Prediction of Local Structural Stabilities of Proteins from Their Amino Acid Sequences

Mapping HDX-MS Data to Protein Conformations through Training Ensemble-Based Models

An original approach that adopts machine learning inference to predict protein structural information using hydrogen-deuterium exchange mass spectrometry (HDX-MS) is described. The method exploits an in-house optimization program that increases the resolution of HDX-MS data from peptides to amino acids. A system is trained using Gradient Tree Boosting as a type of machine learning ensemble technique to assign a protein secondary structure. Using limited training data we generate a discriminative model that uses optimized HDX-MS data to predict protein secondary structure with an accuracy of 75%. This research could form the basis for new methods exploiting artificial intelligence to model protein conformations by HDX-MS.

Predicting DNA-binding protein and coronavirus protein flexibility using protein dihedral angle and sequence feature

The flexibility of protein structure is related to various biological processes, such as molecular recognition, allosteric regulation, catalytic activity, and protein stability. At the molecular level, protein dynamics and flexibility are important factors to understand protein function. DNA-binding proteins and Coronavirus proteins are of great concern and relatively unique proteins. However, exploring the flexibility of DNA-binding proteins and Coronavirus proteins through experiments or calculations is a difficult process. Since protein dihedral rotational motion can be used to predict protein structural changes, it provides key information about protein local conformation. Therefore, this paper introduces a method to improve the accuracy of protein flexibility prediction, DihProFle (Prediction of DNA-binding proteins and Coronavirus proteins flexibility introduces the calculated dihedral Angle information). Based on protein dihedral Angle information, protein evolution information, and amino acid physical and chemical properties, DihProFle realizes the prediction of protein flexibility in two cases on DNA-binding proteins and Coronavirus proteins, and assigns flexibility class to each protein sequence position. In this study, compared with the flexible prediction using sequence evolution information, and physicochemical properties of amino acids, the flexible prediction accuracy based on protein dihedral Angle information, sequence evolution information and physicochemical properties of amino acids improved by 2.2% and 3.1% in the nonstrict and strict conditions, respectively. And DihProFle achieves better performance than previous methods for protein flexibility analysis. In addition, we further analyzed the correlation of amino acid properties and protein dihedral angles with residues flexibility. The results show that the charged hydrophilic residues have higher proportion in the flexible region, and the rigid region tends to be in the angular range of the protein dihedral angle (such as the ψ angle of amino acid residues is more flexible than rigid in the range of 91°-120°). Therefore, the results indicate that hydrophilic residues and protein dihedral angle information play an important role in protein flexibility.

Computational Modeling of Molecular Structures Guided by Hydrogen-Exchange Data

Data produced by hydrogen-exchange monitoring experiments have been used in structural studies of molecules for several decades. Despite uncertainties about the structural determinants of hydrogen exchange itself, such data have successfully helped guide the structural modeling of challenging molecular systems, such as membrane proteins or large macromolecular complexes. As hydrogen-exchange monitoring provides information on the dynamics of molecules in solution, it can complement other experimental techniques in so-called integrative modeling approaches. However, hydrogen-exchange data have often only been used to qualitatively assess molecular structures produced by computational modeling tools. In this paper, we look beyond qualitative approaches and survey the various paradigms under which hydrogen-exchange data have been used to quantitatively guide the computational modeling of molecular structures. Although numerous prediction models have been proposed to link molecular structure and hydrogen exchange, none of them has been widely accepted by the structural biology community. Here, we present as many hydrogen-exchange prediction models as we could find in the literature, with the aim of providing the first exhaustive list of its kind. From purely structure-based models to so-called fractional-population models or knowledge-based models, the field is quite vast. We aspire for this paper to become a resource for practitioners to gain a broader perspective on the field and guide research toward the definition of better prediction models. This will eventually improve synergies between hydrogen-exchange monitoring and molecular modeling.

A Review on Computational Approaches for Analyzing Hydrogen- Deuterium (H/D) Exchange of Proteins

Native state Hydrogen-Deuterium (H/D) exchange method has been used to study the structures and the unfolding pathways for quite a number of proteins. The H/D exchange method is generally monitored using nuclear magnetic resonance (NMR) spectroscopy and mass spectrometry (MS) techniques. NMR-assisted H/D exchange methods primarily monitor the residue level fluctuation of proteins, whereas MS-assisted H/D exchange methods analyze multifold ensemble conformations of proteins. In this connection, quite a large number of computational tools and algorithms have been developed for processing and analyzing huge amount of the H/D exchange data generated from these techniques. In this review, most of the freely available computational tools associated with the H/D exchange of proteins have been comprehensively reviewed and scopes to improve/ develop novel computational approaches for analyzing the H/D exchange data of proteins have also been brought into fore.

Simulated Epidemics in 3D Protein Structures to Detect Functional Properties

The outcome of an epidemic is closely related to the network of interactions between individuals. Likewise, protein functions depend on the 3D arrangement of their residues and the underlying energetic interaction network. Borrowing ideas from the theoretical framework that has been developed to address the spreading of real diseases, we study for the first time the diffusion of a fictitious epidemic inside the protein nonbonded interaction network, aiming to study network features and properties. Our approach allows us to probe the overall stability and the capability of propagating information in complex 3D structures, proving to be very efficient in addressing different problems, from the assessment of thermal stability to the identification of functional sites.

