Adrenomedullin: Not Just Another Gastrointestinal Peptide

Adrenomedullin (AM) and proadrenomedullin N-terminal 20 peptide (PAMP) are two bioactive peptides derived from the same precursor with several biological functions including vasodilation, angiogenesis, or anti-inflammation, among others. AM and PAMP are widely expressed throughout the gastrointestinal (GI) tract where they behave as GI hormones, regulating numerous physiological processes such as gastric emptying, gastric acid release, insulin secretion, bowel movements, or intestinal barrier function. Furthermore, it has been recently demonstrated that AM/PAMP have an impact on gut microbiome composition, inhibiting the growth of bacteria related with disease and increasing the number of beneficial bacteria such as Lactobacillus or Bifidobacterium. Due to their wide functions in the GI tract, AM and PAMP are involved in several digestive pathologies such as peptic ulcer, diabetes, colon cancer, or inflammatory bowel disease (IBD). AM is a key protective factor in IBD onset and development, as it regulates cytokine production in the intestinal mucosa, improves vascular and lymphatic regeneration and function and mucosal epithelial repair, and promotes a beneficial gut microbiome composition. AM and PAMP are relevant GI hormones that can be targeted to develop novel therapeutic agents for IBD, other GI disorders, or microbiome-related pathologies.

Functions of Glycosylation and Related Web Resources for Its Prediction

Glycosylation involves the attachment of carbohydrate sugar chains, or glycans, onto an amino acid residue of a protein. These glycans are often branched structures and serve to modulate the function of proteins. Glycans are synthesized through a complex process of enzymatic reactions that occur in the Golgi apparatus in mammalian systems. Because there is currently no sequencer for glycans, technologies such as mass spectrometry is used to characterize glycans in a biological sample to ascertain its glycome. This is a tedious process that requires high levels of expertise and equipment. Thus, the enzymes that work on glycans, called glycogenes or glycoenzymes, have been studied to better understand glycan function. With the development of glycan-related databases and a glycan repository, bioinformatics approaches have attempted to predict the glycosylation pathway and the glycosylation sites on proteins. This chapter introduces these methods and related Web resources for understanding glycan function.

Discovery of a genetic module essential for assigning left-right asymmetry in humans and ancestral vertebrates

The vertebrate left-right axis is specified during embryogenesis by a transient organ: the left-right organizer (LRO). Species including fish, amphibians, rodents and humans deploy motile cilia in the LRO to break bilateral symmetry, while reptiles, birds, even-toed mammals and cetaceans are believed to have LROs without motile cilia. We searched for genes whose loss during vertebrate evolution follows this pattern and identified five genes encoding extracellular proteins, including a putative protease with hitherto unknown functions that we named ciliated left-right organizer metallopeptide (CIROP). Here, we show that CIROP is specifically expressed in ciliated LROs. In zebrafish and Xenopus, CIROP is required solely on the left side, downstream of the leftward flow, but upstream of DAND5, the first asymmetrically expressed gene. We further ascertained 21 human patients with loss-of-function CIROP mutations presenting with recessive situs anomalies. Our findings posit the existence of an ancestral genetic module that has twice disappeared during vertebrate evolution but remains essential for distinguishing left from right in humans.

PRR11 Promotes Proliferation and Migration of Colorectal Cancer through Activating the EGFR/ERK/AKT Pathway via Increasing CTHRC1

OBJECTIVE

Colorectal cancer (CRC) is a common prevalent malignant tumor globally. The prognosis of CRC patients remains poor due to a lack of effective treatment strategy. Proline-rich 11 (PRR11) is an emerging oncogene in cancers, while its effect in CRC remains unclear. Hence, the present study aimed to identify the function of PRR11 on CRC progression and study the detailed mechanism.

METHODS

Cell proliferation ability was determined by Cell Counting Kit-8 (CCK-8) assay and 5-ethynyl-2'-deoxyuridine (EdU) staining. Transwell invasion assay detected cell invasion ability. Wound healing assay assessed cell migration ability. Xenograft tumor was established to evaluate tumor growth. Quantitative real-time polymerase chain reaction (qRT-PCR), Western blot and immunohistochemistry were performed to determine mRNA or protein levels.

RESULTS

PRR11 was elevated in CRC. PRR11 silencing suppressed CRC cell proliferation, invasion, and migration ability. Besides, PRR11 silencing inhibited EGFR/ ERK/ AKT pathway via restraining Collagen triple helix repeat containing-1 (CTHRC1) expression. Furthermore, knockdown of PRR11 suppressed CRC tumor growth in vivo.

CONCLUSION

PRR11 was highly expressed in CRC. PRR11 silencing suppressed proliferation, invasion, migration, and tumor growth of CRC through inhibiting the EGFR/ERK/AKT pathway via restraining CTHRC1 expression. PRR11 may be a valuable therapeutic target for CRC.

Analyzing causal relationships in proteomic profiles using CausalPath

CausalPath (causalpath.org) evaluates proteomic measurements against prior knowledge of biological pathways and infers causality between changes in measured features, such as global protein and phospho-protein levels. It uses pathway resources to determine potential causality between observable omic features, which are called prior relations. The subset of the prior relations that are supported by the proteomic profiles are reported and evaluated for statistical significance. The end result is a network model of signaling that explains the patterns observed in the experimental dataset. For complete details on the use and execution of this protocol, please refer to Babur et al. (2021).

Multi-Neighborhood Learning for Global Alignment in Biological Networks

The global alignment of biological networks (GABN) aims to find an optimal alignment between proteins across species, such that both the biological structures and the topological structures of the proteins are maximally conserved. The research on GABN has attracted great attention due to its applications on species evolution, orthology detection and genetic analyses. Most of the existing methods for GABN are difficult to obtain a good tradeoff between the conservation of the biological structures and topological structures. In this paper, we propose a multi-neighborhood learning method for solving GABN (called as CLMNA). CLMNA first models GABN as an optimization of a weighted similarity which evaluates the conserved biological and topological similarities of an alignment, and then it combines a first-proximity, second-proximity and individual-aware proximity learning algorithm to solve the modeled problem. Finally, systematic experiments on 10 pairs of biological networks across 5 species show the superiority of CLMNA over the state-of-the-art network alignment algorithms. They also validate the effectiveness of CLMNA as a refinement method on improving the performance of the compared algorithms.

Crowdsourcing biocuration: The Community Assessment of Community Annotation with Ontologies (CACAO)

Experimental data about gene functions curated from the primary literature have enormous value for research scientists in understanding biology. Using the Gene Ontology (GO), manual curation by experts has provided an important resource for studying gene function, especially within model organisms. Unprecedented expansion of the scientific literature and validation of the predicted proteins have increased both data value and the challenges of keeping pace. Capturing literature-based functional annotations is limited by the ability of biocurators to handle the massive and rapidly growing scientific literature. Within the community-oriented wiki framework for GO annotation called the Gene Ontology Normal Usage Tracking System (GONUTS), we describe an approach to expand biocuration through crowdsourcing with undergraduates. This multiplies the number of high-quality annotations in international databases, enriches our coverage of the literature on normal gene function, and pushes the field in new directions. From an intercollegiate competition judged by experienced biocurators, Community Assessment of Community Annotation with Ontologies (CACAO), we have contributed nearly 5,000 literature-based annotations. Many of those annotations are to organisms not currently well-represented within GO. Over a 10-year history, our community contributors have spurred changes to the ontology not traditionally covered by professional biocurators. The CACAO principle of relying on community members to participate in and shape the future of biocuration in GO is a powerful and scalable model used to promote the scientific enterprise. It also provides undergraduate students with a unique and enriching introduction to critical reading of primary literature and acquisition of marketable skills.

