FFAS-3D: improving fold recognition by including optimized structural features and template re-ranking

Biochemical, kinetic, and structural characterization of a Bacillus tequilensis nitroreductase

Nitroreductases (NRs) are NAD(P)H-dependent flavoenzymes that reduce nitro aromatic compounds to their corresponding arylamines via the nitroso and hydroxylamine intermediates. Because of their broad substrate scope and versatility, NRs have found application in multiple fields such as biocatalysis, bioremediation, cell-imaging and prodrug activation. However, only a limited number of members of the broad NR superfamily (> 24 000 sequences) have been experimentally characterized. Within this group of enzymes, only few are capable of amine synthesis, which is a fundamental chemical transformation for the pharmaceutical, agricultural, and textile industries. Herein, we provide a comprehensive description of a recently discovered NR from Bacillus tequilensis, named BtNR. This enzyme has previously been demonstrated to have the capability to fully convert nitro aromatic and heterocyclic compounds to their respective primary amines. In this study, we determined its biochemical, kinetic and structural properties, including its apparent melting temperature (Tm) of 59 °C, broad pH activity range (from pH 3 to 10) and a notably low redox potential (-236 ± 1 mV) in comparison to other well-known NRs. We also determined its steady-state and pre-steady-state kinetic parameters, which are consistent with other NRs. Additionally, we elucidated the crystal structure of BtNR, which resembles the well-characterized Escherichia coli oxygen-insensitive NAD(P)H nitroreductase (NfsB), and investigated the substrate binding in its active site through docking and molecular dynamics studies with four nitro aromatic substrates. Guided by these structural analyses, we probed the functional roles of active site residues by site-directed mutagenesis. Our findings provide valuable insights into the biochemical and structural properties of BtNR, as well as its potential applications in biotechnology.

Structural and Functional Characterization of Obesumbacterium proteus Phytase: A Comprehensive In-Silico Study

Phytate, also known as myoinositol hexakisphosphate, exhibits anti-nutritional properties and possesses a negative environmental impact. Phytase enzymes break down phytate, showing potential in various industries, necessitating thorough biochemical and computational characterizations. The present study focuses on Obesumbacterium proteus phytase (OPP), indicating its similarities with known phytases and its potential through computational analyses. Structure, functional, and docking results shed light on OPP's features, structural stability, strong and stable interaction, and dynamic conformation, with flexible sidechains that could adapt to different temperatures or specific functions. Root Mean Square fluctuation (RMSF) highlighted fluctuating regions in OPP, indicating potential sites for stability enhancement through mutagenesis. The systematic approach developed here could aid in enhancing enzyme properties via a rational engineering approach. Computational analysis expedites enzyme discovery and engineering, complementing the traditional biochemical methods to accelerate the quest for superior enzymes for industrial applications.

Recent Progress of Protein Tertiary Structure Prediction

The prediction of three-dimensional (3D) protein structure from amino acid sequences has stood as a significant challenge in computational and structural bioinformatics for decades. Recently, the widespread integration of artificial intelligence (AI) algorithms has substantially expedited advancements in protein structure prediction, yielding numerous significant milestones. In particular, the end-to-end deep learning method AlphaFold2 has facilitated the rise of structure prediction performance to new heights, regularly competitive with experimental structures in the 14th Critical Assessment of Protein Structure Prediction (CASP14). To provide a comprehensive understanding and guide future research in the field of protein structure prediction for researchers, this review describes various methodologies, assessments, and databases in protein structure prediction, including traditionally used protein structure prediction methods, such as template-based modeling (TBM) and template-free modeling (FM) approaches; recently developed deep learning-based methods, such as contact/distance-guided methods, end-to-end folding methods, and protein language model (PLM)-based methods; multi-domain protein structure prediction methods; the CASP experiments and related assessments; and the recently released AlphaFold Protein Structure Database (AlphaFold DB). We discuss their advantages, disadvantages, and application scopes, aiming to provide researchers with insights through which to understand the limitations, contexts, and effective selections of protein structure prediction methods in protein-related fields.

Protein thermal sensing regulates physiological amyloid aggregation

To survive, cells must respond to changing environmental conditions. One way that eukaryotic cells react to harsh stimuli is by forming physiological, RNA-seeded subnuclear condensates, termed amyloid bodies (A-bodies). The molecular constituents of A-bodies induced by different stressors vary significantly, suggesting this pathway can tailor the cellular response by selectively aggregating a subset of proteins under a given condition. Here, we identify critical structural elements that regulate heat shock-specific amyloid aggregation. Our data demonstrates that manipulating structural pockets in constituent proteins can either induce or restrict their A-body targeting at elevated temperatures. We propose a model where selective aggregation within A-bodies is mediated by the thermal stability of a protein, with temperature-sensitive structural regions acting as an intrinsic form of post-translational regulation. This system would provide cells with a rapid and stress-specific response mechanism, to tightly control physiological amyloid aggregation or other cellular stress response pathways.

Integrating deep learning, threading alignments, and a multi-MSA strategy for high-quality protein monomer and complex structure prediction in CASP15

We report the results of the "UM-TBM" and "Zheng" groups in CASP15 for protein monomer and complex structure prediction. These prediction sets were obtained using the D-I-TASSER and DMFold-Multimer algorithms, respectively. For monomer structure prediction, D-I-TASSER introduced four new features during CASP15: (i) a multiple sequence alignment (MSA) generation protocol that combines multi-source MSA searching and a structural modeling-based MSA ranker; (ii) attention-network based spatial restraints; (iii) a multi-domain module containing domain partition and arrangement for domain-level templates and spatial restraints; (iv) an optimized I-TASSER-based folding simulation system for full-length model creation guided by a combination of deep learning restraints, threading alignments, and knowledge-based potentials. For 47 free modeling targets in CASP15, the final models predicted by D-I-TASSER showed average TM-score 19% higher than the standard AlphaFold2 program. We thus showed that traditional Monte Carlo-based folding simulations, when appropriately coupled with deep learning algorithms, can generate models with improved accuracy over end-to-end deep learning methods alone. For protein complex structure prediction, DMFold-Multimer generated models by integrating a new MSA generation algorithm (DeepMSA2) with the end-to-end modeling module from AlphaFold2-Multimer. For the 38 complex targets, DMFold-Multimer generated models with an average TM-score of 0.83 and Interface Contact Score of 0.60, both significantly higher than those of competing complex prediction tools. Our analyses on complexes highlighted the critical role played by MSA generating, ranking, and pairing in protein complex structure prediction. We also discuss future room for improvement in the areas of viral protein modeling and complex model ranking.

Homology Modeling in the Twilight Zone: Improved Accuracy by Sequence Space Analysis

The analysis of the relationship between sequence and structure similarities during the evolution of a protein family has revealed a limit of sequence divergence for which structural conservation can be confidently assumed and homology modeling is reliable. Below this limit, the twilight zone corresponds to sequence divergence for which homology modeling becomes increasingly difficult and requires specific methods. Either with conventional threading methods or with recent deep learning methods, such as AlphaFold, the challenge relies on the identification of a template that shares not only a common ancestor (homology) but also a conserved structure with the query. As both homology and structural conservation are transitive properties, mining of sequence databases followed by multidimensional scaling (MDS) of the query sequence space can reveal intermediary sequences to infer homology and structural conservation between the query and the template. Here, as a case study, we studied the plethodontid receptivity factor isoform 1 (PRF1) from Plethodon jordani, a member of a pheromone protein family present only in lungless salamanders and weakly related to cytokines of the IL6 family. A variety of conventional threading methods led to the cytokine CNTF as a template. Sequence mining, followed by phylogenetic and MDS analysis, provided missing links between PRF1 and CNTF and allowed reliable homology modeling. In addition, we compared automated models obtained from web servers to a customized model to show how modeling can be improved by expert information.

