Introduction To Protein Structure
01 Jan 2016-
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Posted Content•
TL;DR: This review will provide valuable insights and serve as a starting point for researchers to apply deep learning approaches in their bioinformatics studies and suggest future research directions.
Abstract: In the era of big data, transformation of biomedical big data into valuable knowledge has been one of the most important challenges in bioinformatics. Deep learning has advanced rapidly since the early 2000s and now demonstrates state-of-the-art performance in various fields. Accordingly, application of deep learning in bioinformatics to gain insight from data has been emphasized in both academia and industry. Here, we review deep learning in bioinformatics, presenting examples of current research. To provide a useful and comprehensive perspective, we categorize research both by the bioinformatics domain (i.e., omics, biomedical imaging, biomedical signal processing) and deep learning architecture (i.e., deep neural networks, convolutional neural networks, recurrent neural networks, emergent architectures) and present brief descriptions of each study. Additionally, we discuss theoretical and practical issues of deep learning in bioinformatics and suggest future research directions. We believe that this review will provide valuable insights and serve as a starting point for researchers to apply deep learning approaches in their bioinformatics studies.
704 citations
Cites background from "Introduction To Protein Structure"
...In addition, extracted features from sequences such as a position specific scoring matrices (PSSM) [78], physicochemical properties [79, 80], Atchley factors [81] and one-dimensional structural properties [82, 83] are often used as inputs for deep learning algorithms to alleviate difficulties from complex biological data and improve results....
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TL;DR: The first end‐to‐end differentiable model of protein structure that couples local and global protein structure via geometric units that optimize global geometry without violating local covalent chemistry is reported.
Abstract: Accurate prediction of protein structure is one of the central challenges of biochemistry. Despite significant progress made by co-evolution methods to predict protein structure from signatures of residue-residue coupling found in the evolutionary record, a direct and explicit mapping between protein sequence and structure remains elusive, with no substantial recent progress. Meanwhile, rapid developments in deep learning, which have found remarkable success in computer vision, natural language processing, and quantum chemistry raise the question of whether a deep learning based approach to protein structure could yield similar advancements. A key ingredient of the success of deep learning is the reformulation of complex, human-designed, multi-stage pipelines with differentiable models that can be jointly optimized end-to-end. We report the development of such a model, which reformulates the entire structure prediction pipeline using differentiable primitives. Achieving this required combining four technical ideas: (1) the adoption of a recurrent neural architecture to encode the internal representation of protein sequence, (2) the parameterization of (local) protein structure by torsional angles, which provides a way to reason over protein conformations without violating the covalent chemistry of protein chains, (3) the coupling of local protein structure to its global representation via recurrent geometric units, and (4) the use of a differentiable loss function to capture deviations between predicted and experimental structures. To our knowledge this is the first end-to-end differentiable model for learning of protein structure. We test the effectiveness of this approach using two challenging tasks: the prediction of novel protein folds without the use of co-evolutionary information, and the prediction of known protein folds without the use of structural templates. On the first task the model achieves state-of-the-art performance, even when compared to methods that rely on co-evolutionary data. On the second task the model is competitive with methods that use experimental protein structures as templates, achieving 3-7A accuracy despite being template-free. Beyond protein structure prediction, end-to-end differentiable models of proteins represent a new paradigm for learning and modeling protein structure, with potential applications in docking, molecular dynamics, and protein design.
132 citations
Cites background or methods from "Introduction To Protein Structure"
...In this work, we introduce the building blocks necessary to construct an end-to-end differentiable model of protein structure, and test whether this approach can be made competitive with co-evolution and template-guided methods using two challenging tasks: (1) the prediction of new protein folds without using co-evolutionary information, and (2) the prediction of known protein folds without using experimental structures as templates....
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...Proteins are linear polymers that fold into very specific and ordered three dimensional conformations based on their amino acid sequence (Branden and Tooze, 1999; Dill, 1990)....
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...Achieving this required combining four technical ideas: (1) the adoption of a recurrent neural architecture to encode the internal representation of protein sequence, (2) the parameterization of (local) protein structure by torsional angles, which provides a way to reason over protein conformations without violating the covalent chemistry of protein chains, (3) the coupling of local protein structure to its global representation via recurrent geometric units, and (4) the use of a differentiable loss function to capture deviations between predicted and experimental structures....
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TL;DR: DeepContact as discussed by the authors discovers co-evolutionary motifs and leverages these patterns to enable accurate inference of contact probabilities, particularly when few related sequences are available, and converts hard-to-interpret coupling scores into probabilities.
Abstract: Summary While genes are defined by sequence, in biological systems a protein's function is largely determined by its three-dimensional structure. Evolutionary information embedded within multiple sequence alignments provides a rich source of data for inferring structural constraints on macromolecules. Still, many proteins of interest lack sufficient numbers of related sequences, leading to noisy, error-prone residue-residue contact predictions. Here we introduce DeepContact, a convolutional neural network (CNN)-based approach that discovers co-evolutionary motifs and leverages these patterns to enable accurate inference of contact probabilities, particularly when few related sequences are available. DeepContact significantly improves performance over previous methods, including in the CASP12 blind contact prediction task where we achieved top performance with another CNN-based approach. Moreover, our tool converts hard-to-interpret coupling scores into probabilities, moving the field toward a consistent metric to assess contact prediction across diverse proteins. Through substantially improving the precision-recall behavior of contact prediction, DeepContact suggests we are near a paradigm shift in template-free modeling for protein structure prediction.
