Linear motifs regulating protein secretion, sorting and autophagy in Leishmania parasites are diverged with respect to their host equivalents.

The pathogenic, tropical Leishmania flagellates belong to an early-branching eukaryotic lineage (Kinetoplastida) with several unique features. Unfortunately, they are poorly understood from a molecular biology perspective, making development of mechanistically novel and selective drugs difficult. Here, we explore three functionally critical targeting short linear motif systems as well as their receptors in depth, using a combination of structural modeling, evolutionary sequence divergence and deep learning. Secretory signal peptides, endoplasmic reticulum (ER) retention motifs (KDEL motifs), and autophagy signals (motifs interacting with ATG8 family members) are ancient and essential components of cellular life. Although expected to be conserved amongst the kinetoplastids, we observe that all three systems show a varying degree of divergence from their better studied equivalents in animals, plants, or fungi. We not only describe their behaviour, but also build models that allow the prediction of localization and potential functions for several uncharacterized Leishmania proteins. The unusually Ala/Val-rich secretory signal peptides, endoplasmic reticulum resident proteins ending in Asp-Leu-COOH and atypical ATG8-like proteins are all unique molecular features of kinetoplastid parasites. Several of their critical protein-protein interactions could serve as targets of selective antimicrobial agents against Leishmaniasis due to their systematic divergence from the host.

ELM-the Eukaryotic Linear Motif resource-2024 update.

Short Linear Motifs (SLiMs) are the smallest structural and functional components of modular eukaryotic proteins. They are also the most abundant, especially when considering post-translational modifications. As well as being found throughout the cell as part of regulatory processes, SLiMs are extensively mimicked by intracellular pathogens. At the heart of the Eukaryotic Linear Motif (ELM) Resource is a representative (not comprehensive) database. The ELM entries are created by a growing community of skilled annotators and provide an introduction to linear motif functionality for biomedical researchers. The 2024 ELM update includes 346 novel motif instances in areas ranging from innate immunity to both protein and RNA degradation systems. In total, 39 classes of newly annotated motifs have been added, and another 17 existing entries have been updated in the database. The 2024 ELM release now includes 356 motif classes incorporating 4283 individual motif instances manually curated from 4274 scientific publications and including >700 links to experimentally determined 3D structures. In a recent development, the InterPro protein module resource now also includes ELM data. ELM is available at: http://elm.eu.org.

UniTmp: unified resources for transmembrane proteins.

The UNIfied database of TransMembrane Proteins (UniTmp) is a comprehensive and freely accessible resource of transmembrane protein structural information at different levels, from localization of protein segments, through the topology of the protein to the membrane-embedded 3D structure. We not only annotated tens of thousands of new structures and experiments, but we also developed a new system that can serve these resources in parallel. UniTmp is a unified platform that merges TOPDB (Topology Data Bank of Transmembrane Proteins), TOPDOM (database of conservatively located domains and motifs in proteins), PDBTM (Protein Data Bank of Transmembrane Proteins) and HTP (Human Transmembrane Proteome) databases and provides interoperability between the incorporated resources and an easy way to keep them regularly updated. The current update contains 9235 membrane-embedded structures, 9088 sequences with 536 035 topology-annotated segments and 8692 conservatively localized protein domains or motifs as well as 5466 annotated human transmembrane proteins. The UniTmp database can be accessed at https://www.unitmp.org.

How AlphaFold2 shaped the structural coverage of the human transmembrane proteome.

AlphaFold2 (AF2) provides a 3D structure for every known or predicted protein, opening up new prospects for virtually every field in structural biology. However, working with transmembrane protein molecules pose a notorious challenge for scientists, resulting in a limited number of experimentally determined structures. Consequently, algorithms trained on this finite training set also face difficulties. To address this issue, we recently launched the TmAlphaFold database, where predicted AlphaFold2 structures are embedded into the membrane plane and a quality assessment (plausibility of the membrane-embedded structure) is provided for each prediction using geometrical evaluation. In this paper, we analyze how AF2 has improved the structural coverage of membrane proteins compared to earlier years when only experimental structures were available, and high-throughput structure prediction was greatly limited. We also evaluate how AF2 can be used to search for (distant) homologs in highly diverse protein families. By combining quality assessment and homology search, we can pinpoint protein families where AF2 accuracy is still limited, and experimental structure determination would be desirable.