Functional roles of polyamines and their metabolite acrolein in eukaryotic cells

Among low molecular weight substances, polyamines (spermidine, spermine and their precursor putrescine) are present in eukaryotic cells at the mM level together with ATP and glutathione. It is expected therefore that polyamines play important roles in cell proliferation and viability. Polyamines mainly exist as a polyamine-RNA complex and regulate protein synthesis. It was found that polyamines enhance translation from inefficient mRNAs. The detailed mechanisms of polyamine stimulation of specific kinds of protein syntheses and the physiological functions of these proteins are described in this review. Spermine is metabolized into acrolein (CH2 = CH-CHO) and hydrogen peroxide (H2O2) by spermine oxidase. Although it is thought that cell damage is mainly caused by reactive oxygen species (O2-, H2O2, and •OH), it was found that acrolein is much more toxic than H2O2. Accordingly, the level of acrolein produced becomes a useful biomarker for several tissue-damage diseases like brain stroke. Thus, the mechanisms of cell toxicity caused by acrolein are described in this review.

Accurate prediction of protein structures and interactions using a three-track neural network

DeepMind presented notably accurate predictions at the recent 14th Critical Assessment of Structure Prediction (CASP14) conference. We explored network architectures that incorporate related ideas and obtained the best performance with a three-track network in which information at the one-dimensional (1D) sequence level, the 2D distance map level, and the 3D coordinate level is successively transformed and integrated. The three-track network produces structure predictions with accuracies approaching those of DeepMind in CASP14, enables the rapid solution of challenging x-ray crystallography and cryo-electron microscopy structure modeling problems, and provides insights into the functions of proteins of currently unknown structure. The network also enables rapid generation of accurate protein-protein complex models from sequence information alone, short-circuiting traditional approaches that require modeling of individual subunits followed by docking. We make the method available to the scientific community to speed biological research.

Viperin has species-specific roles in response to herpes simplex virus infection

Viperin is a gene with a broad spectrum of antiviral functions and various mechanisms of action. The role of viperin in herpes simplex virus type 1 (HSV-1) infection is unclear, with conflicting data in the literature that is derived from a single human cell type. We have addressed this gap by investigating viperin during HSV-1 infection in several cell types, spanning species and including immortalized, non-immortalized and primary cells. We demonstrate that viperin upregulation by HSV-1 infection is cell-type-specific, with mouse cells typically showing greater increases compared with those of human origin. Further, overexpression and knockout of mouse, but not human viperin significantly impedes and increases HSV-1 replication, respectively. In primary mouse fibroblasts, viperin upregulation by infection requires viral gene transcription and occurs in a predominantly IFN-independent manner. Further we identify the N-terminal domain of viperin as being required for the anti-HSV-1 activity. Interestingly, this is the region of viperin that differs most between mouse and human, which may explain the apparent species-specific activity against HSV-1. Finally, we show that HSV-1 virion host shutoff (vhs) protein is a key viral factor that antagonises viperin in mouse cells. We conclude that viperin can be upregulated by HSV-1 in mouse and human cells, and that mouse viperin has anti-HSV-1 activity.

Exploring mechanisms of protein influence on calcium oxalate kidney stone formation

Calcium oxalate monohydrate (COM) crystals are the primary constituent of most kidney stones, but urine proteins in stone matrix are believed to be critical elements for stone formation from these crystals. Recent data have shown that hundreds of proteins appear in the stone matrix with no explanation for inclusion of so many proteins. We have proposed a stone formation model with protein stimulated COM aggregation based on polyanion-polycation aggregation, which is supported by finding that matrix is highly enriched in strongly anionic and strongly cationic proteins. Many other proteins may be drawn to such aggregates due to their limited solubility in water or charge effects. Finding similar protein enrichment in both polyarginine (pR) induced aggregates of urine proteins and COM stone matrix would support this hypothesis. Purified proteins (PP) were obtained from random urine samples of six healthy adults by ultradiafiltration. Protein aggregation was induced by adding pR to PP solutions at two concentrations; 0.25 and 0.5 µg pR/µg of PP. Samples of each fraction and the original PP mixture were lyophilized and analyzed by tandem mass spectrometry. Aggregates induced by pR addition to PP samples collected a protein mixture that mimicked the protein distribution observed in COM matrix, supporting our hypothesis. The apparently discordant behavior of certain abundant anionic proteins preferentially joining the pR aggregate, when they had demonstrated reduced abundance in COM stone matrix, suggests that this model was overdriven to aggregate. The reversal of aggregate preference of albumin at low pR addition supports this interpretation.

PredictProtein - Predicting Protein Structure and Function for 29 Years

Since 1992 PredictProtein (https://predictprotein.org) is a one-stop online resource for protein sequence analysis with its main site hosted at the Luxembourg Centre for Systems Biomedicine (LCSB) and queried monthly by over 3,000 users in 2020. PredictProtein was the first Internet server for protein predictions. It pioneered combining evolutionary information and machine learning. Given a protein sequence as input, the server outputs multiple sequence alignments, predictions of protein structure in 1D and 2D (secondary structure, solvent accessibility, transmembrane segments, disordered regions, protein flexibility, and disulfide bridges) and predictions of protein function (functional effects of sequence variation or point mutations, Gene Ontology (GO) terms, subcellular localization, and protein-, RNA-, and DNA binding). PredictProtein's infrastructure has moved to the LCSB increasing throughput; the use of MMseqs2 sequence search reduced runtime five-fold (apparently without lowering performance of prediction methods); user interface elements improved usability, and new prediction methods were added. PredictProtein recently included predictions from deep learning embeddings (GO and secondary structure) and a method for the prediction of proteins and residues binding DNA, RNA, or other proteins. PredictProtein.org aspires to provide reliable predictions to computational and experimental biologists alike. All scripts and methods are freely available for offline execution in high-throughput settings.

DeepGOWeb: fast and accurate protein function prediction on the (Semantic) Web

Understanding the functions of proteins is crucial to understand biological processes on a molecular level. Many more protein sequences are available than can be investigated experimentally. DeepGOPlus is a protein function prediction method based on deep learning and sequence similarity. DeepGOWeb makes the prediction model available through a website, an API, and through the SPARQL query language for interoperability with databases that rely on Semantic Web technologies. DeepGOWeb provides accurate and fast predictions and ensures that predicted functions are consistent with the Gene Ontology; it can provide predictions for any protein and any function in Gene Ontology. DeepGOWeb is freely available at https://deepgo.cbrc.kaust.edu.sa/.

Cross-Species Protein Function Prediction with Asynchronous-Random Walk

Protein function prediction is a fundamental task in the post-genomic era. Available functional annotations of proteins are incomplete and the annotations of two homologous species are complementary to each other. However, how to effectively leverage mutually complementary annotations of different species to further boost the prediction performance is still not well studied. In this paper, we propose a cross-species protein function prediction approach by performing Asynchronous Random Walk on a heterogeneous network (AsyRW). AsyRW first constructs a heterogeneous network to integrate multiple functional association networks derived from different biological data, established homology-relationships between proteins from different species, known annotations of proteins and Gene Ontology (GO). To account for the intrinsic structures of intra- and inter-species of proteins and that of GO, AsyRW quantifies the individual walk lengths of each network node using the gravity-like theory, and then performs asynchronous-random walk with the individual length to predict associations between proteins and GO terms. Experiments on annotations archived in different years show that individual walk length and asynchronous-random walk can effectively leverage the complementary annotations of different species, AsyRW has a significantly improved performance to other related and competitive methods. The codes of AsyRW are available at: http://mlda.swu.edu.cn/codes.php?name=AsyRW.

Generation of mitochondrial reactive oxygen species is controlled by ATPase inhibitory factor 1 and regulates cognition

The mitochondrial ATP synthase emerges as key hub of cellular functions controlling the production of ATP, cellular signaling, and fate. It is regulated by the ATPase inhibitory factor 1 (IF1), which is highly abundant in neurons. Herein, we ablated or overexpressed IF1 in mouse neurons to show that IF1 dose defines the fraction of active/inactive enzyme in vivo, thereby controlling mitochondrial function and the production of mitochondrial reactive oxygen species (mtROS). Transcriptomic, proteomic, and metabolomic analyses indicate that IF1 dose regulates mitochondrial metabolism, synaptic function, and cognition. Ablation of IF1 impairs memory, whereas synaptic transmission and learning are enhanced by IF1 overexpression. Mechanistically, quenching the IF1-mediated increase in mtROS production in mice overexpressing IF1 reduces the increased synaptic transmission and obliterates the learning advantage afforded by the higher IF1 content. Overall, IF1 plays a key role in neuronal function by regulating the fraction of ATP synthase responsible for mitohormetic mtROS signaling.

