Ig-VAE: Generative modeling of protein structure by direct 3D coordinate generation.
Ontology highlight
ABSTRACT:
SUBMITTER: Eguchi RR
PROVIDER: S-EPMC9269947 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:In analogy to proteins, the function of RNA depends on its structure and dynamics, which are encoded in the linear sequence. While there are numerous methods for computational prediction of protein 3D structure from sequence, there have been very few such methods for RNA. This review discusses template-based and template-free approaches for macromolecular structure prediction, with special emphasis on comparison between the already tried-and-tested methods for protein structure modeling and the very recently developed "protein-like" modeling methods for RNA. We highlight analogies between many successful methods for modeling of these two types of biological macromolecules and argue that RNA 3D structure can be modeled using "protein-like" methodology. We also highlight the areas where the differences between RNA and proteins require the development of RNA-specific solutions.
| S-EPMC3168752 | biostudies-literature
Project description: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 102 and 103). Furthermore, the simple structure of our models has distinctive mathematical advantages, which translate into an improved applicability in sequence generation and evaluation. Within these models, we can easily estimate both the probability of a given sequence, and, using the model's entropy, the size of the functional sequence space related to a specific protein family. In the example of response regulators, we find a huge number of ca. 1068 possible sequences, which nevertheless constitute only the astronomically small fraction 10-80 of all amino-acid sequences of the same length. These findings illustrate the potential and the difficulty in exploring sequence space via generative sequence models.
| S-EPMC8490405 | biostudies-literature
Project description:Recently described stochastic models of protein evolution have demonstrated that the inclusion of structural information in addition to amino acid sequences leads to a more reliable estimation of evolutionary parameters. We present a generative, evolutionary model of protein structure and sequence that is valid on a local length scale. The model concerns the local dependencies between sequence and structure evolution in a pair of homologous proteins. The evolutionary trajectory between the two structures in the protein pair is treated as a random walk in dihedral angle space, which is modeled using a novel angular diffusion process on the two-dimensional torus. Coupling sequence and structure evolution in our model allows for modeling both "smooth" conformational changes and "catastrophic" conformational jumps, conditioned on the amino acid changes. The model has interpretable parameters and is comparatively more realistic than previous stochastic models, providing new insights into the relationship between sequence and structure evolution. For example, using the trained model we were able to identify an apparent sequence-structure evolutionary motif present in a large number of homologous protein pairs. The generative nature of our model enables us to evaluate its validity and its ability to simulate aspects of protein evolution conditioned on an amino acid sequence, a related amino acid sequence, a related structure or any combination thereof.
| S-EPMC5850488 | biostudies-literature
Project description:Despite significant progress in recent years, protein structure prediction maintains its status as one of the prime unsolved problems in computational biology. One of the key remaining challenges is an efficient probabilistic exploration of the structural space that correctly reflects the relative conformational stabilities. Here, we present a fully probabilistic, continuous model of local protein structure in atomic detail. The generative model makes efficient conformational sampling possible and provides a framework for the rigorous analysis of local sequence-structure correlations in the native state. Our method represents a significant theoretical and practical improvement over the widely used fragment assembly technique by avoiding the drawbacks associated with a discrete and nonprobabilistic approach.
| S-EPMC2440424 | biostudies-literature
Project description:For eukaryotic cells, the biological processes involving regulatory DNA elements play an important role in cell cycle. Understanding 3D spatial arrangements of chromosomes and revealing long-range chromatin interactions are critical to decipher these biological processes. In recent years, chromosome conformation capture (3C) related techniques have been developed to measure the interaction frequencies between long-range genome loci, which have provided a great opportunity to decode the 3D organization of the genome. In this paper, we develop a new Bayesian framework to derive the 3D architecture of a chromosome from 3C-based data. By modeling each chromosome as a polymer chain, we define the conformational energy based on our current knowledge on polymer physics and use it as prior information in the Bayesian framework. We also propose an expectation-maximization (EM) based algorithm to estimate the unknown parameters of the Bayesian model and infer an ensemble of chromatin structures based on interaction frequency data. We have validated our Bayesian inference approach through cross-validation and verified the computed chromatin conformations using the geometric constraints derived from fluorescence in situ hybridization (FISH) experiments. We have further confirmed the inferred chromatin structures using the known genetic interactions derived from other studies in the literature. Our test results have indicated that our Bayesian framework can compute an accurate ensemble of 3D chromatin conformations that best interpret the distance constraints derived from 3C-based data and also agree with other sources of geometric constraints derived from experimental evidence in the previous studies. The source code of our approach can be found in https://github.com/wangsy11/InfMod3DGen.
| S-EPMC4417147 | biostudies-literature
Project description:MotivationProtein design has become increasingly important for medical and biotechnological applications. Because of the complex mechanisms underlying protein formation, the creation of a novel protein requires tedious and time-consuming computational or experimental protocols. At the same time, machine learning has enabled the solving of complex problems by leveraging large amounts of available data, more recently with great improvements on the domain of generative modeling. Yet, generative models have mainly been applied to specific sub-problems of protein design.ResultsHere we approach the problem of general purpose protein design conditioned on functional labels of the hierarchical Gene Ontology. Since a canonical way to evaluate generative models in this domain is missing, we devise an evaluation scheme of several biologically and statistically inspired metrics. We then develop the conditional generative adversarial network ProteoGAN and show that it outperforms several classic and more recent deep learning baselines for protein sequence generation. We further give insights into the model by analysing hyperparameters and ablation baselines. Lastly, we hypothesize that a functionally conditional model could generate proteins with novel functions by combining labels and provide first steps into this direction of research.AvailabilityCode and data is available at https://github.com/timkucera/proteogan.Supplemental informationSupplemental data are available at Bioinformatics online.
