Genetic manipulation of Patescibacteria provides mechanistic insights into microbial dark matter and the epibiotic lifestyle.

Patescibacteria, also known as the candidate phyla radiation (CPR), are a diverse group of bacteria that constitute a disproportionately large fraction of microbial dark matter. Its few cultivated members, belonging mostly to Saccharibacteria, grow as epibionts on host Actinobacteria. Due to a lack of suitable tools, the genetic basis of this lifestyle and other unique features of Patescibacteira remain unexplored. Here, we show that Saccharibacteria exhibit natural competence, and we exploit this property for their genetic manipulation. Imaging of fluorescent protein-labeled Saccharibacteria provides high spatiotemporal resolution of phenomena accompanying epibiotic growth, and a transposon-insertion sequencing (Tn-seq) genome-wide screen reveals the contribution of enigmatic Saccharibacterial genes to growth on their hosts. Finally, we leverage metagenomic data to provide cutting-edge protein structure-based bioinformatic resources that support the strain Southlakia epibionticum and its corresponding host, Actinomyces israelii, as a model system for unlocking the molecular underpinnings of the epibiotic lifestyle.

A tissue injury sensing and repair pathway distinct from host pathogen defense.

Pathogen infection and tissue injury are universal insults that disrupt homeostasis. Innate immunity senses microbial infections and induces cytokines/chemokines to activate resistance mechanisms. Here, we show that, in contrast to most pathogen-induced cytokines, interleukin-24 (IL-24) is predominately induced by barrier epithelial progenitors after tissue injury and is independent of microbiome or adaptive immunity. Moreover, Il24 ablation in mice impedes not only epidermal proliferation and re-epithelialization but also capillary and fibroblast regeneration within the dermal wound bed. Conversely, ectopic IL-24 induction in the homeostatic epidermis triggers global epithelial-mesenchymal tissue repair responses. Mechanistically, Il24 expression depends upon both epithelial IL24-receptor/STAT3 signaling and hypoxia-stabilized HIF1α, which converge following injury to trigger autocrine and paracrine signaling involving IL-24-mediated receptor signaling and metabolic regulation. Thus, parallel to innate immune sensing of pathogens to resolve infections, epithelial stem cells sense injury signals to orchestrate IL-24-mediated tissue repair.

Cryo-EM structure of a type IV secretion system.

Bacterial conjugation is the fundamental process of unidirectional transfer of DNAs, often plasmid DNAs, from a donor cell to a recipient cell1. It is the primary means by which antibiotic resistance genes spread among bacterial populations2,3. In Gram-negative bacteria, conjugation is mediated by a large transport apparatus-the conjugative type IV secretion system (T4SS)-produced by the donor cell and embedded in both its outer and inner membranes. The T4SS also elaborates a long extracellular filament-the conjugative pilus-that is essential for DNA transfer4,5. Here we present a high-resolution cryo-electron microscopy (cryo-EM) structure of a 2.8 megadalton T4SS complex composed of 92 polypeptides representing 8 of the 10 essential T4SS components involved in pilus biogenesis. We added the two remaining components to the structural model using co-evolution analysis of protein interfaces, to enable the reconstitution of the entire system including the pilus. This structure describes the exceptionally large protein-protein interaction network required to assemble the many components that constitute a T4SS and provides insights on the unique mechanism by which they elaborate pili.

Recent advances in predicting and modeling protein-protein interactions.

Protein-protein interactions (PPIs) drive biological processes, and disruption of PPIs can cause disease. With recent breakthroughs in structure prediction and a deluge of genomic sequence data, computational methods to predict PPIs and model spatial structures of protein complexes are now approaching the accuracy of experimental approaches for permanent interactions and show promise for elucidating transient interactions. As we describe here, the key to this success is rich evolutionary information deciphered from thousands of homologous sequences that coevolve in interacting partners. This covariation signal, revealed by sophisticated statistical and machine learning (ML) algorithms, predicts physiological interactions. Accurate artificial intelligence (AI)-based modeling of protein structures promises to provide accurate 3D models of PPIs at a proteome-wide scale.

What one genus of showy moths can say about migration, adaptation, and wing pattern.

