Predicting conformational switches in proteins

Protein Fold Usages in Ribosomes: Another Glance to the Past

The analysis of protein fold usage, similar to codon usage, offers profound insights into the evolution of biological systems and the origins of modern proteomes. While previous studies have examined fold distribution in modern genomes, our study focuses on the comparative distribution and usage of protein folds in ribosomes across bacteria, archaea, and eukaryotes. We identify the prevalence of certain 'super-ribosome folds,' such as the OB fold in bacteria and the SH3 domain in archaea and eukaryotes. The observed protein fold distribution in the ribosomes announces the future power-law distribution where only a few folds are highly prevalent, and most are rare. Additionally, we highlight the presence of three copies of proto-Rossmann folds in ribosomes across all kingdoms, showing its ancient and fundamental role in ribosomal structure and function. Our study also explores early mechanisms of molecular convergence, where different protein folds bind equivalent ribosomal RNA structures in ribosomes across different kingdoms. This comparative analysis enhances our understanding of ribosomal evolution, particularly the distinct evolutionary paths of the large and small subunits, and underscores the complex interplay between RNA and protein components in the transition from the RNA world to modern cellular life. Transcending the concept of folds also makes it possible to group a large number of ribosomal proteins into five categories of urfolds or metafolds, which could attest to their ancestral character and common origins. This work also demonstrates that the gradual acquisition of extensions by simple but ordered folds constitutes an inexorable evolutionary mechanism. This observation supports the idea that simple but structured ribosomal proteins preceded the development of their disordered extensions.

Prediction and confirmation of a switch-like region within the N-terminal domain of hSIRT1

Many proteins display conformational changes resulting from allosteric regulation. Often only a few residues are crucial in conveying these structural and functional allosteric changes. These regions that undergo a significant change in structure upon receiving an input signal, such as molecular recognition, are defined as switch-like regions. Identifying these key residues within switch-like regions can help elucidate the mechanism of allosteric regulation and provide guidance for synthetic regulation. In this study, we combine a novel computational workflow with biochemical methods to identify a switch-like region in the N-terminal domain of human SIRT1 (hSIRT1), a lysine deacetylase that plays important roles in regulating cellular pathways. Based on primary sequence, computational methods predicted a region between residues 186-193 in hSIRT1 to exhibit switch-like behavior. Mutations were then introduced in this region and the resulting mutants were tested for allosteric reactions to resveratrol, a known hSIRT1 allosteric regulator. After fine-tuning the mutations based on comparison of known secondary structures, we were able to pinpoint M193 as the residue essential for allosteric regulation, likely by communicating the allosteric signal. Mutation of this residue maintained enzyme activity but abolished allosteric regulation by resveratrol. Our findings suggest a method to predict switch-like regions in allosterically regulated enzymes based on the primary sequence. If further validated, this could be an efficient way to identify key residues in enzymes for therapeutic drug targeting and other applications.

A topology-based investigation of protein interaction sites using Hydrophobic Cluster Analysis

Hydrophobic clusters, as defined by Hydrophobic Cluster Analysis (HCA), are conditioned binary patterns, made of hydrophobic and non-hydrophobic positions, whose limits fit well those of regular secondary structures. They were proved to be useful for predicting secondary structures in proteins from the only information of a single amino acid sequence and have permitted to assess, in a comprehensive way, the leading role of binary patterns in secondary structure preference towards a particular state. Here, we considered the available experimental 3D structures of protein globular domains to enlarge our previously reported hydrophobic cluster database (HCDB), almost doubling the number of hydrophobic cluster species (each species being defined by a unique binary pattern) that represent the most frequent structural bricks encountered within protein globular domains. We then used this updated HCDB to show that the hydrophobic amino acids of discordant clusters, i.e. those less abundant clusters for which the observed secondary structure is in disagreement with the binary pattern preference of the species to which they belong, are more exposed to solvent and are more involved in protein interfaces than the hydrophobic amino acids of concordant clusters. As amino acid composition differs between concordant/discordant clusters, considering binary patterns may be used to gain novel insights into key features of protein globular domain cores and surfaces. It can also provide useful information on possible conformational plasticity, including disorder to order transitions.

Inaccurate secondary structure predictions often indicate protein fold switching

Although most proteins conform to the classical one‐structure/one‐function paradigm, an increasing number of proteins with dual structures and functions have been discovered. In response to cellular stimuli, such proteins undergo structural changes sufficiently dramatic to remodel even their secondary structures and domain organization. This “fold‐switching” capability fosters protein multi‐functionality, enabling cells to establish tight control over various biochemical processes. Accurate predictions of fold‐switching proteins could both suggest underlying mechanisms for uncharacterized biological processes and reveal potential drug targets. Recently, we developed a prediction method for fold‐switching proteins using structure‐based thermodynamic calculations and discrepancies between predicted and experimentally determined protein secondary structure (Porter and Looger, Proc Natl Acad Sci U S A 2018; 115:5968–5973). Here we seek to leverage the negative information found in these secondary structure prediction discrepancies. To do this, we quantified secondary structure prediction accuracies of 192 known fold‐switching regions (FSRs) within solved protein structures found in the Protein Data Bank (PDB). We find that the secondary structure prediction accuracies for these FSRs vary widely. Inaccurate secondary structure predictions are strongly associated with fold‐switching proteins compared to equally long segments of non‐fold‐switching proteins selected at random. These inaccurate predictions are enriched in helix‐to‐strand and strand‐to‐coil discrepancies. Finally, we find that most proteins with inaccurate secondary structure predictions are underrepresented in the PDB compared with their alternatively folded cognates, suggesting that unequal representation of fold‐switching conformers within the PDB could be an important cause of inaccurate secondary structure predictions. These results demonstrate that inconsistent secondary structure predictions can serve as a useful preliminary marker of fold switching.

Comprehensive analysis of sequences of a protein switch

Switches form a special class of proteins that dramatically change their three-dimensional structures upon a small perturbation. One possible perturbation that we explore is that of a single point mutation. Building on the pioneering experimental work of Alexander et al. (Alexander et al. PNAS, 2007; 104,11963-11968) that determines switch sequences between α and α+β folds we conduct a comprehensive sequence sampling by a Markov Chain with multiple fitness criteria to identify new switches given the experimental folds. We screen for switch sequences using a combination of contact potential, secondary structure prediction, and finally molecular dynamics simulations. Statistical properties of switch sequences are discussed and illustrated to be most sensitive to mutation at the N- and C- termini of the switch protein. Based on this analysis, a particularly stable putative switch pair is identified and proposed for further experimental analysis.

