Method and system for predicting change in protein stability using neural network model

A system, comprising:

• a memory storing instructions;
• one or more communication interfaces;
• and
• one or more hardware processors coupled to the memory via the one or more communication interfaces, wherein the one or more hardware processors are configured by the instructions to:
• receive (a) a wild-type protein sequence, (b) a mutant-type protein sequence, and (c) a single mutation position (SMP) in the wild-type protein sequence;
• extract a plurality of features, by processing (a) the wild-type protein sequence, (b) the mutant-type protein sequence, and (c) the SMP in the wild-type protein sequence using a pre-trained protein language model;
• separately generate
• (i) a first representation matrix and (ii) a first attention matrix, from a plurality of extracted features of the wild-type protein sequence, and
• (i) a second representation matrix and (ii) a second attention matrix, from the plurality of extracted features of the mutant-type protein sequence;
• select, using a selection algorithm, via the one or more hardware processors, one or more attention scores, by processing the first representation matrix, the first attention matrix, the second representation matrix, the second attention matrix, and the SMP, wherein the one or more attention scores are selected to obtain
• a) a plurality of highly attended amino acids from the wild-type protein sequence with respect to a wild-type residue at the SMP, and
• b) a plurality of highly attended amino acids from the mutant-type protein sequence with respect to a mutant-type residue at the SMP;
• obtain, a residue environment by computing a first final feature representation, and a second final feature representation wherein,
• the first final feature representation aggregates one or more vector representations of each of the plurality of highly attended amino acids and a SMP representation of the wild-type protein sequence extracted from the first representation matrix, and
• the second final feature representation aggregates one or more vector representations of each of the plurality of highly attended amino acids and the SMP representation of the mutant-type protein sequence extracted from the second representation matrix;
• compute a difference vector between the first final feature representation, and the second final feature representation, wherein the difference vector represents a change in amino acid residue and a change in residue environment after a single point mutation of the wild-type protein sequence;
• and
• estimate a change in free energy (ΔΔG) by providing the difference vector to a neural-network based prediction block, wherein value of ΔΔG below 0 indicates that the single point mutation is stable with respect to the wild-type protein sequence.

One or more non-transitory machine-readable information storage mediums comprising one or more instructions which when executed by one or more hardware processors cause:

• receiving (a) a wild-type protein sequence, (b) a mutant-type protein sequence, and (c) a single mutation position (SMP) in the wild-type protein sequence;
• extracting a plurality of features, by processing (a) the wild-type protein sequence, (b) the mutant-type protein sequence, and (c) the SMP in the wild-type protein sequence using a pre-trained protein language model;
• separately generating
• (i) a first representation matrix and (ii) a first attention matrix, from a plurality of extracted features of the wild-type protein sequence, and
• (i) a second representation matrix and (ii) a second attention matrix, from the plurality of extracted features of the mutant-type protein sequence;
• selecting using a selection algorithm one or more attention scores, by processing the first representation matrix, the first attention matrix, the second representation matrix, the second attention matrix, and the SMP, wherein the one or more attention scores are computed to obtain
• a) a plurality of highly attended amino acids from the wild-type protein sequence with respect to a wild-type residue at the SMP, and
• b) a plurality of highly attended amino acids from the mutant-type protein sequence with respect to a mutant-type residue at the SMP;
• obtaining a residue environment by computing a first final feature representation, and a second final feature representation wherein,
• the first final feature representation aggregates one or more vector representations of each of the plurality of highly attended amino acids and a SMP representation of the wild-type protein sequence extracted from the first representation matrix, and
• the second final feature representation aggregates one or more vector representations of each of the plurality of highly attended amino acids and the SMP representation of the mutant-type protein sequence extracted from the second representation matrix;
• computing a difference vector between the first final feature representation, and the second final feature representation, wherein the difference vector represents a change in amino acid residue and a change in residue environment after a single point mutation of the wild-type protein sequence;
• and
• estimating a change in free energy (ΔΔG) by providing the difference vector to a neural-network based prediction block, wherein value of ΔΔG below 0 indicates that the single point mutation is stable with respect to the wild-type protein sequence.