Pan-kinome of Legionella expanded by a bioinformatics survey

The pathogenic Legionella bacteria are notorious for delivering numerous effector proteins into the host cell with the aim of disturbing and hijacking cellular processes for their benefit. Despite intensive studies, many effectors remain uncharacterized. Motivated by the richness of Legionella effector repertoires and their oftentimes atypical biochemistry, also by several known atypical Legionella effector kinases and pseudokinases discovered recently, we undertook an in silico survey and exploration of the pan-kinome of the Legionella genus, i.e., the union of the kinomes of individual species. In this study, we discovered 13 novel (pseudo)kinase families (all are potential effectors) with the use of non-standard bioinformatic approaches. Together with 16 known families, we present a catalog of effector and non-effector protein kinase-like families within Legionella, available at http://bioinfo.sggw.edu.pl/kintaro/ . We analyze and discuss the likely functional roles of the novel predicted kinases. Notably, some of the kinase families are also present in other bacterial taxa, including other pathogens, often phylogenetically very distant from Legionella. This work highlights Nature's ingeniousness in the pathogen-host arms race and offers a useful resource for the study of infection mechanisms.

Methods for discovering catalytic activities for pseudokinases

Pseudoenzymes resemble active enzymes, but lack key catalytic residues believed to be required for activity. Many pseudoenzymes appear to be inactive in conventional enzyme assays. However, an alternative explanation for their apparent lack of activity is that pseudoenzymes are being assayed for the wrong reaction. We have discovered several new protein kinase-like families which have revealed how different binding orientations of adenosine triphosphate (ATP) and active site residue migration can generate a novel reaction from a common kinase scaffold. These results have exposed the catalytic versatility of the protein kinase fold and suggest that atypical kinases and pseudokinases should be analyzed for alternative transferase activities. In this chapter, we discuss a general approach for bioinformatically identifying divergent or atypical members of an enzyme superfamily, then present an experimental approach to characterize their catalytic activity.

SelfAT-Fold: Protein Fold Recognition Based on Residue-Based and Motif-Based Self-Attention Networks

The protein fold recognition is a fundamental and crucial step of tertiary structure determination. In this regard, several computational predictors have been proposed. Recently, the predictive performance has been obviously improved by the fold-specific features generated by deep learning techniques. However, these methods failed to measure the global associations among residues or motifs along the protein sequences. Furthermore, these deep learning techniques are often treated as black boxes without interpretability. Inspired by the similarities between protein sequences and natural language sentences, we applied the self-attention mechanism derived from natural language processing (NLP) field to protein fold recognition. The motif-based self-attention network (MSAN) and the residue-based self-attention network (RSAN) were constructed based on a training set to capture the global associations among the structure motifs and residues along the protein sequences, respectively. The fold-specific attention features trained and generated from the training set were then combined with Support Vector Machines (SVMs) to predict the samples in the widely used LE benchmark dataset, which is fully independent from the training set. Experimental results showed that the proposed two SelfAT-Fold predictors outperformed 34 existing state-of-the-art computational predictors. The two SelfAT-Fold predictors were further tested on an independent dataset SCOP_TEST, and they can achieve stable performance. Furthermore, the fold-specific attention features can be used to analyse the characteristics of protein folds. The trained models and data of SelfAT-Fold can be downloaded from http://bliulab.net/selfAT_fold/.

LOMETS3: integrating deep learning and profile alignment for advanced protein template recognition and function annotation

Deep learning techniques have significantly advanced the field of protein structure prediction. LOMETS3 (https://zhanglab.ccmb.med.umich.edu/LOMETS/) is a new generation meta-server approach to template-based protein structure prediction and function annotation, which integrates newly developed deep learning threading methods. For the first time, we have extended LOMETS3 to handle multi-domain proteins and to construct full-length models with gradient-based optimizations. Starting from a FASTA-formatted sequence, LOMETS3 performs four steps of domain boundary prediction, domain-level template identification, full-length template/model assembly and structure-based function prediction. The output of LOMETS3 contains (i) top-ranked templates from LOMETS3 and its component threading programs, (ii) up to 5 full-length structure models constructed by L-BFGS (limited-memory Broyden–Fletcher–Goldfarb–Shanno algorithm) optimization, (iii) the 10 closest Protein Data Bank (PDB) structures to the target, (iv) structure-based functional predictions, (v) domain partition and assembly results, and (vi) the domain-level threading results, including items (i)–(iii) for each identified domain. LOMETS3 was tested in large-scale benchmarks and the blind CASP14 (14th Critical Assessment of Structure Prediction) experiment, where the overall template recognition and function prediction accuracy is significantly beyond its predecessors and other state-of-the-art threading approaches, especially for hard targets without homologous templates in the PDB. Based on the improved developments, LOMETS3 should help significantly advance the capability of broader biomedical community for template-based protein structure and function modelling.

Profiles of Natural and Designed Protein-Like Sequences Effectively Bridge Protein Sequence Gaps: Implications in Distant Homology Detection

Sequence-based approaches are fundamental to guide experimental investigations in obtaining structural and/or functional insights into uncharacterized protein families. Powerful profile-based sequence search methods rely on a sequence space continuum to identify non-trivial relationships through homology detection. The computational design of protein-like sequences that serve as "artificial linkers" is useful in identifying relationships between distant members of a structural fold. Such sequences act as intermediates and guide homology searches between distantly related proteins. Here, we describe an approach that represents natural intermediate sequences and designed protein-like sequences as HMM (Hidden Markov Models) profiles, to improve the sensitivity of existing search methods. Searches made within the "Profile database" were shown to recognize the parent structural fold for 90% of the search queries at query coverage better than 60%. For 1040 protein families with no available structure, fold associations were made through searches in the database of natural and designed sequence profiles. Most of the associations were made with the Alpha-alpha superhelix, Transmembrane beta-barrels, TIM barrel, and Immunoglobulin-like beta-sandwich folds. For 11 domain families of unknown functions, we provide confident fold associations using the profiles of designed sequences and a consensus from other fold recognition methods. For two DUFs (Domain families of Unknown Functions), we performed detailed functional annotation through comparisons with characterized templates of families of known function.

Decoding the link of microbiome niches with homologous sequences enables accurately targeted protein structure prediction

Information derived from metagenome sequences through deep-learning techniques has significantly improved the accuracy of template free protein structure modeling. However, most of the deep learning-based modeling studies are based on blind sequence database searches and suffer from low efficiency in computational resource utilization and model construction, especially when the sequence library becomes prohibitively large. We proposed a MetaSource model built on 4.25 billion microbiome sequences from four major biomes (Gut, Lake, Soil, and Fermentor) to decode the inherent linkage of microbial niches with protein homologous families. Large-scale protein family folding experiments on 8,700 unknown Pfam families showed that a microbiome targeted approach with multiple sequence alignment constructed from individual MetaSource biomes requires more than threefold less computer memory and CPU (central processing unit) time but generates contact-map and three-dimensional structure models with a significantly higher accuracy, compared with that using combined metagenome datasets. These results demonstrate an avenue to bridge the gap between the rapidly increasing metagenome databases and the limited computing resources for efficient genome-wide database mining, which provides a useful bluebook to guide future microbiome sequence database and modeling development for high-accuracy protein structure and function prediction.

