sequence universe

Intrinsic disorder (ID) in proteins has been extensively described for the last decade; a large‐scale classification of ID in proteins is mostly missing. Here, we provide an extensive analysis of ID in the protein universe on the UniProt database derived from sequence‐based predictions in MobiDB. Almost half the sequences contain an ID region of at least five residues. About 9% of proteins have a long ID region of over 20 residues which are more abundant in Eukaryotic organisms and most frequently cover less than 20% of the sequence. A small subset of about 67,000 (out of over 80 million) proteins is fully disordered and mostly found in Viruses. Most proteins have only one ID, with short ID evenly distributed along the sequence and long ID overrepresented in the center. The charged residue composition of Das and Pappu was used to classify ID proteins by structural propensities and corresponding functional enrichment. Swollen Coils seem to be used mainly as structural components and in biosynthesis in both Prokaryotes and Eukaryotes. In Bacteria, they are confined in the nucleoid and in Viruses provide DNA binding function. Coils & Hairpins seem to be specialized in ribosome binding and methylation activities. Globules & Tadpoles bind antigens in Eukaryotes but are involved in killing other organisms and cytolysis in Bacteria. The Undefined class is used by Bacteria to bind toxic substances and mediate transport and movement between and within organisms in Viruses. Fully disordered proteins behave similarly, but are enriched for glycine residues and extracellular structures.     

Intrinsic disorder in cell-signaling and cancer-associated proteins

The number of intrinsically disordered proteins known to be involved in cell-signaling and regulation is growing rapidly. To test for a generalized involvement of intrinsic disorder in signaling and cancer, we applied a neural network predictor of natural disordered regions (PONDR VL-XT) to four protein datasets: human cancer-associated proteins (HCAP), signaling proteins (AfCS), eukaryotic proteins from SWISS-PROT (EU_SW) and non-homologous protein segments with well-defined (ordered) 3D structure (O_PDB_S25). PONDR VL-XT predicts >or=30 consecutive disordered residues for 79(+/-5)%, 66(+/-6)%, 47(+/-4)% and 13(+/-4)% of the proteins from HCAP, AfCS, EU_SW, and O_PDB_S25, respectively, indicating significantly more intrinsic disorder in cancer-associated and signaling proteins as compared to the two control sets. The disorder analysis was extended to 11 additional functionally diverse categories of human proteins from SWISS-PROT. The proteins involved in metabolism, biosynthesis, and degradation together with kinases, inhibitors, transport, G-protein coupled receptors, and membrane proteins are predicted to have at least twofold less disorder than regulatory, cancer-associated and cytoskeletal proteins. In contrast to 44.5% of the proteins from representative non-membrane categories, just 17.3% of the cancer-associated proteins had sequence alignments with structures in the Protein Data Bank covering at least 75% of their lengths. This relative lack of structural information correlated with the greater amount of predicted disorder in the HCAP dataset. A comparison of disorder predictions with the experimental structural data for a subset of the HCAP proteins indicated good agreement between prediction and observation. Our data suggest that intrinsically unstructured proteins play key roles in cell-signaling, regulation and cancer, where coupled folding and binding is a common mechanism.     

Long indels are disordered: A study of disorder and indels in homologous eukaryotic proteins

Proteins evolve through point mutations as well as by insertions and deletions (indels). During the last decade it has become apparent that protein regions that do not fold into three-dimensional structures, i.e. intrinsically disordered regions, are quite common. Here, we have studied the relationship between protein disorder and indels using HMM–HMM pairwise alignments in two sets of orthologous eukaryotic protein pairs. First, we show that disordered residues are much more frequent among indel residues than among aligned residues and, also are more prevalent among indels than in coils. Second, we observed that disordered residues are particularly common in longer indels. Disordered indels of short-to-medium size are prevalent in the non-terminal regions of proteins while the longest indels, ordered and disordered alike, occur toward the termini of the proteins where new structural units are comparatively well tolerated. Finally, while disordered regions often evolve faster than ordered regions and disorder is common in indels, there are some previously recognized protein families where the disordered region is more conserved than the ordered region. We find that these rare proteins are often involved in information processes, such as RNA processing and translation. This article is part of a Special Issue entitled: The emerging dynamic view of proteins: Protein plasticity in allostery, evolution and self-assembly.     