98 citations
TL;DR: This work reviews the several processing methods developed to prepare advanced SF hydrogel formats, emphasizing a bottom-up approach beginning with critical structural characteristics of silk proteins and their behavior under specific gelation environments.
Abstract: Hydrogels are an attractive class of tunable material platforms that, combined with their structural and functional likeness to biological environments, have a diversity of applications in bioengineering. Several polymers, natural and synthetic, can be used, the material selection being based on the required functional characteristics of the prepared hydrogels. Silk fibroin (SF) is an attractive natural polymer for its excellent processability, biocompatibility, controlled degradation, mechanical properties and tunable formats and a good candidate for the fabrication of hydrogels. Tremendous effort has been made to control the structural and functional characteristic of silk hydrogels, integrating novel biological features with advanced processing techniques, to develop the next generation of functional SF hydrogels. Here, we review the several processing methods developed to prepare advanced SF hydrogel formats, emphasizing a bottom-up approach beginning with critical structural characteristics of silk proteins and their behavior under specific gelation environments. Additionally, the preparation of SF hydrogel blends and other advanced formats will also be discussed. We conclude with a brief description of the attractive utility of SF hydrogels in relevant bioengineering applications.
87 citations
Cites background from "Introduction To Protein Structure"
...In nature, biomolecules, such as peptides and proteins, organize into complex structures that are accredited to functionality [70]....
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Journal Article•
TL;DR: In this article, all-atom molecular dynamics simulations of albumin were carried out to understand how electrostatics can affect the conformation of a single albumin molecule just prior to selfassembly.
Abstract: A better understanding of protein aggregation is bound to translate into critical advances in several areas, including the treatment of misfolded protein disorders and the development of self-assembling biomaterials for novel commercial applications. Because of its ubiquity and clinical potential, albumin is one of the best-characterized models in protein aggregation research; but its properties in different conditions are not completely understood. Here, we carried out all-atom molecular dynamics simulations of albumin to understand how electrostatics can affect the conformation of a single albumin molecule just prior to self-assembly. We then analyzed the tertiary structure and solvent accessible surface area of albumin after electrostatically triggered partial denaturation. The data obtained from these single protein simulations allowed us to investigate the effect of electrostatic interactions between two proteins. The results of these simulations suggested that hydrophobic attractions and counterion binding may be strong enough to effectively overcome the electrostatic repulsions between the highly charged monomers. This work contributes to our general understanding of protein aggregation mechanisms, the importance of explicit consideration of free ions in protein solutions, provides critical new insights about the equilibrium conformation of albumin in its partially denatured state at low pH, and may spur significant progress in our efforts to develop biocompatible protein hydrogels driven by electrostatic partial denaturation.
71 citations
References
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Posted Content•
TL;DR: This review will provide valuable insights and serve as a starting point for researchers to apply deep learning approaches in their bioinformatics studies and suggest future research directions.
Abstract: In the era of big data, transformation of biomedical big data into valuable knowledge has been one of the most important challenges in bioinformatics. Deep learning has advanced rapidly since the early 2000s and now demonstrates state-of-the-art performance in various fields. Accordingly, application of deep learning in bioinformatics to gain insight from data has been emphasized in both academia and industry. Here, we review deep learning in bioinformatics, presenting examples of current research. To provide a useful and comprehensive perspective, we categorize research both by the bioinformatics domain (i.e., omics, biomedical imaging, biomedical signal processing) and deep learning architecture (i.e., deep neural networks, convolutional neural networks, recurrent neural networks, emergent architectures) and present brief descriptions of each study. Additionally, we discuss theoretical and practical issues of deep learning in bioinformatics and suggest future research directions. We believe that this review will provide valuable insights and serve as a starting point for researchers to apply deep learning approaches in their bioinformatics studies.
704 citations
TL;DR: The first end‐to‐end differentiable model of protein structure that couples local and global protein structure via geometric units that optimize global geometry without violating local covalent chemistry is reported.
