NetSurfP-2.0: Improved prediction of protein structural features by integrated deep learning

A histone modifier, ASXL1, interacts with NONO and is involved in paraspeckle formation in hematopoietic cells

Paraspeckles are membraneless organelles formed through liquid-liquid phase separation and consist of multiple proteins and RNAs, including NONO, SFPQ, and NEAT1. The role of paraspeckles and the component NONO in hematopoiesis remains unknown. In this study, we show histone modifier ASXL1 is involved in paraspeckle formation. ASXL1 forms phase-separated droplets, upregulates NEAT1 expression, and increases NONO-NEAT1 interactions through the C-terminal intrinsically disordered region (IDR). In contrast, a pathogenic ASXL mutant (ASXL1-MT) lacking IDR does not support the interaction of paraspeckle components. Furthermore, paraspeckles are disrupted and Nono localization is abnormal in the cytoplasm of hematopoietic stem and progenitor cells (HSPCs) derived from ASXL1-MT knockin mice. Nono depletion and the forced expression of cytoplasmic NONO impair the repopulating potential of HSPCs, as does ASXL1-MT. Our study indicates a link between ASXL1 and paraspeckle components in the maintenance of normal hematopoiesis.

The Cancer Epitope Database and Analysis Resource: A Blueprint for the Establishment of a New Bioinformatics Resource for Use by the Cancer Immunology Community

Recent years have witnessed a dramatic rise in interest towards cancer epitopes in general and particularly neoepitopes, antigens that are encoded by somatic mutations that arise as a consequence of tumorigenesis. There is also an interest in the specific T cell and B cell receptors recognizing these epitopes, as they have therapeutic applications. They can also aid in basic studies to infer the specificity of T cells or B cells characterized in bulk and single-cell sequencing data. The resurgence of interest in T cell and B cell epitopes emphasizes the need to catalog all cancer epitope-related data linked to the biological, immunological, and clinical contexts, and most importantly, making this information freely available to the scientific community in a user-friendly format. In parallel, there is also a need to develop resources for epitope prediction and analysis tools that provide researchers access to predictive strategies and provide objective evaluations of their performance. For example, such tools should enable researchers to identify epitopes that can be effectively used for immunotherapy or in defining biomarkers to predict the outcome of checkpoint blockade therapies. We present here a detailed vision, blueprint, and work plan for the development of a new resource, the Cancer Epitope Database and Analysis Resource (CEDAR). CEDAR will provide a freely accessible, comprehensive collection of cancer epitope and receptor data curated from the literature and provide easily accessible epitope and T cell/B cell target prediction and analysis tools. The curated cancer epitope data will provide a transparent benchmark dataset that can be used to assess how well prediction tools perform and to develop new prediction tools relevant to the cancer research community.

Analysis of the Putative Nucleoporin POM33 in the Filamentous Fungus Sordaria macrospora

In the filamentous fungus Sordaria macrospora (Sm), the STRIPAK complex is required for vegetative growth, fruiting-body development and hyphal fusion. The SmSTRIPAK core consists of the striatin homolog PRO11, the scaffolding subunit of phosphatase PP2A, SmPP2AA, and its catalytic subunit SmPP2Ac1. Among other STRIPAK proteins, the recently identified coiled-coil protein SCI1 was demonstrated to co-localize around the nucleus. Pulldown experiments with SCI identified the transmembrane nucleoporin (TM Nup) SmPOM33 as a potential nuclear-anchor of SmSTRIPAK. Localization studies revealed that SmPOM33 partially localizes to the nuclear envelope (NE), but mainly to the endoplasmic reticulum (ER). We succeeded to generate a Δpom33 deletion mutant by homologous recombination in a new S. macrospora Δku80 recipient strain, which is defective in non-homologous end joining. Deletion of Smpom33 did neither impair vegetative growth nor sexual development. In pulldown experiments of SmPOM33 followed by LC/MS analysis, ER-membrane proteins involved in ER morphology, protein translocation, glycosylation, sterol biosynthesis and Ca²⁺-transport were significantly enriched. Data are available via ProteomeXchange with identifier PXD026253. Although no SmSTRIPAK components were identified as putative interaction partners, it cannot be excluded that SmPOM33 is involved in temporarily anchoring the SmSTRIPAK to the NE or other sites in the cell.

Global Identification and Systematic Analysis of Lysine Malonylation in Maize (Zea mays L.)

Lysine malonylation is a kind of post-translational modifications (PTMs) discovered in recent years, which plays an important regulatory role in plants. Maize (Zea mays L.) is a major global cereal crop. Immunoblotting revealed that maize was rich in malonylated proteins. We therefore performed a qualitative malonylome analysis to globally identify malonylated proteins in maize. In total, 1,722 uniquely malonylated lysine residues were obtained in 810 proteins. The modified proteins were involved in various biological processes such as photosynthesis, ribosome and oxidative phosphorylation. Notably, a large proportion of the modified proteins (45%) were located in chloroplast. Further functional analysis revealed that 30 proteins in photosynthesis and 15 key enzymes in the Calvin cycle were malonylated, suggesting an indispensable regulatory role of malonylation in photosynthesis and carbon fixation. This work represents the first comprehensive survey of malonylome in maize and provides an important resource for exploring the function of lysine malonylation in physiological regulation of maize.

OTX2 Homeoprotein Functions in Adult Choroid Plexus

The choroid plexus is an important blood barrier that secretes cerebrospinal fluid, which essential for embryonic brain development and adult brain homeostasis. The OTX2 homeoprotein is a transcription factor that is critical for choroid plexus development and remains highly expressed in adult choroid plexus. Through RNA sequencing analyses of constitutive and conditional knockdown adult mouse models, we reveal putative functional roles for OTX2 in adult choroid plexus function, including cell signaling and adhesion, and show that OTX2 regulates the expression of factors that are secreted into the cerebrospinal fluid, notably transthyretin. We also show that Otx2 expression impacts choroid plexus immune and stress responses, and affects splicing, leading to changes in the mRNA isoforms of proteins that are implicated in the oxidative stress response and DNA repair. Through mass spectrometry analysis of OTX2 protein partners in the choroid plexus, and in known non-cell-autonomous target regions, such as the visual cortex and subventricular zone, we identify putative targets that are involved in cell adhesion, chromatin structure, and RNA processing. Thus, OTX2 retains important roles for regulating choroid plexus function and brain homeostasis throughout life.

Structure of the human signal peptidase complex reveals the determinants for signal peptide cleavage

The signal peptidase complex (SPC) is an essential membrane complex in the endoplasmic reticulum (ER), where it removes signal peptides (SPs) from a large variety of secretory pre-proteins with exquisite specificity. Although the determinants of this process have been established empirically, the molecular details of SP recognition and removal remain elusive. Here, we show that the human SPC exists in two functional paralogs with distinct proteolytic subunits. We determined the atomic structures of both paralogs using electron cryo-microscopy and structural proteomics. The active site is formed by a catalytic triad and abuts the ER membrane, where a transmembrane window collectively formed by all subunits locally thins the bilayer. Molecular dynamics simulations indicate that this unique architecture generates specificity for SPs based on the length of their hydrophobic segments.

Order from Disorder with Intrinsically Disordered Peptide Amphiphiles

Amphiphilic molecules and their self-assembled structures have long been the target of extensive research due to their potential applications in fields ranging from materials design to biomedical and cosmetic applications. Increasing demands for functional complexity have been met with challenges in biochemical engineering, driving researchers to innovate in the design of new amphiphiles. An emerging class of molecules, namely, peptide amphiphiles, combines key advantages and circumvents some of the disadvantages of conventional phospholipids and block copolymers. Herein, we present new peptide amphiphiles composed of an intrinsically disordered peptide conjugated to two variants of hydrophobic dendritic domains. These molecules, termed intrinsically disordered peptide amphiphiles (IDPA), exhibit a sharp pH-induced micellar phase-transition from low-dispersity spheres to extremely elongated worm-like micelles. We present an experimental characterization of the transition and propose a theoretical model to describe the pH-response. We also present the potential of the shape transition to serve as a mechanism for the design of a cargo hold-and-release application. Such amphiphilic systems demonstrate the power of tailoring the interactions between disordered peptides for various stimuli-responsive biomedical applications.

