Conformational Sampling Research Articles

Calculating linear and nonlinear multi-ensemble slow collective variables for protein folding.

Traditional molecular dynamics simulation of biomolecules suffers from the conformational sampling problem. It is often difficult to produce enough valid data for post analysis such as free energy calculation and transition path construction. To improve the sampling, one practical solution is putting an adaptive bias potential on some predefined collective variables. The quality of collective variables strongly affects the sampling ability of a molecule in the simulation. In the past, collective variables were built with the sampling data at a constant temperature. This is insufficient because of the same sampling problem. In this work, we apply the standard weighted histogram analysis method to calculate the multi-ensemble averages of pairs of time-lagged features for the construction of both linear and nonlinear slow collective variables. Compared to previous single-ensemble methods, the presented method produces averages with much smaller statistical uncertainties. The generated collective variables help a peptide and a miniprotein fold to their near-native states in a short simulation time period. By using the method, enhanced sampling simulations could be more effective and productive.

SOP-MULTI: A Self-Organized Polymer-Based Coarse-Grained Model for Multidomain and Intrinsically Disordered Proteins with Conformation Ensemble Consistent with Experimental Scattering Data.

Multidomain proteins with long flexible linkers and full-length intrinsically disordered proteins (IDPs) are best defined as an ensemble of conformations rather than a single structure. Determining high-resolution ensemble structures of such proteins poses various challenges by using tools from experimental structural biophysics. Integrative approaches combining available low-resolution ensemble-averaged experimental data and in silico biomolecular reconstructions are now often used for the purpose. However, extensive Boltzmann weighted conformation sampling for large proteins, especially for ones where both the folded and disordered domains exist in the same polypeptide chain, remains a challenge. In this work, we present a 2-site per amino-acid resolution SOP-MULTI force field for simulating coarse-grained models of multidomain proteins. SOP-MULTI combines two well-established self-organized polymer models─: (i) SOP-SC models for folded systems and (ii) SOP-IDP for IDPs. For the SOP-MULTI, we introduce cross-interaction terms between the beads belonging to the folded and disordered regions to generate conformation ensembles for full-length multidomain proteins such as hnRNP A1, TDP-43, G3BP1, hGHR-ECD, TIA1, HIV-1 Gag, polyubiquitin, and FUS. When back-mapped to all-atom resolution, SOP-MULTI trajectories faithfully recapitulate the scattering data over the range of the reciprocal space. We also show that individual folded domains preserve native contacts with respect to solved folded structures, and root-mean-square fluctuations of residues in folded domains match those obtained from all-atom molecular dynamics simulation trajectories of the same folded systems. SOP-MULTI force field is made available as a LAMMPS-compatible user package along with setup codes for generating the required files for any full-length protein with folded and disordered regions.

Assessing small molecule conformational sampling methods in molecular docking.

Small molecule conformational sampling plays a pivotal role in molecular docking. Recent advancements have led to the emergence of various conformational sampling methods, each employing distinct algorithms. This study investigates the impact of different small molecule conformational sampling methods in molecular docking using UCSF DOCK 3.7. Specifically, six traditional sampling methods (Omega, BCL::Conf, CCDC Conformer Generator, ConfGenX, Conformator, RDKit ETKDGv3) and a deep learning-based model (Torsional Diffusion) for generating conformational ensembles are evaluated. These ensembles are subsequently docked against the Platinum Diverse Dataset, the PoseBusters dataset and the DUDE-Z dataset to assess binding pose reproducibility and screening power. Notably, different sampling methods exhibit varying performance due to their unique preferences, such as dihedral angle sampling ranges on rotatable bonds. Combining complementary methods may lead to further improvements in docking performance.

Impact of replicas and simulation length on in silico behaviors of a protein domain.

Molecular dynamics (MD) simulations are a powerful tool for life sciences, valuable for their ability to capture atomic-level behavior of molecules over time. To advance knowledge on reasonable timescales, researchers must optimize the amount of useful information extracted from simulation data while frugally managing computational resources. They must balance trajectory lengths and number of replicas, with the aim of maximizing conformational sampling. Identifying this balance is not always intuitive, and lack of standardization among researchers produces variability in results from MD measurements. We investigate how changes in simulation length and replica numbers impact this variability. Using a 231-residue domain, we compare measurements from independent trajectories to a benchmark trajectory of 3x1000-ns replicates. We simulate 27 protein-ligand complexes, allowing us to compare ligand-specific rankings of complexes across replicas. We reveal that some MD measurements are reliably ranked by single trajectories, while others are not. We uncover similar variability in the effects of trajectory lengths on measurements. Our findings suggest that a one-size-fits-all approach to MD simulations is not ideal, and depending on the intended measurements and research question, it is sometimes advantageous to prioritize longer trajectories over multiple replicas. This work provides important considerations for researchers while designing simulation studies.

