OMPdb: A Global Hub of Beta-Barrel Outer Membrane Proteins.

OMPdb (www.ompdb.org) was introduced as a database for ß-barrel outer membrane proteins from Gram-negative bacteria in 2011 and then included 69,354 entries classified into 85 families. The database has been updated continuously using a collection of characteristic profile Hidden Markov Models able to discriminate between the different families of prokaryotic transmembrane ß-barrels. The number of families has increased ultimately to a total of 129 families in the current, second major version of OMPdb. New additions have been made in parallel with efforts to update existing families and add novel families. Here, we present the upgrade of OMPdb, which from now on aims to become a global repository for all transmembrane ß-barrel proteins, both eukaryotic and bacterial.

ANTISOMA: A Computational Pipeline for the Reduction of the Aggregation Propensity of Monoclonal Antibodies.

Monoclonal antibodies (mAbs) constitute a promising class of therapeutics, since ca. 25% of all biotech drugs in development are mAbs. Even though their therapeutic value is now well established, human- and murine-derived mAbs do have deficiencies, such as short in vivo lifespan and low stability. However, the most difficult obstacle to overcome, toward the exploitation of mAbs for disease treatment, is the prevention of the formation of protein aggregates. ANTISOMA is a pipeline for the reduction of the aggregation tendency of mAbs through the decrease in their intrinsic aggregation propensity, based on an automated amino acid substitution approach. The method takes into consideration the special features of mAbs and aims at proposing specific point mutations that could lead to the redesign of those promising therapeutics, without affecting their epitope-binding ability. The method is available online at http://bioinformatics.biol.uoa.gr/ANTISOMA .

The gram-negative outer membrane modeler: Automated building of lipopolysaccharide-rich bacterial outer membranes in four force fields.

Outer membranes are a crucial component of Gram-negative bacteria, containing standard lipids in their inner leaflet, lipopolysaccharides (LPSs) in their outer leaflet, and transmembrane ß-barrels known as outer membrane proteins (OMPs). OMPs regulate functions such as substrate transport and cell movement, while LPSs act as a protective barrier for bacteria and can cause toxic reactions in humans. However, the experimental study of outer membranes is challenging. Molecular dynamics simulations are often used for the computational study of membrane systems, but the preparation of complex, LPS-rich outer membranes is not straightforward. The Gram-Negative Outer Membrane Modeler (GNOMM) is an automated pipeline for preparing simulation systems of OMPs embedded in LPS-containing membranes in four different force fields. Given the physiological and clinical importance of outer membranes and their components, GNOMM can be a useful tool in the study of their structure, function, and implications in diseases. GNOMM is available at http://bioinformatics.biol.uoa.gr/GNOMM. © 2019 Wiley Periodicals, Inc.

Analysis of Single-Nucleotide Polymorphisms in Human Voltage-Gated Ion Channels.

Voltage-gated ion channels (VGICs) are one of the largest groups of transmembrane proteins. Due to their major role in the generation and propagation of electrical signals, VGICs are considered important from a medical viewpoint, and their dysfunction is often associated with Channelopathies. We identified disease-associated mutations and polymorphisms in these proteins through mapping missense single-nucleotide polymorphisms from the UniProt and ClinVar databases on their amino acid sequence, considering their special topological and functional characteristics. Statistical analysis revealed that disease-associated SNPs are mostly found in the voltage sensor domain and the pore loop. Both of these regions are extremely important for the activation and ion conductivity of VGICs. Moreover, among the most frequently observed mutations are those of arginine to glutamine, to histidine or to cysteine, which can probably be attributed to the extremely important role of arginine residues in the regulation of membrane potential in these proteins. We suggest that topological information in combination with genetic variation data can contribute toward a better evaluation of the effect of currently unclassified mutations in VGICs. It is hoped that potential associations with certain disease phenotypes will be revealed in the future with the use of similar approaches.

Structural characterization and molecular dynamics simulations of the caprine and bovine solute carrier family 11 A1 (SLC11A1).