Virtual screening on an α-helix to β-strand switchable region of the FGFR2 extracellular domain revealed positive and negative modulators

The secondary structure of some protein segments may vary between α-helix and β-strand. To predict these switchable segments, we have developed an algorithm, Switch-P, based solely on the protein sequence. This algorithm was used on the extracellular parts of FGF receptors. For FGFR2, it predicted that β4 and β5 strands of the third Ig-like domain were highly switchable. These two strands possess a high number of somatic mutations associated with cancer. Analysis of PDB structures of FGF receptors confirmed the switchability prediction for β5. We thus evaluated if compound-driven α-helix/β-strand switching of β5 could modulate FGFR2 signaling. We performed the virtual screening of a library containing 1.4 million of chemical compounds with two models of the third Ig-like domain of FGFR2 showing different secondary structures for β5, and we selected 32 compounds. Experimental testing using proliferation assays with FGF7-stimulated SNU-16 cells and a FGFR2-dependent Erk1/2 phosphorylation assay with FGFR2-transfected L6 cells, revealed activators and inhibitors of FGFR2. Our method for the identification of switchable proteinic regions, associated with our virtual screening approach, provides an opportunity to discover new generation of drugs with under-explored mechanism of action.

PREDICTION OF PROTEIN SECONDARY STRUCTURE USING PROBABILITY BASED FEATURES AND A HYBRID SYSTEM

In this paper, we propose some co-occurrence probability-based features for prediction of protein secondary structure. The features are extracted using occurrence/nonoccurrence of secondary structures in the protein sequences. We explore two types of features: position-specific (based on position of amino acid on fragments of protein sequences) as well as position-independent (independent of amino acid position on fragments of protein sequences). We use a hybrid system, NEUROSVM, consisting of neural networks and support vector machines for classification of secondary structures. We propose two schemes NSVMps and NSVM for protein secondary structure prediction. The NSVMps uses position-specific probability-based features and NEUROSVM classifier whereas NSVM uses the same classifier with position-independent probability-based features. The proposed method falls in the single-sequence category of methods because it does not use any sequence profile information such as position specific scoring matrices (PSSM) derived from PSI-BLAST. Two widely used datasets RS126 and CB513 are used in the experiments. The results obtained using the proposed features and NEUROSVM classifier are better than most of the existing single-sequence prediction methods. Most importantly, the results using NSVMps that are obtained using lower dimensional features, are comparable to those by other existing methods. The NSVMps and NSVM are finally tested on target proteins of the critical assessment of protein structure prediction experiment-9 (CASP9). A larger dataset is used to compare the performance of the proposed methods with that of two recent single-sequence prediction methods. We also investigate the impact of presence of different amino acid residues (in protein sequences) that are responsible for the formation of different secondary structures.

Extracting knowledge from protein structure geometry

Protein structure prediction techniques proceed in two steps, namely the generation of many structural models for the protein of interest, followed by an evaluation of all these models to identify those that are native-like. In theory, the second step is easy, as native structures correspond to minima of their free energy surfaces. It is well known however that the situation is more complicated as the current force fields used for molecular simulations fail to recognize native states from misfolded structures. In an attempt to solve this problem, we follow an alternate approach and derive a new potential from geometric knowledge extracted from native and misfolded conformers of protein structures. This new potential, Metric Protein Potential (MPP), has two main features that are key to its success. Firstly, it is composite in that it includes local and nonlocal geometric information on proteins. At the short range level, it captures and quantifies the mapping between the sequences and structures of short (7-mer) fragments of protein backbones through the introduction of a new local energy term. The local energy term is then augmented with a nonlocal residue-based pairwise potential, and a solvent potential. Secondly, it is optimized to yield a maximized correlation between the energy of a structural model and its root mean square (RMS) to the native structure of the corresponding protein. We have shown that MPP yields high correlation values between RMS and energy and that it is able to retrieve the native structure of a protein from a set of high-resolution decoys.

Recoding RNA editing of AZIN1 predisposes to hepatocellular carcinoma

A better understanding of human hepatocellular carcinoma (HCC) pathogenesis at the molecular level will facilitate the discovery of tumor-initiating events. Transcriptome sequencing revealed that adenosine-to-inosine (A→I) RNA editing of AZIN1 (encoding antizyme inhibitor 1) is increased in HCC specimens. A→I editing of AZIN1 transcripts, specifically regulated by ADAR1 (encoding adenosine deaminase acting on RNA-1), results in a serine-to-glycine substitution at residue 367 of AZIN1, located in β-strand 15 (β15) and predicted to cause a conformational change, induced a cytoplasmic-to-nuclear translocation and conferred gain-of-function phenotypes that were manifested by augmented tumor-initiating potential and more aggressive behavior. Compared with wild-type AZIN1 protein, the edited form has a stronger affinity to antizyme, and the resultant higher AZIN1 protein stability promotes cell proliferation through the neutralization of antizyme-mediated degradation of ornithine decarboxylase (ODC) and cyclin D1 (CCND1). Collectively, A→I RNA editing of AZIN1 may be a potential driver in the pathogenesis of human cancers, particularly HCC.

Nuclear localization of clathrin involves a labile helix outside the trimerization domain

Clathrin is a trimeric protein involved in receptor-mediated-endocytosis, but can function as a non-trimer outside of endocytosis. We have discovered that the subcellular distribution of a clathrin cysteine mutant we previously studied is altered and a proportion is also localized to nuclear spaces. MALS shows C1573A hub is a mixture of trimer-like and detrimerized molecules. The X-ray structure of the trimerization domain reveals that without light chains, a helix harboring cysteine-1573 is reoriented. We propose clathrin has a detrimerization switch, which suggests clathrin topology can be altered naturally for new functions.

Search for identical octapeptides in unrelated proteins: Structural plasticity revisited

Since proteins are dynamic in nature, they can alter their local structure in response to changes in their environment factors such as temperature, pH, phosphorylation, and binding of other small molecules. These conformational changes are extremely important for the correct folding and functioning of proteins. There are also a number of diseases associated with protein conformational change such as amyloid diseases. To stimulate research into the above factors which specify one conformation over another, different theoretical models have been proposed and tested against sequence similar distant structure protein fragments. In order to simplify the computational complexity of identifying conformational changes in proteins, various local sequence search algorithms were employed and the structural plasticity in unrelated proteins was examined by various research groups. In the present work, we revisit the mechanism of structural plasticity in unrelated proteins with increased number of structures in Protein Data Bank by comparing identical octapeptides in unrelated proteins with dictionary of protein secondary structure extracted from existing experimental data. Our goal is to bring out the influence of hydrophobic residues, hydrophilic residues, flanking residues, difference in secondary structural propensities of surrounding residues, difference in phi-psi angles and local and nonlocal interactions in identical octapeptides adopting different conformations. Also we have used surrounding hydrophobicity, environment dependent interaction energy, atomic mean force potential, structural unit contacts and difference profiles models to explore the factors which cause structural plasticity. The results discussed here may provide insights into protein folding, design and function.