Abstract: Accurate prediction of protein structure is one of the central challenges of biochemistry. Despite significant progress made by co-evolution methods to predict protein structure from signatures of residue-residue coupling found in the evolutionary record, a direct and explicit mapping between protein sequence and structure remains elusive, with no substantial recent progress. Meanwhile, rapid developments in deep learning, which have found remarkable success in computer vision, natural language processing, and quantum chemistry raise the question of whether a deep learning based approach to protein structure could yield similar advancements. A key ingredient of the success of deep learning is the reformulation of complex, human-designed, multi-stage pipelines with differentiable models that can be jointly optimized end-to-end. We report the development of such a model, which reformulates the entire structure prediction pipeline using differentiable primitives. Achieving this required combining four technical ideas: (1) the adoption of a recurrent neural architecture to encode the internal representation of protein sequence, (2) the parameterization of (local) protein structure by torsional angles, which provides a way to reason over protein conformations without violating the covalent chemistry of protein chains, (3) the coupling of local protein structure to its global representation via recurrent geometric units, and (4) the use of a differentiable loss function to capture deviations between predicted and experimental structures. To our knowledge this is the first end-to-end differentiable model for learning of protein structure. We test the effectiveness of this approach using two challenging tasks: the prediction of novel protein folds without the use of co-evolutionary information, and the prediction of known protein folds without the use of structural templates. On the first task the model achieves state-of-the-art performance, even when compared to methods that rely on co-evolutionary data. On the second task the model is competitive with methods that use experimental protein structures as templates, achieving 3-7A accuracy despite being template-free. Beyond protein structure prediction, end-to-end differentiable models of proteins represent a new paradigm for learning and modeling protein structure, with potential applications in docking, molecular dynamics, and protein design.
132 citations
TL;DR: All-atom molecular dynamics simulations of albumin were carried out to understand how electrostatics can affect the conformation of a single albumin molecule just prior to self-assembly and suggested that hydrophobic attractions and counterion binding may be strong enough to effectively overcome the electrostatic repulsions between the highly charged monomers.
Abstract: A better understanding of protein aggregation is bound to translate into critical advances in several areas, including the treatment of misfolded protein disorders and the development of self-assembling biomaterials for novel commercial applications. Because of its ubiquity and clinical potential, albumin is one of the best-characterized models in protein aggregation research; but its properties in different conditions are not completely understood. Here, we carried out all-atom molecular dynamics simulations of albumin to understand how electrostatics can affect the conformation of a single albumin molecule just prior to self-assembly. We then analyzed the tertiary structure and solvent accessible surface area of albumin after electrostatically triggered partial denaturation. The data obtained from these single protein simulations allowed us to investigate the effect of electrostatic interactions between two proteins. The results of these simulations suggested that hydrophobic attractions and counterion binding may be strong enough to effectively overcome the electrostatic repulsions between the highly charged monomers. This work contributes to our general understanding of protein aggregation mechanisms, the importance of explicit consideration of free ions in protein solutions, provides critical new insights about the equilibrium conformation of albumin in its partially denatured state at low pH, and may spur significant progress in our efforts to develop biocompatible protein hydrogels driven by electrostatic partial denaturation.
122 citations
TL;DR: This work reviews the several processing methods developed to prepare advanced SF hydrogel formats, emphasizing a bottom-up approach beginning with critical structural characteristics of silk proteins and their behavior under specific gelation environments.
Abstract: Hydrogels are an attractive class of tunable material platforms that, combined with their structural and functional likeness to biological environments, have a diversity of applications in bioengineering. Several polymers, natural and synthetic, can be used, the material selection being based on the required functional characteristics of the prepared hydrogels. Silk fibroin (SF) is an attractive natural polymer for its excellent processability, biocompatibility, controlled degradation, mechanical properties and tunable formats and a good candidate for the fabrication of hydrogels. Tremendous effort has been made to control the structural and functional characteristic of silk hydrogels, integrating novel biological features with advanced processing techniques, to develop the next generation of functional SF hydrogels. Here, we review the several processing methods developed to prepare advanced SF hydrogel formats, emphasizing a bottom-up approach beginning with critical structural characteristics of silk proteins and their behavior under specific gelation environments. Additionally, the preparation of SF hydrogel blends and other advanced formats will also be discussed. We conclude with a brief description of the attractive utility of SF hydrogels in relevant bioengineering applications.
87 citations
TL;DR: The co‐crystal structure of this riboswitch is determined, which reveals an internally pseudo‐symmetric RNA in which two similar three‐helix‐junction elements associate head‐to‐tail, creating a trough that cradles two c‐di‐AMP molecules making quasi‐equivalent contacts with the Riboswitch.
Abstract: Cyclic diadenosine monophosphate (c-di-AMP) is a second messenger that is essential for growth and homeostasis in bacteria. A recently discovered c-di-AMP-responsive riboswitch controls the expression of genes in a variety of bacteria, including important pathogens. To elucidate the molecular basis for specific binding of c-di-AMP by a gene-regulatory mRNA domain, we have determined the co-crystal structure of this riboswitch. Unexpectedly, the structure reveals an internally pseudo-symmetric RNA in which two similar three-helix-junction elements associate head-to-tail, creating a trough that cradles two c-di-AMP molecules making quasi-equivalent contacts with the riboswitch. The riboswitch selectively binds c-di-AMP and discriminates exquisitely against other cyclic dinucleotides, such as c-di-GMP and cyclic-AMP-GMP, via interactions with both the backbone and bases of its cognate second messenger. Small-angle X-ray scattering experiments indicate that global folding of the riboswitch is induced by the two bound cyclic dinucleotides, which bridge the two symmetric three-helix domains. This structural reorganization likely couples c-di-AMP binding to gene expression.
56 citations