Beta edge strands in protein structure prediction and aggregation

Exploring the effect of high-pressure processing conditions on the deaggregation of natural major royal jelly proteins (MRJPs) fibrillar aggregates

High pressure processing is a safe and green novel non-thermal processing technique for modulating food protein aggregation behavior. However, the systematic relationship between high pressure processing conditions and protein deaggregation has not been sufficiently investigated. Major royal jelly proteins, which are naturally highly fibrillar aggregates, and it was found that the pressure level and exposure time could significantly promote protein deaggregation. The 100-200 MPa treatment favoured the deaggregation of proteins with a significant decrease in the sulfhydryl group content. Contrarily, at higher pressure levels (>400 MPa), the exposure time promoted the formation of disordered agglomerates. Notably, the inter-conversion of α-helix and β-strands in major royal jelly proteins after high pressure processing eliminates the solvent-free cavities inside the aggregates, which exerts a 'collapsing' effect on the fibrillar aggregates. Furthermore, the first machine learning model of the high pressure processing conditions and the protein deaggregation behaviour was developed, which provided digital guidance for protein aggregation regulation.

Reversible and size-controlled assembly of reflectin proteins using a charged azobenzene photoswitch

Disordered proteins often undergo a stimuli-responsive, disorder-to-order transition which facilitates dynamic processes that modulate the physiological activities and material properties of cells, such as strength, chemical composition, and reflectance. It remains challenging to gain rapid and spatiotemporal control over such disorder-to-order transitions, which limits the incorporation of these proteins into novel materials. The reflectin protein is a cationic, disordered protein whose assembly is responsible for dynamic color camouflage in cephalopods. Stimuli-responsive control of reflectin's assembly would enable the design of biophotonic materials with tunable color. Herein, a novel, multivalent azobenzene photoswitch is shown to be an effective and non-invasive strategy for co-assembling with reflectin molecules and reversibly controlling assembly size. Photoisomerization between the trans and cis (E and Z) photoisomers promotes or reduces Coulombic interactions, respectively, with reflectin proteins to repeatedly cycle the sizes of the photoswitch-reflectin assemblies between 70 nm and 40 nm. The protein assemblies formed with the trans and cis isomers show differences in interaction stoichiometry and secondary structure, which indicate that photoisomerization modulates the photoswitch-protein interactions to change assembly size. Our results highlight the utility of photoswitchable interactions to control reflectin assembly and provide a tunable synthetic platform that can be adapted to the structure, assembly, and function of other disordered proteins.

Pathophysiology of Cardiac Amyloidosis

Amyloidosis refers to a heterogeneous group of disorders sharing common pathophysiological mechanisms characterized by the extracellular accumulation of fibrillar deposits consisting of the aggregation of misfolded proteins. Cardiac amyloidosis (CA), usually caused by deposition of misfolded transthyretin or immunoglobulin light chains, is an increasingly recognized cause of heart failure burdened by a poor prognosis. CA manifests with a restrictive cardiomyopathy which progressively leads to biventricular thickening, diastolic and then systolic dysfunction, arrhythmias, and valvular disease. The pathophysiology of CA is multifactorial and includes increased oxidative stress, mitochondrial damage, apoptosis, impaired metabolism, and modifications of intracellular calcium balance.

Rational design of antibody-like peptides for targeting the human complement fragment protein C5a

Human complement fragment 5a (C5a) is one of the most potent glycoproteins generated downstream of C3a and C4a during late-stage activation of the complement signaling cascade. C5a recruits receptors like C5aR1 and C5aR2 and is established to play a critical role in complement-mediated inflammation. Thus, excessive C5a in the plasma due to aberrant activation of the complement contributes to the pathophysiology of several chronic inflammatory diseases. Therefore, restricting the excessive interaction of C5a with its receptors by neutralizing C5a has been one of the most effective therapeutic strategies for the management of inflammatory diseases. Indeed, antibodies targeting C5 (Eculizumab), the precursor of C5a, and C5a (Vilobelimab) have already been approved by the FDA. Still, small designer peptides that work like antibodies and can target and stop C5a from interacting with its receptors seem to be a possible therapeutic alternative to antibodies because they are smaller, cheaper to make, more specific to their target, and can get through membrane barriers. As a proof-of-principle, the current study describes the computational design and evaluation of a pair of peptides that are able to form stable high-affinity complexes with the epitope regions of C5a that are important for the recruitment of C5aR1 and C5aR2. The computational data further supports the potential of designer peptides for mimicking the function of antibodies targeting C5a. However, further experimental studies will be required to establish the structure-function relationship of the designer peptides and also to establish the hypothesis of antibody-like peptides targeting C5a.

A comprehensive review of protein misfolding disorders, underlying mechanism, clinical diagnosis, and therapeutic strategies

INTRODUCTION

Proteins are the most common biological macromolecules in living system and are building blocks of life. They are extremely dynamic in structure and functions. Due to several modifications, proteins undergo misfolding, leading to aggregation and thereby developing neurodegenerative and systemic diseases. Understanding the pathology of these diseases and the techniques used to diagnose them is therefore crucial for their effective management . There are several techniques, currently being in use to diagnose them and those will be discussed in this review.

AIM/OBJECTIVES

Current review aims to discuss an overview of protein aggregation and the underlying mechanisms linked to neurodegeneration and systemic diseases. Also, the review highlights protein misfolding disorders, their clinical diagnosis, and treatment strategies.