Advances in the Prediction of Protein Aggregation Propensity

Background:

Protein aggregation into β-sheet-enriched insoluble assemblies is being found to be associated with an increasing number of debilitating human pathologies, such as Alzheimer’s disease or type 2 diabetes, but also with premature aging. Furthermore, protein aggregation represents a major bottleneck in the production and marketing of proteinbased therapeutics. Thus, the development of methods to accurately forecast the aggregation propensity of a certain protein is of much value.

Methods/Results:

A myriad of in vitro and in vivo aggregation studies have shown that the aggregation propensity of a certain polypeptide sequence is highly dependent on its intrinsic properties and, in most cases, driven by specific short regions of high aggregation propensity. These observations have fostered the development of a first generation of algorithms aimed to predict protein aggregation propensities from the protein sequence. A second generation of programs able to map protein aggregation on protein structures is emerging. Herein, we review the most representative online accessible predictive tools, emphasizing their main distinctive features and the range of applications.

Conclusion:

In this review, we describe representative biocomputational approaches to evaluate the aggregation properties of protein sequences and structures, while illustrating how they can become very useful tools to target protein aggregation in biomedicine and biotechnology.

Establishing synthesis pathway-host compatibility via enzyme solubility

Current pathway synthesis tools identify possible pathways that can be added to a host to produce the desired target molecule through the exploration of abstract metabolic and reaction network space. However, not many of these tools explore gene-level information required to physically realize the identified synthesis pathways, and none explore enzyme-host compatibility. Developing tools that address this disconnect between abstract reactions/metabolic design space and physical genetic sequence design space will enable expedited experimental efforts that avoid exploring unprofitable synthesis pathways. This work describes a workflow, termed Probabilistic Pathway Assembly with Solubility Confidence Scores (ProPASS), which links synthesis pathway construction with the exploration of the physical design space as imposed by the availability of enzymes with predicted characterized activities within the host. Predicted protein solubility propensity scores are used as a confidence level to quantify the compatibility of each pathway enzyme with the host Escherichia coli (E. coli). This study also presents a database, termed Protein Solubility Database (ProSol DB), which provides solubility confidence scores in E. coli for 240,016 characterized enzymes obtained from UniProtKB/Swiss-Prot. The utility of ProPASS is demonstrated by generating genetic implementations of heterologous synthesis pathways in E. coli that target several commercially useful biomolecules.

Native State of Complement Protein C3d Analysed via Hydrogen Exchange and Conformational Sampling

Hydrogen/deuterium exchange detected by mass spectrometry (HDXMS) provides valuable information on protein structure and dynamics. Although HDX-MS data is often interpreted using crystal structures, it was suggested that conformational ensembles produced by molecular dynamics simulations yield more accurate interpretations. In this paper, we analyse the complement protein C3d by performing an HDX-MS experiment, and evaluate several interpretation methodologies using an existing prediction model to derive HDX-MS data from protein structure. To interpret and refine C3d's HDX-MS data, we look for a conformation (or conformational ensemble) of C3d that allows computationally replicating this data. We confirm that crystal structures are not a good choice and suggest that conformational ensembles produced by molecular dynamics simulations might not always be satisfactory either. Finally, we show that coarse-grained conformational sampling of C3d produces a conformation from which its HDX-MS data can be replicated and refined.

Polyglutamine expansion diseases: More than simple repeats

Polyglutamine (polyQ) repeat-containing proteins are widespread in the human proteome but only nine of them are associated with highly incapacitating neurodegenerative disorders. The genetic expansion of the polyQ tract in disease-related proteins triggers a series of events resulting in neurodegeneration. The polyQ tract plays the leading role in the aggregation mechanism, but other elements modulate the aggregation propensity in the context of the full-length proteins, as implied by variations in the length of the polyQ tract required to trigger the onset of a given polyQ disease. Intrinsic features such as the presence of aggregation-prone regions (APRs) outside the polyQ segments and polyQ-flanking sequences, which synergistically participate in the aggregation process, are emerging for several disease-related proteins. The inherent polymorphic structure of polyQ stretches places the polyQ proteins in a central position in protein-protein interaction networks, where interacting partners may additionally shield APRs or reshape the aggregation course. Expansion of the polyQ tract perturbs the cellular homeostasis and contributes to neuronal failure by modulating protein-protein interactions and enhancing toxic oligomerization. Post-translational modifications further regulate self-assembly either by directly altering the intrinsic aggregation propensity of polyQ proteins, by modulating their interaction with different macromolecules or by modifying their withdrawal by the cell quality control machinery. Here we review the recent data on the multifaceted aggregation pathways of disease-related polyQ proteins, focusing on ataxin-3, the protein mutated in Machado-Joseph disease. Further mechanistic understanding of this network of events is crucial for the development of effective therapies for polyQ diseases.