Solvent-amino acid interaction energies in three-dimensional-lattice Monte Carlo simulations of a model 27-mer protein: Folding thermodynamics and kinetics

Amino acid residue-solvent interactions are required for lattice Monte Carlo simulations of model proteins in water. In this study, we propose an interaction-energy scale that is based on the interaction scale by Miyazawa and Jernigan. It permits systematic variation of the amino acid-solvent interactions by introducing a contrast parameter for the hydrophobicity, C(s), and a mean attraction parameter for the amino acids, omega. Changes in the interaction energies strongly affect many protein properties. We present an optimized energy parameter set for best representing realistic behavior typical for many proteins (fast folding and high cooperativity for single chains). Our optimal parameters feature a much weaker hydrophobicity contrast and mean attraction than does the original interaction scale. The proposed interaction scale is designed for calculating the behavior of proteins in bulk and at interfaces as a function of solvent characteristics, as well as protein size and sequence.     

固有无序蛋白与蛋白质相互作用位点残基特征分析

本文对固有无序蛋白(IDPs)与其他蛋白质相互作用位点残基特征进行了研究.首先在数据库中选出满足条件的109条IDPs蛋白质链及与其他配体蛋白形成的299个IDPs-蛋白质复合物,然后提取复合物中作为相互作用位点的IDPs-蛋白质残基.这109条IDPs链中共含有50 031个氨基酸残基,其中处于作用位点的残基有4 822个.通过分析发现,20种氨基酸在形成IDPs-蛋白质相互作用位点残基时具有不同的倾向性,根据形成作用位点残基的倾向性,20种氨基酸可分成三大类:倾向型氨基酸(ILE、LEU、ARG、PHE、TYR、MET、TRP)、中间型氨基酸(GLN、GLU、THR、LYS、VAL、ASP、HIS)、非倾向型氨基酸(PRO、SER、GLY、ALA、ASN、CYS).研究结果还进一步表明,不同氨基酸在有序区域与无序区域形成IDPs-蛋白质作用位点残基的倾向性不同.其中,氨基酸TRP、LEU、ILE、CYS在有序和无序区域形成作用位点残基的差异性尤为明显,而氨基酸GLU、PHE、HIS、ALA则基本没有多大差别.对IDPs-蛋白质相互作用位点残基理化特征进行分析发现:疏水性强、侧链净电荷量较少、极性较小、溶剂可及性表面积较大、侧链体积较大、极化率较大的氨基酸比较倾向于形成作用位点残基.主成分分析结果显示,残基的极化率、侧链体积和溶剂可及表面积对作用位点残基影响最大.     

Short peptides as predictors for the structure of polyarginine sequences in disordered proteins

Intrinsically disordered proteins and intrinsically disordered regions are frequently enriched in charged amino acids. Intrinsically disordered regions are regularly involved in important biological processes in which one or more charged residues is the driving force behind a protein-biomolecule interaction. Several lines of experimental and computational evidence suggest that polypeptides and proteins that carry high net charges have a high preference for extended conformations with average end-to-end distances exceeding expectations for self-avoiding random coils. Here, we show that charged arginine residues even in short glycine-capped model peptides (GRRG and GRRRG) significantly affect the conformational propensities of each other when compared with the intrinsic propensities of a mostly unperturbed arginine in the tripeptide GRG. A conformational analysis based on experimentally determined J-coupling constants from heteronuclear NMR spectroscopy and amide I′ band profiles from vibrational spectroscopy reveals that nearest-neighbor interactions stabilize extended β-strand conformations at the expense of polyproline II and turn conformations. The results from molecular dynamics simulations with a CHARMM36m force field and TIP3P water reproduce our results only to a limited extent. The use of the Ramachandran distribution of the central residue of GRRRG in a calculation of end-to-end distances of polyarginines of different length yielded the expected power law behavior. The scaling coefficient of 0.66 suggests that such peptides would be more extended than predicted by a self-avoiding random walk. Our findings thus support in principle theoretical predictions.     

