MFDp2: Accurate predictor of disorder in proteins by fusion of disorder probabilities, content and profiles

Polêto M, Verli H, Almeida VM, Marana SR, Bender A, Godoi BF, Rodrigues VL, da S. Emery F, Trossini GHG. Fragment Screening Reveals Novel Scaffolds against Sirtuin-2-Related Protein 1 from Trypanosoma brucei

Sirtuin-2 (Sir2) is a histone deacetylase recognized as an antitrypanosomal target, yet there is limited knowledge regarding their potent inhibitors. This investigation employs the fragment-based drug discovery (FBDD) framework to identify novel inhibitors against Trypanosoma brucei Sir2-related protein 1. Initially, frequent residue-ligand interactions extracted from the crystallographic structures of human Sir2 and key features of human and parasitic Sir2 active sites were utilized to curate a targeted fragment library. Screening identified ten fragment hits, which introduced nine novel substructures compared to known Sir2 inhibitors. Among these, fragment 1 was the most potent, with an IC50 value of 17.8 μM and a ligand efficiency of 0.41. Further chemical space exploration of 30 compounds from the two most promising hits confirmed fragment 1 as the most potent. This study underscores the effectiveness of FBDD in discovering chemically distinct starting points with favorable ligand efficiency against protein targets in infectious diseases.

DisorderUnetLM: Validating ProteinUnet for efficient protein intrinsic disorder prediction

The prediction of intrinsic disorder regions has significant implications for understanding protein functions and dynamics. It can help to discover novel protein-protein interactions essential for designing new drugs and enzymes. Recently, a new generation of predictors based on protein language models (pLMs) is emerging. These algorithms reach state-of-the-art accuracy without calculating time-consuming multiple sequence alignments (MSAs). This article introduces the new DisorderUnetLM disorder predictor, which builds upon the idea of ProteinUnet. It uses the Attention U-Net convolutional network and incorporates features from the ProtTrans pLM. DisorderUnetLM achieves top results in the direct comparison with recent predictors exploiting MSAs and pLMs. Moreover, among 43 predictors on the latest CAID-2 benchmark, it ranks 1st for the NOX subset in terms of the ROC-AUC metric (0.844) and 2nd for the AP metric (0.596). For the CAID-2 PDB subset, it ranks in the top 10 (ROC-AUC of 0.924 and AP of 0.862). The code and model are publicly available and fully reproducible at doi.org/10.24433/CO.7350682.v1.

Adaptive strategies of Listeria monocytogenes: An in-depth analysis of the virulent strain involved in an outbreak in Italy through quantitative proteomics

Despite the general classification of L. monocytogenes strains as equally virulent by global safety authorities, molecular epidemiology reveals diverse subtypes in food, processing environments, and clinical cases. This study focuses on a highly virulent strain associated with a listeriosis outbreak in Italy in 2022, providing insights through comprehensive foodomics approaches, with a specific emphasis on quantitative proteomics. In particular, the ST155 strain of L. monocytogenes strain was subjected in vitro to growth stress conditions (NaCl 2.4 %, pH 6.2, T 12 °C), mimicking the conditions present in the frankfurter, its original source. Then, the protein expression patterns were compared with those obtained in optimal growth conditions. Through quantitative proteomic analysis and bioinformatic assessment, different proteins associated with virulence during the exponential growth phase were identified. This study unveils unique proteins specific to each environment, providing insights into how L. monocytogenes adapts to conditions that are similar to those encountered in frankfurters. This investigation contributes valuable insights into the adaptive strategies of L. monocytogenes under stressful conditions, with implications for enhancing food safety practices.

Accurate and Fast Prediction of Intrinsic Disorder Using flDPnn

Intrinsically disordered proteins (IDPs) that include one or more intrinsically disordered regions (IDRs) are abundant across all domains of life and viruses and play numerous functional roles in various cellular processes. Due to a relatively low throughput and high cost of experimental techniques for identifying IDRs, there is a growing need for fast and accurate computational algorithms that accurately predict IDRs/IDPs from protein sequences. We describe one of the leading disorder predictors, flDPnn. Results from a recent community-organized Critical Assessment of Intrinsic Disorder (CAID) experiment show that flDPnn provides fast and state-of-the-art predictions of disorder, which are supplemented with the predictions of several major disorder functions. This chapter provides a practical guide to flDPnn, which includes a brief explanation of its predictive model, descriptions of its web server and standalone versions, and a case study that showcases how to read and understand flDPnn's predictions.

Prediction of folding patterns for intrinsic disordered protein

The conformation flexibility of natural protein causes both complexity and difficulty to understand the relationship between structure and function. The prediction of intrinsically disordered protein primarily is focusing on to disclose the regions with structural flexibility involving relevant biological functions and various diseases. The order of amino acids in protein sequence determines possible conformations, folding flexibility and biological function. Although many methods provided the information of intrinsically disordered protein (IDP), but the results are mainly limited to determine the locations of regions without knowledge of possible folding conformations. Here, the developed protein folding fingerprint adopted the protein folding variation matrix (PFVM) to reveal all possible folding patterns for the intrinsically disordered protein along its sequence. The PFVM integrally exhibited the intrinsically disordered protein with disordering regions, degree of disorder as well as folding pattern. The advantage of PFVM will not only provide rich information for IDP, but also may promote the study of protein folding problem.

Overview Update: Computational Prediction of Intrinsic Disorder in Proteins

There are over 100 computational predictors of intrinsic disorder. These methods predict amino acid-level propensities for disorder directly from protein sequences. The propensities can be used to annotate putative disordered residues and regions. This unit provides a practical and holistic introduction to the sequence-based intrinsic disorder prediction. We define intrinsic disorder, explain the format of computational prediction of disorder, and identify and describe several accurate predictors. We also introduce recently released databases of intrinsic disorder predictions and use an illustrative example to provide insights into how predictions should be interpreted and combined. Lastly, we summarize key experimental methods that can be used to validate computational predictions. © 2023 Wiley Periodicals LLC.

Computational prediction of disordered binding regions

One of the key features of intrinsically disordered regions (IDRs) is their ability to interact with a broad range of partner molecules. Multiple types of interacting IDRs were identified including molecular recognition fragments (MoRFs), short linear sequence motifs (SLiMs), and protein-, nucleic acids- and lipid-binding regions. Prediction of binding IDRs in protein sequences is gaining momentum in recent years. We survey 38 predictors of binding IDRs that target interactions with a diverse set of partners, such as peptides, proteins, RNA, DNA and lipids. We offer a historical perspective and highlight key events that fueled efforts to develop these methods. These tools rely on a diverse range of predictive architectures that include scoring functions, regular expressions, traditional and deep machine learning and meta-models. Recent efforts focus on the development of deep neural network-based architectures and extending coverage to RNA, DNA and lipid-binding IDRs. We analyze availability of these methods and show that providing implementations and webservers results in much higher rates of citations/use. We also make several recommendations to take advantage of modern deep network architectures, develop tools that bundle predictions of multiple and different types of binding IDRs, and work on algorithms that model structures of the resulting complexes.

