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After analysis, the main page for the organism lists the best path for each amino acid (Fig. 6). Gaps are highlighted by color, and known gaps are marked (such as serA or serB in Fig. 6). Each step has hover text with a description of the enzymatic step and the identifier of the top candidate.

Perfringens requires glycine for growth (36), GapMind identified a high-confidence candidate for the serine hydroxymethyltransferase GlyA, which should be sufficient. Perfringens grows in the absence of serine, but GapMind identified only low-confidence candidates for SerB (phosphoserine phosphatase) and medium-confidence candidates for SerC (phosphoserine transaminase). Faecalis is reported to grow in the absence of serine, but GapMind identified only low-confidence candidates for SerA (3-phosphoglycerate dehydrogenase) or SerB and a medium-confidence candidate for SerC. Faecalis (37), only one of the three reactions is present (3-phosphoglycerate dehydrogenase). Most steps are described using enzyme commission (EC) numbers or terms (Fig. 1B).

FIG 2.

Manual examination of 20 low-confidence steps from the nitrogen-fixing subset of the 148 bacteria and archaea. BT3760 was identified as a moderate-confidence candidate because it is less than 40% identical to any characterized enzyme. Most of the pathways in GapMind were taken from the MetaCyc database of metabolic pathways and enzymes (10). In addition, a few variant pathways that are not currently in MetaCyc are included in GapMind. These additional pathways are listed in Text S1 in the supplemental material or are described below.

GapMind attempts to join low-coverage hits from ublast together if the alignments score noticeably higher than other hits (by at least 10 bits) and they are similar to the same characterized or curated protein. GapMind checks that there is little overlap between the alignments (at most 20% of either alignment) and that the combined alignment covers at least 70% of the characterized or curated protein. If the split candidate (the combination of the two alignments) has a higher confidence score (as defined above) than either of the components, then the split is chosen as the candidate instead. The GapMind code should be suitable for reconstructing other metabolic capabilities, such as vitamin synthesis or sugar catabolism. Adding new pathways requires curation effort to describe multisubunit enzymes and to describe reactions that do not have EC numbers.

It is also possible to create ’self-signed‘ certificates. A number of web sites describe the process of creating such certificates. Self-signed certificates are only recommended in certain situations, such as development, testing, or when web site access is restricted to a small population. The problems with self-signed certificates is they are not trusted by browsers. When a modern browser encounters a self-signed certificate, the browser tries to protect the end user.

1. If the microbe was isolated using a complex substrate, such as yeast extract, then nothing is known about its nutritional requirements.
2. Helveticus requires lysine for growth (35), the biosynthetic pathway appears to be complete except for the acetyl-diaminopimelate aminotransferase DapX; GapMind identified a medium-confidence candidate for DapX.
3. When adding a new pathway, it is also important to check the quality of the results and to identify enzymes with ambiguous or incorrect descriptions that should be ignored.
4. Thus, HSERO_RS20920 is a high-confidence candidate for both AroB and AroL.
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6. In the updated GapMind, these proteins (and their homologs) are considered good candidates for either activity.

To list the proteins that are known to carry out each step, GapMind compares EC numbers or terms to the curated descriptions of over 100,000 experimentally characterized protein sequences. The biggest source of characterized proteins is Swiss-Prot (13). GapMind also describes some steps using protein families from TIGRFam (14) or Pfam (15) or by using proteins that we curated based on published papers or genetic data (see below). Our primary goal is to understand how a microbe might be able to grow with minimal nutrients, so we did not include pathways that correspond to unusual nutritional requirements. For example, GapMind does not include glycine synthesis from glycolate (11) or cysteine biosynthesis from sulfocysteine. GapMind also does not include cysteine biosynthesis from serine and methionine, because prototrophic organisms would use the simpler reverse transsulfuration pathway from serine and homocysteine.