Biological impact of mutually exclusive exon switching

Alternative splicing can expand the diversity of proteomes. Homologous mutually exclusive exons (MXEs) originate from the same ancestral exon and result in polypeptides with similar structural properties but altered sequence. Why would some genes switch homologous exons and what are their biological impact? Here, we analyse the extent of sequence, structural and functional variability in MXEs and report the first large scale, structure-based analysis of the biological impact of MXE events from different genomes. MXE-specific residues tend to map to single domains, are highly enriched in surface exposed residues and cluster at or near protein functional sites. Thus, MXE events are likely to maintain the protein fold, but alter specificity and selectivity of protein function. This comprehensive resource of MXE events and their annotations is available at: http://gene3d.biochem.ucl.ac.uk/mxemod/. These findings highlight how small, but significant changes at critical positions on a protein surface are exploited in evolution to alter function.

Protein Complexes Detection Based on Semi-Supervised Network Embedding Model

A protein complex is a group of associated polypeptide chains which plays essential roles in the biological process. Given a graph representing protein-protein interactions (PPI) network, it is critical but non-trivial to detect protein complexes, the subsets of proteins that are tightly coupled, from it. Network embedding is a technique to learn low-dimensional representations of vertices in networks. It has been proved quite useful for community detection in social networks in recent years. However, unlike social networks, PPI network does not contain rich metadata, so that existing network embedding methods cannot fully capture the network structure of PPI to improve the effect of protein complexes detection significantly. We propose a semi-supervised network embedding model by adopting graph convolutional networks to detect densely connected subgraphs effectively. We compare the performance of our model with state-of-the-art approaches on three popular PPI networks with various data sizes and densities. The experimental results show that our approach significantly outperforms other approaches on all three PPI networks.

Generating functional protein variants with variational autoencoders

The vast expansion of protein sequence databases provides an opportunity for new protein design approaches which seek to learn the sequence-function relationship directly from natural sequence variation. Deep generative models trained on protein sequence data have been shown to learn biologically meaningful representations helpful for a variety of downstream tasks, but their potential for direct use in the design of novel proteins remains largely unexplored. Here we show that variational autoencoders trained on a dataset of almost 70000 luciferase-like oxidoreductases can be used to generate novel, functional variants of the luxA bacterial luciferase. We propose separate VAE models to work with aligned sequence input (MSA VAE) and raw sequence input (AR-VAE), and offer evidence that while both are able to reproduce patterns of amino acid usage characteristic of the family, the MSA VAE is better able to capture long-distance dependencies reflecting the influence of 3D structure. To confirm the practical utility of the models, we used them to generate variants of luxA whose luminescence activity was validated experimentally. We further showed that conditional variants of both models could be used to increase the solubility of luxA without disrupting function. Altogether 6/12 of the variants generated using the unconditional AR-VAE and 9/11 generated using the unconditional MSA VAE retained measurable luminescence, together with all 23 of the less distant variants generated by conditional versions of the models; the most distant functional variant contained 35 differences relative to the nearest training set sequence. These results demonstrate the feasibility of using deep generative models to explore the space of possible protein sequences and generate useful variants, providing a method complementary to rational design and directed evolution approaches.

Rosetta design with co-evolutionary information retains protein function

Computational protein design has the ambitious goal of crafting novel proteins that address challenges in biology and medicine. To overcome these challenges, the computational protein modeling suite Rosetta has been tailored to address various protein design tasks. Recently, statistical methods have been developed that identify correlated mutations between residues in a multiple sequence alignment of homologous proteins. These subtle inter-dependencies in the occupancy of residue positions throughout evolution are crucial for protein function, but we found that three current Rosetta design approaches fail to recover these co-evolutionary couplings. Thus, we developed the Rosetta method ResCue (residue-coupling enhanced) that leverages co-evolutionary information to favor sequences which recapitulate correlated mutations, as observed in nature. To assess the protocols via recapitulation designs, we compiled a benchmark of ten proteins each represented by two, structurally diverse states. We could demonstrate that ResCue designed sequences with an average sequence recovery rate of 70%, whereas three other protocols reached not more than 50%, on average. Our approach had higher recovery rates also for functionally important residues, which were studied in detail. This improvement has only a minor negative effect on the fitness of the designed sequences as assessed by Rosetta energy. In conclusion, our findings support the idea that informing protocols with co-evolutionary signals helps to design stable and native-like proteins that are compatible with the different conformational states required for a complex function.

In homologous proteins, functionally or structurally important residues are strongly conserved. Thus, the consideration of conservation signals during protein design protocols can help to create sequences that are more native-like. However, the number of conserved residues is small in many proteins and not all important residues can be captured by conservation analysis. Residues are forming networks whose composition is dictated by protein structure function and thus is visible through the co-evolutionary analysis. Nowadays, advanced methods allow us to deduce these networks from multiple sequence alignments. Thus, we have implemented the novel Rosetta method termed ‘ResCue’ that informs the design protocol with co-evolutionary signals. Recapitulation designs based on ten difficult benchmarks made clear that this protocol creates sequences that are more native-like than three other, state-of-the-art design protocols.

Amino Acid Encoding Methods for Protein Sequences: A Comprehensive Review and Assessment

As the first step of machine-learning based protein structure and function prediction, the amino acid encoding play a fundamental role in the final success of those methods. Different from the protein sequence encoding, the amino acid encoding can be used in both residue-level and sequence-level prediction of protein properties by combining them with different algorithms. However, it has not attracted enough attention in the past decades, and there are no comprehensive reviews and assessments about encoding methods so far. In this article, we make a systematic classification and propose a comprehensive review and assessment for various amino acid encoding methods. Those methods are grouped into five categories according to their information sources and information extraction methodologies, including binary encoding, physicochemical properties encoding, evolution-based encoding, structure-based encoding, and machine-learning encoding. Then, 16 representative methods from five categories are selected and compared on protein secondary structure prediction and protein fold recognition tasks by using large-scale benchmark datasets. The results show that the evolution-based position-dependent encoding method PSSM achieved the best performance, and the structure-based and machine-learning encoding methods also show some potential for further application, the neural network based distributed representation of amino acids in particular may bring new light to this area. We hope that the review and assessment are useful for future studies in amino acid encoding.

TBC1D1 interacting proteins, VPS13A and VPS13C, regulate GLUT4 homeostasis in C2C12 myotubes

Proteins involved in the spaciotemporal regulation of GLUT4 trafficking represent potential therapeutic targets for the treatment of insulin resistance and type 2 diabetes. A key regulator of insulin- and exercise-stimulated glucose uptake and GLUT4 trafficking is TBC1D1. This study aimed to identify proteins that regulate GLUT4 trafficking and homeostasis via TBC1D1. Using an unbiased quantitative proteomics approach, we identified proteins that interact with TBC1D1 in C2C12 myotubes including VPS13A and VPS13C, the Rab binding proteins EHBP1L1 and MICAL1, and the calcium pump SERCA1. These proteins associate with TBC1D1 via its phosphotyrosine binding (PTB) domains and their interactions with TBC1D1 were unaffected by AMPK activation, distinguishing them from the AMPK regulated interaction between TBC1D1 and AMPKα1 complexes. Depletion of VPS13A or VPS13C caused a post-transcriptional increase in cellular GLUT4 protein and enhanced cell surface GLUT4 levels in response to AMPK activation. The phenomenon was specific to GLUT4 because other recycling proteins were unaffected. Our results provide further support for a role of the TBC1D1 PTB domains as a scaffold for a range of Rab regulators, and also the VPS13 family of proteins which have been previously linked to fasting glycaemic traits and insulin resistance in genome wide association studies.