Computational Prediction of Protein Intrinsically Disordered Region Related Interactions and Functions

Intrinsically Disordered Proteins (IDPs) and Regions (IDRs) exist widely. Although without well-defined structures, they participate in many important biological processes. In addition, they are also widely related to human diseases and have become potential targets in drug discovery. However, there is a big gap between the experimental annotations related to IDPs/IDRs and their actual number. In recent decades, the computational methods related to IDPs/IDRs have been developed vigorously, including predicting IDPs/IDRs, the binding modes of IDPs/IDRs, the binding sites of IDPs/IDRs, and the molecular functions of IDPs/IDRs according to different tasks. In view of the correlation between these predictors, we have reviewed these prediction methods uniformly for the first time, summarized their computational methods and predictive performance, and discussed some problems and perspectives.

Distinct conformational changes occur within the intrinsically unstructured pro-domain of pro-Nerve Growth Factor in the presence of ATP and Mg2

Nerve growth factor (NGF), the prototypical neurotrophic factor, is involved in the maintenance and growth of specific neuronal populations, whereas its precursor, proNGF, is involved in neuronal apoptosis. Binding of NGF or proNGF to TrkA, p75NTR , and VP10p receptors triggers complex intracellular signaling pathways that can be modulated by endogenous small-molecule ligands. Here, we show by isothermal titration calorimetry and NMR that ATP binds to the intrinsically disordered pro-peptide of proNGF with a micromolar dissociation constant. We demonstrate that Mg2+ , known to play a physiological role in neurons, modulates the ATP/proNGF interaction. An integrative structural biophysics analysis by small angle X-ray scattering and hydrogen-deuterium exchange mass spectrometry unveils that ATP binding induces a conformational rearrangement of the flexible pro-peptide domain of proNGF. This suggests that ATP may act as an allosteric modulator of the overall proNGF conformation, whose likely distinct biological activity may ultimately affect its physiological homeostasis.

DNA binding and RAD51 engagement by the BRCA2 C-terminus orchestrate DNA repair and replication fork preservation

The tumor suppressor BRCA2 participates in DNA double-strand break repair by RAD51-dependent homologous recombination and protects stressed DNA replication forks from nucleolytic attack. We demonstrate that the C-terminal Recombinase Binding (CTRB) region of BRCA2, encoded by gene exon 27, harbors a DNA binding activity. CTRB alone stimulates the DNA strand exchange activity of RAD51 and permits the utilization of RPA-coated ssDNA by RAD51 for strand exchange. Moreover, CTRB functionally synergizes with the Oligonucleotide Binding fold containing DNA binding domain and BRC4 repeat of BRCA2 in RPA-RAD51 exchange on ssDNA. Importantly, we show that the DNA binding and RAD51 interaction attributes of the CTRB are crucial for homologous recombination and protection of replication forks against MRE11-mediated attrition. Our findings shed light on the role of the CTRB region in genome repair, reveal remarkable functional plasticity of BRCA2, and help explain why deletion of Brca2 exon 27 impacts upon embryonic lethality.

In silico integrative analysis for the characterization of LYT1 a unique protein of Trypanosoma cruzi

Trypanosoma rangeli is the most similar organism to Trypanosoma cruzi. They share distribution areas, hosts, and some vectors. However, there are key differences between them; the first lacks a multiplicative form in the host and does not cause disease, while the second is the etiological agent of the American tripanosomiasis, a tropical disease that still does not have an effective vaccine nor treatment. Aiming to reveal the differences in their gene expression patterns in each life cycle form, the comparison of expression profiles was made parting from the ESTs available in TriTrypDB. We verified that there are no genes unique to T. rangeli in the ESTs. Astonishingly, we determined that T. cruzi has a single copy gene called LYT1, which has no similarity to any other protein of any organism on Earth. LYT1 is involved in invasion, motility, and cell cycle, making it an attractive vaccine target. After its identification, using immunoinformatics programs, we found multiple potential B- and T-cell epitopes in this protein, which is also rich in intrinsically disordered regions. Additionally, an approximation of the 3 D structure was predicted where the B-cell epitopes were located to assess their solvent access. We propose that its particular structural conformation confers the flexibility required for the interactions with multiple proteins, which in part may be performed through N-myristoylation sites. Given its important role in the infectiveness of T. cruzi and its antigenic potential, we highlight the need for future studies focused on its molecular and immunological in vivo characterization.Communicated by Ramaswamy H. Sarma.

SETH predicts nuances of residue disorder from protein embeddings

Predictions for millions of protein three-dimensional structures are only a few clicks away since the release of AlphaFold2 results for UniProt. However, many proteins have so-called intrinsically disordered regions (IDRs) that do not adopt unique structures in isolation. These IDRs are associated with several diseases, including Alzheimer's Disease. We showed that three recent disorder measures of AlphaFold2 predictions (pLDDT, "experimentally resolved" prediction and "relative solvent accessibility") correlated to some extent with IDRs. However, expert methods predict IDRs more reliably by combining complex machine learning models with expert-crafted input features and evolutionary information from multiple sequence alignments (MSAs). MSAs are not always available, especially for IDRs, and are computationally expensive to generate, limiting the scalability of the associated tools. Here, we present the novel method SETH that predicts residue disorder from embeddings generated by the protein Language Model ProtT5, which explicitly only uses single sequences as input. Thereby, our method, relying on a relatively shallow convolutional neural network, outperformed much more complex solutions while being much faster, allowing to create predictions for the human proteome in about 1 hour on a consumer-grade PC with one NVIDIA GeForce RTX 3060. Trained on a continuous disorder scale (CheZOD scores), our method captured subtle variations in disorder, thereby providing important information beyond the binary classification of most methods. High performance paired with speed revealed that SETH's nuanced disorder predictions for entire proteomes capture aspects of the evolution of organisms. Additionally, SETH could also be used to filter out regions or proteins with probable low-quality AlphaFold2 3D structures to prioritize running the compute-intensive predictions for large data sets. SETH is freely publicly available at: https://github.com/Rostlab/SETH.

Intrinsically disordered proteins in viral pathogenesis and infections

Disordered proteins serve a crucial part in many biological processes that go beyond the capabilities of ordered proteins. A large number of virus-encoded proteins have extremely condensed proteomes and genomes, which results in highly disordered proteins. The presence of these IDPs allows them to rapidly adapt to changes in their biological environment and play a significant role in viral replication and down-regulation of host defense mechanisms. Since viruses undergo rapid evolution and have a high rate of mutation and accumulation in their proteome, IDPs' insights into viruses are critical for understanding how viruses hijack cells and cause disease. There are many conformational changes that IDPs can adopt in order to interact with different protein partners and thus stabilize the particular fold and withstand high mutation rates. This chapter explains the molecular mechanism behind viral IDPs, as well as the significance of recent research in the field of IDPs, with the goal of gaining a deeper comprehension of the essential roles and functions played by viral proteins.