Structural and functional analysis of disease-associated mutations in GOT1 gene: An in silico study

Disease-associated single nucleotide polymorphisms (SNPs) alter the natural functioning and the structure of proteins. Glutamic-oxaloacetic transaminase 1 (GOT1) is a gene associated with multiple cancers and neurodegenerative diseases which codes for aspartate aminotransferase. The present study involved a comprehensive in-silico analysis of the disease-associated SNPs of human GOT1. Four highly deleterious nsSNPs (L36R, Y159C, W162C and L345P) were identified through SNP screening using several sequence-based and structure-based tools. Conservation analysis and oncogenic analysis showed that most of the nsSNPs are at highly conserved residues, oncogenic in nature and cancer drivers. Molecular dynamics simulations (MDS) analysis was performed to understand the dynamic behaviour of native and mutant proteins. PTM analysis revealed that the nsSNP Y159C is at a PTM site and will mostly affect phosphorylation at that site. Based on the overall analyses carried out in this study, L36R is the most deleterious mutation amongst the aforementioned deleterious mutations of GOT1.

A secondary structure-based position-specific scoring matrix applied to the improvement in protein secondary structure prediction

Protein secondary structure prediction (SSP) has a variety of applications; however, there has been relatively limited improvement in accuracy for years. With a vision of moving forward all related fields, we aimed to make a fundamental advance in SSP. There have been many admirable efforts made to improve the machine learning algorithm for SSP. This work thus took a step back by manipulating the input features. A secondary structure element-based position-specific scoring matrix (SSE-PSSM) is proposed, based on which a new set of machine learning features can be established. The feasibility of this new PSSM was evaluated by rigid independent tests with training and testing datasets sharing <25% sequence identities. In all experiments, the proposed PSSM outperformed the traditional amino acid PSSM. This new PSSM can be easily combined with the amino acid PSSM, and the improvement in accuracy was remarkable. Preliminary tests made by combining the SSE-PSSM and well-known SSP methods showed 2.0% and 5.2% average improvements in three- and eight-state SSP accuracies, respectively. If this PSSM can be integrated into state-of-the-art SSP methods, the overall accuracy of SSP may break the current restriction and eventually bring benefit to all research and applications where secondary structure prediction plays a vital role during development. To facilitate the application and integration of the SSE-PSSM with modern SSP methods, we have established a web server and standalone programs for generating SSE-PSSM available at http://10.life.nctu.edu.tw/SSE-PSSM.

Hidden Patterns of Anti-HLA Class I Alloreactivity Revealed Through Machine Learning

Detection of alloreactive anti-HLA antibodies is a frequent and mandatory test before and after organ transplantation to determine the antigenic targets of the antibodies. Nowadays, this test involves the measurement of fluorescent signals generated through antibody-antigen reactions on multi-beads flow cytometers. In this study, in a cohort of 1,066 patients from one country, anti-HLA class I responses were analyzed on a panel of 98 different antigens. Knowing that the immune system responds typically to "shared" antigenic targets, we studied the clustering patterns of antibody responses against HLA class I antigens without any a priori hypothesis, applying two unsupervised machine learning approaches. At first, the principal component analysis (PCA) projections of intra-locus specific responses showed that anti-HLA-A and anti-HLA-C were the most distantly projected responses in the population with the anti-HLA-B responses to be projected between them. When PCA was applied on the responses against antigens belonging to a single locus, some already known groupings were confirmed while several new cross-reactive patterns of alloreactivity were detected. Anti-HLA-A responses projected through PCA suggested that three cross-reactive groups accounted for about 70% of the variance observed in the population, while anti-HLA-B responses were mainly characterized by a distinction between previously described Bw4 and Bw6 cross-reactive groups followed by several yet undocumented or poorly described ones. Furthermore, anti-HLA-C responses could be explained by two major cross-reactive groups completely overlapping with previously described C1 and C2 allelic groups. A second feature-based analysis of all antigenic specificities, projected as a dendrogram, generated a robust measure of allelic antigenic distances depicting bead-array defined cross reactive groups. Finally, amino acid combinations explaining major population specific cross-reactive groups were described. The interpretation of the results was based on the current knowledge of the antigenic targets of the antibodies as they have been characterized either experimentally or computationally and appear at the HLA epitope registry.

Predicting Protein Therapeutic Candidates for Bovine Babesiosis Using Secondary Structure Properties and Machine Learning

Bovine babesiosis causes significant annual global economic loss in the beef and dairy cattle industry. It is a disease instigated from infection of red blood cells by haemoprotozoan parasites of the genus Babesia in the phylum Apicomplexa. Principal species are Babesia bovis, Babesia bigemina, and Babesia divergens. There is no subunit vaccine. Potential therapeutic targets against babesiosis include members of the exportome. This study investigates the novel use of protein secondary structure characteristics and machine learning algorithms to predict exportome membership probabilities. The premise of the approach is to detect characteristic differences that can help classify one protein type from another. Structural properties such as a protein’s local conformational classification states, backbone torsion angles ϕ (phi) and ψ (psi), solvent-accessible surface area, contact number, and half-sphere exposure are explored here as potential distinguishing protein characteristics. The presented methods that exploit these structural properties via machine learning are shown to have the capacity to detect exportome from non-exportome Babesia bovis proteins with an 86–92% accuracy (based on 10-fold cross validation and independent testing). These methods are encapsulated in freely available Linux pipelines setup for automated, high-throughput processing. Furthermore, proposed therapeutic candidates for laboratory investigation are provided for B. bovis, B. bigemina, and two other haemoprotozoan species, Babesia canis, and Plasmodium falciparum.

APRANK: Computational Prioritization of Antigenic Proteins and Peptides From Complete Pathogen Proteomes

Availability of highly parallelized immunoassays has renewed interest in the discovery of serology biomarkers for infectious diseases. Protein and peptide microarrays now provide a rapid, high-throughput platform for immunological testing and validation of potential antigens and B-cell epitopes. However, there is still a need for tools to prioritize and select relevant probes when designing these arrays. In this work we describe a computational method called APRANK (Antigenic Protein and Peptide Ranker) which integrates multiple molecular features to prioritize potentially antigenic proteins and peptides in a given pathogen proteome. These features include subcellular localization, presence of repetitive motifs, natively disordered regions, secondary structure, transmembrane spans and predicted interaction with the immune system. We trained and tested this method with a number of bacteria and protozoa causing human diseases: Borrelia burgdorferi (Lyme disease), Brucella melitensis (Brucellosis), Coxiella burnetii (Q fever), Escherichia coli (Gastroenteritis), Francisella tularensis (Tularemia), Leishmania braziliensis (Leishmaniasis), Leptospira interrogans (Leptospirosis), Mycobacterium leprae (Leprae), Mycobacterium tuberculosis (Tuberculosis), Plasmodium falciparum (Malaria), Porphyromonas gingivalis (Periodontal disease), Staphylococcus aureus (Bacteremia), Streptococcus pyogenes (Group A Streptococcal infections), Toxoplasma gondii (Toxoplasmosis) and Trypanosoma cruzi (Chagas Disease). We have evaluated this integrative method using non-parametric ROC-curves and made an unbiased validation using Onchocerca volvulus as an independent data set. We found that APRANK is successful in predicting antigenicity for all pathogen species tested, facilitating the production of antigen-enriched protein subsets. We make APRANK available to facilitate the identification of novel diagnostic antigens in infectious diseases.

The influence of dataset homology and a rigorous evaluation strategy on protein secondary structure prediction

The secondary structure prediction (SSP) of proteins has long been an essential structural biology technique with various applications. Despite its vital role in many research and industrial fields, in recent years, as the accuracy of state-of-the-art secondary structure predictors approaches the theoretical upper limit, SSP has been considered no longer challenging or too challenging to make advances. With the belief that the substantial improvement of SSP will move forward many fields depending on it, we conducted this study, which focused on three issues that have not been noticed or thoroughly examined yet but may have affected the reliability of the evaluation of previous SSP algorithms. These issues are all about the sequence homology between or within the developmental and evaluation datasets. We thus designed many different homology layouts of datasets to train and evaluate SSP prediction models. Multiple repeats were performed in each experiment by random sampling. The conclusions obtained with small experimental datasets were verified with large-scale datasets using state-of-the-art SSP algorithms. Very different from the long-established assumption, we discover that the sequence homology between query datasets for training, testing, and independent tests exerts little influence on SSP accuracy. Besides, the sequence homology redundancy between or within most datasets would make the accuracy of an SSP algorithm overestimated, while the redundancy within the reference dataset for extracting predictive features would make the accuracy underestimated. Since the overestimating effects are more significant than the underestimating effect, the accuracy of some SSP methods might have been overestimated. Based on the discoveries, we propose a rigorous procedure for developing SSP algorithms and making reliable evaluations, hoping to bring substantial improvements to future SSP methods and benefit all research and application fields relying on accurate prediction of protein secondary structures.

