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Upon discovery of the first archaeal species in the 1970s, life has been subdivided into three domains: Eukarya, Archaea, and Bacteria. However, the organization of the three-domain tree of life has been challenged following the discovery of archaeal lineages such as the TACK and Asgard superphyla. The Asgard Superphylum has emerged as the closest archaeal ancestor to eukaryotes, potentially improving our understanding of the evolution of life forms. We characterized the transportomes and their substrates within four metagenome-assembled genomes (MAGs), that is, Odin-, Thor-, Heimdall- and Loki-archaeota as well as the fully sequenced genome ofCandidatusPrometheoarchaeum syntrophicum strain MK-D1 that belongs to the Loki phylum. Using the Transporter Classification Database (TCDB) as reference, candidate transporters encoded within the proteomes were identified based on sequence similarity, alignment coverage, compatibility of hydropathy profiles, TMS topologies and shared domains. Identified transport systems were compared within the Asgard superphylum as well as within dissimilar eukaryotic, archaeal and bacterial organisms. From these analyses, we infer that Asgard organisms rely mostly on the transport of substrates driven by the proton motive force (pmf), the proton electrochemical gradient which then can be used for ATP production and to drive the activities of secondary carriers. The results indicate that Asgard archaea depend heavily on the uptake of organic molecules such as lipid precursors, amino acids and their derivatives, and sugars and their derivatives. Overall, the majority of the transporters identified are more similar to prokaryotic transporters than eukaryotic systems although several instances of the reverse were documented. Taken together, the results support the previous suggestions that the Asgard superphylum includes organisms that are largely mixotrophic and anaerobic but more clearly define their metabolic potential while providing evidence regarding their relatedness to eukaryotes.

The Major Facilitator Superfamily (MFS) is currently the largest characterized superfamily of transmembrane secondary transport proteins. Its diverse members are found in essentially all organisms in the biosphere and function by uniport, symport, and/or antiport mechanisms. In 1993 we first named and described the MFS which then consisted of 5 previously known families that had not been known to be related, and by 2012 we had identified a total of 74 families, classified phylogenetically within the MFS, all of which included only transport proteins. This superfamily has since expanded to 89 families, all included under TC# 2.A.1, and a few transporter families outside of TC# 2.A.1 were identified as members of the MFS. In this study, we assign nine previously unclassified protein families in the Transporter Classification Database (TCDB; http://www.tcdb.org) to the MFS based on multiple criteria and bioinformatic methodologies. In addition, we find integral membrane domains distantly related to partial or full-length MFS permeases in Lysyl tRNA Synthases (TC# 9.B.111), Lysylphosphatidyl Glycerol Synthases (TC# 4.H.1), and cytochrome b(561) transmembrane electron carriers (TC# 5.B.2). Sequence alignments, overlap of hydropathy plots, compatibility of repeat units, similarity of complexity profiles of transmembrane segments, shared protein domains and 3D structural similarities between transport proteins were analyzed to assist in inferring homology. The MFS now includes 105 families.

Bdellovibrio, δ-proteobacteria, including B. bacteriovorus (Bba) and B. exovorus (Bex), are obligate predators of other Gram-negative bacteria. While Bba grows in the periplasm of the prey cell, Bex grows externally. We have analyzed and compared the transport proteins of these 2 organisms based on the current contents of the Transporter Classification Database (TCDB; www.tcdb.org). Bba has 103 transporters more than Bex, 50% more secondary carriers, and 3 times as many MFS carriers. Bba has far more metabolite transporters than Bex as expected from its larger genome, but there are 2 times more carbohydrate uptake and drug efflux systems, and 3 times more lipid transporters. Bba also has polyamine and carboxylate transporters lacking in Bex. Bba has more than twice as many members of the Mot-Exb family of energizers, but both may have energizers for gliding motility. They use entirely different types of systems for iron acquisition. Both contain unexpectedly large numbers of pseudogenes and incomplete systems, suggesting that they are undergoing genome size reduction. Interestingly, all 5 outer-membrane receptors in Bba are lacking in Bex. The 2 organisms have similar numbers and types of peptide and amino acid uptake systems as well as protein and carbohydrate secretion systems. The differences observed correlate with and may account, in part, for the different lifestyles of these 2 bacterial predators.