Understanding the influence of guest-host interactions on the conformation of short peptides in a hydrophobic cavity: a computational study

We performed a computational investigation to understand the conformational preferences of four short peptides in a self-assembled cage based on the experimental work by Y. Hatakeyama et al. (Angew. Chem. Int. Ed.2009, 48, 8695). For this purpose, we combined molecular dynamics simulations, Monte Carlo simulations, and quantum mechanical calculations to obtain energies and structures for several low-lying conformers of four peptides and the corresponding peptide-cage inclusion complexes. Our calculations at both B3LYP and MP2 levels show that for each peptide, the corresponding conformation within the host (as revealed by the crystal structure) does not represent the lowest-energy conformation of this peptide in vacuum. By comparing some low-lying conformers in vacuum and in the cavity (for the same peptide), we found that the cage has a significant influence on the conformational propensities of peptides. First, one carbonyl oxygen of each peptide tends to bind to one Zn(II) atom of the cage, forming a Zn-O bond. The formation of this bond leads to significant charge transfer from the cage to the peptide. Second, this Zn-O bond causes the peptide to go through some local conformational changes. For larger peptides, such as penta- and hexapeptides, our calculations also show that some of their conformers must undergo significant structural changes, due to the confinement of the host. This computational study reveals the noticeable influence of the guest-host interaction on the conformational preferences of short peptides.

Prediction of protein motions from amino acid sequence and its application to protein-protein interaction

BACKGROUND

Structural flexibility is an important characteristic of proteins because it is often associated with their function. The movement of a polypeptide segment in a protein can be broken down into two types of motions: internal and external ones. The former is deformation of the segment itself, but the latter involves only rotational and translational motions as a rigid body. Normal Model Analysis (NMA) can derive these two motions, but its application remains limited because it necessitates the gathering of complete structural information.

RESULTS

In this work, we present a novel method for predicting two kinds of protein motions in ordered structures. The prediction uses only information from the amino acid sequence. We prepared a dataset of the internal and external motions of segments in many proteins by application of NMA. Subsequently, we analyzed the relation between thermal motion assessed from X-ray crystallographic B-factor and internal/external motions calculated by NMA. Results show that attributes of amino acids related to the internal motion have different features from those related to the B-factors, although those related to the external motion are correlated strongly with the B-factors. Next, we developed a method to predict internal and external motions from amino acid sequences based on the Random Forest algorithm. The proposed method uses information associated with adjacent amino acid residues and secondary structures predicted from the amino acid sequence. The proposed method exhibited moderate correlation between predicted internal and external motions with those calculated by NMA. It has the highest prediction accuracy compared to a naïve model and three published predictors.

CONCLUSIONS

Finally, we applied the proposed method predicting the internal motion to a set of 20 proteins that undergo large conformational change upon protein-protein interaction. Results show significant overlaps between the predicted high internal motion regions and the observed conformational change regions.

Predicting continuous local structure and the effect of its substitution for secondary structure in fragment-free protein structure prediction

Local structures predicted from protein sequences are used extensively in every aspect of modeling and prediction of protein structure and function. For more than 50 years, they have been predicted at a low-resolution coarse-grained level (e.g., three-state secondary structure). Here, we combine a two-state classifier with real-value predictor to predict local structure in continuous representation by backbone torsion angles. The accuracy of the angles predicted by this approach is close to that derived from NMR chemical shifts. Their substitution for predicted secondary structure as restraints for ab initio structure prediction doubles the success rate. This result demonstrates the potential of predicted local structure for fragment-free tertiary-structure prediction. It further implies potentially significant benefits from using predicted real-valued torsion angles as a replacement for or supplement to the secondary-structure prediction tools used almost exclusively in many computational methods ranging from sequence alignment to function prediction.

Large-scale analysis of secondary structure changes in proteins suggests a role for disorder-to-order transitions in nucleotide binding proteins

Conformational changes in proteins often involve secondary structure transitions. Such transitions can be divided into two types: disorder-to-order changes, in which a disordered segment acquires an ordered secondary structure (e.g., disorder to alpha-helix, disorder to beta-strand), and order-to-order changes, where a segment switches from one ordered secondary structure to another (e.g., alpha-helix to beta-strand, alpha-helix to turn). In this study, we explore the distribution of these transitions in the proteome. Using a comprehensive, yet highly conservative method, we compared solved three-dimensional structures of identical protein sequences, looking for differences in the secondary structures with which they were assigned. Protein chains in which such secondary structure transitions were detected, were classified into two sets according to the type of transition that is involved (disorder-to-order or order-to-order), allowing us to characterize each set by examining enrichment of gene ontology terms. The results reveal that the disorder-to-order set is significantly enriched with nucleotide binding proteins, whereas the order-to-order set is more diverse. Remarkably, further examination reveals that >22% of the purine nucleotide binding proteins include segments which undergo disorder-to-order transitions, suggesting that such transitions play an important role in this process.

Prion protein misfolding

The crucial event in the development of transmissible spongiform encephalopathies (TSEs) is the conformational change of a host-encoded membrane protein - the cellular PrP^C - into a disease associated, fibril-forming isoform PrP^Sc. This conformational transition from the α-helix-rich cellular form into the mainly β-sheet containing counterpart initiates an ‘autocatalytic’ reaction which leads to the accumulation of amyloid fibrils in the central nervous system (CNS) and to neurodegeneration, a hallmark of TSEs.

The exact molecular mechanisms which lead to the conformational change are still unknown. It also remains to be brought to light how a polypeptide chain can adopt at least two stable conformations. This review focuses on structural aspects of the prion protein with regard to protein-protein interactions and the initiation of prion protein misfolding. It therefore highlights parts of the protein which might play a notable role in the conformational transition from PrP^C to PrP^Sc and consequently in inducing a fatal chain reaction of protein misfolding. Furthermore, features of different proteins, which are able to adopt insoluble fibrillar states under certain circumstances, are compared to PrP in an attempt to understand the unique characteristics of prion diseases.

A sequence-based hybrid predictor for identifying conformationally ambivalent regions in proteins

Background

Proteins are dynamic macromolecules which may undergo conformational transitions upon changes in environment. As it has been observed in laboratories that protein flexibility is correlated to essential biological functions, scientists have been designing various types of predictors for identifying structurally flexible regions in proteins. In this respect, there are two major categories of predictors. One category of predictors attempts to identify conformationally flexible regions through analysis of protein tertiary structures. Another category of predictors works completely based on analysis of the polypeptide sequences. As the availability of protein tertiary structures is generally limited, the design of predictors that work completely based on sequence information is crucial for advances of molecular biology research.

Results

In this article, we propose a novel approach to design a sequence-based predictor for identifying conformationally ambivalent regions in proteins. The novelty in the design stems from incorporating two classifiers based on two distinctive supervised learning algorithms that provide complementary prediction powers. Experimental results show that the overall performance delivered by the hybrid predictor proposed in this article is superior to the performance delivered by the existing predictors. Furthermore, the case study presented in this article demonstrates that the proposed hybrid predictor is capable of providing the biologists with valuable clues about the functional sites in a protein chain. The proposed hybrid predictor provides the users with two optional modes, namely, the high-sensitivity mode and the high-specificity mode. The experimental results with an independent testing data set show that the proposed hybrid predictor is capable of delivering sensitivity of 0.710 and specificity of 0.608 under the high-sensitivity mode, while delivering sensitivity of 0.451 and specificity of 0.787 under the high-specificity mode.