A Multitask Deep-Learning Method for Predicting Membrane Associations and Secondary Structures of Proteins

Prediction of residue-level structural attributes and protein-level structural classes helps model protein tertiary structures and understand protein functions. Existing methods are either specialized on only one class of proteins or developed to predict only a specific type of residue-level attribute. In this work, we develop a new deep-learning method, named Membrane Association and Secondary Structure Predictor (MASSP), for accurately predicting both residue-level structural attributes (secondary structure, location, orientation, and topology) and protein-level structural classes (bitopic, α-helical, β-barrel, and soluble). MASSP integrates a multilayer two-dimensional convolutional neural network (2D-CNN) with a long short-term memory (LSTM) neural network into a multitasking framework. Our comparison shows that MASSP performs equally well or better than the state-of-the-art methods in predicting residue-level secondary structures, boundaries of transmembrane segments, and topology. Furthermore, it achieves outstanding accuracy in predicting protein-level structural classes. MASSP automatically distinguishes the structural classes of input sequences and identifies transmembrane segments and topologies if present, making it broadly applicable to different classes of proteins. In summary, MASSP's good performance and broad applicability make it well suited for annotating residue-level attributes and protein-level structural classes at the proteome scale.

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.

Exploring the sequence features determining amyloidosis in human antibody light chains

The light chain (AL) amyloidosis is caused by the aggregation of light chain of antibodies into amyloid fibrils. There are plenty of computational resources available for the prediction of short aggregation-prone regions within proteins. However, it is still a challenging task to predict the amyloidogenic nature of the whole protein using sequence/structure information. In the case of antibody light chains, common architecture and known binding sites can provide vital information for the prediction of amyloidogenicity at physiological conditions. Here, in this work, we have compared classical sequence-based, aggregation-related features (such as hydrophobicity, presence of gatekeeper residues, disorderness, β-propensity, etc.) calculated for the CDR, FR or V_L regions of amyloidogenic and non-amyloidogenic antibody light chains and implemented the insights gained in a machine learning-based webserver called "V_LAmY-Pred" ( https://web.iitm.ac.in/bioinfo2/vlamy-pred/ ). The model shows prediction accuracy of 79.7% (sensitivity: 78.7% and specificity: 79.9%) with a ROC value of 0.88 on a dataset of 1828 variable region sequences of the antibody light chains. This model will be helpful towards improved prognosis for patients that may likely suffer from diseases caused by light chain amyloidosis, understanding origins of aggregation in antibody-based biotherapeutics, large-scale in-silico analysis of antibody sequences generated by next generation sequencing, and finally towards rational engineering of aggregation resistant antibodies.

Structural analysis of mammalian protein phosphorylation at a proteome level

Phosphorylation is an essential post-translational modification for almost all cellular processes. Several global phosphoproteomics analyses have revealed phosphorylation profiles under different conditions. Beyond identification of phospho-sites, protein structures add another layer of information about their functionality. In this study, we systematically characterize phospho-sites based on their 3D locations in the protein and establish a location map for phospho-sites. More than 250,000 phospho-sites have been analyzed, of which 8,686 sites match at least one structure and are stratified based on their respective 3D positions. Core phospho-sites possess two distinct groups based on their dynamicity. Dynamic core phosphorylations are significantly more functional compared with static ones. The dynamic core and the interface phospho-sites are the most functional among all 3D phosphorylation groups. Our analysis provides global characterization and stratification of phospho-sites from a structural perspective that can be utilized for predicting functional relevance and filtering out false positives in phosphoproteomic studies.

An updated structural model of the A domain of the Pseudomonas putida XylR regulator poses an atypical interplay with aromatic effectors

A revised model of the aromatic binding A domain of the σ⁵⁴ -dependent regulator XylR of Pseudomonas putida mt-2 was produced based on the known 3D structures of homologous regulators PoxR, MopR and DmpR. The resulting frame was instrumental for mapping a number of mutations known to alter effector specificity, which were then reinterpreted under a dependable spatial reference. Some of these changes involved the predicted aromatic binding pocket but others occurred in distant locations, including dimerization interfaces and putative zinc binding site. The effector pocket was buried within the protein structure and accessible from the outside only through a narrow tunnel. Yet, several loop regions of the A domain could provide the flexibility required for widening such a tunnel for passage of aromatic ligands. The model was experimentally validated by treating the cells in vivo and the purified protein in vitro with benzyl bromide, which reacts with accessible nucleophilic residues on the protein surface. Structural and proteomic analyses confirmed the predicted in/out distribution of residues but also supported two additional possible scenarios of interaction of the A domain with aromatic effectors: a dynamic interaction of the fully structured yet flexible protein with the aromatic partner and/or inducer-assisted folding of the A domain.

Predicting the Most Deleterious Missense Nonsynonymous Single-Nucleotide Polymorphisms of Hennekam Syndrome-Causing CCBE1 Gene, In Silico Analysis

Hennekam lymphangiectasia-lymphedema syndrome has been linked to single-nucleotide polymorphisms in the CCBE1 (collagen and calcium-binding EGF domains 1) gene. Several bioinformatics methods were used to find the most dangerous nsSNPs that could affect CCBE1 structure and function. Using state-of-the-art in silico tools, this study examined the most pathogenic nonsynonymous single-nucleotide polymorphisms (nsSNPs) that disrupt the CCBE1 protein and extracellular matrix remodeling and migration. Our results indicate that seven nsSNPs, rs115982879, rs149792489, rs374941368, rs121908254, rs149531418, rs121908251, and rs372499913, are deleterious in the CCBE1 gene, four (G330E, C102S, C174R, and G107D) of which are the highly deleterious, two of them (G330E and G107D) have never been seen reported in the context of Hennekam syndrome. Twelve missense SNPs, rs199902030, rs267605221, rs37517418, rs80008675, rs116596858, rs116675104, rs121908252, rs147974432, rs147681552, rs192224843, rs139059968, and rs148498685, are found to revert into stop codons. Structural homology-based methods and sequence homology-based tools revealed that 8.8% of the nsSNPs are pathogenic. SIFT, PolyPhen2, M-CAP, CADD, FATHMM-MKL, DANN, PANTHER, Mutation Taster, LRT, and SNAP2 had a significant score for identifying deleterious nsSNPs. The importance of rs374941368 and rs200149541 in the prediction of post-translation changes was highlighted because it impacts a possible phosphorylation site. Gene-gene interactions revealed CCBE1's association with other genes, showing its role in a number of pathways and coexpressions. The top 16 deleterious nsSNPs found in this research should be investigated further in the future while researching diseases caused CCBE1 gene specifically HS. The FT web server predicted amino acid residues involved in the ligand-binding site of the CCBE1 protein, and two of the substitutions (R167W and T153N) were found to be involved. These highly deleterious nsSNPs can be used as marker pathogenic variants in the mutational diagnosis of the HS syndrome, and this research also offers potential insights that will aid in the development of precision medicines. CCBE1 proteins from Hennekam syndrome patients should be tested in animal models for this purpose.

AbsoluRATE: An in-silico method to predict the aggregation kinetics of native proteins

Protein aggregation has two aspects, namely, mechanistic and kinetics. Understanding protein aggregation kinetics is critical for prediction of progression of diseases caused by amyloidosis, accumulation of aggregates in biotherapeutics during storage and engineering commercial nano-biomaterials. In this work, we have collected experimentally determined absolute protein aggregation rates and developed an SVM based regression model to predict absolute rates of protein and peptide aggregation near-physiological conditions. The regression model achieved a correlation coefficient of 0.72 with MAE of 0.91 (natural log of k_app, where k_app is in hour^-1) using leave-one-out cross-validation on a dataset of 82 non-redundant proteins/peptides. The model accounts for the experimental conditions (such as temperature, pH, ionic and protein concentration) and sequence-based properties. The amino acid sequence features revealed by this model as being important for aggregation kinetics, are also associated with the aggregation mechanism. In particular, inherent aggregation propensity of the protein/peptide sequence and number of aggregation prone regions (APRs) unpunctuated by the gatekeeping residues, were found to play important roles in the prediction of the absolute aggregation rates. This analysis shows that mechanism and kinetics of protein aggregation are coupled via common sequence attributes. The aggregation kinetic prediction method developed in this work is available at https://web.iitm.ac.in/bioinfo2/absolurate-pred/index.html.