LeishMANIAdb: a comparative resource for Leishmania proteins.

Leishmaniasis is a detrimental disease causing serious changes in quality of life and some forms can lead to death. The disease is spread by the parasite Leishmania transmitted by sandfly vectors and their primary hosts are vertebrates including humans. The pathogen penetrates host cells and secretes proteins (the secretome) to repurpose cells for pathogen growth and to alter cell signaling via host-pathogen protein-protein interactions). Here, we present LeishMANIAdb, a database specifically designed to investigate how Leishmania virulence factors may interfere with host proteins. Since the secretomes of different Leishmania species are only partially characterized, we collated various experimental evidence and used computational predictions to identify Leishmania secreted proteins to generate a user-friendly unified web resource allowing users to access all information available on experimental and predicted secretomes. In addition, we manually annotated host-pathogen interactions of 211 proteins and the localization/function of 3764 transmembrane (TM) proteins of different Leishmania species. We also enriched all proteins with automatic structural and functional predictions that can provide new insights in the molecular mechanisms of infection. Our database may provide novel insights into Leishmania host-pathogen interactions and help to identify new therapeutic targets for this neglected disease. Database URL  https://leishmaniadb.ttk.hu/.

TmAlphaFold database: membrane localization and evaluation of AlphaFold2 predicted alpha-helical transmembrane protein structures.

AI-driven protein structure prediction, most notably AlphaFold2 (AF2) opens new frontiers for almost all fields of structural biology. As traditional structure prediction methods for transmembrane proteins were both complicated and error prone, AF2 is a great help to the community. Complementing the relatively meager number of experimental structures, AF2 provides 3D predictions for thousands of new alpha-helical membrane proteins. However, the lack of reliable structural templates and the fact that AF2 was not trained to handle phase boundaries also necessitates a delicate assessment of structural correctness. In our new database, Transmembrane AlphaFold database (TmAlphaFold database), we apply TMDET, a simple geometry-based method to visualize the likeliest position of the membrane plane. In addition, we calculate several parameters to evaluate the location of the protein into the membrane. This also allows TmAlphaFold database to show whether the predicted 3D structure is realistic or not. The TmAlphaFold database is available at https://tmalphafold.ttk.hu/.

PSINDB: the postsynaptic protein-protein interaction database.

The postsynaptic region is the receiving part of the synapse comprising thousands of proteins forming an elaborate and dynamically changing network indispensable for the molecular mechanisms behind fundamental phenomena such as learning and memory. Despite the growing amount of information about individual protein-protein interactions (PPIs) in this network, these data are mostly scattered in the literature or stored in generic databases that are not designed to display aspects that are fundamental to the understanding of postsynaptic functions. To overcome these limitations, we collected postsynaptic PPIs complemented by a high amount of detailed structural and biological information and launched a freely available resource, the Postsynaptic Interaction Database (PSINDB), to make these data and annotations accessible. PSINDB includes tens of thousands of binding regions together with structural features, mediating and regulating the formation of PPIs, annotated with detailed experimental information about each interaction. PSINDB is expected to be useful for various aspects of molecular neurobiology research, from experimental design to network and systems biology-based modeling and analysis of changes in the protein network upon various stimuli. Database URL https://psindb.itk.ppke.hu/.

DisProt in 2022: improved quality and accessibility of protein intrinsic disorder annotation.

The Database of Intrinsically Disordered Proteins (DisProt, URL: https://disprot.org) is the major repository of manually curated annotations of intrinsically disordered proteins and regions from the literature. We report here recent updates of DisProt version 9, including a restyled web interface, refactored Intrinsically Disordered Proteins Ontology (IDPO), improvements in the curation process and significant content growth of around 30%. Higher quality and consistency of annotations is provided by a newly implemented reviewing process and training of curators. The increased curation capacity is fostered by the integration of DisProt with APICURON, a dedicated resource for the proper attribution and recognition of biocuration efforts. Better interoperability is provided through the adoption of the Minimum Information About Disorder (MIADE) standard, an active collaboration with the Gene Ontology (GO) and Evidence and Conclusion Ontology (ECO) consortia and the support of the ELIXIR infrastructure.