Protein Fold Recognition Based on Auto-Weighted Multi-View Graph Embedding Learning Model

Protein fold recognition is critical for studies of the protein structure prediction and drug design. Several methods have been proposed to obtain discriminative features from the protein sequences for fold recognition. However, the ensemble methods that combine the various features to improve predictive performance remain the challenge problems. In this study, we proposed two novel algorithms: AWMG and EMfold. AWMG used a novel predictor based on the multi-view learning framework for fold recognition. Each view was treated as the intermediate representation of the corresponding data source of proteins, including the evolutionary information and the retrieval information. AWMG calculated the auto-weight for each view respectively and constructed the latent subspace which contains the common information shared by different views. The marginalized constraint was employed to enlarge the margins between different folds, improving the predictive performance of AWMG. Furthermore, we proposed a novel ensemble method called EMfold, which combines two complementary methods AWMG and DeepSS. The later method was a template-based algorithm using the SPARKS-X and DeepFR programs. EMfold integrated the advantages of template-based assignment and machine learning classifier. Experimental results on the two widely datasets (LE and YK) showed that the proposed methods outperformed some state-of-the-art methods, indicating that AWMG and EMfold are useful tools for protein fold recognition.

Molecular modelling of coat protein of the Groundnut bud necrosis tospovirus and its binding with Squalene as an antiviral agent: In vitro and in silico docking investigations

Bud blight disease caused by groundnut bud necrosis virus (GBNV) is a serious constraint in the cultivation of agricultural crops such as legumes, tomato, chilies, potato, cotton etc. Owing to the significant damage caused by GBNV, an attempt was made to identify suitable organic antiviral agents through molecular modelling of the nucleocapsid Coat Protein of GBNV; molecular docking and molecular dynamics that disclosed the interaction of the ligands viz., Squalene and Ganoderic acid-A with coat protein of GBNV. Invitro inhibitory effect of Squalene and Ganoderic acid-A was examined in comparison with different concentrations, against GBNV in cowpea plants under glasshouse condition. The different concentrations of Squalene (50, 100, 150, 250 and 500 ppm) tested in vitro resulted in reduction of lesion numbers (1.69 cm²) as well as reduced virus titre in co-inoculation spray. The present study suggests the antiviral activity of Squalene by effectively fitting into binding site of coat protein of GBNV with favourable hydrophilic as well as strong hydrophobic interactions thereby challenging and blocking the binding of viral replication RNA with coat protein and propagation. The present organic antiviral molecules will be helpful in development of suitable eco-friendly formulations to mitigate GBNV infection disease in plants.

Protein Fold Recognition by Combining Support Vector Machines and Pairwise Sequence Similarity Scores

Protein fold recognition is one of the most essential steps for protein structure prediction, aiming to classify proteins into known protein folds. There are two main computational approaches: one is the template-based method based on the alignment scores between query-template protein pairs and the other is the machine learning method based on the feature representation and classifier. These two approaches have their own advantages and disadvantages. Can we combine these methods to establish more accurate predictors for protein fold recognition? In this study, we made an initial attempt and proposed two novel algorithms: TSVM-fold and ESVM-fold. TSVM-fold was based on the Support Vector Machines (SVMs), which utilizes a set of pairwise sequence similarity scores generated by three complementary template-based methods, including HHblits, SPARKS-X, and DeepFR. These scores measured the global relationships between query sequences and templates. The comprehensive features of the attributes of the sequences were fed into the SVMs for the prediction. Then the TSVM-fold was further combined with the HHblits algorithm so as to improve its generalization ability. The combined method is called ESVM-fold. Experimental results in two rigorous benchmark datasets (LE and YK datasets) showed that the proposed methods outperform some state-of-the-art methods, indicating that the TSVM-fold and ESVM-fold are efficient predictors for protein fold recognition.

Catalytic and structural properties of ATP-dependent caprolactamase from Pseudomonas jessenii

Caprolactamase is the first enzyme in the caprolactam degradation pathway of Pseudomonas jessenii. It is composed of two subunits (CapA and CapB) and sequence-related to other ATP-dependent enzymes involved in lactam hydrolysis, like 5-oxoprolinases and hydantoinases. Low sequence similarity also exists with ATP-dependent acetone- and acetophenone carboxylases. The caprolactamase was produced in Escherichia coli, isolated by His-tag affinity chromatography, and subjected to functional and structural studies. Activity toward caprolactam required ATP and was dependent on the presence of bicarbonate in the assay buffer. The hydrolysis product was identified as 6-aminocaproic acid. Quantum mechanical modeling indicated that the hydrolysis of caprolactam was highly disfavored (ΔG0 '= 23 kJ/mol), which explained the ATP dependence. A crystal structure showed that the enzyme exists as an (αβ)2 tetramer and revealed an ATP-binding site in CapA and a Zn-coordinating site in CapB. Mutations in the ATP-binding site of CapA (D11A and D295A) significantly reduced product formation. Mutants with substitutions in the metal binding site of CapB (D41A, H99A, D101A, and H124A) were inactive and less thermostable than the wild-type enzyme. These residues proved to be essential for activity and on basis of the experimental findings we propose possible mechanisms for ATP-dependent lactam hydrolysis.

An updated classification and mechanistic insights into ligand binding of the substrate-binding proteins

Substrate-binding proteins (SBPs) mediate ligand translocation and have been classified into seven clusters (A-G). Although the substrate specificities of these clusters are known to some extent, their ligand-binding mechanism(s) remain(s) incompletely understood. In this study, the list of SBPs belonging to different clusters was updated (764 SBPs) compared to the previously reported study (504 SBPs). Furthermore, a new cluster referred to as cluster H was identified. Results reveal that SBPs follow different ligand-binding mechanisms. Intriguingly, the majority of the SBPs follow the "one domain movement" rather than the well-known "Venus Fly-trap" mechanism. Moreover, SBPs of a few clusters display subdomain conformational movement rather than the complete movement of the N- and C-terminal domains.

TMEM106B in humans and Vac7 and Tag1 in yeast are predicted to be lipid transfer proteins

TMEM106B is an integral membrane protein of late endosomes and lysosomes involved in neuronal function, its overexpression being associated with familial frontotemporal lobar degeneration, and point mutation linked to hypomyelination. It has also been identified in multiple screens for host proteins required for productive SARS-CoV-2 infection. Because standard approaches to understand TMEM106B at the sequence level find no homology to other proteins, it has remained a protein of unknown function. Here, the standard tool PSI-BLAST was used in a nonstandard way to show that the lumenal portion of TMEM106B is a member of the late embryogenesis abundant-2 (LEA-2) domain superfamily. More sensitive tools (HMMER, HHpred, and trRosetta) extended this to predict LEA-2 domains in two yeast proteins. One is Vac7, a regulator of PI(3,5)P₂ production in the degradative vacuole, equivalent to the lysosome, which has a LEA-2 domain in its lumenal domain. The other is Tag1, another vacuolar protein, which signals to terminate autophagy and has three LEA-2 domains in its lumenal domain. Further analysis of LEA-2 structures indicated that LEA-2 domains have a long, conserved lipid-binding groove. This implies that TMEM106B, Vac7, and Tag1 may all be lipid transfer proteins in the lumen of late endocytic organelles.