METHODOLOGY

Literature related to neurodegenerative and systemic diseases was explored through PubMed, Google Scholar, Scopus, and Medline databases. The keywords used for literature survey and analysis are protein aggregation, neurodegenerative disorders, Alzheimer's disease, Parkinson's disease, systemic diseases, protein aggregation mechanisms, etc. DISCUSSION /CONCLUSION: This review summarises the pathogenesis of neurodegenerative and systemic disorders caused by protein misfolding and aggregation. The clinical diagnosis and therapeutic strategies adopted for the management of these diseases are also discussed to aid in a better understanding of protein misfolding disorders. Many significant concerns about the role, characteristics, and consequences of protein aggregates in neurodegenerative and systemic diseases are not clearly understood to date. Regardless of technological advancements, there are still great difficulties in the management and cure of these diseases. Therefore, for better understanding, diagnosis, and treatment of neurodegenerative and systemic diseases, more studies to identify novel drugs that may aid in their treatment and management are required.

Structural characterization of a novel luciferase-like-monooxygenase fromPseudomonas meliae– an in-silico approach.. [DOI: 10.1101/2023.03.27.534437] [Citation(s) in RCA: 0] [Impact Index Per Article: 0]

Background

Luciferase is a well-known oxidative enzyme that produces bioluminescence. ThePseudomonas meliaeis a plant pathogen that causes wood rot on nectarine and peach and possesses a luciferase-like monooxygenase. After activation, it produces bioluminescence, and the pathogen’s bioluminescence is a visual indicator of diseased plants.

Methods

The present study aims to model and characterize the luciferase-like monooxygenase protein inP. meliaefor its similarity to well-established luciferase. In this study, the luciferase-like monooxygenase fromP. meliaeinfects chinaberry plants has been modeled first and then studied by comparing it with existing known luciferase. Also, the similarities between uncharacterized luciferase fromP. meliaeand template fromGeobacillus thermodenitrificanswere analyzed to find the novelty ofP. meliae.

Results

The results suggest that the absence of bioluminescence inP. meliaecould be due to the evolutionary mutation in positions 138 and 311. The active site remains identical except for two amino acids;P. meliaeTyr138 instead of His138 and Leu311 instead of His311. Therefore, theP. meliaewill have a potential future application, and mutation of the residues 138 and 311 can be restored luciferase light-emitting ability.

Conclusions

This study will help further improve, activate, and repurpose the luciferase fromP. meliaeas a reporter for gene expression.

Potential aggregation hot spots in recombinant human keratinocyte growth factor: a computational study

The recombinant human keratinocyte growth factor (rhKGF) is a highly aggregation-prone therapeutic protein. The high aggregation liability of rhKGF is manifested by loss of the monomeric state, and accumulation of the aggregated species even at moderate temperatures. Here, we analyzed the rhKGF for its vulnerability toward aggregation by detection of aggregation-prone regions (APRs) using several sequence-based computational tools including TANGO, ZipperDB, AGGRESCAN, Zyggregator, Camsol, PASTA, SALSA, WALTZ, SODA, Amylpred, AMYPDB, and structure-based tools including SolubiS, CamSol structurally corrected, Aggrescan3D and spatial aggregation propensity (SAP) algorithm. The sequence-based prediction of APRs in rhKGF indicated that they are mainly located at positions 10-30, 40-60, 61-66, 88-120, and 130-140. Mapping on the rhKGF structure revealed that most of these residues including F16-R25, I43, E45, R47-I56, F61, Y62, N66, L88-E91, E108-F110, A112, N114, T131, and H133-T140 are surface-exposed in the native state which can promote aggregation without major unfolding event, or the conformational change may occur in the oligomers. The other regions are buried in the native state and their contribution to non-native aggregation is mediated by a preceding unfolding event. The structure-based prediction of APRs using the SAP tool limited the number of identified APRs to the dynamically-exposed hydrophobic residues including V12, A50, V51, L88, I89, L90, I118, L135, and I139 mediating the native-state aggregation. Our analysis of APRs in rhKGF identified the regions determining the intrinsic aggregation propensity of the rhKGF which are the candidate positions for engineering the rhKGF to reduce its aggregation tendency.Communicated by Ramaswamy H. Sarma.

The energetic basis for hydroxyapatite mineralization by amelogenin variants provides insights into the origin of amelogenesis imperfecta

Small variations in the primary amino acid sequence of extracellular matrix proteins can have profound effects on the biomineralization of hard tissues. For example, a change in one amino acid within the amelogenin protein can lead to drastic changes in enamel phenotype, resulting in amelogenesis imperfecta, enamel that is defective and easily damaged. Despite the importance of these undesirable phenotypes, there is very little understanding of how single amino acid variation in amelogenins can lead to malformed enamel. Here, we aim to develop a thermodynamic understanding of how protein variants can affect steps of the biomineralization process. High-resolution, in situ atomic force microscopy (AFM) showed that altering one amino acid within the murine amelogenin sequence (natural variants T21 and P41T, and experimental variant P71T) resulted in an increase in the quantity of protein adsorbed onto hydroxyapatite (HAP) and the formation of multiple protein layers. Quantitative analysis of the equilibrium adsorbate amounts revealed that the protein variants had higher oligomer-oligomer binding energies. MMP20 enzyme degradation and HAP mineralization studies showed that the amino acid variants slowed the degradation of amelogenin by MMP20 and inhibited the growth and phase transformation of HAP. We propose that the protein variants cause malformed enamel because they bind excessively to HAP and disrupt the normal HAP growth and enzymatic degradation processes. The in situ methods applied to determine the energetics of molecular level processes are powerful tools toward understanding the mechanisms of biomineralization.

