Proteins are responsible for the most diverse set of functions in biology. The ability to extract information from protein sequences and to predict the effects of mutations is extremely valuable in many domains of biology and medicine. However the mapping between protein sequence and function is complex and poorly understood. Here we present an embedding of natural protein sequences using a Variational Auto-Encoder and use it to predict how mutations affect protein function. We use this unsupervised approach to cluster natural variants and learn interactions between sets of positions within a protein. This approach generally performs better than baseline methods that consider no interactions within sequences, and in some cases better than the state-of-the-art approaches that use the inverse-Potts model. This generative model can be used to computationally guide exploration of protein sequence space and to better inform rational and automatic protein design.
Machine learning and the use of neural networks has increased precipitously over the past few years primarily due to the ever-increasing accessibility to data and the growth of computation power. It has become increasingly easy to harness the power of machine learning for predictive tasks. Protein structure prediction is one area where neural networks are becoming increasingly popular and successful. Although very powerful, the use of ANN require selection of most appropriate input/output encoding, architecture, and class to produce the optimal results. In this investigation we have explored a…
Shiraz University of Medical Science（设拉子医科大学）；University of Tehran（德黑兰大学）
Protein-Protein Interactions (PPIs) perform essential roles in biological functions. Although some experimental techniques have been developed to detect PPIs, they suffer from high false positive and high false negative rates. Consequently, efforts have been devoted during recent years to develop computational approaches to predict the interactions utilizing various sources of information. Therefore, a unique category of prediction approaches has been devised which is based on the protein sequence information. However, finding an appropriate feature encoding to characterize the sequence of pro…
Golisano College of Computing and Information Sciences, Rochester Institute of Technology（罗切斯特理工学院戈利萨诺计算与信息科学学院）；Thomas H. Gosnell School of Life Sciences, Rochester Institute of Technology（罗切斯特理工学院托马斯·H·戈斯内尔生命科学学院）
Predicting protein-protein interactions (PPIs) by learning informative representations from amino acid sequences is a challenging yet important problem in biology. Although various deep learning models in Siamese architecture have been proposed to model PPIs from sequences, these methods are computationally expensive for a large number of PPIs due to the pairwise encoding process. Furthermore, these methods are difficult to interpret because of non-intuitive mappings from protein sequences to their sequence representation. To address these challenges, we present a novel deep framework to model…
Multiplexed, specific and sensitive detection of antigens is critical for the rapid and accurate diagnosis of disease and the informed development of personalized treatment plans. Here, we show that polymer microsphere lasers can be used as photonic sensors to monitor and quantify direct surface binding of biomolecules via changes in the refractive index. The unique spectral signature of each individual laser can be used to find their size and effective refractive index which adds a new encoding dimension when compared to conventional fluorescent beads. We utilize antibody-functionalized micro…
Institute for AI Industry Research (AIR), Tsinghua University（人工智能产业研究院（AIR），清华大学）；Department of Computer Science（计算机科学系）；Technology, Tsinghua University（技术，清华大学）
LightOn（LightOn公司）；University of Oxford（牛津大学）；Harvard Medical School（哈佛医学院）
In this work we introduce RITA: a suite of autoregressive generative models for protein sequences, with up to 1.2 billion parameters, trained on over 280 million protein sequences belonging to the UniRef-100 database. Such generative models hold the promise of greatly accelerating protein design. We conduct the first systematic study of how capabilities evolve with model size for autoregressive transformers in the protein domain: we evaluate RITA models in next amino acid prediction, zero-shot fitness, and enzyme function prediction, showing benefits from increased scale. We release the RITA m…
We propose an optimized parameter set for protein secondary structure prediction using three layer feed forward back propagation neural network. The methodology uses four parameters viz. encoding scheme, window size, number of neurons in the hidden layer and type of learning algorithm. The input layer of the network consists of neurons changing from 3 to 19, corresponding to different window sizes. The hidden layer chooses a natural number from 1 to 20 as the number of neurons. The output layer consists of three neurons, each corresponding to known secondary structural classes viz. alpha helix…
Computation-based Science and Technology Research Center, The Cyprus Institute（塞浦路斯研究所计算科学与技术研究中心）；Faculty of Computing, Engineering and the Built Environment, Birmingham City University（伯明翰城市大学计算、工程与建筑环境学院）
The Influenza virus can be considered as one of the most severe viruses that can infect multiple species with often fatal consequences to the hosts. The Hemagglutinin (HA) gene of the virus can be a target for antiviral drug development realised through accurate identification of its sub-types and possible the targeted hosts. This paper focuses on accurately predicting if an Influenza type A virus can infect specific hosts, and more specifically, Human, Avian and Swine hosts, using only the protein sequence of the HA gene. In more detail, we propose encoding the protein sequences into numerica…