Protein structure prediction using deep learning distance and hydrogen-bonding restraints in CASP14

In this article, we report 3D structure prediction results by two of our best server groups ("Zhang-Server" and "QUARK") in CASP14. These two servers were built based on the D-I-TASSER and D-QUARK algorithms, which integrated four newly developed components into the classical protein folding pipelines, I-TASSER and QUARK, respectively. The new components include: (a) a new multiple sequence alignment (MSA) collection tool, DeepMSA2, which is extended from the DeepMSA program; (b) a contact-based domain boundary prediction algorithm, FUpred, to detect protein domain boundaries; (c) a residual convolutional neural network-based method, DeepPotential, to predict multiple spatial restraints by co-evolutionary features derived from the MSA; and (d) optimized spatial restraint energy potentials to guide the structure assembly simulations. For 37 FM targets, the average TM-scores of the first models produced by D-I-TASSER and D-QUARK were 96% and 112% higher than those constructed by I-TASSER and QUARK, respectively. The data analysis indicates noticeable improvements produced by each of the four new components, especially for the newly added spatial restraints from DeepPotential and the well-tuned force field that combines spatial restraints, threading templates, and generic knowledge-based potentials. However, challenges still exist in the current pipelines. These include difficulties in modeling multi-domain proteins due to low accuracy in inter-domain distance prediction and modeling protein domains from oligomer complexes, as the co-evolutionary analysis cannot distinguish inter-chain and intra-chain distances. Specifically tuning the deep learning-based predictors for multi-domain targets and protein complexes may be helpful to address these issues.

Folding non-homologous proteins by coupling deep-learning contact maps with I-TASSER assembly simulations

Structure prediction for proteins lacking homologous templates in the Protein Data Bank (PDB) remains a significant unsolved problem. We developed a protocol, C-I-TASSER, to integrate interresidue contact maps from deep neural-network learning with the cutting-edge I-TASSER fragment assembly simulations. Large-scale benchmark tests showed that C-I-TASSER can fold more than twice the number of non-homologous proteins than the I-TASSER, which does not use contacts. When applied to a folding experiment on 8,266 unsolved Pfam families, C-I-TASSER successfully folded 4,162 domain families, including 504 folds that are not found in the PDB. Furthermore, it created correct folds for 85% of proteins in the SARS-CoV-2 genome, despite the quick mutation rate of the virus and sparse sequence profiles. The results demonstrated the critical importance of coupling whole-genome and metagenome-based evolutionary information with optimal structure assembly simulations for solving the problem of non-homologous protein structure prediction.

MULTICOM2 open-source protein structure prediction system powered by deep learning and distance prediction

Protein structure prediction is an important problem in bioinformatics and has been studied for decades. However, there are still few open-source comprehensive protein structure prediction packages publicly available in the field. In this paper, we present our latest open-source protein tertiary structure prediction system—MULTICOM2, an integration of template-based modeling (TBM) and template-free modeling (FM) methods. The template-based modeling uses sequence alignment tools with deep multiple sequence alignments to search for structural templates, which are much faster and more accurate than MULTICOM1. The template-free (ab initio or de novo) modeling uses the inter-residue distances predicted by DeepDist to reconstruct tertiary structure models without using any known structure as template. In the blind CASP14 experiment, the average TM-score of the models predicted by our server predictor based on the MULTICOM2 system is 0.720 for 58 TBM (regular) domains and 0.514 for 38 FM and FM/TBM (hard) domains, indicating that MULTICOM2 is capable of predicting good tertiary structures across the board. It can predict the correct fold for 76 CASP14 domains (95% regular domains and 55% hard domains) if only one prediction is made for a domain. The success rate is increased to 3% for both regular and hard domains if five predictions are made per domain. Moreover, the prediction accuracy of the pure template-free structure modeling method on both TBM and FM targets is very close to the combination of template-based and template-free modeling methods. This demonstrates that the distance-based template-free modeling method powered by deep learning can largely replace the traditional template-based modeling method even on TBM targets that TBM methods used to dominate and therefore provides a uniform structure modeling approach to any protein. Finally, on the 38 CASP14 FM and FM/TBM hard domains, MULTICOM2 server predictors (MULTICOM-HYBRID, MULTICOM-DEEP, MULTICOM-DIST) were ranked among the top 20 automated server predictors in the CASP14 experiment. After combining multiple predictors from the same research group as one entry, MULTICOM-HYBRID was ranked no. 5. The source code of MULTICOM2 is freely available at https://github.com/multicom-toolbox/multicom/tree/multicom_v2.0.

FoldRec-C2C: protein fold recognition by combining cluster-to-cluster model and protein similarity network

As a key for studying the protein structures, protein fold recognition is playing an important role in predicting the protein structures associated with COVID-19 and other important structures. However, the existing computational predictors only focus on the protein pairwise similarity or the similarity between two groups of proteins from 2-folds. However, the homology relationship among proteins is in a hierarchical structure. The global protein similarity network will contribute to the performance improvement. In this study, we proposed a predictor called FoldRec-C2C to globally incorporate the interactions among proteins into the prediction. For the FoldRec-C2C predictor, protein fold recognition problem is treated as an information retrieval task in nature language processing. The initial ranking results were generated by a surprised ranking algorithm Learning to Rank, and then three re-ranking algorithms were performed on the ranking lists to adjust the results globally based on the protein similarity network, including seq-to-seq model, seq-to-cluster model and cluster-to-cluster model (C2C). When tested on a widely used and rigorous benchmark dataset LINDAHL dataset, FoldRec-C2C outperforms other 34 state-of-the-art methods in this field. The source code and data of FoldRec-C2C can be downloaded from http://bliulab.net/FoldRec-C2C/download.

Dynamic remodeling of host membranes by self-organizing bacterial effectors

During infection, intracellular bacterial pathogens translocate a variety of effectors into host cells that modify host membrane trafficking for their benefit. We found a self-organizing system consisting of a bacterial phosphoinositide kinase and its opposing phosphatase that formed spatiotemporal patterns, including traveling waves, to remodel host cellular membranes. The Legionella effector MavQ, a phosphatidylinositol (PI) 3-kinase, was targeted to the endoplasmic reticulum (ER). MavQ and the Legionella PI 3-phosphatase SidP, even in the absence of other bacterial components, drove rapid PI 3-phosphate turnover on the ER and spontaneously formed traveling waves that spread along ER subdomains inducing vesicle and tubule budding. Thus, bacteria can exploit a self-organizing membrane-targeting mechanism to hijack host cellular structures for survival.

Novel antimicrobial anionic cecropins from the spruce budworm feature a poly-L-aspartic acid C-terminus

Cecropins form a family of amphipathic α-helical cationic peptides with broad-spectrum antibacterial properties and potent anticancer activity. The emergence of bacteria and cancer cells showing resistance to cationic antimicrobial peptides (CAMPs) has fostered a search for new, more selective and more effective alternatives to CAMPs. With this goal in mind, we looked for cecropin homologs in the genome and transcriptome of the spruce budworm, Choristoneura fumiferana. Not only did we find paralogs of the conventional cationic cecropins (Cfcec⁺ ), our screening also led to the identification of previously uncharacterized anionic cecropins (Cfcec^- ), featuring a poly-l-aspartic acid C-terminus. Comparative peptide analysis indicated that the C-terminal helix of Cfcec^- is amphipathic, unlike that of Cfcec⁺ , which is hydrophobic. Interestingly, molecular dynamics simulations pointed to the lower conformational flexibility of Cfcec^- peptides, relative to that of Cfcec⁺ . Phylogenetic analysis suggests that the evolution of distinct Cfcec⁺ and Cfcec^- peptides may have resulted from an ancient duplication event within the Lepidoptera. Finally, we found that both anionic and cationic cecropins contain a BH3-like motif (G-[KQR]-[HKQNR]-[IV]-[KQR]) that could interact with Bcl-2, a protein involved in apoptosis; this observation is congruent with previous reports indicating that cecropins induce apoptosis. Altogether, our observations suggest that cecropins may provide templates for the development of new anticancer drugs. We also estimated the antibacterial activity of Cfcec-2 and a ∆Cfce-2 peptide as AMPs by testing directly their ability in inhibiting bacterial growth in a disk diffusion assay and their potential for development of novel therapeutics.