Conclusion

Though experimental results show that the hybrid approach designed to exploit the complementary prediction powers of distinctive supervised learning algorithms works more effectively than conventional approaches, there exists a large room for further improvement with respect to the achieved performance. In this respect, it is of interest to investigate the effects of exploiting additional physiochemical properties that are related to conformational ambivalence. Furthermore, it is of interest to investigate the effects of incorporating lately-developed machine learning approaches, e.g. the random forest design and the multi-stage design. As conformational transition plays a key role in carrying out several essential types of biological functions, the design of more advanced predictors for identifying conformationally ambivalent regions in proteins deserves our continuous attention.

Charged single alpha-helix: a versatile protein structural motif

A few highly charged natural peptide sequences were recently suggested to form stable alpha-helical structures in water. In this article we show that these sequences represent a novel structural motif called "charged single alpha-helix" (CSAH). To obtain reliable candidate CSAH motifs, we developed two conceptually different computational methods capable of scanning large databases: SCAN4CSAH is based on sequence features characteristic for salt bridge stabilized single alpha-helices, whereas FT_CHARGE applies Fourier transformation to charges along sequences. Using the consensus of the two approaches, a remarkable number of proteins were found to contain putative CSAH domains. Recombinant fragments (50-60 residues) corresponding to selected hits obtained by both methods (myosin 6, Golgi resident protein GCP60, and M4K4 protein kinase) were produced and shown by circular dichroism spectroscopy to adopt largely alpha-helical structure in water. CSAH segments differ substantially both from coiled-coil and intrinsically disordered proteins, despite the fact that current prediction methods recognize them as either or both. Analysis of the proteins containing CSAH motif revealed possible functional roles of the corresponding segments. The suggested main functional features include the formation of relatively rigid spacer/connector segments between functional domains as in caldesmon, extension of the lever arm in myosin motors and mediation of transient interactions by promoting dimerization in a range of proteins.

FlexPred: a web-server for predicting residue positions involved in conformational switches in proteins

Conformational switches observed in the protein backbone play a key role in a variety of fundamental biological activities. This paper describes a web-server that implements a pattern recognition algorithm trained on the examples from the Database of Macromolecular Movements to predict residue positions involved in conformational switches. Prediction can be performed at an adjustable false positive rate using a user-supplied protein sequence in FASTA format or a structure in a Protein Data Bank (PDB) file. If a protein sequence is submitted, then the web-server uses sequence-derived information only (such as evolutionary conservation of residue positions). If a PDB file is submitted, then the web-server uses sequence-derived information and residue solvent accessibility calculated from this file.

Consensus prediction of amyloidogenic determinants in amyloid fibril-forming proteins

We combine the results of three prediction algorithms on a test set of 21 amyloidogenic proteins to predict amyloidogenic determinants. Two prediction algorithms are recently developed prediction algorithms of amyloidogenic stretches in protein sequences, whereas the third is a secondary structure prediction algorithm capable of identifying 'conformational switches' (regions that have both the propensity for alpha-helix and beta-sheet). Surprisingly, the results of prediction agree well and also agree with experimentally investigated amyloidogenic regions. Furthermore, they suggest several previously not identified amino acid stretches as potential amyloidogenic determinants. Most predicted (and experimentally observed) amyloidogenic determinants reside on the protein surface of relevant solved crystal structures. It appears that a consensus prediction algorithm is more objective than individual prediction methods alone.

The sequence TGAAKAVALVL from glyceraldehyde-3-phosphate dehydrogenase displays structural ambivalence and interconverts between α-helical and β-hairpin conformations mediated by collapsed conformational states

The peptide TGAAKAVALVL from glyceraldehyde-3-phosphate dehydrogenase adopts a helical conformation in the crystal structure and is a site for two hydrated helical segments, which are thought to be helical folding intermediates. Overlapping sequences of four to five residues from the peptide, sample both helical and strand conformations in known protein structures, which are dissimilar to glyceraldehyde-3-phosphate dehydrogenase suggesting that the peptide may have a structural ambivalence. Molecular dynamics simulations of the peptide sequence performed for a total simulation time of 1.2 micros, starting from the various initial conformations using GROMOS96 force field under NVT conditions, show that the peptide samples a large number of conformational forms with transitions from alpha-helix to beta-hairpin and vice versa. The peptide, therefore, displays a structural ambivalence. The mechanism from alpha-helix to beta-hairpin transition and vice versa reveals that the compact bends and turns conformational forms mediate such conformational transitions. These compact structures including helices and hairpins have similar hydrophobic radius of gyration (Rgh) values suggesting that similar hydrophobic interactions govern these conformational forms. The distribution of conformational energies is Gaussian with helix sampling lowest energy followed by the hairpins and coil. The lowest potential energy of the full helix may enable the peptide to take up helical conformation in the crystal structure of the glyceraldehyde-3-phosphate dehydrogenase, even though the peptide has a preference for hairpin too. The relevance of folding and unfolding events observed in our simulations to hydrophobic collapse model of protein folding are discussed.

Structural basis of effector regulation and signal termination in heterotrimeric Galpha proteins

This chapter addresses, from a molecular structural perspective gained from examination of x-ray crystallographic and biochemical data, the mechanisms by which GTP-bound Galpha subunits of heterotrimeric G proteins recognize and regulate effectors. The mechanism of GTP hydrolysis by Galpha and rate acceleration by GAPs are also considered. The effector recognition site in all Galpha homologues is formed almost entirely of the residues extending from the C-terminal half of alpha2 (Switch II) together with the alpha3 helix and its junction with the beta5 strand. Effector binding does not induce substantial changes in the structure of Galpha*GTP. Effectors are structurally diverse. Different effectors may recognize distinct subsets of effector-binding residues of the same Galpha protein. Specificity may also be conferred by differences in the main chain conformation of effector-binding regions of Galpha subunits. Several Galpha regulatory mechanisms are operative. In the regulation of GMP phospodiesterase, Galphat sequesters an inhibitory subunit. Galphas is an allosteric activator and inhibitor of adenylyl cyclase, and Galphai is an allosteric inhibitor. Galphaq does not appear to regulate GRK, but is rather sequestered by it. GTP hydrolysis terminates the signaling state of Galpha. The binding energy of GTP that is used to stabilize the Galpha:effector complex is dissipated in this reaction. Chemical steps of GTP hydrolysis, specifically, formation of a dissociative transition state, is rate limiting in Ras, a model G protein GTPase, even in the presence of a GAP; however, the energy of enzyme reorganization to produce a catalytically active conformation appears to be substantial. It is possible that the collapse of the switch regions, associated with Galpha deactivation, also encounters a kinetic barrier, and is coupled to product (Pi) release or an event preceding formation of the GDP*Pi complex. Evidence for a catalytic intermediate, possibly metaphosphate, is discussed. Galpha GAPs, whether exogenous proteins or effector-linked domains, bind to a discrete locus of Galpha that is composed of Switch I and the N-terminus of Switch II. This site is immediately adjacent to, but does not substantially overlap, the Galpha effector binding site. Interactions of effectors and exogenous GAPs with Galpha proteins can be synergistic or antagonistic, mediated by allosteric interactions among the three molecules. Unlike GAPs for small GTPases, Galpha GAPs supply no catalytic residues, but rather appear to reduce the activation energy for catalytic activation of the Galpha catalytic site.