The Ornate Moth, Utetheisa ornatrix, has served as a model species in chemical ecology studies for decades. Like in the widely publicized stories of the Monarch and other milkweed butterflies, the Ornate Moth and its relatives are tropical insects colonizing whole continents assisted by their chemical defenses. With the recent advances in genomic techniques and evo-devo research, it is becoming a model for studies in other areas, from wing pattern development to phylogeography, from toxicology to epigenetics. We used a genomic approach to learn about Utetheisa's evolution, detoxification, dispersal abilities, and wing pattern diversity. We present an evolutionary genomic analysis of the worldwide genus Utetheisa, then focusing on U. ornatrix. Our reference genome of U. ornatrix reveals gene duplications in the regions possibly associated with detoxification abilities, which allows them to feed on toxic food plants. Finally, comparative genomic analysis of over 100 U. ornatrix specimens from the museum with apparent differences in wing patterns suggest the potential roles of cortex and lim3 genes in wing pattern formation of Lepidoptera and the utility of museum-preserved collection specimens for wing pattern research.

Classification of domains in predicted structures of the human proteome.

Recent advances in protein structure prediction have generated accurate structures of previously uncharacterized human proteins. Identifying domains in these predicted structures and classifying them into an evolutionary hierarchy can reveal biological insights. Here, we describe the detection and classification of domains from the human proteome. Our classification indicates that only 62% of residues are located in globular domains. We further classify these globular domains and observe that the majority (65%) can be classified among known folds by sequence, with a smaller fraction (33%) requiring structural data to refine the domain boundaries and/or to support their homology. A relatively small number (966 domains) cannot be confidently assigned using our automatic pipelines, thus demanding manual inspection. We classify 47,576 domains, of which only 23% have been included in experimental structures. A portion (6.3%) of these classified globular domains lack sequence-based annotation in InterPro. A quarter (23%) have not been structurally modeled by homology, and they contain 2,540 known disease-causing single amino acid variations whose pathogenesis can now be inferred using AF models. A comparison of classified domains from a series of model organisms revealed expansions of several immune response-related domains in humans and a depletion of olfactory receptors. Finally, we use this classification to expand well-known protein families of biological significance. These classifications are presented on the ECOD website (http://prodata.swmed.edu/ecod/index_human.php).

ECOD domain classification of 48 whole proteomes from AlphaFold Structure Database using DPAM2.

Protein structure prediction has now been deployed widely across several different large protein sets. Large-scale domain annotation of these predictions can aid in the development of biological insights. Using our Evolutionary Classification of Protein Domains (ECOD) from experimental structures as a basis for classification, we describe the detection and cataloging of domains from 48 whole proteomes deposited in the AlphaFold Database. On average, we can provide positive classification (either of domains or other identifiable non-domain regions) for 90% of residues in all proteomes. We classified 746,349 domains from 536,808 proteins comprised of over 226,424,000 amino acid residues. We examine the varying populations of homologous groups in both eukaryotes and bacteria. In addition to containing a higher fraction of disordered regions and unassigned domains, eukaryotes show a higher proportion of repeated proteins, both globular and small repeats. We enumerate those highly populated domains that are shared in both eukaryotes and bacteria, such as the Rossmann domains, TIM barrels, and P-loop domains. Additionally, we compare the sampling of homologous groups from this whole proteome set against our stable ECOD reference and discuss groups that have been enriched by structure predictions. Finally, we discuss the implication of these results for protein target selection for future classification strategies for very large protein sets.

Co-component signal transduction systems: Fast-evolving virulence regulation cassettes discovered in enteric bacteria.

Bacterial signal transduction systems sense changes in the environment and transmit these signals to control cellular responses. The simplest one-component signal transduction systems include an input sensor domain and an output response domain encoded in a single protein chain. Alternatively, two-component signal transduction systems transmit signals by phosphorelay between input and output domains from separate proteins. The membrane-tethered periplasmic bile acid sensor that activates the Vibrio parahaemolyticus type III secretion system adopts an obligate heterodimer of two proteins encoded by partially overlapping VtrA and VtrC genes. This co-component signal transduction system binds bile acid using a lipocalin-like domain in VtrC and transmits the signal through the membrane to a cytoplasmic DNA-binding transcription factor in VtrA. Using the domain and operon organization of VtrA/VtrC, we identify a fast-evolving superfamily of co-component systems in enteric bacteria. Accurate machine learning­based fold predictions for the candidate co-components support their homology in the twilight zone of rapidly evolving sequences and provide mechanistic hypotheses about previously unrecognized lipid-sensing functions.