Hydrophobicity regained.

A widespread practice is to use free energies of transfer between organic solvents and water (delta G0transfer to define hydrophobicity scales for the amino acid side chains. A comparison of four delta G0transfer scales reveals that the values for hydrogen-bonding side chains are highly dependent on the non-aqueous environment. This property of polar side chains violates the assumptions underlying the paradigm of equating delta G0transfer with hydrophobicity or even with a generic solvation energy that is directly relevant to protein stability and ligand binding energetics. This simple regaining of the original concept of hydrophobicity reveals a flaw in approaches that use delta G0transfer values to derive generic estimates of the energetics of the burial of polar groups, and allows the introduction of a "pure" hydrophobicity scale for the amino acid residues.     

Utilization of protein intrinsic disorder knowledge in structural proteomics

Intrinsically disordered proteins (IDPs) and proteins with long disordered regions are highly abundant in various proteomes. Despite their lack of well-defined ordered structure, these proteins and regions are frequently involved in crucial biological processes. Although in recent years these proteins have attracted the attention of many researchers, IDPs represent a significant challenge for structural characterization since these proteins can impact many of the processes in the structure determination pipeline. Here we investigate the effects of IDPs on the structure determination process and the utility of disorder prediction in selecting and improving proteins for structural characterization. Examination of the extent of intrinsic disorder in existing crystal structures found that relatively few protein crystal structures contain extensive regions of intrinsic disorder. Although intrinsic disorder is not the only cause of crystallization failures and many structured proteins cannot be crystallized, filtering out highly disordered proteins from structure-determination target lists is still likely to be cost effective. Therefore it is desirable to avoid highly disordered proteins from structure-determination target lists and we show that disorder prediction can be applied effectively to enrich structure determination pipelines with proteins more likely to yield crystal structures. For structural investigation of specific proteins, disorder prediction can be used to improve targets for structure determination. Finally, a framework for considering intrinsic disorder in the structure determination pipeline is proposed.     

Evolution of structurally disordered proteins promotes neostructuralization

Protein structure is generally more conserved than sequence, but for regions that can adopt different structures in different environments, does this hold true? Understanding how structurally disordered regions evolve altered secondary structure element propensities as well as conformational flexibility among paralogs are fundamental questions for our understanding of protein structural evolution. We have investigated the evolutionary dynamics of structural disorder in protein families containing both orthologs and paralogs using phylogenetic tree reconstruction, protein structure disorder prediction, and secondary structure prediction in order to shed light upon these questions. Our results indicate that the extent and location of structurally disordered regions are not universally conserved. As structurally disordered regions often have high conformational flexibility, this is likely to have an effect on how protein structure evolves as spatially altered conformational flexibility can also change the secondary structure propensities for homologous regions in a protein family.     

Protein Structure Classification Based on Conserved Hydrophobic Residues

Protein folding is frequently guided by local residue interactions that form clusters in the protein core. The interactions between residue clusters serve as potential nucleation sites in the folding process. Evidence postulates that the residue interactions are governed by the hydrophobic propensities that the residues possess. An array of hydrophobicity scales has been developed to determine the hydrophobic propensities of residues under different environmental conditions. In this work, we propose a graph-theory-based data mining framework to extract and isolate protein structural features that sustain invariance in evolutionary-related proteins, through the integrated analysis of five well-known hydrophobicity scales over the 3D structure of proteins. We hypothesize that proteins of the same homology contain conserved hydrophobic residues and exhibit analogous residue interaction patterns in the folded state. The results obtained demonstrate that discriminatory residue interaction patterns shared among proteins of the same family can be employed for both the structural and the functional annotation of proteins. We obtained on the average 90 percent accuracy in protein classification with a significantly small feature vector compared to previous results in the area. This work presents an elaborate study, as well as validation evidence, to illustrate the efficacy of the method and the correctness of results reported.     