Compositional Bias of Intrinsically Disordered Proteins and Regions and Their Predictions

Intrinsically disordered regions (IDRs) carry out many cellular functions and vary in length and placement in protein sequences. This diversity leads to variations in the underlying compositional biases, which were demonstrated for the short vs. long IDRs. We analyze compositional biases across four classes of disorder: fully disordered proteins; short IDRs; long IDRs; and binding IDRs. We identify three distinct biases: for the fully disordered proteins, the short IDRs and the long and binding IDRs combined. We also investigate compositional bias for putative disorder produced by leading disorder predictors and find that it is similar to the bias of the native disorder. Interestingly, the accuracy of disorder predictions across different methods is correlated with the correctness of the compositional bias of their predictions highlighting the importance of the compositional bias. The predictive quality is relatively low for the disorder classes with compositional bias that is the most different from the “generic” disorder bias, while being much higher for the classes with the most similar bias. We discover that different predictors perform best across different classes of disorder. This suggests that no single predictor is universally best and motivates the development of new architectures that combine models that target specific disorder classes.

Decoding phage resistance by mpr and its role in survivability of Mycobacterium smegmatis

Bacteria and bacteriophages co-evolve in a constant arms race, wherein one tries and finds newer ways to overcome the other. Phage resistance poses a great threat to the development of phage therapy. Hence, it is both essential and important to understand the mechanism of phage resistance in bacteria. First identified in Mycobacterium smegmatis, the gene mpr, upon overexpression, confers resistance against D29 mycobacteriophage. Presently, the mechanism behind phage resistance by mpr is poorly understood. Here we show that Mpr is a membrane-bound DNA exonuclease, which digests DNA in a non-specific manner independent of the sequence, and shares no sequence or structural similarity with any known nuclease. Exonuclease activity of mpr provides resistance against phage infection, but the role of mpr may very well go beyond just phage resistance. Our experiments show that mpr plays a crucial role in the appearance of mutant colonies (phage resistant strains). However, the molecular mechanism behind the emergence of these mutant/resistant colonies is yet to be understood. Nevertheless, it appears that mpr is involved in the survival and evolution of M. smegmatis against phage. A similar mechanism may be present in other organisms, which requires further exploration.

Intron-Encoded Domain of Herstatin, An Autoinhibitor of Human Epidermal Growth Factor Receptors, Is Intrinsically Disordered

Human epidermal growth factor receptors (HER/ERBB) form dimers that promote cell proliferation, migration, and differentiation, but overexpression of HER proteins results in cancer. Consequently, inhibitors of HER dimerization may function as effective antitumor drugs. An alternatively spliced variant of HER2, called herstatin, is an autoinhibitor of HER proteins, and the intron 8-encoded 79-residue domain of herstatin, called Int8, binds HER family receptors even in isolation. However, the structure of Int8 remains poorly understood. Here, we revealed by circular dichroism, NMR, small-angle X-ray scattering, and structure prediction that isolated Int8 is largely disordered but has a residual helical structure. The radius of gyration of Int8 was almost the same as that of fully unfolded states, although the conformational ensemble of Int8 was less flexible than random coils. These results demonstrate that Int8 is intrinsically disordered. Thus, Int8 is an interesting example of an intrinsically disordered region with tumor-suppressive activity encoded by an intron. Furthermore, we show that the R371I mutant of Int8, which is defective in binding to HER2, is prone to aggregation, providing a rationale for the loss of function.

Dimerization-dependent membrane tethering by Atg23 is essential for yeast autophagy

Eukaryotes maintain cellular health through the engulfment and subsequent degradation of intracellular cargo using macroautophagy. The function of Atg23, despite being critical to the efficiency of this process, is unclear due to a lack of biochemical investigations and an absence of any structural information. In this study, we use a combination of in vitro and in vivo methods to show that Atg23 exists primarily as a homodimer, a conformation facilitated by a putative amphipathic helix. We utilize small-angle X-ray scattering to monitor the overall shape of Atg23, revealing that it contains an extended rod-like structure spanning approximately 320 Å. We also demonstrate that Atg23 interacts with membranes directly, primarily through electrostatic interactions, and that these interactions lead to vesicle tethering. Finally, mutation of the hydrophobic face of the putative amphipathic helix completely precludes dimer formation, leading to severely impaired subcellular localization, vesicle tethering, Atg9 binding, and autophagic efficiency.

Deep learning in prediction of intrinsic disorder in proteins

Intrinsic disorder prediction is an active area that has developed over 100 predictors. We identify and investigate a recent trend towards the development of deep neural network (DNN)-based methods. The first DNN-based method was released in 2013 and since 2019 deep learners account for majority of the new disorder predictors. We find that the 13 currently available DNN-based predictors are diverse in their topologies, sizes of their networks and the inputs that they utilize. We empirically show that the deep learners are statistically more accurate than other types of disorder predictors using the blind test dataset from the recent community assessment of intrinsic disorder predictions (CAID). We also identify several well-rounded DNN-based predictors that are accurate, fast and/or conveniently available. The popularity, favorable predictive performance and architectural flexibility suggest that deep networks are likely to fuel the development of future disordered predictors. Novel hybrid designs of deep networks could be used to adequately accommodate for diversity of types and flavors of intrinsic disorder. We also discuss scarcity of the DNN-based methods for the prediction of disordered binding regions and the need to develop more accurate methods for this prediction.

Cdc6 is sequentially regulated by PP2A-Cdc55, Cdc14, and Sic1 for origin licensing in S. cerevisiae

Cdc6, a subunit of the pre-replicative complex (pre-RC), contains multiple regulatory cyclin-dependent kinase (Cdk1) consensus sites, SP or TP motifs. In Saccharomyces cerevisiae, Cdk1 phosphorylates Cdc6-T7 to recruit Cks1, the Cdk1 phospho-adaptor in S phase, for subsequent multisite phosphorylation and protein degradation. Cdc6 accumulates in mitosis and is tightly bound by Clb2 through N-terminal phosphorylation in order to prevent premature origin licensing and degradation. It has been extensively studied how Cdc6 phosphorylation is regulated by the cyclin-Cdk1 complex. However, a detailed mechanism on how Cdc6 phosphorylation is reversed by phosphatases has not been elucidated. Here, we show that PP2ACdc55 dephosphorylates Cdc6 N-terminal sites to release Clb2. Cdc14 dephosphorylates the C-terminal phospho-degron, leading to Cdc6 stabilization in mitosis. In addition, Cdk1 inhibitor Sic1 releases Clb2·Cdk1·Cks1 from Cdc6 to load Mcm2-7 on the chromatin upon mitotic exit. Thus, pre-RC assembly and origin licensing are promoted by phosphatases through the attenuation of distinct Cdk1-dependent Cdc6 inhibitory mechanisms.

