# US 12,009,060 B2 · Identifying biosynthetic gene clusters

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- Number: US 12,009,060 B2
- Document ID: US12009060B2
- Status: Granted
- Kind: B2
- Application number: 16/362,236
- Filed: 2019-03-22
- Published: 2020-06-18
- Granted: 2024-06-11
- Earliest claimed: 2018-12-14
- Assignees: Merck Sharp & Dohme Corp.; MSD Czech Republic s.r.o.
- Applicants: Merck Sharp & Dohme LLC; MSD Czech Republic s.r.o.
- Inventors: Geoffrey D. Hannigan; David Prihoda; Jindrich Soukup; Christopher Harron Woelk; Danny A. Bitton
- Web page: https://patentgrep.com/patent/US12009060B2

## CPC

- G16B 30/00: ICT specially adapted for sequence analysis involving nucleotides or amino acids
- G16B 40/20: Supervised data analysis
- G06N 3/044: Recurrent networks, e.g. Hopfield networks
- G06N 3/0442: characterised by memory or gating, e.g. long short-term memory [LSTM] or gated recurrent units [GRU]
- G06N 3/045: Combinations of networks
- G06N 3/08: Learning methods
- G06N 3/09: Supervised learning
- G06N 3/048: Activation functions
- G16B: BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
- G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS

## Abstract

A BGC prediction system identifies candidate biosynthetic gene clusters (BGCs) within genomes using machine-learned models, such as a shallow neural network and recurrent neural network (RNN). A set of domains within a genome sequence are identified, each domain corresponds to a set of domain identifiers. A shallow neural network block is applied to each set of domain identifiers to produce a set of vectors. An RNN block is applied to the set of vectors to produce a BGC class score for each domain. The RNN block was trained using an identified set of positive vectors, which represents known BGCs, and a synthesized set of negative vectors, which is unlikely to represent BGCs. Candidate BGCs are selected by averaging BGC class scores across genes within a domain and comparing the average BGC class scores to a threshold. The candidate BGCs are provided for display on a user interface.

## Claims

### Claim 1

A method comprising:
identifying, in a genome sequence, a set of domains, each identified domain corresponding to a set of domain identifiers;
applying a shallow neural network block to each set of domain identifiers to produce a set of vectors, each vector corresponding to a set of domain identifiers;
applying a recurrent neural network (RNN) block to the set of vectors to produce a biosynthetic gene cluster (BGC) class score for each domain, wherein the RNN block was trained by:
identifying a set of positive vectors representing known BGCs;
synthesizing a set of negative vectors unlikely to represent BGCs;
applying the RNN block to the positive and negative sets of vectors to generate predictions of whether each vector is a positive or negative vector; and
updating weights of the RNN block based on the predictions;
selecting candidate BGCs by averaging BGC class scores across genes within a domain and comparing the average BGC class scores to a threshold;
predicting a molecular activity of biosynthetic products derived from the selected BGCs; and
providing for display, on a user interface, the candidate BGCs and predicted molecular activity.

### Claim 2 (depends on 1)

The method of claim 1, further comprising:
processing candidate BGCs, wherein processing includes merging and filtering candidate BGCs based on at least one of: a presence of known BGCs, a cluster length, or a distance between candidate BGCs.

### Claim 3 (depends on 1)

The method of claim 1, further comprising:
merging consecutive candidate BGC genes that are adjacent in the genome sequence.

### Claim 4 (depends on 1)

The method of claim 1, wherein the RNN block is a bi-directional long short-term memory (LSTM) block.

### Claim 5 (depends on 1)

The method of claim 1, wherein the domain identifiers are maintained in genomic order.

### Claim 6 (depends on 1)

The method of claim 1, wherein each vector in the set of vectors comprises one hundred elements, each element being a real number, each element representing a property of the domain based on its genomic context.

