Improved Sampling Strategies for Protein Model Refinement Based on Molecular Dynamics Simulation

Perspectives Toward an Integrative Structural Biology Pipeline With Atomic Force Microscopy Topographic Images

After the recent double revolutions in structural biology, which include the use of direct detectors for cryo-electron microscopy resulting in a significant improvement in the expected resolution of large macromolecule structures, and the advent of AlphaFold which allows for near-accurate prediction of any protein structures, the field of structural biology is now pursuing more ambitious targets, including several MDa assemblies. But complex target systems cannot be tackled using a single biophysical technique. The field of integrative structural biology has emerged as a global solution. The aim is to integrate data from multiple complementary techniques to produce a final three-dimensional model that cannot be obtained from any single technique. The absence of atomic force microscopy data from integrative structural biology platforms is not necessarily due to its nm resolution, as opposed to Å resolution for x-ray crystallography, nuclear magnetic resonance, or electron microscopy. Rather a significant issue was that the AFM topographic data lacked interpretability. Fortunately, with the introduction of the AFM-Assembly pipeline and other similar tools, it is now possible to integrate AFM topographic data into integrative modeling platforms. The advantages of single molecule techniques, such as AFM, include the ability to confirm experimentally any assembled molecular models or to produce alternative conformations that mimic the inherent flexibility of large proteins or complexes. The review begins with a brief overview of the historical developments of AFM data in structural biology, followed by an examination of the strengths and limitations of AFM imaging, which have hindered its integration into modern modeling platforms. This review discusses the correction and improvement of AFM topographic images, as well as the principles behind the AFM-Assembly pipeline. It also presents and discusses a series of challenges that need to be addressed in order to improve the incorporation of AFM data into integrative modeling platform.

Unveiling the affinity-stability relationship in anti-measles virus antibodies: a computational approach for hotspots prediction

Recent years have seen an uptick in the use of computational applications in antibody engineering. These tools have enhanced our ability to predict interactions with antigens and immunogenicity, facilitate humanization, and serve other critical functions. However, several studies highlight the concern of potential trade-offs between antibody affinity and stability in antibody engineering. In this study, we analyzed anti-measles virus antibodies as a case study, to examine the relationship between binding affinity and stability, upon identifying the binding hotspots. We leverage in silico tools like Rosetta and FoldX, along with molecular dynamics (MD) simulations, offering a cost-effective alternative to traditional in vitro mutagenesis. We introduced a pattern in identifying key residues in pairs, shedding light on hotspots identification. Experimental physicochemical analysis validated the predicted key residues by confirming significant decrease in binding affinity for the high-affinity antibodies to measles virus hemagglutinin. Through the nature of the identified pairs, which represented the relative hydropathy of amino acid side chain, a connection was proposed between affinity and stability. The findings of the study enhance our understanding of the interactions between antibody and measles virus hemagglutinin. Moreover, the implications of the observed correlation between binding affinity and stability extend beyond the field of anti-measles virus antibodies, thereby opening doors for advancements in antibody research.

Nanoinformatics based insights into the interaction of blood plasma proteins with carbon based nanomaterials: Implications for biomedical applications

In the past three decades, interest in using carbon-based nanomaterials (CBNs) in biomedical application has witnessed remarkable growth. Despite the rapid advancement, the translation of laboratory experimentation to clinical applications of nanomaterials is one of the major challenges. This might be attributed to poor understanding of bio-nano interface. Arguably, the most significant barrier is the complexity that arises by interplay of several factors like properties of nanomaterial (shape, size, surface chemistry), its interaction with suspending media (surface hydration and dehydration, surface reconstruction and release of free surface energy) and the interaction with biomolecules (conformational change in biomolecules, interaction with membrane and receptor). Tailoring a nanomaterial that minimally interacts with protein and lipids in the medium while effectively acts on target site in biological milieu has been very difficult. Computational methods and artificial intelligence techniques have displayed potential in effectively addressing this problem. Through predictive modelling and deep learning, computer-based methods have demonstrated the capability to create accurate models of interactions between nanoparticles and cell membranes, as well as the uptake of nanomaterials by cells. Computer-based simulations techniques enable these computational models to forecast how making particular alterations to a material's physical and chemical properties could enhance functional aspects, such as the retention of drugs, the process of cellular uptake and biocompatibility. We review the most recent progress regarding the bio-nano interface studies between the plasma proteins and CBNs with a special focus on computational simulations based on molecular dynamics and density functional theory.