Matrin3: Disorder and ALS Pathogenesis

Amyotrophic lateral sclerosis (ALS) is a neurodegenerative disorder characterized by the degeneration of both upper and lower motor neurons in the brain and spinal cord. ALS is associated with protein misfolding and inclusion formation involving RNA-binding proteins, including TAR DNA-binding protein (TDP-43) and fused in sarcoma (FUS). The 125-kDa Matrin3 is a highly conserved nuclear DNA/RNA-binding protein that is implicated in many cellular processes, including binding and stabilizing mRNA, regulating mRNA nuclear export, modulating alternative splicing, and managing chromosomal distribution. Mutations in MATR3, the gene encoding Matrin3, have been identified as causal in familial ALS (fALS). Matrin3 lacks a prion-like domain that characterizes many other ALS-associated RNA-binding proteins, including TDP-43 and FUS, however, our bioinformatics analyses and preliminary studies document that Matrin3 contains long intrinsically disordered regions that may facilitate promiscuous interactions with many proteins and may contribute to its misfolding. In addition, these disordered regions in Matrin3 undergo numerous post-translational modifications, including phosphorylation, ubiquitination and acetylation that modulate the function and misfolding of the protein. Here we discuss the disordered nature of Matrin3 and review the factors that may promote its misfolding and aggregation, two elements that might explain its role in ALS pathogenesis.

Molecular mechanisms underlying the role of the centriolar CEP164-TTBK2 complex in ciliopathies

Cilia formation is essential for human life. One of the earliest events in the ciliogenesis program is the recruitment of tau-tubulin kinase 2 (TTBK2) by the centriole distal appendage component CEP164. Due to the lack of high-resolution structural information on this complex, it is unclear how it is affected in human ciliopathies such as nephronophthisis. Furthermore, it is poorly understood if binding to CEP164 influences TTBK2 activities. Here, we present a detailed biochemical, structural, and functional analysis of the CEP164-TTBK2 complex and demonstrate how it is compromised by two ciliopathic mutations in CEP164. Moreover, we also provide insights into how binding to CEP164 is coordinated with TTBK2 activities. Together, our data deepen our understanding of a crucial step in cilia formation and will inform future studies aimed at restoring CEP164 functionality in a debilitating human ciliopathy.

Surveying over 100 predictors of intrinsic disorder in proteins

INTRODUCTION

Intrinsic disorder prediction field develops, assesses, and deploys computational predictors of disorder in protein sequences and constructs and disseminates databases of these predictions. Over 40 years of research resulted in the release of numerous resources.

AREAS COVERED

We identify and briefly summarize the most comprehensive to date collection of over 100 disorder predictors. We focus on their predictive models, availability and predictive performance. We categorize and study them from a historical point of view to highlight informative trends.

EXPERT OPINION

We find a consistent trend of improvements in predictive quality as newer and more advanced predictors are developed. The original focus on machine learning methods has shifted to meta-predictors in early 2010s, followed by a recent transition to deep learning. The use of deep learners will continue in foreseeable future given recent and convincing success of these methods. Moreover, a broad range of resources that facilitate convenient collection of accurate disorder predictions is available to users. They include web servers and standalone programs for disorder prediction, servers that combine prediction of disorder and disorder functions, and large databases of pre-computed predictions. We also point to the need to address the shortage of accurate methods that predict disordered binding regions.

Subcellular Localization of Seed-Expressed LEA_4 Proteins Reveals Liquid-Liquid Phase Separation for LEA9 and for LEA48 Homo- and LEA42-LEA48 Heterodimers

LEA proteins are involved in plant stress tolerance. In Arabidopsis, the LEA_4 Pfam group is the biggest group with the majority of its members being expressed in dry seeds. To assess subcellular localization in vivo, we investigated 11 seed-expressed LEA_4 proteins in embryos dissected from dry seeds expressing LEA_4 fusion proteins under its native promoters with the Venus fluorescent protein (proLEA_4::LEA_4:Venus). LEA_4 proteins were shown to be localized in the endoplasmic reticulum, nucleus, mitochondria, and plastids. LEA9, in addition to the nucleus, was also found in cytoplasmic condensates in dry seeds dependent on cellular hydration level. Most investigated LEA_4 proteins were detected in 4-d-old seedlings. In addition, we assessed bioinformatic tools for predicting subcellular localization and promoter motifs of 11 seed-expressed LEA_4 proteins. Ratiometric bimolecular fluorescence complementation assays showed that LEA7, LEA29, and LEA48 form homodimers while heterodimers were formed between LEA7-LEA29 and LEA42-LEA48 in tobacco leaves. Interestingly, LEA48 homodimers and LEA42-LEA48 heterodimers formed droplets structures with liquid-like behavior. These structures, along with LEA9 cytoplasmic condensates, may have been formed through liquid-liquid phase separation. These findings suggest possible important roles of LLPS for LEA protein functions.

Metapredict: a fast, accurate, and easy-to-use predictor of consensus disorder and structure

Intrinsically disordered proteins and protein regions make up a substantial fraction of many proteomes in which they play a wide variety of essential roles. A critical first step in understanding the role of disordered protein regions in biological function is to identify those disordered regions correctly. Computational methods for disorder prediction have emerged as a core set of tools to guide experiments, interpret results, and develop hypotheses. Given the multiple different predictors available, consensus scores have emerged as a popular approach to mitigate biases or limitations of any single method. Consensus scores integrate the outcome of multiple independent disorder predictors and provide a per-residue value that reflects the number of tools that predict a residue to be disordered. Although consensus scores help mitigate the inherent problems of using any single disorder predictor, they are computationally expensive to generate. They also necessitate the installation of multiple different software tools, which can be prohibitively difficult. To address this challenge, we developed a deep-learning-based predictor of consensus disorder scores. Our predictor, metapredict, utilizes a bidirectional recurrent neural network trained on the consensus disorder scores from 12 proteomes. By benchmarking metapredict using two orthogonal approaches, we found that metapredict is among the most accurate disorder predictors currently available. Metapredict is also remarkably fast, enabling proteome-scale disorder prediction in minutes. Importantly, metapredict is a fully open source and is distributed as a Python package, a collection of command-line tools, and a web server, maximizing the potential practical utility of the predictor. We believe metapredict offers a convenient, accessible, accurate, and high-performance predictor for single-proteins and proteomes alike.

Metapredict: a fast, accurate, and easy-to-use predictor of consensus disorder and structure

Intrinsically disordered proteins and protein regions make up a substantial fraction of many proteomes in which they play a wide variety of essential roles. A critical first step in understanding the role of disordered protein regions in biological function is to identify those disordered regions correctly. Computational methods for disorder prediction have emerged as a core set of tools to guide experiments, interpret results, and develop hypotheses. Given the multiple different predictors available, consensus scores have emerged as a popular approach to mitigate biases or limitations of any single method. Consensus scores integrate the outcome of multiple independent disorder predictors and provide a per-residue value that reflects the number of tools that predict a residue to be disordered. Although consensus scores help mitigate the inherent problems of using any single disorder predictor, they are computationally expensive to generate. They also necessitate the installation of multiple different software tools, which can be prohibitively difficult. To address this challenge, we developed a deep-learning-based predictor of consensus disorder scores. Our predictor, metapredict, utilizes a bidirectional recurrent neural network trained on the consensus disorder scores from 12 proteomes. By benchmarking metapredict using two orthogonal approaches, we found that metapredict is among the most accurate disorder predictors currently available. Metapredict is also remarkably fast, enabling proteome-scale disorder prediction in minutes. Importantly, metapredict is a fully open source and is distributed as a Python package, a collection of command-line tools, and a web server, maximizing the potential practical utility of the predictor. We believe metapredict offers a convenient, accessible, accurate, and high-performance predictor for single-proteins and proteomes alike.