Computational analysis of propeptide-containing proteins and prediction of their post-cleavage conformation changes.

A propeptide is removed from a precursor protein to generate its active or mature form. Propeptides play essential roles in protein folding, transportation, and activation and are present in about 2.3% of reviewed proteins in the UniProt database. They are often found in secreted or membrane-bound proteins including proteolytic enzymes, hormones, and toxins. We identified a variety of globular and nonglobular Pfam domains in protein sequences designated as propeptides, some of which form intramolecular interactions with other domains in the mature proteins. Propeptide-containing enzymes mostly function as proteases, as they are depleted in other enzyme classes such as hydrolases acting on DNA and RNA, isomerases, and lyases. We applied AlphaFold to generate structural models for over 7000 proteins with propeptides having no less than 20 residues. Analysis of residue contacts in these models revealed conformational changes for over 300 proteins before and after the cleavage of the propeptide. Examples of conformation change occur in several classes of proteolytic enzymes in the families of subtilisins, trypsins, aspartyl proteases, and thermolysin-like metalloproteases. In most of the observed cases, cleavage of the propeptide releases the constraints imposed by the covalent bond between the propeptide and the mature protein, and cleavage enables stronger interactions between the propeptide and the mature protein. These findings suggest that post-cleavage propeptides could play critical roles in regulating the activity of mature proteins.

A Mesostructure Multivariant-Assembly Reinforced Ultratough Biomimicking Superglue.

The imitation of mussels and oysters to create high-performance adhesives is a cutting-edge field. The introduction of inorganic fillers is shown to significantly alter the adhesive's properties, yet the potential of mesoporous materials as fillers in adhesives is overlooked. In this study, the first report on the utilization of mesoporous materials in a biomimetic adhesive system is presented. Incorporating mesoporous silica nanoparticles (MSN) profoundly enhances the adhesion of pyrogallol (PG)-polyethylene imine (PEI) adhesive. As the MSN concentration increases, the adhesion strength to glass substrates undergoes an impressive fivefold improvement, reaching an outstanding 2.5 mPa. The adhesive forms an exceptionally strong bond, to the extent that the glass substrate fractures before joint failure. The comprehensive tests involving various polyphenols, polymers, and fillers reveal an intriguing phenomenon-the molecular structure of polyphenols significantly influences adhesive strength. Steric hindrance emerges as a crucial factor, regulating the balance between π-cation and charge interactions, which significantly impacts the multicomponent assembly of polyphenol-PEI-MSN and, consequently, adhesive strength. This groundbreaking research opens new avenues for the development of novel biomimetic materials.

Stat3 loss in mesenchymal progenitors causes Job syndrome-like skeletal defects by reducing Wnt/ß-catenin signaling.

Job syndrome is a rare genetic disorder caused by STAT3 mutations and primarily characterized by immune dysfunction along with comorbid skeleton developmental abnormalities including osteopenia, recurrent fracture of long bones, and scoliosis. So far, there is no definitive cure for the skeletal defects in Job syndrome, and treatments are limited to management of clinical symptoms only. Here, we have investigated the molecular mechanism whereby Stat3 regulates skeletal development and osteoblast differentiation. We showed that removing Stat3 function in the developing limb mesenchyme or osteoprogenitor cells in mice resulted in shortened and bow limbs with multiple fractures in long bones that resembled the skeleton symptoms in the Job Syndrome. However, Stat3 loss did not alter chondrocyte differentiation and hypertrophy in embryonic development, while osteoblast differentiation was severely reduced. Genome-wide transcriptome analyses as well as biochemical and histological studies showed that Stat3 loss resulted in down-regulation of Wnt/ß-catenin signaling. Restoration of Wnt/ß-catenin signaling by injecting BIO, a small molecule inhibitor of GSK3, or crossing with a Lrp5 gain of function (GOF) allele, rescued the bone reduction phenotypes due to Stat3 loss to a great extent. These studies uncover the essential functions of Stat3 in maintaining Wnt/ß-catenin signaling in early mesenchymal or osteoprogenitor cells and provide evidence that bone defects in the Job Syndrome are likely caused by Wnt/ß-catenin signaling reduction due to reduced STAT3 activities in bone development. Enhancing Wnt/ß-catenin signaling could be a therapeutic approach to reduce bone symptoms of Job syndrome patients.