A novel predicted ADP-ribosyltransferase-like family conserved in eukaryotic evolution

The presence of many completely uncharacterized proteins, even in well-studied organisms such as humans, seriously hampers full understanding of the functioning of the living cells. ADP-ribosylation is a common post-translational modification of proteins; also nucleic acids and small molecules can be modified by the covalent attachment of ADP-ribose. This modification, important in cellular signalling and infection processes, is usually executed by enzymes from the large superfamily of ADP-ribosyltransferases (ARTs). Here, using bioinformatics approaches, we identify a novel putative ADP-ribosyltransferase family, conserved in eukaryotic evolution, with a divergent active site. The hallmark of these proteins is the ART domain nestled between flanking leucine-rich repeat (LRR) domains. LRRs are typically involved in innate immune surveillance. The novel family appears as putative novel ADP-ribosylation-related actors, most likely pseudoenzymes. Sequence divergence and lack of clearly detectable “classical” ART active site suggests the novel domains are pseudoARTs, yet atypical ART activity, or alternative enzymatic activity cannot be excluded. We propose that this family, including its human member LRRC9, may be involved in an ancient defense mechanism, with analogies to the innate immune system, and coupling pathogen detection to ADP-ribosyltransfer or other signalling mechanisms.

A Legionella effector ADP-ribosyltransferase inactivates glutamate dehydrogenase

ADP-ribosyltransferases (ARTs) are a widespread superfamily of enzymes frequently employed in pathogenic strategies of bacteria. Legionella pneumophila, the causative agent of a severe form of pneumonia known as Legionnaire's disease, has acquired over 330 translocated effectors that showcase remarkable biochemical and structural diversity. However, the ART effectors that influence L. pneumophila have not been well defined. Here, we took a bioinformatic approach to search the Legionella effector repertoire for additional divergent members of the ART superfamily and identified an ART domain in Legionella pneumophila gene0181, which we hereafter refer to as Legionella ADP-Ribosyltransferase 1 (Lart1) (Legionella ART 1). We show that L. pneumophila Lart1 targets a specific class of 120-kDa NAD+-dependent glutamate dehydrogenase (GDH) enzymes found in fungi and protists, including many natural hosts of Legionella. Lart1 targets a conserved arginine residue in the NAD+-binding pocket of GDH, thereby blocking oxidative deamination of glutamate. Therefore, Lart1 could be the first example of a Legionella effector which directly targets a host metabolic enzyme during infection.

Phylogenetics-based identification and characterization of a superior 2,3-butanediol dehydrogenase for Zymomonas mobilis expression

BACKGROUND

Zymomonas mobilis has recently been shown to be capable of producing the valuable platform biochemical, 2,3-butanediol (2,3-BDO). Despite this capability, the production of high titers of 2,3-BDO is restricted by several physiological parameters. One such bottleneck involves the conversion of acetoin to 2,3-BDO, a step catalyzed by 2,3-butanediol dehydrogenase (Bdh). Several Bdh enzymes have been successfully expressed in Z. mobilis, although a highly active enzyme is yet to be identified for expression in this host. Here, we report the application of a phylogenetic approach to identify and characterize a superior Bdh, followed by validation of its structural attributes using a mutagenesis approach.

RESULTS

Of the 11 distinct bdh genes that were expressed in Z. mobilis, crude extracts expressing Serratia marcescens Bdh (SmBdh) were found to have the highest activity (8.89 µmol/min/mg), when compared to other Bdh enzymes (0.34-2.87 µmol/min/mg). The SmBdh crystal structure was determined through crystallization with cofactor (NAD⁺) and substrate (acetoin) molecules bound in the active site. Active SmBdh was shown to be a tetramer with the active site populated by a Gln247 residue contributed by the diagonally opposite subunit. SmBdh showed a more extensive supporting hydrogen-bond network in comparison to the other well-studied Bdh enzymes, which enables improved substrate positioning and substrate specificity. This protein also contains a short α6 helix, which provides more efficient entry and exit of molecules from the active site, thereby contributing to enhanced substrate turnover. Extending the α6 helix to mimic the lower activity Enterobacter cloacae (EcBdh) enzyme resulted in reduction of SmBdh function to nearly 3% of the total activity. In great contrast, reduction of the corresponding α6 helix of the EcBdh to mimic the SmBdh structure resulted in ~ 70% increase in its activity.

CONCLUSIONS

This study has demonstrated that SmBdh is superior to other Bdhs for expression in Z. mobilis for 2,3-BDO production. SmBdh possesses unique structural features that confer biochemical advantage to this protein. While coordinated active site formation is a unique structural characteristic of this tetrameric complex, the smaller α6 helix and extended hydrogen network contribute towards improved activity and substrate promiscuity of the enzyme.

Protein structure, amino acid composition and sequence determine proteome vulnerability to oxidation-induced damage

Oxidative stress alters cell viability, from microorganism irradiation sensitivity to human aging and neurodegeneration. Deleterious effects of protein carbonylation by reactive oxygen species (ROS) make understanding molecular properties determining ROS susceptibility essential. The radiation‐resistant bacterium Deinococcus radiodurans accumulates less carbonylation than sensitive organisms, making it a key model for deciphering properties governing oxidative stress resistance. We integrated shotgun redox proteomics, structural systems biology, and machine learning to resolve properties determining protein damage by γ‐irradiation in Escherichia coli and D. radiodurans at multiple scales. Local accessibility, charge, and lysine enrichment accurately predict ROS susceptibility. Lysine, methionine, and cysteine usage also contribute to ROS resistance of the D. radiodurans proteome. Our model predicts proteome maintenance machinery, and proteins protecting against ROS are more resistant in D. radiodurans. Our findings substantiate that protein‐intrinsic protection impacts oxidative stress resistance, identifying causal molecular properties.

Coronavirus3D: 3D structural visualization of COVID-19 genomic divergence

Motivation

As the COVID-19 pandemic is spreading around the world, the SARS-CoV-2 virus is evolving with mutations that potentially change and fine-tune functions of the proteins coded in its genome.

Results

Coronavirus3D website integrates data on the SARS-CoV-2 virus mutations with information about 3D structures of its proteins, allowing users to visually analyze the mutations in their 3D context.

Availability and implementation

Coronavirus3D server is freely available at https://coronavirus3d.org.