Diverse functions of insulin-like 3 peptide

Insulin-like 3 peptide (INSL3) is a member of the insulin-like peptide superfamily and is the only known physiological ligand of relaxin family peptide receptor 2 (RXFP2), a G protein-coupled receptor (GPCR). In mammals, INSL3 is primarily produced both in testicular Leydig cells and in ovarian theca cells, but circulating levels of the hormone are much higher in males than in females. The INSL3/RXFP2 system has an essential role in the development of the gubernaculum for the initial transabdominal descent of the testis and in maintaining proper reproductive health in men. Although its function in female physiology has been less well-characterized, it was reported that INSL3 deletion affects antral follicle development during the follicular phase of the menstrual cycle and uterus function. Since the discovery of its role in the reproductive system, the study of INSL3/RXFP2 has expanded to others organs, such as skeletal muscle, bone, kidney, thyroid, brain, and eye. This review aims to summarize the various advances in understanding the physiological function of this ligand-receptor pair since its first discovery and elucidate its future therapeutic potential in the management of various diseases.

Deep Robust Framework for Protein Function Prediction Using Variable-Length Protein Sequences

The order of amino acids in a protein sequence enables the protein to acquire a conformation suitable for performing functions, thereby motivating the need to analyze these sequences for predicting functions. Although machine learning based approaches are fast compared to methods using BLAST, FASTA, etc., they fail to perform well for long protein sequences (with more than 300 amino acids). In this paper, we introduce a novel method for construction of two separate feature sets for protein using bi-directional long short-term memory network based on the analysis of fixed 1) single-sized segments and 2) multi-sized segments. The model trained on the proposed feature set based on multi-sized segments is combined with the model trained using state-of-the-art Multi-label Linear Discriminant Analysis (MLDA) features to further improve the accuracy. Extensive evaluations using separate datasets for biological processes and molecular functions demonstrate not only improved results for long sequences, but also significantly improve the overall accuracy over state-of-the-art method. The single-sized approach produces an improvement of +3.37 percent for biological processes and +5.48 percent for molecular functions over the MLDA based classifier. The corresponding numbers for multi-sized approach are +5.38 and +8.00 percent. Combining the two models, the accuracy further improves to +7.41 and +9.21 percent, respectively.

Prediction of Protein-Protein Interactions with Local Weight-Sharing Mechanism in Deep Learning

Protein-protein interactions (PPIs) are important for almost all cellular processes, including metabolic cycles, DNA transcription and replication, and signaling cascades. The experimental methods for identifying PPIs are always time-consuming and expensive. Therefore, it is important to develop computational approaches for predicting PPIs. In this paper, an improved model is proposed to use a machine learning method in the study of protein-protein interactions. With the consideration of the factors affecting the prediction of the PPIs, a method of feature extraction and fusion is proposed to improve the variety of the features to be considered in the prediction. Besides, with the consideration of the effect affected by the different input order of the two proteins, we propose a "Y-type" Bi-RNN model and train the network by using a method which both needs backward and forward training. In order to insure the training time caused on the extra training either a backward one or a forward one, this paper proposes a weight-sharing policy to minimize the parameters in the training. The experimental results show that the proposed method can achieve an accuracy of 99.57%, recall of 99.36%, sensitivity of 99.76%, precision of 99.74%, MCC of 99.14%, and AUC of 99.56% under the benchmark dataset.

Anomaly Detection-Based Recognition of Near-Native Protein Structures

The three-dimensional structures populated by a protein molecule determine to a great extent its biological activities. The rich information encoded by protein structure on protein function continues to motivate the development of computational approaches for determining functionally-relevant structures. The majority of structures generated in silico are not relevant. Discriminating relevant/native protein structures from non-native ones is an outstanding challenge in computational structural biology. Inherently, this is a recognition problem that can be addressed under the umbrella of machine learning. In this paper, based on the premise that near-native structures are effectively anomalies, we build on the concept of anomaly detection in machine learning. We propose methods that automatically select relevant subsets, as well as methods that select a single structure to offer as prediction. Evaluations are carried out on benchmark datasets and demonstrate that the proposed methods advance the state of the art. The presented results motivate further building on and adapting concepts and techniques from machine learning to improve recognition of near-native structures in protein structure prediction.

Identification of retinoid acid induced 16 as a novel androgen receptor target in prostate cancer cells

BACKGROUND

Retinoid acid induced 16 (RAI16) was reported to enhance tumorigenesis in hepatocellular carcinoma (HCC). The androgen receptor (AR) is a nuclear hormone receptor that functions as a critical oncogene in several cancer progressions. However, whether RAI16 is a candidate AR target gene that may involve in prostate cancer progression was unclear.

MATERIALS & METHODS

RAI16 expression was detected in prostate cancer cells with or without the AR agonist R1881 treatment by quantitative RT-PCR and Western blot. Direct AR binding to the RAI16 promoter was tested using AR chromatin immunoprecipitation (ChIP) and luciferase assay. Cell viability and colony formation assays in response to R1881 were analyzed in cells with RAI16 knockdown by specific siRNA.

RESULTS

The expression of RAI16 was high in LNCaP(AI), LNCaP(AD), C4-2 expressing AR, but low in Du145 and Pc-3 cells without AR expressing. In addition, the expression of RAI16 could be induced by 10 nM R1881 treatment LNCaP(AD) and C4-2 cells, but inhibited by AR specific siRNA treatment. Furthermore, AR binds directly to ARE3 (-2003~-1982bp) of RAI16 promoter region by ChIP and luciferase assay. RAI16 knockdown inhibited the enhancement of cell viability and colony formation of AR stimulation.

CONCLUSIONS

We demonstrate for the first time that RAI16 is a direct target gene of AR. RAI16 may involved in cell growth of prostate cancer cells in response to AR signaling.

Novel mutations in SPEF2 causing different defects between flagella and cilia bridge: the phenotypic link between MMAF and PCD

Severe asthenozoospermia is a common cause of male infertility. Recent studies have revealed that SPEF2 mutations lead to multiple morphological abnormalities of the sperm flagella (MMAF) without primary ciliary dyskinesia (PCD) symptoms in males, but PCD phenotype was also found in one female individual. Therefore, whether there is a phenotypic continuum ranging from infertile patients with PCD to MMAF patients with no or low noise PCD manifestations remains elusive. Here, we performed whole-exome sequencing in 47 patients with severe asthenozoospermia from 45 unrelated Chinese families. We identified four novel biallelic mutations in SPEF2 (8.9%, 4/45) in six affected individuals (12.8%, 6/47), while no deleterious biallelic variants in SPEF2 were detected in 637 controls, including 219 with oligoasthenospermia, 195 with non-obstructive azoospermia, and 223 fertile controls. Notably, all six patients exhibited PCD-like symptoms, including recurrent airway infections, bronchitis, and rhinosinusitis. Ultrastructural analysis revealed normal 9 + 2 axonemes of respiratory cilia but consistently abnormal 9 + 0 axoneme or disordered accessory structures of sperm flagella, indicating different roles of SPEF2 in sperm flagella and respiratory cilia. Subsequently, a Spef2 knockout mouse model was used to validate the PCD-like phenotype and male infertility, where the subfertility of female Spef2^-/- mice was found unexpectedly. Overall, our data bridge the link between MMAF and PCD based on the association of SPEF2 mutations with both infertility and PCD in males and provide basis for further exploring the molecular mechanism of SPEF2 during spermiogenesis and ciliogenesis.