We evaluated topological predictions for nine different programs, HMMTOP, TMHMM, SVMTOP, DAS, SOSUI, TOPCONS, PHOBIUS, MEMSAT-SVM (hereinafter referred to as MEMSAT), and SPOCTOPUS. These programs were first evaluated using four large topologically well-defined families of secondary transporters, and the three best programs were further evaluated using topologically more diverse families of channels and carriers. In the initial studies, the order of accuracy was: SPOCTOPUS > MEMSAT > HMMTOP > TOPCONS > PHOBIUS > TMHMM > SVMTOP > DAS > SOSUI. Some families, such as the Sugar Porter Family (2.A.1.1) of the Major Facilitator Superfamily (MFS; TC #2.A.1) and the Amino Acid/Polyamine/Organocation (APC) Family (TC #2.A.3), were correctly predicted with high accuracy while others, such as the Mitochondrial Carrier (MC) (TC #2.A.29) and the K+ transporter (Trk) families (TC #2.A.38), were predicted with much lower accuracy. For small, topologically homogeneous families, SPOCTOPUS and MEMSAT were generally most reliable, while with large, more diverse superfamilies, HMMTOP often proved to have the greatest prediction accuracy. We next developed a novel program, TM-STATS, that tabulates HMMTOP, SPOCTOPUS or MEMSAT-based topological predictions for any subdivision (class, subclass, superfamily, family, subfamily, or any combination of these) of the Transporter Classification Database (TCDB; www.tcdb.org) and examined the following subclasses: α-type channel proteins (TC subclasses 1.A and 1.E), secreted pore-forming toxins (TC subclass 1.C) and secondary carriers (subclass 2.A). Histograms were generated for each of these subclasses, and the results were analyzed according to subclass, family and protein. The results provide an update of topological predictions for integral membrane transport proteins as well as guides for the development of more reliable topological prediction programs, taking family-specific characteristics into account.

We have designed a freely accessible program, PhyST, which allows the automated characterization of any family of homologous proteins within the Transporter Classification Database. The program performs an NCBI-PSI-BLAST search and reports (1) the average protein sequence length with standard deviations, (2) the average predicted number of transmembrane segments, (3) the total number of homologues retrieved, (4) a quantitative list of all source phyla, and (5) potential fusion proteins of sizes considerably exceeding the average size of the proteins retrieved. We have applied this program to 58 families of holins, and the results are presented. The results show that holins are very rarely fused to other protein domains, suggesting that holins form transmembrane pores as homooligomers without the participation of other proteins or protein domains.

The Bio-V Suite is a collection of Python scripts designed specifically for bioinformatic research regarding transport protein evolution. The Bio-V Suite contains nine powerful programs for Unix-based environments, each of which can be run as a standalone tool or be accessed in a programmatic manner. These programs and their functions are as follows. The transmembrane alpha-helical statistical prediction tool (TMStats) generates topological statistics for transport proteins. The Global Sequence Alignment Tool (GSAT) performs shuffle-based binary alignments and is fully scalable. It can cross-compare two FASTA files or individual sequences. Protocol1 performs remote PSI-Blast searches and filters redundant/similar sequences and annotates them. Protocol2 finds homologues between FASTA lists and generates graphical reports. The Targeted Smith-Waterman Search (TSSearch) uses a rapid search algorithm to find distant homologues in FASTA files in a heuristic manner. SSearch is the exhaustive version of TSSearch. Genome-Blast (GBlast) will identify potential transport proteins in any genome/proteome file or find similar transport protein homologues between two different genomes/proteomes before generating a graphical report. AncientRep (AR) will find putative transmembrane repeat units using a list of homologues. DefineFamily (DF) will generate a FASTA list to represent an entire Transporter Classification family. These nine programs are tabulated with descriptions of their capabilities in Table 1. [Table: see text].

Protein phylogeny, based on primary amino acid sequence relatedness, reflects the evolutionary process and therefore provides a guide to structure, mechanism and function. Any two proteins that are related by common descent are expected to exhibit similar structures and functions to a degree proportional to the degree of their sequence similarity; but two independently evolving proteins should not. This principle provides the impetus to define protein phylogenetic relationships and interrelate families when possible. In this mini-review, we summarize the computational approaches and criteria we use to establish common evolutionary origin. We apply these tools to define distant superfamily relationships between several previously recognized transport protein families. In some cases, available structural and functional data are evaluated in order to substantiate our claim that molecular phylogeny provides a reliable guide to protein structure and function.

Holins are small “hole-forming” transmembrane proteins that mediate bacterial cell lysis during programmed cell death or following phage infection. We have identified fifty two families of established or putative holins and have included representative members of these proteins in the Transporter Classification Database (TCDB; www.tcdb.org). We have identified the organismal sources of members of these families, calculated their average protein sizes, estimated their topologies and determined their relative family sizes. Topological analyses suggest that these proteins can have 1, 2, 3 or 4 transmembrane α-helical segments (TMSs), and members of a single family are frequently, but not always, of a single topology. In one case, proteins of a family proved to have either 2 or 4 TMSs, and the latter arose by intragenic duplication of a primordial 2 TMS protein-encoding gene resembling the former. Using established statistical approaches, some of these families have been shown to be related by common descent. Seven superfamilies, including 21 of the 52 recognized families were identified. Conserved motif and Pfam analyses confirmed most superfamily assignments. These results serve to expand upon the scope of channel-forming bacterial holins.