Recent Applications of Deep Learning Methods on Evolution- and Contact-Based Protein Structure Prediction

The new advances in deep learning methods have influenced many aspects of scientific research, including the study of the protein system. The prediction of proteins' 3D structural components is now heavily dependent on machine learning techniques that interpret how protein sequences and their homology govern the inter-residue contacts and structural organization. Especially, methods employing deep neural networks have had a significant impact on recent CASP13 and CASP14 competition. Here, we explore the recent applications of deep learning methods in the protein structure prediction area. We also look at the potential opportunities for deep learning methods to identify unknown protein structures and functions to be discovered and help guide drug-target interactions. Although significant problems still need to be addressed, we expect these techniques in the near future to play crucial roles in protein structural bioinformatics as well as in drug discovery.

Molecular characterization and expression analysis of ribosomal L18/L5e gene in Pennisetum glaucum (L.) R. Br

Ribosomal L18/L5e (RL18/L5e) is a member of the ribosomal L18/L5e protein family, which has an essential function in translation of mRNA into protein in the large ribosomal subunit. In this study, RL18/L5e was isolated and sequenced from local Pennisetum glaucum (L.) R. Br. cultivar which is known to adapt to environmental stress. The obtained cDNA for PgRL18/L5e was 699 bp in length, with an open reading frame of 564 bp. The deduced protein sequence contained 187 amino acids and comprised an RL18/L5e domain, which shared high sequence identity with orthologous proteins from Viridiplantae. The obtained PgRL18/L5e cDNA contained two exons of 154 and 545 bp, respectively, and an intron of 1398 bp. Secondary and 3D structures of the deduced PgRL18/L5e protein were predicted using in silico tools. Phylogenetic analysis showed close relationships between the PgRL18/L5e protein and its orthologs from monocot species. Multiple sequence alignment showed high identity in the RL18/L5e domain sequence in all orthologous proteins in Viridiplantae. Moreover, all orthologous RL18/L5e proteins shared the same domain architecture and were nearly equal in length. Quantitative real-time PCR indicated a higher transcript abundance of PgRL18/L5e in shoots than in roots of 3-day-old seedlings. Moreover, the expression of PgRL18/L5e in seedlings under cold and drought stress was substantially lower than that in untreated seedlings, whereas the highest expression was shown under heat stress. This study provides insights into the structure and function of the RL18/L5e gene in tolerant crops, which could facilitate the understanding of the role of the various plant ribosomal proteins in adaptation to extreme environments.

Representation learning applications in biological sequence analysis

Although remarkable advances have been reported in high-throughput sequencing, the ability to aptly analyze a substantial amount of rapidly generated biological (DNA/RNA/protein) sequencing data remains a critical hurdle. To tackle this issue, the application of natural language processing (NLP) to biological sequence analysis has received increased attention. In this method, biological sequences are regarded as sentences while the single nucleic acids/amino acids or k-mers in these sequences represent the words. Embedding is an essential step in NLP, which performs the conversion of these words into vectors. Specifically, representation learning is an approach used for this transformation process, which can be applied to biological sequences. Vectorized biological sequences can then be applied for function and structure estimation, or as input for other probabilistic models. Considering the importance and growing trend for the application of representation learning to biological research, in the present study, we have reviewed the existing knowledge in representation learning for biological sequence analysis.

Remodelling structure-based drug design using machine learning

To keep up with the pace of rapid discoveries in biomedicine, a plethora of research endeavors had been directed toward Rational Drug Development that slowly gave way to Structure-Based Drug Design (SBDD). In the past few decades, SBDD played a stupendous role in identification of novel drug-like molecules that are capable of altering the structures and/or functions of the target macromolecules involved in different disease pathways and networks. Unfortunately, post-delivery drug failures due to adverse drug interactions have constrained the use of SBDD in biomedical applications. However, recent technological advancements, along with parallel surge in clinical research have led to the concomitant establishment of other powerful computational techniques such as Artificial Intelligence (AI) and Machine Learning (ML). These leading-edge tools with the ability to successfully predict side-effects of a wide range of drugs have eventually taken over the field of drug design. ML, a subset of AI, is a robust computational tool that is capable of data analysis and analytical model building with minimal human intervention. It is based on powerful algorithms that use huge sets of 'training data' as inputs to predict new output values, which improve iteratively through experience. In this review, along with a brief discussion on the evolution of the drug discovery process, we have focused on the methodologies pertaining to the technological advancements of machine learning. This review, with specific examples, also emphasises the tremendous contributions of ML in the field of biomedicine, while exploring possibilities for future developments.

SPOT-1D-Single: Improving the Single-Sequence-Based Prediction of Protein Secondary Structure, Backbone Angles, Solvent Accessibility and Half-Sphere Exposures using a Large Training Set and Ensembled Deep Learning

MOTIVATION

Knowing protein secondary and other one-dimensional structural properties are essential for accurate protein structure and function prediction. As a result, many methods have been developed for predicting these one-dimensional structural properties. However, most methods relied on evolutionary information that may not exist for many proteins due to a lack of sequence homologs. Moreover, it is computationally intensive for obtaining evolutionary information as the library of protein sequences continues to expand exponentially. Here we developed a new single-sequence method called SPOT-1D-Single based on a large training dataset of 39120 proteins deposited prior to 2016 and an ensemble of hybrid Long-Short-Term-Memory bidirectional neural network and convolutional neural network.

RESULTS

We showed that SPOT-1D-Single consistently improves over SPIDER3-Single and ProteinUnet for secondary structure, solvent accessibility, contact number, and backbone angles prediction for all seven independent test sets (TEST2018, SPOT-2016, SPOT-2016-HQ, SPOT-2018, SPOT-2018-HQ, CASP12, and CASP13 free-modeling targets). For example, the predicted three-state secondary structure's accuracy ranges from 72.12-74.28% by SPOT-1D-Single, compared to 69.1-72.6% by SPIDER3-Single and 70.6-73% by ProteinUnet. SPOT-1D-Single also predicts SS3 and SS8 with 6.24% and 6.98% better accuracy than SPOT-1D on SPOT-2018 proteins with no homologs (Neff=1), respectively. The new method's improvement over existing techniques is due to a larger training set combined with ensembled learning.

AVAILABILITY

Standalone-version of SPOT-1D-Single is available at https://github.com/jas-preet/SPOT-1D-Single. Direct prediction can also be made at https://sparks-lab.org/server/spot-1d-single. The datasets used in this research can also be downloaded from GitHub.

Antimicrobial Activities of LL-37 Fragment Mutant-Poly (Lactic-Co-Glycolic) Acid Conjugate against Staphylococcus aureus, Escherichia coli, and Candida albicans

Various peptides and their derivatives have been reported to exhibit antimicrobial activities. Although these activities have been examined against microorganisms, novel methods have recently emerged for conjugation of the biomaterials to improve their activities. Here, we prepared CKR12-PLGA, in which CKR12 (a mutated fragment of human cathelicidin peptide, LL-37) was conjugated with poly (lactic-co-glycolic) acid (PLGA), and compared the antimicrobial and antifungal activities of the conjugated peptide with those of FK13 (a small fragment of LL-37) and CKR12 alone. The prepared CKR12-PLGA was characterized by dynamic light scattering and measurement of the zeta potential, critical micellar concentration, and antimicrobial activities of the fragments and conjugate. Although CKR12 showed higher antibacterial activities than FK13 against Staphylococcus aureus and Escherichia coli, the antifungal activity of CKR12 was lower than that of FK13. CKR12-PLGA showed higher antibacterial activities against S. aureus and E. coli and higher antifungal activity against Candida albicans compared to those of FK13. Additionally, CKR12-PLGA showed no hemolytic activity in erythrocytes, and scanning and transmission electron microscopy suggested that CKR12-PLGA killed and disrupted the surface structure of microbial cells. Conjugation of antimicrobial peptide fragment analogues was a successful approach for obtaining increased microbial activity with minimized cytotoxicity.

Uncovering genetic mechanisms of hypertension through multi-omic analysis of the kidney

The kidney is an organ of key relevance to blood pressure (BP) regulation, hypertension and antihypertensive treatment. However, genetically mediated renal mechanisms underlying susceptibility to hypertension remain poorly understood. We integrated genotype, gene expression, alternative splicing and DNA methylation profiles of up to 430 human kidneys to characterize the effects of BP index variants from genome-wide association studies (GWASs) on renal transcriptome and epigenome. We uncovered kidney targets for 479 (58.3%) BP-GWAS variants and paired 49 BP-GWAS kidney genes with 210 licensed drugs. Our colocalization and Mendelian randomization analyses identified 179 unique kidney genes with evidence of putatively causal effects on BP. Through Mendelian randomization, we also uncovered effects of BP on renal outcomes commonly affecting patients with hypertension. Collectively, our studies identified genetic variants, kidney genes, molecular mechanisms and biological pathways of key relevance to the genetic regulation of BP and inherited susceptibility to hypertension.