Proteomic Tools for the Analysis of Cytoskeleton Proteins

Proteomic analyses have become an essential part of the toolkit of the molecular biologist, given the widespread availability of genomic data and open source or freely accessible bioinformatics software. Tools are available for detecting homologous sequences, recognizing functional domains, and modeling the three-dimensional structure for any given protein sequence, as well as for predicting interactions with other proteins or macromolecules. Although a wealth of structural and functional information is available for many cytoskeletal proteins, with representatives spanning all of the major subfamilies, the majority of cytoskeletal proteins remain partially or totally uncharacterized. Moreover, bioinformatics tools provide a means for studying the effects of synthetic mutations or naturally occurring variants of these cytoskeletal proteins. This chapter discusses various freely available proteomic analysis tools, with a focus on in silico prediction of protein structure and function. The selected tools are notable for providing an easily accessible interface for the novice while retaining advanced functionality for more experienced computational biologists.

Identification of Immunogenic Candidate for New Serological Tests for Brucella melitensis by a Proteomic Approach

Background:

The diagnosis of brucellosis by serological tests is based on antigen suspensions derived from smooth lipopolysaccharide extracts, which can give false positive results linked to cross-reactivity with other Gram-negative microorganisms, especially Yersinia enterocolitica O:9 and Escherichia coli O157:H7.

Objective:

The objective of the present study was the characterization by proteomic analysis of specific immunogenic proteins not associated with smooth lipopolysaccharide to improve the diagnostic tests used in the ovine brucellosis eradication programs.

Methods:

The serum from a sheep positive to Brucella melitensis was treated to eliminate all antibodies against such lipopolysaccharide and highlight the reaction towards the immunoreactive proteins in Western Blotting.

Results:

The immunoreactive bands were identified by nLC-MS/MS and through bioinformatic tools, it was possible to select 12 potential candidates as protein antigens specific for Brucella melitensis.

Conclusion:

The detection of new antigens not subjected to cross-reactivity with other Gram-negative microorganisms can offer an additional tool for the serological diagnosis of such disease.

Dimerization underlies the aggregation propensity of intrinsically disordered coiled-coil domain-containing 124

Coiled-coil domain-containing 124 (CCDC124) is a recently discovered ribosome-binding protein conserved in eukaryotes. CCDC124 has regulatory functions on the mediation of reversible ribosomal hibernation and translational recovery by direct attachment to large subunit ribosomal protein uL5, 25S rRNA backbone, and tRNA-binding P/A-site major groove. Moreover, it independently mediates cell division and cellular stress response by facilitating cytokinetic abscission and disulfide stress-dependent transcriptional regulation, respectively. However, the structural characterization and intracellular physiological status of CCDC124 remain unknown. In this study, we employed advanced in silico protein modeling and characterization tools to generate a native-like tertiary structure of CCDC124 and examine the disorder, low sequence complexity, and aggregation propensities, as well as high-order dimeric/oligomeric states. Subsequently, dimerization of CCDC124 was investigated with co-immunoprecipitation (CO-IP) analysis, immunostaining, and a recent live-cell protein-protein interaction method, bimolecular fluorescence complementation (BiFC). Results revealed CCDC124 as a highly disordered protein consisting of low complexity regions at the N-terminus and an aggregation sequence (151-IAVLSV-156) located in the middle region. Molecular docking and post-docking binding free energy analyses highlighted a potential involvement of V153 residue on the generation of high-order dimeric/oligomeric structures. Co-IP, immunostaining, and BiFC analyses were used to further confirm the dimeric state of CCDC124 predominantly localized at the cytoplasm. In conclusion, our findings revealed in silico structural characterization and in vivo subcellular physiological state of CCDC124, suggesting low-complexity regions located at the N-terminus of disordered CCDC124 may regulate the formation of aggregates or high-order dimeric/oligomeric states.

Characterization of the Heat-Stable Proteome during Seed Germination in Arabidopsis with Special Focus on LEA Proteins

During seed germination, desiccation tolerance is lost in the radicle with progressing radicle protrusion and seedling establishment. This process is accompanied by comprehensive changes in the metabolome and proteome. Germination of Arabidopsis seeds was investigated over 72 h with special focus on the heat-stable proteome including late embryogenesis abundant (LEA) proteins together with changes in primary metabolites. Six metabolites in dry seeds known to be important for seed longevity decreased during germination and seedling establishment, while all other metabolites increased simultaneously with activation of growth and development. Thermo-stable proteins were associated with a multitude of biological processes. In the heat-stable proteome, a relatively similar proportion of fully ordered and fully intrinsically disordered proteins (IDP) was discovered. Highly disordered proteins were found to be associated with functional categories development, protein, RNA and stress. As expected, the majority of LEA proteins decreased during germination and seedling establishment. However, four germination-specific dehydrins were identified, not present in dry seeds. A network analysis of proteins, metabolites and amino acids generated during the course of germination revealed a highly connected LEA protein network.

Ubiquitin Interacting Motifs: Duality Between Structured and Disordered Motifs

Ubiquitin is a small protein at the heart of many cellular processes, and several different protein domains are known to recognize and bind ubiquitin. A common motif for interaction with ubiquitin is the Ubiquitin Interacting Motif (UIM), characterized by a conserved sequence signature and often found in multi-domain proteins. Multi-domain proteins with intrinsically disordered regions mediate interactions with multiple partners, orchestrating diverse pathways. Short linear motifs for binding are often embedded in these disordered regions and play crucial roles in modulating protein function. In this work, we investigated the structural propensities of UIMs using molecular dynamics simulations and NMR chemical shifts. Despite the structural portrait depicted by X-crystallography of stable helical structures, we show that UIMs feature both helical and intrinsically disordered conformations. Our results shed light on a new class of disordered UIMs. This group is here exemplified by the C-terminal domain of one isoform of ataxin-3 and a group of ubiquitin-specific proteases. Intriguingly, UIMs not only bind ubiquitin. They can be a recruitment point for other interactors, such as parkin and the heat shock protein Hsc70-4. Disordered UIMs can provide versatility and new functions to the client proteins, opening new directions for research on their interactome.