Coarse-Grained Conformational Sampling of Protein Structure Improves the Fit to Experimental Hydrogen-Exchange Data

Monitoring hydrogen/deuterium exchange (HDX) undergone by a protein in solution produces experimental data that translates into valuable information about the protein's structure. Data produced by HDX experiments is often interpreted using a crystal structure of the protein, when available. However, it has been shown that the correspondence between experimental HDX data and crystal structures is often not satisfactory. This creates difficulties when trying to perform a structural analysis of the HDX data. In this paper, we evaluate several strategies to obtain a conformation providing a good fit to the experimental HDX data, which is a premise of an accurate structural analysis. We show that performing molecular dynamics simulations can be inadequate to obtain such conformations, and we propose a novel methodology involving a coarse-grained conformational sampling approach instead. By extensively exploring the intrinsic flexibility of a protein with this approach, we produce a conformational ensemble from which we extract a single conformation providing a good fit to the experimental HDX data. We successfully demonstrate the applicability of our method to four small and medium-sized proteins.

Exploring the relationships between protein sequence, structure and solubility

Aggregation can be thought of as a form of protein folding in which intermolecular associations lead to the formation of large, insoluble assemblies. Various types of aggregates can be differentiated by their internal structures and gross morphologies (e.g., fibrillar or amorphous), and the ability to accurately predict the likelihood of their formation by a given polypeptide is of great practical utility in the fields of biology (including the study of disease), biotechnology, and biomaterials research. Here we review aggregation/solubility prediction methods and selected applications thereof. The development of increasingly sophisticated methods that incorporate knowledge of conformations possibly adopted by aggregating polypeptide monomers and predict the internal structure of aggregates is improving the accuracy of the predictions and continually expanding the range of applications.

From the Evolution of Protein Sequences Able to Resist Self-Assembly to the Prediction of Aggregation Propensity

Folding of polypeptide chains into biologically active entities is an astonishingly complex process, determined by the nature and the sequence of residues emerging from ribosomes. While it has been long believed that evolution has pressed genomes so that specific sequences could adopt unique, functional three-dimensional folds, it is now clear that complex protein machineries act as quality control system and supervise folding. Notwithstanding that, events such as erroneous folding, partial folding, or misfolding are frequent during the life of a cell or a whole organism, and they can escape controls. One of the possible outcomes of this misbehavior is cross-β aggregation, a super secondary structure which represents the hallmark of self-assembled, well organized, and extremely ordered structures termed amyloid fibrils. What if evolution would have not taken into account such possibilities? Twenty years of research point toward the idea that, in fact, evolution has constantly supervised the risk of errors and minimized their impact. In this review we tried to survey the major findings in the amyloid field, trying to describe what the real pitfalls of protein folding are-from an evolutionary perspective-and how sequence and structural features have evolved to balance the need for perfect, dynamic, functionally efficient structures, and the detrimental effects implicit in the dangerous process of folding. We will discuss how the knowledge obtained from these studies has been employed to produce computational methods able to assess, predict, and discriminate the aggregation properties of protein sequences.

FLEXc: protein flexibility prediction using context-based statistics, predicted structural features, and sequence information

Background

The fluctuation of atoms around their average positions in protein structures provides important information regarding protein dynamics. This flexibility of protein structures is associated with various biological processes. Predicting flexibility of residues from protein sequences is significant for analyzing the dynamic properties of proteins which will be helpful in predicting their functions.

Results

In this paper, an approach of improving the accuracy of protein flexibility prediction is introduced. A neural network method for predicting flexibility in 3 states is implemented. The method incorporates sequence and evolutionary information, context-based scores, predicted secondary structures and solvent accessibility, and amino acid properties. Context-based statistical scores are derived, using the mean-field potentials approach, for describing the different preferences of protein residues in flexibility states taking into consideration their amino acid context.

The 7-fold cross validated accuracy reached 61 % when context-based scores and predicted structural states are incorporated in the training process of the flexibility predictor.

Conclusions

Incorporating context-based statistical scores with predicted structural states are important features to improve the performance of predicting protein flexibility, as shown by our computational results. Our prediction method is implemented as web service called “FLEXc” and available online at: http://hpcr.cs.odu.edu/flexc.

Characterization of folding cores in the cyclophilin A-cyclosporin A complex

Determining the folding core of a protein yields information about its folding process and dynamics. The experimental procedures for identifying the amino acids that make up the folding core include hydrogen-deuterium exchange and Φ-value analysis and can be expensive and time consuming. Because of this, there is a desire to improve upon existing methods for determining protein folding cores theoretically. We have obtained HDX data for the complex of cyclophilin A with the immunosuppressant cyclosporin A. We compare these data, as well as literature values for uncomplexed cyclophilin A, to theoretical predictions using a combination of rigidity analysis and coarse-grained simulations of protein motion. We find that in this case, the most specific prediction of folding cores comes from a combined approach that models the rigidity of the protein using the first software suite and the dynamics of the protein using the froda tool.