Machine Learning-Based Modeling of Olfactory Receptors in Their Inactive State: Human OR51E2 as a Case Study

Atomistic-level investigation of olfactory receptors (ORs) is a challenging task due to the experimental/computational difficulties in the structural determination/prediction for members of this family of G-protein coupled receptors. Here, we have developed a protocol that performs a series of molecular dynamics simulations from a set of structures predicted de novo by recent machine learning algorithms and apply it to a well-studied receptor, the human OR51E2. Our study demonstrates the need for simulations to refine and validate such models. Furthermore, we demonstrate the need for the sodium ion at a binding site near D^2.50 and E^3.39 to stabilize the inactive state of the receptor. Considering the conservation of these two acidic residues across human ORs, we surmise this requirement also applies to the other ∼400 members of this family. Given the almost concurrent publication of a CryoEM structure of the same receptor in the active state, we propose this protocol as an in silico complement to the growing field of ORs structure determination.

A mini-TGA protein modulates gene expression through heterogeneous association with transcription factors

TGA (TGACG-binding) transcription factors, which bind their target DNA through a conserved basic region leucine zipper (bZIP) domain, are vital regulators of gene expression in salicylic acid (SA)-mediated plant immunity. Here, we investigated the role of StTGA2.1, a potato (Solanum tuberosum) TGA lacking the full bZIP, which we named a mini-TGA. Such truncated proteins have been widely assigned as loss-of-function mutants. We, however, confirmed that StTGA2.1 overexpression compensates for SA-deficiency, indicating a distinct mechanism of action compared with model plant species. To understand the underlying mechanisms, we showed that StTGA2.1 can physically interact with StTGA2.2 and StTGA2.3, while its interaction with DNA was not detected. We investigated the changes in transcriptional regulation due to StTGA2.1 overexpression, identifying direct and indirect target genes. Using in planta transactivation assays, we confirmed that StTGA2.1 interacts with StTGA2.3 to activate StPRX07, a member of class III peroxidases (StPRX), which are known to play role in immune response. Finally, via structural modeling and molecular dynamics simulations, we hypothesized that the compact molecular architecture of StTGA2.1 distorts DNA conformation upon heterodimer binding to enable transcriptional activation. This study demonstrates how protein truncation can lead to distinct functions and that such events should be studied carefully in other protein families.

Initiation of fatty acid biosynthesis in Pseudomonas putida KT2440

Deciphering the mechanisms of bacterial fatty acid biosynthesis is crucial for both the engineering of bacterial hosts to produce fatty acid-derived molecules and the development of new antibiotics. However, gaps in our understanding of the initiation of fatty acid biosynthesis remain. Here, we demonstrate that the industrially relevant microbe Pseudomonas putida KT2440 contains three distinct pathways to initiate fatty acid biosynthesis. The first two routes employ conventional β-ketoacyl-ACP synthase III enzymes, FabH1 and FabH2, that accept short- and medium-chain-length acyl-CoAs, respectively. The third route utilizes a malonyl-ACP decarboxylase enzyme, MadB. A combination of exhaustive in vivo alanine-scanning mutagenesis, in vitro biochemical characterization, X-ray crystallography, and computational modeling elucidate the presumptive mechanism of malonyl-ACP decarboxylation via MadB. Given that functional homologs of MadB are widespread throughout domain Bacteria, this ubiquitous alternative fatty acid initiation pathway provides new opportunities to target a range of biotechnology and biomedical applications.

Exploring the role of plant lysin motif receptor-like kinases in regulating plant-microbe interactions in the bioenergy crop Populus

For plants, distinguishing between mutualistic and pathogenic microbes is a matter of survival. All microbes contain microbe-associated molecular patterns (MAMPs) that are perceived by plant pattern recognition receptors (PRRs). Lysin motif receptor-like kinases (LysM-RLKs) are PRRs attuned for binding and triggering a response to specific MAMPs, including chitin oligomers (COs) in fungi, lipo-chitooligosaccharides (LCOs), which are produced by mycorrhizal fungi and nitrogen-fixing rhizobial bacteria, and peptidoglycan in bacteria. The identification and characterization of LysM-RLKs in candidate bioenergy crops including Populus are limited compared to other model plant species, thus inhibiting our ability to both understand and engineer microbe-mediated gains in plant productivity. As such, we performed a sequence analysis of LysM-RLKs in the Populus genome and predicted their function based on phylogenetic analysis with known LysM-RLKs. Then, using predictive models, molecular dynamics simulations, and comparative structural analysis with previously characterized CO and LCO plant receptors, we identified probable ligand-binding sites in Populus LysM-RLKs. Using several machine learning models, we predicted remarkably consistent binding affinity rankings of Populus proteins to CO. In addition, we used a modified Random Walk with Restart network-topology based approach to identify a subset of Populus LysM-RLKs that are functionally related and propose a corresponding signal transduction cascade. Our findings provide the first look into the role of LysM-RLKs in Populus-microbe interactions and establish a crucial jumping-off point for future research efforts to understand specificity and redundancy in microbial perception mechanisms.