ProtFold-DFG: protein fold recognition by combining Directed Fusion Graph and PageRank algorithm

As one of the most important tasks in protein structure prediction, protein fold recognition has attracted more and more attention. In this regard, some computational predictors have been proposed with the development of machine learning and artificial intelligence techniques. However, these existing computational methods are still suffering from some disadvantages. In this regard, we propose a new network-based predictor called ProtFold-DFG for protein fold recognition. We propose the Directed Fusion Graph (DFG) to fuse the ranking lists generated by different methods, which employs the transitive closure to incorporate more relationships among proteins and uses the KL divergence to calculate the relationship between two proteins so as to improve its generalization ability. Finally, the PageRank algorithm is performed on the DFG to accurately recognize the protein folds by considering the global interactions among proteins in the DFG. Tested on a widely used and rigorous benchmark data set, LINDAHL dataset, experimental results show that the ProtFold-DFG outperforms the other 35 competing methods, indicating that ProtFold-DFG will be a useful method for protein fold recognition. The source code and data of ProtFold-DFG can be downloaded from http://bliulab.net/ProtFold-DFG/download.

LOMETS2: improved meta-threading server for fold-recognition and structure-based function annotation for distant-homology proteins

The LOMETS2 server (https://zhanglab.ccmb.med.umich.edu/LOMETS/) is an online meta-threading server system for template-based protein structure prediction. Although the server has been widely used by the community over the last decade, the previous LOMETS server no longer represents the state-of-the-art due to aging of the algorithms and unsatisfactory performance on distant-homology template identification. An extension of the server built on cutting-edge methods, especially techniques developed since the recent CASP experiments, is urgently needed. In this work, we report the recent advancements of the LOMETS2 server, which comprise a number of major new developments, including (i) new state-of-the-art threading programs, including contact-map-based threading approaches, (ii) deep sequence search-based sequence profile construction and (iii) a new web interface design that incorporates structure-based function annotations. Large-scale benchmark tests demonstrated that the integration of the deep profiles and new threading approaches into LOMETS2 significantly improve its structure modeling quality and template detection, where LOMETS2 detected 176% more templates with TM-scores >0.5 than the previous LOMETS server for Hard targets that lacked homologous templates. Meanwhile, the newly incorporated structure-based function prediction helps extend the usefulness of the online server to the broader biological community.

Prochlorococcus phage ferredoxin: structural characterization and electron transfer to cyanobacterial sulfite reductases

Marine cyanobacteria are infected by phages whose genomes encode ferredoxin (Fd) electron carriers. These Fds are thought to redirect the energy harvested from light to phage-encoded oxidoreductases that enhance viral fitness, but it is unclear how the biophysical properties and partner specificities of phage Fds relate to those of photosynthetic organisms. Here, results of a bioinformatics analysis using a sequence similarity network revealed that phage Fds are most closely related to cyanobacterial Fds that transfer electrons from photosystems to oxidoreductases involved in nutrient assimilation. Structural analysis of myovirus P-SSM2 Fd (pssm2-Fd), which infects the cyanobacterium Prochlorococcus marinus, revealed high levels of similarity to cyanobacterial Fds (root mean square deviations of ≤0.5 Å). Additionally, pssm2-Fd exhibited a low midpoint reduction potential (-336 mV versus a standard hydrogen electrode), similar to other photosynthetic Fds, although it had lower thermostability (T_m = 28 °C) than did many other Fds. When expressed in an Escherichia coli strain deficient in sulfite assimilation, pssm2-Fd complemented bacterial growth when coexpressed with a P. marinus sulfite reductase, revealing that pssm2-Fd can transfer electrons to a host protein involved in nutrient assimilation. The high levels of structural similarity with cyanobacterial Fds and reactivity with a host sulfite reductase suggest that phage Fds evolved to transfer electrons to cyanobacterially encoded oxidoreductases.

Association of clinical severity with FANCB variant type in Fanconi anemia

Fanconi anemia (FA) is the most common genetic cause of bone marrow failure and is caused by inherited pathogenic variants in any of 22 genes. Of these, only FANCB is X-linked. We describe a cohort of 19 children with FANCB variants, from 16 families of the International Fanconi Anemia Registry. Those with FANCB deletion or truncation demonstrate earlier-than-average onset of bone marrow failure and more severe congenital abnormalities compared with a large series of FA individuals in published reports. This reflects the indispensable role of FANCB protein in the enzymatic activation of FANCD2 monoubiquitination, an essential step in the repair of DNA interstrand crosslinks. For FANCB missense variants, more variable severity is associated with the extent of residual FANCD2 monoubiquitination activity. We used transcript analysis, genetic complementation, and biochemical reconstitution of FANCD2 monoubiquitination to determine the pathogenicity of each variant. Aberrant splicing and transcript destabilization were associated with 2 missense variants. Individuals carrying missense variants with drastically reduced FANCD2 monoubiquitination in biochemical and/or cell-based assays tended to show earlier onset of hematologic disease and shorter survival. Conversely, variants with near-normal FANCD2 monoubiquitination were associated with more favorable outcome. Our study reveals a genotype-phenotype correlation within the FA-B complementation group of FA, where severity is associated with level of residual FANCD2 monoubiquitination.

Comprehensive in silico modeling of the rice plant PRR Xa21 and its interaction with RaxX21-sY and OsSERK2

The first layer of defense that plants deploy to ward off a microbial invasion comes in the form of pattern-triggered immunity (PTI), which is initiated when the pattern-recognition receptors (PRRs) bind with the pathogen-associated molecular patterns (PAMPs) and co-receptor proteins, and transmit a defense signal. Although several plant PRRs have been discovered, very few of them have been fully characterized, and their functional parameters assessed. In this study, the 3D-model prediction of an entire plant PRR protein, Xa21, was done by implementing multiple in silico modeling techniques. Subsequently, the PAMP RaxX21-sY (sulphated RaxX21) and leucine-rich repeat (LRR) domain of the co-receptor OsSERK2 were docked with the LRR domain of Xa21. The docked complex of these three proteins formed a heterodimer that closely resembles the other crystallographic PTI complexes available. Molecular dynamics simulations and MM/PBSA calculations were applied for an in-depth analysis of the interactions between Xa21 LRR, RaxX21-sY, and OsSERK2 LRR. Arg230 and Arg185 from Xa21 LRR, Val2 and Lys15 from RaxX21-sY and Lys164 from OsSERK2 LRR were found to be the prominent residues which might contribute significantly in the formation of a heterodimer during the PTI process mediated by Xa21. Additionally, RaxX21-sY interacted much more favorably with Xa21 LRR in the presence of OsSERK2 LRR in the complex, which substantiates the necessity of the co-receptor in Xa21 mediated PTI to recognize the PAMP RaxX21-sY. However, the free energy binding calculation reveals the favorability of a heterodimer formation of PRR Xa21 and co-receptor OsSERK2 without the presence of PAMP RaxX21-sY, which validate the previous lab result.

A Legionella effector kinase is activated by host inositol hexakisphosphate

The transfer of a phosphate from ATP to a protein substrate, a modification known as protein phosphorylation, is catalyzed by protein kinases. Protein kinases play a crucial role in virtually every cellular activity. Recent studies of atypical protein kinases have highlighted the structural similarity of the kinase superfamily despite notable differences in primary amino acid sequence. Here, using a bioinformatics screen, we searched for putative protein kinases in the intracellular bacterial pathogen Legionella pneumophila and identified the type 4 secretion system effector Lpg2603 as a remote member of the protein kinase superfamily. Employing an array of biochemical and structural biology approaches, including in vitro kinase assays and isothermal titration calorimetry, we show that Lpg2603 is an active protein kinase with several atypical structural features. Importantly, we found that the eukaryote-specific host signaling molecule inositol hexakisphosphate (IP6) is required for Lpg2603 kinase activity. Crystal structures of Lpg2603 in the apo-form and when bound to IP6 revealed an active-site rearrangement that allows for ATP binding and catalysis. Our results on the structure and activity of Lpg2603 reveal a unique mode of regulation of a protein kinase, provide the first example of a bacterial kinase that requires IP6 for its activation, and may aid future work on the function of this effector during Legionella pathogenesis.