Structural Basis for Voltage Gating and Dalfampridine Binding in the Shaker Potassium Channel.

The generation and propagation of action potentials in neurons depend on the coordinated activation of voltage-dependent sodium and potassium channels. Potassium channels of the Shaker family regulate neuronal excitability through voltage-dependent opening and closing of their ion conduction pore. This family of channels is an important therapeutic target, particularly in multiple sclerosis where the inhibitor dalfampridine (4-aminopyridine) is used to improve nerve conduction. The molecular details of how the voltage sensor domain drives opening of the pore domain has been limited by the lack of closed-state structures, also impairing the search for novel drugs. Using AlphaFold2-based conformational sampling methods we identify a structural model for the closed Shaker potassium channel where movement of the voltage sensor drives the opening trough interactions between the S4-S5 linker and S6 helix. We show experimentally that breakage of a backbone hydrogen bond is a critical part of the activation pathway. Through docking we identify a hydrophobic cavity formed by the pore domain helices that binds dalfampridine in the closed state. Our results demonstrate how voltage sensor movement drives pore opening and provide a structural framework for developing new therapeutic agents targeting the closed state. We anticipate this work will enable structure-based drug design efforts focused on state-dependent modulation of voltage-gated ion channels for the treatment of neurological disorders.

Exploring voltage-gated sodium channel conformations and protein-protein interactions using AlphaFold2.

Voltage-gated sodium (Na V ) channels are vital regulators of electrical activity in excitable cells, playing critical roles in generating and propagating action potentials. Given their importance in physiology, Na V channels are key therapeutic targets for treating numerous conditions, yet developing subtype-selective drugs remains challenging due to the high sequence and structural conservation among Na V family members. Recent advances in cryo-electron microscopy have resolved nearly all human Na V channels, providing valuable insights into their structure and function. However, limitations persist in fully capturing the complex conformational states that underlie Na V channel gating and modulation. This study explores the capability of AlphaFold2 to sample multiple Na V channel conformations and assess AlphaFold Multimer's accuracy in modeling interactions between the Na V α-subunit and its protein partners, including auxiliary β-subunits and calmodulin. We enhance conformational sampling to explore Na V channel conformations using a subsampled multiple sequence alignment approach and varying the number of recycles. Our results demonstrate that AlphaFold2 models multiple Na V channel conformations, including those from experimental structures, new states not yet experimentally identified, and potential intermediate states. Furthermore, AlphaFold Multimer models Na V complexes with auxiliary β-subunits and calmodulin with high accuracy, and the presence of protein partners significantly alters the conformational landscape of the Na V α-subunit. These findings highlight the potential of deep learning-based methods to expand our understanding of Na V channel structure, gating, and modulation, with significant implications for future drug discovery efforts.

Easy and accurate protein structure prediction using ColabFold.

Since its public release in 2021, AlphaFold2 (AF2) has made investigating biological questions, by using predicted protein structures of single monomers or full complexes, a common practice. ColabFold-AF2 is an open-source Jupyter Notebook inside Google Colaboratory and a command-line tool that makes it easy to use AF2 while exposing its advanced options. ColabFold-AF2 shortens turnaround times of experiments because of its optimized usage of AF2's models. In this protocol, we guide the reader through ColabFold best practices by using three scenarios: (i) monomer prediction, (ii) complex prediction and (iii) conformation sampling. The first two scenarios cover classic static structure prediction and are demonstrated on the human glycosylphosphatidylinositol transamidase protein. The third scenario demonstrates an alternative use case of the AF2 models by predicting two conformations of the human alanine serine transporter 2. Users can run the protocol without computational expertise via Google Colaboratory or in a command-line environment for advanced users. Using Google Colaboratory, it takes <2 h to run each procedure. The data and code for this protocol are available at https://protocol.colabfold.com .

Regulating IL-2 immune signaling function via a core allosteric structural network.