A peptide with alternating lysines can act as a highly specific Z-DNA binding domain

Many nucleic acid binding proteins use short peptide sequences to provide specificity in recognizing their targets, which may be either a specific sequence or a conformation. Peptides containing alternating lysine have been shown to bind to poly(dG–d5meC) in the Z conformation, and stabilize the higher energy form [H. Takeuchi, N. Hanamura, H. Hayasaka and I. Harada (1991) FEBS Lett., 279, 253–255 and H. Takeuchi, N. Hanamura and I. Harada (1994) J. Mol. Biol., 236, 610–617.]. Here we report the construction of a Z-DNA specific binding protein, with the peptide KGKGKGK as a functional domain and a leucine zipper as a dimerization domain. The resultant protein, KGZIP, induces the Z conformation in poly(dG–d5meC) and binds to Z-DNA stabilized by bromination with high affinity and specificity. The binding of KGZIP is sufficient to convert poly(dG–d5meC) from the B to the Z form, as shown by circular dichroism. The sequence KGKGKGK is found in many proteins, although no functional role has been established. KGZIP also has potential for engineering other Z-DNA specific proteins for future studies of Z-DNA in vitro and in vivo.

Identifying sequence regions undergoing conformational change via predicted continuum secondary structure

MOTIVATION

Conformational flexibility is essential to the function of many proteins, e.g. catalytic activity. To assist efforts in determining and exploring the functional properties of a protein, it is desirable to automatically identify regions that are prone to undergo conformational changes. It was recently shown that a probabilistic predictor of continuum secondary structure is more accurate than categorical predictors for structurally ambivalent sequence regions, suggesting that such models are suited to characterize protein flexibility.

RESULTS

We develop a computational method for identifying regions that are prone to conformational change directly from the amino acid sequence. The method uses the entropy of the probabilistic output of an 8-class continuum secondary structure predictor. Results for 171 unique amino acid sequences with well-characterized variable structure (identified in the 'Macromolecular movements database') indicate that the method is highly sensitive at identifying flexible protein regions, but false positives remain a problem. The method can be used to explore conformational flexibility of proteins (including hypothetical or synthetic ones) whose structure is yet to be determined experimentally.

AVAILABILITY

The predictor, sequence data and supplementary studies are available at http://pprowler.itee.uq.edu.au/sspred/ and are free for academic use.

Nance-Horan syndrome protein, NHS, associates with epithelial cell junctions

Nance-Horan syndrome, characterized by congenital cataracts, craniofacial, dental abnormalities and mental disturbances, is an X-linked disorder with significant phenotypic heterogeneity. Affected individuals have mutations in the NHS (Nance-Horan syndrome) gene typically resulting in premature truncation of the protein. This report underlines the complexity of the regulation of the NHS gene that transcribes several isoforms. We demonstrate the differential expression of the two NHS isoforms, NHS-A and NHS-1A, and differences in the subcellular localization of the proteins encoded by these isoforms. This may in part explain the pleiotropic features of the syndrome. We show that the endogenous and exogenous NHS-A isoform localizes to the cell membrane of mammalian cells in a cell-type-dependent manner and that it co-localizes with the tight junction (TJ) protein ZO-1 in the apical aspect of cell membrane in epithelial cells. We also show that the NHS-1A isoform is a cytoplasmic protein. In the developing mammalian lens, we found continuous expression of NHS that became restricted to the lens epithelium in pre- and postnatal lens. Consistent with the in vitro findings, the NHS-A isoform associates with the apical cell membrane in the lens epithelium. This study suggests that disturbances in intercellular contacts underlie cataractogenesis in the Nance-Horan syndrome. NHS is the first gene localized at TJs that has been implicated in congenital cataracts.

Computational Sampling of a Cryptic Drug Binding Site in a Protein Receptor: Explicit Solvent Molecular Dynamics and Inhibitor Docking to p38 MAP Kinase

An increasing number of structural studies reveal alternative binding sites in protein receptors that become apparent only when an inhibitor binds, and correct prediction of these situations presents a significant challenge to computer-aided drug design efforts. A striking example is provided by recent crystal structures of the p38 MAP kinase, where a 10A movement of the Phe169 side-chain creates a new binding site adjacent to the ATP binding site that is exploited by the diaryl urea inhibitor BIRB796. Here, we show that this binding site can be successfully and repeatedly identified in explicit-solvent molecular dynamics (MD) simulations of the protein that begin from an unliganded p38 crystal structure. Ligand-docking calculations performed on 5000 different structural snapshots generated during MD indicate that the conformations sampled are often surprisingly competent to bind the inhibitor BIRB796 in the crystallographically correct position and with docked energies that are generally more favorable than those of other positions. Similar docking studies with an ATP-binding site-directed inhibitor suggest that it may be possible to develop hybrid inhibitors that target both the ATP and cryptic binding sites simultaneously. Intriguingly, both inhibitors are occasionally found to dock correctly even with p38's "DFG" motif in the "wrong" conformation and BIRB796 can successfully dock, albeit infrequently, without significant displacement of the Phe169 side-chain; this suggests that the inhibitor might facilitate the latter's conformational change. Finally, two quite different conformations of p38's DFG motif are also sampled for extended periods of time during the simulations; these may provide new opportunities for inhibitor development. The MD simulations reported here, which total 390 ns in length, therefore demonstrate that existing computational methods may be of surprising utility in predicting cryptic binding sites in protein receptors prior to their experimental discovery.

Prediction of protein continuum secondary structure with probabilistic models based on NMR solved structures

BACKGROUND

The structure of proteins may change as a result of the inherent flexibility of some protein regions. We develop and explore probabilistic machine learning methods for predicting a continuum secondary structure, i.e. assigning probabilities to the conformational states of a residue. We train our methods using data derived from high-quality NMR models.

RESULTS

Several probabilistic models not only successfully estimate the continuum secondary structure, but also provide a categorical output on par with models directly trained on categorical data. Importantly, models trained on the continuum secondary structure are also better than their categorical counterparts at identifying the conformational state for structurally ambivalent residues.

CONCLUSION

Cascaded probabilistic neural networks trained on the continuum secondary structure exhibit better accuracy in structurally ambivalent regions of proteins, while sustaining an overall classification accuracy on par with standard, categorical prediction methods.