### Claim 7 (depends on 1)

The method of claim 1, further comprising:
predicting, for each candidate BGC, with a classifier, a secondary metabolite class based on a biosynthetic product and molecular activity of the candidate BGC.

### Claim 8 (depends on 7)

The method of claim 7, wherein the classifier is a random forest classifier.

### Claim 9 (depends on 1)

The method of claim 1, wherein the set of negative vectors are synthesized by:
retrieving a genome sequence with known BGCs;
modifying the genome sequence by replacing a portion of the genes within the known BGCs with random genes of similar length;
generating a set of identifiers for each domain in the modified genome sequence; and
applying a shallow neural network block to each domain in the modified genome sequence to produce a negative set of vectors.

### Claim 10 (depends on 1)

The method of claim 1, wherein applying the RNN block to the set of vectors further comprises applying a sigmoid activation function.

### Claim 11

A non-transitory computer-readable storage medium containing computer program code comprising instructions that, when executed by a processor, causes the processor to:
identify, in a genome sequence, a set of domains, each identified domain corresponding to a set of domain identifiers;
apply a shallow neural network block to each set of domain identifiers to produce a set of vectors, each vector corresponding to a set of domain identifiers;
apply a recurrent neural network (RNN) block to the set of vectors to produce a biosynthetic gene cluster (BGC) class score for each domain, wherein the RNN block was trained by:
identifying a set of positive vectors representing known BGCs;
synthesizing a set of negative vectors unlikely to represent BGCs;
applying the RNN block to the positive and negative sets of vectors to generate predictions of whether each vector is a positive or negative vector; and
updating weights of the RNN block based on the predictions;
select candidate BGCs by averaging BGC class scores across genes within a domain and comparing the average BGC class scores to a threshold;
predict a molecular activity of biosynthetic products derived from the selected BGCs; and
provide for display, on a user interface, the candidate BGCs and predicted molecular activity.

### Claim 12 (depends on 11)

The non-transitory computer-readable storage medium of claim 11, wherein the instructions, when executed by the processor, further cause the processor to:
process candidate BGCs by merging and filtering candidate BGCs based on at least one of: a presence of known BGCs, a cluster length, or a distance between candidate BGCs.

### Claim 13 (depends on 11)

The non-transitory computer-readable storage medium of claim 11, wherein the instructions, when executed by the processor, cause the processor to:
merge consecutive candidate BGC genes.

### Claim 14 (depends on 11)

The non-transitory computer-readable storage medium of claim 11, wherein the RNN block is a bi-directional long short-term memory (LSTM) block.

### Claim 15 (depends on 11)

The non-transitory computer-readable storage medium of claim 11, wherein the domain identifiers are maintained in genomic order.

### Claim 16 (depends on 11)

The non-transitory computer-readable storage medium of claim 11, wherein each vector in the set of vectors comprises one hundred elements, each element being a real number, each element representing a property of the domain based on its genomic context.

### Claim 17 (depends on 11)

The non-transitory computer-readable storage medium of claim 11, wherein the instructions, when executed by the processor, further cause the processor to:
predict, for each candidate BGC, with a classifier, a secondary metabolite class based on a biosynthetic product and molecular activity of the candidate BGC.

### Claim 18 (depends on 17)

The non-transitory computer-readable storage medium of claim 17, wherein the classifier is a random forest classifier.

### Claim 19 (depends on 17)

The non-transitory computer-readable storage medium of claim 17, wherein the set of negative vectors are synthesized by:
retrieving a genome sequence with known BGCs;
modifying the genome sequence by replacing a portion of the genes within the known BGCs with random genes of similar length;
generating a set of identifiers for each domain in the modified genome sequence; and
applying a shallow neural network block to each domain in the modified genome sequence to produce a negative set of vectors.

### Claim 20 (depends on 11)

The non-transitory computer-readable storage medium of claim 11, wherein the instructions that cause the processor to apply the RNN block to the set of vectors further comprise instructions that cause the processor to apply a sigmoid activation function.