Natural Resistance-Associated Macrophage Proteins are a family of transmembrane divalent metal ion transporters, with important implications in life of both bacteria and mammals. Among them, the Solute Carrier family 11 member A1 (SLC11A1) has been implicated with susceptibility to infection by Mycobacterium avium subspecies paratuberculosis (MAP), potentially causing Crohn's disease in humans and paratuberculosis (PTB) in ruminants. Our previous research had focused on sequencing the mRNA of the caprine slc11a1 gene and pinpointed polymorphisms that contribute to caprine SLC11A1's susceptibility to infection by MAP in PTB. Despite its importance, little is known on the structural/dynamic features of mammalian SLC11A1 that may influence its function under normal or pathological conditions at the protein level. In this work we studied the structural architecture of SLC11A1 in Capra hircus and Bos taurus through molecular modeling, molecular dynamics simulations in different, functionally relevant configurations, free energy calculations of protein-metal interactions and sequence conservation analysis. The results of this study propose a three dimensional structure for SLC11A1 with conserved sequence and structural features and provide hints for a potential mechanism through which divalent metal ion transport is conducted. Given the importance of SLC11A1 in susceptibility to PTB, this study provides a framework for further studies on the structure and dynamics of SLC11A1 in other organisms, to gain 3D structural insight into the macromolecular arrangements of SLC11A1 but also suggesting a potential mechanism which divalent metal ion transport is conducted.

αCGRP, another amyloidogenic member of the CGRP family.

The Calcitonin-gene related peptide (CGRP) family is a group of peptide hormones, which consists of IAPP, calcitonin, adrenomedullin, intermedin, αCGRP and ßCGRP. IAPP and calcitonin have been extensively associated with the formation of amyloid fibrils, causing Type 2 Diabetes and Medullary Thyroid Carcinoma, respectively. In contrast, the potential amyloidogenic properties of αCGRP still remain unexplored, although experimental trials have indicated its presence in deposits, associated with the aforementioned disorders. Therefore, in this work, we investigated the amyloidogenic profile of αCGRP, a 37-residue-long peptide hormone, utilizing both biophysical experimental techniques and Molecular Dynamics simulations. These efforts unravel a novel amyloidogenic member of the CGRP family and provide insights into the mechanism underlying the αCGRP polymerization.

Exploring Amyloidogenicity of Clusterin: A Structural and Bioinformatics Analysis.

Clusterin, a multitasking glycoprotein, is a protein highly conserved amongst mammals. In humans, Clusterin is mainly a secreted protein, described as an extracellular chaperone with the capability of interacting with a broad spectrum of molecules. In neurodegenerative diseases, such as Alzheimer's disease, it is an amyloid associated protein, co-localized with fibrillar deposits in amyloid plaques in systemic or localized amyloidoses. An 'aggregation-prone' segment (NFHAMFQ) was located within the Clusterin α-chain sequence using AMYLPRED, a consensus method for the prediction of amyloid propensity, developed in our lab. This peptide was synthesized and was found to self-assemble into amyloid-like fibrils in vitro, as electron microscopy, X-ray fiber diffraction, Attenuated Total Reflectance Fourier-Transform Spectroscopy and Congo red staining studies reveal. All experimental results verify that this human Clusterin peptide-analogue, possesses high aggregation potency. Additional computational analysis highlighted novel and at the same time, unexplored features of human Clusterin.

Mining databases for protein aggregation: a review.

Protein aggregation is an active area of research in recent decades, since it is the most common and troubling indication of protein instability. Understanding the mechanisms governing protein aggregation and amyloidogenesis is a key component to the aetiology and pathogenesis of many devastating disorders, including Alzheimer's disease or type 2 diabetes. Protein aggregation data are currently found "scattered" in an increasing number of repositories, since advances in computational biology greatly influence this field of research. This review exploits the various resources of aggregation data and attempts to distinguish and analyze the biological knowledge they contain, by introducing protein-based, fragment-based and disease-based repositories, related to aggregation. In order to gain a broad overview of the available repositories, a novel comprehensive network maps and visualizes the current association between aggregation databases and other important databases and/or tools and discusses the beneficial role of community annotation. The need for unification of aggregation databases in a common platform is also addressed.

Tracking the amyloidogenic core of IAPP amyloid fibrils: Insights from micro-Raman spectroscopy.