Fat-specific protein 27α inhibits autophagy-dependent lipid droplet breakdown in white adipocytes

AIMS/INTRODUCTION

Fat-specific protein 27 (FSP27) α is the major isoform of FSP27 in white adipose tissue (WAT), and is essential for large unilocular lipid droplet (LD) formation in white adipocytes. In contrast, FSP27β is abundantly expressed in brown adipose tissue (BAT), and plays an important role in small multilocular LD formation. In FSP27 KO mice in which FSP27α and β are both depleted, WAT is characterized by multilocular LD formation, and by increased mitochondrial abundance and energy expenditure, whereas BAT conversely manifests large oligolocular LDs and reduced energy expenditure.

MATERIALS AND METHODS

We investigated the effects of autophagy in WAT and BAT of wild type (WT) and FSP27 knockout (KO) mice. In addition, we examined the effects of FSP27α and FSP27β to the induction of autophagy in COS cells.

RESULTS

Food deprivation induced autophagy in BAT of WT mice, as well as in WAT of FSP27 KO mice, suggesting that enhanced autophagy is characteristic of adipocytes with small multilocular LDs. Pharmacological inhibition of autophagy attenuated the fasting-induced loss of LD area in adipocytes with small multilocular LDs (BAT of WT mice and WAT of FSP27 KO mice), without affecting that in adipocytes with large unilocular or oligolocular LDs (WAT of WT mice or in BAT of FSP27 KO mice). Overexpression of FSP27α inhibited autophagy induction by serum deprivation in COS cells, whereas that of FSP27β had no such effect.

CONCLUSIONS

The present results thus showed that FSP27α inhibits autophagy and might thereby contribute to the energy-storage function of WAT.

AMC-Net: Asymmetric and multi-scale convolutional neural network for multi-label HPA classification

BACKGROUND AND OBJECTIVES

The multi-label Human Protein Atlas (HPA) classification can yield a better understanding of human diseases and help doctors to enhance the automatic analysis of biomedical images. The existing automatic protein recognition methods have been limited to single pattern. Therefore, an automatic multi-label human protein atlas recognition system with satisfactory performance should be conducted. This work aims to build an automatic recognition system for multi-label human protein atlas classification based on deep learning.

METHODS

In this work, an automatic feature extraction and multi-label classification framework is proposed. Specifically, an asymmetric and multi-scale convolutional neural network is designed for HPA classification. Furthermore, this work introduces a combined loss that consists of the binary cross-entropy and F1-score losses to improve identification performance.

RESULTS

Rigorous experiments are conducted to estimate the proposed system. In particular, unlike the current automatic identification systems, which focus on a limited number of patterns, the proposed method is capable of classifying mixed patterns of proteins in microscope images and can handle the subcellular multi-label protein classification task including 28 subcellular localization patterns. The proposed framework based on deep convolutional neural network outperformed the existing approaches with a F1-score of 0.823, which illustrates the robustness and effectiveness of the proposed system.

CONCLUSION

This study proposed a high-performance recognition system for protein atlas classification based on deep learning, and it achieved an automatic multi-label human protein atlas identification framework with superior performance than previous studies.

Multitask Protein Function Prediction through Task Dissimilarity

Automated protein function prediction is a challenging problem with distinctive features, such as the hierarchical organization of protein functions and the scarcity of annotated proteins for most biological functions. We propose a multitask learning algorithm addressing both issues. Unlike standard multitask algorithms, which use task (protein functions) similarity information as a bias to speed up learning, we show that dissimilarity information enforces separation of rare class labels from frequent class labels, and for this reason is better suited for solving unbalanced protein function prediction problems. We support our claim by showing that a multitask extension of the label propagation algorithm empirically works best when the task relatedness information is represented using a dissimilarity matrix as opposed to a similarity matrix. Moreover, the experimental comparison carried out on three model organism shows that our method has a more stable performance in both "protein-centric" and "function-centric" evaluation settings.

Importance of collision cross section measurements by ion mobility mass spectrometry in structural biology

The field of ion mobility mass spectrometry (IM-MS) has developed rapidly in recent decades, with new fundamental advances underpinning innovative applications. This has been particularly noticeable in the field of biomacromolecular structure determination and structural biology, with pioneering studies revealing new structural insight for complex protein assemblies which control biological function. This perspective offers a review of recent developments in IM-MS which have enabled expanding applications in protein structural biology, principally focusing on the quantitative measurement of collision cross sections and their interpretation to describe higher order protein structures.

Genome-wide analyses as part of the international FTLD-TDP whole-genome sequencing consortium reveals novel disease risk factors and increases support for immune dysfunction in FTLD

Frontotemporal lobar degeneration with neuronal inclusions of the TAR DNA-binding protein 43 (FTLD-TDP) represents the most common pathological subtype of FTLD. We established the international FTLD-TDP whole-genome sequencing consortium to thoroughly characterize the known genetic causes of FTLD-TDP and identify novel genetic risk factors. Through the study of 1131 unrelated Caucasian patients, we estimated that C9orf72 repeat expansions and GRN loss-of-function mutations account for 25.5% and 13.9% of FTLD-TDP patients, respectively. Mutations in TBK1 (1.5%) and other known FTLD genes (1.4%) were rare, and the disease in 57.7% of FTLD-TDP patients was unexplained by the known FTLD genes. To unravel the contribution of common genetic factors to the FTLD-TDP etiology in these patients, we conducted a two-stage association study comprising the analysis of whole-genome sequencing data from 517 FTLD-TDP patients and 838 controls, followed by targeted genotyping of the most associated genomic loci in 119 additional FTLD-TDP patients and 1653 controls. We identified three genome-wide significant FTLD-TDP risk loci: one new locus at chromosome 7q36 within the DPP6 gene led by rs118113626 (p value = 4.82e - 08, OR = 2.12), and two known loci: UNC13A, led by rs1297319 (p value = 1.27e - 08, OR = 1.50) and HLA-DQA2 led by rs17219281 (p value = 3.22e - 08, OR = 1.98). While HLA represents a locus previously implicated in clinical FTLD and related neurodegenerative disorders, the association signal in our study is independent from previously reported associations. Through inspection of our whole-genome sequence data for genes with an excess of rare loss-of-function variants in FTLD-TDP patients (n ≥ 3) as compared to controls (n = 0), we further discovered a possible role for genes functioning within the TBK1-related immune pathway (e.g., DHX58, TRIM21, IRF7) in the genetic etiology of FTLD-TDP. Together, our study based on the largest cohort of unrelated FTLD-TDP patients assembled to date provides a comprehensive view of the genetic landscape of FTLD-TDP, nominates novel FTLD-TDP risk loci, and strongly implicates the immune pathway in FTLD-TDP pathogenesis.

Revealing evolutionary constraints on proteins through sequence analysis

Statistical analysis of alignments of large numbers of protein sequences has revealed "sectors" of collectively coevolving amino acids in several protein families. Here, we show that selection acting on any functional property of a protein, represented by an additive trait, can give rise to such a sector. As an illustration of a selected trait, we consider the elastic energy of an important conformational change within an elastic network model, and we show that selection acting on this energy leads to correlations among residues. For this concrete example and more generally, we demonstrate that the main signature of functional sectors lies in the small-eigenvalue modes of the covariance matrix of the selected sequences. However, secondary signatures of these functional sectors also exist in the extensively-studied large-eigenvalue modes. Our simple, general model leads us to propose a principled method to identify functional sectors, along with the magnitudes of mutational effects, from sequence data. We further demonstrate the robustness of these functional sectors to various forms of selection, and the robustness of our approach to the identification of multiple selected traits.