The Eukaryotic Linear Motif resource: 2022 release.

Almost twenty years after its initial release, the Eukaryotic Linear Motif (ELM) resource remains an invaluable source of information for the study of motif-mediated protein-protein interactions. ELM provides a comprehensive, regularly updated and well-organised repository of manually curated, experimentally validated short linear motifs (SLiMs). An increasing number of SLiM-mediated interactions are discovered each year and keeping the resource up-to-date continues to be a great challenge. In the current update, 30 novel motif classes have been added and five existing classes have undergone major revisions. The update includes 411 new motif instances mostly focused on cell-cycle regulation, control of the actin cytoskeleton, membrane remodelling and vesicle trafficking pathways, liquid-liquid phase separation and integrin signalling. Many of the newly annotated motif-mediated interactions are targets of pathogenic motif mimicry by viral, bacterial or eukaryotic pathogens, providing invaluable insights into the molecular mechanisms underlying infectious diseases. The current ELM release includes 317 motif classes incorporating 3934 individual motif instances manually curated from 3867 scientific publications. ELM is available at: http://elm.eu.org.

MemDis: Predicting Disordered Regions in Transmembrane Proteins.

Transmembrane proteins (TMPs) play important roles in cells, ranging from transport processes and cell adhesion to communication. Many of these functions are mediated by intrinsically disordered regions (IDRs), flexible protein segments without a well-defined structure. Although a variety of prediction methods are available for predicting IDRs, their accuracy is very limited on TMPs due to their special physico-chemical properties. We prepared a dataset containing membrane proteins exclusively, using X-ray crystallography data. MemDis is a novel prediction method, utilizing convolutional neural network and long short-term memory networks for predicting disordered regions in TMPs. In addition to attributes commonly used in IDR predictors, we defined several TMP specific features to enhance the accuracy of our method further. MemDis achieved the highest prediction accuracy on TMP-specific dataset among other popular IDR prediction methods.

PolarProtPred: predicting apical and basolateral localization of transmembrane proteins using putative short linear motifs and deep learning.

MOTIVATION: Cell polarity refers to the asymmetric organization of cellular components in various cells. Epithelial cells are the best-known examples of polarized cells, featuring apical and basolateral membrane domains. Mounting evidence suggests that short linear motifs play a major role in protein trafficking to these domains, although the exact rules governing them are still elusive. RESULTS: In this study we prepared neural networks that capture recurrent patterns to classify transmembrane proteins localizing into apical and basolateral membranes. Asymmetric expression of drug transporters results in vectorial drug transport, governing the pharmacokinetics of numerous substances, yet the data on how proteins are sorted in epithelial cells is very scattered. The provided method may offer help to experimentalists to identify or better characterize molecular networks regulating the distribution of transporters or surface receptors (including viral entry receptors like that of COVID-19). AVAILABILITY AND IMPLEMENTATION: The prediction server PolarProtPred is available at http://polarprotpred.ttk.hu. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

PolarProtDb: A Database of Transmembrane and Secreted Proteins showing Apical-Basal Polarity.

Most cells in multicellular organisms are somehow asymmetric, polarized: maintaining separate membrane domains. Typical examples are the epithelial cells (apical-basal polarization), neurons (dendritic-axonal domains), or migratory cells (with a leading and a trailing edge). Here we present the most comprehensive database containing experimentally verified mammalian proteins that display polarized sorting or secretion, focusing on epithelial polarity. In addition to the source cells or tissues, homology-based inferences and transmembrane topology (if applicable) are all provided. PolarProtDb also offers a detailed interface displaying all information that may be relevant for trafficking: including post-translational modifications (glycosylations and phosphorylations), known or predicted short linear motifs conserved across orthologs, as well as potential interaction partners. Data on polarized sorting has so far been scattered across myriads of publications, hence difficult to access. This information can help researchers in several areas, such as scanning for potential entry points of viral agents like COVID-19. PolarProtDb shall be a useful resource to design future experiments as well as for comparative analyses. The database is available at http://polarprotdb.enzim.hu.

Distribution of disease-causing germline mutations in coiled-coils implies an important role of their N-terminal region.

