An official website of the United States government
Proteins perform their biological functions through motion. Although high throughput prediction of the three-dimensional static structures of proteins has proved feasible using deep-learning-based methods, predicting the conformational motions remains a challenge. Purely data-driven machine learning methods encounter difficulty for addressing such motions because available laboratory data on conformational motions are still limited. In this work, we develop a method for generating protein allosteric motions by integrating physical energy landscape information into deep-learning-based methods. We show that local energetic frustration, which represents a quantification of the local features of the energy landscape governing protein allosteric dynamics, can be utilized to empower AlphaFold2 (AF2) to predict protein conformational motions. Starting from ground state static structures, this integrative method generates alternative structures as well as pathways of protein conformational motions, using a progressive enhancement of the energetic frustration features in the input multiple sequence alignment sequences. For a model protein adenylate kinase, we show that the generated conformational motions are consistent with available experimental and molecular dynamics simulation data. Applying the method to another two proteins KaiB and ribose-binding protein, which involve large-amplitude conformational changes, can also successfully generate the alternative conformations. We also show how to extract overall features of the AF2 energy landscape topography, which has been considered by many to be black box. Incorporating physical knowledge into deep-learning-based structure prediction algorithms provides a useful strategy to address the challenges of dynamic structure prediction of allosteric proteins.
more »
« less
Modeling of Protein Structural Flexibility and Large-Scale Dynamics: Coarse-Grained Simulations and Elastic Network Models
Fluctuations of protein three-dimensional structures and large-scale conformational transitions are crucial for the biological function of proteins and their complexes. Experimental studies of such phenomena remain very challenging and therefore molecular modeling can be a good alternative or a valuable supporting tool for the investigation of large molecular systems and long-time events. In this minireview, we present two alternative approaches to the coarse-grained (CG) modeling of dynamic properties of protein systems. We discuss two CG representations of polypeptide chains used for Monte Carlo dynamics simulations of protein local dynamics and conformational transitions, and highly simplified structure-based elastic network models of protein flexibility. In contrast to classical all-atom molecular dynamics, the modeling strategies discussed here allow the quite accurate modeling of much larger systems and longer-time dynamic phenomena. We briefly describe the main features of these models and outline some of their applications, including modeling of near-native structure fluctuations, sampling of large regions of the protein conformational space, or possible support for the structure prediction of large proteins and their complexes.
more »
« less
- Award ID(s):
- 1661391
- PAR ID:
- 10095900
- Date Published:
- Journal Name:
- International Journal of Molecular Sciences
- Volume:
- 19
- Issue:
- 11
- ISSN:
- 1422-0067
- Page Range / eLocation ID:
- 3496
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Molecular search phenomena are observed in a variety of chemical and biological systems. During the search, the participating particles frequently move in complex inhomogeneous environments with random transitions between different dynamic modes. To understand the mechanisms of molecular search with alternating dynamics, we investigate the search dynamics with stochastic transitions between two conformations in a one-dimensional discrete-state stochastic model. It is explicitly analyzed using the first-passage time probability method to obtain a full dynamic description of the search process. A general dynamic phase diagram is developed. It is found that there are several dynamic regimes in the molecular search with conformational transitions, and they are determined by the relative values of the relevant length scales in the system. Theoretical predictions are fully supported by Monte Carlo computer simulations.more » « less
-
Proteins and nucleic acids participate in essentially every biochemical process in living organisms, and the elucidation of their structure and motions is essential for our understanding how these molecular machines perform their function. Nuclear Magnetic Resonance (NMR) spectroscopy is a powerful versatile technique that provides critical information on the molecular structure and dynamics. Spin-relaxation data are used to determine the overall rotational diffusion and local motions of biological macromolecules, while residual dipolar couplings (RDCs) reveal local and long-range structural architecture of these molecules and their complexes. This information allows researchers to refine structures of proteins and nucleic acids and provides restraints for molecular docking. Several software packages have been developed by NMR researchers in order to tackle the complicated experimental data analysis and structure modeling. However, many of them are offline packages or command-line applications that require users to set up the run time environment and also to possess certain programming skills, which inevitably limits accessibility of this software to a broad scientific community. Here we present new science gateways designed for NMR/structural biology community that address these current limitations in NMR data analysis. Using the GenApp technology for scientific gateways (https://genapp.rocks), we successfully transformed ROTDIF and ALTENS, two offline packages for bio-NMR data analysis, into science gateways that provide advanced computational functionalities, cloud-based data management, and interactive 2D and 3D plotting and visualizations. Furthermore, these gateways are integrated with molecular structure visualization tools (Jmol) and with gateways/engines (SASSIE-web) capable of generating huge computer-simulated structural ensembles of proteins and nucleic acids. This enables researchers to seamlessly incorporate conformational ensembles into the analysis in order to adequately take into account structural heterogeneity and dynamic nature of biological macromolecules. ROTDIF-web offers a versatile set of integrated modules/tools for determining and predicting molecular rotational diffusion tensors and model-free characterization of bond dynamics in biomacromolecules and for docking of molecular complexes driven by the information extracted from NMR relaxation data. ALTENS allows characterization of the molecular alignment under anisotropic conditions, which enables researchers to obtain accurate local and long-range bond-vector restraints for refining 3-D structures of macromolecules and their complexes. We will describe our experience bringing our programs into GenApp and illustrate the use of these gateways for specific examples of protein systems of high biological significance. We expect these gateways to be useful to structural biologists and biophysicists as well as NMR community and to stimulate other researchers to share their scientific software in a similar way.more » « less
-
Cell membranes are responsible for a range of biological processes that require interactions between lipids and proteins. While the effects of lipids on proteins are becoming better understood, our knowledge of how protein conformational changes influence membrane dynamics remains rudimentary. Here, we performed experiments and computer simulations to study the dynamic response of a lipid membrane to changes in the conformational state of pH-low insertion peptide (pHLIP), which transitions from a surface-associated (SA) state at neutral or basic pH to a transmembrane (TM) α-helix under acidic conditions. Our results show that TM-pHLIP significantly slows down membrane thickness fluctuations due to an increase in effective membrane viscosity. Our findings suggest a possible membrane regulatory mechanism, where the TM helix affects lipid chain conformations, and subsequently alters membrane fluctuations and viscosity.more » « less
-
Intrinsically disordered proteins (IDPs) are highly prevalent and play important roles in biology and human diseases. It is now also recognized that many IDPs remain dynamic even in specific complexes and functional assemblies. Computer simulations are essential for deriving a molecular description of the disordered protein ensembles and dynamic interactions for a mechanistic understanding of IDPs in biology, diseases, and therapeutics. Here, we provide an in-depth review of recent advances in the multi-scale simulation of disordered protein states, with a particular emphasis on the development and application of advanced sampling techniques for studying IDPs. These techniques are critical for adequate sampling of the manifold functionally relevant conformational spaces of IDPs. Together with dramatically improved protein force fields, these advanced simulation approaches have achieved substantial success and demonstrated significant promise towards the quantitative and predictive modeling of IDPs and their dynamic interactions. We will also discuss important challenges remaining in the atomistic simulation of larger systems and how various coarse-grained approaches may help to bridge the remaining gaps in the accessible time- and length-scales of IDP simulations.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.3390/ijms19113496