Rapid interpretation of small-angle X-ray scattering data

The fundamental aim of structural analyses in biophysics is to reveal a mutual relation between a molecule’s dynamic structure and its physiological function. Small-angle X-ray scattering (SAXS) is an experimental technique for structural characterization of macromolecules in solution and enables time-resolved analysis of conformational changes under physiological conditions. As such experiments measure spatially averaged low-resolution scattering intensities only, the sparse information obtained is not sufficient to uniquely reconstruct a three-dimensional atomistic model. Here, we integrate the information from SAXS into molecular dynamics simulations using computationally efficient native structure-based models. Dynamically fitting an initial structure towards a scattering intensity, such simulations produce atomistic models in agreement with the target data. In this way, SAXS data can be rapidly interpreted while retaining physico-chemical knowledge and sampling power of the underlying force field. We demonstrate our method’s performance using the example of three protein systems. Simulations are faster than full molecular dynamics approaches by more than two orders of magnitude and consistently achieve comparable accuracy. Computational demands are reduced sufficiently to run the simulations on commodity desktop computers instead of high-performance computing systems. These results underline that scattering-guided structure-based simulations provide a suitable framework for rapid early-stage refinement of structures towards SAXS data with particular focus on minimal computational resources and time.

Proteins are the molecular nanomachines in biological cells and thus vital to any known form of life. From the evolutionary perspective, viable protein structure emerges on the basis of a ‘form-follows-function’ principle. A protein’s designated function is inextricably linked to dynamic conformational changes, which can be observed by small-angle X-ray scattering. Intensities from SAXS contain low-resolution information on the protein’s shape at different steps of its functional cycle. We are interested in directly getting an atomistic model of this encoded structure. One powerful approach is to include the experimental data into computational simulations of the protein’s function-related physical motions. We combine scattering intensities with coarse-grained native structure-based models. These models are computationally highly efficient yet describe the system’s dynamics realistically. Here, we present our method for rapid interpretation of scattering intensities from SAXS to derive structural models, using minimal computational resources and time.

Protein-Protein Interaction Identification Using a Similarity-Constrained Graph Model

Protein-protein interaction (PPI) identification is an important task in text mining. Most PPI detection systems make predictions solely based on evidence within a single sentence and often suffer from the heavy burden of manual annotation. This paper approaches PPI detection task from a different paradigm by investigating the context of protein pairs collected from a large corpus and their relations. First, crucial cues in the context are exploited to make initial predictions. Then, relational similarity between protein pairs is calculated. Finally, evidence from the two views is integrated in the framework of minimum cuts algorithm. Experimental results show that the graph model achieves better performance than standard supervised approaches. Using 20 percent data as the training set, our algorithm achieves higher accuracy than support vector machine (SVM) using 80 percent data as training data. Moreover, the semi-supervised settings reveal promising directions for PPI identification exploiting unlabeled data.

Unsupervised Discovery of Geometrically Common Structural Motifs and Long-Range Contacts in Protein 3D Structures

The essential role of small evolutionarily conserved structural units in proteins has been extensively researched and validated. A popular example are serine proteases, where the peptide cleavage reaction is realized by a configuration of only three residues. Brought to spatial proximity during the protein folding process, such structural motifs are often long-range contacts and usually hard to detect at sequence level. Due to the constantly increasing resource of protein 3D structure data, the computational identification of structural motifs can contribute significantly to the understanding of protein fold and function. Thus, we propose a method to discover structural motifs of high geometrical similarity and desired sequence separation in protein 3D structure data. By utilizing methods originated from data mining, no a priori knowledge is required. The applicability of the method is demonstrated by the identification of the catalytic unit of serine proteases and the ion-coordination center of cupredoxins. Furthermore, large-scale analysis of the entire Protein Data Bank points towards the presence of ubiquitous structural motifs, independent of any specific fold or function. We envision that our method is suitable to uncover functional mechanisms and to derive fingerprint libraries of structural motifs, which could be used to assess protein family association.

Tissue-specific expression and post-transcriptional regulation of the ATPase inhibitory factor 1 (IF1) in human and mouse tissues

The ATPase inhibitory factor 1 (IF1) is an intrinsically disordered protein that regulates the activity of the mitochondrial ATP synthase. Phosphorylation of S39 in IF1 prevents it from binding to the enzyme and thus abolishes its inhibitory activity. Dysregulation of IF1 is linked to different human diseases, providing a relevant biomarker of cancer progression. However, the tissue content of IF1 relative to the abundance of the ATP synthase is unknown. In this study, we characterized the tissue-specific expression of IF1 in human and mouse tissues and quantitated the content of IF1 and of ATP synthase. We found relevant differences in IF1 expression between human and mouse tissues and found that in high-energy-demanding tissues, the molar content of IF1 exceeds that of the ATP synthase. In these tissues, a fraction of IF1 is bound to the enzyme, and the other fraction is phosphorylated and hence is unable to bind the enzyme. Post-transcriptional control accounts for most of the regulated expression of IF1, especially in mouse heart, where IF1 mRNA translation is repressed by the leucine-rich pentatricopeptide repeat containing protein. Overall, these findings enlighten the cellular biology of IF1 and pave the way to development of additional models that address its role in pathophysiology.-Esparza-Moltó, P. B., Nuevo-Tapioles, C., Chamorro, M., Nájera, L., Torresano, L., Santacatterina, F., Cuezva, J. M. Tissue-specific expression and post-transcriptional regulation of the ATPase inhibitory factor 1 (IF1) in human and mouse tissues.

CIDEC Is Involved in LPS-Induced Inflammation and Apoptosis in Renal Tubular Epithelial Cells

Adenosine 5'-monophosphate-activated protein kinase (AMPK) has been shown to have anti-inflammatory effect by inhibition of the nuclear factor κB (NF-κB) pathway and is involved in lipopolysaccharide (LPS)-induced inflammation. Cell-death-inducing DFF45-like effector C (CIDEC) can directly down-regulate AMPK activity through interacting with AMPKα subunit. However, whether the AMPK or CIDEC is involved in LPS-induced inflammation in renal tubular epithelial cells is still unknown. Therefore, we studied the role of AMPK and CIDEC in LPS-treated NRK-52E cells. Our results showed that LPS could up-regulate the expression of CIDEC in vitro and in vivo. Silencing CIDEC by CIDEC-siRNA could restore expression of phosphorylated-AMPKα which was decreased by LPS, suppress LPS-induced NF-κB pathway activation, and TNF-α, IL-6, and IL-1β production in NRK-52E cells. Furthermore, silencing CIDEC also partially alleviated LPS-induced epithelial cells apoptosis. In conclusion, the results demonstrated that CIDEC/AMPK signaling pathway played an important role in LPS-induced inflammation and epithelial cells apoptosis.

Structure and Protein Interaction-Based Gene Ontology Annotations Reveal Likely Functions of Uncharacterized Proteins on Human Chromosome 17

Understanding the function of human proteins is essential to decipher the molecular mechanisms of human diseases and phenotypes. Of the 17 470 human protein coding genes in the neXtProt 2018-01-17 database with unequivocal protein existence evidence (PE1), 1260 proteins do not have characterized functions. To reveal the function of poorly annotated human proteins, we developed a hybrid pipeline that creates protein structure prediction using I-TASSER and infers functional insights for the target protein from the functional templates recognized by COFACTOR. As a case study, the pipeline was applied to all 66 PE1 proteins with unknown or insufficiently specific function (uPE1) on human chromosome 17 as of neXtProt 2017-07-01. Benchmark testing on a control set of 100 well-characterized proteins randomly selected from the same chromosome shows high Gene Ontology (GO) term prediction accuracies of 0.69, 0.57, and 0.67 for molecular function (MF), biological process (BP), and cellular component (CC), respectively. Three pipelines of function annotations (homology detection, protein-protein interaction network inference, and structure template identification) have been exploited by COFACTOR. Detailed analyses show that structure template detection based on low-resolution protein structure prediction made the major contribution to the enhancement of the sensitivity and precision of the annotation predictions, especially for cases that do not have sequence-level homologous templates. For the chromosome 17 uPE1 proteins, the I-TASSER/COFACTOR pipeline confidently assigned MF, BP, and CC for 13, 33, and 49 proteins, respectively, with predicted functions ranging from sphingosine N-acyltransferase activity and sugar transmembrane transporter to cytoskeleton constitution. We highlight the 13 proteins with confident MF predictions; 11 of these are among the 33 proteins with confident BP predictions and 12 are among the 49 proteins with confident CC. This study demonstrates a novel computational approach to systematically annotate protein function in the human proteome and provides useful insights to guide experimental design and follow-up validation studies of these uncharacterized proteins.