Structural studies suggest aggregation as one of the modes of action for teixobactin

Teixobactin is a new promising antibiotic that targets cell wall biosynthesis by binding to lipid II and has no detectable resistance thanks to its unique but yet not fully understood mechanism of operation. To aid in the structure-based design of teixobactin analogues with improved pharmacological properties, we present a 3D structure of native teixobactin in membrane mimetics and characterise its binding to lipid II through a combination of solution NMR and fast (90 kHz) magic angle spinning solid state NMR. In NMR titrations, we observe a pattern strongly suggesting interactions between the backbone of the C-terminal "cage" and the pyrophosphate moiety in lipid II. We find that the N-terminal part of teixobactin does not only act as a membrane anchor, as previously thought, but is actively involved in binding. Moreover, teixobactin forms a well-structured and specific complex with lipid II, where the N-terminal part of teixobactin assumes a β conformation that is highly prone to aggregation, which likely contributes to the antibiotic's high bactericidal efficiency. Overall, our study provides several new clues to teixobactin's modes of action.

An amino acid code for irregular and mixed protein packing

To advance our understanding of protein tertiary structure, the development of the knob-socket model is completed in an analysis of the packing in irregular coil and turn secondary structure packing as well as between mixed secondary structure. The knob-socket model simplifies packing based on repeated patterns of two motifs: a three-residue socket for packing within secondary (2°) structure and a four-residue knob-socket for tertiary (3°) packing. For coil and turn secondary structure, knob-sockets allow identification of a correlation between amino acid composition and tertiary arrangements in space. Coil contributes almost as much as α-helices to tertiary packing. In irregular sockets, Gly, Pro, Asp, and Ser are favored, while in irregular knobs, the preference order is Arg, Asp, Pro, Asn, Thr, Leu, and Gly. Cys, His,Met, and Trp are not favored in either. In mixed packing, the knob amino acid preferences are a function of the socket that they are packing into, whereas the amino acid composition of the sockets does not depend on the secondary structure of the knob. A unique motif of a coil knob with an XYZ β-sheet socket may potentially function to inhibit β-sheet extension. In addition, analysis of the preferred crossing angles for strands within a β-sheet and mixed α-helice/β-sheet identifies canonical packing patterns useful in protein design. Lastly, the knob-socket model abstracts the complexity of protein tertiary structure into an intuitive packing surface topology map.

The Antibody Light-Chain Linker Is Important for Domain Stability and Amyloid Formation

The association of light chains (LCs) and heavy chains is the basis for functional antibodies that are essential for adaptive immune responses. However, in some cases, LCs and especially fragments consisting of the LC variable (VL) domain are pathologically deposited in fatal aggregation diseases. The two domains of the LC are connected by a highly conserved linker. We show here that, unexpectedly, the linker residue Arg108 affects the conformational stability and folding of both VLκ and LC constant (CLκ) domains. Interestingly, the extension of VL by Arg108 results in its resistance to amyloid formation, which suggests that the nature of the truncation of the LC plays a crucial role in disease progression. Increased solvation due to the exposed charged C-terminal Arg108 residue explains its stabilizing effects on the VL domain. For the CL domain, the interaction of N-terminal loop residues with Arg108 is important for the integrity of the domain, as the disruption of this interaction results in fluctuation, partial opening of the protein's interior and the exposure of hydrophobic residues that destabilize the domain. This establishes new principles for antibody domain architecture and amyloidogenicity.

CSI 3.0: a web server for identifying secondary and super-secondary structure in proteins using NMR chemical shifts

The Chemical Shift Index or CSI 3.0 (http://csi3.wishartlab.com) is a web server designed to accurately identify the location of secondary and super-secondary structures in protein chains using only nuclear magnetic resonance (NMR) backbone chemical shifts and their corresponding protein sequence data. Unlike earlier versions of CSI, which only identified three types of secondary structure (helix, β-strand and coil), CSI 3.0 now identifies total of 11 types of secondary and super-secondary structures, including helices, β-strands, coil regions, five common β-turns (type I, II, I′, II′ and VIII), β hairpins as well as interior and edge β-strands. CSI 3.0 accepts experimental NMR chemical shift data in multiple formats (NMR Star 2.1, NMR Star 3.1 and SHIFTY) and generates colorful CSI plots (bar graphs) and secondary/super-secondary structure assignments. The output can be readily used as constraints for structure determination and refinement or the images may be used for presentations and publications. CSI 3.0 uses a pipeline of several well-tested, previously published programs to identify the secondary and super-secondary structures in protein chains. Comparisons with secondary and super-secondary structure assignments made via standard coordinate analysis programs such as DSSP, STRIDE and VADAR on high-resolution protein structures solved by X-ray and NMR show >90% agreement between those made with CSI 3.0.

Dissection of the structure-forming activity from the structure-guiding activity of silicatein: a biomimetic molecular approach to print optical fibers

The enzymatically inactive silicatein was used as the platform for the enzymatically active silicatein, which synthesized the silica waveguide.