Human islet amyloid polypeptide (hIAPP) is the major protein component of extracellular amyloid deposits, located in the islets of Langerhans, a hallmark of type II diabetes. The underlying mechanisms of IAPP aggregation have not yet been clearly defined, although the highly amyloidogenic sequence of the protein has been extensively studied. Several segments have been highlighted as aggregation-prone regions (APRs), with much attention focused on the central 8-17 and 20-29 stretches. In this work, we employ micro-Raman spectroscopy to identify specific regions that are contributing to or are excluded from the amyloidogenic core of IAPP amyloid fibrils. Our results demonstrate that both the N-terminal region containing a conserved disulfide bond between Cys residues at positions 2 and 7, and the C-terminal region containing the only Tyr residue are excluded from the amyloid core. Finally, by performing detailed aggregation assays and molecular dynamics simulations on a number of IAPP variants, we demonstrate that point mutations within the central APRs contribute to the reduction of the overall amyloidogenic potential of the protein but do not completely abolish the formation of IAPP amyloid fibrils.

The amyloid interactome: Exploring protein aggregation.

Protein-protein interactions are the quintessence of physiological activities, but also participate in pathological conditions. Amyloid formation, an abnormal protein-protein interaction process, is a widespread phenomenon in divergent proteins and peptides, resulting in a variety of aggregation disorders. The complexity of the mechanisms underlying amyloid formation/amyloidogenicity is a matter of great scientific interest, since their revelation will provide important insight on principles governing protein misfolding, self-assembly and aggregation. The implication of more than one protein in the progression of different aggregation disorders, together with the cited synergistic occurrence between amyloidogenic proteins, highlights the necessity for a more universal approach, during the study of these proteins. In an attempt to address this pivotal need we constructed and analyzed the human amyloid interactome, a protein-protein interaction network of amyloidogenic proteins and their experimentally verified interactors. This network assembled known interconnections between well-characterized amyloidogenic proteins and proteins related to amyloid fibril formation. The consecutive extended computational analysis revealed significant topological characteristics and unraveled the functional roles of all constituent elements. This study introduces a detailed protein map of amyloidogenicity that will aid immensely towards separate intervention strategies, specifically targeting sub-networks of significant nodes, in an attempt to design possible novel therapeutics for aggregation disorders.

Predicting Beta Barrel Transmembrane Proteins Using HMMs.

Transmembrane beta-barrels (TMBBs) constitute an important structural class of membrane proteins located in the outer membrane of gram-negative bacteria, and in the outer membrane of chloroplasts and mitochondria. They are involved in a wide variety of cellular functions and the prediction of their transmembrane topology, as well as their discrimination in newly sequenced genomes is of great importance as they are promising targets for antimicrobial drugs and vaccines. Several methods have been applied for the prediction of the transmembrane segments and the topology of beta barrel transmembrane proteins utilizing different algorithmic techniques. Hidden Markov Models (HMMs) have been efficiently used in the development of several computational methods used for this task. In this chapter we give a brief review of different available prediction methods for beta barrel transmembrane proteins pointing out sequence and structural features that should be incorporated in a prediction method. We then describe the procedure of the design and development of a Hidden Markov Model capable of predicting the transmembrane beta strands of TMBBs and discriminating them from globular proteins.

Predicting Alpha Helical Transmembrane Proteins Using HMMs.

Alpha helical transmembrane (TM) proteins constitute an important structural class of membrane proteins involved in a wide variety of cellular functions. The prediction of their transmembrane topology, as well as their discrimination in newly sequenced genomes, is of great importance for the elucidation of their structure and function. Several methods have been applied for the prediction of the transmembrane segments and the topology of alpha helical transmembrane proteins utilizing different algorithmic techniques. Hidden Markov Models (HMMs) have been efficiently used in the development of several computational methods used for this task. In this chapter we give a brief review of different available prediction methods for alpha helical transmembrane proteins pointing out sequence and structural features that should be incorporated in a prediction method. We then describe the procedure of the design and development of a Hidden Markov Model capable of predicting the transmembrane alpha helices in proteins and discriminating them from globular proteins.

Unraveling the aggregation propensity of human insulin C-peptide.

Over the last 20 years, proinsulin C-peptide emerged as an important player in various biological events. Much time and effort has been spent in exploring all functional features of C-peptide and recording its implications in Diabetes mellitus. Only a few studies, though, have addressed C-peptide oligomerization and link this procedure with Diabetes. The aim of our work was to examine the aggregation propensity of C-peptide, utilizing Transmission Electron Microscopy, Congo Red staining, ATR-FTIR, and X-ray fiber diffraction at a 10 mg ml-1 concentration. Our experimental work clearly shows that C-peptide self-assembles into amyloid-like fibrils and therefore, the aggregation propensity of C-peptide is a characteristic novel feature that should be related to physiological and also pathological conditions. © 2016 Wiley Periodicals, Inc. Biopolymers (Pept Sci) 108: 1-8, 2017.