Necroptosis inhibition as a therapy for Niemann-Pick disease, type C1: Inhibition of RIP kinases and combination therapy with 2-hydroxypropyl-β-cyclodextrin

Niemann-Pick disease, type C1 (NPC1) is an inborn error of metabolism that results in endolysosomal accumulation of unesterified cholesterol. Clinically, NPC1 manifests as cholestatic liver disease in the newborn or as a progressive neurogenerative condition characterized by cerebellar ataxia and cognitive decline. Currently there are no FDA approved therapies for NPC1. Thus, understanding the pathological processes that contribute to neurodegeneration will be important in both developing and testing potential therapeutic interventions. Neuroinflammation and necroptosis contribute to the NPC1 pathological cascade. Receptor Interacting Protein Kinase 1 and 3 (RIPK1 and RIPK3), are protein kinases that play a central role in mediating neuronal necroptosis. Our prior work suggested that pharmacological inhibition of RIPK1 had a significant but modest beneficial effect; however, the inhibitors used in that study had suboptimal pharmacokinetic properties. In this work we evaluated both pharmacological and genetic inhibition of RIPK1 kinase activity. Lifespan in both Npc1^-/- mice treated with GSK'547, a RIPK1 inhibitor with better pharmacokinetic properties, and Npc1^-/-:Ripk1^kd/kd double mutant mice was significantly increased. In both cases the increase in lifespan was modest, suggesting that the therapeutic potential of RIPK1 inhibition, as a monotherapy, is limited. We thus investigated the potential of combining RIPK1 inhibition with 2-hydroxypropyl-β-cyclodextrin (HPβCD) therapy HPβCD has been shown to slow neurological disease progression in NPC1 mice, cats and patients. HPβCD appeared to have an additive positive effect on the pathology and survival of Npc1^-/-:Ripk1^kd/kd mice. RIPK1 and RIPK3 are both critical components of the necrosome, thus we were surprised to observe no increase survival in Npc1^-/-;Ripk3^-/- mice compared to Npc1^-/- mice. These data suggest that although necroptosis is occurring in NPC1, the observed effects of RIPK1 inhibition may be related to its RIPK3-independent role in neuroinflammation and cytokine production.

Biophysical Inference of Epistasis and the Effects of Mutations on Protein Stability and Function

Understanding the relationship between protein sequence, function, and stability is a fundamental problem in biology. The essential function of many proteins that fold into a specific structure is their ability to bind to a ligand, which can be assayed for thousands of mutated variants. However, binding assays do not distinguish whether mutations affect the stability of the binding interface or the overall fold. Here, we introduce a statistical method to infer a detailed energy landscape of how a protein folds and binds to a ligand by combining information from many mutated variants. We fit a thermodynamic model describing the bound, unbound, and unfolded states to high quality data of protein G domain B1 binding to IgG-Fc. We infer distinct folding and binding energies for each mutation providing a detailed view of how mutations affect binding and stability across the protein. We accurately infer the folding energy of each variant in physical units, validated by independent data, whereas previous high-throughput methods could only measure indirect changes in stability. While we assume an additive sequence-energy relationship, the binding fraction is epistatic due its nonlinear relation to energy. Despite having no epistasis in energy, our model explains much of the observed epistasis in binding fraction, with the remaining epistasis identifying conformationally dynamic regions.

Inactivation of Magel2 suppresses oxytocin neurons through synaptic excitation-inhibition imbalance

Prader-Willi and the related Schaaf-Yang Syndromes (PWS/SYS) are rare neurodevelopmental disorders characterized by overlapping phenotypes of high incidence of autism spectrum disorders (ASD) and neonatal feeding difficulties. Based on clinical and basic studies, oxytocin pathway defects are suggested to contribute disease pathogenesis but the mechanism has been poorly understood. Specifically, whether the impairment in oxytocin system is limited to neuropeptide levels and how the functional properties of broader oxytocin neuron circuits affected in PWS/SYS have not been addressed. Using cell type specific electrophysiology, we investigated basic synaptic and cell autonomous properties of oxytocin neurons in the absence of MAGEL2; a hypothalamus enriched ubiquitin ligase regulator that is inactivated in both syndromes. We observed significant suppression of overall ex vivo oxytocin neuron activity, which was largely contributed by altered synaptic input profile; with reduced excitatory and increased inhibitory currents. Our results suggest that dysregulation of oxytocin system goes beyond altered neuropeptide expression and synaptic excitation inhibition imbalance impairs overall oxytocin pathway function.

Learned protein embeddings for machine learning

Motivation

Machine-learning models trained on protein sequences and their measured functions can infer biological properties of unseen sequences without requiring an understanding of the underlying physical or biological mechanisms. Such models enable the prediction and discovery of sequences with optimal properties. Machine-learning models generally require that their inputs be vectors, and the conversion from a protein sequence to a vector representation affects the model's ability to learn. We propose to learn embedded representations of protein sequences that take advantage of the vast quantity of unmeasured protein sequence data available. These embeddings are low-dimensional and can greatly simplify downstream modeling.

Results

The predictive power of Gaussian process models trained using embeddings is comparable to those trained on existing representations, which suggests that embeddings enable accurate predictions despite having orders of magnitude fewer dimensions. Moreover, embeddings are simpler to obtain because they do not require alignments, structural data, or selection of informative amino-acid properties. Visualizing the embedding vectors shows meaningful relationships between the embedded proteins are captured.

Availability and implementation

The embedding vectors and code to reproduce the results are available at https://github.com/fhalab/embeddings_reproduction/.

Supplementary information

Supplementary data are available at Bioinformatics online.

iFeature: a Python package and web server for features extraction and selection from protein and peptide sequences

Summary

Structural and physiochemical descriptors extracted from sequence data have been widely used to represent sequences and predict structural, functional, expression and interaction profiles of proteins and peptides as well as DNAs/RNAs. Here, we present iFeature, a versatile Python-based toolkit for generating various numerical feature representation schemes for both protein and peptide sequences. iFeature is capable of calculating and extracting a comprehensive spectrum of 18 major sequence encoding schemes that encompass 53 different types of feature descriptors. It also allows users to extract specific amino acid properties from the AAindex database. Furthermore, iFeature integrates 12 different types of commonly used feature clustering, selection and dimensionality reduction algorithms, greatly facilitating training, analysis and benchmarking of machine-learning models. The functionality of iFeature is made freely available via an online web server and a stand-alone toolkit.

Availability and implementation

http://iFeature.erc.monash.edu/; https://github.com/Superzchen/iFeature/.

Supplementary information

Supplementary data are available at Bioinformatics online.

IAS: Interaction Specific GO Term Associations for Predicting Protein-Protein Interaction Networks

Proteins carry out their function in a cell through interactions with other proteins. A large scale protein-protein interaction (PPI) network of an organism provides static yet an essential structure of interactions, which is valuable clue for understanding the functions of proteins and pathways. PPIs are determined primarily by experimental methods; however, computational PPI prediction methods can supplement or verify PPIs identified by experiment. Here, we developed a novel scoring method for predicting PPIs from Gene Ontology (GO) annotations of proteins. Unlike existing methods that consider functional similarity as an indication of interaction between proteins, the new score, named the protein-protein Interaction Association Score (IAS), was computed from GO term associations of known interacting protein pairs in 49 organisms. IAS was evaluated on PPI data of six organisms and found to outperform existing GO term-based scoring methods. Moreover, consensus scoring methods that combine different scores further improved performance of PPI prediction.