Bacterial pseudokinase catalyzes protein polyglutamylation to inhibit the SidE-family ubiquitin ligases

Enzymes with a protein kinase fold transfer phosphate from adenosine 5'-triphosphate (ATP) to substrates in a process known as phosphorylation. Here, we show that the Legionella meta-effector SidJ adopts a protein kinase fold, yet unexpectedly catalyzes protein polyglutamylation. SidJ is activated by host-cell calmodulin to polyglutamylate the SidE family of ubiquitin (Ub) ligases. Crystal structures of the SidJ-calmodulin complex reveal a protein kinase fold that catalyzes ATP-dependent isopeptide bond formation between the amino group of free glutamate and the γ-carboxyl group of an active-site glutamate in SidE. We show that SidJ polyglutamylation of SidE, and the consequent inactivation of Ub ligase activity, is required for successful Legionella replication in a viable eukaryotic host cell.

Fold-LTR-TCP: protein fold recognition based on triadic closure principle

As an important task in protein structure and function studies, protein fold recognition has attracted more and more attention. The existing computational predictors in this field treat this task as a multi-classification problem, ignoring the relationship among proteins in the dataset. However, previous studies showed that their relationship is critical for protein homology analysis. In this study, the protein fold recognition is treated as an information retrieval task. The Learning to Rank model (LTR) was employed to retrieve the query protein against the template proteins to find the template proteins in the same fold with the query protein in a supervised manner. The triadic closure principle (TCP) was performed on the ranking list generated by the LTR to improve its accuracy by considering the relationship among the query protein and the template proteins in the ranking list. Finally, a predictor called Fold-LTR-TCP was proposed. The rigorous test on the LE benchmark dataset showed that the Fold-LTR-TCP predictor achieved an accuracy of 73.2%, outperforming all the other competing methods.

MotifCNN-fold: protein fold recognition based on fold-specific features extracted by motif-based convolutional neural networks

Protein fold recognition is one of the most critical tasks to explore the structures and functions of the proteins based on their primary sequence information. The existing protein fold recognition approaches rely on features reflecting the characteristics of protein folds. However, the feature extraction methods are still the bottleneck of the performance improvement of these methods. In this paper, we proposed two new feature extraction methods called MotifCNN and MotifDCNN to extract more discriminative fold-specific features based on structural motif kernels to construct the motif-based convolutional neural networks (CNNs). The pairwise sequence similarity scores calculated based on fold-specific features are then fed into support vector machines to construct the predictor for fold recognition, and a predictor called MotifCNN-fold has been proposed. Experimental results on the benchmark dataset showed that MotifCNN-fold obviously outperformed all the other competing methods. In particular, the fold-specific features extracted by MotifCNN and MotifDCNN are more discriminative than the fold-specific features extracted by other deep learning techniques, indicating that incorporating the structural motifs into the CNN is able to capture the characteristics of protein folds.

DeepSVM-fold: protein fold recognition by combining support vector machines and pairwise sequence similarity scores generated by deep learning networks

Protein fold recognition is critical for studying the structures and functions of proteins. The existing protein fold recognition approaches failed to efficiently calculate the pairwise sequence similarity scores of the proteins in the same fold sharing low sequence similarities. Furthermore, the existing feature vectorization strategies are not able to measure the global relationships among proteins from different protein folds. In this article, we proposed a new computational predictor called DeepSVM-fold for protein fold recognition by introducing a new feature vector based on the pairwise sequence similarity scores calculated from the fold-specific features extracted by deep learning networks. The feature vectors are then fed into a support vector machine to construct the predictor. Experimental results on the benchmark dataset (LE) show that DeepSVM-fold obviously outperforms all the other competing methods.

I-TASSER gateway: A protein structure and function prediction server powered by XSEDE

There is an increasing gap between the number of known protein sequences and the number of proteins with experimentally characterized structure and function. To alleviate this issue, we have developed the I-TASSER gateway, an online server for automated and reliable protein structure and function prediction. For a given sequence, I-TASSER starts with template recognition from a known structure library, followed by full-length atomic model construction by iterative assembly simulations of the continuous structural fragments excised from the template alignments. Functional insights are then derived from comparative matching of the predicted model with a library of proteins with known function. The I-TASSER pipeline has been recently integrated with the XSEDE Gateway system to accommodate pressing demand from the user community and increasing computing costs. This report summarizes the configuration of the I-TASSER Gateway with the XSEDE-Comet supercomputer cluster, together with an overview of the I-TASSER method and milestones of its development.

A Bacterial Effector Mimics a Host HSP90 Client to Undermine Immunity

The molecular chaperone HSP90 facilitates the folding of several client proteins, including innate immune receptors and protein kinases. HSP90 is an essential component of plant and animal immunity, yet pathogenic strategies that directly target the chaperone have not been described. Here, we identify the HopBF1 family of bacterial effectors as eukaryotic-specific HSP90 protein kinases. HopBF1 adopts a minimal protein kinase fold that is recognized by HSP90 as a host client. As a result, HopBF1 phosphorylates HSP90 to completely inhibit the chaperone's ATPase activity. We demonstrate that phosphorylation of HSP90 prevents activation of immune receptors that trigger the hypersensitive response in plants. Consequently, HopBF1-dependent phosphorylation of HSP90 is sufficient to induce severe disease symptoms in plants infected with the bacterial pathogen, Pseudomonas syringae. Collectively, our results uncover a family of bacterial effector kinases with toxin-like properties and reveal a previously unrecognized betrayal mechanism by which bacterial pathogens modulate host immunity.

Deciphering the three-domain architecture in schlafens and the structures and roles of human schlafen12 and serpinB12 in transcriptional regulation

Schlafen proteins are important in cell differentiation and defense against viruses, and yet this family of vertebrate proteins is just beginning to be understood at the molecular level. Here, the three-dimensional architecture and molecular interfaces of human schlafen12 (hSLFN12), which promotes intestinal stem cell differentiation, are analyzed by sequence conservation and structural modeling in light of the functions of its homologs and binding partners. Our analysis shows that the schlafen or divergent AAA ATPase domain described in the N-terminal region of schlafens in databases and the literature is a misannotation. This N-terminal region is conclusively an AlbA_2 DNA/RNA binding domain, forming the conserved core of schlafens and their sequence homologs from bacteria through mammals. Group III schlafens additionally contain a AAA NTPase domain in their C-terminal helicase region. In hSLFN12, we have uncovered a domain matching rho GTPases, which directly follows the AlbA_2 domain in all group II-III schlafens. Potential roles for the GTPase-like domain include antiviral activity and cytoskeletal interactions that contribute to nucleocytoplasmic shuttling and cell polarization during differentiation. Based on features conserved with rSlfn13, the AlbA_2 region in hSLFN12 is likely to bind RNA, possibly as a ribonuclease. We hypothesize that RNA binding by hSLFN12 contributes to an RNA-induced transcriptional silencing/E3 ligase complex, given the functions of hSLFN12's partners, SUV39H1, JMJD6, and PDLIM7. hSLFN12's partner hSerpinB12 may contribute to heterochromatin formation, based on its homology to MENT, or directly regulate transcription via its binding to RNA polymerase II. The analysis presented here provides clear architectural and transcriptional regulation hypotheses to guide experimental design for hSLFN12 and the thousands of schlafens that share its motifs.