β-Bulges: extensive structural analyses of β-sheets irregularities

β-Sheets are quite frequent in protein structures and are stabilized by regular main-chain hydrogen bond patterns. Irregularities in β-sheets, named β-bulges, are distorted regions between two consecutive hydrogen bonds. They disrupt the classical alternation of side chain direction and can alter the directionality of β-strands. They are implicated in protein-protein interactions and are introduced to avoid β-strand aggregation. Five different types of β-bulges are defined. Previous studies on β-bulges were performed on a limited number of protein structures or one specific family. These studies evoked a potential conservation during evolution. In this work, we analyze the β-bulge distribution and conservation in terms of local backbone conformations and amino acid composition. Our dataset consists of 66 times more β-bulges than the last systematic study (Chan et al. Protein Science 1993, 2:1574-1590). Novel amino acid preferences are underlined and local structure conformations are highlighted by the use of a structural alphabet. We observed that β-bulges are preferably localized at the N- and C-termini of β-strands, but contrary to the earlier studies, no significant conservation of β-bulges was observed among structural homologues. Displacement of β-bulges along the sequence was also investigated by Molecular Dynamics simulations.

A method to predict edge strands in beta-sheets from protein sequences

There is a need for rules allowing three-dimensional structure information to be derived from protein sequences. In this work, consideration of an elementary protein folding step allows protein sub-sequences which optimize folding to be derived for any given protein sequence. Classical mechanics applied to this system and the energy conservation law during the elementary folding step yields an equation whose solutions are taken over the field of rational numbers. This formalism is applied to beta-sheets containing two edge strands and at least two central strands. The number of protein sub-sequences optimized for folding per amino acid in beta-strands is shown in particular to predict edge strands from protein sequences. Topological information on beta-strands and loops connecting them is derived for protein sequences with a prediction accuracy of 75%. The statistical significance of the finding is given. Applications in protein structure prediction are envisioned such as for the quality assessment of protein structure models.

Systematic characterization of the specificity of the SH2 domains of cytoplasmic tyrosine kinases

Cytoplasmic tyrosine kinases (CTK) generally contain a Src-homology 2 (SH2) domain, whose role in the CTK family is not fully understood. Here we report the determination of the specificity of 25 CTK SH2 domains by screening one-bead-one-compound (OBOC) peptide libraries. Based on the peptide sequences selected by the SH2 domains, we built Support Vector Machine (SVM) models for the prediction of binding ligands for the SH2 domains. These models yielded support for the progressive phosphorylation model for CTKs in which the overlapping specificity of the CTK SH2 and kinase domains has been proposed to facilitate targeting of the CTK substrates with at least two potential phosphotyrosine (pTyr) sites. We curated 93 CTK substrates with at least two pTyr sites catalyzed by the same CTK, and showed that 71% of these substrates had at least two pTyr sites predicted to bind a common CTK SH2 domain. More importantly, we found 34 instances where there was at least one pTyr site predicted to be recognized by the SH2 domain of the same CTK, suggesting that the SH2 and kinase domains of the CTKs may cooperate to achieve progressive phosphorylation of a protein substrate. This article is part of a Special Issue entitled: From protein structures to clinical applications.

Molecular mechanism by which surface antigen HP0197 mediates host cell attachment in the pathogenic bacteria Streptococcus suis

Streptococcus suis, one of the most important and prevalent pathogens in swine, presents a major challenge to global public health. HP0197 is an S. suis surface antigen that was previously identified by immunoproteomics and can bind to the host cell surface. Here, we investigated the interaction between HP0197 and the host cell surface glycosaminoglycans (GAGs) using indirect immunofluorescence and cell adhesion inhibition assays. In addition, we determined that a novel 18-kDa domain in the N-terminal region of HP0197 functions as the GAG-binding domain. We then solved the three-dimensional structures of the N-terminal 18-kDa and C-terminal G5 domains using x-ray crystallography. Based on this structural information, the GAG-binding sites in HP0197 were predicted and subsequently verified using site-directed mutagenesis and indirect immunofluorescence. The results indicate that the positively charged residues on the exposed surface of the 18-kDa domain, which are primarily lysines, likely play a critical role in the HP0197-heparin interaction that mediates bacterium-host cell adhesion. Understanding this molecular mechanism may provide a basis for the development of effective drugs and therapeutic strategies for treating streptococcal infections.

Supervised learning with decision tree-based methods in computational and systems biology

At the intersection between artificial intelligence and statistics, supervised learning allows algorithms to automatically build predictive models from just observations of a system. During the last twenty years, supervised learning has been a tool of choice to analyze the always increasing and complexifying data generated in the context of molecular biology, with successful applications in genome annotation, function prediction, or biomarker discovery. Among supervised learning methods, decision tree-based methods stand out as non parametric methods that have the unique feature of combining interpretability, efficiency, and, when used in ensembles of trees, excellent accuracy. The goal of this paper is to provide an accessible and comprehensive introduction to this class of methods. The first part of the review is devoted to an intuitive but complete description of decision tree-based methods and a discussion of their strengths and limitations with respect to other supervised learning methods. The second part of the review provides a survey of their applications in the context of computational and systems biology.