The H50Q mutation induces a 10-fold decrease in the solubility of α-synuclein

The conversion of α-synuclein from its intrinsically disordered monomeric state into the fibrillar cross-β aggregates characteristically present in Lewy bodies is largely unknown. The investigation of α-synuclein variants causative of familial forms of Parkinson disease can provide unique insights into the conditions that promote or inhibit aggregate formation. It has been shown recently that a newly identified pathogenic mutation of α-synuclein, H50Q, aggregates faster than the wild-type. We investigate here its aggregation propensity by using a sequence-based prediction algorithm, NMR chemical shift analysis of secondary structure populations in the monomeric state, and determination of thermodynamic stability of the fibrils. Our data show that the H50Q mutation induces only a small increment in polyproline II structure around the site of the mutation and a slight increase in the overall aggregation propensity. We also find, however, that the H50Q mutation strongly stabilizes α-synuclein fibrils by 5.0 ± 1.0 kJ mol⁻¹, thus increasing the supersaturation of monomeric α-synuclein within the cell, and strongly favors its aggregation process. We further show that wild-type α-synuclein can decelerate the aggregation kinetics of the H50Q variant in a dose-dependent manner when coaggregating with it. These last findings suggest that the precise balance of α-synuclein synthesized from the wild-type and mutant alleles may influence the natural history and heterogeneous clinical phenotype of Parkinson disease.

A structure-based approach to predict predisposition to amyloidosis

BACKGROUND

Neurodegenerative diseases and other amyloidoses are linked to the formation of amyloid fibrils. It has been shown that the ability to form these fibrils is coded by the amino acid sequence. Existing methods for the prediction of amyloidogenicity generate an unsatisfactory high number of false positives when tested against sequences of the disease-related proteins.

METHODS

Recently, it has been shown that the three-dimensional structure of a majority of disease-related amyloid fibrils contains a β-strand-loop-β-strand motif called β-arch. Using this information, we have developed a novel bioinformatics approach for the prediction of amyloidogenicity.

RESULTS

The benchmark results show the superior performance of our method over the existing programs.

CONCLUSIONS

As genome sequencing becomes more affordable, our method provides an opportunity to create individual risk profiles for the neurodegenerative, age-related, and other diseases ushering in an era of personalized medicine. It will also be used in the large-scale analysis of proteomes to find new amyloidogenic proteins.

In silico analysis of prion protein mutants: a comparative study by molecular dynamics approach

Polymorphisms in the human prion proteins lead to amino acid substitutions by the conversion of PrPC to PrPSc and amyloid formation, resulting in prion diseases such as familial Creutzfeldt-Jakob disease, Gerstmann-Straussler-Scheinker disease and fatal familial insomnia. Cation-π interaction is a non-covalent binding force that plays a significant role in protein stability. Here, we employ a novel approach by combining various in silico tools along with molecular dynamics simulation to provide structural and functional insight into the effect of mutation on the stability and activity of mutant prion proteins. We have investigated impressions of prevalent mutations including 1E1S, 1E1P, 1E1U, 1E1P, 1FKC and 2K1D on the human prion proteins and compared them with wild type. Structural analyses of the models were performed with the aid of molecular dynamics simulation methods. According to our results, frequently occurred mutations were observed in conserved sequences of human prion proteins and the most fluctuation values appear in the 2K1D mutant model at around helix 4 with residues ranging from 190 to 194. Our observations in this study could help to further understand the structural stability of prion proteins.

ccSOL omics: a webserver for solubility prediction of endogenous and heterologous expression in Escherichia coli

Summary: Here we introduce ccSOL omics, a webserver for large-scale calculations of protein solubility. Our method allows (i) proteome-wide predictions; (ii) identification of soluble fragments within each sequences; (iii) exhaustive single-point mutation analysis.

Results: Using coil/disorder, hydrophobicity, hydrophilicity, β-sheet and α-helix propensities, we built a predictor of protein solubility. Our approach shows an accuracy of 79% on the training set (36 990 Target Track entries). Validation on three independent sets indicates that ccSOL omics discriminates soluble and insoluble proteins with an accuracy of 74% on 31 760 proteins sharing <30% sequence similarity.

Availability and implementation:ccSOL omics can be freely accessed on the web at http://s.tartaglialab.com/page/ccsol_group. Documentation and tutorial are available at http://s.tartaglialab.com/static_files/shared/tutorial_ccsol_omics.html.

Contact:gian.tartaglia@crg.es

Supplementary information:Supplementary data are available at Bioinformatics online.

OneG-Vali: a computational tool for detecting, estimating and validating cryptic intermediates of proteins under native conditions

Unfolding pathway of T4 lysozyme under native conditions as predicted by the OneG-Vali has been illustrated. Also, structural contexts of various states (native (N), cryptic intermediates (CIs) and unfolded (U) conformations) of the protein and the population of three CIs are depicted.