Molecular mechanisms of junctional epidermolysis bullosa: Col 15 domain mutations decrease the thermal stability of collagen XVII

Mutations in the collagen XVII gene, COL17A1, are associated with junctional epidermolysis bullosa. Most COL17A1 mutations lead to a premature termination codon (PTC), whereas only a few mutations result in amino acid substitutions or deletions. We describe here two novel glycine substitutions, G609D and G612R, and a splice site mutation resulting in a deletion of three Gly-X-Y amino acid triplets. In order to investigate the molecular pathomechanisms of non-PTC mutations, G609D and G612R and two previously known substitutions, G627V and G633, and deletion of the amino acids 779-787 were introduced into recombinant collagen XVII. The thermal stability of the mutated collagens was assessed using trypsin digestions at incremental temperatures. All the four glycine substitutions significantly destabilized the ectodomain of collagen XVII, which manifested as 16 degrees C-20 degrees C lower T(m) (midpoint of the helix-to-coil transition). These results were supported by secondary structure predictions, which suggested interruptions of the collagenous triple helix within the largest collagenous domain, Col15. In contrast, deletion of the three full Gly-X-Y triplets, amino acids 779-787, had no overall effect on the stability of the ectodomain, as the deletion was in register with the triplet structure and also generated compensatory changes in the NC15 domain.

The effect of long-range interactions on the secondary structure formation of proteins

The influence of long-range residue interactions on defining secondary structure in a protein has long been discussed and is often cited as the current limitation to accurate secondary structure prediction. There are several experimental examples where a local sequence alone is not sufficient to determine its secondary structure, but a comprehensive survey on a large data set has not yet been done. Interestingly, some earlier studies denied the negative effect of long-range interactions on secondary structure prediction accuracy. Here, we have introduced the residue contact order (RCO), which directly indicates the separation of contacting residues in terms of the position in the sequence, and examined the relationship between the RCO and the prediction accuracy. A large data set of 2777 nonhomologous proteins was used in our analysis. Unlike previous studies, we do find that prediction accuracy drops as residues have contacts with more distant residues. Moreover, this negative correlation between the RCO and the prediction accuracy was found not only for beta-strands, but also for alpha-helices. The prediction accuracy of beta-strands is lower if residues have a high RCO or a low RCO, which corresponds to the situation that a beta-sheet is formed by beta-strands from different chains in a protein complex. The reason why the current study draws the opposite conclusion from the previous studies is examined. The implication for protein folding is also discussed.

Comparative computational analysis of prion proteins reveals two fragments with unusual structural properties and a pattern of increase in hydrophobicity associated with disease-promoting mutations

Prion diseases are a group of neurodegenerative disorders associated with conversion of a normal prion protein, PrPC, into a pathogenic conformation, PrPSc. The PrPSc is thought to promote the conversion of PrPC. The structure and stability of PrPC are well characterized, whereas little is known about the structure of PrPSc, what parts of PrPC undergo conformational transition, or how mutations facilitate this transition. We use a computational knowledge-based approach to analyze the intrinsic structural propensities of the C-terminal domain of PrP and gain insights into possible mechanisms of structural conversion. We compare the properties of PrP sequences to those of a PrP paralog, Doppel, and to the distributions of structural propensities observed in known protein structures from the Protein Data Bank. We show that the prion protein contains at least two sequence fragments with highly unusual intrinsic propensities, PrP(114-125) and helix B. No segments with unusual properties were found in Doppel protein, which is topologically identical to PrP but does not undergo structural rearrangements. Known disease-promoting PrP mutations form a statistically significant cluster in the region comprising helices B and C. Due to their unusual properties, PrP(114-125) and the C terminus of helix B may be considered as primary candidates for sites involved in conformational transition from PrPC to PrPSc. The results of our study also show that most PrP mutations associated with neurodegenerative disorders increase local hydrophobicity. We suggest that the observed increase in hydrophobicity may facilitate PrP-to-PrP or/and PrP-to-cofactor interactions, and thus promote structural conversion.

On the properties and sequence context of structurally ambivalent fragments in proteins

The goal of this work is to characterize structurally ambivalent fragments in proteins. We have searched the Protein Data Bank and identified all structurally ambivalent peptides (SAPs) of length five or greater that exist in two different backbone conformations. The SAPs were classified in five distinct categories based on their structure. We propose a novel index that provides a quantitative measure of conformational variability of a sequence fragment. It measures the context-dependent width of the distribution of (phi,xi) dihedral angles associated with each amino acid type. This index was used to analyze the local structural propensity of both SAPs and the sequence fragments contiguous to them. We also analyzed type-specific amino acid composition, solvent accessibility, and overall structural properties of SAPs and their sequence context. We show that each type of SAP has an unusual, type-specific amino acid composition and, as a result, simultaneous intrinsic preferences for two distinct types of backbone conformation. All types of SAPs have lower sequence complexity than average. Fragments that adopt helical conformation in one protein and sheet conformation in another have the lowest sequence complexity and are sampled from a relatively limited repertoire of possible residue combinations. A statistically significant difference between two distinct conformations of the same SAP is observed not only in the overall structural properties of proteins harboring the SAP but also in the properties of its flanking regions and in the pattern of solvent accessibility. These results have implications for protein design and structure prediction.

Classification and knowledge discovery in protein databases

We consider the problem of classification in noisy, high-dimensional, and class-imbalanced protein datasets. In order to design a complete classification system, we use a three-stage machine learning framework consisting of a feature selection stage, a method addressing noise and class-imbalance, and a method for combining biologically related tasks through a prior-knowledge based clustering. In the first stage, we employ Fisher's permutation test as a feature selection filter. Comparisons with the alternative criteria show that it may be favorable for typical protein datasets. In the second stage, noise and class imbalance are addressed by using minority class over-sampling, majority class under-sampling, and ensemble learning. The performance of logistic regression models, decision trees, and neural networks is systematically evaluated. The experimental results show that in many cases ensembles of logistic regression classifiers may outperform more expressive models due to their robustness to noise and low sample density in a high-dimensional feature space. However, ensembles of neural networks may be the best solution for large datasets. In the third stage, we use prior knowledge to partition unlabeled data such that the class distributions among non-overlapping clusters significantly differ. In our experiments, training classifiers specialized to the class distributions of each cluster resulted in a further decrease in classification error.

The PredictProtein server

PredictProtein (http://www.predictprotein.org) is an Internet service for sequence analysis and the prediction of protein structure and function. Users submit protein sequences or alignments; PredictProtein returns multiple sequence alignments, PROSITE sequence motifs, low-complexity regions (SEG), nuclear localization signals, regions lacking regular structure (NORS) and predictions of secondary structure, solvent accessibility, globular regions, transmembrane helices, coiled-coil regions, structural switch regions, disulfide-bonds, sub-cellular localization and functional annotations. Upon request fold recognition by prediction-based threading, CHOP domain assignments, predictions of transmembrane strands and inter-residue contacts are also available. For all services, users can submit their query either by electronic mail or interactively via the World Wide Web.

Y10W beta(1-40) fluorescence reflects epitope exposure in conformers of Alzheimer's beta-peptide

The Alzheimer's beta-peptide in neutral aqueous solution is characterized variously as a random coil or a heterogeneous mixture of conformers. Under conditions of lowered pH characteristic of intracellular compartments such as endosomes or lysosomes, a different conformation is favored, which is reflected in the biophysical and biological properties of the peptide. The reactivity of the epitope of the monoclonal antibody 6F/3D, encompassing residues 9-14, is drastically reduced. The fluorescence of human sequence beta(1-40) with the tyrosine at position 10 substituted with tryptophan (Y10W beta(1-40)) is quenched nearly 50% when the peptide is shifted to pH 4.6. The exposure of the 6F/3D epitope parallels Y10W beta(1-40) fluorescence changes induced by a variety of perturbations. The linkage of the sensitivity of immunological detection with the potential for monitoring rapid changes by fluorescence offers convergence of biology and biophysics in the study of beta-amyloid peptide conformation.