Structural and functional studies of truncated hemolysin A from Proteus mirabilis

In this study we analyzed the structure and function of a truncated form of hemolysin A (HpmA265) from Proteus mirabilis using a series of functional and structural studies. Hemolysin A belongs to the two-partner secretion pathway. The two-partner secretion pathway has been identified as the most common protein secretion pathway among Gram-negative bacteria. Currently, the mechanism of action for the two-partner hemolysin members is not fully understood. In this study, hemolysis experiments revealed a unidirectional, cooperative, biphasic activity profile after full-length, inactive hemolysin A was seeded with truncated hemolysin A. We also solved the first x-ray structure of a TpsA hemolysin. The truncated hemolysin A formed a right-handed parallel beta-helix with three adjoining segments of anti-parallel beta-sheet. A CXXC disulfide bond, four buried solvent molecules, and a carboxyamide ladder were all located at the third complete beta-helix coil. Replacement of the CXXC motif led to decreased activity and stability according to hemolysis and CD studies. Furthermore, the crystal structure revealed a sterically compatible, dry dimeric interface formed via anti-parallel beta-sheet interactions between neighboring beta-helix monomers. Laser scanning confocal microscopy further supported the unidirectional interconversion of full-length hemolysin A. From these results, a model has been proposed, where cooperative, beta-strand interactions between HpmA265 and neighboring full-length hemolysin A molecules, facilitated in part by the highly conserved CXXC pattern, account for the template-assisted hemolysis.

Potential aggregation prone regions in biotherapeutics: A survey of commercial monoclonal antibodies

Aggregation of a biotherapeutic is of significant concern and judicious process and formulation development is required to minimize aggregate levels in the final product. Aggregation of a protein in solution is driven by intrinsic and extrinsic factors. In this work we have focused on aggregation as an intrinsic property of the molecule. We have studied the sequences and Fab structures of commercial and non-commercial antibody sequences for their vulnerability towards aggregation by using sequence based computational tools to identify potential aggregation-prone motifs or regions. The mAbs in our dataset contain 2 to 8 aggregation-prone motifs per heavy and light chain pair. Some of these motifs are located in variable domains, primarily in CDRs. Most aggregation-prone motifs are rich in beta branched aliphatic and aromatic residues. Hydroxyl-containing Ser/Thr residues are also found in several aggregation-prone motifs while charged residues are rare. The motifs found in light chain CDR3 are glutamine (Q)/asparagine (N) rich. These motifs are similar to the reported aggregation promoting regions found in prion and amyloidogenic proteins that are also rich in Q/N, aliphatic and aromatic residues. The implication is that one possible mechanism for aggregation of mAbs may be through formation of cross-beta structures and fibrils. Mapping on the available Fab-receptor/antigen complex structures reveals that these motifs in CDRs might also contribute significantly towards receptor/antigen binding. Our analysis identifies the opportunity and tools for simultaneous optimization of the therapeutic protein sequence for potency and specificity while reducing vulnerability towards aggregation.

Functional insight into Maelstrom in the germline piRNA pathway: a unique domain homologous to the DnaQ-H 3'-5' exonuclease, its lineage-specific expansion/loss and evolutionarily active site switch

Abstract

Maelstrom (MAEL) plays a crucial role in a recently-discovered piRNA pathway; however its specific function remains unknown. Here a novel MAEL-specific domain characterized by a set of conserved residues (Glu-His-His-Cys-His-Cys, EHHCHC) was identified in a broad range of species including vertebrates, sea squirts, insects, nematodes, and protists. It exhibits ancient lineage-specific expansions in several species, however, appears to be lost in all examined teleost fish species. Functional involvement of MAEL domains in DNA- and RNA-related processes was further revealed by its association with HMG, SR-25-like and HDAC_interact domains. A distant similarity to the DnaQ-H 3'–5' exonuclease family with the RNase H fold was discovered based on the evidence that all MAEL domains adopt the canonical RNase H fold; and several protist MAEL domains contain the conserved 3'–5' exonuclease active site residues (Asp-Glu-Asp-His-Asp, DEDHD). This evolutionary link together with structural examinations leads to a hypothesis that MAEL domains may have a potential nuclease activity or RNA-binding ability that may be implicated in piRNA biogenesis. The observed transition of two sets of characteristic residues between the ancestral DnaQ-H and the descendent MAEL domains may suggest a new mode for protein function evolution called "active site switch", in which the protist MAEL homologues are the likely evolutionary intermediates due to harboring the specific characteristics of both 3'–5' exonuclease and MAEL domains.

Reviewers

This article was reviewed by L Aravind, Wing-Cheong Wong and Frank Eisenhaber. For the full reviews, please go to the Reviewers' Comments section.

Coalescence of spherical beads of retro-HSP12.6 into linear and ring-shaped amyloid nanofibers

The sequence-reversed form of a small heat shock protein, HSP12.6 (retro-HSP12.6), has been reported to fold and assemble into structured tetramers in aqueous solution. Upon raising the protein concentration to ~1.0-1.5 mg/ml, tetrameric retro-HSP12.6 is known to display a tendency to associate further into spherical beads of 18-20 nm in diameter containing folded protein subunits. Here we report that storage of this protein at low temperatures leads to further association of the beaded structures into linear and ring-shaped amyloid nanofibers of 18-20 nm in diameter. The electron micrographs presented in this communication provide the best visual evidence yet that amyloids can form through the association of smaller structured bead-like intermediates. The results also suggest that folded beta-sheet-rich subunits can participate in amyloid formation.