Probing protein ensemble rigidity and hydrogen-deuterium exchange

Protein rigidity and flexibility can be analyzed accurately and efficiently using the program floppy inclusion and rigid substructure topography (FIRST). Previous studies using FIRST were designed to analyze the rigidity and flexibility of proteins using a single static (snapshot) structure. It is however well known that proteins can undergo spontaneous sub-molecular unfolding and refolding, or conformational dynamics, even under conditions that strongly favor a well-defined native structure. These (local) unfolding events result in a large number of conformers that differ from each other very slightly. In this context, proteins are better represented as a thermodynamic ensemble of 'native-like' structures, and not just as a single static low-energy structure. Working with this notion, we introduce a novel FIRST-based approach for predicting rigidity/flexibility of the protein ensemble by (i) averaging the hydrogen bonding strengths from the entire ensemble and (ii) by refining the mathematical model of hydrogen bonds. Furthermore, we combine our FIRST-ensemble rigidity predictions with the ensemble solvent accessibility data of the backbone amides and propose a novel computational method which uses both rigidity and solvent accessibility for predicting hydrogen-deuterium exchange (HDX). To validate our predictions, we report a novel site specific HDX experiment which characterizes the native structural ensemble of Acylphosphatase from hyperthermophile Sulfolobus solfataricus (Sso AcP). The sub-structural conformational dynamics that is observed by HDX data, is closely matched with the FIRST-ensemble rigidity predictions, which could not be attained using the traditional single 'snapshot' rigidity analysis. Moreover, the computational predictions of regions that are protected from HDX and those that undergo exchange are in very good agreement with the experimental HDX profile of Sso AcP.

Breaking the amyloidogenicity code: methods to predict amyloids from amino acid sequence

Numerous studies have shown that the ability to form amyloid fibrils is an inherent property of the polypeptide chain. This has lead to the development of several computational approaches to predict amyloidogenicity by amino acid sequences. Here, we discuss the principles governing these methods, and evaluate them using several datasets. They deliver excellent performance in the tests made using short peptides (~6 residues). However, there is a general tendency towards a high number of false positives when tested against longer sequences. This shortcoming needs to be addressed as these longer sequences are linked to diseases. Recent structural studies have shown that the core element of the majority of disease-related amyloid fibrils is a β-strand-loop-β-strand motif called β-arch. This insight provides an opportunity to substantially improve the prediction of amyloids produced by natural proteins, ushering in an era of personalized medicine based on genome analysis.

Comparison of experimental and theoretical data on hydrogen-deuterium exchange for ten globular proteins

The number of protons available for hydrogen-deuterium exchange was predicted for ten globular proteins using a method described elsewhere by the authors. The average number of protons replaced by deuterium was also determined by mass spectrometry of the intact proteins in their native conformations. Based on these data, we find that two models proposed earlier agree with each other in estimation of the number of protons replaced by deuterium. Using a model with a probability scale for hydrogen bond formation, we estimated a number of protons replaced by deuterium that is close to the experimental data for long-term incubation in D(2)O (24 h). Using a model based on estimations with a scale of the expected number of contacts in globular proteins there is better agreement with the experimental data obtained for a short period of incubation in D(2)O (15 min). Therefore, the former model determines weakly fluctuating parts of a protein that are in contact with solvent only for a small fraction of the time. The latter model (based on the scale of expected number of contacts) predicts either flexible parts of a protein chain exposed to interactions with solvent or disordered parts of the protein.

Biological insights from hydrogen exchange mass spectrometry

Over the past two decades, hydrogen exchange mass spectrometry (HXMS) has achieved the status of a widespread and routine approach in the structural biology toolbox. The ability of hydrogen exchange to detect a range of protein dynamics coupled with the accessibility of mass spectrometry to mixtures and large complexes at low concentrations result in an unmatched tool for investigating proteins challenging to many other structural techniques. Recent advances in methodology and data analysis are helping HXMS deliver on its potential to uncover the connection between conformation, dynamics and the biological function of proteins and complexes. This review provides a brief overview of the HXMS method and focuses on four recent reports to highlight applications that monitor structure and dynamics of proteins and complexes, track protein folding, and map the thermodynamics and kinetics of protein unfolding at equilibrium. These case studies illustrate typical data, analysis and results for each application and demonstrate a range of biological systems for which the interpretation of HXMS in terms of structure and conformational parameters provides unique insights into function. This article is part of a Special Issue entitled: Mass spectrometry in structural biology.

Resolution of the effects induced by W → F substitutions on the conformation and dynamics of the amyloid-forming apomyoglobin mutant W7FW14F

Myoglobin is an alpha-helical globular protein containing two highly conserved tryptophanyl residues at positions 7 and 14 in the N-terminal region. The simultaneous substitution of the two residues increases the susceptibility of the polypeptide chain to misfold, causing amyloid aggregation under physiological condition, i.e., neutral pH and room temperature. The role played by tryptophanyl residues in driving the folding process has been investigated by examining three mutated apomyoglobins, i.e., W7F, W14F, and the amyloid-forming mutant W7FW14F, by an integrated approach based on far-ultraviolet (UV) circular dichroism (CD) analysis, fluorescence spectroscopy, and complementary proteolysis. Particular attention has been devoted to examine the conformational and dynamic properties of the equilibrium intermediate formed at pH 4.0, since it represents the early organized structure from which the native fold originates. The results show that the W → F substitutions at position 7 and 14 differently affect the structural organization of the AGH subdomain of apomyoglobin. The combined effect of the two substitutions in the double mutant impairs the formation of native-like contacts and favors interchain interactions, leading to protein aggregation and amyloid formation.