Rhodopirellula baltica (R. baltica) is a Planctomycete, known to have intracellular membranes. Because of its unusual cell structure and ecological significance, we have conducted comprehensive analyses of its transmembrane transport proteins. The complete proteome of R. baltica was screened against the Transporter Classification Database (TCDB) to identify recognizable integral membrane transport proteins. 342 proteins were identified with a high degree of confidence, and these fell into several different classes. R. baltica encodes in its genome channels (12%), secondary carriers (33%), and primary active transport proteins (41%) in addition to classes represented in smaller numbers. Relative to most non-marine bacteria, R. baltica possesses a larger number of sodium-dependent symporters but fewer proton-dependent symporters, and it has dimethylsulfoxide (DMSO) and trimethyl-amine-oxide (TMAO) reductases, consistent with its Na(+)-rich marine environment. R. baltica also possesses a Na(+)-translocating NADH:quinone dehydrogenase (Na(+)-NDH), a Na(+) efflux decarboxylase, two Na(+)-exporting ABC pumps, two Na(+)-translocating F-type ATPases, two Na(+):H(+) antiporters and two K(+):H(+) antiporters. Flagellar motility probably depends on the sodium electrochemical gradient. Surprisingly, R. baltica also has a complete set of H(+)-translocating electron transport complexes similar to those present in α-proteobacteria and eukaryotic mitochondria. The transport proteins identified proved to be typical of the bacterial domain with little or no indication of the presence of eukaryotic-type transporters. However, novel functionally uncharacterized multispanning membrane proteins were identified, some of which are found only in Rhodopirellula species, but others of which are widely distributed in bacteria. The analyses lead to predictions regarding the physiology, ecology and evolution of R. baltica.

Transport proteins function in the translocation of ions, solutes and macromolecules across cellular and organellar membranes. These integral membrane proteins fall into >600 families as tabulated in the Transporter Classification Database (www.tcdb.org). Recent studies, some of which are reported here, define distant phylogenetic relationships between families with the creation of superfamilies. Several of these are analyzed using a novel set of programs designed to allow reliable prediction of phylogenetic trees when sequence divergence is too great to allow the use of multiple alignments. These new programs, called SuperfamilyTree1 and 2 (SFT1 and 2), allow display of protein and family relationships, respectively, based on thousands of comparative BLAST scores rather than multiple alignments. Superfamilies analyzed include: (1) Aerolysins, (2) RTX Toxins, (3) Defensins, (4) Ion Transporters, (5) Bile/Arsenite/Riboflavin Transporters, (6) Cation:Proton Antiporters, and (7) the Glucose/Fructose/Lactose superfamily within the prokaryotic phosphoenol pyruvate-dependent Phosphotransferase System. In addition to defining the phylogenetic relationships of the proteins and families within these seven superfamilies, evidence is provided showing that the SFT programs outperform programs that are based on multiple alignments whenever sequence divergence of superfamily members is extensive. The SFT programs should be applicable to virtually any superfamily of proteins or nucleic acids.

The ATP-binding cassette (ABC) superfamily consists of both importers and exporters. These transporters have, by tradition, been classified according to the ATP hydrolyzing constituents, which are monophyletic. The evolutionary origins of the transmembrane porter proteins/domains are not known. Using five distinct computer programs, we here provide convincing statistical data suggesting that the transmembrane domains of ABC exporters are polyphyletic, having arisen at least three times independently. ABC1 porters arose by intragenic triplication of a primordial two-transmembrane segment (TMS)-encoding genetic element, yielding six TMS proteins. ABC2 porters arose by intragenic duplication of a dissimilar primordial three-TMS-encoding genetic element, yielding a distinctive protein family, nonhomologous to the ABC1 proteins. ABC3 porters arose by duplication of a primordial four-TMS-encoding genetic element, yielding either eight- or 10-TMS proteins. We assign each of 48 of the 50 currently recognized families of ABC exporters to one of the three evolutionarily distinct ABC types. Currently available high-resolution structural data for ABC porters are fully consistent with our findings. These results provide guides for future structural and mechanistic studies of these important transport systems.

We here describe the transporter classification (TC) system that provides a rational means of classifying all transmembrane transport systems found in living organisms. The availability of this system provides a framework for the use of bioinformatic technologies to answer fundamental questions about the functions, mechanisms, and evolutionary pathways taken for the appearance of these systems. Such advances are discussed.