The PredictProtein server

PredictProtein (PP, http://cubic.bioc.columbia.edu/pp/) is an internet service for sequence analysis and the prediction of aspects of protein structure and function. Users submit protein sequence or alignments; the server returns a multiple sequence alignment, PROSITE sequence motifs, low-complexity regions (SEG), ProDom domain assignments, nuclear localisation signals, regions lacking regular structure and predictions of secondary structure, solvent accessibility, globular regions, transmembrane helices, coiled-coil regions, structural switch regions and disulfide-bonds. Upon request, fold recognition by prediction-based threading is available. For all services, users can submit their query either by electronic mail or interactively from World Wide Web.

In vitro conversion of normal prion protein into pathologic isoforms

The in vitro conversion techniques in cell-free and cell culture systems have provided tools to adequately study the underlying mechanism of TSEs, namely PrP conversion. These systems also have provided tools that make it easier to study the interspecies and intraspecies transmissibilities of TSEs. Finally, these systems also may assist in the discovery of TSE therapeutic strategies and in the development of extremely sensitive TSE detection techniques. In vivo TSE transmission studies are limited to (transgenic) animals (mostly mice). Although the cell culture systems also are restricted in their species-range (mostly mouse), the currently used cell-free systems. Allow studying almost all possible species barriers (including the potential transmission of various TSEs to humans). One advantage of the cell culture systems, however, is that they generate do novo TSE infectivity. Studies using cell cultures also take into account several cofactors in addition to PrP that might be involved in replication the TSE agent. Although the in vitro systems provide accurate tools to study TSE agent parameters, they mainly or only focus on the molecular processes of PrP conversion. Other factors (i.e., host genetic factors [99]) that, for example, determine the differential uptake of the TSE agent from the environment, might play an additional role in determining the susceptibility of hosts for TSEs and on the transmission of the disease among individuals.

Experimental identification of downhill protein folding

Theory predicts the existence of barrierless protein folding. Without barriers, folding should be noncooperative and the degree of native structure should be coupled to overall protein stability. We investigated the thermal unfolding of the peripheral subunit binding domain from Escherichia coli's 2-oxoglutarate dehydrogenase multienzyme complex (termed BBL) with a combination of spectroscopic techniques and calorimetry. Each technique probed a different feature of protein structure. BBL has a defined three-dimensional structure at low temperatures. However, each technique showed a distinct unfolding transition. Global analysis with a statistical mechanical model identified BBL as a downhill-folding protein. Because of BBL's biological function, we propose that downhill folders may be molecular rheostats, in which effects could be modulated by altering the distribution of an ensemble of structures.

Loopy proteins appear conserved in evolution

Over the last decade, structural biologists have unravelled many proteins that appear natively disordered. Common assumptions are that many of these proteins adopt structure through binding and that the structural flexibility enables them to adopt different functions. Here, we investigated regions of more than 70 sequence-consecutive residues that have no regular secondary structure (NORS). Analysing 31 entirely sequenced organisms, we predicted five times as many proteins with NORS regions (loopy proteins) in eukaryotes (20%) than in prokaryotes and archaeas (4%). Thousands of these NORS regions were over 150 residues long. The amino acid composition of NORS regions differed from that of loops in PDB. Although NORS proteins had significantly more residues in low-complexity regions than other proteins, simple cut-off thresholds for sequence bias missed most NORS regions. On average, NORS regions were evolutionarily at least as conserved as their flanking regions. Furthermore, yeast proteins with NORS regions had more protein-protein interaction partners than other proteins. Regulatory and transcription-related functions were over-represented in loopy proteins, biosynthesis and energy metabolism were under-represented. Overall, our analysis confirmed that proteins with non-regular structures appear to play important functional roles, and they may adopt as yet unknown types of protein structures.

Normal mode analysis of macromolecular motions in a database framework: developing mode concentration as a useful classifying statistic

We investigated protein motions using normal modes within a database framework, determining on a large sample the degree to which normal modes anticipate the direction of the observed motion and were useful for motions classification. As a starting point for our analysis, we identified a large number of examples of protein flexibility from a comprehensive set of structural alignments of the proteins in the PDB. Each example consisted of a pair of proteins that were considerably different in structure given their sequence similarity. On each pair, we performed geometric comparisons and adiabatic-mapping interpolations in a high-throughput pipeline, arriving at a final list of 3,814 putative motions and standardized statistics for each. We then computed the normal modes of each motion in this list, determining the linear combination of modes that best approximated the direction of the observed motion. We integrated our new motions and normal mode calculations in the Macromolecular Motions Database, through a new ranking interface at http://molmovdb.org. Based on the normal mode calculations and the interpolations, we identified a new statistic, mode concentration, related to the mathematical concept of information content, which describes the degree to which the direction of the observed motion can be summarized by a few modes. Using this statistic, we were able to determine the fraction of the 3,814 motions where one could anticipate the direction of the actual motion from only a few modes. We also investigated mode concentration in comparison to related statistics on combinations of normal modes and correlated it with quantities characterizing protein flexibility (e.g., maximum backbone displacement or number of mobile atoms). Finally, we evaluated the ability of mode concentration to automatically classify motions into a variety of simple categories (e.g., whether or not they are "fragment-like"), in comparison to motion statistics. This involved the application of decision trees and feature selection (particular machine-learning techniques) to training and testing sets derived from merging the "list" of motions with manually classified ones.

EVA: large-scale analysis of secondary structure prediction

EVA is a web-based server that evaluates automatic structure prediction servers continuously and objectively. Since June 2000, EVA collected more than 20,000 secondary structure predictions. The EVA sets sufficed to conclude that the field of secondary structure prediction has advanced again. Accuracy increased substantially in the 1990s through using evolutionary information taken from the divergence of proteins in the same structural family. Recently, the evolutionary information resulting from improved searches and larger databases has again boosted prediction accuracy by more than 4% to its current height around 76% of all residues predicted correctly in one of the three states: helix, strand, or other. The best current methods solved most of the problems raised at earlier CASP meetings: All good methods now get segments right and perform well on strands. Is the recent increase in accuracy significant enough to make predictions even more useful? We believe the answer is affirmative. What is the limit of prediction accuracy? We shall see. All data are available through the EVA web site at [cubic.bioc.columbia.edu/eva/]. The raw data for the results presented are available at [eva]/sec/bup_common/2001_02_22/.

RIO1, an extraordinary novel protein kinase

RIO1 and Rio-related proteins display little similarity of primary sequence with conventional protein kinases. Based on secondary structure alignments, we show that it contains the domain structure (subdomains I-XI) and conserved secondary structure elements found in conventional protein kinases. We show that recombinant wild-type Rio1p isolated from Escherichia coli displays kinase activity which depends on autophosphorylation and magnesium or manganese as ATP-activating ions. An initial biochemical characterization of Rio1p is presented.