Characterization of non-trivial neighborhood fold constraints from protein sequences using generalized topohydrophobicity

Prediction of key features of protein structures, such as secondary structure, solvent accessibility and number of contacts between residues, provides useful structural constraints for comparative modeling, fold recognition, ab-initio fold prediction and detection of remote relationships. In this study, we aim at characterizing the number of non-trivial close neighbors, or long-range contacts of a residue, as a function of its “topohydrophobic” index deduced from multiple sequence alignments and of the secondary structure in which it is embedded. The “topohydrophobic” index is calculated using a two-class distribution of amino acids, based on their mean atom depths. From a large set of structural alignments processed from the FSSP database, we selected 1485 structural sub-families including at least 8 members, with accurate alignments and limited redundancy. We show that residues within helices, even when deeply buried, have few non-trivial neighbors (0–2), whereas β-strand residues clearly exhibit a multimodal behavior, dominated by the local geometry of the tetrahedron (3 non-trivial close neighbors associated with one tetrahedron; 6 with two tetrahedra). This observed behavior allows the distinction, from sequence profiles, between edge and central β-strands within β-sheets. Useful topological constraints on the immediate neighborhood of an amino acid, but also on its correlated solvent accessibility, can thus be derived using this approach, from the simple knowledge of multiple sequence alignments.

A beta-hairpin comprising the nuclear localization sequence sustains the self-associated states of nucleosome assembly protein 1

The histone chaperone nucleosome assembly protein 1 (NAP1) is implicated in histone shuttling as well as nucleosome assembly and disassembly. Under physiological conditions, NAP1 dimers exist in a mixture of various high-molecular-weight oligomers whose size may be regulated by the cell cycle-dependent concentration of NAP1. Both the functional and structural significance of the observed oligomers are unknown. We have resolved the molecular mechanism by which yeast NAP1 (yNAP1) dimers oligomerize by applying x-ray crystallographic, hydrodynamic, and functional approaches. We found that an extended beta-hairpin that protrudes from the compact core of the yNAP1 dimer forms a stable beta-sheet with beta-hairpins of neighboring yNAP1 dimers. Disruption of the beta-hairpin (whose sequence is conserved among NAP1 proteins in various species) by the replacement of one or more amino acids with proline results in complete loss of yNAP1 dimer oligomerization. The in vitro functions of yNAP1 remain unaffected by the mutations. We have thus identified a conserved structural feature of NAP1 whose function, in addition to presenting the nuclear localization sequence, appears to be the formation of higher-order oligomers.

PPT-DB: the protein property prediction and testing database

The protein property prediction and testing database (PPT-DB) is a database housing nearly 30 carefully curated databases, each of which contains commonly predicted protein property information. These properties include both structural (i.e. secondary structure, contact order, disulfide pairing) and dynamic (i.e. order parameters, B-factors, folding rates) features that have been measured, derived or tabulated from a variety of sources. PPT-DB is designed to serve two purposes. First it is intended to serve as a centralized, up-to-date, freely downloadable and easily queried repository of predictable or 'derived' protein property data. In this role, PPT-DB can serve as a one-stop, fully standardized repository for developers to obtain the required training, testing and validation data needed for almost any kind of protein property prediction program they may wish to create. The second role that PPT-DB can play is as a tool for homology-based protein property prediction. Users may query PPT-DB with a sequence of interest and have a specific property predicted using a sequence similarity search against PPT-DB's extensive collection of proteins with known properties. PPT-DB exploits the well-known fact that protein structure and dynamic properties are highly conserved between homologous proteins. Predictions derived from PPT-DB's similarity searches are typically 85-95% correct (for categorical predictions, such as secondary structure) or exhibit correlations of >0.80 (for numeric predictions, such as accessible surface area). This performance is 10-20% better than what is typically obtained from standard 'ab initio' predictions. PPT-DB, its prediction utilities and all of its contents are available at http://www.pptdb.ca.

An automated high-throughput screening method for the identification of high-yield, soluble protein variants using cell-free expression and systematic truncation

A highly automated method for rapidly identifying soluble protein variants with good expression yields has been developed. This method is based on a commercially available in vitro protein expression system. It consists of two polymerase chain reactions (PCR) followed by in vitro protein expression and protein quantification by dot blot. The PCR protocols have been improved and optimized to allow automation using commercial fluid handling devices. A PCR primer design program has also been implemented to streamline protein variant design. This automated protocol is highly reliable and has tremendously improved the throughput of expression screening as compared to conventional cell-based methods and manual in vitro methods. We have applied this method to 32 problematic targets from the TB Structural Genomics Consortium. Experimental results of these studies are reported.

Ranking the factors that contribute to protein β-sheet folding

The formation of beta-sheet domains in proteins involves five energetically important factors: the formation of networks of hydrogen bonds and hydrophobic faces, and the residue propensities, or preferences, to be found at the edges of the beta-sheet, to adopt the extended conformation, and to make contact with other residues. These relative energy contributions define a potential energy function. Here, we show how optimizing this potential energy function reveals the formation of hydrophobic faces as the utmost factor. The potential energy function was optimized to minimize the Z-scores of the native topologies among the exhaustive sets of over 400 different beta-sheets. These results corroborate with experimental data that showed the environment of a protein is an important modulator of beta-sheet folding. The contact propensities were found to be the least important, which could explain the poor predictive power of beta-strand alignment methods based on pair-wise contact matrices.