Computational Modeling of Virally-encoded Ion Channel Structure

Viroporins are ion channels encoded within a virus's genome, that facilitate a range of devastating infectious diseases such as COVID-19, HIV, and rotavirus. The non-structural protein 4 (NSP4) from rotavirus includes a viroporin domain that disrupts cellular Ca2+ homeostasis, initiating viral replication, and leading to life-threatening vomiting and diarrhea. Though the structure of soluble segments of NSP4 has been determined, membrane-associated regions, including the viroporin domain, remain elusive when utilizing well-established available experimental methods such as x-ray crystallography. However, two recently published protein folding algorithms, AlphaFold2 and trRosetta, demonstrated a high degree of accuracy, when determining the structure of membrane proteins from their primary amino acid sequences, though their training datasets are known to exclude proteins from viral systems. We tested the ability of these non-viral algorithms to predict functional molecular structures of the full-length NSP4 from SA11 rotavirus. We also compared the accuracy of these structures to predictions of other experimental structures of eukaryotic proteins from the Protein Data Banks (PDB), and show that the algorithms predict models more similar to corresponding experimental data than what we saw for the viroporin structure. Our data suggest that while AlphaFold2 and trRosetta each produced distinct NSP4 models, constructs based on either model showed viroporin activity when expressed in E. coli, consistent with that seen from other historical NSP4 sequences.

Structural and functional characterization of NEMO cleavage by SARS-CoV-2 3CLpro

In addition to its essential role in viral polyprotein processing, the SARS-CoV-2 3C-like protease (3CLpro) can cleave human immune signaling proteins, like NF-κB Essential Modulator (NEMO) and deregulate the host immune response. Here, in vitro assays show that SARS-CoV-2 3CLpro cleaves NEMO with fine-tuned efficiency. Analysis of the 2.50 Å resolution crystal structure of 3CLpro C145S bound to NEMO226-234 reveals subsites that tolerate a range of viral and host substrates through main chain hydrogen bonds while also enforcing specificity using side chain hydrogen bonds and hydrophobic contacts. Machine learning- and physics-based computational methods predict that variation in key binding residues of 3CLpro-NEMO helps explain the high fitness of SARS-CoV-2 in humans. We posit that cleavage of NEMO is an important piece of information to be accounted for, in the pathology of COVID-19.

Physics-based protein structure refinement in the era of artificial intelligence

Protein structure refinement is the last step in protein structure prediction pipelines. Physics-based refinement via molecular dynamics (MD) simulations has made significant progress during recent years. During CASP14, we tested a new refinement protocol based on an improved sampling strategy via MD simulations. MD simulations were carried out at an elevated temperature (360 K). An optimized use of biasing restraints and the use of multiple starting models led to enhanced sampling. The new protocol generally improved the model quality. In comparison with our previous protocols, the CASP14 protocol showed clear improvements. Our approach was successful with most initial models, many based on deep learning methods. However, we found that our approach was not able to refine machine-learning models from the AlphaFold2 group, often decreasing already high initial qualities. To better understand the role of refinement given new types of models based on machine-learning, a detailed analysis via MD simulations and Markov state modeling is presented here. We continue to find that MD-based refinement has the potential to improve AI predictions. We also identified several practical issues that make it difficult to realize that potential. Increasingly important is the consideration of inter-domain and oligomeric contacts in simulations; the presence of large kinetic barriers in refinement pathways also continues to present challenges. Finally, we provide a perspective on how physics-based refinement could continue to play a role in the future for improving initial predictions based on machine learning-based methods.

Structural and functional characterization of NEMO cleavage by SARS-CoV-2 3CLpro.. [PMID: 34816264 PMCID: PMC8609902 DOI: 10.1101/2021.11.11.468228] [Citation(s) in RCA: 0] [Impact Index Per Article: 0]

In addition to its essential role in viral polyprotein processing, the SARS-CoV-2 3C-like (3CLpro) protease can cleave human immune signaling proteins, like NF-κB Essential Modulator (NEMO) and deregulate the host immune response. Here, in vitro assays show that SARS-CoV-2 3CLpro cleaves NEMO with fine-tuned efficiency. Analysis of the 2.14 Å resolution crystal structure of 3CLpro C145S bound to NEMO_226–235 reveals subsites that tolerate a range of viral and host substrates through main chain hydrogen bonds while also enforcing specificity using side chain hydrogen bonds and hydrophobic contacts. Machine learning- and physics-based computational methods predict that variation in key binding residues of 3CLpro-NEMO helps explain the high fitness of SARS-CoV-2 in humans. We posit that cleavage of NEMO is an important piece of information to be accounted for in the pathology of COVID-19.