Molecular dynamics simulations and structure-based network analysis reveal structural and functional aspects of G-protein coupled receptor dimer interactions.

A significant amount of experimental evidence suggests that G-protein coupled receptors (GPCRs) do not act exclusively as monomers but also form biologically relevant dimers and oligomers. However, the structural determinants, stoichiometry and functional importance of GPCR oligomerization remain topics of intense speculation. In this study we attempted to evaluate the nature and dynamics of GPCR oligomeric interactions. A representative set of GPCR homodimers were studied through Coarse-Grained Molecular Dynamics simulations, combined with interface analysis and concepts from network theory for the construction and analysis of dynamic structural networks. Our results highlight important structural determinants that seem to govern receptor dimer interactions. A conserved dynamic behavior was observed among different GPCRs, including receptors belonging in different GPCR classes. Specific GPCR regions were highlighted as the core of the interfaces. Finally, correlations of motion were observed between parts of the dimer interface and GPCR segments participating in ligand binding and receptor activation, suggesting the existence of mechanisms through which dimer formation may affect GPCR function. The results of this study can be used to drive experiments aimed at exploring GPCR oligomerization, as well as in the study of transmembrane protein-protein interactions in general.

Intrinsic aggregation propensity of the CsgB nucleator protein is crucial for curli fiber formation.

Several organisms exploit the extraordinary physical properties of amyloid fibrils forming natural protective amyloids, in an effort to support complex biological functions. Curli amyloid fibers are a major component of mature biofilms, which are produced by many Enterobacteriaceae species and are responsible, among other functions, for the initial adhesion of bacteria to surfaces or cells. The main axis of curli fibers is formed by a major structural subunit, known as CsgA. CsgA self-assembly is promoted by oligomeric nuclei formed by a minor curli subunit, known as the CsgB nucleator protein. Here, by implementing AMYLPRED2, a consensus prediction method for the identification of 'aggregation-prone' regions in protein sequences, developed in our laboratory, we have successfully identified potent amyloidogenic regions of the CsgB subunit. Peptide-analogues corresponding to the predicted 'aggregation-prone' segments of CsgB were chemically synthesized and studied, utilizing several biophysical techniques. Our experimental data indicate that these peptides self-assemble in solution, forming fibrils with characteristic amyloidogenic properties. Using comparative modeling techniques, we have developed three-dimensional models of both CsgA and CsgB subunits. Structural analysis revealed that the identified 'aggregation-prone' segments may promote gradual polymerization of CsgB. Briefly, our results indicate that the intrinsic self-aggregation propensity of the CsgB subunit, most probably has a pivotal role in initiating the formation of curli amyloid fibers by promoting the self-assembly process of the CsgB nucleator protein.

MBPpred: Proteome-wide detection of membrane lipid-binding proteins using profile Hidden Markov Models.

A large number of modular domains that exhibit specific lipid binding properties are present in many membrane proteins involved in trafficking and signal transduction. These domains are present in either eukaryotic peripheral membrane or transmembrane proteins and are responsible for the non-covalent interactions of these proteins with membrane lipids. Here we report a profile Hidden Markov Model based method capable of detecting Membrane Binding Proteins (MBPs) from information encoded in their amino acid sequence, called MBPpred. The method identifies MBPs that contain one or more of the Membrane Binding Domains (MBDs) that have been described to date, and further classifies these proteins based on their position in respect to the membrane, either as peripheral or transmembrane. MBPpred is available online at http://bioinformatics.biol.uoa.gr/MBPpred. This method was applied in selected eukaryotic proteomes, in order to examine the characteristics they exhibit in various eukaryotic kingdoms and phyla.

A common 'aggregation-prone' interface possibly participates in the self-assembly of human zona pellucida proteins.

Human zona pellucida (ZP) is composed of four glycoproteins, namely ZP1, ZP2, ZP3 and ZP4. ZP proteins form heterodimers, which are incorporated into filaments through a common bipartite polymerizing component, designated as the ZP domain. The latter is composed of two individually folded subdomains, named ZP-N and ZP-C. Here, we have synthesized six 'aggregation-prone' peptides, corresponding to a common interface of human ZP2, ZP3 and ZP4. Experimental results utilizing electron microscopy, X-ray diffraction, ATR FT-IR spectroscopy and polarizing microscopy indicate that these peptides self-assemble forming fibrils with distinct amyloid-like features. Finally, by performing detailed modeling and docking, we attempt to shed some light in the self-assembly mechanism of human ZP proteins.

