Conformational dynamics of bacterial trigger factor in apo and ribosome-bound states

Sampling of Protein Conformational Space Using Hybrid Simulations: A Critical Assessment of Recent Methods

Recent years have seen several hybrid simulation methods for exploring the conformational space of proteins and their complexes or assemblies. These methods often combine fast analytical approaches with computationally expensive full atomic molecular dynamics (MD) simulations with the goal of rapidly sampling large and cooperative conformational changes at full atomic resolution. We present here a systematic comparison of the utility and limits of four such hybrid methods that have been introduced in recent years: MD with excited normal modes (MDeNM), collective modes-driven MD (CoMD), and elastic network model (ENM)-based generation, clustering, and relaxation of conformations (ClustENM) as well as its updated version integrated with MD simulations (ClustENMD). We analyzed the predicted conformational spaces using each of these four hybrid methods, applied to four well-studied proteins, triosephosphate isomerase (TIM), 3-phosphoglycerate kinase (PGK), HIV-1 protease (PR) and HIV-1 reverse transcriptase (RT), which provide extensive ensembles of experimental structures for benchmarking and comparing the methods. We show that a rigorous multi-faceted comparison and multiple metrics are necessary to properly assess the differences between conformational ensembles and provide an optimal protocol for achieving good agreement with experimental data. While all four hybrid methods perform well in general, being especially useful as computationally efficient methods that retain atomic resolution, the systematic analysis of the same systems by these four hybrid methods highlights the strengths and limitations of the methods and provides guidance for parameters and protocols to be adopted in future studies.

ClustENMD: Efficient sampling of biomolecular conformational space at atomic resolution

Summary

Efficient sampling of conformational space is essential for elucidating functional/allosteric mechanisms of proteins and generating ensembles of conformers for docking applications. However, unbiased sampling is still a challenge especially for highly flexible and/or large systems. To address this challenge, we describe a new implementation of our computationally efficient algorithm ClustENMD that is integrated with ProDy and OpenMM softwares. This hybrid method performs iterative cycles of conformer generation using elastic network model for deformations along global modes, followed by clustering and short molecular dynamics simulations. ProDy framework enables full automation and analysis of generated conformers and visualization of their distributions in the essential subspace.

Availability and implementation

ClustENMD is open-source and freely available under MIT License from https://github.com/prody/ProDy.

Supplementary information

Supplementary data are available at Bioinformatics online.

The Dynamic SecYEG Translocon

The spatial and temporal coordination of protein transport is an essential cornerstone of the bacterial adaptation to different environmental conditions. By adjusting the protein composition of extra-cytosolic compartments, like the inner and outer membranes or the periplasmic space, protein transport mechanisms help shaping protein homeostasis in response to various metabolic cues. The universally conserved SecYEG translocon acts at the center of bacterial protein transport and mediates the translocation of newly synthesized proteins into and across the cytoplasmic membrane. The ability of the SecYEG translocon to transport an enormous variety of different substrates is in part determined by its ability to interact with multiple targeting factors, chaperones and accessory proteins. These interactions are crucial for the assisted passage of newly synthesized proteins from the cytosol into the different bacterial compartments. In this review, we summarize the current knowledge about SecYEG-mediated protein transport, primarily in the model organism Escherichia coli, and describe the dynamic interaction of the SecYEG translocon with its multiple partner proteins. We furthermore highlight how protein transport is regulated and explore recent developments in using the SecYEG translocon as an antimicrobial target.

Towards gaining sight of multiscale events: utilizing network models and normal modes in hybrid methods

With the explosion of normal mode analyses (NMAs) based on elastic network models (ENMs) in the last decade, and the proven precision of MD simulations for visualizing interactions at atomic scale, many hybrid methods have been proposed in recent years. These aim at exploiting the best of both worlds: the atomic precision of MD that often fall short of exploring time and length scales of biological interest, and the capability of ENM-NMA to predict the cooperative and often functional rearrangements of large structures and assemblies, albeit at low resolution. We present an overview of recent progress in the field with examples of successful applications highlighting the utility of such hybrid methods and pointing to emerging future directions guided by advances in experimental characterization of biomolecular systems structure and dynamics.