Towards Extracting Supporting Information About Predicted Protein-Protein Interactions

One of the goals of relation extraction is to identify protein-protein interactions (PPIs) in biomedical literature. Current systems are capturing binary relations and also the direction and type of an interaction. Besides assisting in the curation PPIs into databases, there has been little real-world application of these algorithms. We describe UPSITE, a text mining tool for extracting evidence in support of a hypothesized interaction. Given a predicted PPI, UPSITE uses a binary relation detector to check whether a PPI is found in abstracts in PubMed. If it is not found, UPSITE retrieves documents relevant to each of the two proteins separately, and extracts contextual information about biological events surrounding each protein, and calculates semantic similarity of the two proteins to provide evidential support for the predicted PPI. In evaluations, relation extraction achieved an Fscore of 0.88 on the HPRD50 corpus, and semantic similarity measured with angular distance was found to be statistically significant. With the development of PPI prediction algorithms, the burden of interpreting the validity and relevance of novel PPIs is on biologists. We suggest that presenting annotations of the two proteins in a PPI side-by-side and a score that quantifies their similarity lessens this burden to some extent.

The Frequency Response of Outer Hair Cell Voltage-Dependent Motility Is Limited by Kinetics of Prestin

The voltage-dependent protein SLC26a5 (prestin) underlies outer hair cell electromotility (eM), which is responsible for cochlear amplification in mammals. The electrical signature of eM is a bell-shaped nonlinear capacitance (NLC), deriving from prestin sensor-charge (Qp) movements, which peaks at the membrane voltage, Vh, where charge is distributed equally on either side of the membrane. Voltage dependencies of NLC and eM differ depending on interrogation frequency and intracellular chloride, revealing slow intermediate conformational transitions between anion binding and voltage-driven Qp movements. Consequently, NLC exhibits low-pass characteristics, substantially below prevailing estimates of eM frequency response. Here we study in guinea pig and mouse of either sex synchronous prestin electrical (NLC, Qp) and mechanical (eM) activity across frequencies under voltage clamp (whole cell and microchamber). We find that eM and Qp magnitude and phase correspond, indicating tight piezoelectric coupling. Electromechanical measures (both NLC and eM) show dual-Lorentzian, low-pass behavior, with a limiting (τ2) time constant at Vh of 32.6 and 24.8 μs, respectively. As expected for voltage-dependent kinetics, voltage excitation away from Vh has a faster, flatter frequency response, with our fastest measured τ2 for eM of 18.2 μs. Previous observations of ultrafast eM (τ ≈ 2 μs) were obtained at offsets far removed from Vh We hypothesize that trade-offs in eM gain-bandwith arising from voltage excitation at membrane potentials offset from Vh influence the effectiveness of cochlear amplification across frequencies.SIGNIFICANCE STATEMENT Of two types of hair cells within the organ of Corti, inner hair cells and outer hair cells, the latter evolved to boost sensitivity to sounds. Damage results in hearing loss of 40-60 dB, revealing amplification gains of 100-1000× that arise from voltage-dependent mechanical responses [electromotility (eM)]. eM, driven by the membrane protein prestin, may work beyond 70 kHz. However, this speed exceeds, by over an order of magnitude, kinetics of typical voltage-dependent membrane proteins. We find eM is actually low pass in nature, indicating that prestin bears kinetics typical of other membrane proteins. These observations highlight potential difficulties in providing sufficient amplification beyond a cutoff frequency near 20 kHz. Nevertheless, observed trade-offs in eM gain-bandwith may sustain cochlear amplification across frequency.

Genomic evidence for genes encoding leucine-rich repeat receptors linked to resistance against the eukaryotic extra- and intracellular Brassica napus pathogens Leptosphaeria maculans and Plasmodiophora brassicae

Genes coding for nucleotide-binding leucine-rich repeat (LRR) receptors (NLRs) control resistance against intracellular (cell-penetrating) pathogens. However, evidence for a role of genes coding for proteins with LRR domains in resistance against extracellular (apoplastic) fungal pathogens is limited. Here, the distribution of genes coding for proteins with eLRR domains but lacking kinase domains was determined for the Brassica napus genome. Predictions of signal peptide and transmembrane regions divided these genes into 184 coding for receptor-like proteins (RLPs) and 121 coding for secreted proteins (SPs). Together with previously annotated NLRs, a total of 720 LRR genes were found. Leptosphaeria maculans-induced expression during a compatible interaction with cultivar Topas differed between RLP, SP and NLR gene families; NLR genes were induced relatively late, during the necrotrophic phase of pathogen colonization. Seven RLP, one SP and two NLR genes were found in Rlm1 and Rlm3/Rlm4/Rlm7/Rlm9 loci for resistance against L. maculans on chromosome A07 of B. napus. One NLR gene at the Rlm9 locus was positively selected, as was the RLP gene on chromosome A10 with LepR3 and Rlm2 alleles conferring resistance against L. maculans races with corresponding effectors AvrLm1 and AvrLm2, respectively. Known loci for resistance against L. maculans (extracellular hemi-biotrophic fungus), Sclerotinia sclerotiorum (necrotrophic fungus) and Plasmodiophora brassicae (intracellular, obligate biotrophic protist) were examined for presence of RLPs, SPs and NLRs in these regions. Whereas loci for resistance against P. brassicae were enriched for NLRs, no such signature was observed for the other pathogens. These findings demonstrate involvement of (i) NLR genes in resistance against the intracellular pathogen P. brassicae and a putative NLR gene in Rlm9-mediated resistance against the extracellular pathogen L. maculans.

Moonlighting Activity of Secreted Inflammation-Regulatory Proteins

Moonlighting proteins exhibit multiple activities in different cellular compartments, and their abnormal regulation could play an important role in many diseases. To date, many proteins have been identified with moonlighting activity, and more such proteins are being gradually identified. Among the proteins that possess moonlighting activity, several secreted proteins exhibit multiple activities in different cellular locations, such as the extracellular matrix, nucleus, and cytoplasm. While acute inflammation starts rapidly and generally disappears in a few days, chronic inflammation can last for months or years. This is generally because of the failure to eliminate the cause of inflammation, along with repeated exposure to the inflammatory agent. Chronic inflammation is now considered as an overwhelming burden to the general wellbeing of patients and noted as an underlying cause of several diseases. Moonlighting proteins can contribute to the process of chronic inflammation; therefore, it is imperative to overview some proteins that exhibit multiple functions in inflammatory diseases. In this review, we will focus on inflammation, particularly unravelling several well-known secreted proteins with multiple functions in different cellular locations.

The E3 ubiquitin ligase SP1-like 1 plays a positive role in peroxisome biogenesis in Arabidopsis

Peroxisomes are dynamic organelles crucial for a variety of metabolic processes during the development of eukaryotic organisms, and are functionally linked to other subcellular organelles, such as mitochondria and chloroplasts. Peroxisomal matrix proteins are imported by peroxins (PEX proteins), yet the modulation of peroxin functions is poorly understood. We previously reported that, besides its known function in chloroplast protein import, the Arabidopsis E3 ubiquitin ligase SP1 (suppressor of ppi1 locus1) also targets to peroxisomes and mitochondria, and promotes the destabilization of the peroxisomal receptor-cargo docking complex components PEX13 and PEX14. Here we present evidence that in Arabidopsis, SP1's closest homolog SP1-like 1 (SPL1) plays an opposite role to SP1 in peroxisomes. In contrast to sp1, loss-of-function of SPL1 led to reduced peroxisomal β-oxidation activity, and enhanced the physiological and growth defects of pex14 and pex13 mutants. Transient co-expression of SPL1 and SP1 promoted each other's destabilization. SPL1 reduced the ability of SP1 to induce PEX13 turnover, and it is the N-terminus of SP1 and SPL1 that determines whether the protein is able to promote PEX13 turnover. Finally, SPL1 showed prevalent targeting to mitochondria, but rather weak and partial localization to peroxisomes. Our data suggest that these two members of the same E3 protein family utilize distinct mechanisms to modulate peroxisome biogenesis, where SPL1 reduces the function of SP1. Plants and possibly other higher eukaryotes may employ this small family of E3 enzymes to differentially modulate the dynamics of several organelles essential to energy metabolism via the ubiquitin-proteasome system.