NMR and molecular dynamics simulations revealed distinct conformational dynamics and allosteric networks in computationally re-designed IL-2 superkines compared to wild-type IL-2, despite their similar crystal structures.The superkines S1 and S15 exhibit altered sampling of excited state conformations at an intermediate timescale, with slower conformational exchange rates compared to wild-type IL-2.A rationally designed mutation (L56A) in the S1 superkine's core allosteric network partially reverted its dynamics, receptor binding affinity, and T cell signaling behavior towards that of wild-type IL-2.Our study demonstrates that IL-2 core dynamics play a critical role in receptor binding and signaling function, providing a foundation for engineering more selective IL-2-based immunotherapies.

Eliminating the Deadwood: A Machine Learning Model for CCS Knowledge-Based Conformational Focusing for Lipids.

Accurate elucidation of gas-phase chemical structures using collision cross section (CCS) values obtained from ion-mobility mass spectrometry benefits from a synergism between experimental and in silico results. We have shown in recent work that for a molecule of modest size with a proscribed conformational space we can successfully capture a conformation(s) that can match experimental CCS values. However, for flexible systems such as fatty acids that have many rotatable bonds and multiple intramolecular London dispersion interactions, it becomes necessary to sample a much greater conformational space. Sampling more conformers, however, accrues significant computational cost downstream in optimization steps involving quantum mechanics. To reduce this computational expense for lipids, we have developed a novel machine learning (ML) model to facilitate conformer filtering according to the estimated gas-phase CCS values. Herein we report that the implementation of our CCS knowledge-based approach for conformational sampling resulted in improved structure prediction agreement with experiment by achieving favorable average CCS prediction errors of ∼2% for lipid systems in both the validation set and the test set. Moreover, most of the gas-phase candidate conformations obtained by using CCS focusing achieved lower energy-minimum geometries than the candidate conformations without focusing. Altogether, the implementation of this ML model into our modeling workflow has proven to be beneficial for both the quality of the results and the turnaround time. Finally, while our approach is limited to lipids, it can be readily extended to other molecules of interest.

Chemically Transferable Electronic Coarse Graining for Polythiophenes.

Recent advances in machine-learning-based electronic coarse graining (ECG) methods have demonstrated the potential to enable electronic predictions in soft materials at mesoscopic length scales. However, previous ECG models have yet to confront the issue of chemical transferability. In this study, we develop chemically transferable ECG models for polythiophenes using graph neural networks. Our models are trained on a data set that samples over the conformational space of random polythiophene sequences generated with 15 different monomer chemistries and three different degrees of polymerization. We systematically explore the impact of coarse-grained representation on ECG accuracy, highlighting the significance of preserving the C-β coordinates in thiophene. We also find that integrating unique polymer sequences into training enhances the model performance more efficiently than augmenting conformational sampling for sequences already in the training data set. Moreover, our ECG models, developed initially for one property and one level of quantum chemical theory, can be efficiently transferred to related properties and higher levels of theory with minimal additional data. The chemically transferable ECG model introduced in this work will serve as a foundation model for new classes of chemically transferable ECG predictions across chemical space.

Accurate Conformation Sampling via Protein Structural Diffusion.

Accurate sampling of protein conformations is pivotal for advances in biology and medicine. Although there has been tremendous progress in protein structure prediction in recent years due to deep learning, models that can predict the different stable conformations of proteins with high accuracy and structural validity are still lacking. Here, we introduce UFConf, a cutting-edge approach designed for robust sampling of diverse protein conformations based solely on amino acid sequences. This method transforms AlphaFold2 into a diffusion model by implementing a conformation-based diffusion process and adapting the architecture to process diffused inputs effectively. To counteract the inherent conformational bias in the Protein Data Bank, we developed a novel hierarchical reweighting protocol based on structural clustering. Our evaluations demonstrate that UFConf outperforms existing methods in terms of successful sampling and structural validity. The comparisons with long-time molecular dynamics show that UFConf can overcome the energy barrier existing in molecular dynamics simulations and perform more efficient sampling. Furthermore, We showcase UFConf's utility in drug discovery through its application in neural protein-ligand docking. In a blind test, it accurately predicted a novel protein-ligand complex, underscoring its potential to impact real-world biological research. Additionally, we present other modes of sampling using UFConf, including partial sampling with fixed motif, Langevin dynamics, and structural interpolation.

Conformational Sampling in Computational Studies of Natural Products: Why Is It Important?

Conformational sampling is a vital component of a reliable computational chemistry investigation. With the aim of illustrating the importance of conformational sampling, and building awareness among new practitioners, we present a series of case studies that show how the quality and reliability of computational studies depend on undertaking a thorough conformer search. The examples are drawn from the most common types of research questions in natural products chemistry, but the fundamental principles apply more generally to computational studies of molecular structure and behavior in any field of chemistry.