Protein hydrogen exchange: testing current models

To investigate the determinants of protein hydrogen exchange (HX), HX rates of most of the backbone amide hydrogens of Staphylococcal nuclease were measured by NMR methods. A modified analysis was used to improve accuracy for the faster hydrogens. HX rates of both near surface and well buried hydrogens are spread over more than 7 orders of magnitude. These results were compared with previous hypotheses for HX rate determination. Contrary to a common assumption, proximity to the surface of the native protein does not usually produce fast exchange. The slow HX rates for unprotected surface hydrogens are not well explained by local electrostatic field. The ability of buried hydrogens to exchange is not explained by a solvent penetration mechanism. The exchange rates of structurally protected hydrogens are not well predicted by algorithms that depend only on local interactions or only on transient unfolding reactions. These observations identify some of the present difficulties of HX rate prediction and suggest the need for returning to a detailed hydrogen by hydrogen analysis to examine the bases of structure-rate relationships, as described in the companion paper (Skinner et al., Protein Sci 2012;21:996-1005).

Protein dynamics viewed by hydrogen exchange

To examine the relationship between protein structural dynamics and measurable hydrogen exchange (HX) data, the detailed exchange behavior of most of the backbone amide hydrogens of Staphylococcal nuclease was compared with that of their neighbors, with their structural environment, and with other information. Results show that H-bonded hydrogens are protected from exchange, with HX rate effectively zero, even when they are directly adjacent to solvent. The transition to exchange competence requires a dynamic structural excursion that removes H-bond protection and allows exposure to solvent HX catalyst. The detailed data often make clear the nature of the dynamic excursion required. These range from whole molecule unfolding, through smaller cooperative unfolding reactions of secondary structural elements, and down to local fluctuations that involve as little as a single peptide group or side chain or water molecule. The particular motion that dominates the exchange of any hydrogen is the one that allows the fastest HX rate. The motion and the rate it produces are determined by surrounding structure and not by nearness to solvent or the strength of the protecting H-bond itself or its acceptor type (main chain, side chain, structurally bound water). Many of these motions occur over time scales that are appropriate for biochemical function.

OneG: a computational tool for predicting cryptic intermediates in the unfolding kinetics of proteins under native conditions

Understanding the relationships between conformations of proteins and their stabilities is one key to address the protein folding paradigm. The free energy change (ΔG) of unfolding reactions of proteins is measured by traditional denaturation methods and native hydrogen-deuterium (H/D) exchange methods. However, the free energy of unfolding (ΔG(U)) and the free energy of exchange (ΔG(HX)) of proteins are not in good agreement, though the experimental conditions of both methods are well matching to each other. The anomaly is due to any one or combinations of the following reasons: (i) effects of cis-trans proline isomerisation under equilibrium unfolding reactions of proteins (ii) inappropriateness in accounting the baselines of melting curves (iii) presence of cryptic intermediates, which may elude the melting curve analysis and (iv) existence of higher energy metastable states in the H/D exchange reactions of proteins. Herein, we have developed a novel computational tool, OneG, which accounts the discrepancy between ΔG(U) and ΔG(HX) of proteins by systematically accounting all the four factors mentioned above. The program is fully automated and requires four inputs: three-dimensional structures of proteins, ΔG(U), ΔG(U)(*) and residue-specific ΔG(HX) determined under EX2-exchange conditions in the absence of denaturants. The robustness of the program has been validated using experimental data available for proteins such as cytochrome c and apocytochrome b(562) and the data analyses revealed that cryptic intermediates of the proteins detected by the experimental methods and the cryptic intermediates predicted by the OneG for those proteins were in good agreement. Furthermore, using OneG, we have shown possible existence of cryptic intermediates and metastable states in the unfolding pathways of cardiotoxin III and cobrotoxin, respectively, which are homologous proteins. The unique application of the program to map the unfolding pathways of proteins under native conditions have been brought into fore and the program is publicly available at http://sblab.sastra.edu/oneg.html.

Quantitative assessment of protein structural models by comparison of H/D exchange MS data with exchange behavior accurately predicted by DXCOREX

Peptide amide hydrogen/deuterium exchange mass spectrometry (DXMS) data are often used to qualitatively support models for protein structure. We have developed and validated a method (DXCOREX) by which exchange data can be used to quantitatively assess the accuracy of three-dimensional (3-D) models of protein structure. The method utilizes the COREX algorithm to predict a protein's amide hydrogen exchange rates by reference to a hypothesized structure, and these values are used to generate a virtual data set (deuteron incorporation per peptide) that can be quantitatively compared with the deuteration level of the peptide probes measured by hydrogen exchange experimentation. The accuracy of DXCOREX was established in studies performed with 13 proteins for which both high-resolution structures and experimental data were available. The DXCOREX-calculated and experimental data for each protein was highly correlated. We then employed correlation analysis of DXCOREX-calculated versus DXMS experimental data to assess the accuracy of a recently proposed structural model for the catalytic domain of a Ca(2+)-independent phospholipase A(2). The model's calculated exchange behavior was highly correlated with the experimental exchange results available for the protein, supporting the accuracy of the proposed model. This method of analysis will substantially increase the precision with which experimental hydrogen exchange data can help decipher challenging questions regarding protein structure and dynamics.