Interrogating the Role of the Two Distinct Fructose-Bisphosphate Aldolases of Bacillus methanolicus by Site-Directed Mutagenesis of Key Amino Acids and Gene Repression by CRISPR Interference

The Gram-positive Bacillus methanolicus shows plasmid-dependent methylotrophy. This facultative ribulose monophosphate (RuMP) cycle methylotroph possesses two fructose bisphosphate aldolases (FBA) with distinct kinetic properties. The chromosomally encoded FBA^C is the major glycolytic aldolase. The gene for the major gluconeogenic aldolase FBA^P is found on the natural plasmid pBM19 and is induced during methylotrophic growth. The crystal structures of both enzymes were solved at 2.2 Å and 2.0 Å, respectively, and they suggested amino acid residue 51 to be crucial for binding fructose-1,6-bisphosphate (FBP) as substrate and amino acid residue 140 for active site zinc atom coordination. As FBA^C and FBA^P differed at these positions, site-directed mutagenesis (SDM) was performed to exchange one or both amino acid residues of the respective proteins. The aldol cleavage reaction was negatively affected by the amino acid exchanges that led to a complete loss of glycolytic activity of FBA^P. However, both FBA^C and FBA^P maintained gluconeogenic aldol condensation activity, and the amino acid exchanges improved the catalytic efficiency of the major glycolytic aldolase FBA^C in gluconeogenic direction at least 3-fold. These results confirmed the importance of the structural differences between FBA^C and FBA^P concerning their distinct enzymatic properties. In order to investigate the physiological roles of both aldolases, the expression of their genes was repressed individually by CRISPR interference (CRISPRi). The fba ^C RNA levels were reduced by CRISPRi, but concomitantly the fba ^P RNA levels were increased. Vice versa, a similar compensatory increase of the fba ^C RNA levels was observed when fba ^P was repressed by CRISPRi. In addition, targeting fba ^P decreased tkt ^P RNA levels since both genes are cotranscribed in a bicistronic operon. However, reduced tkt ^P RNA levels were not compensated for by increased RNA levels of the chromosomal transketolase gene tkt ^C.

Computational and SAXS-based structure insights of pectin acetyl esterase (CtPae12B) of family 12 carbohydrate esterase from Clostridium thermocellum ATCC 27405

Pectin is a complex form of polysaccharide and is composed of several structural components that require the concerted action of several pectinases for its complete degradation. In this study, in silico and solution structure of a pectin acetyl esterase (CtPae12B) of family 12 carbohydrate esterase (CE12) from Clostridium thermocellum was determined. The CtPae12B modelled structure, showed a new α/β hydrolase fold, similar to the fold found in the crystal structures of its nearest homologues from CE12 family, which differed from α/β hydrolase fold found in glycoside hydrolases. In the active site of CtPae12B, two loops (loop1 and loop6) play an important role in the formation of a catalytic triad Ser15-Asp187-His190, where Ser15 acts as a nucleophile. The structural stability of CtPae12B and its catalytic site was detected by performing molecular dynamic (MD) simulation which showed stable and compact conformation of the structure. Molecular docking method was employed to analyse the conformations of various suitable ligands docked at the active site of CtPae12B. The stability and structural specificity of the catalytic residues with the ligand, 4-nitrophenyl acetate (4-NPA) was confirmed by MD simulation of CtPae12B-4NPA docked complex. Moreover, it was found that the nucleophile Ser15, forms hydrophobic interaction with 4-NPA in the active site to complete covalent catalysis. Small angle X-ray scattering analysis of CtPae12B at 3 mg/mL displayed elongated, compact and monodispersed nature in solution. The ab initio derived dummy model showed that CtPae12B exists as a homotrimer at 3 mg/mL which was also confirmed by dynamic light scattering.Communicated by Ramaswamy H. Sarma.

Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences

In the field of artificial intelligence, a combination of scale in data and model capacity enabled by unsupervised learning has led to major advances in representation learning and statistical generation. In the life sciences, the anticipated growth of sequencing promises unprecedented data on natural sequence diversity. Protein language modeling at the scale of evolution is a logical step toward predictive and generative artificial intelligence for biology. To this end, we use unsupervised learning to train a deep contextual language model on 86 billion amino acids across 250 million protein sequences spanning evolutionary diversity. The resulting model contains information about biological properties in its representations. The representations are learned from sequence data alone. The learned representation space has a multiscale organization reflecting structure from the level of biochemical properties of amino acids to remote homology of proteins. Information about secondary and tertiary structure is encoded in the representations and can be identified by linear projections. Representation learning produces features that generalize across a range of applications, enabling state-of-the-art supervised prediction of mutational effect and secondary structure and improving state-of-the-art features for long-range contact prediction.

Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences

In the field of artificial intelligence, a combination of scale in data and model capacity enabled by unsupervised learning has led to major advances in representation learning and statistical generation. In the life sciences, the anticipated growth of sequencing promises unprecedented data on natural sequence diversity. Protein language modeling at the scale of evolution is a logical step toward predictive and generative artificial intelligence for biology. To this end, we use unsupervised learning to train a deep contextual language model on 86 billion amino acids across 250 million protein sequences spanning evolutionary diversity. The resulting model contains information about biological properties in its representations. The representations are learned from sequence data alone. The learned representation space has a multiscale organization reflecting structure from the level of biochemical properties of amino acids to remote homology of proteins. Information about secondary and tertiary structure is encoded in the representations and can be identified by linear projections. Representation learning produces features that generalize across a range of applications, enabling state-of-the-art supervised prediction of mutational effect and secondary structure and improving state-of-the-art features for long-range contact prediction.

Structure analysis of deleterious nsSNPs in human PALB2 protein for functional inference

Partner and Localizer of BRCA2 or PALB2 is a typical tumor suppressor protein, that responds to DNA double stranded breaks through homologous recombination repair. Heterozygous mutations in PALB2 are known to contribute to the susceptibility of breast and ovarian cancer. However, there is no comprehensive study characterizing the structural and functional impacts of SNPs located in the PALB2 gene. Therefore, it is of interest to document a comprehensive analysis of coding and non-coding SNPs located at the PALB2 loci using in silico tools. The data for 1455 non-synonymous SNPs (nsSNPs) located in the PALB2 loci were retrieved from the dbSNP database. Comprehensive characterization of the SNPs using a combination of in silico tools such as SIFT, PROVEAN, PolyPhen, PANTHER, PhD-SNP, Pmut, MutPred 2.0 and SNAP-2, identified 28 functionally important SNPs. Among these, 16 nsSNPs were further selected for structural analysis using conservation profile and protein stability. The most deleterious nsSNPs were documented within the WD40 domain of PALB2. A general outline of the structural consequences of each variant was developed using the HOPE project data. These 16 mutant structures were further modelled using SWISS Model and three most damaging mutant models (rs78179744, rs180177123 and rs45525135) were identified. The non-coding SNPs in the 3' UTR region of the PALB2 gene were analyzed for altered miRNA target sites. The comprehensive characterization of the coding and non-coding SNPs in the PALB2 locus has provided a list of damaging SNPs with potential disease association. Further validation through genetic association study will reveal their clinical significance.

Recent Advances in the Prediction of Protein Structural Classes: Feature Descriptors and Machine Learning Algorithms

In the postgenomic age, rapid growth in the number of sequence-known proteins has been accompanied by much slower growth in the number of structure-known proteins (as a result of experimental limitations), and a widening gap between the two is evident. Because protein function is linked to protein structure, successful prediction of protein structure is of significant importance in protein function identification. Foreknowledge of protein structural class can help improve protein structure prediction with significant medical and pharmaceutical implications. Thus, a fast, suitable, reliable, and reasonable computational method for protein structural class prediction has become pivotal in bioinformatics. Here, we review recent efforts in protein structural class prediction from protein sequence, with particular attention paid to new feature descriptors, which extract information from protein sequence, and the use of machine learning algorithms in both feature selection and the construction of new classification models. These new feature descriptors include amino acid composition, sequence order, physicochemical properties, multiprofile Bayes, and secondary structure-based features. Machine learning methods, such as artificial neural networks (ANNs), support vector machine (SVM), K-nearest neighbor (KNN), random forest, deep learning, and examples of their application are discussed in detail. We also present our view on possible future directions, challenges, and opportunities for the applications of machine learning algorithms for prediction of protein structural classes.