Expanding Automated Multiconformer Ligand Modeling to Macrocycles and Fragments.

Small molecule ligands exhibit a diverse range of conformations in solution. Upon binding to a target protein, this conformational diversity is generally reduced. However, ligands can retain some degree of conformational flexibility even when bound to a receptor. In the Protein Data Bank (PDB), a small number of ligands have been modeled with distinct alternative conformations that are supported by X-ray crystallography density maps. However, the vast majority of structural models are fit to a single ligand conformation, potentially ignoring the underlying conformational heterogeneity present in the sample. We previously developed qFit-ligand to sample diverse ligand conformations and to select a parsimonious ensemble consistent with the density. While this approach indicated that many ligands populate alternative conformations, limitations in our sampling procedures often resulted in non-physical conformations and could not model complex ligands like macrocycles. Here, we introduce several improvements to qFit-ligand, including the use of routines within RDKit for stochastic conformational sampling. This new sampling method greatly enriches low energy conformations of small molecules and macrocycles. We further extended qFit-ligand to identify alternative conformations in PanDDA-modified density maps from high throughput X-ray fragment screening experiments. The new version of qFit-ligand improves fit to electron density and reduces torsional strain relative to deposited single conformer models and our previous version of qFit-ligand. These advances enhance the analysis of residual conformational heterogeneity present in ligand-bound structures, which can provide important insights for the rational design of therapeutic agents.

AlphaFold with conformational sampling reveals the structural landscape of homorepeats

Homorepeats are motifs with reiterations of the same amino acid. They are prevalent in proteins associated with diverse physiological functions but also linked to several pathologies. Structural characterization of homorepeats has remained largely elusive, primarily because they generally occur in the disordered regions or proteins. Here, we address this subject by combining structures derived from machine learning with conformational sampling through physics-based simulations. We find that hydrophobic homorepeats have a tendency to fold into structured secondary conformations, while hydrophilic ones predominantly exist in unstructured states. Our data show that the flexibility rendered by disorder is a critical component besides the chemical feature that drives homorepeats composition toward hydrophilicity. The formation of regular secondary structures also influences their solubility, as pathologically relevant homorepeats display a direct correlation between repeat expansion, induction of helicity, and self-assembly. Our study provides critical insights into the conformational landscape of protein homorepeats and their structure-activity relationship.

Insights into Ligand-Mediated Activation of an Oligomeric Ring-Shaped Gene-Regulatory Protein from Solution- and Solid-State NMR

The 91 kDa oligomeric ring-shaped ligand binding protein TRAP (trp RNA binding attenuation protein) regulates the expression of a series of genes involved in tryptophan (Trp) biosynthesis in bacilli. When cellular Trp levels rise, the free amino acid binds to sites buried in the interfaces between each of the 11 (or 12, depending on the species) protomers in the ring. Crystal structures of Trp-bound TRAP show the Trp ligands are sequestered from solvent by a pair of loops from adjacent protomers that bury the bound ligand via polar contacts to several threonine residues. Binding of the Trp ligands occurs cooperatively, such that successive binding events occur with higher apparent affinity but the structural basis for this cooperativity is poorly understood. We used solution methyl-TROSY NMR relaxation experiments focused on threonine and isoleucine sidechains, as well as magic angle spinning solid-state NMR 13C–13C and 15N-13C chemical shift correlation spectra on uniformly labeled samples recorded at 800 and 1200 MHz, to characterize the structure and dynamics of the protein. Methyl 13C relaxation dispersion experiments on ligand-free apo TRAP revealed concerted exchange dynamics on the µs-ms time scale, consistent with transient sampling of conformations that could allow ligand binding. Cross-correlated relaxation experiments revealed widespread disorder on fast timescales. Chemical shifts for methyl-bearing side chains in apo- and Trp-bound TRAP revealed subtle changes in the distribution of sampled sidechain rotameric states. These observations reveal a pathway and mechanism for induced conformational changes to generate homotropic Trp-Trp binding cooperativity.