Positive selection and intrinsic disorder are associated with multifunctional C4(AC4) proteins and geminivirus diversification

Viruses within the Geminiviridae family cause extensive agricultural losses. Members of four genera of geminiviruses contain a C4 gene (AC4 in geminiviruses with bipartite genomes). C4(AC4) genes are entirely overprinted on the C1(AC1) genes, which encode the replication-associated proteins. The C4(AC4) proteins exhibit diverse functions that may be important for geminivirus diversification. In this study, the influence of natural selection on the evolutionary diversity of 211 C4(AC4) genes relative to the C1(AC1) sequences they overlap was determined from isolates of the Begomovirus and Curtovirus genera. The ratio of nonsynonymous (d_N) to synonymous (d_S) nucleotide substitutions indicated that C4(AC4) genes are under positive selection, while the overlapped C1(AC1) sequences are under purifying selection. Ninety-one of 200 Begomovirus C4(AC4) genes encode elongated proteins with the extended regions being under neutral selection. C4(AC4) genes from begomoviruses isolated from tomato from native versus exotic regions were under similar levels of positive selection. Analysis of protein structure suggests that C4(AC4) proteins are entirely intrinsically disordered. Our data suggest that non-synonymous mutations and mutations that increase the length of C4(AC4) drive protein diversity that is intrinsically disordered, which could explain C4/AC4 functional variation and contribute to both geminivirus diversification and host jumping.

A combined evolutionary and structural approach to disclose the primary structural determinants essential for proneurotrophins biological functions

The neurotrophins, i.e., Nerve Growth Factor (NGF), Brain-Derived Neurotrophic Factor (BDNF), Neurotrophin 3 (NT3) and Neurotrophin 4 (NT4), are known to play a range of crucial functions in the developing and adult peripheral and central nervous systems. Initially synthesized as precursors, i.e., proneurotrophins (proNTs), that are cleaved to release C-terminal mature forms, they act through two types of receptors, the specific Trk receptors (Tropomyosin-related kinases) and the pan-neurotrophin receptor p75NTR, to initiate survival and differentiative responses. Recently, all the proNTs but proNT4 have been demonstrated to be not just inactive precursors, but signaling ligands that mediate opposing actions in fundamental aspects of the nervous system with respect to the mature counterparts through dual-receptor complexes formation with a member of the VPS10 family and p75NTR. Despite the functional relevance, the molecular determinants underpinning the interactions between the pro-domains and their receptors are still elusive probably due to their intrinsically disordered nature. Here we present an evolutionary approach coupled to an experimental study aiming to uncover the structural and dynamical basis of the biological function displayed by proNGF, proBDNF and proNT3 but missing in proNT4. A bioinformatic analysis allowed to elucidate the functional adaptability of the proNTs family in vertebrates, identifying conserved key structural features. The combined biochemical and SAXS experiments shed lights on the structure and dynamic behavior of the human proNTs in solution, giving insights on the evolutionary conserved structural motifs, essential for the multifaceted roles of proNTs in physiological as well as in pathological contexts.

Computational Biology and Machine Learning Approaches to Understand Mechanistic Microbiome-Host Interactions

The microbiome, by virtue of its interactions with the host, is implicated in various host functions including its influence on nutrition and homeostasis. Many chronic diseases such as diabetes, cancer, inflammatory bowel diseases are characterized by a disruption of microbial communities in at least one biological niche/organ system. Various molecular mechanisms between microbial and host components such as proteins, RNAs, metabolites have recently been identified, thus filling many gaps in our understanding of how the microbiome modulates host processes. Concurrently, high-throughput technologies have enabled the profiling of heterogeneous datasets capturing community level changes in the microbiome as well as the host responses. However, due to limitations in parallel sampling and analytical procedures, big gaps still exist in terms of how the microbiome mechanistically influences host functions at a system and community level. In the past decade, computational biology and machine learning methodologies have been developed with the aim of filling the existing gaps. Due to the agnostic nature of the tools, they have been applied in diverse disease contexts to analyze and infer the interactions between the microbiome and host molecular components. Some of these approaches allow the identification and analysis of affected downstream host processes. Most of the tools statistically or mechanistically integrate different types of -omic and meta -omic datasets followed by functional/biological interpretation. In this review, we provide an overview of the landscape of computational approaches for investigating mechanistic interactions between individual microbes/microbiome and the host and the opportunities for basic and clinical research. These could include but are not limited to the development of activity- and mechanism-based biomarkers, uncovering mechanisms for therapeutic interventions and generating integrated signatures to stratify patients.

The mammalian cholesterol synthesis enzyme squalene monooxygenase is proteasomally truncated to a constitutively active form

Squalene monooxygenase (SM, also known as squalene epoxidase) is a rate-limiting enzyme of cholesterol synthesis that converts squalene to monooxidosqualene and is oncogenic in numerous cancer types. SM is subject to feedback regulation via cholesterol-induced proteasomal degradation, which depends on its lipid-sensing N-terminal regulatory domain. We previously identified an endogenous truncated form of SM with a similar abundance to full-length SM, but whether this truncated form is functional or subject to the same regulatory mechanisms as full-length SM is not known. Here, we show that truncated SM differs from full-length SM in two major ways: it is cholesterol resistant and adopts a peripheral rather than integral association with the endoplasmic reticulum membrane. However, truncated SM retains full SM activity and is therefore constitutively active. Truncation of SM occurs during its endoplasmic reticulum–associated degradation and requires the proteasome, which partially degrades the SM N-terminus and disrupts cholesterol-sensing elements within the regulatory domain. Furthermore, truncation relies on a ubiquitin signal that is distinct from that required for cholesterol-induced degradation. Using mutagenesis, we demonstrate that partial proteasomal degradation of SM depends on both an intrinsically disordered region near the truncation site and the stability of the adjacent catalytic domain, which escapes degradation. These findings uncover an additional layer of complexity in the post-translational regulation of cholesterol synthesis and establish SM as the first eukaryotic enzyme found to undergo proteasomal truncation.

Order from disorder in the sarcomere: FATZ forms a fuzzy but tight complex and phase-separated condensates with α-actinin

In sarcomeres, α-actinin cross-links actin filaments and anchors them to the Z-disk. FATZ (filamin-, α-actinin-, and telethonin-binding protein of the Z-disk) proteins interact with α-actinin and other core Z-disk proteins, contributing to myofibril assembly and maintenance. Here, we report the first structure and its cellular validation of α-actinin-2 in complex with a Z-disk partner, FATZ-1, which is best described as a conformational ensemble. We show that FATZ-1 forms a tight fuzzy complex with α-actinin-2 and propose an interaction mechanism via main molecular recognition elements and secondary binding sites. The obtained integrative model reveals a polar architecture of the complex which, in combination with FATZ-1 multivalent scaffold function, might organize interaction partners and stabilize α-actinin-2 preferential orientation in Z-disk. Last, we uncover FATZ-1 ability to phase-separate and form biomolecular condensates with α-actinin-2, raising the question whether FATZ proteins can create an interaction hub for Z-disk proteins through membraneless compartmentalization during myofibrillogenesis.

APOD: accurate sequence-based predictor of disordered flexible linkers

Motivation

Disordered flexible linkers (DFLs) are abundant and functionally important intrinsically disordered regions that connect protein domains and structural elements within domains and which facilitate disorder-based allosteric regulation. Although computational estimates suggest that thousands of proteins have DFLs, they were annotated experimentally in <200 proteins. This substantial annotation gap can be reduced with the help of accurate computational predictors. The sole predictor of DFLs, DFLpred, trade-off accuracy for shorter runtime by excluding relevant but computationally costly predictive inputs. Moreover, it relies on the local/window-based information while lacking to consider useful protein-level characteristics.