Intrinsic dynamics is evolutionarily optimized to enable allosteric behavior

Allosteric behavior is central to the function of many proteins, enabling molecular machinery, metabolism, signaling and regulation. Recent years have shown that the intrinsic dynamics of allosteric proteins defined by their 3-dimensional architecture or by the topology of inter-residue contacts favors cooperative motions that bear close similarity to structural changes they undergo during their allosteric actions. These conformational motions are usually driven by energetically favorable or soft modes at the low frequency end of the mode spectrum, and they are evolutionarily conserved among orthologs. These observations brought into light evolutionary adaptation mechanisms that help maintain, optimize or regulate allosteric behavior as the evolution from bacterial to higher organisms introduces sequential heterogeneities and structural complexities.

Pre- and post-docking sampling of conformational changes using ClustENM and HADDOCK for protein-protein and protein-DNA systems

Incorporating the dynamic nature of biomolecules in the modeling of their complexes is a challenge, especially when the extent and direction of the conformational changes taking place upon binding is unknown. Estimating whether the binding of a biomolecule to its partner(s) occurs in a conformational state accessible to its unbound form (“conformational selection”) and/or the binding process induces conformational changes (“induced‐fit”) is another challenge. We propose here a method combining conformational sampling using ClustENM—an elastic network‐based modeling procedure—with docking using HADDOCK, in a framework that incorporates conformational selection and induced‐fit effects upon binding. The extent of the applied deformation is estimated from its energetical costs, inspired from mechanical tensile testing on materials. We applied our pre‐ and post‐docking sampling of conformational changes to the flexible multidomain protein‐protein docking benchmark and a subset of the protein‐DNA docking benchmark. Our ClustENM‐HADDOCK approach produced acceptable to medium quality models in 7/11 and 5/6 cases for the protein‐protein and protein‐DNA complexes, respectively. The conformational selection (sampling prior to docking) has the highest impact on the quality of the docked models for the protein‐protein complexes. The induced‐fit stage of the pipeline (post‐sampling), however, improved the quality of the final models for the protein‐DNA complexes. Compared to previously described strategies to handle conformational changes, ClustENM‐HADDOCK performs better than two‐body docking in protein‐protein cases but worse than a flexible multidomain docking approach. However, it does show a better or similar performance compared to previous protein‐DNA docking approaches, which makes it a suitable alternative.

Exploring the roles of substrate-binding surface of the chaperone site in the chaperone activity of trigger factor

Trigger factor (TF) is a key component of the prokaryotic chaperone network, which is involved in many basic cellular processes, such as protein folding, protein trafficking, and ribosome assembly. The major chaperone site of TF has a cradle-like structure in which protein substrate may fold without interference from other proteins. Here, we investigated in vivo and in vitro the roles of hydrophobic and charged patches on the edge and interior of cradle during TF-assisted protein folding. Our results showed that most of the surface of the cradle was involved in TF-assisted protein folding, which was larger than found in early studies. Although the inner surface of cradle was mostly hydrophobic, both hydrophobic and electrostatic patches were indispensable for TF to facilitate correct protein folding. However, hydrophobic patches were more important for the antiaggregation activity of TF. Furthermore, it was found that the patches on the surface of cradle were involved in TF-assisted protein folding in a spatial and temporal order. These results suggest that the folding-favorable interface between the cradle and substrate was dynamic during TF-assisted protein folding, which enabled TF to be involved in the folding of substrate in an aggressive manner rather than acting as a classic holdase.-Fan, D., Cao, S., Zhou, Q., Zhang, Y., Yue, L., Han, C., Yang, B., Wang, Y., Ma, Z., Zhu, L., Liu, C. Exploring the roles of substrate-binding surface of chaperone site in the chaperone activity of trigger factor.

Molecular simulations of the ribosome and associated translation factors

The ribosome is a macromolecular complex which is responsible for protein synthesis in all living cells according to their transcribed genetic information. Using X-ray crystallography and, more recently, cryo-electron microscopy (cryo-EM), the structure of the ribosome was resolved at atomic resolution in many functional and conformational states. Molecular dynamics simulations have added information on dynamics and energetics to the available structural information, thereby have bridged the gap to the kinetics obtained from single-molecule and bulk experiments. Here, we review recent computational studies that brought notable insights into ribosomal structure and function.