An integrative characterization of proline cis and trans conformers in a disordered peptide

Intrinsically disordered proteins (IDPs) often contain proline residues that undergo cis/trans isomerization. While molecular dynamics (MD) simulations have the potential to fully characterize the proline cis and trans subensembles, they are limited by the slow timescales of isomerization and force field inaccuracies. NMR spectroscopy can report on ensemble-averaged observables for both the cis-proline and trans-proline states, but a full atomistic characterization of these conformers is challenging. Given the importance of proline cis/trans isomerization for influencing the conformational sampling of disordered proteins, we employed a combination of all-atom MD simulations with enhanced sampling (metadynamics), NMR, and small-angle x-ray scattering (SAXS) to characterize the two subensembles of the ORF6 C-terminal region (ORF6CTR) from SARS-CoV-2 corresponding to the proline-57 (P57) cis and trans states. We performed MD simulations in three distinct force fields: AMBER03ws, AMBER99SB-disp, and CHARMM36m, which are all optimized for disordered proteins. Each simulation was run for an accumulated time of 180–220 μs until convergence was reached, as assessed by blocking analysis. A good agreement between the cis-P57 populations predicted from metadynamic simulations in AMBER03ws was observed with populations obtained from experimental NMR data. Moreover, we observed good agreement between the radius of gyration predicted from the metadynamic simulations in AMBER03ws and that measured using SAXS. Our findings suggest that both the cis-P57 and trans-P57 conformations of ORF6CTR are extremely dynamic and that interdisciplinary approaches combining both multiscale computations and experiments offer avenues to explore highly dynamic states that cannot be reliably characterized by either approach in isolation.

Integration of AlphaFold with Molecular Dynamics for Efficient Conformational Sampling of Transporter Protein NarK.

Transporter proteins carry their substrate across the cell membrane by changing their conformation. Thus, conformational dynamics are crucial for transport function. However, clarifying the complete transport cycle is challenging even with the current structural biology approach. Molecular dynamics (MD) simulation is a computational approach that can provide the time-resolved conformational dynamics of transporter proteins in atomic details but suffers from a high computational cost. Here, we integrate state-of-the-art protein structure prediction AI, AlphaFold2 (AF2), with MD simulation to reduce the computational cost. Focusing on the transporter protein NarK, we first show that AF2 sampled broad conformations of NarK, including the inward-open, occluded, and outward-open states. We also applied the coevolution-informed mutation in AF2, identifying state-shifting mutations. Then, we show that MD simulations from AF2-generated outward-open conformation, which is experimentally unresolved, captured the essence of the conformational state. We also found that MD simulations from AF2-generated intermediates showed transient dynamics like a transition state connecting two conformational states. This study paves the way for efficient conformational sampling of transporter proteins.

A Comprehensive Study of Factors Affecting the Prediction of the pKa Shift of Asp26 in Thioredoxin Protein.

The stable protonation state of ionizable amino acids in a protein can be predicted by computing the pKa shift of that residue within the protein environment. Thermodynamic Integration (TI) is an ideal molecular dynamics-based approach for predicting the pKa shift of ionizable protein residues. Here, we probe TI-based simulation protocols for their ability to accurately predict the pKa shift of Asp26 in thioredoxin. While implicit solvent models can predict the pKa shift accurately, explicit solvent models result in substantial errors. To understand the underlying reason for this surprising discrepancy, we investigate the role of various factors such as solvent models, conformational sampling, background charges, and polarization.

Recent Advances in Protein Folding Pathway Prediction through Computational Methods.

The protein folding mechanisms are crucial to understanding the fundamental processes of life and solving many biological and medical problems. By studying the folding process, we can reveal how proteins achieve their biological functions through specific structures, providing insights into the treatment and prevention of diseases. With the advancement of AI technology in the field of protein structure prediction, computational methods have become increasingly important and promising for studying protein folding mechanisms. In this review, we retrospect the current progress in the field of protein folding mechanisms by computational methods from four perspectives: simulation of an inverse folding pathway from native state to unfolded state; prediction of early folding residues by machine learning; exploration of protein folding pathways through conformational sampling; prediction of protein folding intermediates based on templates. Finally, the challenges and future perspectives of the protein folding problem by computational methods are also discussed.

Roles of Accelerated Molecular Dynamics Simulations in Predictions of Binding Kinetic Parameters.

Rational predictions on binding kinetics parameters of drugs to targets play significant roles in future drug designs. Full conformational samplings of targets are requisite for accurate predictions of binding kinetic parameters. In this review, we mainly focus on the applications of enhanced sampling technologies in calculations of binding kinetics parameters and residence time of drugs. The methods involved in molecular dynamics simulations are applied to not only probe conformational changes of targets but also reveal calculations of residence time that is significant for drug efficiency. For this review, special attention are paid to accelerated molecular dynamics (aMD) and Gaussian aMD (GaMD) simulations that have been adopted to predict the association or disassociation rate constant. We also expect that this review can provide useful information for future drug design.