CHARMM-GUI LBS Finder & Refiner for Ligand Binding Site Prediction and Refinement

A protein performs its task by binding a variety of ligands in its local region that is also known as the ligand-binding-site (LBS). Therefore, accurate prediction, characterization, and refinement of LBS can facilitate protein functional annotations and structure-based drug design. In this work, we present CHARMM-GUI LBS Finder & Refiner (https://www.charmm-gui.org/input/lbsfinder) that predicts potential LBS, offers interactive features for local LBS structure analysis, and prepares various molecular dynamics (MD) systems and inputs by setting up distance restraint potentials for LBS structure refinement. LBS Finder & Refiner supports 5 different commonly used simulation programs, such as NAMD, AMBER, GROMACS, GENESIS, and OpenMM, for LBS structure refinement together with hydrogen mass repartitioning. The capability of LBS Finder & Refiner is illustrated through LBS structure predictions and refinements of 48 modeled and 20 apo benchmark target proteins. Overall, successful LBS structure predictions and refinements are seen in our benchmark tests. We hope that LBS Finder & Refiner is useful to predict, characterize, and refine potential LBS on any given protein of interest.

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.

Fast and effective protein model refinement using deep graph neural networks

Protein model refinement is the last step applied to improve the quality of a predicted protein model. Currently the most successful refinement methods rely on extensive conformational sampling and thus, take hours or days to refine even a single protein model. Here we propose a fast and effective model refinement method that applies GNN (graph neural networks) to predict refined inter-atom distance probability distribution from an initial model and then rebuilds 3D models from the predicted distance distribution. Tested on the CASP (Critical Assessment of Structure Prediction) refinement targets, our method has comparable accuracy as two leading human groups Feig and Baker, but runs substantially faster. Our method may refine one protein model within ~11 minutes on 1 CPU while Baker needs ~30 hours on 60 CPUs and Feig needs ~16 hours on 1 GPU. Finally, our study shows that GNN outperforms ResNet (convolutional residual neural networks) for model refinement when very limited conformational sampling is allowed.

Accurate Structure Prediction for Protein Loops Based on Molecular Dynamics Simulations with RSFF2C

Protein loops, connecting the α-helices and β-strands, are involved in many important biological processes. However, due to their conformational flexibility, it is still challenging to accurately determine three-dimensional (3D) structures of long loops experimentally and computationally. Herein, we present a systematic study of the protein loop structure prediction via a total of ∼850 μs molecular dynamics (MD) simulations. For a set of 15 long (10-16 residues) and solvent-exposed loops, we first evaluated the performance of four state-of-the-art loop modeling algorithms, DaReUS-Loop, Sphinx, Rosetta-NGK, and MODELLER, on each loop, and none of them could accurately predict the structures for most loops. Then, temperature replica exchange molecular dynamics (REMD) simulations were conducted with three recent force fields, RSFF2C with TIP3P water model, CHARMM36m with CHARMM-modified TIP3P, and AMBER ff19SB with OPC. We found that our recently developed residue-specific force field RSFF2C performed the best and successfully predicted 12 out of 15 loops with a root-mean-square deviation (RMSD) < 1.5 Å. As an alternative with lower computational cost, normal MD simulations at high temperatures (380, 500, and 620 K) were investigated. Temperature-dependent performance was observed for each force field, and, for RSFF2C+TIP3P, we found that three independent 100-ns MD simulations at 500 K gave comparable results with REMD simulations. These results suggest that MD simulations, especially with enhanced sampling techniques such as replica exchange, with the RSFF2C force field could be useful for accurate loop structure prediction.

Can molecular dynamics simulations improve the structural accuracy and virtual screening performance of GPCR models?

The determination of G protein-coupled receptor (GPCR) structures at atomic resolution has improved understanding of cellular signaling and will accelerate the development of new drug candidates. However, experimental structures still remain unavailable for a majority of the GPCR family. GPCR structures and their interactions with ligands can also be modelled computationally, but such predictions have limited accuracy. In this work, we explored if molecular dynamics (MD) simulations could be used to refine the accuracy of in silico models of receptor-ligand complexes that were submitted to a community-wide assessment of GPCR structure prediction (GPCR Dock). Two simulation protocols were used to refine 30 models of the D3 dopamine receptor (D3R) in complex with an antagonist. Close to 60 μs of simulation time was generated and the resulting MD refined models were compared to a D3R crystal structure. In the MD simulations, the receptor models generally drifted further away from the crystal structure conformation. However, MD refinement was able to improve the accuracy of the ligand binding mode. The best refinement protocol improved agreement with the experimentally observed ligand binding mode for a majority of the models. Receptor structures with improved virtual screening performance, which was assessed by molecular docking of ligands and decoys, could also be identified among the MD refined models. Application of weak restraints to the transmembrane helixes in the MD simulations further improved predictions of the ligand binding mode and second extracellular loop. These results provide guidelines for application of MD refinement in prediction of GPCR-ligand complexes and directions for further method development.