Results

We conceptualize, design and test APOD (Accurate Predictor Of DFLs), the first highly accurate predictor that utilizes both local- and protein-level inputs that quantify propensity for disorder, sequence composition, sequence conservation and selected putative structural properties. Consequently, APOD offers significantly more accurate predictions when compared with its faster predecessor, DFLpred, and several other alternative ways to predict DFLs. These improvements stem from the use of a more comprehensive set of inputs that cover the protein-level information and the application of a more sophisticated predictive model, a well-parametrized support vector machine. APOD achieves area under the curve = 0.82 (28% improvement over DFLpred) and Matthews correlation coefficient = 0.42 (180% increase over DFLpred) when tested on an independent/low-similarity test dataset. Consequently, APOD is a suitable choice for accurate and small-scale prediction of DFLs.

Availability and implementation

https://yanglab.nankai.edu.cn/APOD/.

Prediction of secondary structure population and intrinsic disorder of proteins using multitask deep learning

Recent research in predicting protein secondary structure populations (SSP) based on Nuclear Magnetic Resonance (NMR) chemical shifts has helped quantitatively characterise the structural conformational properties of intrinsically disordered proteins and regions (IDP/IDR). Different from protein secondary structure (SS) prediction, the SSP prediction assumes a dynamic assignment of secondary structures that seem correlate with disordered states. In this study, we designed a single-task deep learning framework to predict IDP/IDR and SSP respectively; and multitask deep learning frameworks to allow quantitative predictions of IDP/IDR evidenced by the simultaneously predicted SSP. According to independent test results, single-task deep learning models improve the prediction performance of shallow models for SSP and IDP/IDR. Also, the prediction performance was further improved for IDP/IDR prediction when SSP prediction was simultaneously predicted in multitask models. With p53 as a use case, we demonstrate how predicted SSP is used to explain the IDP/IDR predictions for each functional region.

Intracellular arginine-dependent translation sensor reveals the dynamics of arginine starvation response and resistance in ASS1-negative cells

BACKGROUND

Many cancers silence the metabolic enzyme argininosuccinate synthetase 1 (ASS1), the rate-limiting enzyme for arginine biosynthesis within the urea cycle. Consequently, ASS1-negative cells are susceptible to depletion of extracellular arginine by PEGylated arginine deiminase (ADI-PEG20), an agent currently being developed in clinical trials. As the primary mechanism of resistance to arginine depletion is re-expression of ASS1, we sought a tool to understand the temporal emergence of the resistance phenotype at the single-cell level.

METHODS

A real-time, single-cell florescence biosensor was developed to monitor arginine-dependent protein translation. The versatile, protein-based sensor provides temporal information about the metabolic adaptation of cells, as it is able to quantify and track individual cells over time.

RESULTS

Every ASS1-deficient cell analyzed was found to respond to arginine deprivation by decreased expression of the sensor, indicating an absence of resistance in the naïve cell population. However, the temporal recovery and emergence of resistance varied widely amongst cells, suggesting a heterogeneous metabolic response. The sensor also enabled determination of a minimal arginine concentration required for its optimal translation.

CONCLUSIONS

The translation-dependent sensor developed here is able to accurately track the development of resistance in ASS1-deficient cells treated with ADI-PEG20. Its ability to track single cells over time allowed the determination that resistance is not present in the naïve population, as well as elucidating the heterogeneity of the timing and extent of resistance. This tool represents a useful advance in the study of arginine deprivation, while its design has potential to be adapted to other amino acids.

Genome-wide and structural analysis of the Myb-SHAQKYF family in Entamoeba histolytica

Amoebiasis is the third leading cause of death among protozoon parasitic diseases in the lower-middle income countries. Understanding the molecular events that control gene expression such as transcription factors, their DNA binding mode and target sequences can help to develop new antiamoebic drugs against Entamoeba histolytica. In this paper we performed a genome and structural analysis of a specific transcription factor. The genome of E. histolytica codifies for 9 EhMybSHAQKYF proteins, which are a family within a large group of 34 Myb-DNA-binding domain (Myb-DBD) containing proteins. Here we compared Entamoeba Myb-SHAQKYF proteins with Myb-like proteins from the Reveille (RVE) family, important regulators of plant circadian networks. This comparison could lead to stablish their role in E. histolytica life cycle. We show that the ehmybshaqkyf genes are differentially expressed in trophozoites under basal cell culture conditions. An in-silico analysis predicts that members of this group harbor a highly conserved and structured Myb-DBD and a large portion of intrinsically disordered residues. As the Myb-DBD of these proteins harbors a distinctive Q[VI]R[ST]HAQK[YF]F sequence in its putative third α-helix, we consider relevant to determine the three-dimensional (3D) structure of one of them. An NMR structure of the Myb-DBD of EhMybS3 shows that this protein is composed of three α-helices stabilized by a hydrophobic core, similar to Myb proteins of different kingdoms. It is remarkable that despite not sharing similarities in their amino acid sequences, the structure of the Myb-DBD of the EhMybS3 is well conserved in this early branching eukaryote.

APOD: accurate sequence-based predictor of disordered flexible linkers

MOTIVATION

Disordered flexible linkers (DFLs) are abundant and functionally important intrinsically disordered regions that connect protein domains and structural elements within domains and which facilitate disorder-based allosteric regulation. Although computational estimates suggest that thousands of proteins have DFLs, they were annotated experimentally in <200 proteins. This substantial annotation gap can be reduced with the help of accurate computational predictors. The sole predictor of DFLs, DFLpred, trade-off accuracy for shorter runtime by excluding relevant but computationally costly predictive inputs. Moreover, it relies on the local/window-based information while lacking to consider useful protein-level characteristics.

RESULTS

We conceptualize, design and test APOD (Accurate Predictor Of DFLs), the first highly accurate predictor that utilizes both local- and protein-level inputs that quantify propensity for disorder, sequence composition, sequence conservation and selected putative structural properties. Consequently, APOD offers significantly more accurate predictions when compared with its faster predecessor, DFLpred, and several other alternative ways to predict DFLs. These improvements stem from the use of a more comprehensive set of inputs that cover the protein-level information and the application of a more sophisticated predictive model, a well-parametrized support vector machine. APOD achieves area under the curve = 0.82 (28% improvement over DFLpred) and Matthews correlation coefficient = 0.42 (180% increase over DFLpred) when tested on an independent/low-similarity test dataset. Consequently, APOD is a suitable choice for accurate and small-scale prediction of DFLs.

AVAILABILITY AND IMPLEMENTATION

https://yanglab.nankai.edu.cn/APOD/.