Atomic and residue hydrophilicity in the context of folded protein structures

Water-protein interactions drive protein folding, stabilize the folded structure, and influence molecular recognition and catalysis. We analyzed the closest protein contacts of 10,837 water molecules in crystallographic structures to define a specific hydrophilicity scale reflecting specific rather than bulk solvent interactions. The tendencies of different atom and residue types to be the nearest protein neighbors of bound water molecules correlated with other hydrophobicity scales, verified the relevance of crystallographically determined water positions, and provided a direct experimental measure of water affinity in the context of the folded protein. This specific hydrophilicity was highly correlated with hydrogen-bonding capacity, and correlated better with experimental than computationally derived measures of partitioning between aqueous and organic phases. Atoms with related chemistry clustered with respect to the number of bound water molecules. Neutral and negatively charged oxygen atoms were the most hydrophilic, followed by positively-charged then neutral nitrogen atoms, followed by carbon and sulfur atoms. Agreement between observed side-chain specific hydrophilicity values and values derived from the atomic hydrophilicity scale showed that hydrophilicity values can be synthesized for different functional groups, such as unusual side or main chains, discontinuous epitopes, and drug molecules. Two methods of atomic hydrophilicity analysis provided a measure of complementarity in the interfaces of trypsin:pancreatic trypsin inhibitor and HIV protease:U-75875 inhibitor complexes. © 1995 Wiley-Liss, Inc.     

The importance of intrinsic disorder for protein phosphorylation

Reversible protein phosphorylation provides a major regulatory mechanism in eukaryotic cells. Due to the high variability of amino acid residues flanking a relatively limited number of experimentally identified phosphorylation sites, reliable prediction of such sites still remains an important issue. Here we report the development of a new web-based tool for the prediction of protein phosphorylation sites, DISPHOS (DISorder-enhanced PHOSphorylation predictor, http://www.ist.temple. edu/DISPHOS). We observed that amino acid compositions, sequence complexity, hydrophobicity, charge and other sequence attributes of regions adjacent to phosphorylation sites are very similar to those of intrinsically disordered protein regions. Thus, DISPHOS uses position-specific amino acid frequencies and disorder information to improve the discrimination between phosphorylation and non-phosphorylation sites. Based on the estimates of phosphorylation rates in various protein categories, the outputs of DISPHOS are adjusted in order to reduce the total number of misclassified residues. When tested on an equal number of phosphorylated and non-phosphorylated residues, the accuracy of DISPHOS reaches 76% for serine, 81% for threonine and 83% for tyrosine. The significant enrichment in disorder-promoting residues surrounding phosphorylation sites together with the results obtained by applying DISPHOS to various protein functional classes and proteomes, provide strong support for the hypothesis that protein phosphorylation predominantly occurs within intrinsically disordered protein regions.     

Resolving the ambiguity: Making sense of intrinsic disorder when PDB structures disagree

Missing regions in X‐ray crystal structures in the Protein Data Bank (PDB) have played a foundational role in the study of intrinsically disordered protein regions (IDPRs), especially in the development of in silico predictors of intrinsic disorder. However, a missing region is only a weak indication of intrinsic disorder, and this uncertainty is compounded by the presence of ambiguous regions, where more than one structure of the same protein sequence “disagrees” in terms of the presence or absence of missing residues. The question is this: are these ambiguous regions intrinsically disordered, or are they the result of static disorder that arises from experimental conditions, ensembles of structures, or domain wobbling? A novel way of looking at ambiguous regions in terms of the pattern between multiple PDB structures has been demonstrated. It was found that the propensity for intrinsic disorder increases as the level of ambiguity decreases. However, it is also shown that ambiguity is more likely to occur as the protein region is placed within different environmental conditions, and even the most ambiguous regions as a set display compositional bias that suggests flexibility. The results suggested that ambiguity is a natural result for many IDPRs crystallized under different conditions and that static disorder and wobbling domains are relatively rare. Instead, it is more likely that ambiguity arises because many of these regions were conditionally or partially disordered.