The consensus Nglyco -X-S/T motif and a previously unknown Nglyco -N-linked glycosylation are necessary for growth and pathogenicity of Phytophthora

Asparagine (Asn, N)-linked glycosylation within N_glyco -X-S/T; X ≠ P motif is a ubiquitously distributed post-translational modification that participates in diverse cellular processes. In this work, N-glycosylation inhibitor was shown to prevent Phytophthora sojae growth, suggesting that N-glycosylation is necessary for oomycete development. We conducted a glycoproteomic analysis of P. sojae to identify and map N-glycosylated proteins and to quantify differentially expressed glycoproteins associated with mycelia, asexual cyst, and sexual oospore developmental stages. A total of 355 N-glycosylated proteins was found, containing 496 glycosites, potentially involved in glycan degradation, carbon metabolism, glycolysis, or other metabolic pathways. Through PNGase F deglycosylation assays and site-directed mutagenesis of a GPI transamidase protein (GPI16) upregulated in cysts and a heat shock protein 70 (HSP70) upregulated in oospores, we demonstrated that both proteins were N-glycosylated and that the N_glyco -N motif is a target site for asparagine - oligosaccharide linkage. Glycosite mutations of Asn 94 N_glyco -X-S/T in the GPI16 led to impaired cyst germination and pathogenicity, while mutation of the previously unknown Asn 270 N_glyco -N motif in HSP70 led to decreased oospore production. In addition to providing a map of the oomycete N-glycoproteome, this work confirms that P. sojae has evolved multiple N-glycosylation motifs essential for growth.

Design of a Peptide-Carrier Vaccine Based on the Highly Immunogenic Fasciola hepatica Leucine Aminopeptidase

Many studies have shown that the degree of organization and repetitiveness of an antigen correlates with its efficiency to induce a B-cell response and production of neutralizing antibodies. Here we describe the design of a chimeric protein based on the hexamer form of the highly immunogenic Fasciola hepatica leucine aminopeptidase as a carrier system of small peptides with potential use as a multiepitope vaccine.

Proteomic Profiling of Lysine Acetylation Indicates Mitochondrial Dysfunction in the Hippocampus of Gut Microbiota-Absent Mice

Major depressive disorder (MDD) is a leading cause of disability around the world and contributes greatly to the global burden of disease. Mounting evidence suggests that gut microbiota dysbiosis may be involved in the pathophysiology of MDD through the microbiota–gut–brain axis. Recent research suggests that epigenetic modifications might relate to depression. However, our knowledge of the role of epigenetics in host–microbe interactions remains limited. In the present study, we used a combination of affinity enrichment and high-resolution liquid chromatography tandem mass spectrometry analysis to identify hippocampal acetylated proteins in germ-free and specific pathogen-free mice. In total, 986 lysine acetylation sites in 543 proteins were identified, of which 747 sites in 427 proteins were quantified. Motif analysis identified several conserved sequences surrounding the acetylation sites, including D^∗Kac, DKac, KacY, KacD, and D^∗∗Kac. Gene ontology annotations revealed that these differentially expressed acetylated proteins were involved in multiple biological functions and were mainly located in mitochondria. In addition, pathway enrichment analysis demonstrated that oxidative phosphorylation and the tricarboxylic acid cycle II (eukaryotic), both of which are exclusively localized to the mitochondria, were the primarily disturbed functions. Taken together, this study indicates that lysine acetylation alterations may play a pivotal role in mitochondrial dysfunction and may be a mechanism by which gut microbiota regulate brain function and behavioral phenotypes.

Implementation of computational approaches to explore the deleterious effects of non-synonymous SNPs on pRB protein

Retinoblastoma 1 (RB1) is the first discovered tumor suppressor gene and recognized as the simple model system whose encoded defective protein can cause a pediatric cancer retinoblastoma. It functions as a negative regulator of the cell cycle through the interactions with members of the E2F transcription factors family. The protein of the RB1 gene (pRB) is engaged in various cell cycle processes including apoptosis, cell cycle arrest and chromatin remodeling. Recent studies on Retinoblastoma also exhibited multiple sets of point mutation in the associated protein due to its large polymorphic information in the local database. In this study, we identified the list of disease associated non-synonymous single nucleotide polymorphisms (nsSNPs) in RB1 by incorporating different computational algorithms, web servers, modeling of the mutants and finally superimposing it. Out of 826 nsSNPs, W516G and W563G were predicted to be highly deleterious variants in the conserved regions and found to have an impact on protein structure and protein-protein interaction. Moreover, our study concludes the effect of W516G variant was more detrimental in destabilizing protein's nature as compared to W563G variant. We also found defective binding of pRB having W516G mutation with E2F2 protein. Findings of this study will aid in shortening of the expensive experimental cost of identifying disease associated SNPs in retinoblastoma for which specialized personalized treatment or therapy can be formulated.Communicated by Ramaswamy H. Sarma.

Dromedary camels as a natural source of neutralizing nanobodies against SARS-CoV-2

The development of prophylactic and therapeutic agents for coronavirus disease 2019 (COVID-19) is a current global health priority. Here, we investigated the presence of cross-neutralizing antibodies against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in dromedary camels that were Middle East respiratory syndrome coronavirus (MERS-CoV) seropositive but MERS-CoV free. The tested 229 dromedaries had anti–MERS-CoV camel antibodies with variable cross-reactivity patterns against SARS-CoV-2 proteins, including the S trimer and M, N, and E proteins. Using SARS-CoV-2 competitive immunofluorescence immunoassays and pseudovirus neutralization assays, we found medium-to-high titers of cross-neutralizing antibodies against SARS-CoV-2 in these animals. Through linear B cell epitope mapping using phage immunoprecipitation sequencing and a SARS-CoV-2 peptide/proteome microarray, we identified a large repertoire of Betacoronavirus cross-reactive antibody specificities in these dromedaries and demonstrated that the SARS-CoV-2–specific VHH antibody repertoire is qualitatively diverse. This analysis revealed not only several SARS-CoV-2 epitopes that are highly immunogenic in humans, including a neutralizing epitope, but also epitopes exclusively targeted by camel antibodies. The identified SARS-CoV-2 cross-neutralizing camel antibodies are not proposed as a potential treatment for COVID-19. Rather, their presence in nonimmunized camels supports the development of SARS-CoV-2 hyperimmune camels, which could be a prominent source of therapeutic agents for the prevention and treatment of COVID-19.

Computational prediction of the effects of non-synonymous single nucleotide polymorphisms on the GPI-anchor transamidase subunit GPI8p of Plasmodium falciparum

Drug resistance is increasingly evolving in malaria parasites; hence, it is important to discover and establish alternative drug targets. In this context, GPI-anchor transamidase (GPI-T) is a potential drug target primarily of its crucial role in the development and survival of the parasite in the GPI anchor biosynthesis pathway. The present investigation was undertaken to explore the plausible effects of nsSNP on the structure and functions of GPI-T subunit GPI8p of Plasmodium falciparum. The GPI8p (PF3D7_1128700) was analyzed using various sequence-based and structure-based computational tools such as SIFT, PROVEAN, PredictSNP, SNAP2, I-Mutant, MuPro, ConSurf, NetSurfP, MUSTER, COACH server and STRING server. Of the 34 nsSNPs submitted for functional analysis, 18 nsSNPs (R124 L, N143 K, Y145 F, V157I, T195S, K379E, I392 K, I437 T, Y438H, N439D, Y441H, N442D, N448D, N451D, D457A, D457Y, I458 L and N460 K) were predicted to have deleterious effects on the protein GPI8p. Additionally, I-Mutant 2.0 and MuPro both showed a decrease in stability after mutation as a result of these nsSNPs, suggesting the destabilization of protein. ConSurf findings suggest that most of the regions were highly conserved. In addition, COACH server was used to predict the ligand binding sites. It was found that no mutation was present at the predicted ligand binding site. The results of the STRING database showed that the protein GPI8p interacts with those proteins which either involve the biosynthetic process of attaching GPI anchor to protein or GPI anchor. The present study suggested that the GPI8p could be a novel target for anti-malarial drugs, which provides significant details for further experimentation.