Improving the prediction of protein secondary structure in three and eight classes using recurrent neural networks and profiles

Secondary structure predictions are increasingly becoming the workhorse for several methods aiming at predicting protein structure and function. Here we use ensembles of bidirectional recurrent neural network architectures, PSI-BLAST-derived profiles, and a large nonredundant training set to derive two new predictors: (a) the second version of the SSpro program for secondary structure classification into three categories and (b) the first version of the SSpro8 program for secondary structure classification into the eight classes produced by the DSSP program. We describe the results of three different test sets on which SSpro achieved a sustained performance of about 78% correct prediction. We report confusion matrices, compare PSI-BLAST to BLAST-derived profiles, and assess the corresponding performance improvements. SSpro and SSpro8 are implemented as web servers, available together with other structural feature predictors at: http://promoter.ics.uci.edu/BRNN-PRED/.

Species-specific and isoform-specific RNA binding of human and mouse fragile X mental retardation proteins

The loss of the fragile X RNA binding protein, FMRP, causes macroorchidism and mental retardation in man. The discovery of a mouse ortholog led to the development of several FMRP knockout mouse strains that recapitulate some features of the disease. As mouse and human FMRPs differ in several amino acids in their RNA binding domains, we compared the RNA binding profiles of these two orthologs. Five variant FMRPs, whose differences arose from alternative splicing and mutation within the conserved RNA binding domains, were examined. Homoribopolymer binding studies showed that human FMRPs (hFMRP) bound a broader range of single-stranded mimetics than mouse FMRPs (mFMRP) and these interactions were both complex and cooperative. hFMRP and mFMRP also displayed significant preferences toward binding their own mRNA; specifically we found that the mFMRP isoforms bind mFMR1 mRNA much more tightly than their human counterparts. Finally, these data demonstrate that each FMRP variant binds RNAs uniquely, resulting in a set of proteins with differing affinities.

Continuum secondary structure captures protein flexibility

The DSSP program assigns protein secondary structure to one of eight states. This discrete assignment cannot describe the continuum of thermal fluctuations. Hence, a continuous assignment is proposed. Technically, the continuum results from averaging over ten discrete DSSP assignments with different hydrogen bond thresholds. The final continuous assignment for a single NMR model successfully reflected the structural variations observed between all NMR models in the ensemble. The structural variations between NMR models were verified to correlate with thermal motion; these variations were captured by the continuous assignments. Because the continuous assignment reproduces the structural variation between many NMR models from one single model, functionally important variation can be extracted from a single X-ray structure. Thus, continuous assignments of secondary structure may affect future protein structure analysis, comparison, and prediction.

Intrinsically disordered protein

Proteins can exist in a trinity of structures: the ordered state, the molten globule, and the random coil. The five following examples suggest that native protein structure can correspond to any of the three states (not just the ordered state) and that protein function can arise from any of the three states and their transitions. (1) In a process that likely mimics infection, fd phage converts from the ordered into the disordered molten globular state. (2) Nucleosome hyperacetylation is crucial to DNA replication and transcription; this chemical modification greatly increases the net negative charge of the nucleosome core particle. We propose that the increased charge imbalance promotes its conversion to a much less rigid form. (3) Clusterin contains an ordered domain and also a native molten globular region. The molten globular domain likely functions as a proteinaceous detergent for cell remodeling and removal of apoptotic debris. (4) In a critical signaling event, a helix in calcineurin becomes bound and surrounded by calmodulin, thereby turning on calcineurin's serine/threonine phosphatase activity. Locating the calcineurin helix within a region of disorder is essential for enabling calmodulin to surround its target upon binding. (5) Calsequestrin regulates calcium levels in the sarcoplasmic reticulum by binding approximately 50 ions/molecule. Disordered polyanion tails at the carboxy terminus bind many of these calcium ions, perhaps without adopting a unique structure. In addition to these examples, we will discuss 16 more proteins with native disorder. These disordered regions include molecular recognition domains, protein folding inhibitors, flexible linkers, entropic springs, entropic clocks, and entropic bristles. Motivated by such examples of intrinsic disorder, we are studying the relationships between amino acid sequence and order/disorder, and from this information we are predicting intrinsic order/disorder from amino acid sequence. The sequence-structure relationships indicate that disorder is an encoded property, and the predictions strongly suggest that proteins in nature are much richer in intrinsic disorder than are those in the Protein Data Bank. Recent predictions on 29 genomes indicate that proteins from eucaryotes apparently have more intrinsic disorder than those from either bacteria or archaea, with typically > 30% of eucaryotic proteins having disordered regions of length > or = 50 consecutive residues.

Environmentally induced reversible conformational switching in the yeast cell adhesion protein alpha-agglutinin

The yeast cell adhesion protein alpha-agglutinin is expressed on the surface of a free-living organism and is subjected to a variety of environmental conditions. Circular dichroism (CD) spectroscopy shows that the binding region of alpha-agglutinin has a beta-sheet-rich structure, with only approximately 2% alpha-helix under native conditions (15-40 degrees C at pH 5.5). This region is predicted to fold into three immunoglobulin-like domains, and models are consistent with the CD spectra as well as with peptide mapping and site-specific mutagenesis. However, secondary structure prediction algorithms show that segments comprising approximately 17% of the residues have high alpha-helical and low beta-sheet potential. Two model peptides of such segments had helical tendencies, and one of these peptides showed pH-dependent conformational switching. Similarly, CD spectroscopy of the binding region of alpha-agglutinin showed reversible conversion from beta-rich to mixed alpha/beta structure at elevated temperatures or when the pH was changed. The reversibility of these changes implied that there is a small energy difference between the all-beta and the alpha/beta states. Similar changes followed cleavage of peptide or disulfide bonds. Together, these observations imply that short sequences of high helical propensity are constrained to a beta-rich state by covalent and local charge interactions under native conditions, but form helices under non-native conditions.

Review: protein secondary structure prediction continues to rise

Methods predicting protein secondary structure improved substantially in the 1990s through the use of evolutionary information taken from the divergence of proteins in the same structural family. Recently, the evolutionary information resulting from improved searches and larger databases has again boosted prediction accuracy by more than four percentage points to its current height of around 76% of all residues predicted correctly in one of the three states, helix, strand, and other. The past year also brought successful new concepts to the field. These new methods may be particularly interesting in light of the improvements achieved through simple combining of existing methods. Divergent evolutionary profiles contain enough information not only to substantially improve prediction accuracy, but also to correctly predict long stretches of identical residues observed in alternative secondary structure states depending on nonlocal conditions. An example is a method automatically identifying structural switches and thus finding a remarkable connection between predicted secondary structure and aspects of function. Secondary structure predictions are increasingly becoming the work horse for numerous methods aimed at predicting protein structure and function. Is the recent increase in accuracy significant enough to make predictions even more useful? Because the recent improvement yields a better prediction of segments, and in particular of beta strands, I believe the answer is affirmative. What is the limit of prediction accuracy? We shall see.