Abstract: A method of automated control of a microscope in cryogenic electron microscopy (cryo-EM), wherein the microscope is configured to collect high-magnification micrographs of particles suspended in vitreous ice. Such particles are found in grid squares, and a square contains holes from which high-magnification micrographs are imaged. The method is carried out during an active data collection session, leveraging a pipeline that comprises a set of models. The pipeline evaluates a set of collection locations to determine whether to continue collection at a current grid/square or instead at a new grid/square. The evaluation is based on a set of one or more quality scores derived from one or more pretrained models and machine learning-based active learning. Based on the determination, control information is provided to automatically control the microscope to move to a next target for data collection.

Abstract: A retrieval-augmented framework leverages a generative protein language model of whole protein families. The model is configured and trained on homologous sequences and learns to generate sets of related proteins as sequences-of-sequences across very large numbers (e.g., tens of millions) of natural protein sequence clusters. In order to capture conditioning between sequences in an order independent manner (typically, the order of sequences within a family is arbitrary) and to generalize to large context lengths, the model leverages a transformer layer that models order-dependence between tokens within sequences and order-independence between sequences. Upon training, the model is used in protein engineering workflows, such as controllable design of protein sequences and variant effect prediction.

Abstract: A method of automated control of a microscope in cryogenic electron microscopy (cryo-EM), wherein the microscope is configured to collect high-magnification micrographs of particles suspended in vitreous ice. Such particles are found in grid squares, and a square contains holes from which high-magnification micrographs are imaged. The method is carried out during an active data collection session, leveraging a pipeline that comprises a set of models. The pipeline evaluates a set of collection locations to determine whether to continue collection at a current grid/square or instead at a new grid/square. The evaluation is based on a set of one or more quality scores derived from one or more pretrained models and machine learning-based active learning. Based on the determination, control information is provided to automatically control the microscope to move to a next target for data collection.

Abstract: A method for efficient search of protein sequence databases for proteins that have sequence, structural, and/or functional homology with respect to information derived from a search query. The method involves transforming the protein sequences into vector representations and searching in a vector space. Given a database of protein sequences and a learned embedding model, the embedding model is applied to each amino acid sequence to transform it into a sequence of vector representations. A query sequence is also transformed into a sequence of vector representations, preferably using the same learned embedding model. Once the query has been embedded in this manner, proteins are retrieved from the database based on distance between the query embedding and the protein embeddings contained within the database. Rapid and accurate search of the vector space is carried out using exact search using metric data structures, or approximate search using locality sensitive hashing.

Abstract: A method for efficient search of protein sequence databases for proteins that have sequence, structural, and/or functional homology with respect to information derived from a search query. The method involves transforming the protein sequences into vector representations and searching in a vector space. Given a database of protein sequences and a learned embedding model, the embedding model is applied to each amino acid sequence to transform it into a sequence of vector representations. A query sequence is also transformed into a sequence of vector representations, preferably using the same learned embedding model. Once the query has been embedded in this manner, proteins are retrieved from the database based on distance between the query embedding and the protein embeddings contained within the database. Rapid and accurate search of the vector space is carried out using exact search using metric data structures, or approximate search using locality sensitive hashing.

Abstract: A method for efficient search of protein sequence databases for proteins that have sequence, structural, and/or functional homology with respect to information derived from a search query. The method involves transforming the protein sequences into vector representations and searching in a vector space. Given a database of protein sequences and a learned embedding model, the embedding model is applied to each amino acid sequence to transform it into a sequence of vector representations. A query sequence is also transformed into a sequence of vector representations, preferably using the same learned embedding model. Once the query has been embedded in this manner, proteins are retrieved from the database based on distance between the query embedding and the protein embeddings contained within the database. Rapid and accurate search of the vector space is carried out using exact search using metric data structures, or approximate search using locality sensitive hashing.

Abstract: Genetic sequence information representative of a first set of organisms is accessed. The first set of organisms can include organisms that include an organism feature and organisms that do not include the organism feature. A latent space representation of k-mers within the first genetic sequence information is generated, for instance using a generative topic model. A generative interpolation model is generated using the latent space representation. The generative interpolation model is configured to classify genetic sequence information representation of a target organism to determine whether the target organism includes the organism feature. Second genetic sequence information representation of each of a second set of organisms is accessed. The generative interpolation model is then applied to the second genetic sequence information to identify which of the second set of organisms are likely to include the organism feature.