β-Edge Interactions in a Pentadecameric Human Antibody Vκ Domain

Antibodies are the archetypal molecules of the Ig-fold superfamily. Their highly conserved beta-sheet architecture has evolved to avoid aggregation by protecting edge strands. However, the crystal structure of a human V kappa domain described here, reveals an exposed beta-edge strand which mediates assembly of a helical pentadecameric oligomer. This edge strand is highly conserved in V kappa domains but is both shortened and capped by the use of two sequential trans-proline residues in V lambda domains. We suggest that the exposure of this beta-edge in V kappa domains may explain why light-chain deposition disease is mediated predominantly by kappa antibodies.

The molecular mechanisms of the anti-amyloid effects of phenols

Previous investigations demonstrated that various aromatic compounds, many of which are known antioxidants, inhibit amyloid fibril formation. Yet, the mechanism of action of these compounds is not fully understood and contribution of their antioxidative potency has not been addressed. In recent publications, Ono et al. (2003, 2004) studied the anti-amyloid effects of 11 phenols on each of three consecutive processes: (1) seeding (formation) of nascent fibrils, (2) elongation (extension) of the fibrils, and (3) depolymerization (destabilization) of the formed assemblies. The aim of the present study was to evaluate the molecular mechanisms that mediate the effects of the studied inhibitors on each of these processes. Hierarchical clustering analyses indicated that the studied inhibitors can be categorized into three groups: 'slightly active' inhibitors, 'highly active' inhibitors and 'selective inhibitors' that differ markedly in their effects on these three stages. Analyses of the correlations between the effects of the studied compounds on the various stages of amyloid fibril formation, and their known physicochemical properties provided novel insights on the specific role of hydrophobic and aromatic interactions as well as the antioxidative potency on the process of amyloid fibril formation and dissociation. Specifically, the hydrophobic and/or aromatic character of the compounds makes the major contribution to the anti-formation and anti-extension effects, whereas the antioxidative potency relates mostly to the promotion of destabilization.

An algorithm for predicting protein-protein interaction sites: Abnormally exposed amino acid residues and secondary structure elements

Multiprotein systems mediate most regulatory processes in living organisms. Although the structures of the individual proteins are often defined, less is known of the structures of multiprotein systems. Computational methods for predicting interfaces, using evolutionary conservation and/or physicochemical data, have been developed. Here we consider the use of solvent accessibility, residue propensity, and hydrophobicity, in conjunction with secondary structure data, as prediction parameters. We analyze the influence of residue type and secondary structure on solvent accessibility and define a measure of "relative exposedness." Clustering abnormally high scoring residues provides a basis for predicting interaction sites. The analysis is extended to investigate abnormally exposed secondary structure elements, particularly beta-sheet strands. We show that surface-exposed beta-strands lacking protective features are more likely to be found at protein-protein interfaces, allowing us to create an algorithm with approximately 68% and approximately 75% accuracy in differentiating between interacting and edge strands in isolated beta-strands and beta-sheet strands, respectively. These methods of identifying abnormally exposed surface regions are combined in an algorithm, which, on a data set of 77 unbound and disjoint (single chain extracted from complex) structures, predicts 79% of the protein-protein interfaces correctly. If enzyme-inhibitor complexes, where the inhibitor mimics a nonprotein substrate, are excluded, the accuracy increases to 85%.

Physicochemical descriptors to discriminate protein-protein interactions in permanent and transient complexes selected by means of machine learning algorithms

Analyzing protein-protein interactions at the atomic level is critical for our understanding of the principles governing the interactions involved in protein-protein recognition. For this purpose, descriptors explaining the nature of different protein-protein complexes are desirable. In this work, the authors introduced Epic Protein Interface Classification as a framework handling the preparation, processing, and analysis of protein-protein complexes for classification with machine learning algorithms. We applied four different machine learning algorithms: Support Vector Machines, C4.5 Decision Trees, K Nearest Neighbors, and Naïve Bayes algorithm in combination with three feature selection methods, Filter (Relief F), Wrapper, and Genetic Algorithms, to extract discriminating features from the protein-protein complexes. To compare protein-protein complexes to each other, the authors represented the physicochemical characteristics of their interfaces in four different ways, using two different atomic contact vectors, DrugScore pair potential vectors and SFCscore descriptor vectors. We classified two different datasets: (A) 172 protein-protein complexes comprising 96 monomers, forming contacts enforced by the crystallographic packing environment (crystal contacts), and 76 biologically functional homodimer complexes; (B) 345 protein-protein complexes containing 147 permanent complexes and 198 transient complexes. We were able to classify up to 94.8% of the packing enforced/functional and up to 93.6% of the permanent/transient complexes correctly. Furthermore, we were able to extract relevant features from the different protein-protein complexes and introduce an approach for scoring the importance of the extracted features.

A new catalog of protein beta-sheets

Systematic protein folding studies depend on protein three-dimensional structure annotation, the assignment of amino acid structural types from atomic coordinates. Significant stabilizing factors between adjacent beta-sheet peptide chains have recently been characterized and were not considered during the development of previously published annotation methods. To produce an accurate beta-sheet domain catalog and to encompass the full beta-sheet spectacle, we developed a method, beta-Spider, which evaluates a packing energy between adjacent peptide chains in accordance with the newly discovered stabilizing factors. While considering important energetic factors, our approach also minimizes the use of subjective criteria, such as (phi,psi) boundaries and sets of H-bonding motifs that are used in other existing methods. As a result of the application of beta-Spider to a set of available high-resolution X-ray crystal structures, we present here a new beta-sheet catalog that differs considerably from the one produced by the most acclaimed DSSP method. The catalog includes new H-bonding motifs that were never reported.