Exome-sequencing confirms DNAJC5 mutations as cause of adult neuronal ceroid-lipofuscinosis

We performed whole-exome sequencing in two autopsy-confirmed cases and an elderly unaffected control from a multigenerational family with autosomal dominant neuronal ceroid lipofuscinosis (ANCL). A novel single-nucleotide variation (c.344T>G) in the DNAJC5 gene was identified. Mutational screening in an independent family with autosomal dominant ANCL found an in-frame single codon deletion (c.346_348 delCTC) resulting in a deletion of p.Leu116del. These variants fulfill all genetic criteria for disease-causing mutations: they are found in unrelated families with the same disease, exhibit complete segregation between the mutation and the disease, and are absent in healthy controls. In addition, the associated amino acid substitutions are located in evolutionarily highly conserved residues and are predicted to functionally affect the encoded protein (CSPα). The mutations are located in a cysteine-string domain, which is required for membrane targeting/binding, palmitoylation, and oligomerization of CSPα. We performed a comprehensive in silico analysis of the functional and structural impact of both mutations on CSPα. We found that these mutations dramatically decrease the affinity of CSPα for the membrane. We did not identify any significant effect on palmitoylation status of CSPα. However, a reduction of CSPα membrane affinity may change its palmitoylation and affect proper intracellular sorting. We confirm that CSPα has a strong intrinsic aggregation propensity; however, it is not modified by the mutations. A complementary disease-network analysis suggests a potential interaction with other NCLs genes/pathways. This is the first replication study of the identification of DNAJC5 as the disease-causing gene for autosomal dominant ANCL. The identification of the novel gene in ANCL will allow us to gain a better understanding of the pathological mechanism of ANCLs and constitutes a great advance toward the development of new molecular diagnostic tests and may lead to the development of potential therapies.

Amyloid-like aggregates sequester numerous metastable proteins with essential cellular functions

Protein aggregation is linked with neurodegeneration and numerous other diseases by mechanisms that are not well understood. Here, we have analyzed the gain-of-function toxicity of artificial β sheet proteins that were designed to form amyloid-like fibrils. Using quantitative proteomics, we found that the toxicity of these proteins in human cells correlates with the capacity of their aggregates to promote aberrant protein interactions and to deregulate the cytosolic stress response. The endogenous proteins that are sequestered by the aggregates share distinct physicochemical properties: They are relatively large in size and significantly enriched in predicted unstructured regions, features that are strongly linked with multifunctionality. Many of the interacting proteins occupy essential hub positions in cellular protein networks, with key roles in chromatin organization, transcription, translation, maintenance of cell architecture and protein quality control. We suggest that amyloidogenic aggregation targets a metastable subproteome, thereby causing multifactorial toxicity and, eventually, the collapse of essential cellular functions.

Predicting protein flexibility through the prediction of local structures

Protein structures are valuable tools for understanding protein function. However, protein dynamics is also considered a key element in protein function. Therefore, in addition to structural analysis, fully understanding protein function at the molecular level now requires accounting for flexibility. However, experimental techniques that produce both types of information simultaneously are still limited. Prediction approaches are useful alternative tools for obtaining otherwise unavailable data. It has been shown that protein structure can be described by a limited set of recurring local structures. In this context, we previously established a library composed of 120 overlapping long structural prototypes (LSPs) representing fragments of 11 residues in length and covering all known local protein structures. On the basis of the close sequence-structure relationship observed in LSPs, we developed a novel prediction method that proposes structural candidates in terms of LSPs along a given sequence. The prediction accuracy rate was high given the number of structural classes. In this study, we use this methodology to predict protein flexibility. We first examine flexibility according to two different descriptors, the B-factor and root mean square fluctuations from molecular dynamics simulations. We then show the relevance of using both descriptors together. We define three flexibility classes and propose a method based on the LSP prediction method for predicting flexibility along the sequence. The prediction rate reaches 49.6%. This method competes rather efficiently with the most recent, cutting-edge methods based on true flexibility data learning with sophisticated algorithms. Accordingly, flexibility information should be taken into account in structural prediction assessments.

Interactions in the native state of monellin, which play a protective role against aggregation

A series of recent studies have provided initial evidence about the role of specific intra-molecular interactions in maintaining proteins in their soluble state and in protecting them from aggregation. Here we show that the amino acid sequence of the protein monellin contains two aggregation-prone regions that are prevented from initiating aggregation by multiple non-covalent interactions that favor their burial within the folded state of the protein. By investigating the behavior of single-chain monellin and a series of five of its mutational variants using a variety of biochemical, biophysical and computational techniques, we found that weakening of the non-covalent interaction that stabilizes the native state of the protein leads to an enhanced aggregation propensity. The lag time for fibrillation was found to correlate with the apparent midpoint of thermal denaturation for the series of mutational variants, thus showing that a reduced thermal stability is associated with an increased aggregation tendency. We rationalize these findings by showing that the increase in the aggregation propensity upon mutation can be predicted in a quantitative manner through the increase in the exposure to solvent of the amyloidogenic regions of the sequence caused by the destabilization of the native state. Our findings, which are further discussed in terms of the structure of monellin and the perturbation by the amino acid substitutions of the contact surface between the two subdomains that compose the folded state of monellin, provide a detailed description of the specific intra-molecular interactions that prevent aggregation by stabilizing the native state of a protein.