A switch to feeding on cycads generates parallel accelerated evolution of toxin tolerance in two clades of Eumaeus caterpillars (Lepidoptera: Lycaenidae).

We assembled a complete reference genome of Eumaeus atala, an aposematic cycad-eating hairstreak butterfly that suffered near extinction in the United States in the last century. Based on an analysis of genomic sequences of Eumaeus and 19 representative genera, the closest relatives of Eumaeus are Theorema and Mithras We report natural history information for Eumaeus, Theorema, and Mithras Using genomic sequences for each species of Eumaeus, Theorema, and Mithras (and three outgroups), we trace the evolution of cycad feeding, coloration, gregarious behavior, and other traits. The switch to feeding on cycads and to conspicuous coloration was accompanied by little genomic change. Soon after its origin, Eumaeus split into two fast evolving lineages, instead of forming a clump of close relatives in the phylogenetic tree. Significant overlap of the fast evolving proteins in both clades indicates parallel evolution. The functions of the fast evolving proteins suggest that the caterpillars developed tolerance to cycad toxins with a range of mechanisms including autophagy of damaged cells, removal of cell debris by macrophages, and more active cell proliferation.

Impact of Asp/Glu-ADP-ribosylation on protein-protein interaction and protein function.

PARylation plays critical role in regulating multiple cellular processes such as DNA damage response and repair, transcription, RNA processing, and stress response. More than 300 human proteins have been found to be modified by PARylation on acidic residues, that is, Asp (D) and Glu (E). We used the deep-learning tool AlphaFold to predict protein-protein interactions (PPIs) and their interfaces for these proteins based on coevolution signals from joint multiple sequence alignments (MSAs). AlphaFold predicted 260 confident PPIs involving PARylated proteins, and about one quarter of these PPIs have D/E-PARylation sites in their predicted PPI interfaces. AlphaFold predictions offer novel insights into the mechanisms of PARylation regulations by providing structural details of the PPI interfaces. D/E-PARylation sites have a preference to occur in coil regions and disordered regions, and PPI interfaces containing D/E-PARylation sites tend to occur between short linear sequence motifs in disordered regions and globular domains. The hub protein PCNA is predicted to interact with more than 20 proteins via the common PIP box motif and the structurally variable flanking regions. D/E-PARylation sites were found in the interfaces of key components of the RNA transcription and export complex, the SF3a spliceosome complex, and H/ACA and C/D small nucleolar ribonucleoprotein complexes, suggesting that systematic PARylation have a profound effect in regulating multiple RNA-related processes such as RNA nuclear export, splicing, and modification. Finally, PARylation of SUMO2 could modulate its interaction with CHAF1A, thereby representing a potential mechanism for the cross-talk between PARylation and SUMOylation in regulation of chromatin remodeling.

Human mitochondrial protein complexes revealed by large-scale coevolution analysis and deep learning-based structure modeling.

MOTIVATION: Recent development of deep-learning methods has led to a breakthrough in the prediction accuracy of 3D protein structures. Extending these methods to protein pairs is expected to allow large-scale detection of protein-protein interactions (PPIs) and modeling protein complexes at the proteome level. RESULTS: We applied RoseTTAFold and AlphaFold, two of the latest deep-learning methods for structure predictions, to analyze coevolution of human proteins residing in mitochondria, an organelle of vital importance in many cellular processes including energy production, metabolism, cell death and antiviral response. Variations in mitochondrial proteins have been linked to a plethora of human diseases and genetic conditions. RoseTTAFold, with high computational speed, was used to predict the coevolution of about 95% of mitochondrial protein pairs. Top-ranked pairs were further subject to modeling of the complex structures by AlphaFold, which also produced contact probability with high precision and in many cases consistent with RoseTTAFold. Most top-ranked pairs with high contact probability were supported by known PPIs and/or similarities to experimental structural complexes. For high-scoring pairs without experimental complex structures, our coevolution analyses and structural models shed light on the details of their interfaces, including CHCHD4-AIFM1, MTERF3-TRUB2, FMC1-ATPAF2 and ECSIT-NDUFAF1. We also identified novel PPIs (PYURF-NDUFAF5, LYRM1-MTRF1L and COA8-COX10) for several proteins without experimentally characterized interaction partners, leading to predictions of their molecular functions and the biological processes they are involved in. AVAILABILITY AND IMPLEMENTATION: Data of mitochondrial proteins and their interactions are available at: http://conglab.swmed.edu/mitochondria. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Genomics Reveals the Origins of Historical Specimens.