Prediction of beta-strand packing interactions using the signature product

The prediction of beta-sheet topology requires the consideration of long-range interactions between beta-strands that are not necessarily consecutive in sequence. Since these interactions are difficult to simulate using ab initio methods, we propose a supplementary method able to assign beta-sheet topology using only sequence information. We envision using the results of our method to reduce the three-dimensional search space of ab initio methods. Our method is based on the signature molecular descriptor, which has been used previously to predict protein-protein interactions successfully, and to develop quantitative structure-activity relationships for small organic drugs and peptide inhibitors. Here, we show how the signature descriptor can be used in a Support Vector Machine to predict whether or not two beta-strands will pack adjacently within a protein. We then show how these predictions can be used to order beta-strands within beta-sheets. Using the entire PDB database with ten-fold cross-validation, we have achieved 74.0% accuracy in packing prediction and 75.6% accuracy in the prediction of edge strands. For the case of beta-strand ordering, we are able to predict the correct ordering accurately for 51.3% of the beta-sheets. Furthermore, using a simple confidence metric, we can determine those sheets for which accurate predictions can be obtained. For the top 25% highest confidence predictions, we are able to achieve 95.7% accuracy in beta-strand ordering. [Figure: see text].

From chance to frequent encounters: Origins of β2-microglobulin fibrillogenesis

It is generally accepted that amyloid formation requires partial, but not complete unfolding of a polypeptide chain. Amyloid formation by beta-2 microglobulin (beta2m), however, readily occurs under strongly native conditions provided that there is exposure to specific transition metal cations. In this review, we discuss transition metal catalyzed conformational changes in several amyloidogenic systems including prion protein, Alzheimer's and Parkinson's diseases. For some systems, including beta2m from dialysis related amyloidosis (DRA), catalysis overcomes an entropic barrier to protein aggregation. Recent data suggest that beta2m samples conformations that are under thermodynamic control, resulting in local or partial unfolding under native conditions. Furthermore, exposure to transition metal cations stabilizes these partially unfolded states and promotes the formation of small oligomers, whose structures are simultaneously near-native and amyloid-like. By serving as a tether, Cu(2+) enables the encounter of amyloidogenic conformations to occur on time scales which are significantly more rapid than would occur between freely diffusing monomeric protein. Once amyloid formation occurs, the requirement for Cu(2+) is lost. We assert that beta2m amyloid fiber formation at neutral pH may be facilitated by rearrangements catalyzed by the transient and pair wise tethering of beta2m at the blood/dialysate interface present during therapeutic hemodialysis.

Visualization of conformational distribution of short to medium size segments in globular proteins and identification of local structural motifs

Analysis of the conformational distribution of polypeptide segments in a conformational space is the first step for understanding a principle of structural diversity of proteins. Here, we present a statistical analysis of protein local structures based on interatomic C(alpha) distances. Using principal component analysis (PCA) on the intrasegment C(alpha)-C(alpha) atomic distances, the conformational space of protein segments, which we call the protein segment universe, has been visualized, and three essential coordinate axes, suitable for describing the universe, have been identified. Three essential axes specified radius of gyration, structural symmetry, and separation of hairpin structures from other structures. Among the segments of arbitrary length, 6-22 residues long, the conservation of those axes was uncovered. Further application of PCA to the two largest clusters in the universe revealed local structural motifs. Although some of motifs have already been reported, we identified a possibly novel strand motif. We also showed that a capping box, which is one of the helix capping motifs, was separated into independent subclusters based on the C(alpha) geometry. Implications of the strand motif, which may play a role for protein-protein interaction, are discussed. The currently proposed method is useful for not only mapping the immense universe of protein structures but also identification of structural motifs.

Improved prediction of protein-protein binding sites using a support vector machines approach

MOTIVATION

Structural genomics projects are beginning to produce protein structures with unknown function, therefore, accurate, automated predictors of protein function are required if all these structures are to be properly annotated in reasonable time. Identifying the interface between two interacting proteins provides important clues to the function of a protein and can reduce the search space required by docking algorithms to predict the structures of complexes.

RESULTS

We have combined a support vector machine (SVM) approach with surface patch analysis to predict protein-protein binding sites. Using a leave-one-out cross-validation procedure, we were able to successfully predict the location of the binding site on 76% of our dataset made up of proteins with both transient and obligate interfaces. With heterogeneous cross-validation, where we trained the SVM on transient complexes to predict on obligate complexes (and vice versa), we still achieved comparable success rates to the leave-one-out cross-validation suggesting that sufficient properties are shared between transient and obligate interfaces.

AVAILABILITY

A web application based on the method can be found at http://www.bioinformatics.leeds.ac.uk/ppi_pred. The dataset of 180 proteins used in this study is also available via the same web site.

CONTACT

westhead@bmb.leeds.ac.uk

SUPPLEMENTARY INFORMATION

http://www.bioinformatics.leeds.ac.uk/ppi-pred/supp-material.