This ensures that pathways are considered even if they have gaps due to as-yet-unknown variant enzymes. The confidence of a pathway is the lowest confidence of any step in that pathway. Second, predicting enzymatic activity from a protein’s sequence is challenging if the sequence is very different from that of any protein that has been studied experimentally. To increase their coverage, comparative tools often rely on databases of annotated proteins, including annotations for proteins that have not been studied experimentally. Unfortunately, many of the enzyme annotations in databases such as GenBank, KEGG, or SEED are incorrect (7, 8). Another problem is that comparative tools often rely on identifying best hits, which does not work well for fusion proteins or split proteins.

1. The browser presents the user with a number of prompts requiring him or her to recognize the risk of the unknown certificate, examine the certificate, and accept it.
2. GapMind also does not consider whether a candidate gene clusters with other proteins in the pathway.
3. This hypothetical organism might require either methionine or cysteine for growth because it might not be able to assimilate sulfide.
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5. Address correspondence to Morgan N. Price, , or Adam P. Arkin,
6. We used usearch/ublast 10.0 (the free 32-bit version) and HMMER 3.1b2.

Second, neither ornithine carbamoyltransferase (ArgI) nor acetylornithine carbamoyltransferase was identified with high confidence. BT3717 was identified as a candidate for acetylornithine carbamoyltransferase, but BT3717 is nearly identical to a characterized enzyme from Bacteroides fragilis that acts on N-succinylornithine instead (22). Fragilis was proposed to synthesize arginine via succinylated intermediates (22) instead of acetylated intermediates (Fig. 3A and B). Fragilis is an N-succinylglutamate kinase, not an N-acetylglutamate kinase (23), which confirms that B. Overall, we used the genetic data to reduce the total number of gaps in these 35 bacteria from 130 to 31.

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Each step is defined by one or more EC numbers, terms, or UniProt identifiers. EC numbers can be matched to curated descriptions and to families in TIGRFam. EC numbers work well for most steps, but some steps do not have fully specified four-digit EC numbers or are catalyzed by heteromeric protein complexes.

To improve GapMind, we tested it on 35 diverse bacteria that can make all 20 amino acids and for which we have large-scale genetic data from pools of transposon mutants (8, 19–21). Nevertheless, across all the pathways, the average bacterium had 3.7 gaps, or steps lbl komodo that were on the best path but were not high confidence. These gaps included 0.8 low-confidence steps and 2.9 medium-confidence steps per bacterium. Most of the diverged candidates were already annotated in UniProt with the functions that we confirmed; the six exceptions are explained in Data Set S2.

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This hypothetical organism might require either methionine or cysteine for growth because it might not be able to assimilate sulfide. We built a tool, GapMind, to reconstruct and annotate amino acid biosynthesis pathways in prokaryotic genomes. Given our limited understanding of biosynthetic pathways and the challenges of automated annotation, GapMind does not predict whether a biosynthetic capability is present or not. Instead, it identifies the most plausible pathway for making each amino acid based on current knowledge, and it highlights potential gaps.

(B) Split candidates for vitamin B12-dependent methionine synthase (MetH) in Burkholderia phytofirmans PsJN and Bacteroides thetaiotaomicron VPI-5482. Predicting growth requirements automatically is challenging for several reasons. First, many bacteria do not use the standard biosynthetic pathways from Escherichia coli or Bacillus subtilis that are described in textbooks. These variant pathways are often missing from the databases that automated tools rely on (3, 6). Variant pathways and variant enzymes continue to be discovered, so accurate prediction of microbial growth capabilities from genome sequences alone may not yet be possible (3).

The conservation of most gaps implies that known gaps will be useful for understanding other organisms. If a new genome appears to have a gap but is related to an organism that has the same gap and grows in minimal media, then this known gap should not be considered evidence that the organism lacks the pathway. We also examined the microbe with the most gaps, which was the hyperthermophilic archaeon Pyrolobus fumarii 1A.