| S-EPMC9237736 | biostudies-literature
Project description:Rational compound design remains a challenging problem for both computational methods and medicinal chemists. Computational generative methods have begun to show promising results for the design problem. However, they have not yet used the power of three-dimensional (3D) structural information. We have developed a novel graph-based deep generative model that combines state-of-the-art machine learning techniques with structural knowledge. Our method ("DeLinker") takes two fragments or partial structures and designs a molecule incorporating both. The generation process is protein-context-dependent, utilizing the relative distance and orientation between the partial structures. This 3D information is vital to successful compound design, and we demonstrate its impact on the generation process and the limitations of omitting such information. In a large-scale evaluation, DeLinker designed 60% more molecules with high 3D similarity to the original molecule than a database baseline. When considering the more relevant problem of longer linkers with at least five atoms, the outperformance increased to 200%. We demonstrate the effectiveness and applicability of this approach on a diverse range of design problems: fragment linking, scaffold hopping, and proteolysis targeting chimera (PROTAC) design. As far as we are aware, this is the first molecular generative model to incorporate 3D structural information directly in the design process. The code is available at https://github.com/oxpig/DeLinker.
| S-EPMC7189367 | biostudies-literature
Project description:Generative models have increasingly been proposed as a solution to the molecular design problem. However, it has proved challenging to control the design process or incorporate prior knowledge, limiting their practical use in drug discovery. In particular, generative methods have made limited use of three-dimensional (3D) structural information even though this is critical to binding. This work describes a method to incorporate such information and demonstrates the benefit of doing so. We combine an existing graph-based deep generative model, DeLinker, with a convolutional neural network to utilise physically-meaningful 3D representations of molecules and target pharmacophores. We apply our model, DEVELOP, to both linker and R-group design, demonstrating its suitability for both hit-to-lead and lead optimisation. The 3D pharmacophoric information results in improved generation and allows greater control of the design process. In multiple large-scale evaluations, we show that including 3D pharmacophoric constraints results in substantial improvements in the quality of generated molecules. On a challenging test set derived from PDBbind, our model improves the proportion of generated molecules with high 3D similarity to the original molecule by over 300%. In addition, DEVELOP recovers 10× more of the original molecules compared to the baseline DeLinker method. Our approach is general-purpose, readily modifiable to alternate 3D representations, and can be incorporated into other generative frameworks. Code is available at https://github.com/oxpig/DEVELOP.
| S-EPMC8580048 | biostudies-literature
Project description:The 3D structure of the genome plays a vital role in biological processes such as gene interaction, gene regulation, DNA replication and genome methylation. Advanced chromosomal conformation capture techniques, such as Hi-C and tethered conformation capture, can generate chromosomal contact data that can be used to computationally reconstruct 3D structures of the genome. We developed a novel restraint-based method that is capable of reconstructing 3D genome structures utilizing both intra-and inter-chromosomal contact data. Our method was robust to noise and performed well in comparison with a panel of existing methods on a controlled simulated data set. On a real Hi-C data set of the human genome, our method produced chromosome and genome structures that are consistent with 3D FISH data and known knowledge about the human chromosome and genome, such as, chromosome territories and the cluster of small chromosomes in the nucleus center with the exception of the chromosome 18. The tool and experimental data are available at https://missouri.box.com/v/LorDG.
| S-EPMC5430849 | biostudies-literature
Project description:Devoid of all known canonical actin-binding proteins, the prevalent parasite Giardia lamblia uses an alternative mechanism for cytokinesis. Unique aspects of this mechanism can potentially be leveraged for therapeutic development. Here, live-cell imaging methods were developed for Giardia to establish division kinetics and the core division machinery. Surprisingly, Giardia cytokinesis occurred with a median time that is ?60 times faster than mammalian cells. In contrast to cells that use a contractile ring, actin was not concentrated in the furrow and was not directly required for furrow progression. Live-cell imaging and morpholino depletion of axonemal Paralyzed Flagella 16 indicated that flagella-based forces initiated daughter cell separation and provided a source for membrane tension. Inhibition of membrane partitioning blocked furrow progression, indicating a requirement for membrane trafficking to support furrow advancement. Rab11 was found to load onto the intracytoplasmic axonemes late in mitosis and to accumulate near the ends of nascent axonemes. These developing axonemes were positioned to coordinate trafficking into the furrow and mark the center of the cell in lieu of a midbody/phragmoplast. We show that flagella motility, Rab11, and actin coordination are necessary for proper abscission. Organisms representing three of the five eukaryotic supergroups lack myosin II of the actomyosin contractile ring. These results support an emerging view that flagella play a central role in cell division among protists that lack myosin II and additionally implicate the broad use of membrane tension as a mechanism to drive abscission.
| S-EPMC5530689 | biostudies-other