Next-generation sequencing resulted in the identification of a huge number of naturally occurring variations in human proteins. The correct interpretation of the functional effects of these variations necessitates the understanding of how they modulate protein structure. Coiled-coils are α-helical structures responsible for a diverse range of functions, but most importantly, they facilitate the structural organization of macromolecular scaffolds via oligomerization. In this study, we analyzed a comprehensive set of disease-associated germline mutations in coiled-coil structures. Our results suggest an important role of residues near the N-terminal part of coiled-coil regions, possibly critical for superhelix assembly and folding in some cases. We also show that coiled-coils of different oligomerization states exhibit characteristically distinct patterns of disease-causing mutations. Our study provides structural and functional explanations on how disease emerges through the mutation of these structural motifs.

PlaToLoCo: the first web meta-server for visualization and annotation of low complexity regions in proteins.

Low complexity regions (LCRs) in protein sequences are characterized by a less diverse amino acid composition compared to typically observed sequence diversity. Recent studies have shown that LCRs may co-occur with intrinsically disordered regions, are highly conserved in many organisms, and often play important roles in protein functions and in diseases. In previous decades, several methods have been developed to identify regions with LCRs or amino acid bias, but most of them as stand-alone applications and currently there is no web-based tool which allows users to explore LCRs in protein sequences with additional functional annotations. We aim to fill this gap by providing PlaToLoCo - PLAtform of TOols for LOw COmplexity-a meta-server that integrates and collects the output of five different state-of-the-art tools for discovering LCRs and provides functional annotations such as domain detection, transmembrane segment prediction, and calculation of amino acid frequencies. In addition, the union or intersection of the results of the search on a query sequence can be obtained. By developing the PlaToLoCo meta-server, we provide the community with a fast and easily accessible tool for the analysis of LCRs with additional information included to aid the interpretation of the results. The PlaToLoCo platform is available at: http://platoloco.aei.polsl.pl/.

Sequence and Structure Properties Uncover the Natural Classification of Protein Complexes Formed by Intrinsically Disordered Proteins via Mutual Synergistic Folding.

Intrinsically disordered proteins mediate crucial biological functions through their interactions with other proteins. Mutual synergistic folding (MSF) occurs when all interacting proteins are disordered, folding into a stable structure in the course of the complex formation. In these cases, the folding and binding processes occur in parallel, lending the resulting structures uniquely heterogeneous features. Currently there are no dedicated classification approaches that take into account the particular biological and biophysical properties of MSF complexes. Here, we present a scalable clustering-based classification scheme, built on redundancy-filtered features that describe the sequence and structure properties of the complexes and the role of the interaction, which is directly responsible for structure formation. Using this approach, we define six major types of MSF complexes, corresponding to biologically meaningful groups. Hence, the presented method also shows that differences in binding strength, subcellular localization, and regulation are encoded in the sequence and structural properties of proteins. While current protein structure classification methods can also handle complex structures, we show that the developed scheme is fundamentally different, and since it takes into account defining features of MSF complexes, it serves as a better representation of structures arising through this specific interaction mode.

Sequential, Structural and Functional Properties of Protein Complexes Are Defined by How Folding and Binding Intertwine.

Intrinsically disordered proteins (IDPs) fulfill critical biological roles without having the potential to fold on their own. While lacking inherent structure, the majority of IDPs do reach a folded state via interaction with a protein partner, presenting a deep entanglement of the folding and binding processes. Protein disorder has been recognized as a major determinant in several properties of proteins, such as sequence, adopted structure upon binding and function. However, the way the binding process is reflected in these features in general lacks a detailed description. Here, we defined three categories of protein complexes depending on the unbound structural state of the interactors and analyzed them in detail. We found that strikingly, the properties of interactors in terms of sequence and adopted structure are defined not only by the intrinsic structural state of the protein itself but also to a comparable extent by the structural state of the binding partner. The three different types of interactions are also regulated through divergent molecular tactics of post-translational modifications. This not only widens the range of biologically relevant sequence and structure spaces defined by ordered proteins but also presents distinct molecular mechanisms compatible with specific biological processes, separately for each interaction type. The distinct attributes of different binding modes identified in this study can help to understand how various types of interactions serve as building blocks for the assembly of tightly regulated and highly intertwined regulatory networks.