Centuries of zoological studies have amassed billions of specimens in collections worldwide. Genomics of these specimens promises to reinvigorate biodiversity research. However, because DNA degrades with age in historical specimens, it is a challenge to obtain genomic data for them and analyze degraded genomes. We developed experimental and computational protocols to overcome these challenges and applied our methods to resolve a series of long-standing controversies involving a group of butterflies. We deduced the geographical origins of several historical specimens of uncertain provenance that are at the heart of these debates. Here, genomics tackles one of the greatest problems in zoology: countless old specimens that serve as irreplaceable embodiments of species concepts cannot be confidently assigned to extant species or population due to the lack of diagnostic morphological features and clear documentation of the collection locality. The ability to determine where they were collected will resolve many on-going disputes. More broadly, we show the utility of applying genomics to historical museum specimens to delineate the boundaries of species and populations, and to hypothesize about genotypic determinants of phenotypic traits.

Genomes of skipper butterflies reveal extensive convergence of wing patterns.

For centuries, biologists have used phenotypes to infer evolution. For decades, a handful of gene markers have given us a glimpse of the genotype to combine with phenotypic traits. Today, we can sequence entire genomes from hundreds of species and gain yet closer scrutiny. To illustrate the power of genomics, we have chosen skipper butterflies (Hesperiidae). The genomes of 250 representative species of skippers reveal rampant inconsistencies between their current classification and a genome-based phylogeny. We use a dated genomic tree to define tribes (six new) and subtribes (six new), to overhaul genera (nine new) and subgenera (three new), and to display convergence in wing patterns that fooled researchers for decades. We find that many skippers with similar appearance are distantly related, and several skippers with distinct morphology are close relatives. These conclusions are strongly supported by different genomic regions and are consistent with some morphological traits. Our work is a forerunner to genomic biology shaping biodiversity research.

Gypsy moth genome provides insights into flight capability and virus-host interactions.

Since its accidental introduction to Massachusetts in the late 1800s, the European gypsy moth (EGM; Lymantria dispar dispar) has become a major defoliator in North American forests. However, in part because females are flightless, the spread of the EGM across the United States and Canada has been relatively slow over the past 150 years. In contrast, females of the Asian gypsy moth (AGM; Lymantria dispar asiatica) subspecies have fully developed wings and can fly, thereby posing a serious economic threat if populations are established in North America. To explore the genetic determinants of these phenotypic differences, we sequenced and annotated a draft genome of L. dispar and used it to identify genetic variation between EGM and AGM populations. The 865-Mb gypsy moth genome is the largest Lepidoptera genome sequenced to date and encodes ∼13,300 proteins. Gene ontology analyses of EGM and AGM samples revealed divergence between these populations in genes enriched for several gene ontology categories related to muscle adaptation, chemosensory communication, detoxification of food plant foliage, and immunity. These genetic differences likely contribute to variations in flight ability, chemical sensing, and pathogen interactions among EGM and AGM populations. Finally, we use our new genomic and transcriptomic tools to provide insights into genome-wide gene-expression changes of the gypsy moth after viral infection. Characterizing the immunological response of gypsy moths to virus infection may aid in the improvement of virus-based bioinsecticides currently used to control larval populations.

Genomics-based higher classification of the species-rich Hairstreaks (Lepidoptera: Lycaenidae: Eumaeini).

