773 research outputs found
Machine learning-guided directed evolution for protein engineering
Machine learning (ML)-guided directed evolution is a new paradigm for
biological design that enables optimization of complex functions. ML methods
use data to predict how sequence maps to function without requiring a detailed
model of the underlying physics or biological pathways. To demonstrate
ML-guided directed evolution, we introduce the steps required to build ML
sequence-function models and use them to guide engineering, making
recommendations at each stage. This review covers basic concepts relevant to
using ML for protein engineering as well as the current literature and
applications of this new engineering paradigm. ML methods accelerate directed
evolution by learning from information contained in all measured variants and
using that information to select sequences that are likely to be improved. We
then provide two case studies that demonstrate the ML-guided directed evolution
process. We also look to future opportunities where ML will enable discovery of
new protein functions and uncover the relationship between protein sequence and
function.Comment: Made significant revisions to focus on aspects most relevant to
applying machine learning to speed up directed evolutio
Deep Learning for Genomics: A Concise Overview
Advancements in genomic research such as high-throughput sequencing
techniques have driven modern genomic studies into "big data" disciplines. This
data explosion is constantly challenging conventional methods used in genomics.
In parallel with the urgent demand for robust algorithms, deep learning has
succeeded in a variety of fields such as vision, speech, and text processing.
Yet genomics entails unique challenges to deep learning since we are expecting
from deep learning a superhuman intelligence that explores beyond our knowledge
to interpret the genome. A powerful deep learning model should rely on
insightful utilization of task-specific knowledge. In this paper, we briefly
discuss the strengths of different deep learning models from a genomic
perspective so as to fit each particular task with a proper deep architecture,
and remark on practical considerations of developing modern deep learning
architectures for genomics. We also provide a concise review of deep learning
applications in various aspects of genomic research, as well as pointing out
potential opportunities and obstacles for future genomics applications.Comment: Invited chapter for Springer Book: Handbook of Deep Learning
Application
Generative Pretrained Autoregressive Transformer Graph Neural Network applied to the Analysis and Discovery of Novel Proteins
We report a flexible language-model based deep learning strategy, applied
here to solve complex forward and inverse problems in protein modeling, based
on an attention neural network that integrates transformer and graph
convolutional architectures in a causal multi-headed graph mechanism, to
realize a generative pretrained model. The model is applied to predict
secondary structure content (per-residue level and overall content), protein
solubility, and sequencing tasks. Further trained on inverse tasks, the model
is rendered capable of designing proteins with these properties as target
features. The model is formulated as a general framework, completely
prompt-based, and can be adapted for a variety of downstream tasks. We find
that adding additional tasks yields emergent synergies that the model exploits
in improving overall performance, beyond what would be possible by training a
model on each dataset alone. Case studies are presented to validate the method,
yielding protein designs specifically focused on structural proteins, but also
exploring the applicability in the design of soluble, antimicrobial
biomaterials. While our model is trained to ultimately perform 8 distinct
tasks, with available datasets it can be extended to solve additional problems.
In a broader sense, this work illustrates a form of multiscale modeling that
relates a set of ultimate building blocks (here, byte-level utf8 characters
that define the nature of the physical system at hand) to complex output. This
materiomic scheme captures complex emergent relationships between universal
building block and resulting properties via a synergizing learning capacity to
express a set of potentialities embedded in the knowledge used in training, via
the interplay of universality and diversity
Applications of deep neural networks to protein structure prediction
Professor Yi Shang, Dissertation Advisor; Professor Dong Xu, Dissertation Co-advisor.Includes vita.Field of Study: Computer science."July 2018."Protein secondary structure, backbone torsion angle and other secondary structure features can provide useful information for protein 3D structure prediction and protein functions. Deep learning offers a new opportunity to significantly improve prediction accuracy. In this dissertation, several new deep neural network architectures are proposed for protein secondary structure prediction: deep inception-inside-inception (Deep3I) networks and deep neighbor residual (DeepNRN) networks for secondary structure prediction; deep residual inception networks (DeepRIN) for backbone torsion angle prediction; deep dense inception networks (DeepDIN) for beta turn prediction; deep inception capsule networks (DeepICN) for gamma turn prediction. Every tool was then implemented as a standalone tool integrated into MUFold package and freely available to research community. A webserver called MUFold-SS-Angle is also developed for protein property prediction. The input feature to those deep neural networks is a carefully designed feature matrix corresponding to the primary amino acid sequence of a protein, which consists of a rich set of information derived from individual amino acid, as well as the context of the protein sequence. Specifically, the feature matrix is a composition of physio-chemical properties of amino acids, PSI-BLAST profile, HHBlits profile and/or predicted shape string. The deep architecture enables effective processing of local and global interactions between amino acids in making accurate prediction. In extensive experiments on multiple datasets, the proposed deep neural architectures outperformed the best existing methods and other deep neural networks significantly: The proposed DeepNRN achieved highest Q8 75.33, 72.9, 70.8 on CASP 10, 11, 12 higher than previous state-of-the-art DeepCNF-SS with 71.8, 72.3, and 69.76. The proposed MUFold-SS (Deep3I) achieved highest Q8 76.47, 74.51, 72.1 on CASP 10, 11, 12. Compared to the recently released state-of-the-art tool, SPIDER3, DeepRIN reduced the Psi angle prediction error by more than 5 degrees and the Phi angle prediction error by more than 2 degrees on average. DeepDIN outperformed significantly BetaTPred3 in both two-class and nine-class beta turn prediction on benchmark BT426 and BT6376. DeepICN is the first application of using capsule network to biological sequence analysis and outperformed all previous gamma-turn predictors on benchmark GT320.Includes bibliographical references (pages 114-131)
The promises of large language models for protein design and modeling.
The recent breakthroughs of Large Language Models (LLMs) in the context of natural language processing have opened the way to significant advances in protein research. Indeed, the relationships between human natural language and the language of proteins invite the application and adaptation of LLMs to protein modelling and design. Considering the impressive results of GPT-4 and other recently developed LLMs in processing, generating and translating human languages, we anticipate analogous results with the language of proteins. Indeed, protein language models have been already trained to accurately predict protein properties, generate novel functionally characterized proteins, achieving state-of-the-art results. In this paper we discuss the promises and the open challenges raised by this novel and exciting research area, and we propose our perspective on how LLMs will affect protein modeling and design
- …