Occurrence of Ordered and Disordered Structural Elements in Postsynaptic Proteins Supports Optimization for Interaction Diversity.

The human postsynaptic density is an elaborate network comprising thousands of proteins, playing a vital role in the molecular events of learning and the formation of memory. Despite our growing knowledge of specific proteins and their interactions, atomic-level details of their full three-dimensional structure and their rearrangements are mostly elusive. Advancements in structural bioinformatics enabled us to depict the characteristic features of proteins involved in different processes aiding neurotransmission. We show that postsynaptic protein-protein interactions are mediated through the delicate balance of intrinsically disordered regions and folded domains, and this duality is also imprinted in the amino acid sequence. We introduce Diversity of Potential Interactions (DPI), a structure and regulation based descriptor to assess the diversity of interactions. Our approach reveals that the postsynaptic proteome has its own characteristic features and these properties reliably discriminate them from other proteins of the human proteome. Our results suggest that postsynaptic proteins are especially susceptible to forming diverse interactions with each other, which might be key in the reorganization of the postsynaptic density (PSD) in molecular processes related to learning and memory.

Structural Principles Governing Disease-Causing Germline Mutations.

Advancements in sequencing in the past decades enabled not only the determination of the human proteome but also the identification of a large number of genetic variations in the human population. The phenotypic effects of these mutations range from neutral for polymorphisms to severe for some somatic mutations. Disease-causing germline mutations (DCMs) represent a special and largely understudied class with relatively weak phenotypes. While for somatic mutations their effect on protein structure and regulation has been extensively studied in select cases, for germline mutations, this information is currently largely missing. In this analysis, a large amount of DCMs were analyzed and contrasted to polymorphisms from a structural point of view. Our results delineate the characteristic features of DCMs starting at the global level of partitioning proteins into globular, disordered and transmembrane classes, moving toward smaller structural units describing secondary structure elements and molecular surfaces, reaching down to the smallest structural entity, post-translational modifications. We show how these structural entities influence the emergence and possible phenotypic effects of DCMs.

Identification of Extracellular Segments by Mass Spectrometry Improves Topology Prediction of Transmembrane Proteins.

Transmembrane proteins play crucial role in signaling, ion transport, nutrient uptake, as well as in maintaining the dynamic equilibrium between the internal and external environment of cells. Despite their important biological functions and abundance, less than 2% of all determined structures are transmembrane proteins. Given the persisting technical difficulties associated with high resolution structure determination of transmembrane proteins, additional methods, including computational and experimental techniques remain vital in promoting our understanding of their topologies, 3D structures, functions and interactions. Here we report a method for the high-throughput determination of extracellular segments of transmembrane proteins based on the identification of surface labeled and biotin captured peptide fragments by LC/MS/MS. We show that reliable identification of extracellular protein segments increases the accuracy and reliability of existing topology prediction algorithms. Using the experimental topology data as constraints, our improved prediction tool provides accurate and reliable topology models for hundreds of human transmembrane proteins.

TSTMP: target selection for structural genomics of human transmembrane proteins.

The TSTMP database is designed to help the target selection of human transmembrane proteins for structural genomics projects and structure modeling studies. Currently, there are only 60 known 3D structures among the polytopic human transmembrane proteins and about a further 600 could be modeled using existing structures. Although there are a great number of human transmembrane protein structures left to be determined, surprisingly only a small fraction of these proteins have 'selected' (or above) status according to the current version the TargetDB/TargetTrack database. This figure is even worse regarding those transmembrane proteins that would contribute the most to the structural coverage of the human transmembrane proteome. The database was built by sorting out proteins from the human transmembrane proteome with known structure and searching for suitable model structures for the remaining proteins by combining the results of a state-of-the-art transmembrane specific fold recognition algorithm and a sequence similarity search algorithm. Proteins were searched for homologues among the human transmembrane proteins in order to select targets whose successful structure determination would lead to the best structural coverage of the human transmembrane proteome. The pipeline constructed for creating the TSTMP database guarantees to keep the database up-to-date. The database is available at http://tstmp.enzim.ttk.mta.hu.