DiffPack: A Torsional Diffusion Model for Autoregressive Protein Side-Chain Packing
Mila - Québec AI Institute（米拉-魁北克人工智能研究所）；Université de Montréal（蒙特利尔大学）；Intel（英特尔公司）；HEC Montréal（蒙特利尔高等商学院）；CIFAR AI Research Chair（加拿大高级研究所人工智能研究主席）
Equi-mRNA: Protein Translation Equivariant Encoding for mRNA Language Models
Department of Computer Science University of Central Florida（计算机科学系 佛罗里达中央大学）；Department of Industrial Engineering University of Central Florida（工业工程系 佛罗里达中央大学）；Department of Computer Science and Industrial Engineering University of Central Florida（计算机科学与工业工程系 佛罗里达中央大学）
Massachusetts Institute of Technology（麻省理工学院）；Simons Machine Learning Center（西蒙斯机器学习中心）；New York Structural Biology Center（纽约结构生物学中心）
We present the MSA-to-protein transformer, a generative model of protein sequences conditioned on protein families represented by multiple sequence alignments (MSAs). Unlike existing approaches to learning generative models of protein families, the MSA-to-protein transformer conditions sequence generation directly on a learned encoding of the multiple sequence alignment, circumventing the need for fitting dedicated family models. By training on a large set of well-curated multiple sequence alignments in Pfam, our MSA-to-protein transformer generalizes well to protein families not observed duri…
École Polytechnique Fédérale de Lausanne（洛桑联邦理工学院）；SIB Swiss Institute of Bioinformatics（瑞士生物信息学研究所）
Self-supervised neural language models with attention have recently been applied to biological sequence data, advancing structure, function and mutational effect prediction. Some protein language models, including MSA Transformer and AlphaFold's EvoFormer, take multiple sequence alignments (MSAs) of evolutionarily related proteins as inputs. Simple combinations of MSA Transformer's row attentions have led to state-of-the-art unsupervised structural contact prediction. We demonstrate that similarly simple, and universal, combinations of MSA Transformer's column attentions strongly correlate wit…
Accurate information about protein content in the organism is instrumental for a better understanding of human biology and disease mechanisms. While the presence of certain types of proteins can be life-threatening, the abundance of others is an essential condition for an individual's overall well-being. Protein microarray is a technology that enables the quantification of thousands of proteins in hundreds of human samples in a parallel manner. In a series of studies involving protein microarrays, we have explored and implemented various data science methods for all-around analysing of these d…
Evolution in its course found a variety of solutions to the same optimisation problem. The advent of high-throughput genomic sequencing has made available extensive data from which, in principle, one can infer the underlying structure on which biological functions rely. In this paper, we present a new method aimed at extracting sites encoding structural and func- tional properties from a set of protein primary sequences, namely a Multiple Sequence Alignment. The method, called Critical Variable Selection, is based on the idea that subsets of relevant sites cor- respond to subsequences that occ…
Vector Institute for AI Research（向量人工智能研究所）；University of Toronto（多伦多大学）
Protein function is inherently linked to its localization within the cell, and fluorescent microscopy data is an indispensable resource for learning representations of proteins. Despite major developments in molecular representation learning, extracting functional information from biological images remains a non-trivial computational task. Current state-of-the-art approaches use autoencoder models to learn high-quality features by reconstructing images. However, such methods are prone to capturing noise and imaging artifacts. In this work, we revisit deep learning models used for classifying m…
De novo design of molecules has recently enjoyed the power of generative deep neural networks. Current approaches aim to generate molecules either resembling the properties of the molecules of the training set or molecules that are optimized with respect to specific physicochemical properties. None of the methods generates molecules specific to a target protein. In the approach presented here, we introduce a method which is conditioned on the protein target sequence to generate de novo molecules that are relevant to the target. We use an implementation adapted from Google's "Show and Tell" ima…
Generative models emerge as promising candidates for novel sequence-data driven approaches to protein design, and for the extraction of structural and functional information about proteins deeply hidden in rapidly growing sequence databases. Here we propose simple autoregressive models as highly accurate but computationally efficient generative sequence models. We show that they perform similarly to existing approaches based on Boltzmann machines or deep generative models, but at a substantially lower computational cost (by a factor between $10^2$ and $10^3$). Furthermore, the simple structure…
From Scarce Functional Labels to Label-Aware Generation in Homologous Protein Families
École Normale Supérieure, CNRS, Laboratoire de Physique - LPENS（巴黎高等师范学院，法国国家科学研究中心，物理实验室）；Sorbonne Université, CNRS, Dept. of Computational, Quantitative and Synthetic Biology - CQSB（索邦大学，法国国家科学研究中心，计算、定量与合成生物学系）；Institut Universitaire de France (IUF)（法兰西学院）；Dipartimento di Fisica, Sapienza Università di Roma（罗马智慧大学物理系）