GprotPRED: Annotation of Gα, Gß and Gγ subunits of G-proteins using profile Hidden Markov Models (pHMMs) and application to proteomes.

Heterotrimeric G-proteins form a major protein family, which participates in signal transduction. They are composed of three subunits, Gα, Gß and Gγ. The Gα subunit is further divided in four distinct families Gs, Gi/o, Gq/11 and G12/13. The goal of this work was to detect and classify members of the four distinct families, plus the Gß and the Gγ subunits of G-proteins from sequence alone. To achieve this purpose, six specific profile Hidden Markov Models (pHMMs) were built and checked for their credibility. These models were then applied to ten (10) proteomes and were able to identify all known G-protein and classify them into the distinct families. In a separate case study, the models were applied to twenty seven (27) arthropod proteomes and were able to give more credible classification in proteins with uncertain annotation and in some cases to detect novel proteins. An online tool, GprotPRED, was developed that uses these six pHMMs. The sensitivity and specificity for all pHMMs were equal to 100% with the exception of the Gß case, where sensitivity equals to 100%, while specificity is 99.993%. In contrast to Pfam's pHMM which detects Gα subunits in general, our method not only detects Gα subunits but also classifies them into the appropriate Gα-protein family and thus could become a useful tool for the annotation of G-proteins in newly discovered proteomes. GprotPRED online tool is publicly available for non-commercial use at http://bioinformatics.biol.uoa.gr/GprotPRED and, also, a standalone version of the tool at https://github.com/vkostiou/GprotPRED.

A ß-solenoid model of the Pmel17 repeat domain: insights to the formation of functional amyloid fibrils.

Pmel17 is a multidomain protein involved in biosynthesis of melanin. This process is facilitated by the formation of Pmel17 amyloid fibrils that serve as a scaffold, important for pigment deposition in melanosomes. A specific luminal domain of human Pmel17, containing 10 tandem imperfect repeats, designated as repeat domain (RPT), forms amyloid fibrils in a pH-controlled mechanism in vitro and has been proposed to be essential for the formation of the fibrillar matrix. Currently, no three-dimensional structure has been resolved for the RPT domain of Pmel17. Here, we examine the structure of the RPT domain by performing sequence threading. The resulting model was subjected to energy minimization and validated through extensive molecular dynamics simulations. Structural analysis indicated that the RPT model exhibits several distinct properties of ß-solenoid structures, which have been proposed to be polymerizing components of amyloid fibrils. The derived model is stabilized by an extensive network of hydrogen bonds generated by stacking of highly conserved polar residues of the RPT domain. Furthermore, the key role of invariant glutamate residues is proposed, supporting a pH-dependent mechanism for RPT domain assembly. Conclusively, our work attempts to provide structural insights into the RPT domain structure and to elucidate its contribution to Pmel17 amyloid fibril formation.

Exploring the 'aggregation-prone' core of human Cystatin C: A structural study.

Amyloidogenic proteins like human Cystatin C (hCC) have been shown to form dimers and oligomers by exchange of subdomains of the monomeric proteins. Normally, the hCC monomer, a low molecular type 2 Cystatin, consists of 120 amino acid residues and functions as an inhibitor of cysteine proteases. The oligomerization of hCC is involved in the pathophysiology of a rare form of amyloidosis namely Icelandic hereditary cerebral amyloid angiopathy, in which an L68Q mutant is deposited as amyloid in brain arteries of young adults. In order to find the shortest stretch responsible to drive the fibril formation of hCC, we have previously demonstrated that the LQVVR peptide forms amyloid fibrils, in vitro (Tsiolaki et al., 2015). Predictions by AMYLPRED, an amyloidogenic determinant prediction algorithm developed in our lab, led us to synthesize and experimentally study two additional predicted peptides derived from hCC. Along with our previous findings, in this work, we reveal that these peptides self-assemble, in a similar way, into amyloid-like fibrils in vitro, as electron microscopy, X-ray fiber diffraction, ATR FT-IR spectroscopy and Congo red staining studies have shown. Further to our experimental results, all three peptides seem to have a fundamental contribution in forming the "aggregation-prone" core of human Cystatin C.