Amyloidogenic sequences in native protein structures

Numerous short peptides have been shown to form beta-sheet amyloid aggregates in vitro. Proteins that contain such sequences are likely to be problematic for a cell, due to their potential to aggregate into toxic structures. We investigated the structures of 30 proteins containing 45 sequences known to form amyloid, to see how the proteins cope with the presence of these potentially toxic sequences, studying secondary structure, hydrogen-bonding, solvent accessible surface area and hydrophobicity. We identified two mechanisms by which proteins avoid aggregation: Firstly, amyloidogenic sequences are often found within helices, despite their inherent preference to form beta structure. Helices may offer a selective advantage, since in order to form amyloid the sequence will presumably have to first unfold and then refold into a beta structure. Secondly, amyloidogenic sequences that are found in beta structure are usually buried within the protein. Surface exposed amyloidogenic sequences are not tolerated in strands, presumably because they lead to protein aggregation via assembly of the amyloidogenic regions. The use of alpha-helices, where amyloidogenic sequences are forced into helix, despite their intrinsic preference for beta structure, is thus a widespread mechanism to avoid protein aggregation.

Prediction of amino acid residues protected from hydrogen-deuterium exchange in a protein chain

We have investigated the possibility to predict protection of amino acid residues from hydrogen-deuterium exchange. A database containing experimental hydrogen-deuterium exchange data for 14 proteins for which these data are known has been compiled. Different structural parameters related to flexibility of amino acid residues and their amide groups have been analyzed to answer the question whether these parameters can be used for predicting the protection of amino acid residues from hydrogen-deuterium exchange. A method for prediction of protection of amino acid residues, which uses only the amino acid sequence of a protein, has been elaborated.

Relative stabilities of conserved and non-conserved structures in the OB-fold superfamily

The OB-fold is a diverse structure superfamily based on a beta-barrel motif that is often supplemented with additional non-conserved secondary structures. Previous deletion mutagenesis and NMR hydrogen exchange studies of three OB-fold proteins showed that the structural stabilities of sites within the conserved beta-barrels were larger than sites in non-conserved segments. In this work we examined a database of 80 representative domain structures currently classified as OB-folds, to establish the basis of this effect. Residue-specific values were obtained for the number of Calpha-Calpha distance contacts, sequence hydrophobicities, crystallographic B-factors, and theoretical B-factors calculated from a Gaussian Network Model. All four parameters point to a larger average flexibility for the non-conserved structures compared to the conserved beta-barrels. The theoretical B-factors and contact densities show the highest sensitivity. Our results suggest a model of protein structure evolution in which novel structural features develop at the periphery of conserved motifs. Core residues are more resistant to structural changes during evolution since their substitution would disrupt a larger number of interactions. Similar factors are likely to account for the differences in stability to unfolding between conserved and non-conserved structures.

Influence of proline on the thermostability of the active site and membrane arrangement of transmembrane proteins

Proline residues play a fundamental and subtle role in the dynamics, structure, and function in many membrane proteins. Temperature derivative spectroscopy and differential scanning calorimetry have been used to determine the effect of proline substitution in the structural stability of the active site and transmembrane arrangement of bacteriorhodopsin. We have analyzed the Pro-to-Ala mutation for the helix-embedded prolines Pro50, Pro91, and Pro186 in the native membrane environment. This information has been complemented with the analysis of the respective crystallographic structures by the FoldX force field. Differential scanning calorimetry allowed us to determine distorted membrane arrangement for P50A and P186A. The protein stability was severely affected for P186A and P91A. In the case of Pro91, a single point mutation is capable of strongly slowing down the conformational diffusion along the denaturation coordinate, becoming a barrier-free downhill process above 371 K. Temperature derivative spectroscopy, applied for first time to study thermal stability of proteins, has been used to monitor the stability of the active site of bacteriorhodopsin. The mutation of Pro91 and Pro186 showed the most striking effects on the retinal binding pocket. These residues are the Pro in closer contact to the active site (activation energies for retinal release of 60.1 and 76.8 kcal/mol, respectively, compared to 115.8 kcal/mol for WT). FoldX analysis of the protein crystal structures indicates that the Pro-to-Ala mutations have both local and long-range effects on the structural stability of residues involved in the architecture of the protein and the active site and in the proton pumping function. Thus, this study provides a complete overview of the substitution effect of helix-embedded prolines in the thermodynamic and dynamic stability of a membrane protein, also related to its structure and function.

Prediction of Residue Status to Be Protected or Not Protected From Hy-drogen Exchange Using Amino Acid Sequence Only

We have outlined here some structural aspects of local flexibility. Important functional properties are related to flexible segments. We try to predict regions that have been shown to exhibit the highest probability of being folded in the equilibrium intermediate or native state and will be protected from hydrogen exchange using amino acid sequence only. Our approach FoldUnfold for the prediction of unstructured regions has been applied to seven different proteins. For 80% of the residues considered in this paper we can predict correctly their status: will they be protected or not from hydrogen exchange. An additional goal of our study is to assess whether properties inferred using the bioinformatics approach are easily applicable to predict behavior of proteins in solution.