Combining random microseed matrix screening and the magic triangle for the efficient structure solution of a potential lysin from bacteriophage P68

Two commonly encountered bottlenecks in the structure determination of a protein by X-ray crystallography are screening for conditions that give high-quality crystals and, in the case of novel structures, finding derivatization conditions for experimental phasing. In this study, the phasing molecule 5-amino-2,4,6-triiodoisophthalic acid (I3C) was added to a random microseed matrix screen to generate high-quality crystals derivatized with I3C in a single optimization experiment. I3C, often referred to as the magic triangle, contains an aromatic ring scaffold with three bound I atoms. This approach was applied to efficiently phase the structures of hen egg-white lysozyme and the N-terminal domain of the Orf11 protein from Staphylococcus phage P68 (Orf11 NTD) using SAD phasing. The structure of Orf11 NTD suggests that it may play a role as a virion-associated lysin or endolysin.

Metabolic control of BRISC-SHMT2 assembly regulates immune signalling

Serine hydroxymethyltransferase 2 (SHMT2) regulates one-carbon transfer reactions that are essential for amino acid and nucleotide metabolism, and uses pyridoxal-5'-phosphate (PLP) as a cofactor. Apo SHMT2 exists as a dimer with unknown functions, whereas PLP binding stabilizes the active tetrameric state. SHMT2 also promotes inflammatory cytokine signalling by interacting with the deubiquitylating BRCC36 isopeptidase complex (BRISC), although it is unclear whether this function relates to metabolism. Here we present the cryo-electron microscopy structure of the human BRISC-SHMT2 complex at a resolution of 3.8 Å. BRISC is a U-shaped dimer of four subunits, and SHMT2 sterically blocks the BRCC36 active site and inhibits deubiquitylase activity. Only the inactive SHMT2 dimer-and not the active PLP-bound tetramer-binds and inhibits BRISC. Mutations in BRISC that disrupt SHMT2 binding impair type I interferon signalling in response to inflammatory stimuli. Intracellular levels of PLP regulate the interaction between BRISC and SHMT2, as well as inflammatory cytokine responses. These data reveal a mechanism in which metabolites regulate deubiquitylase activity and inflammatory signalling.

Generation of tumor antigen-specific murine CD8+ T cells with enhanced anti-tumor activity via highly efficient CRISPR/Cas9 genome editing

Immunotherapies have led to the successful development of novel therapies for cancer. However, there is increasing concern regarding the adverse effects caused by non-tumor-specific immune responses. Here, we report an effective strategy to generate high-avidity tumor-antigen-specific CTLs, using Cas9/single-guide RNA (sgRNA) ribonucleoprotein (RNP) delivery. As a proof-of-principle demonstration, we selected the gp100 melanoma-associated tumor antigen, and cloned the gp100-specific high-avidity TCR from gp100-immunized mice. To enable rapid structural dissection of the TCR, we developed a 3D protein structure modeling system for the TCR/antigen-major histocompatibility complex (pMHC) interaction. Combining these technologies, we efficiently generated gp100-specific PD-1(-) CD8+ T cells, and demonstrated that the genetically engineered CD8+ T cells have high avidity against melanoma cells both in vitro and in vivo. Our methodology offers computational prediction of the TCR response, and enables efficient generation of tumor antigen-specific CD8+ T cells that can neutralize tumor-induced immune suppression leading to a potentially powerful cancer therapeutic.

Protein tertiary structure modeling driven by deep learning and contact distance prediction in CASP13

Predicting residue‐residue distance relationships (eg, contacts) has become the key direction to advance protein structure prediction since 2014 CASP11 experiment, while deep learning has revolutionized the technology for contact and distance distribution prediction since its debut in 2012 CASP10 experiment. During 2018 CASP13 experiment, we enhanced our MULTICOM protein structure prediction system with three major components: contact distance prediction based on deep convolutional neural networks, distance‐driven template‐free (ab initio) modeling, and protein model ranking empowered by deep learning and contact prediction. Our experiment demonstrates that contact distance prediction and deep learning methods are the key reasons that MULTICOM was ranked 3rd out of all 98 predictors in both template‐free and template‐based structure modeling in CASP13. Deep convolutional neural network can utilize global information in pairwise residue‐residue features such as coevolution scores to substantially improve contact distance prediction, which played a decisive role in correctly folding some free modeling and hard template‐based modeling targets. Deep learning also successfully integrated one‐dimensional structural features, two‐dimensional contact information, and three‐dimensional structural quality scores to improve protein model quality assessment, where the contact prediction was demonstrated to consistently enhance ranking of protein models for the first time. The success of MULTICOM system clearly shows that protein contact distance prediction and model selection driven by deep learning holds the key of solving protein structure prediction problem. However, there are still challenges in accurately predicting protein contact distance when there are few homologous sequences, folding proteins from noisy contact distances, and ranking models of hard targets.

Protein fold recognition based on multi-view modeling

Motivation

Protein fold recognition has attracted increasing attention because it is critical for studies of the 3D structures of proteins and drug design. Researchers have been extensively studying this important task, and several features with high discriminative power have been proposed. However, the development of methods that efficiently combine these features to improve the predictive performance remains a challenging problem.

Results

In this study, we proposed two algorithms: MV-fold and MT-fold. MV-fold is a new computational predictor based on the multi-view learning model for fold recognition. Different features of proteins were treated as different views of proteins, including the evolutionary information, secondary structure information and physicochemical properties. These different views constituted the latent space. The ε-dragging technique was employed to enlarge the margins between different protein folds, improving the predictive performance of MV-fold. Then, MV-fold was combined with two template-based methods: HHblits and HMMER. The ensemble method is called MT-fold incorporating the advantages of both discriminative methods and template-based methods. Experimental results on five widely used benchmark datasets (DD, RDD, EDD, TG and LE) showed that the proposed methods outperformed some state-of-the-art methods in this field, indicating that MV-fold and MT-fold are useful computational tools for protein fold recognition and protein homology detection and would be efficient tools for protein sequence analysis. Finally, we constructed an update and rigorous benchmark dataset based on SCOPe (version 2.07) to fairly evaluate the performance of the proposed method, and our method achieved stable performance on this new dataset. This new benchmark dataset will become a widely used benchmark dataset to fairly evaluate the performance of different methods for fold recognition.

Supplementary information

Supplementary data are available at Bioinformatics online.

Structure-Guided Exploration of SDS22 Interactions with Protein Phosphatase PP1 and the Splicing Factor BCLAF1

SDS22 is an ancient regulator of protein phosphatase-1 (PP1). Our crystal structure of SDS22 shows that its twelve leucine-rich repeats adopt a banana-shaped fold that is shielded from solvent by capping domains at its extremities. Subsequent modeling and biochemical studies revealed that the concave side of SDS22 likely interacts with PP1 helices α5 and α6, which are distal from the binding sites of many previously described PP1 interactors. Accordingly, we found that SDS22 acts as a "third" subunit of multiple PP1 holoenzymes. The crystal structure of SDS22 also revealed a large basic surface patch that enables binding of a phosphorylated form of splicing factor BCLAF1. Taken together, our data provide insights into the formation of PP1:SDS22 and the recruitment of additional interaction proteins, such as BCLAF1.