We propose a higher classification of the lycaenid hairstreak tribe Eumaeini - one of the youngest and most species-rich butterfly tribes - based on autosome, Lepidopteran Z sex chromosome, and mitochondrial protein-coding genes. The subtribe Neolycaenina Korb is a synonym of Callophryidina Tutt, and subtribe Tmolusina Bálint is a synonym of Strephonotina K. Johnson, Austin, Le Crom, & Salazar. Proposed names are Rhammina Prieto & Busby, new subtribe; Timaetina Busby & Prieto, new subtribe; Atlidina Martins & Duarte, new subtribe; Evenina Faynel & Grishin, new subtribe; Jantheclina Robbins & Faynel, new subtribe; Paiwarriina Lamas & Robbins, new subtribe; Cupatheclina Lamas & Grishin, new subtribe; Parrhasiina Busby & Robbins, new subtribe; Ipideclina Martins & Grishin, new subtribe; and Trichonidina Duarte & Faynel, new subtribe. Phylogenetic results from the autosome and Z sex chromosome analyses are similar. Future analyses of datasets with hundreds of terminal taxa may be more practical time-wise by focussing on the smaller number of sex chromosome sequences (2.6% of nuclear protein-coding sequences). The phylogenetic classification and biological summaries for each subtribe suggest that a variety of factors affected Eumaeini diversification. About a dozen kinds of male secondary sexual organs with frequent evolutionary gains and losses occur in Atlidina, Evenina, and Jantheclina (141 species combined). Females have been shown to use these organs to discriminate between conspecific and non-conspecific males, facilitating sympatry among close relatives. Eumaeina, Rhammina, and Timaetina (140 species combined) are overwhelmingly montane with some evidence for a higher incidence of sympatric diversification. Seven Neotropical lineages in five subtribes invaded the temperate parts of the Nearctic Region with a diversification increase in the Callophryidina (262 species). North American Satyrium and Callophrys then invaded the Palearctic at least once each, with a major species-richness increase in Satyrium. The evolution of litter feeding detritivores within Calycopidina (172 species) resulted in an increase in diversification rate compared with its flower-feeding sister lineage. Atlidina, Strephonotina, Parrhasiina, and Strymonina (562 species combined) each contain a mixture of genera that specialize on one or two caterpillar food plant families and genera that are polyphagous. These would be appropriate subtribes to assess how the breadth of caterpillar food plants and the frequency of host shifts affected diversification.

Enpp1 inhibits ectopic joint calcification and maintains articular chondrocytes by repressing hedgehog signaling.

The differentiated phenotype of articular chondrocytes of synovial joints needs to be maintained throughout life. Disruption of the articular cartilage, frequently associated with chondrocyte hypertrophy and calcification, is a central feature in osteoarthritis (OA). However, the molecular mechanisms whereby phenotypes of articular chondrocytes are maintained and pathological calcification is inhibited remain poorly understood. Recently, the ecto-enzyme Enpp1, a suppressor of pathological calcification, was reported to be decreased in joint cartilage with OA in both human and mouse, and Enpp1 deficiency causes joint calcification. Here, we found that hedgehog (Hh) signaling activation contributes to ectopic joint calcification in the Enpp1-/- mice. In the Enpp1-/- joints, Hh signaling was upregulated. Further activation of Hh signaling by removing the patched 1 gene in the Enpp1-/- mice enhanced ectopic joint calcification, whereas removing Gli2 partially rescued the ectopic calcification phenotype. In addition, reduction of Gαs in the Enpp1-/- mice enhanced joint calcification, suggesting that Enpp1 inhibits Hh signaling and chondrocyte hypertrophy by activating Gαs-PKA signaling. Our findings provide new insights into the mechanisms underlying Enpp1 regulation of joint integrity.

Protein contact prediction using metagenome sequence data and residual neural networks.

MOTIVATION: Almost all protein residue contact prediction methods rely on the availability of deep multiple sequence alignments (MSAs). However, many proteins from the poorly populated families do not have sufficient number of homologs in the conventional UniProt database. Here we aim to solve this issue by exploring the rich sequence data from the metagenome sequencing projects. RESULTS: Based on the improved MSA constructed from the metagenome sequence data, we developed MapPred, a new deep learning-based contact prediction method. MapPred consists of two component methods, DeepMSA and DeepMeta, both trained with the residual neural networks. DeepMSA was inspired by the recent method DeepCov, which was trained on 441 matrices of covariance features. By considering the symmetry of contact map, we reduced the number of matrices to 231, which makes the training more efficient in DeepMSA. Experiments show that DeepMSA outperforms DeepCov by 10-13% in precision. DeepMeta works by combining predicted contacts and other sequence profile features. Experiments on three benchmark datasets suggest that the contribution from the metagenome sequence data is significant with P-values less than 4.04E-17. MapPred is shown to be complementary and comparable the state-of-the-art methods. The success of MapPred is attributed to three factors: the deeper MSA from the metagenome sequence data, improved feature design in DeepMSA and optimized training by the residual neural networks. AVAILABILITY AND IMPLEMENTATION: http://yanglab.nankai.edu.cn/mappred/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.