Proteome-Wide Analysis of Lysine 2-Hydroxyisobutyrylated Proteins in Fusarium oxysporum

Protein lysine 2-hydroxyisobutyrylation (K _hib ), a new type of post-translational modification, occurs in histones and non-histone proteins and plays an important role in almost all aspects of both eukaryotic and prokaryotic living cells. Fusarium oxysporum, a soil-borne fungal pathogen, can cause disease in more than 150 plants. However, little is currently known about the functions of K _hib in this plant pathogenic fungus. Here, we report a systematic analysis of 2-hydroxyisobutyrylated proteins in F. oxysporum. In this study, 3782 K _hib sites in 1299 proteins were identified in F. oxysporum. The bioinformatics analysis showed that 2-hydroxyisobutyrylated proteins are involved in different biological processes and functions and are located in diverse subcellular localizations. The enrichment analysis revealed that K _hib participates in a variety of pathways, including the ribosome, oxidative phosphorylation, and proteasome pathways. The protein interaction network analysis showed that 2-hydroxyisobutyrylated protein complexes are involved in diverse interactions. Notably, several 2-hydroxyisobutyrylated proteins, including three kinds of protein kinases, were involved in the virulence or conidiation of F. oxysporum, suggesting that K _hib plays regulatory roles in pathogenesis. Moreover, our study shows that there are different K _hib levels of F. oxysporum in conidial and mycelial stages. These findings provide evidence of K _hib in F. oxysporum, an important filamentous plant pathogenic fungus, and serve as a resource for further exploration of the potential functions of K _hib in Fusarium species and other filamentous pathogenic fungi.

Structural determinants of cholesterol recognition in helical integral membrane proteins

Cholesterol is an integral component of mammalian membranes. It has been shown to modulate membrane fluidity and dynamics and alter integral membrane protein function. However, understanding the molecular mechanisms of how cholesterol impacts protein function is complicated by limited and conflicting structural data. Because of the nature of the crystallization and cryo-EM structure determination, it is difficult to distinguish between specific and biologically relevant interactions and a nonspecific association. The only widely recognized search algorithm for cholesterol-integral-membrane-protein interaction sites is sequence based, i.e., searching for the so-called "Cholesterol Recognition/interaction Amino acid Consensus" motif. Although these motifs are present in numerous integral membrane proteins, there is inconclusive evidence to support their necessity or sufficiency for cholesterol binding. Here, we leverage the increasing number of experimental cholesterol-integral-membrane-protein structures to systematically analyze putative interaction sites based on their spatial arrangement and evolutionary conservation. This analysis creates three-dimensional representations of general cholesterol interaction sites that form clusters across multiple integral membrane protein classes. We also classify cholesterol-integral-membrane-protein interaction sites as either likely-specific or nonspecific. Information gleaned from our characterization will eventually enable a structure-based approach to predict and design cholesterol-integral-membrane-protein interaction sites.

Evolution and Diversity of Semaphorins and Plexins in Choanoflagellates

Semaphorins and plexins are cell surface ligand/receptor proteins that affect cytoskeletal dynamics in metazoan cells. Interestingly, they are also present in Choanoflagellata, a class of unicellular heterotrophic flagellates that forms the phylogenetic sister group to Metazoa. Several members of choanoflagellates are capable of forming transient colonies, whereas others reside solitary inside exoskeletons; their molecular diversity is only beginning to emerge. Here, we surveyed genomics data from 22 choanoflagellate species and detected semaphorin/plexin pairs in 16 species. Choanoflagellate semaphorins (Sema-FN1) contain several domain features distinct from metazoan semaphorins, including an N-terminal Reeler domain that may facilitate dimer stabilization, an array of fibronectin type III domains, a variable serine/threonine-rich domain that is a potential site for O-linked glycosylation, and a SEA domain that can undergo autoproteolysis. In contrast, choanoflagellate plexins (Plexin-1) harbor a domain arrangement that is largely identical to metazoan plexins. Both Sema-FN1 and Plexin-1 also contain a short homologous motif near the C-terminus, likely associated with a shared function. Three-dimensional molecular models revealed a highly conserved structural architecture of choanoflagellate Plexin-1 as compared to metazoan plexins, including similar predicted conformational changes in a segment that is involved in the activation of the intracellular Ras-GAP domain. The absence of semaphorins and plexins in several choanoflagellate species did not appear to correlate with unicellular versus colonial lifestyle or ecological factors such as fresh versus salt water environment. Together, our findings support a conserved mechanism of semaphorin/plexin proteins in regulating cytoskeletal dynamics in unicellular and multicellular organisms.

Comprehensive Study on Enhancing Low-Quality Position-Specific Scoring Matrix with Deep Learning for Accurate Protein Structure Property Prediction: Using Bagging Multiple Sequence Alignment Learning

Accurate predictions of protein structure properties, for example, secondary structure and solvent accessibility, are essential in analyzing the structure and function of a protein. Position-specific scoring matrix (PSSM) features are widely used in the structure property prediction. However, some proteins may have low-quality PSSM features due to insufficient homologous sequences, leading to limited prediction accuracy. To address this limitation, we propose an enhancing scheme for PSSM features. We introduce the "Bagging MSA" (multiple sequence alignment) method to calculate PSSM features used to train our model, adopt a convolutional network to capture local context features and bidirectional long short-term memory for long-term dependencies, and integrate them under an unsupervised framework. Structure property prediction models are then built upon such enhanced PSSM features for more accurate predictions. Moreover, we develop two frameworks to evaluate the effectiveness of the enhanced PSSM features, which also bring proposed method into real-world scenarios. Empirical evaluation of CB513, CASP11, and CASP12 data sets indicates that our unsupervised enhancing scheme indeed generates more informative PSSM features for structure property prediction.

SAINT: self-attention augmented inception-inside-inception network improves protein secondary structure prediction

MOTIVATION

Protein structures provide basic insight into how they can interact with other proteins, their functions and biological roles in an organism. Experimental methods (e.g. X-ray crystallography and nuclear magnetic resonance spectroscopy) for predicting the secondary structure (SS) of proteins are very expensive and time consuming. Therefore, developing efficient computational approaches for predicting the SS of protein is of utmost importance. Advances in developing highly accurate SS prediction methods have mostly been focused on 3-class (Q3) structure prediction. However, 8-class (Q8) resolution of SS contains more useful information and is much more challenging than the Q3 prediction.

RESULTS

We present SAINT, a highly accurate method for Q8 structure prediction, which incorporates self-attention mechanism (a concept from natural language processing) with the Deep Inception-Inside-Inception network in order to effectively capture both the short- and long-range interactions among the amino acid residues. SAINT offers a more interpretable framework than the typical black-box deep neural network methods. Through an extensive evaluation study, we report the performance of SAINT in comparison with the existing best methods on a collection of benchmark datasets, namely, TEST2016, TEST2018, CASP12 and CASP13. Our results suggest that self-attention mechanism improves the prediction accuracy and outperforms the existing best alternate methods. SAINT is the first of its kind and offers the best known Q8 accuracy. Thus, we believe SAINT represents a major step toward the accurate and reliable prediction of SSs of proteins.

AVAILABILITY AND IMPLEMENTATION

SAINT is freely available as an open-source project at https://github.com/SAINTProtein/SAINT.

Synergistic role of nucleotides and lipids for the self-assembly of Shs1 septin oligomers

Budding yeast septins are essential for cell division and polarity. Septins assemble as palindromic linear octameric complexes. The function and ultra-structural organization of septins are finely governed by their molecular polymorphism. In particular, in budding yeast, the end subunit can stand either as Shs1 or Cdc11. We have dissected, here, for the first time, the behavior of the Shs1 protomer bound to membranes at nanometer resolution, in complex with the other septins. Using electron microscopy, we have shown that on membranes, Shs1 protomers self-assemble into rings, bundles, filaments or two-dimensional gauzes. Using a set of specific mutants we have demonstrated a synergistic role of both nucleotides and lipids for the organization and oligomerization of budding yeast septins. Besides, cryo-electron tomography assays show that vesicles are deformed by the interaction between Shs1 oligomers and lipids. The Shs1-Shs1 interface is stabilized by the presence of phosphoinositides, allowing the visualization of micrometric long filaments formed by Shs1 protomers. In addition, molecular modeling experiments have revealed a potential molecular mechanism regarding the selectivity of septin subunits for phosphoinositide lipids.