Comparative Assessment of Intrinsic Disorder Predictions with a Focus on Protein and Nucleic Acid-Binding Proteins

With over 60 disorder predictors, users need help navigating the predictor selection task. We review 28 surveys of disorder predictors, showing that only 11 include assessment of predictive performance. We identify and address a few drawbacks of these past surveys. To this end, we release a novel benchmark dataset with reduced similarity to the training sets of the considered predictors. We use this dataset to perform a first-of-its-kind comparative analysis that targets two large functional families of disordered proteins that interact with proteins and with nucleic acids. We show that limiting sequence similarity between the benchmark and the training datasets has a substantial impact on predictive performance. We also demonstrate that predictive quality is sensitive to the use of the well-annotated order and inclusion of the fully structured proteins in the benchmark datasets, both of which should be considered in future assessments. We identify three predictors that provide favorable results using the new benchmark set. While we find that VSL2B offers the most accurate and robust results overall, ESpritz-DisProt and SPOT-Disorder perform particularly well for disordered proteins. Moreover, we find that predictions for the disordered protein-binding proteins suffer low predictive quality compared to generic disordered proteins and the disordered nucleic acids-binding proteins. This can be explained by the high disorder content of the disordered protein-binding proteins, which makes it difficult for the current methods to accurately identify ordered regions in these proteins. This finding motivates the development of a new generation of methods that would target these difficult-to-predict disordered proteins. We also discuss resources that support users in collecting and identifying high-quality disorder predictions.

Predicting the effect of disordered linkers on effective concentrations and avidity with the "Ceff calculator" app

Linkers are crucial to the functions of multidomain proteins as they couple functional units to encode regulation such as auto-inhibition, enzyme targeting or tuning of interaction strength. A linker changes reactions from bimolecular to unimolecular, and the equilibrium and kinetics is thus determined by the properties of the linker rather than concentrations. We present a theoretical workflow for estimating the functional consequences of tethering by a linker. We discuss how to: (1) Identify flexible linkers from sequence. (2) Model the end-to-end distance distribution for a flexible linker using a worm-like chain. (3) Estimate the effective concentration of a ligand tethered by a flexible linker. (4) Calculate the decrease in binding affinity caused by auto-inhibition. (5) Calculate the expected avidity enhancement of a bivalent interaction from effective concentration. The worm-like chain modeling is available through a web application called the "C_eff calculator" (http://ceffapp.chemeslab.org), which will allow user-friendly prediction of experimentally inaccessible parameters.

ODiNPred: comprehensive prediction of protein order and disorder

Structural disorder is widespread in eukaryotic proteins and is vital for their function in diverse biological processes. It is therefore highly desirable to be able to predict the degree of order and disorder from amino acid sequence. It is, however, notoriously difficult to predict the degree of local flexibility within structured domains and the presence and nuances of localized rigidity within intrinsically disordered regions. To identify such instances, we used the CheZOD database, which encompasses accurate, balanced, and continuous-valued quantification of protein (dis)order at amino acid resolution based on NMR chemical shifts. To computationally forecast the spectrum of protein disorder in the most comprehensive manner possible, we constructed the sequence-based protein order/disorder predictor ODiNPred, trained on an expanded version of CheZOD. ODiNPred applies a deep neural network comprising 157 unique sequence features to 1325 protein sequences together with the experimental NMR chemical shift data. Cross-validation for 117 protein sequences shows that ODiNPred better predicts the continuous variation in order along the protein sequence, suggesting that contemporary predictors are limited by the quality of training data. The inclusion of evolutionary features reduces the performance gap between ODiNPred and its peers, but analysis shows that it retains greater accuracy for the more challenging prediction of intermediate disorder.

Systematic analysis of the IL-17 receptor signalosome reveals a robust regulatory feedback loop

IL-17 mediates immune protection from fungi and bacteria, as well as it promotes autoimmune pathologies. However, the regulation of the signal transduction from the IL-17 receptor (IL-17R) remained elusive. We developed a novel mass spectrometry-based approach to identify components of the IL-17R complex followed by analysis of their roles using reverse genetics. Besides the identification of linear ubiquitin chain assembly complex (LUBAC) as an important signal transducing component of IL-17R, we established that IL-17 signaling is regulated by a robust negative feedback loop mediated by TBK1 and IKKε. These kinases terminate IL-17 signaling by phosphorylating the adaptor ACT1 leading to the release of the essential ubiquitin ligase TRAF6 from the complex. NEMO recruits both kinases to the IL-17R complex, documenting that NEMO has an unprecedented negative function in IL-17 signaling, distinct from its role in NF-κB activation. Our study provides a comprehensive view of the molecular events of the IL-17 signal transduction and its regulation.

Current pivotal strategies leading a difficult target protein to a sample suitable for crystallographic analysis

Crystallographic structural analysis is an essential method for the determination of protein structure. However, crystallization of a protein of interest is the most difficult process in the analysis. The process is often hampered during the sample preparation, including expression and purification. Even after a sample has been purified, not all candidate proteins crystallize. In this mini-review, the current methodologies used to overcome obstacles encountered during protein crystallization are sorted. Specifically, the strategy for an effective crystallization is compared with a pipeline where various expression hosts and constructs, purification and crystallization conditions, and crystallization chaperones as target-specific binder proteins are assessed by a precrystallization screening. These methodologies are also developed continuously to improve the process. The described methods are useful for sample preparation in crystallographic analysis and other structure determination techniques, such as cryo-electron microscopy.

Identification of an α-MoRF in the Intrinsically Disordered Region of the Escargot Transcription Factor

Molecular recognition features (MoRFs) are common in intrinsically disordered proteins (IDPs) and intrinsically disordered regions (IDRs). MoRFs are in constant order-disorder structural transitions and adopt well-defined structures once they are bound to their targets. Here, we study Escargot (Esg), a transcription factor in Drosophila melanogaster that regulates multiple cellular functions, and consists of a disordered N-terminal domain and a group of zinc fingers at its C-terminal domain. We analyzed the N-terminal domain of Esg with disorder predictors and identified a region of 45 amino acids with high probability to form ordered structures, which we named S2. Through 54 μs of molecular dynamics (MD) simulations using CHARMM36 and implicit solvent (generalized Born/surface area (GBSA)), we characterized the conformational landscape of S2 and found an α-MoRF of ∼16 amino acids stabilized by key contacts within the helix. To test the importance of these contacts in the stability of the α-MoRF, we evaluated the effect of point mutations that would impair these interactions, running 24 μs of MD for each mutation. The mutations had mild effects on the MoRF, and in some cases, led to gain of residual structure through long-range contacts of the α-MoRF and the rest of the S2 region. As this could be an effect of the force field and solvent model we used, we benchmarked our simulation protocol by carrying out 32 μs of MD for the (AAQAA)₃ peptide. The results of the benchmark indicate that the global amount of helix in shorter peptides like (AAQAA)₃ is reasonably predicted. Careful analysis of the runs of S2 and its mutants suggests that the mutation to hydrophobic residues may have nucleated long-range hydrophobic and aromatic interactions that stabilize the MoRF. Finally, we have identified a set of residues that stabilize an α-MoRF in a region still without functional annotations in Esg.