Dimerisation of the PICTS complex via LC8/Cut-up drives co-transcriptional transposon silencing in Drosophila

In animal gonads, the PIWI-interacting RNA (piRNA) pathway guards genome integrity in part through the co-transcriptional gene silencing of transposon insertions. In Drosophila ovaries, piRNA-loaded Piwi detects nascent transposon transcripts and instructs heterochromatin formation through the Panoramix-induced co-transcriptional silencing (PICTS) complex, containing Panoramix, Nxf2 and Nxt1. Here, we report that the highly conserved dynein light chain LC8/Cut-up (Ctp) is an essential component of the PICTS complex. Loss of Ctp results in transposon de-repression and a reduction in repressive chromatin marks specifically at transposon loci. In turn, Ctp can enforce transcriptional silencing when artificially recruited to RNA and DNA reporters. We show that Ctp drives dimerisation of the PICTS complex through its interaction with conserved motifs within Panoramix. Artificial dimerisation of Panoramix bypasses the necessity for its interaction with Ctp, demonstrating that conscription of a protein from a ubiquitous cellular machinery has fulfilled a fundamental requirement for a transposon silencing complex.

Molecular characterization of coat color gene in Sahiwal versus Karan Fries bovine

BACKGROUND

Melanocortin-1-receptor gene (MC1R) plays a significant role in signaling cascade of melanin production. In cattle, the coat colors, such as red and black, are an outcome of eumelanin and pheomelanin pigments, respectively. The coat colors have become critical factors in the animal selection process. This study is therefore aimed at the molecular characterization of reddish-brown coat-colored Sahiwal cattle in comparison to the black and white-colored Karan Fries.

RESULTS

The Sequence length of the MC1R gene was 954 base pairs in Sahiwal cattle. The sequences were examined and submitted to GenBank Acc.No. MG373575 to MG373605. Alignment of both (Sahiwal and Karan Fries) protein sequences by applying ClustalO multiple sequence alignment programs revealed 99.8-96.8% sequence similarity within the bovine. MC1R gene phylogenetic studies were analyzed by MEGA X. The gene MC1R tree, protein confines, and hereditary difference of cattle were derived from Ensemble Asia Cow Genome Browser 97. One unique single-nucleotide polymorphism (c.844C>A) (SNP) was distinguished. Single amino acid changes were detected in the seventh transmembrane structural helix region, with SNP at p.281 T>N of MC1R gene in Karan Fries cattle.

CONCLUSIONS

In this current research, we first distinguished the genomic sequence of the MC1R gene regions that showed evidence of coat variation between Indian indigenous Sahiwal cattle breed correlated with crossbreed Karan Fries. These variations were found in the Melanocortin 1 receptor coding regions of the diverse SNPs. The conclusions of this research provide new insights into understanding the coat color variation in crossbreed compared to the Indian Sahiwal cattle.

iCysMod: an integrative database for protein cysteine modifications in eukaryotes

As important post-translational modifications, protein cysteine modifications (PCMs) occurring at cysteine thiol group play critical roles in the regulation of various biological processes in eukaryotes. Due to the rapid advancement of high-throughput proteomics technologies, a large number of PCM events have been identified but remain to be curated. Thus, an integrated resource of eukaryotic PCMs will be useful for the research community. In this work, we developed an integrative database for protein cysteine modifications in eukaryotes (iCysMod), which curated and hosted 108 030 PCM events for 85 747 experimentally identified sites on 31 483 proteins from 48 eukaryotes for 8 types of PCMs, including oxidation, S-nitrosylation (-SNO), S-glutathionylation (-SSG), disulfide formation (-SSR), S-sulfhydration (-SSH), S-sulfenylation (-SOH), S-sulfinylation (-SO2H) and S-palmitoylation (-S-palm). Then, browse and search options were provided for accessing the dataset, while various detailed information about the PCM events was well organized for visualization. With human dataset in iCysMod, the sequence features around the cysteine modification sites for each PCM type were analyzed, and the results indicated that various types of PCMs presented distinct sequence recognition preferences. Moreover, different PCMs can crosstalk with each other to synergistically orchestrate specific biological processes, and 37 841 PCM events involved in 119 types of PCM co-occurrences at the same cysteine residues were finally obtained. Taken together, we anticipate that the database of iCysMod would provide a useful resource for eukaryotic PCMs to facilitate related researches, while the online service is freely available at http://icysmod.omicsbio.info.

Solvent Accessibility of Residues Undergoing Pathogenic Variations in Humans: From Protein Structures to Protein Sequences

Solvent accessibility (SASA) is a key feature of proteins for determining their folding and stability. SASA is computed from protein structures with different algorithms, and from protein sequences with machine-learning based approaches trained on solved structures. Here we ask the question as to which extent solvent exposure of residues can be associated to the pathogenicity of the variation. By this, SASA of the wild-type residue acquires a role in the context of functional annotation of protein single-residue variations (SRVs). By mapping variations on a curated database of human protein structures, we found that residues targeted by disease related SRVs are less accessible to solvent than residues involved in polymorphisms. The disease association is not evenly distributed among the different residue types: SRVs targeting glycine, tryptophan, tyrosine, and cysteine are more frequently disease associated than others. For all residues, the proportion of disease related SRVs largely increases when the wild-type residue is buried and decreases when it is exposed. The extent of the increase depends on the residue type. With the aid of an in house developed predictor, based on a deep learning procedure and performing at the state-of-the-art, we are able to confirm the above tendency by analyzing a large data set of residues subjected to variations and occurring in some 12,494 human protein sequences still lacking three-dimensional structure (derived from HUMSAVAR). Our data support the notion that surface accessible area is a distinguished property of residues that undergo variation and that pathogenicity is more frequently associated to the buried property than to the exposed one.

Diverse viral proteases activate the NLRP1 inflammasome

The NLRP1 inflammasome is a multiprotein complex that is a potent activator of inflammation. Mouse NLRP1B can be activated through proteolytic cleavage by the bacterial Lethal Toxin (LeTx) protease, resulting in degradation of the N-terminal domains of NLRP1B and liberation of the bioactive C-terminal domain, which includes the caspase activation and recruitment domain (CARD). However, natural pathogen-derived effectors that can activate human NLRP1 have remained unknown. Here, we use an evolutionary model to identify several proteases from diverse picornaviruses that cleave human NLRP1 within a rapidly evolving region of the protein, leading to host-specific and virus-specific activation of the NLRP1 inflammasome. Our work demonstrates that NLRP1 acts as a 'tripwire' to recognize the enzymatic function of a wide range of viral proteases and suggests that host mimicry of viral polyprotein cleavage sites can be an evolutionary strategy to activate a robust inflammatory immune response.

The immune system recognizes disease-causing microbes, such as bacteria and viruses, and removes them from the body before they can cause harm. When the immune system first detects these foreign invaders, a multi-part structure known as the inflammasome launches an inflammatory response to help fight the microbes off. Several sensor proteins can activate the inflammasome, including one in mice called NLRP1B. This protein has evolved a specialized site that can be cut by a bacterial toxin. Once cleaved, this region acts like a biological tripwire and sparks NLRP1B into action, allowing the sensor to activate the inflammasome system.

Humans have a similar protein called NLRP1, but it is unclear whether this protein has also evolved a tripwire region that can sense microbial proteins. To answer this question, Tsu, Beierschmitt et al. set out to find whether NLRP1 can be activated by viruses in the Picornaviridae family, which are responsible for diseases like polio, hepatitis A, and the common cold. This revealed that NLRP1 contains a cleavage site for enzymes produced by some, but not all, of the viruses in the picornavirus family. Further experiments confirmed that when a picornavirus enzyme cuts through this region during a viral infection, it triggers NLRP1 to activate the inflammasome and initiate an immune response.

The enzymes from different viruses were also found to cleave human NLRP1 at different sites, and the protein’s susceptibility to cleavage varied between different animal species. For instance, Tsu, Beierschmitt et al. discovered that NLRP1B in mice is also able to sense picornaviruses, and that different enzymes activate and cleave NLRP1B and NLRP1 to varying degrees: this affected how well the two proteins are expected to be able to sense specific viral infections. This variation suggests that there is an ongoing evolutionary arms-race between viral proteins and the immune system: as viral proteins change and new ones emerge, NLRP1 rapidly evolves new tripwire sites that allow it to sense the infection and launch an inflammatory response.

What happens when NLRP1B activates the inflammasome during a viral infection is still an open question. The discovery that mouse NLRP1B shares features with human NLRP1 could allow the development of animal models to study the role of the tripwire in antiviral defenses and the overactive inflammation associated with some viral infections. Understanding the types of viruses that activate the NLRP1 inflammasome, and the outcomes of the resulting immune response, may have implications for future treatments of viral infections.